Women and Industrialization in Asia (Routledge Studies in the Growth Economies of Asia, 3)

WOMEN AND INDUSTRIALIZATION IN ASIA

It is well known that the female work force has played a large part in the Asian ‘export miracle’, yet their role has commonly been depicted as confined to sweat shops and tea houses. This book examines the bigger picture regarding women in the labour market and how this has been changing in the course of development and industrialization. Drawing on labour force survey data from across the continent, the book analyses: • how women’s participation, employment patterns and relative pay have changed over time and in the course of industrialization; • labour market institutions, including legislation on equal pay, equal employment opportunity and maternity leave; • the progress of women from the light export industries into white collar occupations; • how women’s pay has risen relative to men’s in many Asian countries, at a rate far faster than in most OECD countries; • studies on India, Indonesia, Japan, Korea, Malaysia, Philippines and Thailand. Written in an accessible style and with the key issues amply supported by up-todate quantitative data. Women and Industrialization in Asia produces some surprising results and dispels some common myths regarding the position of female workers in the region. Susan Horton is Professor of Economics at the University of Toronto. Her research interests have focused on aspects of labour markets and poverty in developing countries. She has also worked as a consultant for the World Bank, UNICEF and FAO.

Routledge Studies in the Growth Economies of Asia

1

Changing Capital Markets in East Asia Edited by Ky Cao 2

3

4

Financial Reform in China Edited by On Kit Tam

Women and Industrialization in Asia Edited by Susan Horton

Japan’s Trade Policy. Action or Reaction? Yumiko Makanagi 5

The Japanese Election System. Three Analytical Perspectives Junichiro Wada

WOMEN AND INDUSTRIALIZATION IN ASIA

Edited by Susan Horton

London and New York

First published 1996 by Routledge 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Routledge 29 West 35th Street, New York, NY 10001 Routledge is an imprint of the Taylor & Francis Group This edition published in the Taylor & Francis e-Library, 2003. © 1996 Susan Horton All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication Data A catalogue record for this book has been requested ISBN 0-203-43436-6 Master e-book ISBN

ISBN 0-203-74260-5 (Adobe eReader Format) ISBN 0-415-12907-9 (Print Edition) ISSN 1359-7876

CONTENTS

List of Illustrations Notes on contributors Acknowledgements

vi xvi xviii

1 WOMEN AND INDUSTRIALIZATION IN ASIA: OVERVIEW Susan Horton

1

2 WOMEN IN THE INDIAN LABOUR FORCE: A TEMPORAL AND SPATIAL ANALYSIS Sarthi Acharya

43

3 WOMEN AND THE LABOUR MARKET IN INDONESIA DURING THE 1980s Dwayne Benjamin

81

4 WOMEN IN THE JAPANESE ECONOMY M.Anne Hill

134

5 WOMEN’S EMPLOYMENT STRUCTURE AND MALE-FEMALE WAGE DIFFERENTIALS IN KOREA Moo Ki Bai and Woo Hyun Cho

165

6 WOMEN IN THE LABOUR MARKET IN MALAYSIA Jamilah Ariffin, Susan Horton and Guilherme Sedlacek

207

7 WOMEN IN THE LABOUR MARKET IN THE PHILIPPINES Ruperto Alonzo, Susan Horton and Reema Nayar

244

8 CHANGES IN WOMEN’S ECONOMIC ROLE IN THAILAND Mathana Phananiramai

274

Index

307 v

ILLUSTRATIONS

FIGURES 1.1

Participation rates of women in seven Asian countries, 1970–1990 1.2 Participation rates of men in seven Asian countries, 1970–1990 1.3 Participation by age, women, in seven Asian countries, 1987–1990 1.4 Participation by age, men, in seven Asian countries, 1987–1990 1.5 Labour force participation, women, India, 1972–1988 1.6 Labour force participation, women, Indonesia, 1980–1990 1.7 Labour force participation, women, Japan, 1970–1988 1.8 Labour force participation, women, Korea, 1963–1990 1.9 Labour force participation, women, Malaysia, 1975–1987 1.10 Labour force participation, women, Philippines, 1978–1988 1.11 Labour force participation, women, Thailand, 1980–1989 1.12 Female-male earnings by age in seven Asian countries, 1987–1990 2.1 Labour force participation rates by age, sex and location, all India, 1972–1973 to 1987–1988 2.2 Labour force participation rates by age, sex and location, all India, 1987–1988 2.3 Labour force participation rates by education, sex, and location for ages 16 and above, all India, 1987–1988 2.4 Labour force participation rates of women by states and region, India, 1987–1988 3.1 Activities: Indonesia, urban males, 1980 3.2 Activities: Indonesia, urban males, 1990 3.3 Activities: Indonesia, urban females, 1980 3.4 Activities: Indonesia, urban females, 1990 3.5 Activities: Indonesia, rural males, 1980 3.6 Activities: Indonesia, rural males, 1990 3.7 Activities: Indonesia, rural females, 1980 vi

8 9 11 12 13 13 14 14 15 15 16 30 48 49 51 52 88 89 89 90 91 91 92

ILLUSTRATIONS

3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 3.19 3.20 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 3.30 3.31 3.32 4.1 4.2

Activities: Indonesia, rural females, 1990 Labour force participation: Indonesia, urban, 1981: by age and sex Labour force participation: Indonesia, rural, 1981: by age and sex Labour force participation: Indonesia, urban females, 1981: by age and marital status Labour force participation: Indonesia, rural females, 1981: by age and marital status Labour force participation: Indonesia, urban males, 1981: employment status by education Labour force participation: Indonesia, urban males, 1991: employment status by education Labour force participation: Indonesia, urban females, 1981: employment status by education Labour force participation: Indonesia, urban females, 1991: employment status by education Labour force participation: Indonesia, rural males, 1981: employment status by education Labour force participation: Indonesia, rural males, 1991: employment status by education Labour force participation: Indonesia, rural females, 1981: employment status by education Labour force participation: Indonesia, rural females, 1991: employment status by education Participation rate: Indonesia, urban males, 1980–1991: by age Participation rate: Indonesia, urban females, 1980–1991: by age Participation rate: Indonesia, rural males, 1980–1991: by age Participation rate: Indonesia, rural females, 1980–1991: by age Unemployment rate: Indonesia, urban males, 1980–1991: by age Unemployment rate: Indonesia, urban females, 1980–1991: by age Unemployment rate: Indonesia, rural males, 1980–1991: by age Unemployment rate: Indonesia, rural females, 1980–1991: by age Hours worked: Indonesia, urban males, 1980–1991: by age Hours worked: Indonesia, urban females, 1980–1991: by age Hours worked: Indonesia, rural males, 1980–1991: by age Hours worked: Indonesia, rural females, 1980–1991: by age Labour force participation, Japan, 1950–1990 Age-specific participation rates, women and men, overall rate, Japan, 1970 and 1988 vii

92 93 93 94 94 95 96 96 97 97 98 98 99 100 100 101 101 102 102 103 103 105 105 106 106 137 140

ILLUSTRATIONS

4.3 4.4 4.5 4.6 4.7 4.8 5.1 5.2 5.3 5.4 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14

Age-specific participation rates, women, employees only, Japan, 1970–1988 Age-specific participation rates, married women, employees and workers, Japan, 1978 and 1988 Women’s relative earnings, Japan, 1962–1988 Women’s relative earnings and wages, adjusted, Japan, 1965–1988 Age-tenure profiles, Japan, 1965 and 1988 Total monthly earnings-age profiles, Japan, 1965 and 1988 Labour force participation rates of women by age group, Korea, 1963–1990 Age-wage profiles by occupation and sex, Korea, 1989 Age-wage profiles by high-wage/low-wage firm, men, Korea, 1989 Age-wage profiles by high-wage/low-wage firm, women, Korea, 1989 Participation rates of rural women, Peninsular Malaysia, 1975–1987 Participation rates of urban women, Peninsular Malaysia, 1975–1987 Participation rates of rural men, Peninsular Malaysia, 1975–1987 Participation rates of urban men, Peninsular Malaysia, 1975–1987 Participation rates by race: females, urban Peninsular Malaysia, 1987 Participation rates by race: males, urban Peninsular Malaysia, 1987 Annual income by race: females, urban Peninsular Malaysia, 1987 Annual income by race: males, urban Peninsular Malaysia, 1987 Hourly income by race: females, urban Peninsular Malaysia, 1987 Hourly income by race: males, urban Peninsular Malaysia, 1987 Mean years of education by age: entire sample, all races, urban Peninsular Malaysia, 1987 Mean years of education by age: entire sample, Malay, urban Peninsular Malaysia, 1987 Mean years of education by age: entire sample, Chinese, urban Peninsular Malaysia, 1987 Mean years of education by age: entire sample, Indian, urban Peninsular Malaysia, 1987 viii

141 141 150 154 155 155 168 187 193 194 211 212 212 213 214 214 221 222 222 223 223 224 224 225

ILLUSTRATIONS

6.15 Female-male ratio of annual income by race, urban Peninsular Malaysia, 1987 6.16 Female-male ratio of hourly income by race, urban Peninsular Malaysia, 1987 6.17 Mean hours worked: all workers, urban Peninsular Malaysia, 1987 6.18 Mean hours worked: Malay workers only, urban Peninsular Malaysia, 1987 6.19 Mean hours worked: Chinese workers only, urban Peninsular Malaysia, 1987 6.20 Mean hours worked: Indian workers only, urban Peninsular Malaysia, 1987 6.21 Mean years of education by age: all workers, urban Peninsular Malaysia, 1987 6.22 Mean years of education by age: Malay workers only, urban Peninsular Malaysia, 1987 6.23 Mean years of education by age: Chinese workers only, urban Peninsular Malaysia, 1987 6.24 Mean years of education by age: Indian workers only, urban Peninsular Malaysia, 1987 6.25 Adjusted female-male ratio of annual income by race, urban Peninsular Malaysia, 1987 6.26 Adjusted female-male ratio of hourly income by race, urban Peninsular Malaysia, 1987 7.1 Women’s earnings relative to men’s by age group, Philippines, 1988

226 226 227 227 228 228 229 230 230 231 232 232 256

TABLES 1.1 1.2 1.3 1.4 1.5 1.6 1.7

Total basic socio-economic indicators, seven selected Asian countries, 1965 and 1990 Employment distribution of women workers by industry (%) in seven Asian countries, 1950–1990 Percentage of women workers by industry (%) in seven Asian countries, 1950–1990 Duncan index by sector and occupation, seven Asian countries, 1950–1990 Regression results for employment distribution of women workers by industry (based on Table 1.2) Employment distribution of women workers by occupation (%) in seven Asian countries, 1957–1990 Percentage of women workers by occupation (%) in seven Asian countries, 1957–1990 ix

2 18 19 20 21 23 24

ILLUSTRATIONS

1.8

Regression results for employment distribution of women workers by occupation (based on Table 1.6) 1.9 Employment distribution of women workers by employment status (%) in seven Asian countries, 1950–1990 1.10 Percentage of women workers by employment status (%) in seven Asian countries, 1955–1990 1.11 Regression results for employment distribution of women workers by employment status (based on Table 1.9) 1.12 Earnings equation results, seven Asian countries, 1968–1990 1.13 Oaxaca decomposition results, seven Asian countries, 1968–1990 A1.1 Participation definitions A1.2 Earnings, definitions and sources 2.1 Labour force participation by sex and location, all India, 1972–1973 to 1987–1988 2.2 Percentage of women outside the labour force engaged in self-provision by location, all India, 1987–1988 2.3 Labour time allocation of rural adult men and women, in different regions and countries, South Asia, 1970s and 1980s 2.4 Logistic regression results of the determinants of female work participation rates, rural and urban Maharashtra, India, 1987–1988 2.5 Percentage distribution of the usually employed by status of employment and rural-urban location, all India, 1972–1973 to 1987–1988 2.6 Percentage distribution of workers by age, status of employment and urban-rural location, all India, 1977–1978 and 1987–1988 2.7 Percentage distribution of workers in selected industries, India, 1972–1973 to 1987–1988 2.8 Size and growth of non-agricultural organized-unorganized labour, India, 1970s and 1980s 2.9 Women’s employment in the organized manufacturing industrial sector by three-digit industry, all India, 1975–1976 and 1985–1986 2.10 Percentage distribution of workers (usual status) by occupation and rural-urban location, India, 1977–1978 and 1987–1988 2.11 Number of days worked in a typical (reference) week, by region, age group and sex, all India, 1977–1978 and 1987–1988 2.12 Determinants of women’s wages, rural and urban Maharashtra, India, 1987–1988 2.13 Determinants of women’s and men’s wages, expanded model, rural and urban Maharashtra, India, 1987–1988 x

25 26 27 28 31 33 39 41 45 47 54

58

60

61 64–5 66

66–7

68 69 70 72

ILLUSTRATIONS

2.14 Oaxaca decomposition results for earnings, rural and urban Maharashtra, India, 1987–1988 A2.1 Distribution of population by sex and residence 3.1 Labour force participation probits, urban Indonesia 1980 and 1990 3.2 Labour force participation probits, rural Indonesia 1980 and 1990 3.3 Employment status, Indonesia 1981, 1986 and 1990 by age 3.4 Distribution of employment across industries, Indonesia 1980 and 1990 3.5 Earnings equations, urban Indonesia 1980 3.6 Earnings equations, rural Indonesia 1980 3.7 Earnings equations, urban Indonesia 1990 3.8 Earnings equations, rural Indonesia 1990 3.9 Selectivity correction probits, urban Indonesia 1980 and 1990 3.10 Selectivity correction probits, rural Indonesia 1980 and 1990 3.11 Oaxaca decompositions, Indonesia 1980 and 1990 3.12 Comparison of labour market earnings across samples, Indonesia 1980, 1981 and 1990 3.13 Sources of household income, Indonesia 1981 and 1990 A3.1 Education categories and years of schooling 4.1 Labour force participation rates and the distribution of the labour force by employment status, Japan, 1948–1990 4.2 Labour force participation of married and total women, Japan, 1960–1988 4.3 Employment distribution of women and percentage of workers who are female, by industry, Japan, 1950–1990 4.4 Employment distribution of women and percentage of workers who are female, by detailed industry, Japan, 1985 4.5 Employment distribution of women and percentage of workers who are female, by occupation, Japan, 1955–1990 4.6 Employment distribution of women and percentage of workers who are female, by detailed occupation, Japan, 1985 4.7 Wages, hours and relative wages for men and women, Japan, 1962–1988 4.8 Wage differentials by industry, firm size and gender, Japan, 1987 4.9 Estimated log hourly wage regressions, Japan, 1968–1988 4.10 Estimated log monthly earnings regressions, Japan, 1968–1988 4.11 Decomposing the gender gap in earnings and wages, Japan, 1968–1988 4.12 Distribution of total population 15 and older by highest level of schooling completed (%), Japan, 1960–1980 xi

73 77 107 109 111 112 116 117 118 119 121 122 123 125 126 131 136 138 143 144–5 146 147 148 151 156 156 157 158

ILLUSTRATIONS

4.13 Percentage of persons entering higher education, Japan, 1950–1988 4.14 Proportion of four-year college graduates who find employment (%), Japan, 1960–1990 4.15 Changing marriage behaviour, Japan, 1950–1985 5.1 Size of the labour force and labour force participation by sex and farm/non-farm households, Korea, 1963–1990 5.2 Labour force participation rates by sex and age group, Korea, 1963–1990 5.3 The number of advancers and trends in advancement rates to higher educational institutions, Korea, 1966–1990 5.4 Distribution of employed persons by employment status, Korea, 1963–1990 5.5 Employment distribution of women and percentage of workers who are female, by industry, Korea, 1960–1990 5.6 Employment distribution of women in manufacturing industry and percentage of workers who are female, Korea, 1966–1989 5.7 Distribution of women workers in the fabricated metals industry by a three-digit industry classification and share of workers who are female, Korea, 1989 5.8 Distribution of employed persons by size of establishment, Korea, 1989 5.9 Average monthly wage by industry, Korea, 1989 5.10 Employment distribution of women and the percentage of workers who are female, by occupation, Korea, 1960–1990 5.11 Relative share of women workers and relative wage of women workers in female-dominant occupations, Korea, 1989 5.12 Distribution of high-wage and low-wage firms, Korea, 1973–1989 5.13 Composition of workers by high-wage and low-wage firms, Korea, 1973–1989 5.14 Number of vacancies and vacancy rate by industry, Korea, 1985–1990 5.15 Number of vacancies and vacancy rate by occupation, Korea, 1985–1990 5.16 Job opening rate, Korea, 1990 5.17 Labour force, unemployment and discouraged workers by sex and age group, and sex and education, Korea, 1989 5.18 Definitions of variables in the estimation of the earnings function, Korea, 1984 and 1989 5.19 Least squares estimates of the earnings function by sex, Korea, 1984 and 1989 5.20 Mean values of regressors for Korea, 1984 and 1989 xii

159 159 160 167 169 170 171 172

173

174 174 175 176 177 180 181 182 183 184 185 190 191 192

ILLUSTRATIONS

6.1

Participation rates and unemployment rates by sex and urban-rural location, Peninsular Malaysia, 1975–1987 6.2 Employed persons by industry, rural-urban location and race, Malaysia, 1986 6.3 Employed persons by race, sex and employment status, Malaysia, 1986 6.4 Employment distribution of women and percentage of workers who are female by industry (one-digit), Malaysia, 1957–1987 6.5 Employment distribution of women, and percentage of workers who are female, by industry (three-digit), urban manufacturing sector, Malaysia, 1987 6.6 Employment distribution of women, and percentage of workers who are female, by occupation, Peninsular Malaysia, 1957–1987 6.7 Employment distribution of women, and percentage of workers who are female, by employment status, Malaysia, 1957–1987 6.8 Earnings functions, employees, annual earnings, Malaysia, 1973 6.9 Earnings functions, employees, annual earnings, Malaysia, 1987 6.10 Selectivity correction probits, paid employment, Malaysia, 1973–1987 6.11 Oaxaca decomposition of male-female earnings differentials, Malaysia, 1973 6.12 Oaxaca decomposition of male-female earnings differentials, Malaysia, 1987 7.1 Participation rates by age and sex, selected years, Philippines, 1956–1988 7.2 Participation rates by sex and urban-rural location, Philippines, 1976–1992 7.3 Unemployment rates by sex and urban-rural location, Philippines, 1976–1992 7.4 Employment distribution of women and percentage of workers who are female, by industry (one-digit), Philippines, 1960–1986 7.5 Employment distribution of women, and percentage of workers who are female, by three-digit industry, urban manufacturing sector, Philippines, 1978 and 1988 7.6 Percentage distribution of non-traditional manufactured exports by industry, Philippines, 1978 and 1988 7.7 Employment distribution of women, and percentage of workers who are female, by occupation, Philippines, 1960–1986 xiii

210 216 217 218

219

220

221 233 234 235 237 237 247 248 250

252

253 254

255

ILLUSTRATIONS

7.8 7.9 7.10 7.11 7.12 7.13 7.14 7.15 7.16 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11 8.12 8.13

Employment distribution of women, and percentage of workers who are female, by employment status, Philippines, 1960–1990 Average number of weekly hours worked by women in primary job by industry, Philippines, 1980, 1984 and 1989 Sectoral distribution by marital status: women, urban Philippines, 1988 Sectoral distribution by marital status: men, urban Philippines, 1988 Earnings functions, urban employees, nominal quarterly earnings, Philippines, 1978 and 1988 Earnings functions, urban employees, nominal hourly earnings, Philippines, 1978 and 1988 Mean values of variables for earnings equations, Philippines, 1978 and 1988 Participation probability logits, paid employment, Philippines, 1978 and 1988 Oaxaca decomposition of male-female earnings differential, Philippines, 1978 and 1988 Labour force participation rate by sex and location, Thailand, 1971–1989 Labour force participation rate by age and location, Thailand, 1980 and 1989 Labour force participation rate of population aged 25–60 by educational level and location, Thailand, 1980 and 1989 Labour force participation rate of population aged 25–60 by marital status and location, Thailand, 1980 and 1989 Percentage of total employment by industry, Thailand, 1980 and 1989 Employment and GDP share in female labour-intensive industries, Thailand, 1980 and 1989 Percentage of total employment by work status, Thailand, 1980 and 1989 Percentage of total employment by work status, sex and age, Thailand, 1980 and 1989 Percentage of total employment by occupation, sex and location, Thailand, 1980 and 1989 Average hours worked per week by age and sex, Thailand, 1980 and 1989 Average monthly wage rate of employees by age, sex and location, Thailand, 1980 and 1989 Average monthly wage rate of employees by education, sex and location, Thailand, 1980 and 1989 Average monthly wage rate of employees by industry, sex and location, Thailand, 1980 and 1989 xiv

256 258 259 260 261 262 263 264 266 276 278 279 280 281 283 284 285 287 288 289 291 292

ILLUSTRATIONS

8.14 Regression equations on monthly earnings, Thailand, 1980 and 1989 8.15 Probit estimates of being employee, Thailand, 1980 and 1989 8.16 Regression equation on monthly earnings with selectivity bias correction, Thailand, 1980 and 1989 8.17 Means of selected variables, Thailand, 1980 and 1989 8.18 Wage difference decomposition, Thailand, 1980 and 1989

xv

293 294 296 297 298

CONTRIBUTORS

Sarthi Acharya is Professor, Rural Studies Division, Tata Institute of Social Sciences, Bombay. He has also been a Visiting Scholar at the Institute for Social Studies, the Hague, the Asian Studies Center, Boston University, and the Agricultural University at Bogor, Indonesia. He is the author of many papers on the rural economy in India, including work on the labour market, poverty and women. Ruperto Alonzo is Professor, School of Economics, University of the Philippines, Quezon City and Director of its Program in Development Economics. He has been a consultant of the ADB, ILO/ARTEP, UN/ESCAP and UNIDO. He has published articles on Philippine development issues such as labour markets, education and the informal sector. He was editor of the Philippine Economic Journal from 1985 to 1991. Jamilah Ariffin is Associate Professor, Faculty of Economics and Administration, University of Malaya. Her work focuses on women and development, and industrialization and social change. She was a member of the Commonwealth Expert Group on ‘Women and Structural Adjustment’. She is the author and editor of several books, book chapters and journal articles on women, and women workers in Malaysia. Moo Ki Bai is Professor, Department of Economics, and also Director of the Institute of Economic Research, Seoul National University, Korea. He has also been a member of several important national committees such as the National Committee for Industrial Relations. He has written and edited eight books and numerous publications on labour issues in Korea. Dwayne Benjamin is Associate Professor of Economics at the University of Toronto. He has also been a consultant for the World Bank. His fields of research include labour economics, development economics and econometrics, and he has several papers and publications on labour markets including work on rural labour markets in Indonesia, women in the labour force in China, and immigrants in the labour market in Canada. xvi

CONTRIBUTORS

Woo Hyun Cho is Associate Professor, Department of Economics, Soong Sil University, Korea. He has also been General Secretary of the Korea Labour Economics Association, and Assistant Professor at the State University of New York at Fredonia. He has published three books and several articles on labour economics, labour market institutions and trade unions in Korea. M.Anne Hill is Professor of Economics, Queens College of the City University of New York, and Senior Research Associate, Center for the Study of Business and Government, Baruch College of the City University of New York. She has published three books and numerous other works on the topics of women’s work, women’s education, labour markets in Japan, and the economics of disability. Susan Horton is Professor of Economics, University of Toronto. She has also been a consultant to the World Bank, FAO, and UNICEF. She is the editor of two books and author of several publications on labour markets, health and nutrition in developing countries. Reema Nayar is an economist at the World Bank. She has worked on the informal sector, labour markets, education, fertility, health and nutrition in developing countries. Mathana Phananiramai is Associate Professor of Economics, Thammasat University, Thailand, and Research Fellow of Thailand Development Research Institute. She is the author of a number of publications on population, labour and household structure issues in Thailand. Guilherme Sedlacek is an economist at the World Bank. He has worked on labour markets especially in Latin America, and at the time of the project was working in the Women in Development Division of the Bank.

xvii

ACKNOWLEDGEMENTS

We are grateful to a number of organizations and individuals for their assistance in completing this study. Funding for the project was provided by IDRC (International Development Research Centre, Ottawa, Canada), the Japanese Government (via a Trust Fund at the World Bank), External Affairs Canada (Asia-Pacific Fund) and SSHRC (Social Sciences and Humanities Research Council, Canada). The two project workshops were hosted by the Institute for Policy Analysis, University of Toronto, and Seoul National University. Professor Moo Ki Bai kindly made all the arrangements at Seoul National University. Dipak Mazumdar (World Bank) helped greatly with the overall direction and coordination of the research, and it was his idea to undertake research on this topic. He obtained the initial research funding and commissioned the India study. Without his assistance this project would not have begun. At the workshop in Toronto, Julie Anderson-Schaffner (Stanford University), Albert Berry (University of Toronto), Suganya Hutaserani (Thai Development Research Institute), Dipak Mazumdar (World Bank) and Jaime Tenjo (University of Toronto) all participated and provided valuable comments. At the workshop in Seoul, Jae Hee Jeon (Ministry of Labour), Sung Jin Kim (Federation of Korea Trade Unions), Mi-na Lee (Hong Ik University), Chonghoon Rhee (Korea Development Institute), and Soo Bong Uh (Korea Labour Institute) commented on the papers and greatly enhanced the discussions. Project administration was capably undertaken by Sharon Bolt (Institute for Policy Analysis, University of Toronto). Preparation of the book manuscript involved excellent assistance with typing and organization by Corrine Sellars (also at the Institute for Policy Analysis), and painstaking and thorough editorial assistance by Priscilla Chiu and Johnson Ongking (at the time both were graduate students in the Department of Economics, University of Toronto). Thanks to Scott Anderson and Mayvis Rebeira for compiling the index. Many individuals and organizations contributed to the country studies. Susan Horton thanks Edgard Rodriguez for research assistance for the overview chapter. Sarthi Acharya is grateful to NSSO of India, particularly its Chairman Pravin Visaria, for providing him access to data for the India study, and to Alpana Thadani for research assistance. Dwayne Benjamin is grateful to the Biro Pusat Statistik in Jakarta for help in obtaining the data for the Indonesia study, Alex Korns for useful xviii

ACKNOWLEDGEMENTS

discussions about the data, the personnel of the ILO office in Jakarta for help in obtaining information on Indonesian labour legislation and the SSHRC (grant #410– 92–0975) for additional funding. Anne Hill would like to acknowledge the additional support of PSC-CUNY grant number 6–62394 for the Japan study, and the able research assistance of Edward McGovern. Moo Ki Bai and Woo Hyun Cho would like to thank Ms Hye Ja Kwon for research assistance for the Korea study. Ruperto Alonzo, Susan Horton and Reema Nayar thank the NSO of the Philippines for providing access to the Labour Force Survey data. Thanks to Gray Graffam, Ashu Handa, Johnson Ongking, Edgard Rodriguez and Scott Anderson for assistance with the figures.

xix

1 WOMEN AND INDUSTRIALIZATION IN ASIA Overview Susan Horton Women’s work has played an important part in the process of industrialization. Nowhere has this been more evident than in Asia, where women’s work in the export industries has played such an important role in successful growth. Key light export industries such as textiles and electronics rely heavily on young, relatively unskilled, women workers. Women’s contribution to earning foreign exchange via the promotion of tourism (e.g. Thailand) and migrant worker remittances (the Philippines) has also been considerable. There have been many anthropological and sociological studies of women’s market work in Asia, documenting the long hours, low pay, poor working conditions, and often dead-end jobs. The effect on women’s lives has also been examined, whereby young, often rural and less well educated women work in manufacturing industry in the period before marriage and childbearing. There have been far fewer studies of the bigger picture, namely the overall role that women play in the labour market, and how this has been changing in the process of development. This book tries to examine that bigger picture, using labour force surveys for seven Asian countries at several points in time. The focus is on examining how women’s labour market participation, employment patterns and earnings have changed over time in the process of industrialization. The seven countries range from low income developing countries (India, Indonesia) to upper middle income developing countries (Korea) and one OECD country (Japan). Two of the seven have had relatively slow growth of per capita GNP over the last twenty-five years (India and the Philippines), whilst the others have had growth in per capita GDP exceeding 4 per cent per annum (World Bank, 1992). Background information on levels of per capita GDP, women’s education and fertility is given in Table 1.1. Some potentially interesting cases (such as socialist countries) had to be omitted because of lack of comparable data. The

1

WOMEN AND INDUSTRIALIZATION IN ASIA Table 1.1 Total basic socio-economic indicators, seven selected Asian countries, 1965 and 1990

seven country studies (India, Indonesia, Japan, Korea, Malaysia, the Philippines and Thailand), alphabetically ordered, form Chapters two to eight of this book. Relatively few previous studies have attempted this type of comparative analysis, since in general labour force surveys for developing countries are underutilized, and cross-country comparisons therefore become a large undertaking. A few previous studies have used individual country survey data (Schultz 1990, Psacharopoulos and Tzannatos, 1992, Horton, Kanbur and Mazumdar, 1991). Others have used international compilations, usually the ILO Yearbook (Anker and Hein, 1986, Standing, 1989). Access to individual country surveys is preferable: internationally-compiled data on earnings are particularly weak, there are advantages to using individual not aggregate data, and often there are important discontinuities in country-level data, not always well documented in international yearbooks. Without wishing to give away all the conclusions at the very outset, there are two broad findings of this book. First, the process of industrialization itself seems to enforce broad regularities on women’s work in all countries, notwithstanding the large differences across countries. The changes occurring in Asia have interesting parallels to the US at earlier stages of industrialization. At the same time the cross-country differences in women’s earnings relative to men’s are larger within Asia than probably any other region. Second, studies focusing on individual sectors and industries miss the ‘big picture’. Women’s participation in the urban market economy in Asia has been increasing, not only in production-related occupations, but also in other occupations including those higherpaid professional and administrative ones. And, consistent with the relative increase in women’s education relative to men’s, women’s relative earnings have also been rising. 2

WOMEN AND INDUSTRIALIZATION: OVERVIEW

In this chapter we first survey previous studies (pp. 3–7), and then synthesize in turn the country study results on women’s participation (pp. 7–16), employment patterns (pp. 16–28) and earnings (pp. 29–32). The section beginning on p. 32 discusses the different institutions affecting women’s work in the countries concerned, including the ‘marriage bar’, protective legislation and special benefits for women workers, equal pay and equal employment opportunity legislation and provision of day care. The final section of the chapter (see p. 36–8) contains conclusions. LITERATURE SURVEY As mentioned above, there are more comparative studies on women’s participation and employment patterns than on earnings, since statistics on participation and employment are more readily available (the main source being the ILO Yearbook). Previous studies using ILO data for Asia include Lim (1993) and UN ESCAP (1989). Schultz (1990) examines some worldwide trends, using Census data, Bakker (1988) undertakes a comparative study of OECD countries, and there is one study of Latin America using a methodology similar to the one used in this book (Psacharopoulos and Tzannatos, 1992). Finally, Goldin’s (1990) pathbreaking study of the long sweep of patterns of women’s work and pay in the US provides a fascinating base for comparison. Participation The main determining factor in the trend of women’s participation over time is the rural-urban division. Goldin (1990) argues that for the US there was a long-run Ushaped pattern of participation over time. This was explained by an initially higher participation rate for women in rural than urban areas, since women’s rural activities were more compatible with child care and household responsibilities. This led to an initial decline in overall women’s participation with urbanization. As urbanization continued and was accompanied by the demographic transition, women’s participation rates within urban areas began to increase, and eventually aggregate rates rose also. This same broad hypothesis seems to explain patterns elsewhere in the world, although there are important variations across regions. In Latin America for example women’s rural participation rates are traditionally lower than urban rates, such that urbanization is associated consistently with increased participation (Psacharopoulos and Tzannatos, 1992). For OECD countries recently women’s participation rates have been either constant or rising (Bakker, 1988). Schultz (1990), using ILO data, finds that women’s overall participation rate is decreasing worldwide, using population-weighted data. When the data are disaggregated by region, participation rates for women are increasing in the high-income countries, in Latin America and in East Asia. The worldwide decrease is heavily influenced 3

WOMEN AND INDUSTRIALIZATION IN ASIA

by trends in South and West Asia, where definitional changes in the censuses make inter-year comparisons unreliable. (The fifth and last region is North Africa, where women’s participation is also decreasing: there are inadequate data for sub-Saharan Africa.) There are also interesting differences observed in age-participation profiles for women in different regions (which also change with time). At least three different patterns can be characterized. The ‘double peaked’ pattern whereby women participate prior to marriage and child bearing, and then return to the labour force when children are older, is exhibited by the US for example (Goldin, 1990). This contrasts with the ‘single peaked’ pattern of early participation without a later return to the labour force, which Lim (1993) and UNESCAP (1989) identify in Singapore and Hong Kong, and which Lim attributes to Chinese-Confucianist cultural beliefs that married women should attend to the needs of their families. The third pattern can be described as a ‘plateau’, which is more evident in rural areas, whereby women continue their labour force participation with little interruption for child rearing. (This same pattern is typical for men almost everywhere.) Lim (1993) describes the Philippines, Thailand, Malaysia and Indonesia as following a ‘high plateau’ pattern, which she attributes to ‘the less age-selective nature of female employment in agriculture or in domestic service, petty trade or handicrafts where the combination of childcare and work was possible’. She describes the South Asian countries (Nepal, Bangladesh, Sri Lanka, India and Pakistan) as following a ‘low plateau’ pattern, which she attributes to the cultural sanctions against married women participating in the work force. Employment patterns Previous studies have focused on at least four different ways of categorizing employment, namely by industry (usually the nine one-digit SITC categories presented in the ILO Yearbook), by occupation (usually the eight one-digit ISCO categories in the ILO Yearbook), by employment status (employee/self employed/ unpaid family worker, etc.), and finally by public/private status. Studies by industry status show that in the course of development labour tends to shift from the agricultural sector, mainly to manufacturing, commerce and services (Schultz, 1990). According to Schultz, in most regions women’s employment shifts more into commerce and services relative to men. There is however considerable regional variation in theshare of women in different industrial sectors. The share of women in total employment in the four main sectors ranges as follows: 0.118 to 0.363 (agriculture: the figures correspond to Latin America and East Asia respectively), 0.109 to 0.439 (manufacturing: North Africa and East Asia respectively), 0.05 to 0.438 (commerce: East Asia and high income countries

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WOMEN AND INDUSTRIALIZATION: OVERVIEW

respectively), and 0.178 to 0.52 (services: North Africa and high income countries respectively). Other studies have focused on the feminization of employment in certain industrial sectors, particularly manufacturing. For Latin America Psacharopoulos and Tzannatos (1992) find that women tend to be overly represented in services, manufacturing, and commerce. Standing (1989) views with concern the increased share of female employment in the non-agricultural sector and amongst production workers, arguing that this is part of a worldwide trend to using more flexible, cheaper labour. Wood (1991) focuses on the feminization of labour in manufacturing in developing countries and tries to explain why this has apparently not eliminated corresponding female employment in manufacturing in developed countries. Lim (1993) documents a trend to feminization in particular export industries (electronics, textiles, garments, food processing, footwear, chemicals, rubber and plastic) as well as in services and clerical work in Asia. For OECD countries, women overwhelmingly work in the service sector, according to Bakker (1988). Examining employment by occupation, several interesting studies for individual occupations and individual countries have documented the phenomenon whereby certain occupations have changed from being maledominated to becoming female-dominated (often accompanied by reductions in pay relative to other occupations). This has happened for example to clerical work, to teaching and may be occurring to family-practice doctors and to some law specialities (Routh, 1965, Cohn, 1985, Goldin, 1990). There are fewer systematic cross-country studies. One exception is Psacharopoulos and Tzannatos (1992), who undertake a Duncan index analysis of dissimilarity of men’s and women’s employment, both for seven occupational categories and eight industrial categories, for fifteen Latin American countries. They could not find any trend in the degree of dissimilarity of men’s and women’s occupational distribution (it decreased over time in seven countries, increased in six, and no information was available for the other two countries). Considering only employees, however, women’s occupational distribution tended to become more similar to men’s. Development is also associated with a shift towards employee status, associated with the decline of family farms and the incorporation of businesses. This shift occurs for women just as for men (Psacharopoulos and Tzannatos, 1992, for Latin America), although less quickly for women (Schultz, 1990, worldwide data). However, Standing (1989) also documentsan increase in the proportion of women who are self-employed outside agriculture (worldwide data), confirmed by Lim (1993) for Asia, who comments on the increase of women as contract workers in the home. As regards the public/private composition of employment, Standing notes an increase in the share of women in public sector employment (worldwide data), a trend corroborated by Lim (1993) for Asia. 5

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Earnings Few comparative studies of earnings trends exist, since the problems of obtaining reliable earnings data are more severe than of obtaining participation and employment data. Even for individual countries such as the US it has taken much work to determine what has happened to female earnings relative to male over time, and how the changes can be explained. Goldin (1990) provides a fascinating analysis of the gender gap in earnings for the US over time. Annual earnings for women were around 46 per cent of men’s in 1890, rose to around 56 per cent by 1930, remained virtually constant at 60 per cent between 1950 and 1980, and have since risen again. Prior to 1890 only manufacturing earnings are available. Women’s relative earnings were 30 per cent of men’s in 1820, rose to 56 per cent by 1895, and have since then not changed. Goldin explains the trends as follows: the initial earnings gap reflected women’s lesser physical strength, compared to that of men, a disadvantage that was offset by mechanization during the industrial revolution. The shift into white collar activities which accelerated towards the end of the nineteenth century led to an increase in rewards to education and experience. As women’s education and experience both increased relative to men’s, this improved women’s relative pay. At the same time ‘wage discrimination’ emerged somewhere between 1890 and 1940, in the form of differential rewards for men and women for the same characteristics, and related to quite separate male and female ‘job ladders’ in firms. ‘Marriage bars’ to employment also became important somewhere prior to 1930. Political changes since the 1960s laid the foundations for the increase in women’s relative pay in the 1980s. Psacharopoulos and Tzannatos (1992) examine earnings data and earnings functions for fifteen Latin American countries. They conclude that men’s earnings are around 30 per cent higher than women’s (or that the female/male earnings ratio is 75 per cent). Of the 30 per cent figure, 20 percentage points are due to differences in endowments (women’s lower hours worked and lower experience, partially offset by higher education levels for women), whilst the other 10 percentage points are due to different returns to the same characteristics. Using a selectivity correction reduces the difference by which men’s earnings exceed women’s to around 25 per cent (on average women with somewhat poorer humancapital characteristics select themselves into the labour force in Latin America). There are many other individual country studies of earnings (see country studies for references to Asian countries); however, there are relatively few which document trends over time. For the OECD countries, women earn 20–40 per cent less than men, and the narrowing of the gap over the 1970s was slow and minimal (Bakker, 1988). 6

WOMEN AND INDUSTRIALIZATION: OVERVIEW

WOMEN’S LABOUR MARKET PARTICIPATION The previous section hypothesized that women’s participation rates might have a U-shaped pattern in the course of development, explained by rural-urban shifts. In the seven Asian countries examined (Figure 1.1), women’s participation rates were rising over the period 1970–1990 for at least two countries (Korea and Thailand). For Korea, the increase dated back even earlier (data in Chapter 5 go back to 1963). Women’s participation also increased for Indonesia and the Philippines, but the time series are quite short. The increases in women’s participation in Asia are, however, less marked than in the industrialized countries and in Latin America over the same period, and this is a little surprising in view of the much faster growth in five of the seven Asian countries. The main contrast with Latin America is that women’s participation rates in Asia have been traditionally high, whereas they have been relatively low in Latin America. Most of the country studies provide information separately by urban and rural area, and these show that rural rates have in general been higher than urban rates. Thus rural-urban migration has tended to decrease women’s participation in aggregate, but this has been offset by rising rates within urban areas. Levels of women’s participation are fairly similar in five of the seven countries (around 40–50 per cent) (Figure 1.1). The Indian rates, though apparently lower in Figure 1.1, are in fact more similar, if the different definition of working age is taken into account. Calculations suggest that redefining the Indian working population to exclude those less than 10 or 15 would raise reported participation rates by 10 or 15 percentage points respectively. Women’s participation in Thailand is however markedly higher than in the other countries, in a way not explained by differences in definitions. The results for men (Figure 1.2) show less variance across countries: there is uniformly high participation by men in prime working ages across countries (again the lower Indian figures are partially the result of the different definition of working age in the population). In both Japan and Korea a trend decrease can be observed, similar to that observed in the industrialized countries.

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Figure 1.1 Participation rates of women in seven Asian countries, 1970–1990

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Figure 1.2 Participation rates of men in seven Asian countries, 1970–1990

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On p. 4 we discussed some stylized age-participation profiles (‘single peaked’, ‘double peaked’ and ‘plateau’). Participation rates by age from cross-sectional data need to be interpreted somewhat carefully, since they are not the rates observed over the life-cycle of any group of women, but rather contain both cohort and time trend effects. The female age-participation profile for the seven countries (Figure 1.3) seem to conform either to the ‘double peaked’ or to the ‘plateau’ patterns. The ‘plateau’ pattern exhibited by India, Indonesia, the Philippines and Thailand is similar to that for men. This pattern is more typical for women in rural economies, but for the Philippines and Thailand the pattern persists in urban areas. The ‘double peaked’ pattern (Japan, Korea) is more similar to that observed for industrialized countries, with exit after marriage and a return after childrearing. Malaysia has a somewhat modified ‘double peaked’ pattern also (which contrasts with Lim’s classification in UN ESCAP, 1989, using earlier data). In fact the data for rural Malaysia show a marked double peak, with only a single peak at young ages for urban areas (Mazumdar, 1994). The levels of women’s participation differ between countries. Thailand tends to have the highest rates at all ages. After age 25 Korea and Indonesia tend to have the next highest levels, and India and Japan the lowest levels. The differences are in part attributable to institutional and cultural forces, discussed further on pp. 32–36. In Japan and Korea until recently there has been a ‘marriage bar’ in that women automatically resigned jobs upon marriage (or possibly on pregnancy). This, plus the lack of available day care (either from public provision or from the extended family, which had tended to break down with rapid urbanization), and societal expectations that child care was women’s prime concern, also led to the sorting of women into dead-end jobs, which reduced the incentive to stay in the labour force. By contrast, in the Philippines and Thailand the extended family has tended to provide day care and there has been more of a tradition of women continuing work. Rural-urban composition of the labour force also matters. The plateau shape in the two low-income countries (India and Indonesia) may be related to their more rural-based economies and strong family ties. Participation rates for men (Figure 1.4) are fairly uniform across countries. Between 90 and 100 per cent of men of prime working age work, with lower proportions in the earlier and later age groups, depending on the length of time individuals stay in school, and the usual retirement age (retirement being more an urban than a rural phenomenon). Since most of the Asian countries examined have been growing rapidly, it is not surprising that the age-participation profiles for women have been changing over time (Figures 1.5 to 1.11). Several countries exhibit increased participation by older women as one might expect with shrinking family sizes. In Japan and Korea this has filled in the trough between the 10

WOMEN AND INDUSTRIALIZATION: OVERVIEW

Figure 1.3 Participation by age, women, in seven Asian countries, 1987–1990

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Figure 1.4 Participation by age, men, in seven Asian countries, 1987–1990

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Figure 1.5 Labour force participation, women, India, 1972–1988

Figure 1.6 Labour force participation, women, Indonesia, 1980–1990 13

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Figure 1.7 Labour force participation, women, Japan, 1970–1988

Figure 1.8 Labour force participation, women, Korea, 1963–1990

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Figure 1.9 Labour force participation, women, Malaysia, 1975–1987

Figure 1.10 Labour force participation, women, Philippines, 1978–1988 15

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Figure 1.11 Labour force participation, women, Thailand, 1980–1989

two peaks for labour force participation, implying increased participation by married women, much as has occurred over the last half century in the US (Goldin, 1990). There is somewhat more mixed evidence of a similar phenomenon in Malaysia, and in the Philippines, participation rates have risen for women over 35. For India, the labour market over the recent past has evidently been fairly static (economic growth has not far exceeded demographic pressure). No strong trends are observable for Indonesia, the other low income country. For Thailand the most noticeable change is the decline in rural participation rates for women of all ages, perhaps associated with rapid rural-urban migration of those women with higher propensities to work. PATTERNS OF EMPLOYMENT We analyse here three sets of patterns of employment—by industry, occupation, and employment status. Since employment patterns differ both with the level of development and by gender, there are two sets of tables, firstly the percentage distribution of women by employment status, and secondly the percentage of workers within each industry/occupation/status who are women. The latter controls

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for shifts of men’s employment in the course of development. Subsequently the Duncan indices of dissimilarity are calculated using the formula:

where i=1 indexes the sectors of interest, and fi and mi are the sectoral employment ratios of men and women to their respective labour force. Note that D=0 implies identical employment distributions and D=1 implies completely dissimilar ones for men and for women. Finally some simple regression results are presented, using women’s share of employment in a particular industry or sector or employment status as the dependent variable. The independent variables include per capita GDP (there are two variants: using exchange rates to convert to international prices, and using PPP exchange rates), country intercept dummies (Thailand is the base case) and a time trend. Employment patterns by industry In the course of development, women’s employment shifts out of agriculture into three other main sectors, namely manufacturing, commerce and services (Table 1.2). Since the female proportion in agriculture is relatively high, the sectoral shift leads to an increase in the female proportion in the other sectors, with feminization being particularly high in manufacturing, commerce and services (Table 1.3: note that the overall female proportion in the total labour force does not rise much, with the exception of Korea). There are some anomalies, such as the relatively higher proportion of construction workers who are women in India (15–25 per cent). There are also interesting parallels between descriptions of current Asian light export factories (where young single women workers, often recent migrants from rural areas, live in dormitories), with Goldin’s (1990) description of young single women workers living in boarding-houses in the US and working in the textile industry in the early nineteenth century. The Duncan indices using industry data (Table 1.4: eight industrial categories) are in the region 0.10–0.25, with the exception of the Philippines, where the value is closer to 0.4. By comparison, similar indices for Latin America (also with eight industrial categories) yielded a value of around 0.4. The greater similarity of male and female employment distributions in Asia seems attributable to the high participation rates of women in agriculture. By contrast, women’s participation in agriculture in the Philippines and in Latin America is much lower, hence the higher score on the index of dissimilarity. Regression results (Table 1.5) confirm that women’s employment shifts out of agriculture and into manufacturing, commerce and services in the 17

WOMEN AND INDUSTRIALIZATION IN ASIA

course of development. The only significant time trend is an increase in the proportion of women in commerce. There are country-specific differences, as shown by the country intercept: Thailand and Malaysia have a relatively higher share of their female workers in agriculture, and less in commerce and manufacturing than in the other countries, whereas the Philippines tends to have the lowest share of women workers in agriculture and the highest in manufacturing and services, relative to other countries. Employment information at the one-digit industry level may conceal important differences within industries. Most of the country studies include

20

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information on the distribution of women’s employment and the proportion of women workers for selected three-digit industries. They confirm the well-known fact that many of the light export industries are female-dominated throughout Asia. Employment patterns by occupation In the course of development there is a decline in the percentage of women employed as agricultural workers, and a shift into clerical, production, sales and service occupations (Table 1.6). Evidently the shift of women into clerical work occurs relatively later in the development process (as women shift out of light manufacturing industries, once the heavy industry phase of development commences), since only in Japan, and in Korea by 1990, is there much evidence of this shift. There are other differences across countries: in Japan the share of women amongst craft workers is particularly high, and it is also fairly high in Korea. As in other countries, some occupations are female-dominated and others maledominated (Table 1.7). Sales and service occupations have relatively high proportions of their workers who are women, and the share in clerical work is rising in all seven countries. Women also form an increasing share of professional workers (largely in activities such as teaching and nursing) but a low proportion of administrative (managerial) workers. This parallels the trend in the presently industrialized countries (Routh, 1965, Cohn, 1985, Goldin, 1990). The Duncan indices for occupation (Table 1.4, using seven occupational categories) are quite low, ranging from 0.05 to 0.25, again with the exception of the Philippines (0.30 to 0.40). By comparison, the value for Latin American countries, also using seven occupational categories, is around 0.49 (Psacharopoulos and Tzannatos, 1992). Women’s higher participation in agriculture in Asia (with the exception of the Philippines) again seems to be the main explanation. Regression results (Table 1.8) confirm that women’s employment shifts out of agriculture and into clerical, production, and service occupations in the course of development. There is a significant time trend into professional and administrative and sales occupations. Thailand and Malaysia again have a relatively higher share of their female employment in agriculture and a lower share in sales, and (for Thailand but not Malaysia) a lower share in services. The Philippines is at the opposite extreme, with the lowest share in agricultural occupations and the highest share in professional/administrative, production and services (the PPP results for production are an exception).

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Employment patterns by employment status The process of development is accompanied by a shift of women workers from the status of unpaid family worker to that of employee (albeit, in the case of India, to the less advantageous status of casual employee) (Table 1.9). There is also a smaller decline in the proportion of women who are self-employed (for all countries except India). The shift to paid employee status would be expected to improve women’s bargaining power within the household, since cash earnings from the market seem to affect decision making more than the same number of hours of unpaid work.

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In the course of development, the female proportion in each employment status increases. This is a compositional effect: unpaid family work is the most heavily feminized employment status, at least until quite late in the development process, and this is the declining sector in the course of development (Table 1.10). The regression results (Table 1.11) confirm the shift out of unpaid family work and into employee status for women in the course of development. There is no significant change in the proportion of self-employed with development, and no significant time trends in any employment status.

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Thailand has the lowest share of women employees and the highest share of unpaid family workers, probably reflecting women’s concentration in agriculture. India and the Philippines represent the opposite extreme, with the highest share of women employees and the lowest share of unpaid family workers: for the Philippines this reflects women’s low concentration in agriculture.

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EARNINGS Women’s earnings relative to men’s have tended to have more than purely economic significance. Relative earnings in developed countries have been a rallying point for political movements. However, earnings data are more problematic to interpret than participation and employment data, as discussed in Appendix 1. Age-earnings profiles for women’s relative earnings are presented here for the seven Asian countries, for the late 1980s (Figure 1.12) and the corresponding earnings definitions are in Appendix Table A1.2. These profiles tell a story which is consistent with the age-participation profiles (Figure 1.3). As is also the case for industrialized countries, women’s earnings in Asia tend to decline relative to men’s with age, reflecting time spent out of the labour force and hence lower accumulation of experience, lower investments in training, and choice of more dead-end occupations (whether because of worker choice or employer insistence or both). The relative decline in women’s earnings is, unsurprisingly, least in Thailand and the Philippines (where women’s participation remains relatively high throughout the lifecycle) and where women’s relative earnings at age 50 are still 70–80 per cent of men’s. In India, Japan and Malaysia women’s earnings fall to 50 per cent of men’s by age 50, and in Korea and Indonesia where the decline is the greatest, women’s earnings fall to 30–40 per cent of men’s by age 50. Malaysia, Japan and Korea all exhibit the double-peaked age-participation profiles where women do not have continuous attachment to the labour force, which is likely to adversely affect earnings of older women. India and Indonesia by contrast exhibit plateaushaped age-participation profiles: one possibility is that in these low-income, rural economies the number of older women in paid employment is low and the figures are not very informative. The country studies provide a little further information as to the change in the age-earnings profile over time for Indonesia, Japan and Thailand. In each case the profile has shifted up over time (consistent with the overall increase in women’s relative earnings) and the increase becomes particularly marked at older ages (where the increase in each case is more than 10 percentage points over a ten or fifteen year period). A likely explanation is the narrowing gap in men’s and women’s education levels in the course of development, combined with the increase over time in women’s participation at older ages. However, since these profiles mix life cycle and cohort effects, they should be interpreted cautiously. Multivariate analysis is helpful to sort out some of the effects. Earnings equations were estimated for the seven countries for two points in time (data were only available for one year for India) (Table 1.12). It is somewhat difficult to generalize due to the differences in specification adopted.Although most studies used a similar set of explanatory

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Figure 1.12 Female-male earnings by age in seven Asian countries, 1987–1990

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WOMEN AND INDUSTRIALIZATION: OVERVIEW

variables (education, age, location, sometimes marital status, and in one case race), some added other variables such as occupation and industry, which are potentially more endogenous. Two of the studies used data other than household labour force surveys (namely Japan, where aggregate published data were used, and Korea, where establishment survey data were used). For these latter two countries selectivity-corrected estimates could not be obtained. The other five studies obtained both OLS and selectivity-corrected estimates. Five of the seven also allowed for hours differences between men and women explaining part of the earnings differential, either by including hours as a regressor (in one case this was instrumented), or by estimating separate equations for monthly and hourly earnings. 31

WOMEN AND INDUSTRIALIZATION IN ASIA

For the five countries for which selectivity-corrected earnings equations were estimated, participation equations were required (usually probits). Demographic variables were used to identify the model (i.e. these variables were thought to affect participation but not directly to affect earnings). There was no consistent pattern to the results concerning the coefficient on the selection term. It was significant in about half the cases (both for men and for women, and for rural as well as urban areas), and similarly was negative in about half the cases (again, true both for men and for women, and for rural as well as urban areas). Perhaps the most important conclusion from undertaking selectivity corrections, is that these are equally important for men as for women. Whereas the original efforts to undertake these corrections focused on the choice (primarily for women) whether or not to participate in the labour force, in fact there is also a selection bias affecting the choice between employee and other employment status, which affects both women and men. A standard Oaxaca decomposition was done using the earnings equations, to examine the components of the male-female earnings differential (Table 1.13), using male coefficients and female means, i.e.:

Ln(w) here refers to the log of average wages, b to the regression coefficients, and x to the independent variable means, and the subscripts m and f denote male and female respectively. There are encouraging trends in that women’s relative earnings improve with time within all countries (data for India were only available for one year). There is also a tendency for a slightly smaller proportion of the differential to be attributable to characteristics with time (the change in Japan from 1978 to 1988 in hourly wages being the main exception). The most likely explanation for the narrowing gap is the increase in women’s education relative to men’s over time and with development, as well as the increase in women’s labour market experience (which was most apparent for Japan, Korea and Malaysia, in Figures 1.5–1.11). The Philippines is an outlier, in that women’s labour market characteristics would predict higher wages than men’s, evaluated at men‘s coefficients: this largely reflects the higher educational attainment of women than men in the Philippines. In this as in other characteristics the Philippines again resembles Latin American rather than other Asian countries. INSTITUTIONS This chapter has documented substantial variations across countries in women’s participation, employment and earnings, even once level of

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development is accounted for (using a measure such as per capita GDP). There are clearly important social and cultural factors involved, beyond the scope of discussion by economists, affecting women’s role in the family and in society. Different social values affect the likelihood that women participate in the labour market. For example, the Confucian ethic in Japan and Korea, Muslim influence in parts of Malaysia and Indonesia, and traditions of female seclusion in parts of India, all stress women’s role in the family and downplay their role in the labour market. By contrast some observers point to historical factors which have enhanced women’s role in the market economy in Thailand. In the Philippines inheritance traditions (whereby land is transmitted to sons) may have contributed to the emphasis on education of daughters and hence women’s importance in the urban labour market. Other disciplines such as anthropology and women’s studies might also characterize the seven countries somewhat differently as to the strength of patriarchal traditions. 33

WOMEN AND INDUSTRIALIZATION IN ASIA

More amenable to discussion by economists are labour market institutions in different countries which affect women’s labour market participation. Before undertaking the study, it seemed plausible that in those countries where women’s pay was relatively higher, labour market institutions would be more favourable to women. This would not necessarily imply that it was the institutions which led to higher relative pay: both could be affected by underlying social factors which were more conducive to women’s participation in the market economy. However, the country studies suggested that the relationship between institutions and labour market outcomes was less clearcut. Of the two countries with the highest relative pay for women, and where women seem to have made the greatest advances into higher-paying occupations (the Philippines and Thailand), one has institutions explicitly favouring women’s labour market role (the Philippines) whilst the other does not (Thailand). Of the other five countries where women’s relative pay is lower, three have equal pay laws (India, Japan and Korea) and two have recently enacted quite strong equal-opportunity legislation (Japan and Korea). There are two different strands in the legislation affecting women’s market work which often come into conflict, namely protective legislation (generally related to women’s maternal role, including restrictions on night and hazardous work, and providing for maternity leave), and equity-promoting legislation. The protective legislation tends to predate the equity-promoting legislation. A number of specific institutions or measures were examined for each country in the study (note that the existence of legislation does not imply its enforcement, and the issue of enforcement is discussed later). The protective measures examined included whether or not the country was signatory to ILO Convention 89 (1948) (on night work for women), and whether there was pregnancy or maternity leave legislated. The equity-promoting legislation included whether or not the country was signatory to ILO Convention 100 (1951) (on equal remuneration for equal work) and to the 1988 UNCDAW (UN Convention on Removal of all Forms of Discrimination Against Women). In each case it was necessary to examine whether or not there was domestic legislation to enact the provisions of the international conventions signed. In the case of the UNCDAW, specific domestic measures examined included the existence of equal opportunity legislation, ‘marriage bars’ to employment, sex-segregated job advertisements and discrimination in retirement age. In some countries family law which discriminates against women may affect women’s ability to obtain equal treatment at work. Note that Korea was not a member of the ILO until recently and has therefore not yet signed many of the conventions. Considering protective legislation, only two of the six countries have signed ILO Convention 89 on night work, namely the Philippines and India. However, most of the countries do have domestic legislation limiting night work and work deemed too heavy or hazardous for women (mining, work involving heavy lifting, 34

WOMEN AND INDUSTRIALIZATION: OVERVIEW

etc.). At the same time these laws are not rigidly enforced. There is usually a provision for exceptions, and night work by women in export factories is prevalent in many countries. Maternity leave is also generally legislated, and of similar length (three months in India and Indonesia, sixty days in Korea, the Philippines and the private sector in Malaysia, six weeks in the public sector in Malaysia). The amount of pay, and who pays, varies, and this has implications. In Japan at least part is paid for by the Social Security system whereas in most of the other countries the main cost falls on employers, which can be a disincentive to hire women (especially older and married women). Korea, for example, is considering shifting part of the cost to the Social Security system for this reason. In Thailand the employer and the Social Security system each pay half of the cost of maternity leave. Equal pay legislation is also of fairly long standing in most of the countries. India, Indonesia, Japan and the Philippines are all signatory to ILO Convention 100 on equal pay for equal work, and both Korea and Thailand also have domestic legislation with the same intent. Only in Malaysia are domestic laws silent on this issue. Equal opportunity legislation is relatively newer. The signing of the UNCDAW (1988) implies corresponding changes in domestic legislation. Malaysia is not a signatory to this convention, but most of the other countries are. Japan and Korea both passed domestic equal opportunity legislation in 1988. This legislation removed a number of previous highly discriminatory practices. In Japan women used to retire ten years earlier than men, and there had been marriage bars both in the private sector and in government jobs up until the late 1970s. In Korea marriage bars had been common into the 1980s, and sex-segregated job advertisements were the norm (vacancy information was published by sex). It is now no longer permissible to discriminate against married women in promotion and dismissal, although it remains permissible in hiring. In the Philippines the 1987 constitution explicitly calls for equality between the sexes, and unequal provisions of the family code of law have been removed. Sex-segregated job advertisements are common, but there are no official marriage bars except in the armed forces. In the other countries there are few domestic laws prohibiting discriminatory practices. In Malaysia, since the federal constitution does not make reference to sex it is difficult to obtain legal redressal on this basis. In Indonesia, there is legislation prohibiting discrimination against anyone in employment, which implicitly prohibits discrimination against women: however, family law recognizes the husband as head of the household, and discrimination against married women in hiring is permitted. In Thailand marriage bars persist and sexsegregated job advertisements are tolerated. In India sex-segregated job advertisements are also common. 35

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Domestic legislation is of limited value if there is no enforcement, and enforcement of provisions affecting women is particularly difficult if women do not work in the formal sector. The proportion of women in the formal sector is smaller, the poorer the country. Thus, although India has on paper a number of laws regulating women’s employment, the very small proportion of women in the formal sector inhibits enforcement. In the Philippines, where lack of growth has hindered employment creation, more of the complaints concerning violations of minimum wage laws are from women. At the same time the strength of domestic political movements representing women may play some role. Some observers in Korea and Japan argue that groups representing women may not be strong enough to ensure genuine enforcement of (on paper) relatively strong equity-promoting legislation. By comparison women’s political role in the Philippines may appear stronger. Leaving aside further discussion of political issues, there are practical issues which are difficult to solve, affecting women’s career advancement. Child care is one of the main impediments to participation of married women. There are very few formal day care centres in these countries (although Korea has legislation encouraging large establishments to provide child care). The extended family is less likely to be able to provide child care if the trend to a nuclear family continues in Asia. The same issues face working mothers in Asia as in industrialized countries. Women are observed to work shorter hours (Thai country study), shift to jobs with more flexible hours (Philippines country study), and there are ‘mommy tracks’ of career advancement (Japan). These factors all tend to limit women’s career advancement and relative pay, but are difficult to deal with. CONCLUSIONS This study attempted the (possibly Herculean) task of comparing women’s participation, employment and earnings patterns over time for seven Asian countries, mostly in East and Southeast Asia, with the addition of India. Although such work has been done previously for OECD countries (Bakker 1988) and Latin American countries (Psacharopoulos and Tzannatos, 1992) there are no such previous studies for Asia, to our knowledge. There are some interesting similarities between women’s progress in the labour force in Asia and that in the now-industrialized countries. There is evidence of a similar U-shaped pattern of participation over time, whereby participation falls with urbanization and rises subsequently once the demographic transition is completed. The rise in participation occurs particularly for older (usually married) women. The employment shifts also are similar: by industry there is a concentration of women in manufacturing, commerce and services; there is a 36

WOMEN AND INDUSTRIALIZATION: OVERVIEW

trend towards feminization of certain occupations (e.g. clerical work and some professions); and a shift for women (as for men) from unpaid family work to employee status. There is some encouraging evidence of a narrowing gap in earnings relative to men as women’s education and labour market experience rise relative to men’s, and a possible tendency towards a smaller proportion of the earnings gap being explained by women’s labour market characteristics. East Asia does exhibit some differences from other regions. Women’s participation rates are relatively high, and the share of women in manufacturing and in agriculture is particularly high relative to other regions. Within Asia there are differences amongst countries. Some of the differences are related to different levels of development of the countries within the region (the preceding paragraph has mentioned some of the systematic trends in participation, employment and earnings noted in the course of development). Other differences are country-specific. Thailand is unusual in its high level of female participation and female/male earnings ratio. The Philippines is unusual in its structure of female employment, which more resembles that of Latin American countries, with the level of women’s education exceeding that of men, low levels of female participation in rural areas, and high levels in urban areas. Some of the country differences are probably related to social and cultural differences. Some may be explained by different labour market institutions, although the institutions themselves are also likely to be shaped by prevailing social and cultural values, such that simply changing the institutions may do relatively little to change women’s participation in employment. One limitation of the study is that information on women’s work and market earnings does not directly inform us about women’s (or their families’) welfare. If increased women’s participation is at the cost of longer work days for women, inferior quality child care, a higher propensity for marital breakdown, and poor or unhealthy work conditions, these may offset the benefits of work outside the home and higher earnings. It was impossible to examine the large and important issue of welfare within this book. Much interesting work remains to be done. This book did not explore finer details of the pattern of women’s work and earnings. Important topics remain for future research, such as the patterns of women’s unemployment relative to men’s, the trend in part-time work (which has been such an important component of the recent increase in women’s participation in OECD countries), and how women switch occupations/industries and change hours worked with the life cycle. The research gaps are greatest in work on earnings, which preferably requires access to national survey data, and where international data compilations such as the ILO Yearbook are least informative. Given the rapidity of change in women’s role in 37

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East Asia (due to rapid industrialization and demographic transition), it makes a particularly interesting region for further study. APPENDIX: DATA AND DEFINITIONS When undertaking cross-country and time series comparisons, it is important to discuss the data and definitions. Almost all the data used here are from labour force surveys (or, in the case of some of the ILO data, from Censuses). There are, however, differences in the exact definitions used. Methodological studies (e.g. Anker, Khan and Gupta, 1988) have shown that women’s labour force participation is particularly sensitive to the exact questions asked. This is especially important in rural societies where the demarcation between work in the household and for the market is somewhat fuzzy. Anker et al.’s study for India found that while sex and proxy status (self-reported or reported by another) of the respondent did not affect reporting of labour force activity, and sex of interviewer had a weak and inconsistent effect, the wording of the question did matter. Using the ILO definition of the labour force, a question involving the key phrases ‘main activity’, ‘secondary activity’ and ‘any other activity done for earnings’ resulted in a reported female labour force participation rate for Uttar Pradesh of 47.6 per cent. Adding specific questions on ‘family farm/ business or other cash earning activity’, ‘other specific subsistence activities such as animal husbandry, sewing, processing food for storage, gathering fuel’ and ‘any other time consuming activities’ raised reported rates to 55.3 per cent, 89.8 per cent and 93.0 per cent respectively. Information as to the question asked is rarely available in publications of labour force surveys, but may cause variations across countries and over time. There are also both cross-country and intertemporal differences in labour force definitions (see Appendix Table A1.1). The greatest problems in the existing country sample are with the data for India. The Indian Census data (those reported in the ILO Yearbook) have sufficiently large changes in the labour force definition that intertemporal comparisons are hazardous. The reference period changed from the ‘last fifteen days and last season’ (1961 Census) to ‘last seven days and last season’ (1971 Census) to ‘last one year’ (1981 Census). The 1961 Census asked about a certain small number of activities, the 1971 about ‘main and secondary occupation’, and the 1981 about ‘whether worked in last year’ and ‘main activity’. As a result, reported female labour force participation (for individuals aged 5 and over) changed from 31 per cent in 1961, to 16 per cent in 1971, to 24 per cent in 1981. The Indian NSS data used here are more consistent over time, but the age definition for participation (workers 5 and over, divided by total female population) is not highly comparable to the other countries where 38

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the minimum age is at least 10 or 15. Using the age distribution data for women in the Indian population for 1981 (ILO Yearbook), a reported average activity rate for women of around 29 per cent (using participation data by age for 1983, Chapter 2, Figure 2.2) would be converted to around 40 per cent if only women aged 10 and over were included, and around 45 per cent if only women aged 15 and over were included (author’s calculation).

39

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Similarly for the Philippines there are some problems of intertemporal comparisons, due to large changes in definitions (see Appendix to Chapter 7). This includes a change in the definition in the age of working population from 10+ up to 1976 and 15+ thereafter. The reference period was one week between 1956 and 1976, and since then has been one quarter. The labour force definition also expanded in 1976 to include some minor activities (home gardening, raising crops, hogs, poultry, fishing, etc.) provided that there were some implied earnings in the quarter. These definitional changes were associated with an increase of 15 to 20 percentage points in the labour force participation of women as reported in the ILO Yearbook, comparing the 1970s and 1990. For Indonesia, there would seem to be differences in definitions or coverage between the SUSENAS and SAKERNAS surveys (see country study). For Thailand there were changes in the age definition and in the definition of the labour force in 1989. There are also inter-country differences in the minimum hours requirement for unpaid family workers (more than one in Indonesia, Japan, and for 1988 in Thailand; more than eighteen in Korea; and more than twenty for 1980 in Thailand). There are also probable differences in the way in which workers are grouped into occupations and industries in different countries, but these are not well documented. Finally, earnings data are likely to be the most problematic for undertaking cross-country and intertemporal comparisons. Different reference periods may affect measurement of earnings due to differences in ability to recall. Earnings of employees are typically more accurately reported than those of the selfemployed, and earnings for farm households are particularly problematic. Most earnings functions analysis is therefore restricted to employees. However, secular increases in the proportion of the labour force who are employees implies compositional shifts in the data and changes in the fraction of the labour force covered. The data reported in the ILO Yearbook include both results from household surveys and those from establishment surveys. The results from the two types of surveys are not directly comparable. Establishment surveys usually entail less accurate information for the informal sector, and thus labour force survey data (as used here in the country studies) are preferable, even if one is analysing only employees. However, for Korea and Japan labour force survey data are not available: in this case the exact coverage of the survey is important, as there are certain excluded sectors (see Appendix Table A1.2). Another conceptual problem is the length of the reporting period for earnings. This is either monthly or quarterly in the country sample here, and there may be different errors in recall with different periods. A more important distinction is between hourly and weekly/monthly/quarterly earnings: in most countries women work shorter hours than men (young 40

WOMEN AND INDUSTRIALIZATION: OVERVIEW

women in the Philippines being an exception). Thus female earnings tend to be higher relative to men’s on an hourly basis, than on a weekly or monthly basis. Similarly there are differences in the exact definition of earnings. In some countries, particularly Japan and Korea in this sample, bonuses and special payments form a relatively large share of earnings (these payments are also not trivial for Thailand). However, the relative share of bonuses and special payments in total earnings differs for men and women. (For women in Japan, contract earnings form about 76 per cent of annual pay, overtime 4 per cent, and annual special payments 20 per cent: for men the corresponding figures are 70 per cent, 8 per cent and 22 per cent, respectively: see Chapter 4, Table 4.7, data for 1988.) It is therefore preferable to try to use the most inclusive measure of earnings as far as possible. For Japan these payments are included in the reported earnings: for Korea and Thailand they are not. BIBLIOGRAPHY Anker, R. and Hein, C. (1986) Sex Inequalities in Urban Employment in the Third World, Houndmills: Macmillan, for ILO. Anker, R., Khan, M.E. and Gupta, R.B. (1988) Women’s Participation in the Labour Force: a Methods Test in India for Improving its Measurement, Geneva: ILO. Bakker, I. (1988) ‘Women’s employment in comparative perspective’, in J.Jenson, E.Hagen and C.Reddy (eds) Feminization of the Labour Force, Cambridge: Polity Press. Cohn, S. (1985) The Process of Occupational Sex-Typing: the Feminization of Clerical Labor in Great Britain, Philadelphia: Temple University Press. Goldin, C. (1990) Understanding the Gender Gap: an Economic History of American Women, New York: Oxford University Press. 41

WOMEN AND INDUSTRIALIZATION IN ASIA Horton, S., Kanbur, R. and Mazumdar, D. (1991) ‘Labour markets in an era of adjustment: evidence from 12 developing countries’, International Labour Review 130:531–558. ILO (various years) Yearbook of Labour Statistics, Geneva: ILO. ——(1991) Yearbook of Labour Statistics: retrospective edition on population censuses 1945– 1989, Geneva: ILO. IMF (various years) International Financial Statistics, Washington DC: International Monetary Fund. Lim, L.L. (1993) ‘The feminization of labour in the Asian Pacific rim countries: from contributing to economic dynamism to bearing the brunt of structural adjustment’, in H.Ogawa and J.Williamson (eds) Human Resources in Development Along the AsiaPacific Rim, Singapore and New York: Oxford University Press. Mazumdar, D. (1994) ‘Labour markets in structural adjustment in Malaysia’, in S. Horton, R.Kanbur and D.Mazumdar (eds) Labour markets in an era of adjustment, Washington DC: World Bank EDI. Psacharopoulos, G. and Tzannatos, Z. (1992) Women’s Employment and Pay in Latin America, Washington DC: World Bank, Regional and Sectoral Studies. Routh, G. (1965) Occupation and Pay in Britain 1906–60, Cambridge: Cambridge University Press. Schultz, T.P. (1990) ‘Women’s changing participation in the labor force: a world perspective’, Economic Development and Cultural Change 38:457–488. Standing, G. (1989) ‘Global feminization through flexible labour’, World Development 17:1077–1095. Summers and Heston (1991) ‘The Penn World Table (Mark 5); an expanded set of international comparisons, 1950–1988’, Quarterly Journal of Economics 106: 327–368. UN ESCAP (1989) ‘Status of women in Asia and the Pacific region’, Bangkok: UN ESCAP publication no. ST/ESCAP/417. Wood, A. (1991) ‘North-South trade and female labour in manufacturing: an asymmetry’, Journal of Development Studies 27:168–189. World Bank (1992) World Development Report 1992, New York: Oxford University Press.

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2 WOMEN IN THE INDIAN LABOUR FORCE A temporal and spatial analysis Sarthi Acharya

BACKGROUND Women’s participation in market economic activities in India is low compared to most other parts of Asia. There is also wide regional variation within the country. Detailed field studies as well as large survey data reveal a high sensitivity of women’s measured participation to definitional changes. Minor alterations in the definition result in the inclusion of, or exclusion from, the labour force of a few million (Dixon, 1982, Anker, 1983, Anker and Khan, 1988). Debates on the nature of women’s work, to a great extent, are constrained by these definitional problems; therefore considerable effort is wasted in classifying economic activities and women’s participation in them. Consequently much of the macroeconomic research on women’s work is descriptive and based on data sets which are not necessarily comparable with each other. It is not difficult to trace the genesis of this ambiguity. Unlike most countries of East and South East Asia, India has not experienced rapid economic growth on a sustained basis in the contemporary period. The long term trend growth in the real GDP over 1951–1990 was 3.5 per cent per annum, and for per capita income, 1.5 per cent per annum over the same period. The GDP growth rate exceeded 5 per cent per annum for the first time in the 1980s. Moreover, the growth process has been uneven across regions and has not been labour intensive (GOI, 1990). Consequently evolution of labour markets has remained stunted. Large parts of the country have stayed within the peasant sectors in which it is difficult to differentiate between activities related to self-provision and the market (Jose, 1989). In support of this argument a few statistics may usefully be quoted here. The latest available labour data (for 1987–1988) show that about 70 per cent of the Indian work force resides in the rural areas and over two-thirds is engaged in a largely subsistence-oriented agriculture. Over half the workers are engaged in activities termed as ‘self-employment’ or ‘unpaid family work’. A substantial section of the population is engaged in unskilled jobs (Visaria and Minhas, 1991). In the 43

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absence of significant changes in the structure of the labour force, pre-industrial configurations continue to influence the work pattern (Ramachandran, 1990). Research on women’s work has been extensive since the mid-1970s, and a number of studies have tried to highlight women’s (in) visibility and importance in the economy (GOI, 1975, Mitra, 1978, IRRI, 1985, World Bank, 1991). However, as stated above, there have been problems relating to the definitions of work, enumeration and coverage, which have marred construction of systematic profiles for temporal and regional comparison. The classification of female workers has been subject to question, since some scholars find their work to be non-standardized (Harris, Kannan and Rodgers, 1989). Since the regional variation in the levels of development is high, cross-regional comparison of many features of the work force have not been found to be very meaningful. The only broad contention that has been agreed upon is that there are very wide, often inexplicable, differences in the labour force characteristics across regions and over time (Krishnamurty, 1984). Also, most studies on women in the labour market have not covered the decade of the 1980s, the only time when the country experienced a relatively high growth rate. This paper is written with a view to constructing profiles of labour participation, activity and sectoral distribution of female workers, identifying the causes of the wide variation in women’s work participation across states, and understanding their earnings behaviour. The main data used in the text are drawn from the National Sample Surveys (NSS) of India and the Ministry of Labour. Details on these sources are given in the Appendix. The period covered is that of the 1970s and 1980s. PARTICIPATION RATES Aggregate participation rates No two decennial censuses in India have had comparable definitions of workers in the recent decades except those conducted in 1981 and 1991. Since the 1991 reports are not available as yet, census data are not used for comparing labour participation rates.1 The NSS, since its twenty-seventh round of data collection in 1972–1973, has conducted quinquennial surveys on employment/unemployment in which comparable definitions have been used.2 These surveys, though conducted on a less than 1 per cent sample, are highly representative at the aggregate level. They are also more accurate than the census data, since their collection is staggered round the year to capture the effect of seasonality. The NSS uses three alternative criteria for enumerating workers: the usual status (US), the current weekly status (CWS) and the current daily status (CDS) (defined in the 44

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Appendix). More than one definition is deemed necessary in the Indian setting since the (pre-industrial) nature of work is difficult to capture by any single definition. Participation according to each of these is discussed in turn here. A person is included in the labour force as a main worker by the usual status if he/she has spent a major part of the last 365 days in gainful economic activity or is actively seeking/available for work. If a person works for (inclusive of is available for) only a minor part of the year he/she is deemed a subsidiary worker. Table 2.1 shows the percentage of persons in the labour force, as obtained from the four rounds of the NSS, namely, the twenty-seventh round (1972–1973), thirty-second round (1977–1978), thirty-eighth round (1983) and forty-third round (1987–1988).3 There is virtually no difference observed in the total (main plus subsidiary) as well as main workers’ participation rates (US) over the said years, for both male and female workers, in rural as well as urban areas. A fluctuation of 1 to 1.5 per cent is observed from one round to another but this could be attributed to sampling errors. The other two measures have a reference period of a week. Because of the short reference period, these measures exhibit a high degree of sensitivity to frequent withdrawal from and entry into the labour force. According to the CWS a person is deemed to be in the labour force if he/she has worked or is available for work for any time during the refer-

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ence week preceding the date of interview; while according to the CDS the labour participation is calculated as a proportion of the number of person-days worked (plus available/seeking work) to total person days on an average day in the reference week.4 Columns 4, 5, 8 and 9 in Table 2.1 show that the proportion of workers as enumerated by the CWS are lower than those by the US, and by the CDS, still lower. The participation rates by CWS/CDS are 20– 25 per cent lower than by US (main plus subsidiary) among women. While this pattern is observed even among male workers, the magnitude of the drop is much smaller. There are three main observations that call for attention here. The first is the low and invariant participation of women, owing to which the gap between the male and female participation is large. The second is the high and persistent presence of female subsidiary workers, in the range of 8–10 per cent of the population in rural areas and 3–4 per cent in urban areas. This forms about a fourth of the total female labour in each case. The third is the visible gap between the usual and current status participation rates. All three have their origin in the nature of constraints faced by women in a relatively slow-growing, poor agrarian economy. Women’s work participation is closely related to their overall work burden, which in turn is dependent upon the level of development of an economy. In India commercialization and division of labour have all along been low. To a great extent this can be attributed to slow economic growth and the associated low labour absorption (Acharya and Papanek, 1989). In large parts of rural and urban areas women are engaged in self-provision, due to which families are able to sustain themselves at amazingly low earnings levels of $2–3 per day.5 Table 2.2 presents data at the all-India level on women’s involvement in self providing activities, such as free fuel wood collection, cow dung cake preparation, fetching water, etc. This table shows that up to 88 per cent of the rural and 66 per cent of the urban women are engaged in some such activity and as a result they are constrained from entering the labour market as regular workers (see also Nagraj, 1988). 6 Aside from self provisioning, women also have reproductive responsibilities, due to which their temporary withdrawal from the labour force often renders them subsidiary workers. The birthrate in India is still in the range of 30–32 per thousand population. Again, because of the slow economic activity there is relatively low labour demand, due to which wages and incomes are low. Also, agriculture, the single largest employer, is a seasonal activity in which women’s role is quite rigidly defined. They participate only in certain activities, mainly sowing, seed bed preparation, plant care, weeding and harvesting, and frequently withdraw from the labour market for short periods. The fact that over the fifteen-year period women’s participation is virtually unaltered suggests that economic growth has barely kept up with demographic pressure. 46

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Labour participation by age Data on age distribution in Indian demographic- and socio-economic surveys are very weak (Krishnamurty, 1984). This is because the respondents often have little idea of their exact age. This bias can somewhat be neutralized if the age brackets are kept relatively large. The NSS tabulates data by fifteen year age brackets for the population belonging to age 15 years old and above, and by five-year brackets for those below 15. Figure 2.1 provides patterns of labour participation (according to US total), by sex and residence (rural/urban), as obtained from four surveys. The age group (30–44) years has the largest participation rate both among men and women. The reasons appear obvious. Younger men and women are in education in the earlier age brackets. Some women join the labour force after completing their reproductive responsibilities (i.e., after the age of 30–35). Next, given the fact that the average life span of an Indian during the 1980s was 55 years, both men and women tend to drop out of the labour force after the age of 45 due to poor health. Figure 2.2, which shows the age specific participation for 1987–1988 by five-year age brackets (for earlier years this was not tabulated), confirms the usual inverted U shaped curve with a peak at the age of 35–40 in both rural and urban areas. There is a striking similarity between the data from the different rounds which suggests that not much change has occurred during these fifteen years. A significant feature, though, is a drop in the labour participation rates of both men and women in rural as well as urban areas, in the age groups 5–9 and 10–14 years, between 1983 and 1987–1988. A fall is also observed in the 15–29 age bracket in all groups except urban women. Over this period the NSS data on school attendance report that in the 5–9 age group, the ratio of rural male school attendees to total in that 47

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age group rose from 49.6 per cent to 52.5 per cent, whilst the ratio for rural females rose from 35.5 per cent to 40.4 per cent. The corresponding increases in urban areas were from 71.1 per cent to 73.0 per cent (males) and from 65.9 per cent to 67.9 per cent (females). For the age group of 10–14 the increases were from 61.5 per cent to 66.1 per cent (males) and 33.9 per cent to 41.9 per cent (females) in rural areas; and 78.5 per cent to 79.9 per cent (males) and 67.0 per cent to 71.9 (females) in urban areas. These statistics show that during this period female school attendance has increased much more than male, which may account for the decline in labour participation in young age groups. An important exercise in understanding the age-specific labour participation rates is to compare age cohort participation rates. The NSS authorities tabulate data by fifteen-year age brackets and the time span between 1972–1973 and 1987– 1988 is fifteen years, permitting only a two point comparison. This is unlikely to yield meaningful results. The decennial census permits a comparison of ten-year cohorts for labour force of age 20 and above. A cohort comparison was done using data from the 1961, 1971 and 1981 census reports, but it did not yield meaningful conclusions owing to incomparability in the definition of workers. The results are therefore not presented here.7

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Labour participation by education The pattern on participation rates by broad educational attainments, sex and location, for age groups 15 years and above, are given in Figure 2.3. These data show that in virtually every case there is a U-shaped pattern; the illiterate enter the labour market in largest proportions followed by the just literate and then the middle school graduates. Except for urban women, the participation of secondary school graduates is the lowest, and participation then rises for university and college graduates and above. (For urban women the increase in participation begins at the secondary school education level.) Arguably the markets faced by the illiterate (thereby also the poor) are mainly the informal unskilled ones—both in urban and rural areas—in which entry is not very difficult. At the same time the compulsion to work for survival by the poor is acute. University graduates also have a compulsion to work owing to the large number of years of education put in. The intermediate education groups neither have such high supply compulsions nor is the demand for their skills strong. Thus those in the middle education groups do not participate, or else do so only as subsidiary workers. This is particularly the case for women. Regional variation in labour participation There is wide variation in the inter-state labour participation levels of women workers as seen from Figure 2.4. Their participation in the capacity of main workers ranges from less than 10 per cent in Punjab to over 40 per cent in Maharashtra. An age-specific standardization of the distribution (not presented here) reduces the variation by a bit but large differences still persist. The coefficient of variation across states is above 50 per cent. Several attempts have been made in the literature to explain the interstate variation in female work participation. In a recent exercise, under the (implicit) assumption that India is predominantly an agrarian economy, Sundram (1988) argues that women have household responsibilities which hamper their labour participation, and that women withdraw from the labour market when affluence increases. He also argues that, given the fact that women tend to work in informal systems where it is possible for them to simultaneously undertake household responsibilities, access to physical resources within the household will enhance their participation. While Sundram’s model is statistically significant, it is ahistorical and based on supply side logic only. More important, it is unlikely that the vast regional variation in women’s work can be explained by such a simplistic formulation.8 In another group of exercises it has been suggested that women work more in paddy growing areas. It is argued that they mainly work in 50

Source: National Sample Survey (NSS)

Figure 2.3 Labour force participation rates by education, sex and location for ages 16 and above, all India, 1987–1988

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monotonous and low paying occupations such as transplanting and weeding for which male labour is hard to get (Sen, 1985, Unnvehr and Stanford, 1985). This explanation does not, however, hold uniformly for the whole country since the eastern states, which are mainly paddy growing, exhibit very low labour participation rates compared to the millet zones in central and western India. Socio-cultural reasons are also argued to inhibit women’s participation in the labour force. Among the major constraining factors are caste and religion. Since India has had slow economic growth and limited presence of market forces, noneconomic factors continue to assume an important place. In this context one important constraint is purdah (or ‘seclusion’) which is widely (though not exclusively) practised in the east and north and on which a lot has been written (see Beteille, 1975, Abdullah and Ziedenstein, 1982). While this explanation carries 52

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a lot more weight than the other propositions, there are gaps in knowledge here too, such as why purdah affects women differentially across regions, given that it has been prevalent in most parts of the sub-continent. A macro-level estimate of participation probability This paper estimates a simple model to explain inter-state and micro-level variation in women’s participation, based on some of the factors discussed above. There are clear regional patterns in women’s participation. According to the main worker criterion, the eastern states, namely West Bengal, Assam and Bihar, along with the ones in the north, namely, Uttar Pradesh, Punjab and Haryana, show very low (less than 20 per cent) female labour participation rates. Orissa, an eastern state, also shows a fairly low participation, although higher than in the other eastern states. These states also have a high proportion of women working as subsidiary workers. In contrast, the states in the west and south, namely Rajasthan, Gujarat, Maharashtra, Madhya Pradesh, Andhra Pradesh, Karnataka and Tamil Nadu, show a relatively high participation rate (greater than 30 per cent). (Kerala, another southern state, is the only exception.) These states also have a low proportion of women subsidiary workers. This pattern of dispersion broadly coincides with the geographic ‘north and east’ versus ‘south and west’ divide of the country. Each of these regions has historical/cultural as well as agro-climatic characteristics which distinguish them. In the Indian context, to some extent, participation in the labour force has its genesis in the historical shaping of land systems. The eastern and northern regions, which receive high rainfall and/or lie in the fertile Indo-Gangetic plains, have historically been under land control systems which are characterized by a complex hierarchy of intermediaries involving castes and classes (the Zamindari system). Manual work on land is tedious (and low paying) and the social values attached to it have traditionally been such that it is to be performed by persons belonging to the lowest rungs of the society. In this multi-layer structure, people have traditionally tended to emulate the behaviour of those higher up in the hierarchy. Both men and women thus tend to shun manual wage-work, and this may tend to support the institution of purdah as a means of avoiding it. Since agriculture was (and still is) the dominant occupation, its influence has spread over other occupations, including those in urban areas (Acharya and Shah, 1987). In the south and west, regions which are characterized by relatively low rainfall, aridity and rocky terrains, historically the land systems (the Ryotwari system) assigned less rigid caste/class/sex roles to the populace. In these regions, unlike the Zamindari areas, land has been relatively more 53

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equitably distributed and the castes that control it belong to the middle cadre in the hierarchy of castes. The tendency to emulate the life styles of the landed does not inhibit others from working on land, since people belonging to the middle and landed castes themselves are not averse to working outside the household. Women’s work participation in these areas has also been aided by the fact that historically these areas have been sparsely populated and the sheer demand for labour has pulled women into the labour force. Again, agriculture, the principal employer, exercises influence on the other sectors, including those in urban areas; hence the overall participation rate is high. Within this broad north/east-south/west divide, the inter-state variation can be explained by socio-economic factors which govern the supply and demand for labour. Several micro studies show that women’s work burden in peasant societies is uniform at about 8–10 hours a day in diverse settings; the difference lies in what they do in which setting (Table 2.3).9 One way to analyse work is to divide it between market-oriented jobs (Wm) and expenditure saving activities (Wd). Wm is that component of work which is usually enumerated in the calculations of labour participation rates. Wd is that component of work which is not directly income generating but is economically and socially useful to the household; and if not performed, the household may have to expend money to buy the corresponding goods/services. Typical examples of these activities are animal care, food processing, weaving, tailoring, stitching, etc. (see Table 2.2). The choice between Wm and Wd depends upon the relative economic and social advantage derived from each activity. In each region, labour force participation depends on the choices available to workers (how attractive the returns are to Wm, relative to Wd). Two considerations warrant attention. First, dispossessment is an important reason for women to join the labour force. Agricultural land is the major resource in most of India and those who do not possess it join the labour force for survival (the returns to Wd become very low). Landless women in almost all cases are able to do little else other than manual jobs; they thus join the labour force in the capacity of casual labourers (Acharya and Panwalkar, 1988). Second, in a slow growing economy those women whose income (socio-economic status) is high do not join the labour force, since they do not find it meaningful to compete for work with low wages and difficult conditions. Instead, they engage themselves in ‘status production’ activities which Papanek (1979) describes as those which enhance the status of the household through human/ physical capital formation in the domestic sector (i.e., the returns to Wd are high relative to Wm). The choice between Wd and Wm is also influenced by human capital endowment. It was noted earlier that some education tends to inhibit women’s participation rate while more education may promote it. It is argued here, and empirical studies also show, that the demand for labour in the Indian markets for the less educated is not very high (Jose, 1989). In the face of slack demand conditions, those who can, withdraw from the labour force. By the same argument, 55

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the better educated tend to join it, because demand for highly educated labour is stronger. In an attempt to examine the empirical validity of the above explanation, grouped data on women’s labour participation rates from fifteen major states of India (disaggregated by rural and urban areas), as obtained from the NSS surveys of 1977–1978, 1983 and 1987–1988, have been pooled together.10 A Chow test permits such a pooling. The dependent variable is the percentage of women participating in the labour force to total women (US main), standardized for age distribution across states (LFPRf). The independent variables include two dummies, one for differentiating the erstwhile Zamindari and Ryotwari regions (NSD: 1=west/south) and the other for identifying the rural and urban areas (RUD: 1=rural). The reason for introducing the latter is to differentiate between labour markets in the two locales as the activities in them are different. Next, the percentage of women casual labourers (the dispossessed) to total women workers is included as a proxy for compulsion to work in the market (CL). Income is included (monthly per capita household expenditure, MPCE) to control for income effects on work decisions (ideally this should exclude the woman’s own earnings, but this was not feasible with the data).11 The model is estimated using a Generalized Least Squares (GLS) procedure since the off-diagonal elements in the variance-covariance matrix may not be zero in pooled panel data sets. The estimated equation is as follows (t values in brackets):

All variables are significant at 5 per cent level with the correct signs. In sum, it is suggested that at the very broad level, agro-climatic conditions and land systems shape women’s work participation, while within each region, choices/ compulsions operate according to the socio-economic status of households. A micro-level model of participation Microeconomic exercises in understanding labour participation are very scarce for India; there is therefore little to draw upon from the literature. The model here has been tested using primary records of the NSS for the province of Maharashtra, using persons age 10 and over for 1987–1988.12 Separate estimates are made for rural and urban areas. The dependent variable is labour force participation; if the woman participates the variable assumes value equal to unity, and zero otherwise.

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The model tested is essentially the same as before, with a few changes that are pertinent in micro-level estimation. An age variable (dummy) is included (1 in ages 30–45 and zero otherwise). This takes account of the childbearing activity prior to age 30, and the tendency to retire from the labour force after age 45 as health deteriorates. In an alternative specification, age and age squared are included in place of an age dummy. The number of children in the house is an equally important factor in keeping women from work. However, data on this variable are not available, and instead, the overall size of the household is included as a (poor) proxy. Two sociological explanatory variables included are marriage and religion. A dummy variable has been introduced that assumes a value zero for the married and unity otherwise. Under the assumption that certain religions influence work participation, another dummy is introduced which assumes a value zero for Islamic women (since they practice seclusion the most) and unity otherwise. The two variables used for representing withdrawal of women from the work force by the affluent and high castes are the monthly per capita expenditure and the caste status. The former is a continuous variable, and for the latter a dummy variable has been constructed which assumes a value unity for the ‘scheduled’ categories (socially and economically least advantaged) and zero for others. Next, female-headed households have been identified to be the poorest in India. The participation of women in these households is expected to be high. The variable is measured by a dummy assuming value zero for female-headed households and unity otherwise. The impact of education on participation is measured by constructing two dummy variables; one for up to secondary grade education (junior education), and another for education beyond secondary level (senior education). A continuous years of education variable would have been ideal but that is not possible here since data are available only for completed years of schooling at discrete intervals. Next, under the assumption that technical education would encourage people to participate in outside work, an indexed variable has been introduced, which assumes a next higher value as the technical grade rises. The model is fitted using the logistic regression method. The choice of this method over probit is made because much of the literature on this subject bases itself on the logit model. The estimates are given in Table 2.4. In specification one, all the variables in the equation on rural female participation are significant and bear the right signs. In the urban participation equation too, eight out of the ten coefficients are significant. Household expenditure and market status do not seem to be important 57

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in urban areas. It can be argued that the demand conditions here offer a wider choice of jobs, thereby permitting women the possibility of working for a wage/ salary without compromising their social status. This is corroborated by a positive sign on the higher education coefficient, which is negative in the equation for rural women. Specification two also shows very similar results, with only minor differences. The estimates confirm the hypotheses discussed.

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EMPLOYMENT PATTERNS Employment status The ILO classifies workers according to whether they are employers, ownaccount workers, employees or unpaid family workers. In the Indian setting, it is less useful to separate the first two categories since ‘employer’ as a category is statistically insignificant as well as non-exclusive. This is because the peasant sector in agriculture has a multitude of owners who are mainly self-employed but also hire-in/out labour. Next, it is useful to disaggregate employees into regular and casual workers since the work availability and earnings of these two are quite different. A distribution of workers by status categories as obtained from the four rounds of the NSS is given in Table 2.5. The category of unpaid family workers was not tabulated separately for 1977–1978 and 1987–1988, but was merged with the self-employed. In the rural areas a majority of both men and women are self-employed. The next most important group is casual labourers and then regular employees. A higher proportion of women are casual workers compared to men, and a lower proportion are regular employees. Since casual workers hold an inferior position compared to regular employees (see Visaria, 1981), women occupy an inferior position in this category of workers. In the urban areas the largest group of men are regular employees, while the largest group of women are self-employed. This is due to a higher rate of commercialization in urban than in rural areas, particularly in the male labour market. A breakdown of the self-employed into unpaid family workers and own account workers is available for 1972–1973 and 1983. These data show that in rural areas a major proportion of self-employed women are actually unpaid family workers, who are engaged in work which generates money income, but who have no control over their earnings. By contrast, a major proportion of men in rural areas are own-account workers. Fewer urban women workers are unpaid family workers, possibly owing to a higher degree of commercialization of the labour markets in urban areas. Table 2.5 also shows a trend towards casualization of the labour force over this period, which is more pronounced in rural than in urban areas. This is inevitable in a stagnant economy facing demographic pressure. There is distinctly a gender specific concentration of workers by status, and to judge whether this has changed over time, a Duncan index is computed.13 This index shows that over time there has been a reduction in segregation by employment status, larger in rural than in urban areas. However, this is computed based on only four observations. Age specific data on worker employment status at two points of time, 1977–1978 and 1987–1988 (Table 2.6), were examined to observe any 59

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change in the age-status roles. Workers in prime working ages in the Indian context (15–44) are more likely to be employees than workers in younger or older age groups. There is no discernible trend seen over time. Women’s employment by industry Table 2.7 presents the broad industrial division of male and female workers in rural and urban areas, for 1972–1973, 1977–1978, 1983 and 1987–1988. A priori it should be mentioned that workers in the primary sectors—mainly agriculture— have a lower status in the labour force, and earnings are low compared to the nonagricultural sectors. This table shows that over three-quarters of the labour force in the rural areas is concentrated in the primary sectors; more so among the women workers. In the urban areas too, almost a third of the women and about a tenth of the men are engaged in agriculture. In each case there is a higher proportion of women (than men) in agriculture, which strengthens the earlier findings about their lower status in the labour force. In the rural areas there has been a shift away from agriculture, of about 9 per cent among men and 5 per cent among women workers, and a corresponding rise in the other sectors, over the period 1972–1973 to 1987–1988. In proportional terms women’s employment has increased most in manufacturing and men’s in services in rural areas. In contrast, in the urban areas there has been little or no sectoral shift observed over this period. It is not encouraging to note that agriculture is a significant and only very slowly diminishing occupation in urban areas. A more detailed (two digit) breakdown of agriculture and allied activities in rural areas (Table 2.7) shows that livestock is the only agricultural activity in which there has been a significant increase in the proportion of women workers between 1972–1973 and 1987–1988. This is perhaps due to the stepping up of rural diversification programmes during this period, in which the principal activity promoted was animal husbandry. A corresponding two-digit breakdown of the manufacturing activities in urban areas (Table 2.7) shows a minor rise in the share of women workers in the industry groups of chemicals, electrical machinery, and others, but the absolute share of these in the overall employment structure is so small that the changes are insignificant. More striking, unlike in most of South and East Asia, there is no visible growth of employment in the ‘sunrise’ or export industries, and no visible alteration in the composition of the labour force. A Duncan index of dissimilarity was computed to judge the differences in the gender distribution of workers and the shift over time. For the rural areas this index assumes values 0.07, 0.08, 0.10 and 0.11 in the four survey years, whilst the corresponding figures for urban areas are, 0.30, 0.33, 0.32 and 0.34. There is a 62

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small movement towards dissimilarity as industry diversification has occurred over time. However, the results should be interpreted with caution because the dominance of one sector reduces the sensitivity of this index (as is the case here with agriculture in the rural sector). The industry distribution results support part of the findings of the employment status distribution (see pp 59–62) that the relative position of women in the labour market is inferior to that of men, but it has not deteriorated (compared to men’s) over the period. Employment in organized and unorganized sectors Workers in India are engaged in economic enterprises as diverse as modern automated industries and household and cottage enterprises. For analytical purposes, it is useful to distinguish the organized sector—i.e., those protected by various labour laws—and the unorganized sector (Bremen, 1980, Papola, 1981). Agriculture and allied activities are almost wholly outside the purview of the organized sector except some plantations, some livestock activities and specific agricultural services. Hence the non-agricultural sector alone is discussed here. Table 2.8 presents data on the breakdown of the non-agricultural employment by organized and unorganized sectors for 1972–1973 and 1983. The size of the organized sector was low (less than 10 per cent) when compared to the size of the total labour force of about 260 million (including agriculture) in the early 1980s. In the early 1970s the percentage of male workers in the non-agricultural sectors who were not organized was 68 per cent. By the 1980s this proportion rose to about 72 per cent. For female workers these percentages rose from 83 per cent to 85 per cent over the same period. It is evident that the share of the unprotected labour in the total labour force is larger for women than for men, and rising over time for both sexes. The organized manufacturing sector Most modern medium and large industries in India fall under the organized sector. Women’s employment in the organized manufacturing sector, broken down to the two- and three-digit level (the two-digit level breakdown is used for four industries with low female employment), is given in Table 2.9. This table reiterates the earlier finding that women are mainly concentrated in food and textile industries and there is little change in this pattern over the decade from the mid-1970s to the mid-1980s. The most striking feature found is that up to 50 per cent of women in the organized sector are concentrated in three industries (out of the fifty-nine listed in the table): textiles, cashew processing and tobacco processing (1985–1986 figures). The much talked about employment of women in 63

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the garment and electronic industries is perhaps outside the organized sector. In most high growth sectors (i.e., those which have experienced employment growth of 50 per cent or more) the absolute number of women has been too small to change the pattern. On aggregate the growth in employment has been less than 15 per cent over the ten years. Export industries are identified by an asterisk in this table. Among the export industries that have shown promise of high earnings in recent years are textiles, garments, electrical machinery (including electronics), gems and jewels, engineering goods, processed food, and petroleum products. Women’s employment is important in textiles and cashew processing, but not the others. Occupational distribution Studies on occupational distribution (Polachek, 1987) find that women seem to be concentrated in a small number of occupations. Table 2.10 contains data on the occupational distribution of workers according to a one digit classification. In rural areas there is a predominance of both sexes in agriculture, which, though decreasing over time (from 1977–1978 to 1987–1988), has stayed large.

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The urban areas show a concentration of workers of both sexes in production and transport. The next highest concentration of women workers appears in agriculture, followed by services, and then professional occupations. Over the decade there is a significant rise of women among professionals, administrative and clerical staff, and a corresponding fall in agriculture. There is considerable gender segregation in the occupational distribution (the Duncan index is about 0.28 for both years). Days of work Table 2.11 presents data on the number of days worked per week by men and women in rural and urban areas, for the years 1977–1978 and 1987–1988. (The NSS does not collect data on hours worked.) The definition of work is such that any work up to four hours is counted as a half day, and more than four hours work is counted as a day. Thus, data on days worked has deficiencies. It is no surprise that the figures given in Table 2.11 show virtually no variation. A coefficient of variation calculated on the Maharashtra data (mentioned earlier) shows an extremely small figure. In spite of this, effort was made to set up a model to explain variation in the days of work put in by female labour for Maharashtra. All the known explanatory variables combined were unable to explain more than 1 per cent of the variation in both rural and urban areas. 14 Arguably, it is labour demand which largely determines hours worked in India (evidence for this is the fact that a quarter of women,

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EARNINGS BEHAVIOUR (MICRO ANALYSIS) As stated earlier, primary data for the whole country are not available; therefore the NSS data set, available for the state of Maharashtra, has been used for estimating the earnings functions. Separate equations are estimated for rural and urban areas. The model estimated is based on the classical human capital theory of the Mincer type which uses individual attributes of workers to explain variations in earnings. Earnings behaviour in India has been studied by Tilak (1979), Datta (1985), Rao and Datta (1989), and Acharya and Jose (1991), among others. Most studies, however, base their findings on small samples drawn on individuals belonging to a section of the society, or a specific geographical setting. Analyses based on large and random samples are few, if any. In a recent global survey of literature done by 70

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Terrell (1992), not a single Indian study is quoted. It is with this backdrop that the present study is undertaken. Columns one and three of Table 2.12 present OLS estimates of this model for rural and urban areas respectively. The explanatory variables are age, age squared and five education dummies representing completion of different levels of education. In a statistical sense the equations are good fits with all the coefficients significant at 5 per cent confidence. The square of age bears a negative sign as expected. The education (dummy) coefficients increase in magnitude with increasing education as expected, implying that the mean earnings rise with education. The R2 value, particularly in the urban equation, is high, though it is not unacceptably low in the rural equation, given the fact that these equations are fitted using cross-section data. A two step self-selection correction procedure, suggested by Heckman (1976, 1980), has been followed and these results are in columns two and four. In both the equations the lambda (selection) terms are statistically only significant at the 10 per cent level, neither the R2 values nor the coefficient values of the explanatory variables change significantly, implying that the coefficients of the uncorrected equation are not biased. The Mincer model is expanded to include variables such as occupation and marital status (Table 2.13). The occupation variable is a dummy, with a value of unity for professional and technical-type occupations and zero for lower and unskilled jobs. Marital status is also represented by a dummy assuming a value of unity for the unmarried and zero otherwise. The estimates, presented in Table 2.13, show that all the coefficients in all four equations, other than marital status in the rural women’s earnings equation, are statistically significant. Lambda is no longer significant at 10 per cent level. Columns one and three on the one hand and two and four on the other, which are the estimates for the uncorrected and corrected versions of the model, show a fall in the magnitude of most coefficients when compared with their counterparts in Table 2.12. This shows that part of the education effect works via choice of occupation. The marital status coefficient indicates that married women are paid less than unmarried ones (from Table 2.4, they are also more likely to participate). The occupation variable is highly significant, with the expected sign. OLS estimates for male earnings, minus the self-selection correction variable, using NSS primary data for Maharashtra, are also given in Table 2.13, columns five and six. All the coefficients are statistically significant at the 5 per cent level and bear the right signs in both rural and urban equations. The gender wage gap is next disaggregated using the Oaxaca (1973) decomposition formula. This offers a method by which the wage gap due to explained or measured factors (Xs) can be separated from the 71

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unexplained or unmeasured ones. The difference between the mean of male and female wages can be explained as,

where W is the mean wage, the Xs are the means of explanatory variables and the Bs are the coefficients of Xs. The subscripts m and f refer to male and female, and subscript i refers to individual i. The decomposition is undertaken using columns one, three, five and six in Table 2.13, and the results are given in Table 2.14. The total wage gap explained in rural areas is 35 per cent, and in urban areas it is 26 per cent. Thus, a large portion stays unexplained. This is often thought of as

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the upper bound to gender discrimination, although demand side factors and unmeasured individual characteristics may also be important. This decomposition exercise is perhaps the first of its kind on Indian data. SOCIAL AND INSTITUTIONAL ASPECTS Traditionally, Indian women are viewed as housewives; their principal functions are restricted to reproduction and homemaking. Although this is not true for all Indian women, nevertheless, the status of women’s work continues to be viewed as subsidiary. The whole analysis of this paper bears testimony to this. Indian society is segregated owing to its incomplete transformation into a market economy. As a result, there are few economic practices common to all segments and regions. As far as market work is concerned, marriage and childbirth affect participation, but in ways specific to class and region. In rural areas most women continue to work, while in urban areas many withdraw from the labour force. The affluent can afford to drop out more than the less affluent. Women in the formal sector, particularly the government, are more likely to continue work since the law permits certain concessions such as maternity leave and paid leave. However, women with several children are likely to drop out of the labour force, whatever their employment. The laws that have been enacted that seek to regulate women’s work include the Maternity Benefit Act (1961) and the Equal Remuneration Act (1976). The Maternity Benefit Act seeks to protect women during pregnancy by allotting them three months of paid leave and regulating their work conditions to prevent damage 73

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to their health. Both this act and the Equal Remuneration Act are characterized by poor enforcement. The enforcement mechanisms are virtually non-existent in the unorganized sectors. The Equal Remuneration Law (1976) was enacted to prevent discriminatory practices with regard to labour recruitment and pay. Its main features are as follows: 1 Both men and women should get equal remuneration for the same work or work of a similar nature. 2 In order to comply with the above provision, the employer should not reduce the rate of remuneration. 3 The higher of the two rates is final. 4 Discriminatory practices in recruitment, promotion, training, transfers, etc. are prohibited. Often differential interpretations are attached to the above. The words ‘same work or work of similar nature’ is interpreted to suit the employers’ advantage. Thus women’s work is often graded as being qualitatively different and of less value than men’s. Moreover, many women are employed for seasonal work and often are not given regular wages (or salaries) as per the stated law. Closely related to this is the Minimum Wages Act (1948) which stipulates that men and women get an equal minimum wage. Again, in the absence of other regulating mechanisms, this is seldom enforced outside the organized sector. Eighty per cent of women work in the unorganized sectors and these women are not effectively covered by the Minimum Wages Act, Contract Labour Act (1970) or the Inter-state Migrant Work Act (1979). In the agricultural sector, minimum wage is prescribed for all manual work. There is also a provision of equal pay for equal work. There is, however, no administrative machinery to follow it up. In the organized sector (in non-agriculture) there are laws which provide for minimum wages for each job skill and these are equal for both the sexes. Work hour regulations, paid leave, medical insurance, job security and maternity leave, are all part of the package available to the organized sector workers. There is administrative machinery to support the legislations and powerful trade unions to follow them up. The organized sector, however, comprises only a small section of the work force and hence the impact of the laws is only partial. There is no legal prohibition about gender-segregated advertisements. It is not uncommon to see advertisements that exclusively seek applications from one gender or the other.

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Women in India are largely semi-literate and are socially and politically ignorant. Because of the lack of awareness and the resulting disempowerment, they are unable to fight for what is legally theirs. Coupled with an inefficient and half-hearted enforcing system, the situation is one of discrimination and exploitation. Certain workers are totally unprotected by the law. This is because they are inaccessible. They include home workers, domestic workers, vendors, hawkers and construction workers. CONCLUSION This chapter attempts to put together existing secondary data on women’s labour participation, their work status and occupations, the industry groups they are employed in, and their earnings, in a temporal and regional framework. The period covered is from the early 1970s to the early 1980s. The results indicate that there has been little change in women’s participation rates over time but that wide regional variations exist. The major reasons for these variations lie in the historical shaping of land systems, the choices people exercise in working for a wage vis à vis work at home, and some socio-cultural influences. There has been a rise in the casualization of the labour force owing to economic stagnation and demographic pressure. Gender segregation exists in the work of men and women by industry and occupation. Individual earnings functions of both men and women show that the classical human capital theory is applicable in the Indian conditions. An attempt was also made to correct for self-selection bias in the earnings functions; it was found that this bias was not prominent. Oaxaca decomposition exercises showed that up to 25–35 per cent of the male-female wage gap can be explained by measured variables. Women’s relatively weak economic position is also reflected in weaknesses of the institutions affecting women’s position in the labour force. This weakness is related to the level of economic development and to demographic pressure in labour supply. APPENDIX: DATA SETS AND DEFINITIONS The data sets used in this paper are drawn from the National Sample Survey Organization (NSS) on labour participation rates, employment status, occupation and industry distribution, and (for Maharashtra only) earnings, and from the Ministry of Labour on organized sector workers. It needs to be mentioned that primary data are not available from any government agency; only grouped data are accessible. Details on sample sizes, designs and definitions of the two surveys are given below. Some selected individual data on earnings were made available to the author, for Maharashtra only. 75

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THE NSS The NSS has conducted 4 quinquennial surveys since 1972–1973; in the year 1977– 1978, 1983 (calendar year) and 1987–1988, on employment and unemployment. A stratified two-stage sample design has been adopted for rural and urban areas separately. The size of the sample is not very large: it is about a tenth of 1 per cent of the population, but it is highly representative. Data collection is staggered through a full year to capture the effects of seasonality. Work participation definitions Recognizing the fact that India is a predominantly agrarian economy and a single definition of the work status of a person does not fully describe his/her activity, three criteria are simultaneously used for defining a worker. The status of activity on which a person spent a major part of the last 365 days before the date of the survey is considered the main (or principal) usual status of a person. A person is deemed to be in the labour force as per the usual status (main) if he/she has worked, sought work or is available for work, for a greater part of the last year. Conversely if a person spent a greater part of the last year in activities not considered economically gainful he/she is deemed to be a non-worker. A non-worker (on the basis of the principal usual activity status) who pursued some gainful activity for a minor part of the year is considered to be working in the capacity of a subsidiary worker. The main and subsidiary workers added together form the total work force as per the usual status. In the first survey (1972–1973) the questions were so posed that differentiation between main and subsidiary workers was not possible. Comparisons with the later surveys are accordingly limited. For classification of a person according to the current weekly status, he/she is first assigned a unique activity for the seven-day period preceding the survey. A person is deemed to be a worker if he/she is engaged in gainful economic activity or looking for it for at least one hour during the past week; or else considered a non-worker. For classifying a person according to the current daily status, up to two activity statuses are permissible per person each day (of the reference week). A person is deemed to be a worker for the entire day if he/ she has worked/been available for work for four hours, or more during a day. If a person has worked/been available for work for at least one hour a day but less than four hours he/she is considered a worker for half a day. The aggregate of person-days classified by different activities for all the seven days of a week gives the distribution of ‘working days’ and ‘out of labour force days’ during the survey week. 76

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As stated earlier the sample is highly representative of the population. For calculating weighted averages, the weights are to be drawn from the population rather than the sample. The distribution of the population by sex and residence for the four surveys is given below (in millions):

Definition of employment status • Self employed: persons who are engaged on their own farms/non-farm enterprises. Some operate without any hired hands while others may hire in workers. Some operate by exclusively hiring in workers, and they are termed ‘employers’. • Unpaid family workers: persons engaged in economically gainful activity in household enterprises but who do not receive any cash payment or share in earnings for the work performed. They are household members and in most cases related to the household head. They get food and shelter as a member of the household. • Regular employees: persons working in others’ enterprises and getting in return salary or wages on a regular basis (and not on the basis of daily or periodic renewal of work contract). • Casual labourers: persons engaged on others’ enterprise and getting in return wages according to the terms of a daily or periodic work contract. Their earnings are considerably lower than those of the regular employees owing to large gaps in wages and number of days of work between the two categories of workers.

Ministry of Labour The Ministry of Labour collates labour data collected by different Government departments on a range of items. Data are collected from all factories, commercial establishments and service departments, which are above a certain size. Units above a certain size are required to file returns with the government so that their activities can be regulated (e.g. for taxes, capacity utilization, obtaining import licences, and the like). Workers engaged in these enterprises are extended legal protection, such as a defined minimum wage, acceptable working conditions (including hours of 77

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work), paid leave, medical insurance, etc. Since workers engaged in these enterprises are protected by the law, in almost all cases they are economically more secure and better off than others. These workers are also members of recognized trade unions. Hence they are referred to as organized sector workers. The size of the unorganized sectors is calculated by subtracting the organized sector workers from the total sectoral worker strength obtained from the NSS sources. NOTES 1 There are other problems related to enumeration in census data. It is a single time-point inquiry and the accuracy is not very high. This is particularly so in regard to data on work participation and age of the respondent. 2 The basis of these definitions was evolved by a Committee of Experts on Unemployment, popularly known as the Dantwala Committee. See GOI (1970). 3 Subsidiary workers are not shown for 1972–1973 since in that round they were not enumerated separately. 4 The exact method is to count the total number of half days worked plus available for work in the reference week and divide it by seven. 5 A dollar, of course, buys much more in India compared to western countries, since the general price level is low, yet these remain low levels of income on which to support a family. 6 Data from micro studies show that these activities could take up to four to six hours per day. See Acharya and Shah (1987). 7 It is important to compute female work participation by marital status. However in most of India—both rural and urban—women tend to marry between the ages of 15– 20, and marriage is a near universal phenomenon. Hence such a classification is not discussed here. 8 Sundram’s main equations to explain the inter-state variation in female labour participation, using the thirty-second round of NSS pertaining to 1977–1978, are

Where FR=fertility rate; NSA/HH=land sown per household; LABPROD =labour productivity; AGHH=proportion of urban labour engaged in agriculture; and MPCE=monthly per-capita expenditure. Figures in brackets are t values. 9 This analysis draws heavily on an earlier work by the author (Acharya and Shah, 1987), wherein time allocation data from different studies were also analysed. 10 Primary data are not accessible for almost any large scale inquiry in India. In many cases there are laws enacted to prohibit access to primary records. 11 An education variable in a grouped data exercise is not expected to be meaningful. It is therefore not included.

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WOMEN IN THE INDIAN LABOUR FORCE 12 13

Primary records of the NSS have been made available to us for one province as a special courtesy. The Duncan index is as follows: D=0.5SUM (fi–mi)

14

where n is the number of job categories, and fi and mi are the employment ratios of women and men respectively in each category. The only other study which estimates the days of work done in the Indian context, is by Bardhan (1979). But it relates to casual labourers’ work in an agrarian context.

BIBLIOGRAPHY Abdullah T. and Ziedenstein, S. (1982) Women in Bangladesh: Prospects of Change, London: Pergamon. Acharya, S. and Jose, A.V. (1991) ‘Employment and mobility: a study of low income households in Bombay’, New Delhi: ILO-ARTEP. Acharya, S. and Panwalkar, V.G. (1988) ‘Labour force participation in rural Maharashtra’, New Delhi: ILO-ARTEP. Acharya, S. and Papanek, G.F. (1989) ‘Agricultural wages and rural poverty’, Discussion Paper No. 39, Boston: Asian Center, Boston University. Acharya, S. and Shah I. (1987) ‘Understanding female labour participation in rural Asia: an alternative premise’. Paper presented at a UNU Sponsored Seminar on Family Strategies and Labour, Katmandu: CWD. Anker, R. (1983) ‘Female labour force participation in developing countries’, International Labour Review 122, 6:709–723. Anker, R. and Khan, M.E. (1988) Women’s Participation in the Labour Force: a Methods Test in India, Geneva: ILO. Bardhan, P.K. (1979) ‘Labour supply functions in a poor agrarian economy’, American Economic Review LXIX, 1:73–83. Beteille, A. (1975) Inequality and Social Change, Delhi: Oxford University Press. Bremen, J. (1980) ‘Informal sector in research, theory and practice’, Rotterdam: CASP, Erasmus University. Datta, R.C. (1985) ‘Schooling, expenditure and earnings: an empirical analysis’, Margin 17, 2:60–73. Dixon, R. (1982) ‘Women in agriculture: counting labour force in developing countries’, Population and Development Review 8, 3:539–566. Government of India (1970) ‘Report of the expert committee on unemployment’, New Delhi: Planning Commission. ——(1975) ‘Report of the committee on the status of women’, New Delhi: Planning Commission. ——(1990) ‘A note on employment in the eighth plan’, New Delhi: Planning Commission. Harris, J., Kannan, K.P. and Rodgers, G. (1989) ‘Urban labour market structure and job access in India’, DP/15/1989, Labour Market Programme, Geneva: IILS. Heckman, J. (1976) ‘The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models’, The Annals of Economic and Social Measurement 5, 4:475–492. ——(1980) ‘Sample selection bias as a specification error’, in J.P Smith (ed.), Female Labour Supply, New Jersey: Princeton University Press. IRRI (International Rice Research Institute) (1985) Women in Rice Farming: Proceedings of a Conference on Women in Rice Farming Systems, Aldershot: Gower. Jose, A.V. (ed.) (1989) Limited Options, New Delhi: ILO-ARTEP. 79

WOMEN AND INDUSTRIALIZATION IN ASIA Krishnamurty, J. (1984) ‘Changes in the Indian labour force’, Economic and Political Weekly XIX, 50:2121–2128. Mitra, A. (1978) ‘India’s population: aspects of quality and control’, New Delhi: Abhinav. Nagraj, K. (1988) Female Workers in Rural Tamil Nadu, New Delhi: ILO-ARTEP. Oaxaca, R. (1973) ‘Male-female wage differentials in urban labour markets’, International Economic Review 14, 3:693–709. Papanek, H. (1979) ‘Family status production: work and non-work of women’, Signs 4, 4:775–781. Papola, T.S. (1981) Informal Sector in a Developing Economy, New Delhi: Vikas. Polacheck, S. (1987) ‘Occupational segregation and gender wage gap’, Population Research and Policy Review 6:47–67. Ramachandran, V.K. (1990) Wage Labour and Unfreedom in Agriculture, Oxford: Oxford University Press. Rao, M.J.M. and Datta, R.C. (1989) ‘Rates of return in the Indian private sector’, Economics Letters 30, 4:373–378. Sen, G. (1985) ‘Paddy production and processing and women workers in India’, in IRRI, Women and Rice Farming Systems, London: Gower. Sundram, K. (1988) ‘Inter-state variations in work participation rates in India’, New Delhi: ILO-ARTEP. Terrell, K. (1992) ‘Female-male earnings differential and occupational structure’, International Labour Review 131, 4 and 5:387–404. Tilak, B.J. (1979) ‘Inequality in education in India’, unpublished PhD Thesis, Delhi: University of Delhi. Unnvehr, L. and Stanford, M.L. (1985) ‘Technology and demand for labour in Asian farming’, in IRRI, Women and Rice Farming Systems, London: Gower. Visaria, P. (1981) ‘Poverty and unemployment in India’, World Development 9, 3: 277–300. Visaria, P. and Minhas, B.S. (1991) ‘Evolving an employment policy for the 1990s’, Economic and Political Weekly 13 April:969–979. World Bank (1991) Gender and poverty in India, Washington DC: World Bank.

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3 WOMEN AND THE LABOUR MARKET IN INDONESIA DURING THE 1980s Dwayne Benjamin

Women have always played an important economic role in Indonesian households. Indeed, this role has not been confined to traditional, unpaid ‘women’s work’, but has always included an important component of remunerative employment.1 Very little of this work, however, took place in a labour market working for wages, but was concentrated in either casual agriculture or other forms of self-employment. The same was largely true for men. All this is changing. With recent development and structural change occurring in the Indonesian economy, the labour market is becoming a more important source of economic livelihood, and women are increasingly participating in this market. This paper focuses on the economic role of women in Indonesia over the 1980s. My emphasis, partially data determined, is on the increasing importance of the labour market as a source of employment. It is men who have been most affected by this change, but women have participated as well. An important point to note, however, is that individuals are not necessarily the correct unit of observation. Most observers of the Indonesian economy emphasize the household or team nature of economic decision making.2 This partially explains the generally optimistic view of the position of women in the economy. All family members are expected (and must) contribute to family earnings. Thus, while women merit study in isolation, their economic roles should be taken in the context of broader household decision making. This chapter proceeds as follows. In the first section on pp. 82–85 I review some of the more general changes that have occurred in the Indonesian economy over the 1980s as a backdrop for our labour market study. I then introduce the data that will be used in this study, focusing on some of its limitations. The most difficult challenge is gleaning a consistent picture of labour force trends in Indonesia. Because of data comparability problems, it is unwise to rely on any two surveys to establish trends. Consequently, I use several different data sets spanning the elevenyear period 1980–1991. The quantity of data: labour market information of varying types on over two and a half million observations is quite staggering, and the resulting analysis can be quite cumbersome. Nevertheless, a consistent picture 81

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does emerge. In the section on pp. 86–110, I look at participation rates of men and women, and explore changes in the nature of women’s participation. In the section on pp. 110–113, I briefly describe employment patterns. Some of the most interesting results are in the section on pp. 113–127, in which I examine labour market earnings, with an eye to explaining differences in the level of earnings between men and women, as well as a preliminary investigation into the source of changes in this difference. To help put these changes into a larger context, I summarize the role of women’s earnings in the household. In the section beginning on p. 127, I provide a summary of the institutional and policy framework that is expected to accommodate the changing economic role of women; my conclusions are summarized on pp. 129–130. BACKGROUND The Indonesian economy in the 1980s Before exploring the changes in women’s roles in the labour market, it is useful to examine the broader changes that occurred in Indonesia over the 1980s. Changes in the labour market are invariably determined to some extent by such economic and structural changes. Furthermore, we will frequently be comparing labour markets at two points in time. Rather than look at any two years in isolation, it is important to see where each year fits into other trends in the economy: two points do not identify a reliable trend. There are three key features of the Indonesian economy that reflect on or raise questions about the labour market. First, economic growth in the 1980s was slow but steady, at least after a bad start. Second, there was considerable structural change within the economy, as the country became less dependent on oil as a source of export earnings and government revenue. Third, despite all this change, the 1980s saw a continued reduction in the number of people in poverty. In terms of growth rates of GDP, Indonesia lies somewhere in the middle of the Asian economies. The 1980s saw growth rates above 5 per cent in most years, typically around 7 per cent. This rate is lower than the occasionally ‘stellar’ performance of Korea, but higher than the Philippines. There were two bad years: 1982 and 1985. These years corresponded first to a worldwide recession, and second to an approximately 60 per cent decline in oil prices. Since taxes from oil revenue accounted for about 70 per cent of government revenues prior to 1982, such a price decline had serious macroeconomic consequences. To reduce the impact of these shocks on the economy, the government instituted a series of reforms over the 1980s. There was a reduction in public sector growth, including cuts in civil 82

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service pay. Tighter monetary policies were adopted and the Rupiah was devalued. Trade barriers were reduced. These reforms were designed to reduce the dependence of Indonesia on oil, primarily by promoting the development of alternative export industries. Most commentators on these reforms, such as Anata et al. (1988), World Bank (1991), and Booth (1992) agree that the reforms were successful in creating macroeconomic stability, as well as fostering the development of non-oil exporting industries. Despite the consistent growth performance, there was significant structural change. These changes are well documented and discussed in the volume edited by Booth (1992). For the purpose at hand, we are most interested in the implications for changing patterns of employment. The biggest change in sectoral composition is the size of manufacturing. This industry has benefited most from the exportoriented policy environment. The manufacturing share of GDP went from 8.4 per cent in 1980 to 14.3 per cent in 1990. As a share of non-oil exports, manufacturing has gone from 8 per cent in 1980 to over 32 per cent in 1990. Real growth rates for the manufacturing sector exceeded 10 per cent during the 1980s (except for 1982). The textile, garment, and footwear industries are among the fastest growing. These industries are also significant employers of female labour. One important question surrounds the size distribution of firms. Is growth dominated by new, larger factories, or are more traditional cottage industries holding their own? Unfortunately, due to data difficulties, it is difficult to answer this question. What we know is that in 1986, cottage industries accounted for 13.6 per cent of output, but over 44 per cent of employment. Given the possible differences in working conditions, and long run viability between factory and cottage industries, more research on this matter would be useful. While manufacturing has grown quickly, as have other industries like trade and construction, the largest employer of men and women remains agriculture. Agriculture’s share of GDP declined from 25 per cent in 1980 to 21 per cent in 1988, but this is a case where the numerator has simply increased more slowly than the denominator. Real growth in agriculture averaged 3.1 per cent in the 1980s versus 6.1 per cent prior to 1980. Employment growth is smaller than output growth, as newer technologies displace some amount of labour. However, declines in employment growth are smaller than originally feared when green revolution technologies were first adopted. For this study, one important implication of the remaining importance of agriculture is that given its strong self-employment component, work for wages is not going to be as important in rural Indonesia as it is in more industrialized economies. However, if current growth rates continue, agriculture will eventually be a less important source of employment and income.

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The labour market will be a more important source of livelihood, and women a larger fraction of the workforce. As we will see, this process has already begun. By most accounts, households have been quite successful at maintaining their standard of living over the 1980s, even during a difficult period of macroeconomic adjustment. Ravallion and Huppi (1991) and World Bank (1990a) provide a careful statistical analysis of the incidence of poverty, and the level of income inequality over the mid-1980s. Both the incidence of poverty and the number of people living in poverty declined, as did the level of income inequality. This ‘success’ has been partially attributed to the diffuse, robust nature of the economy. With a smallholder based rural economy, and even a small-holder urban economy, most households were able to withstand changes in the prices of imports and cuts in government expenditures. Apparently both households and markets were sufficiently flexible to cope with these shocks. An interesting question is what role women played in maintaining or improving household income levels. Far from displacing men, does an economy where women are more integrated into the labour market make household adjustment easier? It is important to note, however, that there are still a lot of poor people in Indonesia. With population growth rates around 2 per cent, and increasing life-expectancies, the size of the working-age population is increasing at approximately 2.5 per cent a year. This demands a high level of growth in the economy as a whole. Whether or not household members can obtain jobs at sufficiently high wages will very much depend on the flexibility and capacity of the labour market, especially as rural land resources become more scarce. Data constraints Obviously, the types of questions anyone can answer are limited by the data. Ideally, I would like a series of comprehensive household income/labour force surveys over the 1980s. A survey along the lines of the World Bank’s LSMS (Living Standards Measurement Survey), with a large sample would allow a relatively accurate measure of labour force activity and remuneration, including the possible estimation of returns from self-employment. Such surveys are rare. In Indonesia, labour force data are regularly collected in two surveys: the SUSENAS and the SAKERNAS. The SUSENAS is a traditional household consumption/ expenditure survey. In 1980, 1981, and 1982, detailed labour force questions, as well as income questions, were asked. I use the 1980, 1981, and 1982 SUSENAS to provide information on the early 1980s. Unfortunately, as we shall see, there are several important differences among these surveys, and between them and the labour force surveys of the latter half of the decade. After 1982, most labour force questions were removed from the SUSENAS, and asked almost exclusively in the 84

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SAKERNAS (labour force) survey. The SAKERNAS surveys were conducted in 1986, and onwards. The post-1982 SUSENAS surveys ask only limited labour force questions. However, I also use the 1990 SUSENAS survey for purposes of comparison with the 1990 SAKERNAS in this chapter. As we shall see, the data limit some of the answers that can be obtained to some of the simpler questions about the changing role of women. Some of these limitations are documented in the Appendix. However, I do obtain earnings data over the entire period for all individuals who worked in the labour market: this allows some of the most interesting questions regarding women in the labour market to be addressed. The limitations of labour force surveys in Indonesia are well documented in several sources: Moir (1980), Soemantri (1982), Lluch and Mazumdar (1985), Hugo et al. (1987), Korns (1987), Cremer (1990), Bazargan (1991), Mehran (1991), and Jones and Manning (1992), to name a few. The most serious problems have nothing to do with Indonesia in particular, but rather with the difficulty of measuring a labour force that is mostly employed on family farms or in family businesses. Self-employed and unpaid family workers do not fit neatly into the boxes of surveys designed primarily for use in industrialized countries. When enterprises are family run, it is even difficult to attribute any particular activity to a single individual. Thus, the measurement of the labour force, at least for those individuals not employed for wages, is difficult and requires well-trained surveyors. Two problems emerge for the project at hand. First, it is the measurement of women in the labour force that suffers most, since women are likely to be self-employed or unpaid family workers for some part of their working lives. Second, as these measurement problems have become better known, and survey expertise has evolved at the BPS (Biro Pusat Statistik, the Central Bureau of Statistics), the measurement of the labour force has improved, and undercounting has been substantially reduced. Unfortunately, this means that comparability of labour force rates over time, especially for women, is very difficult. Another problem limits the usefulness of labour force surveys, even when perfectly executed. Labour force survey questions end when a respondent indicates she is self-employed or an own-account worker. We get no further information on earnings for this status of worker. If this is an important category of worker, then we will miss this important source of earnings. While imperfect, self-employment earnings are reported in the SUSENAS, at least at the household level. Nevertheless, there is a clear upper boundary on what we can learn about women and self-employment from these data. For the remainder of this chapter, I endeavour to highlight the data compromises that I had to make, and suggest possible biases in my conclusions. I also try to limit my study to questions that can more confidently be answered with the surveys to which I have access. 85

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LABOUR FORCE PARTICIPATION Previous studies There are several excellent studies of the labour force in Indonesia. LocherScholten and Niehof (1987) outline results from Dutch surveys from the 1920s, the census of 1930, and budget surveys of estate workers in the 1930s and 1940s. In the 1920s, women performed 50–80 per cent of labour in rice farming. Most of this labour was in ‘female-specific’ rice tasks like planting, weeding, and harvesting. In historical studies surveyed by White (1985) and Sajogyo (1985) evidence from many smaller, more intense surveys confirms the historical and current large role that women play in rice farming. According to Locher-Scholten and Niehof though, the later surveys suggest that it would be wrong to generalize the rice figures to other agriculture, because women’s market participation would be overstated. Nevertheless, most surveys agreed that women played a large role in agriculture. Many subsequent studies use the 1961, 1971, and 1980 censuses as their bases. Soemantri (1982) focuses on changes in the 1961–71 period. Lluch and Mazumdar (1985) and Hugo et al. (1987) review changes in the structure of the labour force up to 1980. Jones and Manning (1992) extend these studies through 1987. Several broad conclusions can be drawn. First, all authors note the difficulty of making comparisons of labour force participation rates over time, especially for females. This problem is not isolated to Indonesia: Goldin (1990) explains the difficulties in the American context, for example. The labour force is supposed to represent the economically active population. Many of women’s traditional jobs, gathering firewood, raising poultry, etc., were not considered economic activities. Consequently, their economic contribution has tended to be undercounted, even when surveyors use a fairly constant definition of economic activity. These conceptual problems continue through the 1980s. Despite the difficulties in comparisons over time, most studies agree that there is a significant regional component to labour force participation, especially for women. Rural participation rates are generally higher than urban rates, and Java’s rates are generally higher than those of other islands, though there is also substantial variation in Java. Another important feature of labour force activity in Indonesia, in common with other countries, is the life-cycle pattern of labour force participation. The main difference between the Indonesian and Western experience is that there is a less pronounced decline in activity after 20 years of age (at marriage), i.e., the Western ‘two peaked’ age profile does not seem to apply in Indonesia. As pointed out in Goldin (1990), the twopeaked age profile is partially explained by the convolution of cross-section (age) and cohort effects. Perhaps this convolution is not so much of a problem 86

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in Indonesia. Jones and Manning (1992) show that participation rates for women of all ages have been increasing over the 1980s. There are also several sectoral conclusions. In particular, women, like men, are working less in agriculture. According to Hugo et al. (1987), the percentage of women in agriculture has declined from 72 per cent in 1961 to 58 per cent in 1980. Jones and Manning (1992) report that non-agricultural employment has been growing at 4–5 per cent per year since 1970, versus 1–2 per cent in agriculture. Trade and manufacturing are the industries that account for a growing fraction of female workers. As most authors emphasize, this is not purely a result of dynamic manufacturing and trade sectors, but also a decrease in the ability of agriculture to provide employment. There has been an increase in landlessness, especially in Java, and a decrease in farm size. While productivity has increased, some of the technological change, such as increased use of herbicides and a shift to contract and sickle harvesting has directly reduced the demand for female farm labour. What is more, rice harvesting was traditionally the most lucrative form of agricultural employment for women, with wages sometimes exceeding the wage for male ploughing labour.3 Nevertheless, we should not get carried away with the increased importance of manufacturing: it is still a relatively small sector. As Jones and Manning emphasize, we must also be careful about our impressions of these manufacturing jobs: these are not jobs at IBM or General Motors. As mentioned previously, a disproportionate share of workers is employed in the largely inefficient (by domestic and international standards) cottage sector. Still, given the present importance of agriculture, and the attendant importance of self-employment, the wage labour market is probably not going to be the most important source of employment for women. Finally, most authors, for example White (1985), recognize the importance of addressing the role of women within the context of the household. The determinants of female labour force participation will involve more than factors that only affect women. For example, relative male and female wages, as well as the wealth level of the household, will help determine whether women work off the farm. According to White, ‘the sexual division of labour within the household is a practice not so clear-cut as ideology suggests. Men for example, will sometimes stay home to baby-sit and cook a meal while adult women and girls are off harvesting, or trading at the market’ (White, 1985:132). Furthermore, we must be very careful in attaching any welfare interpretation to participation figures. If increased participation is a result of reduced barriers to employment, women and households are probably better off. However, if increased participation is a result of decreased household income and a necessity to scrounge a living in low paying jobs at the expense of spending time in home activities, the welfare interpretation would be quite different. 87

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Results from the SUSENAS and SAKERNAS I now look at what the data have to say about patterns of labour force attachment over the 1980s. For most variables, I present results for only two years, 1980 and 1990, for example. However, we can detect important patterns within a given year. Cross-section results should provide suggestions as to which variables might contribute to changing patterns of labour force attachment over the 1980s. Unfortunately, as Goldin (1990) and others have emphasized, we cannot comfortably extrapolate dynamic variables from cross sections because of cohort and time effects. At least with different cross sections, we can control for the cohort, if not the time effects. The following exercise is by no means a perfect substitute for having more complete data. In the results that follow, there is an almost infinite number of possible breakdowns: urban versus rural, and Java versus other islands are two examples. In order to keep the number of tables and figures to a manageable level, I report urban and rural separately, with only limited controls for Java. For many purposes, this is probably enough disaggregation.4 However, the regional variation of employment outcomes, even within Java, is a potentially important research question itself, especially if one is interested in issues of poverty and income distribution (see Jones and Manning (1992), for example). Figures 3.1 to 3.8 show labour force activities for males and females by age group. The 1980 results are based on the SUSENAS while the 1990 results come from the SAKERNAS. I maintain the survey categories for ‘main activity’ during the survey week. While the focus is on women, men’s results are presented to provide a baseline for discussion of women’s

Figure 3.1 Activities: Indonesia, urban males, 1980 88

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Figure 3.2 Activities: Indonesia, urban males, 1990

Figure 3.3 Activities: Indonesia, urban femles, 1980

behaviour. For urban males (Figures 3.1, 3.2) the most striking feature of the two figures is their similarity. Indeed this is true for all the figures. Slightly fewer 10–14- and 15–24-yearolds are working, as an increased number are in school. The changes for urban females (Figures 3.3, 3.4) are more pronounced. At most age levels above 15, there is an increase 89

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Figure 3.4 Activities: Indonesia, urban females, 1990

in the percentage of women working, with just under 40 per cent of prime age women working. For ages 10–14 and 15–24 there has been a substantial increase in the number either working or in school. In 1980, just over 50 per cent of women between the ages of 15–24 were in one of the two categories, but almost 70 per cent were in 1990. There has been an especially large increase in the fraction of girls attending school. For females, the largest competing category to the labour force is ‘home duties’ or ‘house work’. One can imagine the potential measurement problems in a rural economy in distinguishing home duties from unpaid family labour in auxiliary activities. This distinction is probably not a problem with males, as their principal competing category is school. Very few men report participation in home duties. One wonders whether home duties are implicitly defined as women’s work. The corresponding rural results are presented in Figures 3.5–3.8. For men, we see higher participation rates than in urban areas for all age groups. Fewer males attend school in rural areas. These patterns are quite stable over the two data sets. Rural females also have higher participation rates than those in urban areas, with participation approaching 50 per cent for most age groups. Slightly more women attend school than did so in 1980. More conventional age profiles of labour force participation are presented in Figures 3.9 and 3.10 for 1981. The graph plots the average participation rate for five-year age categories, using the midpoints as the point of reference. Participation follows a very typical pattern; however, there are a couple of interesting points. First, the urban profiles are 90

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Figure 3.5 Activities: Indonesia, rural males, 1980

Figure 3.6 Activities: Indonesia, rural males, 1990

steeper. This reflects the higher school attendance rates of the young, which keep more of them out of the labour force until they are older. Note also that there is a less well defined notion of ‘retirement’. The elderly, especially in rural areas, maintain more attachment to the labour force than is the case in most Western economies. We have to be careful though. As Goldin (1990) emphasizes, these cross-section profiles may not 91

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Figure 3.7 Activities: Indonesia, rural females, 1980

Figure 3.8 Activities: Indonesia, rural females, 1990

represent genuine life-cycle patterns. If participation rates are changing over time and the young are particularly affected, then the cross section profile will be flatter than the ‘real’ lifecycle profile. In all likehood, 30-year-olds in 1990 will participate more than 30-year-olds in 1980,and the cross section will under predict the aging effect. One interesting feature of these profiles is the absence of a decline in participation for 92

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Figure 3.9 Labour force participation: Indonesia, urban, 1981: by age and sex

Figure 3.10 Labour force participation: Indonesia, rural, 1981: by age and sex

females of child bearing ages. We do not see the ‘two-peaked’ profile that is common in Western data sets. Children have a smaller effect on the labour force participation decision in Indonesia, perhaps due to extended families or the type of work being done by women. In the US and Canada, there has been a marked difference in the historical change of labour force behaviour between married and single women. In Figures 3.11 and 3.12 I split female participation by marital status. Here, I delineate between currently married, and ‘single’, an amalgamation 93

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Figure 3.11 Labour force participation: Indonesia, urban females, 1981: by age and marital status

Figure 3.12 Labour force participation: Indonesia, rural females, 1981: by age and marital status

of the never married, widowed, and divorced. Never married women are not very common in Indonesia, at least above 20 years of age. ‘Single’ women have higher participation rates, the gap being higher in urban than rural areas. The difference between married and single is smaller than the traditional difference in North 94

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America. That marital status plays an important role in affecting female labour force participation cannot be surprising if we believe that the participation decision is taken in the context of a broader household decision making model. Notice again, the absence of lower participation rates for women in child-bearing years, even among married women. However, the gap between married and single is highest for women of 25–35 years of age. This suggests that the age profile is contaminated by cohort effects. The decline in labour force participation during child-bearing years is swamped by the increased labour force attachment for this cohort, born ten years after the stock of 35–45 year olds in 1981. Unfortunately, since marital status is not asked in the SAKERNAS, this issue cannot be more precisely analysed. Figures 3.13–3.20 show the important role that education plays in determining labour force status. In these figures I break conventionally measured labour force participation into four categories: work as an employee (for wages in the labour market), paid self-employment, unpaid family work, and unemployment. For males, who do not have such variable participation rates as females, there is a less pronounced effect of education on participation. Only in the middle of the education range, corresponding to incomplete secondary school, is the labour force participation rate much lower. This at least partially reflects the fact that this group is younger and still in school. Nevertheless, the more educated have slightly higher participation rates. More interesting than the overall rate is the pattern of participation. Unemployment rates appear to increase with education, at least until people have post-secondary education. Unemployment among high school graduates is an ongoing problem, and

Figure 3.13 Labour force participation: Indonesia, urban males, 1981: employment status by education 95

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Figure 3.14 Labour force participation: Indonesia, urban males, 1991: employment status by education

Figure 3.15 Labour force participation: Indonesia, urban females, 1981: employment status by education

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Figure 3.16 Labour force participation: Indonesia, urban females, 1991: employment status by education

Figure 3.17 Labour force participation: Indonesia, rural males, 1981: employment status by education

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Figure 3.18 Labour force participation: Indonesia, rural males, 1991: employment status by education

Figure 3.19 Labour force participation: Indonesia, rural females, 1981: employment status by education

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Figure 3.20 Labour force participation: Indonesia, rural females, 1991: employment status by education

is discussed in Jones and Manning (1992). While the higher unemployment rates probably reflect a lack of opportunity for these individuals, their higher reservation wages may also reflect a greater ability to withstand spells of unemployment. Simply stated, the more educated are probably richer. The second pattern is the importance of work for pay, i.e., work for wages. Employment in the so-called formal sector is much more important for educated men. The participation patterns are similar for females, except that the effects on unemployment and work for pay are much starker. Highly educated females have significantly higher participation rates than lower educated women, but also face higher unemployment rates. Of interest for the regressions that follow, it is also apparent that simple linear controls for years of schooling are unlikely to capture the patterns of education effects that we see using education categories. As was the case in the previous set of figures, we do not see dramatic changes in the participation patterns by education level across the ten years. For men there is a slight decrease in participation rates, while for women there is a slight increase, especially in urban areas. Women also faced higher levels of unemployment in 1991 than 1981. This may reflect more accurate measurement of unemployment, as measurement of unemployment can be difficult since attachment to the labour force is still required, and may have been difficult to detect in the SUSENAS (see Cremer 1990 for a related discussion). One final, important feature is worth emphasizing: increases in employment have not occurred only in the formal (work for pay) labour 99

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market. Both sexes have seen increases in the percentage of the potential labour force engaged in various forms of paid self-employment. The previous figures were designed to look at cross-sectional features of labour force participation, as well as to suggest changes that may have occurred over the last decade. As suggested earlier, comparability issues make this a potentially risky exercise, so there is a high pay off to looking at many points in time in order to detect trends. This is the purpose of Figures 3.21–3.32, though for differing groups of variables. Here we use all available data to verify the trends suggested in the previous figures.

Figure 3.21 Participation rate: Indonesia, urban males, 1980–1991: by age

Figure 3.22 Participation rate: Indonesia, urban females, 1980–1991: by age 100

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Figure 3.23 Participation rate: Indonesia, rural males, 1980–1991: by age

Figure 3.24 Participation rate: Indonesia, rural females, 1980–1991: by age

Figures 3.21–3.24 show participation rates by age groups over time. Comparison of an age group ten years apart, at two points in time ten years apart allows one to follow a particular cohort. My focus though will be on the trend and age effects. For urban males, we see large age effects, and a slight increase in participation rates, at least for 15–24-year-olds over the latter part of the decade. For urban women, there are less pronounced age effects (in absolute terms), though 15–24and 55–65-year101

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Figure 3.25 Unemployment rate: Indonesia, urban males, 1980–1991: by age

Figure 3.26 Unemployment rate: Indonesia, urban females, 1980–1991: by age

olds participate less than prime age women. There appears to have been a steady increase in participation rates for ‘all ages’, though most of this increase is occurring with younger women. For rural men, the age effects are weaker than for urban men: only the youngest and oldest men are different from the prime age groups. There has not been much change over time. For rural females, there are distinct age effects, though for all ages, there seems to be a slight decrease in participation rates. 102

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Figure 3.27 Unemployment rate: Indonesia, rural males, 1980–1991: by age

Figure 3.28 Unemployment rate: Indonesia, rural females, 1980–1991: by age

Figures 3.25–3.28 illustrate the behaviour of the unemployment rate over the 1980s. I will focus on the age and temporal features of the graphs. For urban areas, the unemployment rate for males and females has behaved similarly over the 1980s. The male rate is slightly higher than for females, and unemployment is concentrated almost exclusively in the younger age groups, especially 15–24. Over the last five years of the 1980s, however, the unemployment rate has declined for this group. The picture is quite different 103

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in rural areas. First, the rates are quite low compared to urban Indonesia. This may reflect greater opportunity, or more likely it reflects supply side behaviour. Recall from the education figures that unemployment is a ‘luxury’ enjoyed by the higher educated as they wait for ‘formal sector’ type jobs. As the countryside becomes more developed, we can probably expect to see more of this type of unemployment. Indeed, it appears that unemployment is increasing for this age group, in stark contrast to the urban results. As was the case in urban areas, unemployment is concentrated in the youngest age group, and male and female patterns are very similar. So far, the only measure of labour supply we have examined is the dichotomous variable: participate or not. Another important measure of labour market attachment is hours: conditional on working, how much time does an individual spend working, and how does this differ between men and women. Figures 3.29–3.32 show the behaviour of hours worked over the 1980s for various age groups. Comparability between the SUSENAS and SAKERNAS (early and late 1980s) figures might be problematic, so I focus on trends identified in the SAKERNAS data. Note, though, that cross-sectional differences in ages and sexes appear similar over the entire sample of data sets. For urban areas, differences in hours worked between males and females are much smaller than the differences in participation rates. Indeed, for the 15–24-year-old age group, hours worked by females are about the same as men. Furthermore, for this age of women, hours have been increasing steadily over the 1980s. In rural areas, the difference between men’s and women’s hours is greater. Also, rural women work fewer hours than women in urban areas. Again this is a different result than held when we looked at participation. Nevertheless, there has been a slight increase in average weekly hours worked for most age-sex groups in rural areas. Labour force participation probits While we have covered a huge number of figures, they have painted a fairly consistent picture of the labour market in Indonesia over the last decade. However, regression type analysis must be employed to hold more than one factor constant, which is the best that figures can accomplish. The following, more rigorous analysis will not overturn the results or impressions gleaned from the previous informal discussion, but it will allow more concrete, precise conclusions to be drawn. One objective of this exercise is to identify variables (factors) that may help explain some of the changes that occurred in the 1980s. If variables like education or age at first marriage are changing, we can surely expect to see accompanying changes in labour force behaviour, if indeed they are important determinants of labour supply. In Tables 3.1 and 3.2, probit estimates for 104

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Figure 3.29 Hours worked: Indonesia, urban males, 1980–1991: by age

Figure 3.30 Hours worked: Indonesia, urban females, 1980–1991: by age

labour force participation are presented for 1980 and 1990 (and by urban and rural, as usual). In Table 3.1 I show estimates of labour force participation probit equations for 1980 and 1990 for urban Indonesia. First notice the means of the dependent variables. The male participation rate declined somewhat, from 74.6 per cent to 73.8 per cent, while the female rate increased dramatically from 32.6 per cent to 38.8 per cent. We have to 105

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Figure 3.31 Hours worked: Indonesia, rural males, 1980–1991: by age

Figure 3.32 Hours worked: Indonesia, rural females, 1980–1991, by age

be careful in interpreting the parameters of these equations. Specifically, interpretation of the coefficients, as representing some structural feature of labour supply behaviour, is perilous in the absence of estimation of a more formal labour supply model. Most importantly, we do not have measures of non-labour income or the market wage in our participation equations. The inverted U-shape of life-cycle participation is captured by the 106

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quadratic in age: age has a positive effect on participation, and the effect decreases with age. The effects of education are measured by the education indicator variables. The education effects are negative (compared with those who have no education) until the highest levels of schooling are reached. The schooling effects are especially difficult to interpret without estimating a full labour supply model. Presumably income and substitution effects offset each other in different ways at different levels of education. For example, while middle educated women may have higher wages than uneducated women, their higher wealth may induce lower participation rates if ‘leisure’ is a normal good. What is more, if age does not fully capture elements of the schooling decision, then middle educated individuals may prefer remaining in school to working. Women in Java are slightly more likely to participate than women from the other islands. Clearly, household demographic structure has large effects on the labour supply decisions of both men and women. Young children (under 10 years old) have a negative effect on the probability that a woman works, whereas they have a positive effect on a man’s propensity to work. This is similar to the results found in North America. Other adults in the household also matter, especially since we cannot control for marital status. For example, having more prime age men in a household lowers the probability that a woman (or man) will work, having more prime age women increases a woman’s likelihood of working and decreases a man’s. Comparing the coefficients across years, one is struck by the similarity of the age and demographic effects. Only the schooling coefficients appear to change in magnitude, though the basic patterns are very similar between the two years. In Table 3.2, the rural results are shown. Here, the male participation rates declined from 90 per cent to 89 per cent, and the female rate increased from 56.9 per cent to 57.9 per cent. While the means are different than in the urban sample, the coefficient patterns are quite similar. Ideally, it would be informative to use these estimates to decompose the change in participation rates between 1980 and 1990 into that part due to changes in worker characteristics, and that due to changes in the behaviour of individuals. While not formally conducted, we can still discuss changes in some of the important variables that have been identified in Tables 3.1 and 3.2. First, more women are getting more education, and we know from Table 3.1 and 3.2 that education, particularly attainment of high school education, affects labour force participation, especially into work for wages. Oey-Gardiner (1991) provides an analysis of changing school attendance rates by sex in the 1980s, comparing 1980 and 1985. She finds that school attendance rates for primary aged children (7–12) have gone from 84 per cent to 94 per cent for males, and 83 per cent to 94 per cent for females over the five-year period. Primary education is virtually universal. It is in secondary attendance that females fall behind 108

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males, though the gap is narrowing. School attendance rates for 13–15-and 16– 18-year-old males were 65 per cent and 39 per cent in 1980 and 76 per cent and 53 per cent in 1985. Comparable figures for females are 56 per cent and 24 per cent in 1980 and 70 per cent and 41 per cent in 1985. In addition, the gap between Java and the other islands has narrowed. An important caveat must be made. While I control for age in these regressions, the dramatic increases in school enrolment have been relatively recent, and negative education effects for some categories may still partially reflect age or cohort components, and ‘still in school’ status. The second set of important variables is the family structure, or, ideally, marital status variables. Marital status affects labour market participation both directly, and indirectly as it affects fertility. The changes in these demographic variables are documented in Williams (1990) and World Bank (1990b). Total fertility rates (TFR) have been declining at 3 per cent per year over the 1980s. The TFR was 5.5 in 1950, 5.4 in 1974, and 3.4 in 1989. By age, the decline in fertility has been most pronounced in the 15–19-year-old groups. This is primarily the result of delayed first marriage. The age at first marriage has increased by over a year in the 1980s. In 1980, 30 per cent of women 15–49 years of age were married before they were 20 years old. By 1987 this figure had fallen to 19 per cent. The changing age of first marriage is reflected in the means of my own data. The percentage never married (not controlling for age) has increased in all regions, the highest increases being outside Java. The fertility rate has also declined due to increased contraceptive use. In 1960, only 10 per cent of women reported using contraceptives. In 1980 the figure was 30 per cent, and in 1988, it was 46 per cent. These changes in fertility will almost certainly affect female labour supply. EMPLOYMENT Before moving to earnings, further background is provided by looking at employment patterns of those who work. Table 3.3 shows employment status by age and sex for 1981, 1986, and 1990. These numbers are useful for several reasons. First, they allow us to focus on paid workers (self-employed and employees) rather than all workers including unpaid family workers. Korns (1987) has argued that difficulties in measuring participation in unpaid family activities, especially for women, make paid workers a better measure of labour force attachment that at least can be compared consistently over time. Second, they allow us to see how important the labour market is becoming as a source of paid employment (versus self-employment). Unfortunately, even while unpaid family labour is possibly mismeasured, the distinction between employee and selfemployed has also fluctuated. For example, among the 1980, 1981, and 1982 SUSENAS’s, the categorization of agricultural contract labourers differs by survey. In 110

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1980, there are more employees than in subsequent years. This must be kept in mind when we estimate our earnings functions. Regarding the measurement of trends, as before, the most reliable information comes from comparing across the SAKERNAS surveys (1986 and 1990 in Table 3.3). Most of the changes that occurred over the 1980s apply to the youngest age group in urban areas (the areas most likely to be affected by industrialization). From 1986 to 1990, employment of all urban women in both paid activities (E or S in the table) increased from 26 per 111

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cent to 30 per cent, while for urban men the increase was from 64 per cent to 66 per cent. The status of employee grew the most. Korns’ insight into the labour force signal is clear in the rural numbers. Here we clearly see the relative stability of the earning workers (self-employed or employee) category compared to the unpaid family worker category. While participation in both paid activities has increased, when we look at earnings we will not be able to study the earnings of the self-employed. Clearly, interesting information, especially in rural areas, is missing. Before turning to earnings, I present one final table of descriptive statistics. In Table 3.4 I show employment by industry and sex in 1980 and 1990. Unfortunately, this is as fine a disaggregation of industry as is possible, so a specific study of export industries cannot be done. We can still see some definite shifts in employment patterns over the ten-year period, though the magnitudes are not very large. In urban areas, employment patterns are very similar for men and women, the exceptions being that men are more concentrated in construction and transportation, while women are more engaged in trade. Over the 1980s though, manufacturing has become more important, increasing its share by 2 per cent to 3 percentage points. For women, employment in textiles and food processing, both exportoriented industries, has increased by about a percentage point each. In rural areas, male-female employment patterns outside agriculture are similar to urban areas. The most important fact, however, is that agriculture is by far the most important industry. Nevertheless, its share has declined from 75 per cent to 70 per cent over the 1980s, with most of the shift going towards manufacturing and trade. LABOUR MARKET EARNINGS In measuring the changing importance of women in the economy, participation (and hours) represents one important dimension. Another is compensation, or earnings. In this section we use standard techniques in labour economics to examine several features of the determinants of women’s and men’s labour market earnings. Before presenting the results, however, it is important to recognize what we are missing. Most studies of labour force participation in developed countries focus on the narrow but relatively well defined and measurable concept of work for pay. Applied to developing countries this means ignoring unpaid family workers and the self-employed. In a broad study of economic activity, much would be missing from this limited focus. However, it is still useful to look at the wage labour market in isolation. As the wage labour market becomes a more important means of allocating labour, with less work done in the home or family farm, we can ask questions about how women fare in this market relative to men, and what factors determine an individual’s participation in the wage labour market. Furthermore, for studying wage discrimination, or the level of earnings in general, employment earnings are almost certainly measured more accurately than other components of household income. Also, it is in the wage labour market, as opposed to self-employment, where we might most expect to see discrimination. 113

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Finally, as the wage labour market becomes a larger source of employment, labour market policies become a more important consideration. For example, minimum wage laws, workplace safety regulations, and industrial relations structure are less important when few people work in the formal labour market. Currently in Indonesia, the minimum wage is not viewed to be a binding constraint. Nor is the state labour union regarded as a vociferous protector of female worker rights. This does not mean there is no role for these institutions, but rather that they are not currently important.5 Their importance will probably grow as the labour market becomes a larger source of employment. I address some of these questions in the final section of this chapter. To maximize the time spread between samples, I use the 1980 SUSENAS and 1990 SAKERNAS. These data sets each have measures of hours worked, a potentially important ingredient in a model of earnings, though there will still be comparability problems. There are several interesting questions one can ask about these labour market earnings. First, has the level of earnings changed over the 1980s for men or women? Have relative male-female earnings changed? What are the principal determinants of labour market earnings? How have these changed over the 1980s? To answer these questions, I estimate standard human capital earnings functions. The reported earnings from the SUSENAS surveys are total wage and salary earnings during the previous month for all individuals. For the SAKERNAS, earnings are only recorded for the primary job, and only for individuals whose main employment status is ‘employee’. This may induce comparability problems with the SUSENAS. Even with controls for hours worked, it may not be appropriate to compare earnings of workers whose secondary activities are in the labour market with those whose primary activities are in the labour market. Certainly, it makes it difficult to compare the proportions of individuals with labour market earnings in the two years. For this reason, I restrict the 1980 sample to those workers who report ‘employee’ as their main employment status. Of course, as I mentioned earlier, the 1980 survey had a broader definition of employee than did the subsequent surveys, including short term contract (casual) workers who were classified as self-employed in 1990. So, even this restriction of the 1980 sample may not yield perfectly comparable samples of workers between years. Except for replacing age with potential experience (age minus years of schooling minus five) and dropping the demographic variables, the earnings regressions include the same regressors as the previous participation probits. In order to compare earnings over time, they must be expressed in comparable currency units. I use the Indonesian CPI to inflate the 1980 earnings to the 1990 level of prices. The factor 114

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is 2.38. This will only be a crude control for price level if there have been differential changes in regional price levels. Thus, comparisons of levels and differences of earnings across regions are probably not advisable without a more sophisticated accounting of price changes. The Java dummy variable, in addition to capturing other differences between Java and the other islands, will hopefully capture some of these regional differences in the price level as well. Tables 3.5–3.8 show the results of estimating the earnings functions for urban and rural, males and females, in 1980 and 1990, while means of the dependent variables (log earnings) are shown in Table 3.11 (see p. 123). There are several interesting features. First, from Table 3.11 we see that real wage earnings appear to have risen for all groups over the past decade, especially in rural Indonesia. In both urban and rural Indonesia, women’s earnings increased more than men’s, though men still earn more than women. Second, looking at the OLS estimates in Tables 3.5–3.8, the returns to education are phenomenally high, especially for females. The high rates of return to schooling are in accord with Byron and Takahashi (1989) who looked at a subset of the 1981 SUSENAS data, and Behrman and Deolalikar (1993) who used the 1986 SAKERNAS. As was the case with the participation equations, the estimated effects of schooling (returns to schooling) have fallen over the 1980s. This may be a result of the increasing supply of educated workers. Third, note that earnings are significantly lower in Java, though the differential has declined somewhat over the 1980s. This may simply reflect higher price levels on the other islands. As is well known, hours worked is an important determinant of earnings. I thus include hours (log hours) as a regressor in the second specification for each sample. Inclusion of the hours variable does not have a significant impact on the male coefficients, though the female schooling coefficients rise substantially. This suggests that hours are negatively correlated with education (ceteris paribus, possibly indicating an income effect). Basically, higher education raises women’s hourly wages more than it raises their earnings. Since hours and earnings (or wages) are possibly jointly determined, or at least hours are correlated with the error term of the earnings equation, it is necessary to instrument hours. The OLS hours coefficient may be overstated if, for example, highly motivated individuals both work more and have higher wages. Alternatively, the hours coefficient may be biased downward due to well known measurement error problems with hours data. Of course, instrumenting hours is easier said than done, since we need valid instruments. These would be variables that affect hours but not wages (or earnings, except through hours). Household demographic variables are reasonable candidates, though reasonable does not mean the same thing as valid. For 115

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now, I maintain the same set of possibly invalid instruments for each equation, and report the results of over-identification tests, which test the conditional validity of the instruments. I also report the results of the Hausman tests for the endogeneity of the hours variable. This test compares the OLS and 2SLS hours’ coefficients to determine whether they are significantly different. The results of this exercise are not particularly successful. The over-identification tests frequently fail, especially in rural samples, and the 2SLS hours coefficients are poorly determined. Nevertheless, the basic patterns suggest that the true coefficient is probably higher than the OLS coefficient, i.e. that measurement error is a problem. Furthermore, the Hausman tests suggest that this difference is often significant. On the positive side, the other coefficients are not appreciably affected by this procedure either. An alternative, possibly better, approach would be to create a wage variable, essentially restricting the log hours coefficient to be 1. Another well known possible problem with the OLS estimated earnings functions is selectivity bias. This may be an even greater problem with these data because the sample is not simply restricted to participating men and women, but to the smaller sample of those who work in the wage labour market (as opposed to selfemployment). The returns to schooling, for example, may be understated if unobserved comparative advantage in the labour market is negatively correlated with acquired education. This would happen if low educated employees had higher unobserved skills (on average) in the labour market than the higher educated. The standard Heckman correction for selectivity, like the instrumenting of hours, requires exclusion restrictions: variables that determine wage labour market participation, but not earnings: a tall order indeed. I use the same demographic variables as identifying variables. The probit selection equations are reported in Tables 3.9 and 3.10. Not surprisingly, the coefficients look similar to those in the participation equations, except for the stronger education effects. As we saw in the earlier figures, higher education is associated with wage earner status, as well as being an important determinant of participation. Thus we would expect education to have a greater influence in these probit equations. In the end, the selectivity corrections do not affect the coefficients of interest very much, suggesting that selectivity bias is not a problem in our data, or at least that our excluded instruments do not work. This is basically the same conclusion reached by Behrman and Deolalikar (1993). The one exception is for rural men. Here, we find significant positive Heckman selection terms, suggesting a positive selection of men into wage employment, as discussed above. Consistent with this story, the returns to schooling are estimated to be much higher in the selectivity corrected equation. Note, however, that conclusions about the direction of selection biases, inherently based on untestable assumptions, must be treated with caution. 120

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One use that can be made of the earnings equations is to compute Oaxaca decompositions, a breakdown of the male-female earnings differential into that part which can be ‘explained’ by differences in characteristics (like education), and ‘unexplained’, that part which is due to differences in the regression coefficients (sometimes called discrimination). The results of these decompositions are reported in Table 3.11. For tractability, I only use the OLS specifications, with and without controls

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for hours. While using OLS is ‘hands above the table’, and avoids using more complicated, possibly less robust techniques, this does not mean that the biases they are designed to address are not important. In 1980, the raw differential was higher in urban than rural areas, but by 1990 both differentials declined, with the urban differential now smaller than the rural. To express the differentials in another way, the largest differential of 0.942 implies females earned 39 per cent of what men 122

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earned in 1980, and 0.609, the smallest, implies a ratio of 54 per cent in 1990, a significant improvement. While accounting for differences in hours helps increase the portion of the differential that is ‘explained’, there remains a significant part that is not explainable by differences in characteristics. The unexplained female to male earnings ratios are 69 per cent in urban, and 65 per cent in rural areas. While still implying a large difference between men and women, these ratios represent a considerable improvement over 1980, especially in urban areas. This result, however, requires further evaluation, particularly with regard to the differing types of samples in 1980 and 1990. The results in Table 3.11 suggest a very large decrease in the male-female earnings differential. This could be real, or it may be a consequence of the more restricted sample of workers in the 1990 SAKERNAS. To explore this issue further, I look at wage earnings differentials for other samples of workers. In addition to the 1980 and 1990 samples analysed above, I examine the full 1980 SUSENAS earnings sample, the 1981 full sample of earners, and the 1990 SUSENAS full sample of earners. In the first panel of Table 3.12, the ‘participation rates’ in these samples are presented. The full SUSENAS 1980 generally has the highest rates, especially in rural areas. It is in the rural areas that job multiplicity is highest (i.e. many workers have secondary earnings from the labour market) and the distinction between employee and self-employed is weakest. The rural female participation rate is quite low for the 1990 SAKERNAS, suggesting that this might be a more select sample than in the SUSENAS surveys. The 1990 SUSENAS numbers do not suggest a large increase in the number of individuals with labour market earnings, except for urban females. In urban areas, the comparability of samples is also less of a problem. In the second panel I explore the consequences of the differing samples on the measured wage differential. The 1980 and 1981 SUSENAS (any sample) yield quite similar male-female differentials. For 1990, urban females’ earnings are similar across the two surveys, while urban males have slightly higher earnings in the SUSENAS sample. Since the SUSENAS includes earnings from secondary jobs as well as primary jobs, this difference probably reflects the fact that men hold more jobs than women. Thus the 1990 SUSENAS yields a larger male-female differential than the SAKERNAS, but still considerably smaller than the 1980 differential. In rural areas, the story is a little bit different in 1990. By including many more individuals, including those whose labour earnings are marginal, the SUSENAS sample suggests lower average earnings for both men and women than the SAKERNAS. Nevertheless, the differential, while slightly larger than that from the SAKERNAS, is also significantly smaller than the 1980 or 1981 differential. In conclusion, while the results of Table 3.12 suggest that there are comparability problems 124

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between the SUSENAS and SAKERNAS earnings data, it nonetheless appears that the earnings of women rose faster than those of men over the 1980s, reducing the earnings gap. The contribution of female earnings to household income We have seen that (possibly) slightly more people work in the labour market in 1990 than did in 1980, and that their earnings from this activity are also higher. Does this mean that the labour market is becoming a more important source of income for households? Has female labour income in particular become more important for households? In Table 3.13 I tabulate the major sources of income for households in 1981 and 1 990 using the SUSENAS data. I report the mean for each type of income, the fraction of households who report that type of income, and the share of total household income represented by that type of income. The first

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thing to notice is that average real household incomes have generally risen over the decade. Since household size has declined, per capita income has probably increased. In all parts of Indonesia, labour market earnings have become a more important source of income, especially in rural areas. In rural areas, agriculture as a share of income has declined by more than 10 percentage points, even while more than 70 per cent of households still earn some income from agriculture. This reflects the overall decline of agriculture as a share of GDP that was reported earlier in the chapter. While female earnings have become a larger share of household earnings (increasing from 6.4 per cent to 9.7 per cent in urban areas, 3.8 per cent to 5.4 per cent in rural areas) the increase has been larger for men (37 per cent to 44 per cent in urban Indonesia, 18 per cent to 25 per cent in rural areas). Clearly the labour market is becoming a more important part of economic life in Indonesia, but the increase is proportionately larger for males. Undoubtedly, given the overall increases in participation for females, women are probably moving into other types of work that men are leaving: on the family farm or in family enterprises. Whether this reflects discrimination or choice is certainly not discernible from these data. LABOUR MARKET REGULATIONS IN INDONESIA As more workers participate in the labour market for wages, the set of workers subject to labour market regulation expands. This makes an understanding of these regulations more relevant for researchers studying the Indonesian labour market. What is more, as women increasingly work in the ‘formal’ sector, the possibilities that legislation can improve their position increases. Obviously, a few paragraphs cannot do justice to the complexity of issues that arise in evaluating the labourlegal framework in Indonesia, even with the limited focus on how it pertains to women. Furthermore, there are severe data limitations that prevent a discussion of issues of enforcement of existing legislation, an aspect of the legal framework that is critical to any evaluation. All I will attempt here is a thumbnail sketch of some of the key features of the legislation pertaining to women. At the highest level of legislation, there is at least an implicit recognition of the equality of men and women in the labour market. For example, Article 27 of the 1945 Constitution asserts that all individuals have the right to work, and while not explicitly mentioning women, they are implicitly included. The main labour law of 1951 has few references to women, except for some elements of protective legislation. More explicitly, Indonesia is a signatory (Act No. 80/1957) to ILO Convention 100 on equal pay for men and women for work of equal value, and this act prohibits wage discrimination against women. The Manpower Act of 1969, while not explicitly referring to women, also decrees that discrimination against 127

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anyone is illegal. In Act No. 7/1989, Indonesia ratified the 1988 UN Convention concerning discrimination against women. As another example of the at least implicit recognition of the equality of men and women, while the minimum wage varies by region according to the cost of reaching a ‘minimum physical need’, it does not vary by sex. Of course, there may occur many spontaneous, illegal strikes over the non-enforcement of the minimum wage law, so that the application of this law may in effect be unevenly applied to men and women. In summary then, it is illegal to discriminate against women in terms of pay. As in any country, enforcement of these laws which prohibit discrimination is quite another matter. In addition, in juxtaposition to the labour oriented laws, the Marriage Law of 1974 explicitly recognizes the husband as the head and chief provider of the household, and the wife as the mother. This legal/societal recognition of the reproductive role of women is in turn reflected in a number of protective legislative measures that may discriminate against women. The Labour law of 1951 put into place several elements of protective legislation pertaining to women. In Article 13 of Act No. 1/1951, women were granted the right to three months of paid maternity leave. As the leave was to be paid by the firm, the act was accompanied by detailed regulations that allowed it to be feasibly implemented. In the same act, women were also provided with the right to nursing breaks. Particular to Indonesia, women were also granted two days per month of unpaid menstrual leave. As with any protective legislation, enforcement is a potential problem. Furthermore, the provisions of this law make hiring married women, particularly those of child-bearing age, more expensive to employers. The act did not explicitly forbid discrimination against married women in hiring, nor did it explicitly forbid firing pregnant women (except, of course, in granting the maternity leave to women in the first place). That abuse and neglect of this law was a problem is evident in ministerial decree PER-03/MEN/1989 on the ‘Prohibition of Termination of Employment For Women Workers Due to be Married, Pregnant, or Giving Birth’. This decree stated that considering the law of 1951, and the fact that, ‘there is still happening requirements issued by companies which do not accept candidacy for women if they are married, pregnant, or giving birth’, that employers will be prohibited from terminating the employment of women workers for these reasons. The decree also reemphasized that employers were obliged to provide maternity leave according to the 1951 Act. This decree, however, stopped short of prohibiting discrimination against married women in hiring. The 1951 Act also placed restrictions on the employment of women at night, though this has been relaxed and refined by decree PER-04/MEN/1989 which allows for women’s night work under well specified conditions. The 1951 Act also contains protective legislation prohibiting women from working in mines, or in work dangerous to 128

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their health or safety. New labour legislation is in the works to update the 1951 law, but striking a balance between protective legislation and the promotion of equality has proven to be difficult. The issue of occupational health and safety, and its balance with the desire for employment growth, is an important legal question that applies as much to men as women as industrialization proceeds. In the end, there are laws that if enforced would prevent discrimination against women. However, enforcement depends primarily on labour inspectors of the Department of Manpower. There cannot possibly be enough inspectors, especially given the dispersed nature of industrialization. The refinement of legislation and regulation also probably depends, as it did in the United States, on the accumulation of experience and the development of a body of case law. As there are more examples of enforcement of the laws, either through the state trade union, or by groups of individuals at the plant level through the legal system, we can expect the legislation to become more precise and enforceable. We can almost surely expect this to take a long time. Further complicating matters, as we saw in the previous sections, not all of the increase in employment is occurring in the formal wage labour market, as much of the expansion, especially in rural areas, is in various forms of ‘selfemployment’, which includes workers contracting in various ways with larger firms. How labour legislation will apply to these non-traditional employees remains an open question. CONCLUSIONS Few simple, easy to report, conclusions without qualifications have emerged. As we have seen, this is primarily a result of comparability problems with the data sets. These comparability problems are rooted in some of the institutional subtleties of the Indonesian labour market (i.e. the definition of employee) as well as changing objectives of the surveys. Nevertheless, a few patterns of levels and changes of female labour market outcomes are apparent. First, especially in the growing urban areas, female participation rates, including participation as wage employees, rose over the 1980s. By examining hours, we also see that a focus on participation alone understates the relative attachment of urban women to the labour market. Second, while the real earnings of all workers rose over the 1980s, female earnings rose more quickly than men’s, leading to a decline in the otherwise large malefemale wage gap. Clearly, more research remains to be done. Three pieces of research stand out as being most important. First, there is a need to construct as consistent as possible a series of labour market earnings over the 1980s, so that trends in absolute and relative earnings can be more clearly established. This probably means focusing only on the SAKERNAS data from 1986 onwards. Second, there is a need to measure the returns to other 129

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forms of labour besides that traded in the labour market: various forms of selfemployment (i.e. the ‘informal sector’) are growing as quickly as wage employment. Third, it would be useful to cast this analysis in a more formal family labour supply model in order to test for changes in the role of women that can be attributed to changes in market conditions, and other ‘unexplained’ changes. The last two projects clearly depend on obtaining better household survey data. APPENDIX: DATA SOURCES FOR INDONESIA As mentioned in the paper, the data sources I employ are the 1980, 1981, 1982, and 1990 SUSENAS, and the 1986–1991 SAKERNAS. The purpose of this appendix is to describe the labour force components of the SUSENAS and SAKERNAS, and also to provide some detail on variable construction in this paper. SUSENAS The SUSENAS is the oldest regular household survey in Indonesia, carried out in 14 of the years between 1963 and 1987. It has varied in structure over these years, as modules were added and removed. It is designed to be flexible, so that in each year special surveys can be appended. In 1979, 1980, 1981, and 1982, the survey included substantial information on the labour force. The surveys that I use have data on: employment, unemployment, industry, status in employment, occupation, and earnings. Hours of work are not included for 1981. Definitions of employment status (self-employed vs. employee) also changed across surveys. The labour force is defined as all individuals who were economically active at least one hour during the reference week, or those who were unemployed and seeking work. There is also information on individual age and schooling in the demographic component of the survey. After 1982, most labour force questions were removed from the SUSENAS to eliminate duplication with the SAKERNAS. Surveys conducted in 1984, 1987 and 1990 have available consumption and income data which can be used to study some aspects of the labour market. However, the 1990 SUSENAS, because it was not designed as a labour force survey, has no specific labour force information. The labour force information I use from this survey is gleaned from the income section of the survey. I obtain information on individual age and education from the demographic module of the survey. From the income section, I obtain individual salary and wage income during the previous month. It is for this reason that my study of 1990 SUSENAS is limited to wage and salary employees, and there is no information on industry, employment status, etc. 130

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SAKERNAS For a thorough account of the SAKERNAS labour force survey, see Bazargan (1991) and Mehran (1991). To summarize briefly, however, the SAKERNAS is a household based labour force survey conducted quarterly. The survey were conducted in 1976, 1977, 1978, 1986, and every year thereafter. The survey uses the same definition of employment as the SUSENAS did (the standard one hour during the reference week, though refinements on definition of ‘economically active’ have occurred). One can obtain information on employment, unemployment, hours of work, industry, status in employment, and the level of education. Information on occupation was not collected after 1986, and industry definitions have changed occasionally. We obtain most of the useful information only for the individual’s main job. This means secondary employment and earnings information is very limited, and comparability with the SUSENAS total earnings may be a problem where secondary jobs are important, as in rural areas. Finally, there is only limited household level information, and no information on marital status. Other data issues Most of the variables in this paper are self-explanatory, but a few require further information. The potential experience variable was constructed as age—years of schooling—five, as is the case in most studies. I converted the education categories to years of schooling according to the table presented in Byron and Takahashi (1989):

All means and regressions are wieghted, so that each observation receives the same relative weight as its probability of sampling in the total population.

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All available observations were used. Only observations where critical information was missing, or were coded ‘9999999’ were dropped. NOTES 1 See Locher-Scholten and Niehof (1987) for a presentation of the early twentieth-century role of women in Java. 2 See Williams (1990) for an excellent description of the economic structure of the family in Indonesia. 3 See IRRI (1985), in particular the White and Sajogyo chapters, for a variety of studies that discuss the potential impact of technological change in rice agriculture on women’s employment, and in turn household income, in several Asian economies. 4 For the sample of 15–65-year-olds that I work with, in 1980 77 per cent of the sample lived in rural areas, whereas only 68 per cent of individuals were classified as rural in 1990. 5 Also see White (1990) for a more specific discussion of some of the regulations that affect women factory workers.

BIBLIOGRAPHY Anata, A., Alatas, S. and Tjiptoherijanto, P. (1988) ‘Labour market developments and structural change in Indonesia’, in P.E.Fong (ed.) Labour Market Developments and Structural Change: The Experience of ASEAN and Australia, Singapore: Singapore University Press. Bazargan, A. (1991) ‘Sources, and Types of Data on Employment and Manpower in Indonesia’, DEPNAKER/UNDP/ILO Technical Report Series B, No. 1. Jakarta, February. Behrman, J.R. and Deolalikar, A.B. (1993) ‘Unobserved household and community heterogeneity and the labour market impact of schooling: a case study of Indonesia’, in Economic Development and Cultural Change, 461–488. Booth, A. (ed.) (1992) The Oil Boom and After: Indonesian Economic Policy and Performance in the Soeharto Era, Singapore: Oxford University Press. Byron, R.P. and Takahashi, H. (1989) ‘An Analysis of the Effect of Schooling, Experience and Sex on Earnings in the Government and Private Sectors of Urban Java’, Bulletin of Indonesian Economic Studies 25, 1:105–117. Cremer, G. (1990) ‘Who are those misclassified as others? A note on Indonesian labour force statistics’, Bulletin of Indonesian Economic Studies 26, 1:69–90. Goldin, C. (1990) Understanding the Gender Gap: An Economic History of American Women, New York: Oxford University Press. Hugo, G., Hull, T., Hull, V. and Jones, G. (1987) The Demographic Dimension in Indonesian Economic Development, Singapore: Oxford University Press. IRRI (International Rice Research Institute) (1985) Women in Rice Farming: Proceedings of a Conference on Women in Rice Farming Systems, Brookfield: Gower. Jones, G. and Manning, C. (1992) ‘Labour Force and Employment during the 1980s’, in A.Booth (ed.) The Oil Boom and After: Indonesian Economic Policy and Performance in the Soeharto Era, Singapore: Oxford University Press: 363–410. Korns, Alex (1987) ‘Distinguishing signal from noise in labour force data for Indonesia’, DSP Working Paper, No. 5. Lluch, C. and Mazumdar, D. (1985) Indonesia Wages and Employment, A World Bank Country Study, Washington, DC: The World Bank. Locher-Scholten, E. and Niehof, A. (1987) Indonesian Women in Focus, The Netherlands : Foris Publications. Mehran, F. (1991) ‘Employment data in Indonesia: report of a consultancy mission’, Jakarta: DEPNAKER/UNDP/ILO Technical Report Series A, No. 6. 132

WOMEN AND THE LABOUR MARKET IN INDONESIA Moir, H. (1980) Economic Activities of Women in Rural Java: Are the Data Adequate?, Canberra: Development Studies Centre, Australian National University. Oey-Gardiner, M. (1991) ‘Gender differences in schooling in Indonesia’, Bulletin of Indonesian Economic Studies 27, 1:57–80. Ravallion, M. and Huppi, M. (1991) ‘Measuring changes in poverty: a methodological case study of Indonesia during an adjustment period’, World Bank Economic Review 5, 1:57– 84. Sajogyo, P. (1985) ‘The impact of new farming technology on women’s employment’, in IRRI Women in Rice Farming: Proceedings of a Conference on Women in Rice Farming Systems, Brookfield: Gower: 149–170. Soemantri, S. (1982) Study of Indonesia’s Economically Active Population, Yogyakarta: Gadjah Mada University Press. White, B. (1985) ‘Women and the modernization of rice agriculture: some general issues and a Javanese case study’, in IRRI Women in Rice Farming: Proceedings of a Conference on Women in Rice Farming Systems, Brookfield: Gower: 119–148. White, M. (1990) ‘Improving the welfare of women factory workers: lessons from Indonesia’, International Labour Review 129:121–133. Williams, L.B. (1990) Development, Demography, and Family Decision-Making: The Status of Women in Rural Java, Brown University Studies in Population and Development, Boulder: Westview Press. World Bank (1990a) Indonesia: Strategy for a Sustained Reduction in Poverty, A World Bank Country Study, Washington DC: The World Bank. ——(1990b) Indonesia: Family Planning Perspectives in the 1990s, A World Bank Country Study, Washington DC: The World Bank. ——(1991) Indonesia: Employment and Training Foundations for Industrialization in the 1990s, World Bank Internal Report No. 9350-IND, 13 June 1991.

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4 WOMEN IN THE JAPANESE ECONOMY M.Anne Hill

BACKGROUND1 Japan’s economic miracle and steady growth have been widely heralded. Less is known about the extent to which Japanese women have contributed to this growth. During the post-World War II period, Japan’s annual rate of growth in real GNP increased from an average of 8.5 per cent (1955 to 1960) to 10 per cent (1960 to 1965), and reached 12.3 per cent from 1966 through 1970. While Japan experienced slowdowns with the oil shocks (especially 1973–1975), real GNP began to expand again in the 1980s, with annual growth rates (at about 4.5 per cent) exceeding those in the United States and Europe. During this period, the proportion of GDP accounted for by tertiary industry rose from 46.7 per cent in 1955 to 50.9 per cent in 1970 to 61.0 per cent in 1988, with the proportion of all employment in tertiary industry rising concomitantly (Japan Institute of Labour, 1980, 1986, Elwell, 1990). Women in Japan have represented a large reserve force of workers who have contributed flexibility to overall employment. Japan’s low measured unemployment rate (which reached a high of 2.8 per cent in 1986) has been due at least in part to the large number of women employed in temporary positions, who appear to leave the labour force altogether during business downturns. The majority of working men have retained lifetime employment with little inter-firm mobility. With stark reductions in fertility, rising levels of female education, and changing social attitudes, women have entered the formal labour sector in increasing numbers, with rising proportions working as regular employees. These trends, especially among married women, have been dramatic. As the female share of the labour force rises, Japan may witness initial slowing of productivity growth, since many of the women who enter the labour force will have less experience and training than their male counterparts. As described below, the changing composition of the female labour force has influenced the malefemale wage gap, which closed rapidly during the 1960s to mid-1970s, with progress toward equalizing wages slowing during the late 1970s and 1980s. However, as more and more women become permanently attached to the work force, we can anticipate increases both in their productivity and relative wages. 134

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If the proportion of women working in the formal sector of the Japanese labour force maintains its upward trend, Japan can anticipate some of the concomitant social changes experienced by her Western sisters: further reductions in fertility, higher measured family income with more two-earner families, rising demand for time-saving consumer goods and services (among them, child care) that could ease domestic responsibilities, and perhaps movements to change the nature of work in Japan, especially calls for reducing the length of work days, work weeks, and the number of geographic moves. This chapter addresses historical trends in women’s work, wages, schooling and fertility in Japan and closes with prospects for the future. LABOUR FORCE PARTICIPATION Employment relationships in many Japanese firms possess three distinctive features which influence women’s employment, wages, and the gender wage gap: the seniority-merit wage system (nenko joretsu chingin seido) which ties wages to an employee’s family status and life-cycle needs as well as to productivity related characteristics (Umemura, 1980); biannual bonus payments (usually representing roughly one-quarter of total annual earnings) (Hashimoto, 1979); and lifetime employment.2 Generally, however, only regular employees are entitled to the benefits of the seniority-merit wage system, to biannual bonus payments, and to lifetime employment. Regular employees are defined as those who are employed with a contract for an indefinite period or who have worked for more than one month, or more than a specific probationary period, after which regular employees effectively are granted tenure. Other employees are temporary employees, who have a contract of a specified length (e.g., two weeks or even two months) and whose contracts may or may not be renewed, depending on the demand for their services.3 In 1990, 72.3 per cent of all working Japanese women and 80.8 per cent of Japanese men were employees (Table 4.1). Most male employees were regular employees, while about 80 per cent of female employees were regular employees. The remaining labour force participants were either self-employed or family workers. Self-employed workers are those who own and operate unincorporated firms, while family workers are family members employed in such firms.4 Through the post-World War II period, with Japan’s rapid economic transition, resources and workers have shifted from the agricultural sector and from small family-run businesses to more highly industrialized activities. As the data in Table 4.1 illustrate, the overall labour force participation rate of Japanese men has declined only gradually during this period, falling from 135

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83.9 in 1948 to 77.2 in 1990. However, the shift across employment statuses has been remarkable. In 1948, more than half of all men in the Japanese labour force were either self-employed or family workers. As opportunities for gainful employment declined for self-employed and family workers, Japanese men shifted fairly steadily into work as employees. While Japanese women currently participate as actively in the labour force as do American women, in the US nearly all women are employees, working for someone else. In contrast 11 per cent of all Japanese working women are selfemployed (predominantly homeworkers) and an additional 17 per cent work in family-run enterprises. Only 72 per cent work as employees. Just over half of all working Japanese women attain the status of regular employee. The historically large numbers of self-employed and family workers have had a significant effect on the trends in overall female labour force participation (see Figure 4.1). With the decline of the agricultural sector, some women who would otherwise have been employed in family enterprises or small family farms appear to have left the labour force altogether. Consequently, the overall labour force participation rate for Japanese women rose from a 1948 level of 47.4 per cent to a peak of 56.7 per cent in 1955, then declined to a 1975 level of 45.7 per cent before beginning again to rise slowly. As a result, Japan stands in stark contrast to most industrialized countries where post-war female labour force participation rates have risen steadily.

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The present overall female labour force participation rate in Japan differs little from that in other industrialized countries, yet the proportion of all working women who remain self-employed and family workers exceeds that of most industrialized countries, and is somewhat more comparable to that in other Asian countries, especially Korea and the Philippines (Table 1.6, p. 23). While the proportion of working women engaged as employees has risen fairly steadily from 24.5 per cent in 1948 to 72.3 per cent in 1990, nearly 30 per cent of all working women remain self-employed and family workers. The Japanese labour force continues to retain features characteristic of a developing country, especially the existence of a large informal sector. That labour supply would differ between Japanese men and women is perhaps not surprising, since women’s career paths have tended to diverge from men’s due largely to differences in family responsibilities. However, in Japan, institutional factors exacerbate this tendency. The Labour Standards Law of 1947, for example, instituted protective labour practices that effectively limited female participation in particular sectors of the economy. While the Labour Standards Law guaranteed equal pay for equal work, it also prohibited night work, set maximum overtime hours, prohibited heavy lifting, underground work, and work at any height above five metres (Cook and Hayashi, 1980). Also, until a 1966 court case ruled against such practices, many large firms required that women resign upon marriage. Yet, even five years after the 1966 ruling, all female employees were single in 14 per cent of Japanese establishments. Many firms maintained practices requiring mandatory retirement upon marriage (8.9 per cent), pregnancy or childbirth (8.8 per cent), or at age 40 or less (11.0 per cent).5 Often, when women leave a firm, their job tenure is broken. Upon return, they are generally not credited with previous service. Moreover, frequent transfers combined with a long working day and work week effectively limit continuity in employment of married women, especially those who have children (Hayashi, 1991). As Table 4.2 illustrates, labour force participation of married women as employees in non-agricultural industries has risen dramatically during the past thirty years, nearly quadrupling from 8.8 per cent in 1960 to 31.1 per cent in 1988. Moreover, the participation patterns of married women have become increasingly similar to those for women overall. Yet the current proportion of married Japanese women who work as employees in the non-agricultural sector approaches a comparable rate of 31.7 per cent experienced by married women in the US in 1960.6 Figure 4.2 presents overall age-specific participation rates for women and men for 1970 and 1988. There has been little change in these patterns for men, with upward of 90 per cent participation rates between the ages of 25 and 59. However, the age-participation curve from women has shifted upward, with rising participation at all ages between 20 and 59. 139

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The overall participation patterns retain the familiar M-shape, with labour force withdrawals occurring after marriage and childbirth and reentry after the childrearing period. However, among more recent cohorts, the M-shape appears to be flattening, with fewer women leaving the labour force during their mid-20s and early 30s. Figure 4.3 displays these rates for female employees only, including also detail for 1978 and 1983. Women younger than 20 have lengthened their school careers, with a resulting decline in participation. However, sharp increases in participation can be observed at other ages. In 1970, women participated as employees at relatively high rates, exited in their mid-20s and generally did not return; after ages 25 to 29, the participation profile is relatively flat. By 1988, this pattern had changed markedly; women reentered the labour force as employees, with participation rates approaching 50 per cent among women aged 35 to 54. Figure 4.4 concentrates on married women age 20 and older, for 1978 and 1988, and displays separate graphs of the overall participation rate and that for employees. Among married women, the age profile for participation overall is much higher than that for employees alone, with marked differences occurring especially after age 34. Married women who reenter the labour force have tended to work in the informal sector, with greater flexibility of employment and hours. Yet while the age profiles 140

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overall and for employees have each shifted upward in the decade considered, the increase is especially notable for employees. This figure confirms the picture portrayed statistically in Table 4.2; married women are working increasingly in the formal sector of Japan’s economy. EMPLOYMENT STATUS Overall, women represented about 40 per cent of the labour force in 1990, roughly the same proportion as in 1950. In the interim, the share of all jobs held by women shrank, with both the relative and absolute number of women in the labour force declining between 1970 and 1975. Table 4.3 displays the proportion of all jobs held by women and the distribution of employed women by industry. Nearly half of all agricultural and forestry workers are women. In 1990, women represented more than half of all workers in the service industry, and nearly half in wholesale and retail trade and finance, insurance, and real estate. The government, utilities, communications, mining, and construction remain predominantly male industries. However, both the proportion of all construction industry workers who are women and the industry’s share of all employed women have risen considerably during this forty-year period. In 1950, more than half of all employed women worked in agriculture and forestry. Today, only 8 per cent of working women are employed in that sector. Larger proportions now work in services (27.8 per cent, up from 11.8 per cent in 1950) and wholesale and retail trade (27.2 per cent). The proportion of all women employed in manufacturing increased from 12.0 per cent in 1950 to 25.9 per cent in 1970 and has fallen marginally since. Some more detailed industrial categories exhibit very high proportions of women; for example, more than half of textile and three-quarters of apparel industry employees are women. Plastics, leather, electrical machinery, and precision instruments industries engage a work force that is more than 40 per cent female. Within the wholesale and retail trade sector, employees in department stores, food and beverage stores, and restaurants are also disproportionately female (see Table 4.4). Table 4.5 illustrates the proportion of all employees in an occupation who are women and the distribution of employed women by occupation. The occupational composition of the female labour force has changed dramatically since 1955 with a dramatic decline in the proportion of all women who work as farmers, and the movement of women into other occupations. The share of all professional and technical jobs held by women has risen from 36.3 per cent in 1955 to 42.0 per cent in 1990. (However, within all professional occupations, medical and health professionals combined with teachers account for many of these positions, as indicated in Table 4.6.) Japanese women have failed to attain managerial 142

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jobs. In 1990, only 8 per cent of all managerial posts were held by women and less than 1 per cent of all women worked as managers. However, this proportion has increased from a 1975 low of 3.7 per cent. Japanese men retain nearly half of all clerical jobs, although clerical jobs are increasingly held by women. Clerical jobs were held by 8 per cent of all women in 1955 and by 27.4 per cent of all women in 1990. Surprisingly, Japanese 143

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women represent nearly a third of all craft and production workers and more than 40 per cent of all labourers. In the United States, less than 20 per cent of such jobs are held by women. THE EARNINGS GAP IN JAPAN The overall pay gap between Japanese men and women remains dramatic. In 1988, the average female employee in Japan earned 57 per cent of the 145

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average male employee’s earnings. However, before exploring the determinants of this wage gap it is imperative to clarify what is meant by wages in Japan. Unfortunately, wage information generally focuses on employees. There are no readily available data for the wages of self-employed and family workers. As a result, the extent to which reported wage data cover the entire labour force differs for men and women; and for both over time, as a rising proportion of all workers have become employees.7 Moreover, earnings embody several components: 147

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1 contract earnings (including the basic wage, as well as various allowances for housing, family, location, position, shift work, inter alia); 2 non-scheduled earnings (overtime pay, night-work pay, holiday pay); 3 special annual payments (bi-annual bonus payments, and lump sum retirement payments). Table 4.7 displays monthly earnings (both total and contract) annual special payments and total monthly hours for men and women who are employed by firms covered by the Basic Survey of Wage Structure (the survey excludes part-time workers). These data are used to estimate an hourly wage, including bonus payments, and also to calculate measures of women’s relative earnings. During the post-war period, rapid economic growth in the secondary and tertiary sectors produced dramatic wage growth for women. Women’s relative wages rose considerably, as both nominal and real wage growth for women surpassed that for men. Simple regressions relating nominal wage growth to GNP growth during 1955 to 1986 reveal that for women, a one percentage point rise in the GNP growth rate produced a 0.85 percentage point increase in women’s wage growth and only a 0.65 percentage point increase in the rate of men’s wage growth. The ratio of women’s total monthly earnings to men’s increased from about 0.50 in the late 1950s to a peak of 0.59 in 1975. Relative wages attained a maximum as female labour force participation reached a trough. As the female labour force began to grow, relative wages fell, declining to about 0.56 during the late 1970s and early 1980s, but have recently begun to rise (see Figure 4.5). Part of the gender disparity can be explained by men’s higher payments for non-scheduled earnings and annual bonus payments. Generally, women have received less than half the bonus payment amounts received by men. However, women’s annual bonus payments have increased sharply in relative terms, serving to close the overall pay gap. Some of the gender wage gap can be explained by shorter working hours for women. In 1988, women worked an average of 185 hours a month compared to 200 for men. As depicted in Figure 4.5, women’s regular (or contract) earnings, are closest to men’s, although only 60.5 per cent in 1988. Translating annual bonus payments into an hourly measure, women’s relative hourly earnings follow a trend similar to contract earnings and at 60.3 per cent in 1988, approach the female-male ratio for contract earnings. However, the hourly wage ratio followed a similar upward trend, peaking in 1975, and being stable until the mid-1980s, when relative wages began to climb again. Women’s wages peaked as the female labour force declined both relative to men and in absolute numbers. While Japanese women just entering the labour force fare reasonably well relative to men, the pay gap increases dramatically with age. In 1986, 149

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Figure 4.5 Women’s relative earnings, Japan, 1962–1988

women aged 18 to 19 earned 84.1 per cent as much as men of that age. (This ratio is down slightly from 86.9 per cent in 1975.) Relative wages fall to 65.2 per cent by age 30 to 34 and to less than 50 per cent for women aged 45 to 54. Yet the average employee has become proportionately older. Women aged 40 to 54 now represent more than one-third of all female employees, compared to 19 per cent in 1965.8 Some of the gender wage gap can be attributed to differences in the distribution of men and women by size of firm. Historically, wages in the largest Japanese firms have exceeded those in smaller firms. 9 Women are employed disproportionately in smaller firms. For example, in 1960 women represented 36.5 per cent of all employees in firms with fewer than thirty employees and only 25.9 per cent of employees in firms with 500 or more workers. By 1988, women’s representation had increased to 36.8 per cent overall, but to only 30.3 per cent of employees in the largest firms and 41.3 per cent of workers in firms with fewer than thirty employees. Table 4.8 uses Monthly Labour Survey data to illustrate current wage levels by size of firm, industry, and gender for 1987. 10 Wholesale and retail trade, which has experienced rising levels of female employment, pays women low wages, both relative to men and relative to women in other industries. The same holds true for manufacturing, where women in firms with thirty or more employees earn only 42 per cent as much as men. However, the service industry, also with rising female proportions, pays 150

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well both in absolute and relative terms. For example, women working in service firms with thirty or more employees earn 61 per cent as much as men and 62 per cent more than women in manufacturing. Within the manufacturing industry, women appear to receive lower wages in those sectors which employ large numbers of women, notably in food and apparel production. Determinants of earnings Much work has been done on the relationships between earnings and individual characteristics, especially schooling, job experience, and tenure. Human capital theory (see Becker, 1964, and Mincer, 1970, 1974), and applications to Japan by Hashimoto (1979), and Hill (1992) holds several basic propositions: that individuals gain skill with time on the job, that these skills may be both general (transferable) and specific to the firm (non-transferable), and that productivity and therefore wages increase with investment on the job. Also, schooling improves productivity and earnings. To estimate empirically the effect of investment in human capital on earnings, years (and the nature) of schooling, time on the current job (or tenure), and total labour market experience serve as proxies for investment in human capital. Earnings models estimated using data for Japanese men and women yield estimated returns to labour market experience and schooling that are similar to those reported for the US and, within employment status, reasonably comparable results for men and women (see Hill, 1989, and Hill, 1991). Investigations of the male-female wage gap in the US have revealed that a significant proportion of the male-female difference in earnings can be explained by gender differences in educational attainment, labour force experience, occupation, and family status (see especially Mincer and Polachek, 1974, 1978, Corcoran and Duncan, 1979, O’Neill, 1985, Smith and Ward, 1985, and Blau and Beller, 1988). Yashiro (1981) and Osawa (1984) provide exemplary research efforts which use human capital theory to explain male-female earnings differences in Japan. Yashiro employs aggregate cross-sectional data for men and women and, for 1976, finds that 46.8 per cent of the wage gap can be accounted for by difference in job tenure, with an additional 7.2 per cent due to men’s higher educational attainment. Osawa uses individual data for Japanese men and women to estimate human capital earnings functions. She also discovers that differences in experience and schooling account for much of the wage gap, with schooling accounting for 11 per cent and experience differences explaining 26.8 per cent. It appears that women’s employment suffered during the dramatic labour market adjustment and structural change that Japan experienced after the 1973–1974 oil shock. As women moved out of the labour force, their measured relative earnings 152

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rose, apparently indicating that the stayers had higher levels of schooling, greater tenure, and consequently higher wages. Tan (1980) reports empirical results from a very interesting case study of the labour adjustment experienced by a medium-sized Japanese electrical machinery firm during this period. His data, compiled from the company’s annual reports and personnel files, reveal that women appear to have borne the brunt of the firm’s adjustment; women as a proportion of the firm’s work force fell from 56 per cent in 1969 to only 12.5 per cent in 1978. During the intervening years, women’s wage growth exceeded men’s and the women who stayed with the firm were characterized by higher levels of schooling, tenure, and not surprisingly, higher wages, when compared with those women who left. Adjusted relative earnings Wages are related to tenure on the job, size of employer, occupation, schooling, the continuity of experience, and the occupation and industry chosen. In order to analyse the extent to which changes in the underlying determinants of wages can explain the recent downward trend in female relative wages, I used data for the period from 1965 to 1988 from the Basic Survey on Wage Structure, which reports total and contract earnings as well as annual bonus payments, age, and job tenure, by sex-education-firm size categories. For each year, two dependent variables were used: the natural log of total monthly earnings (including a monthly measure of the annual bonus), and the hourly wage. Each dependent variable for the cell was regressed on sex, age, tenure, and a dummy variable for firm size if larger than 1,000 employees. Thus, two equations were estimated for each year. The exponential of the sex coefficient provides an estimate of women’s adjusted relative wages. Figure 4.6 presents these adjusted wage ratios. After accounting for differentials in tenure and age, the adjusted earnings ratio ranged from 0.73 to more than 0.80. Further accounting for hours differentials increases adjusted relative wages to between 0.75 and 0.84, closing much of the gender wage gap. As with the actual earnings, adjusted earnings rose markedly until 1975, levelling off in the late 1970s and not increasing further. Why has the gender wage gap failed to close further? The proportion of the female labour force who are new entrants (or who recently changed jobs) has increased recently. This proportion has risen to 13 per cent in 1987 from about 10 per cent during the 1959 to 1974 period. The comparable rate for men is 7 per cent.11 The stability in the unadjusted wage gap can be accounted for in part by the expansion of the female labour force with the entry of relatively less experienced women. And one of the most important and often-cited determinants of wages is tenure on 153

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Figure 4.6 Women’s relative earnings and wages, adjusted, Japan, 1965–1988

the job. Figure 4.7 illustrates the age-tenure relationship for men and women in 1965 and 1988. This graph reinforces the impression yielded by the ageparticipation profiles described earlier. Women appear increasingly to be retaining jobs as employees even through ages typically associated with early family formation. At ages up to 30–34, the age-tenure relationship among women is similar to that for men. While tenure increases steeply with age for men until ages 50–59, the relationship is less steep for women. However, the female pattern in 1988 is quite different from the 1965 picture, with higher levels of tenure at all ages above 25, and a rising, rather than flat, age-tenure profile. But how do these tenure gains translate into earnings profiles? Figure 4.8 illustrates the relationship between total monthly earnings and age for the same period. While men experience steep earnings growth with age, women have very flat wage profiles. It is clear that the increases in women’s tenure have not been converted into steeper wage profiles. Japanese firms may adjust gradually to the apparent increasing commitment of women to long-term stays in the labour force. Decomposing trends in the wage gap Table 4.11 summarizes the results from regressions estimated with somewhat more detailed data from the Basic Survey of Wage Structure (the 154

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Figure 4.7 Age-tenure profiles, Japan, 1965 and 1988

Figure 4.8 Total monthly earnings-age profiles, Japan, 1965 and 1988 (including annual special payments)

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regressions are displayed in Tables 4.9 and 4.10). Aggregate wage survey results for 1968, 1978, and 1988 include both contract (or regular) earnings as well as bonus payments for men and women. These data are reported in the form of cell means for monthly wage payments, annual bonus payments, monthly working hours, age, and job tenure, by industry, firm size, sex, employment type, i.e., production worker (blue collar) or 156

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salaried employee (white collar workers), and by education. For each of the three years, two models are estimated. First, the natural log of total earnings (and then the log of the wage) for each cell is regressed on age, tenure, a dummy variable indicating completion of at least upper secondary school, and a dummy variable if a salaried employee. The sample includes information for the manufacturing, the wholesale and retail trade, and the finance, insurance, and real estate industries.12 The overall earnings differential is somewhat smaller after we account for differences in working hours. Both measures have decreased over time. In 1968 log differential in annual earnings was 73 per cent; the wage differential was 68 per cent. The earnings difference narrowed to 59 per cent in 1988 and decreased to 53 per cent for the wage measure. Since the wage structure itself may differ by sex, I estimate earnings and wage equations separately. Using these separate models, we can decompose the extent to which the total earnings (wage) differential is attributable to differences in characteristics of men and women, and the extent to which it may be accounted for by the way in which firms reward those characteristics. However, the relative importance of characteristics and coefficients in this decomposition depends on how the decomposition is implemented. Table 4.11 presents both methods. The effect of the difference in means appears to explain a smaller share of the earnings and wage differentials over time. The decline in the adjusted wage differential appears to have resulted from changing characteristics of female workers, with increasingly larger shares of the earnings (wage) gap accounted for by the wage structure itself. As women’s job tenure and education increase in the future, we can expect the earnings gap to diminish. As employers adjust their expectations 157

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regarding women’s likely tenure on the job, the manner in which the market compensates women for their skill levels may begin to change as well. HUMAN CAPITAL AND THE FAMILY IN JAPAN The rise in female labour force participation along with women’s rising wages have been experienced concurrently with rising educational attainment, reductions in the proportion of women who marry, and declining fertility. As the data in Tables 4.12 and 4.13 attest, the educational attainment of Japanese men clearly exceeds that of women. In 1985, only 2.8 per cent of all Japanese women had attained a college education, compared with 13.5 per cent of Japanese men. However, the proportion of Japanese women continuing their education has risen considerably over time. In 1989, a higher proportion of female junior high school graduates continued on to senior high school (95.3 per cent versus 93.0 per cent of men). Although college-going rates are similar for men and women, the majority of women attend two-year colleges or technical schools. Only 14.7 per cent of female high school graduates entered a four-year college or university, as against 34.1 per cent for men. Perhaps equally important is the extent to which young women who do complete a college education are then able to enter the labour force. In

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1960, less than two-thirds of women graduating from a four-year college (a rare group themselves) entered the labour force, compared to 86.3 per cent of male college graduates (Table 4.13). By 1990, the proportions for men and women had equalized at 81 per cent (Table 4.14). Table 4.15 depicts the proportion of women remaining single by age 20–24, 25–29, and finally 40–44. In 1950, only 55 per cent of women 20–24 had never married and this fell to 15 per cent by ages 25–29, with only two per cent still single by ages 40–44. While the proportion never married by ages 40–44 has increased gradually to 5 per cent, young women appear to be delaying marriage. Since 1975, the percentage single by age 20–24 has increased to 81.4, and for women 25–29 to 30.6 per cent, with much of this change occurring in the past decade. This has also affected fertility.

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Japan experienced rapid fertility declines in the early to mid 1950s, with the live birth rate fairly stable through the following twenty years.13 However, the live birth rate dropped precipitously during the mid- to late 1970s and 1980s, falling to 10.8 per 1,000 and the total fertility rate below replacement level at 1.66 in 1988. The rising numbers of single women, delayed marriages, and reduction in fertility have caused considerable political concern in Japan, with recent pronatalist policies being considered and adopted. PROSPECTS FOR THE FUTURE While wage discrimination on the basis of sex has been illegal since 1947, it was not until 1985 that Japan enacted legislation mandating that women be offered equal employment opportunities as well.14 Recent legislation also overturned many of Japan’s protective work rules including overtime restrictions and prohibition of night shifts, as well as statutes barring women from ‘dangerous or harmful’ work mentioned previously (see p. 139). Sugeno (1988) reports that some of the early effects of implementation of these new laws include: elimination of sex-specific job advertisements; expanded recruitment of female four-year college graduates; equalizing male and female retirement ages; and the introduction in many large companies of a two-track employment system, consisting of a ‘managerial’ track and a ‘clerical’ track. Sugeno further describes the managerial track as requiring comprehensive job rotation and transfers, and having unlimited promotion possibilities, while the clerical track requires no transfers and limited job rotation but also limits upward mobility. There exists some concern that the managerial track will become male and the clerical track female. Yet, the two-track system may in fact enable Japanese women to enjoy long-term employment. Whether and by how much these recent changes can close the gender pay gap and equalize the 160

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economic position of women in Japan remains a question to be answered during the coming years. APPENDIX Labour force data Labour force data are drawn primarily from the Labour Force Survey, a national household survey conducted monthly and covering persons 15 years old and over. Employed persons are those who work any hours for pay or profit during the reference survey week, including unpaid family workers. Unemployed workers are those not working who actively sought work the week before the survey date. Self-employed workers own and operate unincorporated enterprises. Family workers are persons who work in an unincorporated enterprise owned by a family worker (and who may or may not be paid). Employees work for wages or profit in an unincorporated enterprise, company, corporation or association, or the government. Wage data Two national surveys yield detailed wage information for Japan. The Basic Survey on Wage Structure, an establishment survey conducted annually by the Ministry of Labour, covers establishments with at least ten regular employees, which in 1986 encompassed 70,000 establishments and 1.4 million workers. Firms in agriculture, forestry and fisheries, the government, and domestic services are normally excluded from the survey (Japan Statistical Yearbook, 1988:68.). Data provided generally consist of an employee’s age, sex, schooling, size of firm, industry, and whether he or she is a production or white collar worker. Wage information includes monthly contract earnings, total earnings, monthly hours, and annual special payments as well. The Ministry of Labour also conducts a Monthly Labour Survey of establishments with thirty or more regular workers, and covers workers in all industries except agriculture, forestry and fisheries, government, domestic services, and services provided by foreign governments in Japan. Additional surveys, reporting details by sex for most years, cover smaller establishments, with 5–29 and 1–4 regular workers (Japan Statistical Yearbook, 1988:67).

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NOTES 1 This chapter builds on recent work, Hill (1990) and Hill (1991). 2 For a thorough description of the Japanese wage and employment system, including labour laws that impinge on this system, see Hanami (1981). 3 See Japan Institute of Labour (1974), p. 197 and Hanami (1981). 4 Family workers are persons who work in unincorporated businesses operated by a family member. In Japan, family workers are included as labour force participants if working at least one hour during the survey week, if unpaid. In the United States, for example, the cutoff is 15 hours. Persons who receive remuneration for their work are included in the labour force, regardless of hours worked. 5 US Department of Labor (1976) pp. 40 and 57. 6 US Department of Commerce (1986). 7 Data from Japan’s National Survey of Family Income and Expenditure include information by gender on the monthly earnings for individuals in one-person households. While clearly a select sub-sample of the entire population, these monthly earnings data are based on a household survey and are not contingent on either employment status or firm size. These data depict a similar trend. In 1969, women in one-person households earned 76 per cent as much as comparable men. Women’s relative earnings rose to 82 per cent in 1974, then fell to 80 per cent in 1979 and further to 75 per cent in 1984. 8 Japan Statistical Bureau, Japan Statistical Yearbook, 1987. 9 For analysis of the effect of firm size on wages, see e.g., Stoikov (1973) and Hashimoto and Raisian (1985). 10 The Monthly Labour Survey data include contract earnings and extra payments, except for employees in firms with one to four employees. 11 Employment Status Survey data as reported in the Japan Statistical Yearbook, various issues. 12 Published data included these three sets of industries consistently across years. 13 Apart from the sharp drop in 1966 attributed to ‘Hinoeuma’ and the superstition that any woman born that year would be too aggressive to be an attractive marriage partner (Hashimoto, 1974). 14 For detailed descriptions of the Equal Employment Opportunity Law and its potential implications for the female labour force in Japan see Suwa (1988) and Edwards (1988).

BIBLIOGRAPHY Anderson, L.H. and Hill, M.A. (1983) ‘Marriage and labor market discrimination in Japan’, Southern Economic Journal 49, 4:941–953. Becker, G. (1964) Human Capital: a Theoretical and Empirical Analysis, NBER, New York: Columbia University Press. Blau, F.D. and Beller, A.H. (1988) ‘Trends in earnings differentials by gender, 1971–1981’, Industrial and Labour Relations Review 41, 4:513–529. Cook, A. and Hayashi, H. (1980) Working Women in Japan: Discrimination, Resistance and Reform, Ithaca, New York: New York State School of Industrial and Labor Relations. Corcoran, M. and Duncan, G. (1979) ‘Work history, labour force attachment, and earnings differences between the races and sexes’, Journal of Human Resources 14:3–20. Edwards, L.N. (1988) ‘Equal employment opportunity in Japan: A view from the west’, Industrial and Labour Relations Review 41, 2:240–250.

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WOMEN IN THE JAPANESE ECONOMY ——(1992) ‘The status of women in Japan: has the equal employment opportunity law made a difference?’, unpublished manuscript, New York: Queens College, City University of New York. Elwell, C. (1990) ‘Recent trends and outlook for the Japanese economy’, in Japan’s Economic Challenge, study papers submitted to the Joint Economic Committee, Congress of the United States, Washington, DC: US Government Printing Office. Hanami, T. (1981) Labour Relations in Japan Today, Tokyo: Kodansha International, Ltd. Hashimoto, M. (1974) ‘Economics of postwar fertility in Japan’, in T.W.Schultz (ed.) Economics of the Family: Marriage, Children, and Human Capital, Chicago: The University of Chicago Press, 225–249. ——(1979) ‘Bonus payment, on-the-job training, and lifetime employment in Japan’, Journal of Political Economy 87:1086–1104. Hashimoto, M. and Raisian, J. (1985) ‘Employment tenure and earnings profiles in Japan and the United States’, American Economic Review 75:721–735. Hayashi, H. (1991) ‘Legal issues on wages of Japanese women workers’, International Review of Comparative Public Policy: 3:243–260. Hill, M.A. (1983) ‘Female labour force participation in developing and developed countries— consideration of the informal sector’, Review of Economics and Statistics 65, 3:459–468. ——(1989) ‘Female labour supply in Japan: implications of the informal sector for labour force participation and hours of work’, Journal of Human Resources 24, 1:143–161. ——(1990) ‘Women in the Japanese labour force’, in Japan’s Economic Challenge, study papers submitted to the Joint Economic Committee, Congress of the United States. ——(1991) ‘Women’s relative wages in post-war Japan’, International Review of Comparative Public Policy 3:261–286. ——(1992) ‘Employment status and earnings of Japanese men’, Research in Asian Economics 4B:363–377. Japan Institute of Labour (1974, 1980, 1986) Japan Labour Statistics, Tokyo, Japan. Japan Statistical Bureau, Japan Statistical Yearbook, various issues. Mincer, J.A. (1970) ‘The distribution of labour incomes’, Journal of Economic Literature 8:1–26. ——(1974) Schooling, Experience, and Earnings, New York: Columbia University Press. Mincer, J. and Polachek, S. (1974) ‘Family investments in human capital’, Journal of Political Economy 82:S76–S108. ——(1978) ‘Women’s earnings reexamined’, Journal of Human Resources 13: 118–133. O’Neill, J. (1985) ‘The trend in the male-female wage gap in the United States’, Journal of Labour Economics 3, 1, Pt. 2:S91-S116. Osawa, M. (1984) ‘Women’s skill formation, labour force participation, and fertility in Japan’, unpublished PhD dissertation, Southern Illinois University. Rodosho Fujinkyoku (Women’s Labour Bureau) (1988 and 1989) Fujinrodo no Jitsujo (Women’s Labour Force Situation), Tokyo: Okurasho Insatsukyoku. Smith, J.P. and Ward, M.P. (1985) ‘Time series changes in the labour force’, Journal of Labour Economics 3, 1, Pt. 2:232–238. Stoikov, V. (1973) ‘Earnings in Japanese manufacturing: a human capital approach’, Journal of Political Economy 81:340–355. Sugeno, K. (1988) ‘The impact of the equal employment opportunity law at its first stage enforcement’, reprinted in Highlights in Japanese Industrial Relations, Vol. II, Tokyo: The Japan Institute of Labour.

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WOMEN AND INDUSTRIALIZATION IN ASIA Suwa, Y. (1988) ‘The equal employment opportunity law’, reprinted in Highlights in Japanese Industrial Relations, Vol. II, Tokyo: The Japan Institute of Labour. Tan, H. (1980) ‘Labour market adjustment in Japan in the 1970s: a case study’, unpublished paper, Australian National University. Umemura, M. (1980) ‘The seniority-merit wage system in Japan’, in S.Nishikawa (ed.) The Labour Market in Japan, Tokyo: University of Tokyo Press, 177–187. U.S. Department of Commerce (1986) Statistical Abstract of the US, 1986, Washington, DC: US Government Printing Office. U.S. Department of Labor (1976) The Role and Status of Women Workers in the United States and Japan. Yashiro, N. (1981) ‘Male-female wage differentials—rational explanation’, Japan Economic Studies 9 (Winter):28–61.

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5 WOMEN’S EMPLOYMENT STRUCTURE AND MALE-FEMALE WAGE DIFFERENTIALS IN KOREA Moo Ki Bai and Woo Hyun Cho

INTRODUCTION This chapter examines the factors that restrict female employment and cause wide wage differentials between men and women workers, in order that specific policy proposals can be made to improve the economic status of female workers in Korea. The central hypotheses are that the Korean labour market is segmented into highwage and low-wage firms (or sectors), and that employment problems of women such as low productivity and low wages are related to their being employed in lowwage firms (or sectors). Even though the female labour force has experienced many kinds of mobility (regional, industrial and occupational), and increases in skill level, it will be shown that women workers are mainly hired in low-wage firms (or sectors) requiring only a low level of skills. As a result, they usually cannot escape from disadvantages with respect to wage and employment opportunities. This issue will be the main theme of the section beginning on p. 166. In the section on pp. 186–193, we analyse the male-female wage differential in the segmented labour market. In order to formulate public policy that will reduce discrimination in the labour market and misallocation of resources, and also achieve more equitable income distribution, one has to find out how much of the gender wage disparity is due to inequality of treatment and how much is due to differences in characteristics. Our empirical results show that the elimination of wage discrimination in Korea could result in an increase of the ratio of average female to average male wage from 51 per cent in 1989 to approximately 80 per cent. In the section on pp. 193–200, we argue that any policy to promote sexual equality should be based on the reality that the Korean labour market is segmented and hence should be sector-specific in nature. We examine existing institutions, and suggest three types of government policies and programmes to reduce gender 165

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disparities in wages and employment opportunities: those for high-wage firms, those for low-wage firms, and general ones. EMPLOYMENT STRUCTURE AND CHARACTERISTICS OF FEMALE WORKERS Changes in the structure of female labour supply As Korea has developed into a semi-industrialized country, female participation in economic activity has increased. Over the period 1963 to 1990, the size of the female population aged 15 years or over has grown at an annual average rate of 2.6 per cent while the size of the female labour force (employed workers plus unemployed workers) has grown at an annual average rate of 3.5 per cent. By contrast, the two comparable growth rates for males were 2.5 per cent and 2.7 per cent, respectively, over the same sample period. Profound structural changes in the female labour supply have occurred, such as urban migration, increased participation of married women in the labour force, and higher levels of education. The discussion below examines each of these changes in detail. According to Table 5.1, the female labour force participation rate rose from 36.3 per cent in 1963 to 47.0 per cent in 1990. Over the same period, the labour force participation rate for males gradually declined from 76.4 per cent in 1963 to 73.9 per cent in 1990. Two additional factors are likely to promote a further increase of female participation in future economic activity. First, the demand for married women is expected to rise as the rural unskilled labour supply is almost exhausted. Second, the increasing numbers of highly educated women coupled with changes in females’ individual and social perceptions about jobs and work, should result in growing labour force participation by more-educated females. Migration has been remarkable for both men and women. The majority of the male and female working population (59.4 per cent and 64.1 per cent, respectively) were located in rural areas in 1963, but these proportions had dropped to only 18.7 per cent and 22.8 per cent respectively in 1990. Out-migration in younger age groups was particularly striking. In 1990, the age groups 15–19, 20–24 and 25–29 comprised only 1.7 per cent, 4.9 per cent and 6.0 per cent, respectively, of the 3.5 million rural labour force population. Meanwhile, the age groups 30–39, 40–49 and 50 or over comprised 14.6 per cent, 20.8 per cent and a striking 51.9 per cent, respectively. It is useful to note that there is little difference in the age distribution of the rural labour force population between men and women. The massive urban migration led to a concentration of the labour force in urban areas as well as an aging rural labour force. According to 166

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estimates made by Tai Whan Kwon (1978, 1988) using data from the Population and Housing Census, the volume of rural-urban migration was 3.5 million between 1960 and 1970, 4.4 million between 1970 and 1980 and 1.9 million between 1980 and 1985, making a total of 9.8 million of the rural population who moved into urban areas during the period 1960–1985. Using the same data, Jin Do Park (1991) estimated the extent to which the increase in the urban labour force population can be attributed to the migration of the rural labour force population. Unfortunately, Park utilized time series data spanning only from 1965–1980. Park found that 53.0 167

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per cent of the increase in the urban labour force population between 1965 and 1970 could be attributed to rural-urban migration. His figure for the period between 1970 and 1980 was 44.0 per cent. We expect to see a further decline in the contribution to the urban labour market made by rural-urban migration in the decade of the 1980s, which is likely to continue in the 1990s. Figure 5.1 depicts the M-shaped distribution of female labour force participation by age categories in 1963, 1970 and 1990, based on Table 5.2. At the infant stage of industrialization between 1963 and 1970, Korea experienced a dramatic upsurge of labour force participation by young unmarried women aged 15–19. Participation rates of those 20–24 were also high until the marriage age of 25–29 years, after which women tend to reduce their labour force participation. Once they are in the mid-30s, they once again return to the labour force and there is a second peak between age 40–44, followed by a substantial decline thereafter. In 1990, the labour force participation rate of young unmarried women aged 15–19 declined due to increasing enrolment in middle or high schools. In contrast to the period between 1963 and 1970, however, in 1990 participation rates of women in all other age groups rose.

Figure 5.1 Labour force participation rates of women by age group, Korea, 1963–1990 Source: EPB, Annual Report on the Economically Active Population Survey, 1963, 1970, 1980, 1990 168

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Between 1960 and 1990 the education level of the labour force in Korea experienced a substantial improvement. In 1960, 79.5 per cent of employed men were composed of elementary school graduates, while 9.1 per cent, 7.8 per cent and 3.1 per cent of employed men were middle school, high school and college graduates, respectively.1 The comparable shares in 1990 were 21.6 per cent for elementary school graduates, 19.2 per cent for middle school graduates, 41.9 per cent for high school graduates and 17.4 per cent for college graduates.2 A similar trend is detected in the case of employed women. In 1960, middle school, high school and college graduates formed 3 per cent, 2 per cent and 0.2 per cent, respectively, of all employed women, while elementary school graduates formed the majority, at 95 per cent. Though the elementary school graduates still were the largest group of female workers 169

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(40.7 per cent) in 1990, the share of high school and college graduates increased significantly to 20.0 per cent for middle school graduates, 31.0 per cent for high school graduates and 8.3 per cent for college graduates. Table 5.3 documents the increase in educational enrolment in the Korean population, and in particular the sharp increase in female enrolment in middle schools in the 1970s, and in college in the 1980s. Changes in the distribution of employment by status The proportion of working women engaged as employees (i.e., persons who are engaged in paid employment) among employed women in the labour force has risen steadily from 21.8 per cent in 1963 to 56.6 per cent in 1990. This means that the proportion of working women engaged as own-account workers and unpaid family workers has shrunk correspondingly (see Table 5.4). Not only did the informal sector shrink drastically, but women workers’ status has also been improving from temporary or casual job status to regular or permanent status. Table 5.4 indicates that the proportion of female daily 3 or casual workers out of female employees has dropped drastically from 70.6 per cent in 1963 to 22.6 per cent in 1990. But it should be noted that a large informal sector still continues to be a major characteristic of the Korean labour force. Furthermore, as indicated by the fact that 13.2 per cent of male employees and 22.6 per cent of female employees are casual or daily workers, female workers undoubtedly experience greater employment instability than male workers. We will investigate this issue further in the industrial and occupational compositions of the labour force and in the section on pp. 179–180, which

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discusses segmentation of the labour market into high- and low-wage paying firms. Changes in the distribution of employed persons by industry In 1960, 69.3 per cent of female labour was employed in the agricultural and fishery sector, while the rest were distributed among the social and personal service sector (13.1 per cent), the retail and wholesale sector (9.3 per cent) and the manufacturing sector (6.3 per cent). By 1990, the majority of female employment was in the low-income sectors of retail and wholesale, and manufacturing (56.2 per cent), while the rest were in the agricultural and fishery sector (20.4 per cent) and in social and personal services (15.4 per cent) (Table 5.5). Among the major industries where the female labour force is heavily concentrated, the manufacturing sector showed the highest growth rate of employment in the 1960s and 1970s, but was surpassed by social and personal services and the retail and wholesale sectors in the 1980s. A further categorization of the manufacturing sector by a two-digit industrial classification shows that 73.4 per cent of female employment in manufacturing was in textiles, clothing and leather, and 11.1 per cent in food and beverages in 1966, which changed to 42.5 per cent in textiles, clothing and leather, 22.8 per cent in fabricated metal and 10.4 per cent

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in food and beverages in 1989 (Table 5.6). It is to be noted that the textiles, clothing and leather industry is a labour-intensive, simple assembly and processing type of industry. On a three-digit classification of the fabricated metal industry, 68.9 per cent of female workers employed in this industry are in the electrical machinery equipment manufacturing industry, in which the female workers constituted 48.6 per cent of total employees in 1989 (Table 5.7). The electrical machinery equipment manufacturing industry mainly produces home appliances and assembles intermediate products into final products; therefore unskilled women are often employed in this industry as the work required is standardized, simple and repetitive. Tables 5.6 and 5.7 together suggest that the majority of female workers in manufacturing are employed in labour-intensive, assembly and processing type of industries including textiles, clothing and leather, food and beverages and electrical machinery equipment manufacturing industries.

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The two sectors other than manufacturing and agriculture with large female employment in 1989 (Table 5.5) were the wholesale and retail industry, and social and personal services. According to Table 5.8 (from the EPB, Report on Employment Structure Survey, 1989), 82.0 per cent of female workers in the retail and wholesale sector were employed in establishments with less than five employees, while 7.9 per cent were employed in establishments with 5–9 employees. The comparable shares of female workers in the social and personal services sector in firms of these sizes were 54.0 per cent and 7.9 per cent, respectively. Since a majority of female employees in the tertiary industry are hired in the establishments with petty capital and small size, it can be conjectured that these women are faced with instability of employment. Before proceeding to the occupational composition and its change of

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female labour force, it must be emphasized that female employment is concentrated in low-wage industries. Those industries where the majority of female workers are employed, including textiles, clothing and leather, other manufacturing, electrical machinery equipment manufacturing and scientific measuring and controlling machinery sectors tend to pay wages lower than the average for manufacturing as a whole. On the other hand, a relatively high wage is paid in machinery, transportation equipment, and chemical and primary metal sectors where male employment takes a majority share (Table 5.9). Table 5.9 reports wage indices for various industries for 1989, based on setting the average wage level in manufacturing equal to one hundred. The wage indices of the textiles, clothing and leather, electrical machinery

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equipment manufacturing, other manufacturing, and scientific measuring and controlling machinery sectors, where female workers are heavily concentrated, are 74.3, 97.0, 72.3 and 91.1, respectively, which are in sharp contrast with the wage indices in those sectors where the majority of male workers are employed (149.1 in the basic metal, 118.2 in the machinery manufacturing and 139.3 in the transportation equipment sector). The manufacturing sector is a low wage sector relative to the service industry. Among tertiary industry, however, the retail and wholesale sector, in which female workers are heavily concentrated, is a low-wage sector. On the other hand, females are a minority in the high-wage sectors of electricity and gas, construction and banking and insurance. This apparent industrial segregation of the sexes, with men concentrated in high-wage sectors and women concentrated in low-wage sectors, causes larger male-female wage differentials.

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Changes in the distribution of employed persons by occupation Table 5.10 shows that there have been considerable occupational changes for women between 1963 and 1990. The share of farm workers has been rapidly declining and there have been dramatic increases in the share of clerical workers, professionals and managers, and production workers. However, a more detailed study indicates that the female employment structure by occupation reflects industrial segregation. We first examine the distribution of female production workers by three-digit occupational classifications. Female production workers are primarily employed in sewing, weaving, spinning, textile apprenticeships, electrical and electronic assembly, unclassified electronics, shoemaking and bakery. Among the total of 118 three-digit classifications of production and related occupations, twelve occupations (shown in Table 5.11) account for 69.0 per cent of female production workers. Table 5.11 also suggests that the female-dominated occupations pay lower wages. As has already been mentioned, female production workers are mainly concentrated in the low-wage manufacturing sector industries such as paper, textiles, clothing

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and footwear, electrical machinery assembly, and food and beverages. A rising demand for unskilled or semi-skilled labour in the assembly and processing of textiles, clothing and footwear and electronics where precision and exactness are required, drove female workers into the occupations of sewing, weaving, spinning, shoemaking and electrical and electronic assembly. Thus, whereas Mi Hye Roh (1990) and Soo Bong Uh (1991) argue that occupational segregation generates the severity of wage differentials between male and female workers, we would argue that it is industrial sex segregation which gives rise to occupational segregation between different sexes. That is, female workers employed in the low-wage industry are driven into the low-wage occupations within the same industry, and the crowding of female workers to low-wage occupations in turn generates the severe malefemale wage differentials. The same phenomenon is evident in sales-related occupations. According to the Occupational Wage Survey, there are a total of six occupations by a three-digit classification in sales-related occupations. However, the single three-digit occupation of salespersons and shop assistants accounts for 98.5 per cent of total female workers in sales-related occupations (Table 5.11). According to the Report on the Employment Structure Survey, which provides the statistics on the industrial distribution of women working in sales-related occupations, the retail and wholesale, and food and lodging industries accounted for 90.6 per cent of female employees in this category in 1989. This industrial concentration leads to the occupational concentration observed. In service-related occupations, there are twelve three-digit occupations. The three categories cook, cleaner, and non-classified services, constitute 85.4 per cent of women in service-related occupations (Table 5.11). As 95.8 per cent of women in services industries are distributed in the wholesale, retail and lodging and social and personal service industries, it can be conjectured that again the industrial characteristics determine women’s occupations as well. Female workers in production, sales and service occupations comprise 75 per cent of total female workers, excluding those working in agricultural and fishery occupations. We conclude that the argument by Mi Hye Roh and Soo Bong Uh, urging government policy to reduce the degree of occupational segregation in order to reduce male-female wage differentials, is not convincing. Rather, more efforts must be put into reducing industrial segregation. The one case where reducing occupational segregation seems important is in the context of the administrative, professional and management occupations. There are seventy-seven occupations by a three-digit classification in the cases of professional workers, of which nurses and non-classified nurses account for 44.6 per cent of total female professional workers. Similarly, four occupations (typist, bookkeeper, transport conductor and telephone operator) account for 47.0 per cent 178

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of total female clerical workers whereas there are in total nineteen occupations by a three-digit classification. By contrast, only 1 per cent of managers are female, which reflects social custom in Korea that male workers are reluctant to be supervised by a female manager. Therefore, female professional workers are forced to take the role of assisting a medical doctor or the male managers or administrators in white collar occupations. Industrial dualism, segmented labour markets and female concentration on low-wage paying firms Even though female employment is concentrated in labour-intensive, assembly and processing industries and in low-wage occupations, it is important to examine whether the industrial segregation of the female labour force is improving or worsening, since this may have important effects on female employment opportunities. Research concerning the relationship between industrial dualism and labour market structure has been done in the past among others by Bluestone (1970), Wachtel and Betsey (1972), Thurow (1975), and Piore (1980). Dualist theories argue that the primary labour market consists of workers for monopolistic core firms, whilst there is a secondary labour market supplying competitive periphery firms. Increased sub-contracting is argued to have intensified dualism in the developing countries in the 1960s (Standing 1987, 1989). Cho (1991) analysed industrial dualism in Korea. He defined a low wage as two-thirds or less of the median wage received by male workers in the manufacturing sector in Korea. He then calculated the percentage of low-wage workers within a firm. Firms were then categorized into four classes: the top class of high-wage firms, the middle class of high-wage firms, the middle class of low-wage firms and the bottom class of low-wage firms if the share of low-wage paid workers in the firm fell in the ranges 0–24 per cent, 25–49 per cent, 50–74 per cent and 75–100 per cent, respectively. Table 5.12 reports the firms’ distribution in Korea according to the above categorization. The first characteristic evident in Table 5.12 is that the middle class of highwage firms is very weak in Korea. The distribution of firms has a sandglass shape, clearly indicating a dual industrial structure. This type of industrial dualism may cause the labour market to be segmented into the primary labour market and the secondary labour market. Cho (1991) further demonstrated that there were indeed significant wage differentials among observationally equivalent workers between high-wage firms and low-wage firms. The second characteristic displayed in Table 5.12 is that the firms’ distribution changed after 1973 and it is not until 1989 that the firms’

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distribution again becomes similar to that in 1973. The reduction in the share of the top and middle class of high-wage firms in 1978 and 1984 suggests the increase in importance of low-wage firms in the industrialization process since 1975. As the economy turned its attention to the heavy and chemical industries, the increasing number of subcontracts given to small and medium size firms was apparently accompanied by the quantitative expansion of low-wage firms. Another factor may have been expanding employment of unskilled, and semi-skilled workers within the top class of high-wage firms, associated with the increase in number of assembly factories specialized in mass production. As the number of low-wage workers employed in the top class of high-wage firms increased, the share of the top class of high-wage firms declined between 1978 and 1984. But as the economy enjoyed a boom over the period between 1986 and 1989, the profitability of those firms increased. Furthermore, the negotiating power of labour unions in large scale production sites became strengthened after the massive strike waves of 1987. Therefore we can conjecture that the industrial dualism in Korea is shrinking rather than expanding. Table 5.13 documents the strong relation between firm wage category and gender composition. The concentration of women in low-wage firms intensified during the heavy industry phase after 1975 but diminished again by 1989. Disequilibrium of labour supply and demand in Korea In contrast to the 3.1 per cent average growth rate per annum of total employment across all industries in Korea between 1960 and 1990, female and male manufacturing workers’ employment grew by 9.7 per cent and 7.1 per cent, respectively, over the same period. Recently, labour shortage has emerged as a serious problem in Korea. The labour shortage problem is especially marked in the manufacturing sector, where low-wage firms are in the most difficult situation. In particular, there is a severe shortage of production and related workers. The number of vacancies and the vacancy rate by industry appear in 180

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Table 5.14. As of March 1990, Korean industry was 192,100 workers short of full capacity, with a vacancy rate of 4.3 per cent. This rate compares to one of 1.8 per cent in 1985 and 3.2 per cent in 1989. The manufacturing sector faces the most difficult labour shortage problem: in 1990, 78 per cent of all vacancies were in manufacturing. Within the manufacturing sector, the textile, clothing and leather, and the metal assembly industries were heavily hurt by the labour shortage problem, with vacancy rates of 8.3 per cent and 4.6 per cent respectively. As was discussed earlier, these are the sectors where female workers are massively employed. The mining industry, one of the declining industries in Korea, also experienced a relatively high vacancy rate (7.11 per cent in 1990). By contrast, the retail, wholesale and lodging industries, the banking, insurance and real estate industries, and social and personal services each have a vacancy rate barely exceeding 1 per cent. The data on vacancy rates by occupation confirm the pattern given in the industry data. As indicated in Table 5.15, as of March 1990, 86.3 per cent of the total number of vacancies were in production or related occu-

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pations. The labour shortage in production is relatively recent, as information on vacancies over time shows. One cause of the shortage may be the increase in the number of college graduates and a decline in those with high school education who might enter production work. Korea’s Ministry of Labour provides occupation-specific information which allows a more systematic study of the degree and the scope of male and female labour shortage (Table 5.16). The job opening rate is measured by dividing the number of job offers by the number of job seekers. A job opening ratio exceeding 1.0 represents labour shortage, while labour surplus is represented by a rate below 1.0. The average job opening ratio is 1.18 across all occupations registered in the National Employment Stability Institute. Separate ratios are calculated for vacancies for females and for males (since 1990 it has become illegal to issue sex-specific job advertisements). The job opening ratios for women are high in many female labour-intensive occupations: 1.58 for female nurses, 2.05 for 182

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female nurses’ assistants, and as high as 8.51 for female weavers and 8.11 for female shoemakers. The on-going labour shortage suggests the necessity of revision in customary discrimination practices against women in order to solve the labour shortage problem, which will be discussed in the section beginning on p. 197. In spite of the current serious labour shortage in Korea, we observe a substantial number of discouraged female workers in the age group 25–39 and among highly educated married women. Statistics on both the unemployment rates and the hidden unemployment rates of men and women by age group and education level are compiled in the Report on the Employment Structure Survey, 1983, 1986 and 1989. The hidden unemployed are those in the survey who were not actively searching for work because they believed no suitable work was available. As seen in Table 5.17, the (open) unemployment rates of male and female workers in 1989 were 3.0 per cent and 2.2 per cent, respectively. But the hidden unemployment rates of male and female workers in 1989 were 3.3 per cent and 16.7 per cent, respectively. Even though women’s open unemployment rate is lower than men’s, the women’s hidden unemployment rate is more than five times that of men. In 1989, the number of women who were out of the labour force but willing to work if reasonable work was available, totalled 1,319,000, far exceeding the total number of workers in shortage in Korea, which was 192,100. Analysed by age group, the hidden unemployed women are heavily concentrated in the age groups of 25–29 and 30–39. The hidden unemployed rate of females aged 15–19 was highest but the absolute number

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was only 4,000 persons, which was relatively small. Analysed by education level, the hidden unemployment rate is highest among women junior-college graduates. In 1989, the hidden unemployment rates for women of middle school, high school, junior-college and university graduates were 13.7 per cent, 21.2 per cent, 30.8 per cent and 18.2 per cent respectively. Reducing employment and wage discrimination in Korea seems essential to utilizing women’s human resources adequately.

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WOMEN AND INDUSTRIALIZATION IN ASIA MALE-FEMALE WAGE DIFFERENTIALS IN THE SEGMENTED LABOUR MARKETS IN KOREA Male-female wage differentials According to the Occupational Wage Survey, women’s wages were 45.4 per cent of men’s in 1971 when the survey was conducted for the first time. This percentage fell to 41.2 in 1975, but then reversed its course and edged up to 42.9 in 1980 and to 52.8 in 1989. Historical figures from the Yearbook of Statistics, issued by the Japanese Government-General of Korea, reported that the ratio of Korean women’s earnings to Korean men’s was 53 per cent in 1930 and 54 per cent in 1935, and the Industrial Labour Force and Wage Survey for South Korea in 1946, conducted after the liberation from Japanese rule in 1945, reported that the ratio was 57 per cent. Thus the wage gap remained fairly constant until 1946, then got steadily wider up to 1975, then it started again to narrow very slowly. While women’s labour force participation has risen dramatically, it nonetheless remains the case that the gender wage gap has remained wide. Data published in the 1989–1990 ILO Yearbook of Labour Statistics suggest that Japan and Korea exhibit the largest wage differential by sex of any country. Several factors account for the large differential in Korea. Cho’s (1991) work discussed above (Table 5.13) suggested that women were disproportionately employed in low-wage firms. We believe that intense competition for securing employment in high wage firms produces labour queues. Taking advantage of this situation, high-wage firms often show preference for male workers. One customary practice is that only male workers are hired through regular job opening advertisements, while female workers get their jobs through personal connections such as recommendations from friends and relatives. This type of discriminatory practice may be called ‘discrimination at the port of entry’, and tends to produce unequal job opportunities. Since a large fraction of human capital stock possessed by individuals is acquired from learning on the job or on-the-job training, discrimination at the port of entry will generate unequal on-the-job training (or learning) opportunities over individuals’ working lives and subsequent wage inequalities between men and women. Seniority is very important in the determination of wages in Korea. Under this wage system, female workers are at a disadvantage because they tend to quit their jobs because of marriage, child birth or child-care problems. Shorter job tenure limits the prospects for wage increases for female workers, even though there are few gender differentials in the wages of the newly hired workers aged 17–19 or 20–24. The small number of highly-paid middle-aged or older female workers, coupled with the tendency for married women who reenter the labour force after raising 186

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Figure 5.2 Age-wage profiles by occupation and sex, Korea, 1989 Source: Ministry of Labour, Occupational Wage Survey, 1989

children to be paid wages similar to those of young unmarried female workers, leads to the large wage differentials between male and female workers. Figure 5.2 presents the age-wage profiles for both men and women, for blue and white collar workers. Wage differentials between male and female production workers are small up to age 25, but for female production workers wages tend to decline after age 25. For those female production workers who reenter the market after age 30, low wages prevail and the gap relative to male wages increases continuously. A similar phenomenon affects female white collar workers. Female workers tend to receive infrequent and inadequate on-the-job training and receive few promotion opportunities. Female white collar workers are assigned to secondary jobs such as typing and bookkeeping and serve as assistants to male white collar workers. This implies that they have little opportunity for regular job transfers which would help them gain managerial and firm-specific knowledge. Therefore, promotion to higher positions in the management level is highly restricted. Likewise, discriminatory practices in the internal labour market against female production workers start with their being allocated to the simple assembly or processing type of jobs. Table 5.11 indicated that female workers are concentrated 187

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in sewing, weaving, electric and electronics assembly and shoemaking, all of which are simple assembly and processing types of occupations. The requirements of these occupations are simply precision, speed and no absence from work. The firms’ unwillingness to provide female workers with training, as well as the limited promotion opportunities for women, are important elements behind the large wage differentials between male and female workers. The wage paid by Korean firms includes the cost of living allowance, family support, education allowances and housing allowances. These fringe benefits are normally offered only to male workers. This is an additional factor that creates wage differentials between male and female workers.4 Se II Park (1984) describes sex discrimination in the Korean labour market as follows: The employment discrimination in the labour market can be decomposed into occupational segregation and discrimination in the internal labour market. In respect of the former, female workers are driven into lowwage occupations even when they have the same individual characteristics as the male workers such as the same education level and job experiences. As regards the latter, firms’ employment policy or practices relating to job assignment and transfer, training, promotion and retirement are disadvantageous to female workers. Another form of sex discrimination in the labour market is wage discrimination. It refers to the relatively low wages paid to female workers owing to the firms’ policy or practices on wage payment, despite the fact that the female workers have the same education level and job experiences and are in the same occupation as the male workers. (Park, 1984) Se I1 Park regarded occupational discrimination and internal labour market discrimination as the major sources of employment discrimination against women. We would argue that there is also discrimination at port of entry, and that this is more important than the other two sources of discrimination. Female workers are over-represented or crowded in low-wage firms and sectors, which constrain their opportunities for on-the-job training and promotions throughout their working lives. This may be a more important factor for explaining the wage differentials by gender, than occupational discrimination, internal labour market discrimination, and innate productivity differences between men and women.

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Oaxaca decomposition of male-female differentials We next undertake earnings functions analysis and a Oaxaca decomposition to better understand the source of the large male-female wage gap. Following Oaxaca, the average ¯ ) and women (W ¯ ) are taken to depend respectively on a hourly wages of men (W m f ¯ and X ¯ in the following way. number of mean values of observed characteristics X m f

where bm and bf are column vectors of estimated coefficients of the earning functions ¯ and X ¯ are the corresponding row vectors for men and women, respectively, and X m f of mean values of the regressors of the earnings functions. Thus the wage difference between men and women can be represented by

^ as a measure of discrimination, then, If we define the discrimination coefficient D

¯ /W ¯ is the observed male-female wage ratio, and (W ¯ /W ¯ )° is the ratio in where W m f m f the absence of discrimination. ¯ -X ¯ )b could be a proxy for the natural log of the maleIf we assume that (X m f m female wage ratio in the absence of discrimination, then

¯ -X ¯ )b as a proxy for the natural log of the male-female wage If instead we use (X m f f ratio in the absence of discrimination, then

^ is thus the estimated coefficient when (w¯ /w¯ )° is approximated by using male D 1 m f ^ is the estimated discrimination coefficient wage regression coefficients, and D 2 when (W¯m/W¯f)° is approximated by using female wage regression coefficients. Table 5.18 defines the variables used in the earnings functions, and the earnings functions results are in Table 5.19, for manufacturing (mean ^ fell from 0.52874 values for regressors are in Table 5.20). The value of D 1 189

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in 1984 to 0.43090 in 1989, while that for D^2 fell from 0.24334 to 0.24078 over the same period, indicating that discrimination by sex decreased slightly.

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Segmented labour markets and male-female wage differentials We have discussed earlier the segmentation of the Korean labour market into highwage firms and low-wage firms. An important feature of this labour market structure is that workers may not be free to move from low-wage to high-wage firms. While individuals are free to leave their current job, they may not get the desired position in higher wage firms. Figure 5.3 depicts the age-wage profiles for male employees in blue collar and white collar occupations in high-wage and low-wage firms as well as (for comparison) that of female blue collar workers in low-wage firms. We notice that there exists a remarkable wage differential between white collar male workers in low-wage and high-wage firms, and that the same is true for blue collar male workers. The wage differences among women between high- and low-wage firms are much narrower than for men (Figure 5.4). Thus the wage differential between male workers in the high-wage firms and female workers in the low-wage firms particularly after age 25 constitutes another influential determinant of the wage gap between male and female workers in Korea.

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Figure 5.3 Age-wage profiles by high-wage/low-wage firm, men, Korea, 1989 Source: Ministry of Labour, Occupational Wage Survey, 1989

Additional work by Bai and Cho (1992b) estimating separate earnings functions for low-wage and high-wage firms suggests that the discrimination coefficients ^ and D ^ ) are much higher in high-wage firms, and that policy against (D 1 2 discrimination should focus on high-wage firms. POLICY PROPOSALS FOR WOMEN’S EMPLOYMENT AND WAGES Citing the much-quoted phrase, ‘People are poor because the rate of wages paid by the industries of the United States will not permit them to be anything but poor,’ Wachtel and Betsey (1972) once argued, ‘In our society work is invariably prescribed as the path out of poverty. However, for a significant proportion of the poor this remedy falls on deaf ears, since they work but are poor. The working poor earn their poverty.’ Does the fact that female workers in Korea are largely employed in low-wage industries and low-wage firms force them to remain in poverty, as Wachtel and Betsey argued? Or is industrialization changing this situation? One feature that is consistently emphasized throughout this chapter is that women’s employment and earnings problems are related to their 193

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Figure 5.4 Age-wage profiles by high-wage/low-wage firm, women, Korea, 1989 Source: Ministry of Labour, Occupational Wage Survey, 1989

being employed in low-wage firms. Therefore, government policies that have the purpose of facilitating female employment and its upward mobility must be reorganized in accordance with industrial restructuring. Since the industrial structure in Korea is polarized into high-wage firms and low-wage firms, whilst middle-wage firms have been weak, a government policy promoting sexual equality across all manufacturing sectors would conflict with economic growth and efficiency. First of all, we review the current legal framework and government policies for women’s employment. Then we suggest four major policies to reduce sex differentials which, at the same time, encourage growth. We first discuss macroeconomic and industrial policy as the best policy for reducing pay inequality between high-wage firms and low-wage firms. We then examine micro-level labour market policies relevant to high-wage (or low-wage) firms, and microeconomic policy proposals based on human capital theory are also discussed.

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Institutions and government policies for women’s employment and welfare Korean women’s participation in the labour force is affected by various laws, government institutions and policies, and the legal framework has also been changing quite rapidly in the recent past. The Equal Employment Opportunity Law, promulgated on 4 December 1987 and amended on 1 April 1989, has as its purpose to secure equal opportunity and treatment for employees regardless of their gender, as well as to protect the maternal role of women. The law contains a number of provisions regarding discrimination. Employers are required to provide equal opportunity to females and males with regard to recruitment and employment. Therefore, gender-segregated job advertisements are prohibited, as are restrictions applied only to women, eg. age and/or marital status. The law requires employers to offer equal pay for equal-value work in the same enterprise, where equal value is defined by skill, endeavour, responsibility, working conditions, or other things which are required in the process of work. The law also states that employers shall not discriminate on the basis of gender, with regard to education, placement, and promotion of employees. Lastly, the employer shall not discriminate, on the basis of gender, against employees with regard to retirement age or dismissal, and shall not enter into an employment contract which provides that marriage, pregnancy or child birth of female employees will be cause for retirement. The Labour Standards Law, promulgated in 1953, had been the only legal protection for female workers before the Equal Employment Opportunity Law. The Labour Standards Law prohibits night work, or work in a mine for female workers, and limits women’s overtime working hours. Female workers are not authorized to work between ten p.m. and six a.m. or on public holidays, unless there is both consent from the worker and approval from the Ministry of Labour. The law also allows menstruation and maternity leaves and nursing hours. Female workers are allowed one day’s leave with pay for menstruation every month. Pregnant female workers are allowed sixty days leave with pay, thirty days of which shall be reserved for use after child birth, and female workers with infants under one year of age are allowed breaks twice a day for nursing, of not less than thirty minutes each. The Equal Employment Opportunity Law of 1988 modified the maternity leave provisions, and provided for up to a year’s leave following child bearing, including the paid maternity leave (sixty days) before and/or after child birth. The period of leave for child-rearing is to be included in the length of service, which affects promotion and retirement bonuses. Also, employers are required to provide female employees with necessary facilities for nursing infants at the workplace.

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Enforcement of the law is important. Marriage bars and gender-segregated job advertisements have been common in the past.5 Therefore, the government, via the Ministry of Labour, has actively publicized the provisions of the Equal Employment Opportunity Law since 1988, via pamphlets, and workshops held jointly with research institutes or women’s organizations. In addition, the government has undertaken regular inspections for the observance of the law. Orders have been issued for the corrections of discrimination in retirement and dismissal in establishments with ten or more workers (in 1988), in some large hospitals and hotels (in 1990), and in the banking and finance industry (in 1993). Between 1988 and 1992 there were 4,886 such corrections orders of which those for retirement/dismissal formed 40 per cent and another 30 per cent were regarding leave for child-rearing. Other efforts to publicize the law include a government plan to nominate March as The Month of Equal Employment, which includes World Women’s Day (8 March). Central and local governments and other public sector enterprises are being required to take the initiative in abolishing their gender-discriminating personnel and human resource management systems and to develop better programmes for maternity protection. The government is also considering inspecting the employment rules of all establishments with one hundred or more employees. Furthermore, the government is now seriously considering imposing quotas for women’s employment for selected industries and/or occupations, where women’s employment usually has been restricted. Regarding enforcement of protective legislation concerning women, a survey conducted in August 1991 shows that, out of 520 establishments which have 300 or more female employees, only 317 establishments (61 per cent) allow leave of absence for child-rearing. The government is therefore considering transferring some of the cost of such leave to social insurances such as medical insurance or an unemployment insurance programme, a change which was made in Japan in 1975. The government is also considering whether some of the protective legislation in the Labour Standards Law is now out of date. The prohibition of women’s employment in certain industries and occupations has lost its meaning due to factory automation. Also, restriction of female workers’ overtime, night and holiday work tends to be an obstacle to women’s employment. In addition, menstruation leave with pay (Indonesia is the only other country allowing such leave) also has a negative effect on women’s employment. Therefore, the government is considering amendments to some articles of the Labour Standards Law in the near future.

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Limited availability of day care inhibits participation by married women. The existing legislation requires that establishments in which more than 1,000 female workers are employed must provide a child-care facility, and since 1987 the Korean government has been operating eighteen model child-care centres in major public and agricultural production sites. Nonetheless, there is a great shortage of childcare centres. According to an estimate made by Ministry of Health and Social Affairs in 1993, there are 3.95 million children of the age of 0–5 raised by married female workers. Of them only 3.6 per cent received the benefits of public and private child-care centres. The government plans to eliminate current restrictions on the facilities and to provide tax incentives for private facilities. Industrial restructuring and economic growth Women are primarily employed in low-wage industries or low-wage firms. The substantial wage premium paid to workers in high-wage firms has been shown empirically. It appears that the economic status of workers, both male and female, in export-led economic development can be more effectively improved through fostering the knowledge-intensive and technology-intensive firms, and small and medium-sized firms producing parts and raw materials. Thus, industrial policy which aims to transform the industrial structure and encourage small and mediumsized firms may help to increase equity in the labour market as well as further economic efficiency. Another alternative measure to enhance the economic status of female workers, which is no less important than industrial restructuring, is to maintain continued economic growth and increase the share of the manufacturing sector in GNP. This can enable female workers to move from low-productivity sectors to highproductivity ones, and facilitate the improvement of their wages and employment. Micro-level labour market policy for female employment in high- and low-wage firms Female workers face disadvantages in being hired for good jobs, and once hired, face discrimination in terms of training, job assignment, job transfer, promotion, dismissal and retirement. Discriminatory action against female workers tends to take the form of industrial and occupational segregation and/or wage discrimination. The Korean government should show a strong commitment to the Equal Employment Opportunity Law to eliminate deep-rooted sex discriminations in the high-wage sector. For this to be accomplished, detailed criteria should be established to determine whether a discriminatory action has occurred, and the penalty against any violation of Equal Employment Opportunity Law, which is currently minimal,

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should be increased. The abolition of unjustified discrimination against female workers can be justified both on equity and efficiency grounds. The government itself has not been in the forefront of equity in hiring. There are 90,000 low-level government officials in Korea ranging from the ninth level to the sixth level, among which 12.5 per cent are female. On the other hand, only 1.2 per cent of 20,000 high-level government officials between the fifth and the first level are female. Sex discrimination in public enterprises can be illustrated by the following figures. While 38.1 per cent of total employees in the private sector are women, female workers account for only 18.0 per cent of employees in public enterprises. The combined annual budget of the public enterprises is 1.7 times that of the central government, and they account for 10.5 per cent of the value-added and 2.5 per cent of employment in Korea’s non-agricultural sectors (Won Duk Lee, 1989). Public enterprises definitely belong to the high-wage sector. If we assume that Korea’s level of industrialization is about 20–30 years behind the advanced countries, then the seriousness of sex discrimination in the public sector can be detected when the current statistics of female employment in the public enterprises of Korea are compared with those in the developed countries 20–30 years ago. In 1975, the proportions of female employment in public sectors were 53.4 per cent in the UK, 47.5 per cent in the US, 64.4 per cent in Sweden and 29.6 per cent in Japan (Eun Cho, 1991). We therefore suggest a quota system be initially implemented in the public sector which should be extended to the private highwage sectors after a brief period of evaluation of its performance. We also propose that in low-wage firms where more than 500 female workers are employed, occupational segregation must be eliminated so that promotion of female workers to managerial posts can increase. In Korea, female managers are almost non-existent, and it is difficult for women in white collar jobs to be promoted to managerial posts. Human capital formation and subsequent wage increase for female workers Opportunities for on-the-job training for workers in the manufacturing sector are rare, and such training is also very poor in its content. This has not changed much for the last fifteen years. The extremely low level of training within manufacturing firms is observed consistently across numerous studies. For example, a recent survey in 1991 by Bai and Cho showed that only 27.3 per cent of manufacturing workers surveyed in Seoul area received on-the-job training. This indicates little improvement over the past fifteen years. On-the-job training for women is even more scarce than that for men. Bai and Cho’s (1991) survey showed that only 20.7 per cent of female employees have undergone a job training programme compared with 33.7 per cent of men. 198

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The 1990 report of the Ministry of Labour on women’s job training through the in-plant training centre reported similar results. As of 1990, only 4,665 women had gone through the in-plant training programme, compared with a figure of 21,025 for men. Employers generally prefer paying for job training levies to conducting an in-plant training programme. They are even more conservative when it comes to providing job training for women. It should also be noted that, as of 1990, 84.4 per cent of in-house training for women was concentrated on the assembly processes in labour intensive industries such as textiles, electronics, footwear, sewing, and so on. We would therefore argue that in addition to the current public job training system, job training centres exclusively for women should be established and the programme should fully reflect the supply and demand characteristics of the female labour force. At the same time, the Ministry of Labour should consider the establishment of technical colleges or technical high schools for women, especially in such fields as electrical and electronics, communications, information processing, safety management, quality control, environment management, etc. Such vocational education would make a substantial contribution to ensuring long-term industrial competitiveness and to sustaining economic growth, and would broaden women’s employment opportunities in high-wage sectors. An effective way to enhance women’s upward mobility in industry is to offer them more educational opportunities aimed at imparting a higher level of skills. The level of higher educational advancement in Korea has already reached that of many developed countries. The problem in higher education in Korea is quality, not quantity. As of 1990, only 0.3 per cent of girls and 15.6 per cent of boys were in technical high schools, and the numbers of technical high school graduates have been stagnant for the last ten years, while those from academic high schools have been increasing rapidly. In order to upgrade technical training for female workers, it is necessary not only to establish girls’ technical high schools to provide training in appropriate types of skills, but also to give special emphasis to vocational education to all high school students, regardless of sex. As for college students, humanities degrees have been traditionally preferred to science and engineering degrees in Korea, particularly for women. For example, 15.4 per cent of female and 38.7 per cent of male college students are working for science and engineering degrees. The ratio of college graduates studying science and engineering to those studying humanities and social sciences changed from 46.7:53.3 in 1981 to 37.2:62.8 in 1990, reflecting a trend towards emphasis on humanities and social science degrees. In the face of intensified competition from other developing countries in the traditional labour-intensive industries such as textiles and electronics, and in light of the application of computer aided design and manufacturing (CAD/CAM) in the traditional labour-intensive industries in developed countries, the cheap and 199

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abundant labour in Korea no longer provides a cost effective substitute for computer aided assembling or factory line work. Continuous advancement and adjustment of industrial structure, based on changing international comparative advantage, should take place in Korea. Demand for machine and equipment operators, engineers and technicians in micro-processing, micro-electronics, CAD/CAM and robotics, and demand for ‘hybrid’ managers who possess complex and multiple skills in the fields of engineering, information technology and general management are expected to increase rapidly in the near future. There is an urgent need for linking education and training with changing skill demands, i.e., demand for information- and knowledge-intensive workers. To enhance the upward mobility of female workers in industry, there is a need to increase the intake of women in those courses offered at the certificate, diploma and degree levels by the educational institutions. Government policy should focus on the qualitative improvement of higher education in science and engineering and develop a close link between the changes in skill requirement and the manpower supply of educational institutions. The aging population of the developed world is likely to be less flexible and hence less amenable to the challenges of knowledge- and information-intensive jobs. The requisite expertise will be in short supply because of the almost stagnant or even declining size of the young college graduated work force in the developed countries. If Korea succeeds in ‘factor creation’ of the requisite expertise, companies in the developed countries will have an incentive to relocate the information- and knowledge-intensive firms and jobs to Korea. If this occurs, Korea may have a good chance to move out of a labour-intensive, assembly and processing type of industrial structure towards a more knowledge-intensive industrial structure. The most important problem that Korea must resolve is how to make women’s higher education more relevant and responsive to the rapidly changing skill requirements of the economy. CONCLUSIONS In Korea, despite the advances made in women’s participation and the changes in their employment, women’s employment remains concentrated in low-paying industries. Recent legislation such as the Equal Employment Opportunity Law has been much more favourable to women’s employment than was the practice in the past. The labour movement can potentially help efforts to improve women’s relative position. In Korea, however, labour movements historically have not been very strong. Also union leaders have been interested mainly in improving the wages and working conditions of male workers, neglecting those of female workers, in a society where men are perceived to be superior to women. This parallels the case of Germany where the industrial labour unions have strong power, but the wage differences 200

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between the sexes which existed in 1913 still remain today (Schneider, 1991). It therefore implies that the strengthening of labour movements should proceed with special attention given to the problems of female workers. We have discussed industrial and economic policies in the previous section. Other long-term social changes may be important. Women should be encouraged to be equally responsible for household income, just as their husbands should be expected to assume some responsibility for housework. APPENDIX: MAJOR DATA SOURCES FOR KOREA The Economically Active Population Survey The primary purpose of this survey is to collect up-to-date information on economic status of the population and changes in the activity pattern of the labour force needed for the formulation and implementation of various government policies. The survey has been conducted since 1963. It covers all persons aged 15 years old and over who usually reside within the territory of the Republic of Korea at the time of enumeration. The armed forces, prisoners and foreigners are excluded from this survey. As of 1990, the sample size was 32,500 households. Interviews are conducted during the week just after the reference period. The reference period refers to the week containing the 15th of the month. The survey is undertaken by resident enumerators with a direct interview method. Twenty-six survey items have been employed since January 1985 as follows:

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The survey defines working age as 15 years old and over. The concepts and definitions of employment and unemployment used in the survey are categorized as follows: 1 The population above the working age limit except the armed forces, prisoners and foreigners is divided into the economically active population and the not economically active population. The economically active population comprises all persons who were employed or unemployed during the reference week. 2 The employed comprise all persons who worked at least one hour or more for pay or profit, or who worked eighteen hours or more as unpaid family workers during the reference week. The persons who had a job but were temporarily absent from work are categorized as the employed. 3 The unemployed comprise all persons who were not at work but were available for work and were actively looking for work during the reference week. Persons not looking for work on account of bad weather, temporary illness, or having made arrangements to start a new job within a month subsequent to the reference week, are also considered as the unemployed. The Employment Structure Survey This survey has been conducted triennially to produce detailed information on the regional structure of employment, unemployment, underemployment and the mobility of the labour force since 1983, and the 1989 survey is, therefore, the third one conducted. The survey covers all persons aged 15 years old or above, who usually reside within the territory of the Republic of Korea at the time of enumeration, but the armed forces, prisoners and foreigners are excluded from the survey. As of 1989, a total of 141,955 households were sampled and surveyed. Interviews were conducted during the ten days just after the reference period. The reference period refers to the week containing 15 November. The methodology used for the survey is the same as for the Economically Active Population Survey. In addition to the same twenty-six survey items as those in the Economically Active Population Survey, the other twenty-seven survey items are as follows:

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Occupational Wage Survey The purpose of this survey is to collect data on the wage level of different occupations. The data serve as a basis for planning and improving labour market policies and the wage systems. The survey has been conducted since 1971. This survey is carried out primarily by a questionnaire sent out by mail. The company clerk is normally in charge of filling out the questionnaires, based on the information given in the workers’ wage ledger. The survey was carried out in July every year, and the reference period was one month (1 June–30 June). This survey covers about 3,300 establishments selected by a stratified random sampling method from all establishments, except agriculture, forestry, hunting and fishing, which employ ten and more regular employees. But government or local administrative agencies, army or police and national or public educational institutions are excluded. The stratification variable is size of establishment

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(numbers employed). One hundred per cent of all establishments with less than 100 employees are sampled, and a smaller fraction of larger enterprises.

Report on Employment Prospects This survey collects data on the number of workers in redundancy or shortage by industry, by occupation, and by skill categories. It is carried out in April of every year, using the same methodology as the Occupational Wage Survey. This survey covers about 3,300 establishments selected by a stratified random sampling method from all establishments except agriculture, forestry, hunting and fishing, which employ ten and more regular employees. But government or local administrative agencies, army, police and national or public educational agencies are excluded. Job Seeking, Job Opening and Employment Trends The National Employment Stability Institute of the Ministry of Labour collects information on the number of job openings, job applicants and job placements by sex, occupation and region from a total of 707 employment agencies, which are either national, public or private. NOTES 1 Years of schooling for elementary school, middle school and high school graduates are 6, 9, and 12 years, respectively. Junior college takes two years, and university as a rule takes four years. 2 The data sources of the composition of employed workers by education level are the Annual Reports on the Economically Active Population of the Economic Planning Board of Korea in 1990. Except 1990, they are from the Population and Housing Census. 3 The daily workers are those whose terms of contracts expire in less than one month. 4 An ILO-ARTEP study in 1991 in Seoul found that 38 per cent of men reported the availability of cost of living allowances as compared to 17 per cent of women, while for educational allowances, the figures were 42 per cent and 23 per cent, and for severance pay 47 per cent and 37 per cent.

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In 1990, before the Equal Employment Opportunity Law was enforced, 39.1 per cent of newspaper advertisements indicated ‘male only’, 24 per cent indicated ‘female only’, while only 33.7 per cent indicated ‘both sexes’ (Korea Women’s Development Institute, 1991).

BIBLIOGRAPHY Bai, M.K. and Cho, W.H. (1991) ‘The employment structure and upward mobility of working females in Seoul’s manufacturing sector’ (mimeo). ——(1992a) ‘Synthesis report on employment and upward mobility in urban labour markets of Manila, Bangkok, Kuala Lumpur and Seoul with special reference to women in the manufacturing sector’. ——(1992b) ‘Male-female wage differentials in the segmented labour markets of Korea’ Korean Journal of Labour Economics 15:1–15. Bai, M.K. and Park, J.Y. (1977) A Study on Korea’s Industrial Labour, Institute of Economic Research, Seoul National University (in Korean). Bluestone, B. (1970) ‘The tripartite economy: labour markets and the working poor’, Poverty and Human Resources July–August, 5, 4:15–35. Cho, E. (1991) ‘Female employment and female labour policy in Japan and UK’, paper presented at Committee for the 21st Century (in Korean). Cho, W.H. (1991) ‘Labour demand-side characteristics and the determination of wages and wage structures during the industrialization process of Korea’, in Industrialization and Labour Force in Korea Vol. II, Korean Economic Research Institute (in Korean). ——(1993) ‘Industrial dualism and the wage determination process in Korea’ Kyoung Je Hak Yon Gu (Journal of the Korean Economic Association) 40, 1: 1–39. Economic Planning Board (1963, 1970, 1980, 1990) Annual Report on the Economically Active Population Survey , Seoul: Government of Korea. ——(1989) Report on the Employment Structure Survey, Seoul: Government of Korea. ——(1960, 1966, 1970, 1980) Report on Population and Housing Census, Seoul: Government of Korea. ILO (1989–90) Yearbook of Labour Statistics, Geneva: International Labour Organization Industrial Labour Force and Wage Survey for South Korea (1946), Government of Korea: 40, Seoul: Department of Labour of South Korean Interim Government. Japanese Government-General of Korea, Yearbook of Statistics, 1930 and 1935. Korea Women’s Development Institute (1991) White Paper on Women (in Korean), Seoul, Korea Women’s Development Institute. Korea Women’s Development Institute (1994) Statistical Yearbook on Women, Seoul: Korea Women’s Development Institute. Kwon, T.W (1978) ‘Estimates of net internal migration of Korea 1970–5’, Bulletin of the Population and Development Studies Center, 7. ——(1988) ‘Estimates of net internal migration of Korea 1975–85’, Bulletin of the Population and Development Studies Center, 17. Lee, W.D. (1989) ‘Characteristics of labour unions in the public enterprises’, paper presented at the Seminar of the Korean Public Enterprises Research Association (in Korean) . Ministry of Education, Statistical Yearbook of Education, 1966, 1970, 1980, 1990, Seoul. Ministry of Health and Social Affairs (1993) White Paper on Health and Social Affairs, Seoul. Ministry of Labour (1985, 1989, 1990) Report on Employment Prospects. ——(1990) Yearbook of Labour Statistics, Seoul. ——(1991) Job Seeking, Job Opening and Employment Trend, Employment Security Office, Seoul. 205

WOMEN AND INDUSTRIALIZATION IN ASIA ——(1991) Report on Job Training, Seoul. ——(1973, 1978, 1983, 1988, 1989) Report on Occupational Wage Survey, Seoul. Oaxaca, R. (1973) ‘Male-female wage differentials in urban labor markets’, International Economic Review, 14, 3. Park, J.D. (1991) ‘Industrialization and urban migration of rural labour force’, 7 (in Korean). Park, S.I. (1984) ‘Problems in female labour market and wage differentials between male and female workers’, in Wage Structure in Korea, Korea Development Institute, 181–216 (in Korean). Piore, M.J. (1980) ‘Technical foundation of dualism and discontinuity’, in S.Berger and S.Piore (eds) Dualism and Discontinuity in Industrial Countries, New York: Cambridge University Press. Roh, M.H., Kim, T.H., Kim, Y.O., Yung, S.J. and Moon, Y.K. (1990) ‘Wage structure of male and female workers’, Korea Institute for Women’s Development (in Korean). Schneider, M. (1991) A Brief History of the German Trade Unions, translated by Barrie Selman, Verlage J.H.W.Dietz Nachf. Standing, G. (1987) ‘Vulnerable groups in urban labour process’. WEP Research Working Paper, Labour Market Analysis and Employment Working Paper No. 13, Geneva: ILO. ——(1989) ‘The growth of external flexibility in a nascent NIC: Malaysian labour flexibility survey’, WEP Research Working Paper, Labour Market Analysis and Employment Working Paper No. 35, Geneva: ILO. Thurow, L. (1975) Generating Inequality, New York Basic Books Inc. Uh, S.B. (1991) ‘Occupational segregation and male-female wage differentials’, Korea Labour Review, 2, Korea Labour Institute (in Korean). ——(1991) ‘Changes in labour market and the labour policy agenda’, Korea Labour Institute (in Korean) . Wachtel, H.M. and Betsey, C. (1972) ‘Employment at low wages’, The Review of Economics and Statistics 54:121–129.

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6 WOMEN IN THE LABOUR MARKET IN MALAYSIA Jamilah Ariffin, Susan Horton and Guilherme Sedlacek The Malaysian labour market is interesting in that the country has grown rapidly in recent years, and changed its structure rather dramatically. The rise of export industries (often employing young, single women) has been an important component of the growth success. Prior to 1970 the labour market was characterized by extreme segmentation along ethnic lines, which the government has made strenuous (and largely successful) attempts to change, as part of the New Economic Policy. The data analysed in this chapter suggest that women’s employment has changed rapidly along with the other changes in the Malaysian economy. There have been dramatic increases in the share of women workers in certain sectors, particularly in manufacturing and clerical occupations. Women’s earnings have risen substantially relative to men’s. However, child care and child-rearing remain women’s primary responsibility, and the lack of day care arrangements mean that women tend to leave the labour force permanently in urban areas after having children, and to leave temporarily in rural areas and return once the children are older. This, combined with the relatively high birthrate, may both limit women’s further advancement in the labour force, and eventually lead to labour shortage in some sectors. The first section of this chapter (see pp. 207–209) presents background material on recent macroeconomic performance and summarizes aspects of the New Economic Policy which have had important effects on the labour market. The section beginning on p. 209 discusses participation, the section on pp. 213–219, employment patterns, that on pp. 219–236 earnings; the section beginning on p. 236 describes some of the labour market institutions affecting women, and the final section (p. 240) contains conclusions. THE MACROECONOMY AND BACKGROUND TO THE LABOUR MARKET At the time of Independence in 1957, Malaysia was a dualistic economy based on peasant agriculture and foreign-owned plantations and tin mines. Another colonial heritage was the large share of the population who were not of the native ethnic group, the bumiputras (mainly Malays). The immigrants were mainly Chinese 207

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(who had immigrated to work as artisans and in the tin mines) and Indians (who had been recruited for the rubber plantations and as infrastructural workers) (see Lim, 1988). Up to 1970 industrialization focused on import-substitution, as with many other newly independent developing countries. Government policy was rather laissezfaire and one outcome was deepening foreign domination of the economy. The main objective of the First Malaysia Plan (1966–1970) was economic growth. This policy emphasis shifted, following racial riots in 1969, to eradicating poverty and eliminating racial differences in incomes and vocation. The New Economic Policy (NEP), which enshrined these concerns, lasted for 20 years, until 1990. The NEP involved a reorientation of priorities, including an increased role of government in the economy, as well as a shift towards export-oriented industrialization as a means of creating employment opportunities (see Mehmet, 1972). Export Processing Zones were introduced in 1971. During the two decades of the NEP the economy grew very successfully. Average growth of GNP (constant prices) exceeded 7 per cent in the 1970s (with one bad year in 1975), and exceeded 6 per cent in the 1980s (with a short but severe recession in 1985 and 1986) (unpublished World Bank figures). The oil price hikes of the 1970s benefitted Malaysia, as an oil exporter, which used revenue to help finance investment programmes. Like other oil exporters Malaysia adjusted somewhat belatedly to the fall in the terms of trade (which for Malaysia began after 1980). Malaysia accumulated foreign debt at a rather alarming rate until the severe recession in 1985–1986. Since then economic performance has turned around and World Bank unpublished estimates for 1991 put growth of real GNP at 9.0 per cent. The country is now moving towards a new phase of higher technology-based and heavier industrialization. The Sixth Malaysia Plan (1991–1995) (the New Development Plan) reemphasizes the role of the private sector in development and envisages a diminishing of the government intervention which had been a feature of the NEP. (Mazumdar, 1994, discusses macroeconomic performance under the NEP in more detail.) Much of the analysis of the labour market and income distribution in Malaysia has focused on differences in economic activities and incomes by race. Bumiputras (mainly Malays) constituted 57 per cent of the labour force in 1986 (Department of Statistics, 1988) and a similar share of the population, whilst the Chinese accounted for 33 per cent, Indians 9 per cent and others just less than 1 per cent. At the time the NEP was introduced Malays earned markedly less than the other two main ethnic groups. Figures for 1976–1977 from the Malaysian Family Life Survey (Smith, 1983) suggest that Indian men earned 60 per cent more than Malay men on average, and Chinese men 108 per cent more. Whereas Malay workers were 208

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heavily concentrated in agriculture, the Chinese dominated the trade sector, and Indians were concentrated in rubber and palm estates in rural areas, and in professions and services in urban areas (Smith, 1983). The NEP used fiscal incentives to enforce employment quotas of 30 per cent for bumiputras for all categories of jobs, especially in the modern urban sector (Lim, 1988) and succeeded in substantially increasing employment of bumiputras particularly in the category of community, social and personal services, which includes the government (see the section beginning on p. 213). Nevertheless, in 1987 only just over a third of Malay workers were in urban areas as compared to almost two thirds of workers from other ethnic groups, and industrial and occupational differences remained (see pp. 213–219). Earnings differentials by race did narrow (see pp. 220–223). Given the focus on racial differentials in the labour force, there has been relatively less attention paid to differentials between men and women. Each of the three main ethnic groups are associated with traditional patriarchal values which do not necessarily encourage women’s participation in the paid labour force. Fertility rates in Malaysia remain relatively high (as compared to other Asian countries with similar income levels) and the government since 1986 has been pursuing pronatalist policies in the interests of economic growth. Female labour force participation has therefore not been particularly high. Nevertheless, there have been significant challenges to traditional mores and some industries important to Malaysia’s manufacturing export success do rely on female labour, as the next two sections show. WOMEN’S LABOUR MARKET PARTICIPATION There is not a large economic literature on women and the labour market in Malaysia. In Ariffin’s (1991c) survey of 140 research papers on women in economic development in Malaysia, only a very small proportion are written by economists. The earliest published papers on women and labour force participation were mainly demographic in orientation (Jones, 1965, Fong, 1974, Hirschman and Aghajarian, 1980, Kwok, 1981 and Chia, 1987). There have been a number of socioeconomic studies focusing on women in the manufacturing sector (Lim, 1978, Ariffin, 1978, 1982 and 1984, Ali, 1983, Sodhy, 1984 and Lee and Sivananthiran, 1992). Other studies of specific sectors and industries include Yeoh (1982) on the informal sector, Sulaiman (1984) on the market and household production sectors, Osman Rani and Jomo (1980) on wage trends in manufacturing, and Chong (1992) on manufacturing companies in the Klang valley. Finally, studies which look at trends and patterns of women’s participation include Yatim (1983), Lim (1983), Tham

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(1983), Mazumdar (1981), Berma and Shahadan (1989), Ariffin (1990), Yahya (1990) and Mazumdar (1994). The labour market situation for women in 1975 is described in Mazumdar (1981). Unmarried women tended to participate more in the labour force than married women, and employment rates for married women increased with income (i.e. those married women who worked tended to have higher educational and social backgrounds). For unmarried women, employment rates were lower for Malay than for Chinese women, related to social norms among the predominantly Muslim Malays. Women reported high rates of unemployment and underemployment. The unemployment rate for women (using the active definition: see Appendix) was twice that of men, and women formed three quarters of the passively unemployed (discouraged workers). Malay working women faced barriers to employment in certain sectors in urban areas: they were over-represented in clerical, professional and technical, and labourer occupations, but under-represented elsewhere. One would expect that some of the barriers facing Malay women in 1975 might have diminished by the late 1980s, with the NEP and the growth of export-oriented industries employing female workers. The data (Tables 6.1–6.7 and Figures 6.1–6.4, 6.5–6.6, based on Labour Force Survey data) indicate that this is indeed the case.

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Women’s participation rates on aggregate in Malaysia are similar to those in the Philippines, Indonesia and Korea—in the 40–45 per cent range for the working age population, with a slight upward trend in the 1980s (Table 6.1). More careful disaggregation reveals differences by urban/rural location, by race and by age. Female participation rates have been declining over time in rural areas and increasing in urban areas. In 1975 and 1980 rural participation rates exceeded urban for women, whereas in 1984 and 1987 the reverse was true (Table 6.1). Participation rates of women also have marked age patterns. Whereas in urban areas age-participation profiles are single-peaked (women tend to work before they marry or have children, but then leave the labour force), rural profiles are double-peaked (women whose children are older return to the labour force) (Figures 6.1 and 6.2). The corresponding profiles for men are in Figures 6.3 and 6.4. In rural areas women’s work is more likely to be compatible with household duties. In urban areas the stereotype is of young, single women working in export industries or sales, relatively dead-end jobs. There is also a smaller group of more educated women in urban areas who combine work with household responsibilities: most of these women are in feminized occupations such as nursing, teaching, and clerical work. There are also differences by race in women’s age-participation profiles (Figures 6.5 and 6.6). Participation by Malay women has a relatively flatter age profile than that for Indian women, and the profile is steepest for

Figure 6.1 Participation rates of rural women, Peninsular Malaysia, 1975–1987 211

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Figure 6.2 Participation rates of urban women, Peninsular Malaysia, 1975–1987

Figure 6.3 Participation rates of rural men, Peninsular Malaysia, 1975–1987

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Figure 6.4 Participation rates of urban men, Peninsular Malaysia, 1975–1987

Chinese women. There are quite large differences in participation rates by race at some ages. For example, participation by Malay women in the 18–23 age group is 14 percentage points lower than for Chinese women, a similar pattern to that found by Mazumdar (1981). This reflects traditional views among Malay Muslim groups which have in the past discouraged work by young single women, a practice which has changed somewhat over time with the demand for young single women workers in export industries. EMPLOYMENT PATTERNS The tables on employment (Tables 6.2–6.7) document some of the advances made under the NEP by Malay women, as well as some of the advances made by all women in the process of industrialization. We discuss first the effects of race, since this is a prominent topic in the literature: the Labour Force Survey Report (Department of Statistics, 1988) for example tabulates most of the main tables by race (but very few by race and by sex). Prior to the NEP, Malays were concentrated in peasant agriculture. The Chinese, the majority of whom had begun as production workers in mining, had also moved into processing of metals, food and wood in manufacturing and dominated commerce and trade. Indians worked mainly in

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Figure 6.5 Participation rates by race: females, urban Peninsular Malaysia, 1987

Figure 6.6 Participation rates by race: males, urban Peninsular Malaysia, 1987

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rubber and palm estates in rural areas, and a minority of them (due to higher levels of education than other ethnic groups in the early 1970s) worked in professions and services in urban areas (Smith, 1983, using data from 1976–1977). Ten years later, statistics from the Labour Force Survey (Department of Statistics, 1988) show that employment patterns had changed somewhat. The main change was the increase in the proportion of urban Malays in government employment (included in the category ‘community, social and personal services’). Whereas for urban Malays only 18 per cent worked in trade and almost 40 per cent in community, social and personal services, for Chinese the proportions were almost exactly reversed, 34 per cent and 17 per cent respectively. Figures for Indians were somewhat intermediate. In rural areas Malays still predominated in agriculture (50 per cent of rural Malays were in agriculture as compared to only 27 per cent of rural Chinese) whilst 25 per cent of rural Chinese were in trade but only 10 per cent of rural Malays (the figures for rural Indians are similar to those for Malays) (Table 6.2). Detailed tabulations of employment by two-digit industry by race and sex (available from the authors) confirm the hypothesis that specialization by race affects both men and women. There are nine (out of thirty-two) two-digit industries where Chinese men are over-represented (where they form over 50 per cent of the male workers, as compared to 34 per cent of the employed male population, and ten where Chinese women are over-represented (using an analogous definition of over-representation for women): in six of these, both Chinese men and Chinese women are over-represented (paper and basic metal manufacturing, construction, trade categories 61 and 62 and services category 95). Similarly Indian men are over-represented in six industries (where they form more than 20 per cent of male workers, despite being only 13 per cent of the employed male population) and Indian women in eight, and in two cases these coincide (services categories 92 and 96). Malay men are over-represented in eight industries (where they form more than 65 per cent of male workers, as compared to 53 per cent of the employed male population) and Malay women in two, which coincide in two cases (transport category 72 and services category 91). The correlation coefficients between the percentage of men in an industry from a particular ethnic group, and the percentage of women from the same ethnic group in the same industry are .382 for Malays, .513 for Indians and .786 for Chinese (based on the twenty-nine two-digit industries where there are both men and women workers), showing there is a strong effect of race on employment by industry which affects both men and women workers. The differences by race in economic function also translate into similar differences in employment status (Table 6.3). A larger share of Chinese men are employers, often in trade (9 per cent versus 2–3 per cent of Malay 215

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and Indian men). Similarly a larger share of Malay men are own-account workers (30 per cent, as against 22 per cent for Chinese men and 10 per cent for Indians), reflecting ownership of family farms. A particularly large proportion of Indian men are employees, either in professions in urban areas, or on plantations in rural areas. For women, there are similar patterns of economic function by race as for men. Indian women are largely employees, in the same activities as Indian men. Malay women are more likely to be own-account workers or unpaid family workers, again probably reflecting work on the family farm in rural areas. Chinese women are more likely than other women to be employers, although the numbers are small. In general women are less likely than men to be employers and own-account workers, and 216

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more likely to be unpaid family workers, a pattern found in many other countries. The rapid growth in the Malaysian economy in the recent past has been accompanied by changes in women’s employment over time. Women have moved out of agriculture into manufacturing, commerce and services over time (Table 6.4). As the overall proportion of women in the labour force has gone up over time (from 26 per cent in 1957–1960 to 35 per cent in 1987), the proportion of workers who are women in manufacturing, commerce and services has risen quite rapidly. Manufacturing in particular has a large proportion of female workers (46 per cent in 1987). Mazumdar (1994) documents similar trends with Labour Force Survey rather than Census data. Table 6.5 goes into more detail as to the current allocation of women by industry, giving figures by three-digit industry within manufacturing for 1987. Women form the majority of workers in other grain mill products (56 per cent), textiles (58 per cent), garments (78 per cent), footwear (63 per cent), other chemicals (50 per cent), electrical machinery (72 per cent), professional and scientific equipment (65 per cent) and other (57 per cent—includes toys and jewellery, etc.). As in other countries, relatively few industries account for most of women’s manufacturing employment: 21 per cent is in the garments industry, and 20 per cent in electrical machinery (including electronics). Women’s occupations have changed at the same time as they have moved into different industries. Women’s employment has shifted out of

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agriculture and into professional, clerical, sales, production and service occupations (Table 6.6). Women are over-represented in professional, clerical and service occupations (there is a higher proportion of women workers in these occupations than in the labour force overall), and these are recent developments of the 1980s. The change in clerical occupations is particularly striking. In 1957 less than 10 per cent of clerical workers were women, but they now constitute the majority of this occupation. Finally, employment status has also changed over time (Table 6.7). There has been a shift from unpaid family work, into employee status for women, the usual pattern accompanying urbanization and the expansion of the market economy. A higher proportion of women are employees than men (Mazumdar, 1994). The change in women’s employment and the large increase in female 218

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participation rates has been accompanied by an increase in women’s relative wages over the period 1973–1987, as the next section shows. WOMEN’S EARNINGS RELATIVE TO MEN Previous work on earnings includes Mazumdar (1981), Chapman and Harding (1985), Mazumdar (1994) and Anand (1983). Figures 6.7–6.26 and Tables 6.8 to 6.12 summarize information about earnings in urban Malaysia. The figures provide information about urban workers (paid employees, employers and own account workers: unpaid family members

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are excluded) in peninsular Malaysia. The tables are for a slightly different sample, namely those reporting wages (mainly paid employees), for the whole of Malaysia, for 1973 and 1987. One important goal of the New Economic Policy was to narrow income differentials by race, and this policy seems to have been successful if the 1987 data (Figures 6.7, 6.8, 6.9, 6.10) is compared with the 1970s data reported in Smith (1983). The gaps are narrower for the three younger age cohorts than the older three (note that the Indians are the smallest group and some of the fluctuations observed for this group may be due to small cell size). The narrowing may partly be explained by a corresponding narrowing of educational differentials, since in the older age cohorts urban Indians had higher education than other ethnic groups (Figures 6.11–6.14). When women’s earnings are compared to men’s, it is seen that there are strong effects of age (Figures 6.15 and 6.16). Women’s earnings (on

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an annual basis) are around 80 per cent of men’s for the younger age groups, but fall to 50 per cent or less of men’s after age 35. This is consistent with the pattern of female labour force participation, where participation is interrupted by marriage, with a return to the labour force following child rearing in rural areas (although an alternate explanation may be that there are strong cohort effects). The differences in the male-female earnings ratio by race are rather small: the ratio for Malays is slightly higher until the oldest age cohort, and the figures for Indians are somewhat variable due to the small cell size for working Indian women. The gap in earnings between men and women can partly be explained by differences in education and in hours worked. Women work shorter hours than men (Figures 6.17 to 6.20) hence the female-male earnings ratio of hourly earnings is less unfavourable to women (the ratio is 80 per cent for the youngest age group, it actually increases slightly for the age group 24–29, and drops to around 60 per cent after age 35). And whereas men work relatively constant numbers of hours per week over the lifecycle, women tend to work fewer hours as they get older (Figures 6.17–6.20). Similarly, relatively higher male education helps to explain the earnings differential. Men’s education exceeds women’s in all except the youngest

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age group, and the gap widens with age (in the oldest age group women have on average three years of education, half that of men: Figures 6.11–6.14). Clearly women’s education has been catching up with men’s in Malaysia in recent years. Comparing Figures 6.11–6.14 (education of all working age population) and 6.21– 6.24 (education of all workers) suggests that the more educated women on average self-select themselves into the labour force. Since almost all men of working age are in the labour market, this self-selection effect is less important for men. When only workers are compared, women workers in all the first three age groups (up to age 35) have levels of education at least as high as men. Thus ‘adjusted’ earnings ratios in Figures 6.25 and 6.26 show that much of the steep drop in female-male earnings with age can be explained by education (Figure 6.25) and education combined with hours worked (Figure 6.26). The adjustment procedure utilized earnings functions (not presented here) where log earnings was regressed on region dummies and interactions between all age group and education dummies, separately for men and women. There were six regions used (Northwest—Kedah, Perlis and Pulau Pinang; Northeast—Kelantan, Negri Sembilan, Pahang and Trengganu; Southeast—Johor; capital region—Selangor and Kuala Lumpur; Perak—adjoining the capital; and Southwest—Melaka). Six age groups were distinguished as in the Figures (18–23, 24–29, 30–35, 36–41, 42–47 and 48–59). Five education groups were used (incomplete primary,

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primary, lower secondary, upper secondary, post-secondary and university, corresponding to less than 5, 5–6, 8–9, 10–11, and 12 or more years of education respectively). The earnings adjustment was undertaken by assigning women the level of education of men in the same region and age group (but retaining women’s education coefficients). (Full analysis is available from the authors on request.) Tables 6.8 and 6.9 report a slightly different set of earnings functions, using an abbreviated number of independent variables in order to allow comparison between 1973 and 1988. The variables included are age and age squared, six education dummies (the seventh, omitted, dummy is primary education) and two race dummies (the third, omitted, dummy is Indian). (Note that primary education corresponds to six years of schooling, Lower Certificate to nine years, Malaysian Certificate to eleven years, Higher School Certificate to thirteen years, a college diploma implies thirteen to fifteen years of education, and a university degree sixteen to seventeen years.) Urban/ rural and region variables were omitted as they were not available for 1973: in any case these variables explain relatively little of the earnings differential between men and women. Three different specifications are used in the earnings functions, namely OLS, OLS adding log hours as an independent variable, and finally a selectioncorrected specification. Adding log hours increases the explanatory 231

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power of the regressions but does not change the other coefficients greatly (except to increase the returns to a college diploma). Ideally hours should be instrumented, since this is a choice variable. The Heckman selection correction variable (lambda) is significant and sizeable only for men (the selection probits used to obtain Lamda are in Table 6.10). The inclusion of a selection correction term for men alters the coefficients on age and ethnic origin (in particular it affects the earnings of Indians relative to other groups). The latter effect is consistent with the earlier discussion

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that there is considerable self-selection into different occupations by ethnic origin, and evidently the characteristics affecting occupational selection also affect earnings. The results in Tables 6.8 and 6.9 show that women have lower returns to age than men, as might be expected. Whereas for men age and work experience increase hand in hand, for women accumulation of work experience with age is much slower, due to interruptions in labour force participation. The age coefficient for women increases between 1973 and 234

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1987, consistent with women’s increased participation rates. The returns to education are systematically higher for women than for men (a feature noted also by Mazumdar, 1994). Earnings differentials by race narrow over time, but do not completely disappear. The advantage of about 0.36 for Chinese men over Malay men in 1987 is less than that in 1973, but still implies that Chinese men’s earnings are 143 per cent of Malay earnings. Chinese women earn about 138 per cent of what Malay women earn, Indian men earn about 122 per cent of what Malay men earn, and Indian women about 110 per cent of what Malay women earn in 1987. It is likely that although the differences in earnings by race have narrowed considerably within urban areas (Figures 6.7–6.26 are for urban areas only), nationwide a gap still remains (as observed in Table 6.9) partly because Malays are considerably more likely to live in rural areas than are other groups. Mazumdar (1994) presents other earnings functions which allow for differences in returns to education and experience by race. An Oaxaca decomposition of the female-male earnings differential was undertaken for annual earnings (Tables 6.11–6.12). Women’s relative earnings increase over time from 57 per cent in 1973 to 69 per cent in 1987. Relatively little of the earnings gap is explained by differences in characteristics between women and men. Of these characteristics, age is the most important: working women are disadvantaged by being younger than working men. Women’s relatively lower education does not seem to matter very much. Differences in coefficients explain the largest share of the earnings gap, and of these, the effect of the age coefficient dominates. This is usually interpreted as an experience effect. Men tend to have a higher coefficient on age than do women, because age is a proxy for experience, and men tend to participate more continuously over the life-cycle. Although women have higher returns to education, the net effect of the education variables in explaining the earnings gap is small. There is also a large unexplained component in 1973, due to differences in the intercept for men and for women. INSTITUTIONS The high participation rates for young women in Malaysia (see pp. 211–213) suggest that women tend to stop work at the age of marriage or onset of child bearing. However, the attrition rate varies by sector, and is higher for urban than for rural women. Economic analysis of secondary data cannot explain these differences. However, there is information from other social science studies, from the socioeconomic, sociological and anthropological literatures. Longitudinal studies of women in the labour market are very useful in this regard, and although there is no national study, there is one from the HAWA project at the University of Malaya 236

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which has done follow-up studies since 1980 on samples of factory women in both urban and rural areas (Ariffin, 1991a). The Institute for the Advancement of Malaysian women (whose acronym is IKWAM) has also gathered detailed information on the work history of poor working women in all four economic sectors (industrial, informal, plantation and agriculture) (Ariffin, 1992a), and Lee and Sivananthiran (1992) also have information for a smaller sample in urban manufacturing. These studies show that most Malaysian women workers do not stop work upon marriage, but due to the dual-role burden imposed by motherhood. Lee and Sivananthiran found that the majority of married respondents in the urban sector did not discontinue work: of the small proportion who did, about 50 per cent of them did so because of pregnancy and child birth. Most (80 per cent) rejoined employment within a period of four years. Ariffin (1992b) found that although husbands of working women are supportive of their wives’ continued employment, it is the problems associated with child-care responsibilities which hinder women’s work. Child-care facilities at the work place are lacking, and support of the extended family has been diminishing. Women in dead-end jobs are the most likely to discontinue work, whereas those in jobs which are perceived to offer a good chance for occupational mobility are more likely to continue working. Whether women remain at work also varies with labour market conditions affecting the bargaining power of married women workers, and the type of technology utilized. In the mid-1980s, when there was no shortage of women workers and the technology in electronics factories was very labour-intensive and strenuous (for example eye-scope work in bonding electronic chips), most factory managers in the manufacturing sector were of the opinion that the productivity level of women production operators declined rapidly when they had young children. Industrial companies preferred to employ single women so as to avoid additional costs of maternity leave and maternity benefits (Lim, 1978). However, by the early 1990s, with rapid economic growth and labour shortages, particularly in electronics, and where the technology in electronics has become highly automated and less strenuous, the situation has reversed. Employers currently cannot afford to lose their women workers, and now provide them with wage incentives to induce them to stay on despite marriage and motherhood. However, there still remain problems with child-care responsibilities. Ariffin’s (1992a) study showed that women would stay home for a short time after the birth of a child, and then go back to informal sector activities where the more flexible work-hours are more conducive to dual-role responsibilities. There are other institutional factors affecting women’s labour market participation. We now examine the role of protective legislation, minimum wage laws, equal-wage and equal-opportunity legislation. It is useful to note at the outset 238

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that the Federal Constitution of Malaysia makes no reference to sex or gender. Thus it is difficult to obtain redressal for discrimination from the courts. Nor do the labour laws in Malaysia contain clauses with reference to gender or sex, and the implementation of these laws applies equally to both sexes. However, there are some provisions in the labour laws which apply exclusively to women workers, usually with the apparent intent of protecting women workers. However, other observers argue that these laws are aimed at protecting the interests of male workers, by retaining the segmented nature of the labour market (Ariffin, 1992b). The Employment Act of 1955 (revised in 1981) is the law which regulates all labour relations, such as contracts of service, wages, rest days, hours of work, holidays, termination, layoffs and retirement. There are two parts specifically applying to women only, namely part VIII ‘Employment of women’ and part IX ‘Maternity protection’. The act applies to all employees who fall within the definition used in the act. The section on employment of women contains section 34, which prohibits night work for female employees in the industrial or agricultural sector between the hours of ten p.m. and 5 a.m. However: any female employee employed in shift work in any approved undertaking which operates at least 2 shifts per day may work at such times within the hours of 10 o’clock in the evening and 5 o’clock in the morning as the Minister may approve. (Employment (Women Shift-Workers) Regulation, 1970) In fact, in the light industries sector in particular, night-shift work for female employees has become the general rule rather than the exception, and section 34 is thus not enforced. Section 35 of the same act also prohibits female employment in underground work. Another segregating force in the labour market is that there is no prohibition on gender-specific job advertisements. In fact job advertisements for female factory workers (young, single and educated) abound in local newspapers and as public posters. The same applies to other ‘feminine’ jobs in the social-escort service. Maternity leave is available. The government sector provides six weeks’, and the private sector sixty days’ leave. One omission in the labour laws is that there has been no legal recognition of the principle of ‘equal pay for equal work’. In the public sector the principle is by no means new, and has been implemented for at least three decades. Although the private sector accepts the concept in principle, in practice there are many instances in which women workers are subject to wage discrimination, for which there is no legal recourse. Nor has there been any ‘equal opportunity’ legislation. And because 239

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of the overriding importance of redressing inequalities by ethnic background, the Malaysian government has been reluctant to endorse formally the ratification of the 1988 Convention on Elimination of Discrimination Against Women (CEDAW). To conclude this section, there have been enormous changes in Malaysian labour market institutions in the last twenty years in at least three important ways. First, labour laws which originally aimed at protecting women workers have been amended to adapt to the new industrial situation where the majority of factory labour is female. The type of industries brought in by export-oriented industrialization since the 1970s (in particular electronics, semi-conductors and the garment industry) all depend on women’s labour. Second, because of urbanization and the increased cost of living, many urban families in Malaysia depend on two incomes. Most husbands do not object to their wives working outside the home and bringing in a much-needed supplement to household income. Women’s increased education, delayed marriage, decreased fertility, and the influence of Westernization have also contributed to this trend. However child-care arrangements have not as yet adapted to this situation. Third, the rapid growth of the Malaysian economy in recent years has led to acute labour shortages in manufacturing. However, the lack of suitable childcare, the reluctance of employers to adapt institutions to meet the needs of women workers, and some cultural constraints have meant that female labour has been underutilized. This is a potential bottle-neck in the Malaysian labour market. CONCLUSIONS Women’s participation, employment and earnings in Malaysia have undergone the same rapid changes as the rest of the economy over the last twenty years. However, institutions have not necessarily changed to accommodate women’s increased importance in the labour force. In urban areas much of women’s participation is prior to child bearing, and there is a marked withdrawal from the labour force once children are born, related to important constraints on the availability of child care. In rural areas women tend to return to the labour force once their children are grown. These age-participation patterns are quite similar to those in Korea and Japan. Given the relatively high birth rate in Malaysia (relative to its level of per capita income) and the pronatalist policies, this may in future hinder further increases in women’s participation and advancement into more permanent careers, as well as limit further advance in women’s relative earnings. Relatively little attention has been paid to gender equality in Malaysia, in view of the overriding political importance under the New Economic Policy of promoting greater equality by ethnic origin. 240

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APPENDIX: DATA SOURCES FOR MALAYSIA The main source of labour force data is the Labour Force Survey (Department of Statistics, various years), which has been conducted annually since 1974. The results are published in The Labour Force Survey Report. Some earlier information was collected in the National Survey on Employment, unemployment and underemployment in 1962, two smaller surveys in 1964 and 1965 in five urban centres, and a Sample Survey of Households in 1967–1978. Official estimates of employment, unemployment and labour force are also included in the Yearbook of Statistics published by the Department of Statistics. They are based on the Labour Force Survey, but are adjusted for some sectors ‘in view of a more complete coverage from administrative records’ (Department of Statistics, 1986). The Labour Force Surveys cover both urban and rural areas of Peninsular Malaysia, Sabah and Sarawak, and cover persons living in private households. The working age population is defined as those aged 15–64. Those employed were those who during the reference week did any work for pay, profit or family gain, as well as those not at work for reasons of illness, temporary layoff, etc., but who had a job to return to. The unemployed are divided into the actively unemployed (who were actively looking for work during the reference week), and the inactively unemployed (who were not looking because they believed no work was available or if available they were not qualified). Some changes have occurred in the data over time. Surveys in 1981 and 1983 were undertaken on a quarterly basis, whereas the 1984 survey was from September 1984 to January 1985. From 1984 onwards coverage in Sabah and Sarawak was extended to more remote areas. Also, the Industrial Classification used was revised in the 1982 survey. These issues are discussed in different issues of The Labour Force Survey Report. BIBLIOGRAPHY Ali, Z. (1983) ‘Rural-urban migration: a case study of some problems confronting women workers in the electronics industry in Selangor’, MEc thesis, University of Malaya, Kuala Lumpur. Anand, S. (1983) Inequality and Poverty in Malaysia, New York: Oxford University Press. Ariffin, J. (1978) ‘Rural-urban migration and the status of factory women workers in a developing society: a case study of Peninsular Malaysia’, paper presented at Conference of the Sociological Association of Australia and New Zealand, Brisbane, Queensland. ——(1982) ‘Industrialization, female labour migration and the changing pattern of Malay women’s labour force participation’, Journal of South East Asian Studies 19:412–425. ——(1984) ‘Industrial development and rural-urban migration of Malay women workers in Peninsular Malaysia’, PhD thesis, University of Queensland. ——(1990) ‘Economic development and women in the manufacturing sector 1957–1987’, in J.Ariffin and S.R.Yahye (eds) Proceedings of the Colloquium on Women and Development, Kuala Lumpur: Population Studies Unit, University of Malaya. ——(1999a) ‘HAWA I: A study of migrant women workers in the Klang Valley’, report submitted to the Institute of Higher Studies, University of Malaya, Kuala Lumpur. ——(1991b) ‘Identifying the needs of women in development and family welfare’, summary report of the IKWAM research project seminar, Kuala Lumpur. 241

WOMEN AND INDUSTRIALIZATION IN ASIA ——(1991c) Women Studies in Malaysia: an Overview and a Reference Bibliography, Kuala Lumpur: National Population and Family Development Board. ——(1992a) ‘Identifying the needs of poor women in development and family welfare: overall report of the IKWAM research project’, Kuala Lumpur: Population Studies Unit, University of Malaya. ——(1992b) Women and Development in Malaysia, Kuala Lumpur: Pelanduk Publications. Berma, M. and Shahadan, F. (1989) ‘Economic development trends and women’s participation in the service sector’, in Proceedings of the Colloquium on Women and Development, Kuala Lumpur: Population Studies Unit, University of Malaya. Chapman, B.J. and Harding, J.R. (1985) ‘Sex differences in earnings: an analysis of Malaysian wage data’, Journal of Development Studies 21:362–376. Chia, S.Y. (1987) ‘Women’s economic participation in Malaysia’, in United Nations, Women’s Economic Participation in Asia and the Pacific, UN Economic and Social Commission for Asia and Pacific. Department of Statistics (1986) Yearbook of Statistics, Kuala Lumpur: Government of Malaysia, Department of Statistics. ——(1988) The Labour Force Survey Report: Malaysia, 1985–1986, Kuala Lumpur: Government of Malaysia, Department of Statistics. Fong, M.S. (1974) ‘Social and economic correlates of female labour force participation in West Malaysia’, PhD thesis, University of Hawaii. ——(1975) ‘Female labour force participation in a modernising society (Malaysia and Singapore 1921–1957)’, Honolulu: East-West Population Institute Paper no. 4. Hirschman, C. and Aghajarian, A. (1980) ‘Women’s labour force participation and socioeconomic development: the case of Peninsular Malaysia 1957–1970’, Journal of Southeast Asian Studies 11:30–49. ILO (International Labour Organization) (various years) Yearbook of Labour Statistics, Geneva: ILO. Jones, G. (1965) ‘Female participation in the labour force in a plural economy: the Malayan example’, Malaysian Economic Review 10, 2:61–82. Kwok, K.K. (1981) ‘Female labour force participation in peninsular Malaysia’, in Report of the Population Seminar, Population and Sectoral Development, Kuala Lumpur: Population Studies Unit, University of Malaya. Lee, K.H. and Sivananthiran, A. (1992) ‘Report on employment, occupational mobility and earnings in the Kuala Lumpur urban labour market with special reference to women in the manufacturing sector’, Kuala Lumpur: Faculty of Economics and Administration, University of Malaya, report submitted to ILO/ARTEP. Lim, A.C. (1992) ‘A study on employee turnover in selected manufacturing companies in the Klang valley’, MBA thesis, University of Malaya. Lim, L. (1978) ‘Women workers in multinational corporations: the case of the electronics industry in Malaysia and Singapore’, Michigan: PhD dissertation, Michigan University. Lim, L.L. (1983) ‘The role of women in the Malaysian economy’, in Wanita MCA Seminar Women in the 80s: Their Achievements and Challenges Ahead, Kuala Lumpur. ——(1988) ‘Labour markets, labour flows and structural change in Peninsular Malaysia’, in P.E.Fong (ed.) Labour Market Developments and Structural Change, Singapore: Singapore University Press. Mazumdar, D. (1981) The Urban Labor Market and Income Distribution: A Study of Malaysia, New York: Oxford University Press. ——(1994) ‘Labor markets in structural adjustment in Malaysia’, in S.Horton, D.Mazumdar, R.Kanbur (eds) Labor Markets in an Era of Adjustment, Washington DC: World Bank, EDI. Mehmet, O. (1972) ‘Manpower planning and labour markets in developing countries: a case study of West Malaysia’, Journal of Development Studies 8:277–289. 242

WOMEN IN THE LABOUR MARKET IN MALAYSIA Osman Rani, H. and Jomo, K.S. (1980) ‘Wage trends in Peninsular Malysia’s manufacturing industries 1963–1973’, paper presented at the Sixth Convention of the Malysian Economic Association, Penang. Smith, J.P. (1983) ‘Income and growth in Malaysia’, Santa Monica, California: Rand Corporation, Report No R2941 AID. Sodhy, S. (1984) ‘Women in the manufacturing sector of Peninsular Malaysia: their distribution and occupation status’, MEc thesis, University of Malaya. Sulaiman, H. (1984) ‘The productive activities of Malaysian women in the market and household production sectors’, PhD thesis, Ohio State University. Tham, A.F. (1983) ‘Female participation in labour forces: some socio-economic correlates’, in Seminar on Economic Activities of Women in Malaysia, Johor Bahru. UN ESCAP (1989) ‘Status of women in Asia and the Pacific region’, Bangkok: UN ESCAP publication No. ST/ESCAP/417. Yahye, S.R. (1990) ‘The development process and women’s labour force participation: a macro-level analysis of patterns and trends 1957–1987’, in J.Ariffin and S.R.Yahye (eds) Proceedings of the Colloquium on Women and Development, Kuala Lumpur: Population Studies Unit, University of Malaya. Yatim, M.M. (1983) ‘Employment of women in Malaysia’, in Seminar on Economic Activities of Women in Malaysia, Johor Bahru. Yeoh, S.P. (1982) ‘Informal sector participation and women’s economic position in West Malaysia’, PhD thesis, Duke University.

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7 WOMEN IN THE LABOUR MARKET IN THE PHILIPPINES Ruperto Alonzo, Susan Horton and Reema Nayar

The Philippines seems to be somewhat of an outlier as compared to other East and Southeast Asian countries. In fact some observers have argued humorously that it is a country which accidentally became detached from Latin America and floated across the Pacific to its current location. Certainly there are features of its macroeconomic performance, behaviour of its labour market and the role of women in the labour market, which resemble those of Latin American countries and which differ markedly from those of other East and Southeast Asian countries. In the first section of this chapter we discuss briefly the macroeconomic events of the recent past and provide some background on the labour market. The section on pp. 246–251 analyses women’s labour market participation, the section beginning on p. 251 deals with employment patterns, and that on pp. 255–265 discusses earnings and hours. The section on pp. 265–269 describes some of the important labour market institutions affecting women, and the conclusions are contained in the final section on pp. 269–270. OVERVIEW OF RECENT MACROECONOMIC PERFORMANCE The Philippines’ macroeconomic performance compares unfavourably with that of many of the neighbouring countries. Of the seven countries in this study, the Philippines has experienced the slowest growth of per capita GDP over the period 1965–1990. Five of the countries had per capita GDP growth rates in excess of 4 per cent per annum; India’s rate was 1.9 per cent, whilst that of the Philippines was 1.3 per cent. The Philippines was the only one in the group to experience negative per capita growth in the years 1980–1990 (World Bank, World Development Report 1992). The adverse effects of slow growth on employment creation were exacerbated by the import-substitution policies initiated after World War II. Particularly in the 244

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1950s and 1960s, the incentive structure favoured capital-intensive growth and led both to relatively high unemployment and underemployment, as well as to a concentration of industrial production in Greater Manila. By the 1970s a more export-oriented strategy was adopted which led to faster growth of exports and non-traditional manufactured exports. This strategy included the creation of special export-processing zones. Unfortunately the timing of the oil price rises stalled this recovery. Due to heavy foreign borrowing both in the 1960s and 1970s, the Philippines came increasingly under external financial pressure. The Aquino assassination in 1983 precipitated a crisis of confidence which led to a freezing of trade credits and a subsequent fall in employment and production. (This section relies heavily on Tidalgo, 1988.) Some indicators of the debt problem are as follows: using World Bank data for 1986, there were eleven non-African countries where the size of the public debt exceeded 60 per cent of GDP. Of these, there were two in Asia, namely Malaysia and the Philippines. In terms of total public debt outstanding, the Philippines ranked eleventh (the other Asian countries with larger debts were Indonesia and Korea). Despite the Philippines’ rather unimpressive macroeconomic performance, the Philippines compares favourably on educational enrolment to the other Asian countries in this study. The Philippines has a higher proportion of the age group enrolled in secondary education than either Malaysia or Thailand (both of which have higher per capita GDP), and is the only country of the seven studied here which has a higher proportion of females than males enrolled in secondary education (Table 1.1, p. 2). In the latter respect the Philippines is similar to many Latin American countries and different from all the other Asian countries except Sri Lanka (using data from World Bank, World Development Report 1992). This favourable education performance, particularly for women, is a somewhat mixed blessing. In combination with a slow growth of labour demand it has led to high unemployment and underemployment for the more educated, and provided an incentive for emigration. The ‘brain drain’ (see Tidalgo, 1988) consists of permanent migrants, the large majority of whom go to the US. Over the period 1965–1982 the average number of permanent migrants was 32,100 per year, of whom 59 per cent were female. The majority of these migrants are professional, technical and related workers and clerical workers. More important in terms of numbers has been the ‘brawn drain’ (Tidalgo, 1988) of overseas contract workers, the majority of whom go to the Middle East. These averaged 440,600 per year between 1986 and 1990, of whom 47 per cent were female in 1987 (Bureau of Women and Young Workers, undated). Whilst overseas migration has some advantages in terms of relieving pressure in the labour market, and in terms of remittances (in 1988 for example remittances were US $388 million, 5.5 per cent of merchandise exports: World Bank, World

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Development Report 1990), there are disadvantages to losing highly educated and skilled workers. Women in the Philippines, associated with their relatively high level of education, have a higher profile than in some neighbouring countries. For example, women compose a slight majority of the civil service (51 per cent) and the career civil service (56 per cent), albeit that women have a relatively more difficult time being promoted to the highest echelons (although women form 69 per cent of the career executives—second level—hey are only 31 per cent of those in the highest positions—third level: Bureau of Women and Young Workers, undated). These and other concerns have led to an explicit Philippines Development Plan for Women 1989–1992, to accompany the Five Year Plan for 1987–1992. WOMEN’S LABOUR MARKET PARTICIPATION When examining trends in participation and employment in the Philippines, it is important to note that there is a discontinuity in the main data series from the Labour Force Survey (see the Appendix to this chapter). The tabulations undertaken for this chapter therefore use the October 1978 and October 1988 labour force surveys to give as long as possible time series, with comparable data, for years of fairly similar levels of labour market activity. One important factor which affects women’s labour market participation and pattern of earnings in the Philippines is women’s relatively high level of education, which tends to make urban/rural patterns of women’s participation, and industrial and occupational patterns of women’s employment, more similar to those of Latin American countries than to neighbouring Asian countries. However, the Philippines is similar to the other East and Southeast Asian countries in this study in the concentration of women workers in a relatively few industries and occupations, in particular key export sectors such as garments and electronics. The Philippines also shares some characteristics of Thailand in that the existence of the extended family and the assistance of grandmothers in child care permits fairly constant women’s labour market participation by age, a feature which does not exist in the other countries in this study. Tables 7.1–7.3 provide the data for this section. Most previous studies on labour force participation and employment in the Philippines, while acknowledging the data limitations and discontinuities in the series due to changes in the definition of labour force, employment, and unemployment concepts already mentioned, continue nevertheless with their analyses of labour market trends as if there were no data problems and therefore come up with conclusions that may not be warranted. For example, the review of labour market trends by Reyes, Milan and Sanchez (1989) notes a high growth rate in the labour force of 4.0 per cent per year

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between 1975 and 1980, compared to only 3.1 per cent per year between 1970 and 1975, when in fact the 1975 data include 10–14-year-olds, and use a narrower definition of work. Thus, for women, the apparent jump in labour force growth rates is even more pronounced, from 3.2 per cent per year from 1970–1975 to 8.1 per cent per year from 1975–1980. Tables 7.1 and 7.2 contain participation rates by sex over time. One feature is that women’s participation rates by age are relatively flat, i.e. there is no pronounced exit from and reentry into the labour force coinciding with child bearing and child rearing. It is somewhat difficult to examine long-run trends in the aggregate level of participation, due to the break in the data series between August and October 1976 (and some continued definitional changes between 1976 and 1978). The data suggest that there was a decline in participation rates for both men and women between 1956 and 1976 (see Table 7.1: this is probably associated with urbanization and increased education). 247

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For the period 1976–1992 there seems to be an increase in participation after the debt crisis year of 1982 (Table 7.2), evident both for the last quarter and last week reference period series. With the third quarter reference period for 1976–1986, regressions of the labour force participation rate (LFPR) against time and a dummy variable (1 if after 1982, 0 otherwise) show the following results: for males, the LFPR is significantly higher by 3.8 percentage points in the post-1982 period, with a declining time trend of -0.6 percentage point per year; for females, the LFPR is higher by 6.6 percentage points for the post1982 period, with no significant time trend. With the preceding week as reference period for 1980–1992, the male LFPR is higher by 4.0 percentage points, the female LFPR by 4.5 percentage points after 1982, with no significant time trend for either. What would help explain the rise in LFPRs in the post-1982 period? It has been consistently observed in cross-section studies for the Philippines that at low levels of family income, even as wages decline in real terms, women tend to join the labour force, i.e. an ‘added worker’ effect (Encarnacion, 1973, Canlas, 1978). This cross-section phenomenon may apply as well to the labour market behaviour over time of all family members. Since the onset of the economic crisis in the mid-1980s, the Philippines has yet to reattain the real per capita income level of 1982. Costello and Ferrer (1993) use the observed age- and gender-specific labour force participation rates across regions to make projections of the labour force in the year 2000. For males and females separately, they run OLS regressions of LFPRs against time for each age group and administrative region, using time series on average LFPRs from 1981–1991. The projected LFPRs for the year 2000 are then applied to the regional base population projections which take into account the age- and sex-specific census survival ratios and migration rates observed for the period 1980–1990. Upon aggregation, they arrive at the following projections: the male labour force would increase at only 3.2 per cent per year, while the female labour force would grow at 4.2 per cent per year, so that by the year 2000 women will comprise 39.1 per cent of the total labour force, compared to only 36.9 per cent in 1990. The age groups displaying the fastest growth rates, based on historical trends, are the younger ones. One problem with the projection methodology however, is its rather deterministic nature, with participation depending only on time trends. The high growth rates for the younger age groups, for example, may be running counter to increased schooling participation rates at the secondary level that have recently been brought about by the government’s free secondary education policy. Tables 7.2 and 7.3 also highlight some interesting urban-rural differences. Rural participation rates for men are about 10 percentage points higher than urban rates, whereas there is very little difference for women.

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And whilst in urban areas male unemployment rates exceed female, the converse is true in rural areas. The Philippines evidently has fewer rural (agricultural) opportunities for women, but more in urban areas (related to women’s higher educational attainment than men’s). This pattern is typical in Latin America, but unusual in Asia. EMPLOYMENT Employment trends over time should be examined carefully, again due to the break in the data series after 1976 (see Appendix to this chapter). Different publications also present slightly different series. The figures in the ILO Yearbook of Labour Statistics (using IHS data) use the last week reference period, whereas those in the Philippine Statistical Yearbook (NEDA, 1989) use the last quarter (also based on IHS data). The data show that female employment shifted out of agriculture and into the tertiary sector between 1960 and 1975 (Table 7.4). The observed decline in the share of female employment in the manufacturing sector is somewhat unusual in the course of development, and probably reflects the excessively capital-intensive industrialization policies implemented. The figures from 1976 onwards suggest that the declining share of women’s employment in manufacturing and the increasing share in commerce continued, whilst the share of employment in services stagnated and the shift out of agricultural employment reversed. One possible explanation is that the change in definition of work in 1976 (see Appendix to this chapter) increased the reported number of women working in agriculture. However the annual series (in NEDA) suggests that there were jumps in female employment in agriculture in 1973, 1978 and 1983, and that the shift back to agriculture may not simply be a statistical artefact, but may be related to deteriorating urban employment opportunities. The figures on the proportion of workers who are female, by industry (also in Table 7.4) show that, as in other countries, women are concentrated in certain industries. In the Philippines women form over half the work force in manufacturing, commerce and services, but only a quarter of the work force in agriculture. This pattern is similar to that of Latin America (although there the fraction female in each sector is lower), and different from the other East and Southeast Asian countries where women are usually more highly represented in agriculture than in urban activities. Further analysis by more detailed industry classification shows that women workers are concentrated in quite narrow industrial categories. Table 7.5 presents information by two-digit industry for the manufacturing sector. Women form the majority of workers in one of the two-digit categories in manufacturing, namely textiles, wearing apparel and leather (other two-digit industries outside manufacturing where women predominate include education services—where women are 73.8 per cent of the 251

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work force, medical, dental, veterinary and related workers—73.3 per cent, retail trade—67.7 per cent, personal and health services—62.7 per cent, restaurants and hotels—57.3 per cent and production of livestock, poultry and other animals— 56.7 per cent: these figures are averages for 1980, 1984 and 1989, from the Bureau of Women and Young Workers, undated). Once the information for manufacturing is examined at the three-digit industry (also in Table 7.5) women are still highly concentrated, and form the majority (or close to half) of workers in tobacco, textiles, wearing apparel, leather, footwear, electrical machinery, professional and scientific equipment, and other manufacturing. This coincides with areas of non-traditional export success (Table 7.6). Garments and electrical machinery (the latter category includes electronics) were 54 per cent of the Philippines non-traditional exports in 1978, and 59 per cent in 1988. Both

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industries rely heavily on imported inputs (domestically produced textiles are highly protected and costly), and much of the production occurs in export-processing zones. Low cost labour is crucial, and the government has used measures to try to ensure that it is available. There are ‘restrictions on labour organizing in these zones and in the export sector in general’ as well as ‘liberal adoption of apprenticeships enabling firms to obtain labour costing below the required legal minimum wage’ (Tidalgo, 1988:168). Garment and electronics exports have been very important sources of foreign exchange: non-traditional manufactured exports grew from 8.3 per cent of all exports in 1970 to 31.4 per cent in 1978 (Tidalgo, 1988) to 66.0 per cent in 1988 (calculated from NEDA, 1989). Garments and electrical machinery (including electronics) alone were almost 40 per cent of all merchandise exports in 1988. Thus women’s work in manufacturing has been extremely important in earning foreign exchange. Table 7.7 provides information on the distribution of women’s employment by occupation, and the proportion of workers in each occupation who are female. Again, the discontinuity in the data affects the interpretation of long-term trends, but it seems that the growing share of employment for women was in professional, clerical, sales, and (until 1975) service occupations. The share in agricultural occupations was declining up until 1975, and thereafter increased. As in Table 7.4, part of the increase in the share of agriculture after 1976 may be due to the change in work

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definition, but part appears to be a response to years of labour market stagnation and economic crisis. The final table analysing employment, Table 7.8, focuses on the employment distribution of women, and proportion of workers who are female, by employment status. This table shows the usual shift out of unpaid family work into employee status in the course of development, both between 1960 and 1975, and between 1980 and 1990. The changes in definition in 1976 appear to have a marked effect (leading to a sharp increase in the numbers of female unpaid family workers reported, most likely in agriculture). HOURS AND EARNINGS In most countries women, especially married women, tend to work fewer hours than men and to interrupt labour market participation to have children, b o t h fa c t o r s w h i c h t e n d t o re d u c e r e l at iv e fe m a l e e a r n i n g s . I n

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the Philippines, however, urban working women have a relatively strong attachment to the labour force and work similar hours to men, which seems to be correlated with a relatively flat age profile for female-male relative earnings. However, women do not have parity in earnings relative to men, a factor which is explained largely by differences in relative returns (coefficients in earnings functions) rather than differences in endowments. In fact, based on measured endowments women might expect higher earnings than men (based on their relatively higher education). One factor which may contribute to lower relative earnings for women is industrial and occupational segregation, discussed in the previous section. Figure 7.1 and Tables 7.9–7.16 present evidence on earnings and hours. Figure 7.1 presents information on life-cycle earnings of women relative to men, which are relatively flat. There is little difference whether hourly or quarterly earnings are used, except for young women workers. Women and men tend to work a similar number of hours, except for young women who work very long hours (probably due in part to the long hours worked by women domestic workers). Note that Figure 7.1 is restricted to urban employed, employers and self-employed and does not include unpaid family workers. Table 7.9 provides information on the number of days worked in the last quarter in the primary job for men and women, by industry and occupation (urban and rural areas combined). Clearly in many urban activities the standard work-week is either five or six days, except for manufacturing, where workers may be laid off due to temporary work shortages. The work-week is also shorter in agriculture. Women work similar numbers of days to men in most industries, except for manufacturing where women worked fewer days than men in 1980 and 1984 (but more in 1989). This may possibly depend on the nature of demand in different sectors of manufacturing, given that women workers are particularly concentrated in a few areas, especially in garments and electronics exports. Women agricultural workers also work shorter weeks than men, further evidence of women’s subsidiary role in agriculture in the Philippines, and of the pressures for women to migrate for urban jobs. The occupational data (also in Table 7.9) tell a similar story, namely that men and women work similar numbers of days in most occupations except production (i.e. manufacturing) and agricultural occupations. Tables 7.10 and 7.11, from Gill, Sedlacek and Nayar (1993) examine hours worked and employment composition by sex, industrial sector, employment status and marital status. Based on similar tabulations for other countries, they argue that for women marriage is significantly associated with change in industrial sector and in employment status, whereas the same is not true for men. They suggest that this is related to greater familial responsibilities entailed by care of children, and that

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married women choose jobs with greater flexibility in terms of hours of work. In the Philippines, women shift out of the service industry and into trade (there is also a small increase in agriculture, although this sector is not very important in urban areas). There is a corresponding shift out of employee status (mainly private employees) and into self-employment. Clearly self-employment is likely to offer greater flexibility in terms of hours, since the coefficient of variation is higher in this sector than in others (however mean hours worked by self-employed women are higher than for employees). It is less obvious from the data presented why the shift from services to trade occurs. Both industries have similar coefficients of variation in hours, and women in trade in fact work higher mean hours than in any other industry. Worse still, women’s hourly earnings are lower in trade than in any other industry. Possibly the explanation is that being self-employed in trade is more compatible with household duties in that children can be present at the work place, or that the hours can be worked at flexible times of day. Tables 7.12–7.16 summarize the results of earnings functions for urban (paid) employees. Table 7.12 contains the results for quarterly earnings and Table 7.13 for hourly earnings. In each case the functions are estimated

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with and without the Heckman selectivity correction. The lambda terms (indicating selection effects) are larger for men than for women, and tend to reduce the size of many other coefficients, particularly for men. It should be remembered that the selection here is into the status of paid employee (those excluded include the self-employed, employers, and unpaid family workers as well as non-workers). For men the important selection decision (in terms of fractions of those of working age involved) is between paid employee status and self-employed/employer status, whereas for women the important decision is between paid employee status and non-work. Apparently the unmeasured characteristics selecting men into the status of employee are characteristics which also adversely affect earnings. The presence of these unmeasured characteristics also biases upwards the

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coefficients on measured characteristics, if the selection correction is omitted. For women the selection effect is somewhat different, has a smaller effect on earnings, and causes little bias on the coefficients of the measurable characteristics if the selection correction is not made. Mean values for the variables used in the earnings regressions are given in Table 7.14, and Table 7.15 contains the selection logit equations. Participation rates are highest in central regions of the country (the National Capital Region and Central Luzon). Participation rates have a U-shaped pattern by education for women (being highest for women with no education and with tertiary education), whereas for men they increase monotonically with education. The demographic variables (the proportion of women of different ages in the household, and the same variables interacted with education level of these women) predictably affect women’s participation more than men’s. The presence of other adult women in the household tends to increase women’s participation. Table 7.16 contains the standard Oaxaca decomposition by sex. Women’s hourly earnings rose from 80 per cent of men’s in 1978 to 84 per cent in 1988, and quarterly earnings rose from 74 per cent to 81 per cent correspondingly. Women would in fact have an advantage over men in terms of earnings if only measured endowments mattered, due to the relatively higher level of women’s education. However, this is offset by differential earnings coefficients, in particular the higher returns to age for men (true for all cases except hourly earnings in 1978) and higher intercept terms for men. INSTITUTIONS In terms of official legislation and stated government policies concerning the status of women, the Philippines would perhaps not be far behind even the developed countries. The government has ratified the ILO conventions dealing with women in the work place, such as No. 100 (Equal Remuneration for Men and Women Workers for Work of Equal Value, 1951), No. 111 (Discrimination in Respect of Employment and Occupation, 1958), and No. 142 (Human Resources Development, 1975).

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The government has also ratified the United Nations Convention on the Elimination of All Forms of Discrimination Against Women (1979) and has committed itself to the Nairobi Forward-Looking Strategies for the Advancement of Women, two of the important documents that emanated from the International Women’s Decade (1976–1986). Several government agencies deal directly with women’s concerns. The National Commission on the Role of Filipino Women (NCRFW) was created in 1975 with the mandate to ‘work towards the full integration of women for social, economic, political and cultural development at national regional and international levels on 266

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a basis of equality with men’. The Department of Labour and Employment has a Bureau of Women and Young Workers which looks after the protection and welfare of women workers. The Department of Social Welfare and Development has its own Bureau of Women’s Welfare which is concerned with the ‘prevention and eradication of exploitation of women in any form, including prostitution and illegal recruitment, as well as the promotion of skills for employment and selfactualization’. The 1987 Constitution explicitly says that ‘The State recognizes the role of women in nation-building, and shall ensure the fundamental equality before the law of women and men’ (Article II, Section 14), and that ‘The State shall protect working women by providing safe and healthful working conditions, taking into account their maternal functions, and such facilities and opportunities that will enhance their welfare and enable them to realize their full potential in the service of the nation’ (Article XIII, Section 14). Also in 1987, Executive Order 227, or ‘The New Family Code of the Philippines’ was issued by the president eliminating many discriminatory provisions in the Civil Code which were remnants of Spanish colonial law. In 1989 the Republic Act (RA) 6725 was passed making discrimination against women with respect to terms and conditions of employment a criminal offence. Discrimination is defined as ‘payment of a lesser compensation to a female employee as against a male employee, for work of equal value; and favouring a male employee over a female employee with respect to security of tenure, promotion, training opportunities, study and scholarship grants solely on account of their sexes’. It is clarified in the law’s implementing rules and regulations, however, that differences in compensation based on such factors as length of service or seniority and location or geographical area of employment are allowable. The clause on scholarship grants erases the government policy of disqualifying a mother with a child below two years old from availing of an overseas scholarship or training. In December 1991 the Women in Development and Nation Building Act (RA 7192) was passed, which, among others, allows women to enter into contracts in the same respect as men. Previous to this, marital consent was required of a married woman before she could enter into a contract or obtain a loan. The law also allows women equal access to club memberships and to admission to military schools. In February 1992 RA 7322 was ratified, increasing maternity benefits for women workers in the private sector who are covered by the Social Security System from the old forty-five days’ to sixty days’ leave with pay. This puts the private sector woman worker on an equal footing with her regular government counterpart; however, casual and temporary women workers in government get only thirty days paid maternity leave. A government employee also has to be married to avail of maternity benefits, while any private sector employee may avail of such benefits. 267

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Before 1975, maternity leave was actually for as long as four months (Santos and Liamzon, 1985). Most of these recent pieces of legislation concerning women deal with issues that have been identified in the Philippine Development Plan for Women, 1989– 1992 (NCRFW 1989). The plan was prepared to serve as a companion piece to the Medium-Term Philippine Development Plan. The coordination for the plan preparation was handled by the NCRFW, which formed technical working groups composed of representatives from government line agencies and non-governmental organizations. The Plan for Women presents an excellent review of economic, social and political issues concerning women in development, and proposes programmes and projects to address gender-related problems. The document is significant especially in light of the fact that, in the Medium-Term Plans for 1978– 1982 and 1983–1987, official concern for women in development centred on the role of women as wives and mothers more than anything else: out of twelve statements mentioning women, seven referred to the health and nutrition needs of pregnant women and lactating mothers (Santos and Liamzon, 1985). The Plan for Women, however, recognizes that additional ‘protective’ labour laws may be self-defeating and keeping women out of the job market. ‘Since existing protective labour laws already exact negative impact on the employment of women, provision of more without the necessary consciousness-raising among employers and women themselves would be difficult’ (NCRFW 1989). Legislation is one thing, but enforcement is another matter. Officially, marriage bars do not exist except in the armed forces; a 1984 Presidential Decree provides that any female commissioned officer or enlisted woman who gets married shall automatically be separated from the service, unless she has already completed at least three years of continuous service. In the private sector, employers in the labourintensive export-oriented industries such as electronics are said to prefer young, single women for their visual acuity, finger dexterity, and docility (del Rosario, 1985). After five or six years of work their ‘skills’ deteriorate and a new batch of young and single women comes in. In the semi-conductor industry, 85 per cent of employment in 1984 was female. In the export processing zones, women (mostly young and single) comprised 70 per cent of the work force in the mid-1980s (Ministry of Labour and Employment 1984, as cited in del Rosario, 1985). Gender-segregated job advertisements are illegal, but they are tolerated, as a look at the classified ads section of any newspaper would reveal. While there is no separate minimum wage law for women, one study notes that the majority of complaints concerning violations of labour standards were from women, with most of the grievances aired being wage-related, including underpayment of minimum wage, non-payment of the thirteenth month pay, etc. (Cortes, 1982, as cited in del Rosario, 1985). A 1980 survey of employment in the Bataan Export Processing 268

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Zone also showed 40 per cent of female workers receiving less than the stipulated minimum wage of P13 per day at that time, compared to only 17 per cent of male workers (Castro, 1982). In 1990, out of 25,000 establishments inspected by the Bureau of Working Conditions of the Department of Labour and Employment, 30 per cent were found to be violating the minimum wage law, while 14 per cent were not giving the thirteenth month pay. While the department notes these violations, prosecution is initiated only if a worker files a formal complaint. In many instances, workers probably realize that strict compliance by their employer may mean the loss of their jobs. But the important thing to point out here is that women appear to be over-represented in the number of complainants. While there is no separate minimum wage legislation for women, there is a separate minimum wage law for household help, most of whom are women. The laws governing household help not only specify the minimum wage level but also require employers to provide proper working conditions such as food and lodging, medical attention, opportunity for at least elementary education, a maximum of ten hours of work per day, and four days off per month. The Plan for Women notes, however, that the recent trend in overseas migration of domestic helpers has helped to increase the pay of those left behind, so that actual earnings for household help are often above the mandated minimum (NCRFW 1989). CONCLUSIONS The beginning of this chapter suggested that there were a number of parallels between the Philippines and the Latin American countries. When examining women’s role in the labour market, common underlying factors include the high level of women’s education relative to men’s, early capital-intensive industrialization based on import-substitution policies, and economic stagnation following the debt crisis. These factors lead to similarities in the composition of employment, with better opportunities for women in urban than in rural areas, and a corresponding incentive for urban migration. Trends over time in the composition of employment by industry, occupation and employment status have also been similar in the Philippines and in many Latin American countries, in that the manufacturing sector has failed to generate enough jobs and employment has been forced into the tertiary sector and back into agriculture, and there have been incentives for outmigration. The Philippines does however share some characteristics of the neighbouring Asian countries, one of which is the importance of women in earning foreign exchange. Women form almost half the work force in electrical machinery and three quarters in garments, both important export sectors. Almost half of temporary migrants and more than half of permanent migrants are women, and their remittances are an important source of foreign exchange. 269

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In its patterns of women’s life-cycle participation and earnings relative to men the Philippines is quite similar to Thailand. Those women who work tend to remain in the work force throughout their working life. This more permanent attachment to the labour force in turn explains why women’s earnings relative to men’s are higher than in most other countries in this study, and diminish less with age. Women workers in the Philippines also share some characteristics of women workers worldwide. They earn less than men, which cannot be explained by shorter hours or part time work (in the Philippines women’s hours are similar to men’s, they work as many days per month in their primary job, and participation rates are relatively constant over the lifecycle). It may, however, be relevant that women workers are concentrated in relatively few industries and occupations, which are (not coincidentally) low paying. Although women have advanced into professional and government jobs, they find advancement to the highest echelons difficult. APPENDIX: LABOUR MARKET DATA IN THE PHILIPPINES The main sources of labour force data for the Philippines are described in Tidalgo and Esguerra (undated). The National Statistical Office has conducted regular household surveys, including a labour force survey, since 1956. The survey is now called the Integrated Survey of Households (ISH), and includes the Labour Force Survey, the Family Income and Expenditures Survey, and other special modules, e.g. the National Demographic Survey and the National Health Survey. The Labour Force Survey was collected biannually 1956–1969 (usually in May and October), and quarterly since 1971 (usually January, April, July and October: the results refer to the preceding quarter). The results are published by the NSO in the Integrated Survey of Households Bulletin (Labour Force), which began as the Philippine Statistical Survey of Households Bulletin in 1956. The Department of Labour and Employment reprints the survey information in its biennial Yearbook of Labour Statistics. The series from May 1956 to August 1976 is quite comparable across surveys, with a population coverage of those aged 10 years old and above, and with the week preceding the survey interview as reference period for employment data. Beginning with the third quarter of 1976, major changes were introduced. The labour force was made to include only those aged 15 years old and above, in conformity with the Labour Code’s legal minimum employable age; this not only created a break in the series, but also prevented the monitoring of the employment of the 10–14-year-olds. At the same time the reference period became the preceding calendar quarter, which was again changed in July 1987 to the preceding week. The survey itself, however, covers both reference periods; the 270

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third quarter rounds from 1978–1986 show the average labour force participation rate to be only 0.4 percentage points higher, but the average unemployment rate 3.9 percentage points lower, for the past quarter than for the past week. The definition of unemployment was also changed four times between 1976 and 1978. The net effect of these changes on measured unemployment rates is likely to be larger for women than for men. The definition of work was also expanded in October 1976, to include minor home activities in crops and gardening, livestock and poultry raising, fishing and manufacturing. (Between May 1956 and August 1976, work was defined as working for pay or profit, or working without pay on the farm or business enterprise operated by a member of the same household, or having a job but not being at work because of temporary illness, vacation, strike or other reasons, or being about to start work within thirty days. In October 1976 those doing minor activities in home gardening, raising crops, fruits, etc., raising hogs, poultry, etc. were added, provided that within the quarter there was some harvest such that earnings were derived from the activity.) This broader definition of work may have contributed to a relatively larger number of unemployed, especially in agriculture. Finally, there have also been changes in industrial, regional and occupation classification at several times (Tidalgo and Esguerra, undated). The data analysis for this chapter therefore takes account of the discontinuity in the series between October 1976 and August 1978. The original tabulations undertaken here are for October 1978 and October 1988, to give a time series (which is as long as possible), without problems of comparability, using years of reasonably similar levels of economic activity. The compensation data from the Labour Force Survey are total earnings, which consist of basic wages, a thirteenth month bonus (paid at Christmas) for some wage and salary workers in the formal sector, plus other payments (including overtime), in cash or in kind. No regular series is published using the ISH data, although unpublished tables for 1976 to 1986 are available for public use at the NSO central office. (Bureau of Women and Young Workers, undated, contains information for three years.) The NSO discontinued the reporting of earnings data from the Labour Force Survey in 1989. In addition to the Integrated Survey of Households, the NSO conducts the Annual Survey of Establishments and the quinquennial Economic Census, which report the total annual compensation and the number of employees (for non-agricultural establishments, with coverage changes in the 1980s). Several other government agencies also collect earnings data. The National Wages Council used to conduct the Occupational Wages Survey for Non-Agricultural Establishments (on monthly 271

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wages plus COLA, the Cost of Living Allowance); this function was transferred in 1990 to the Bureau of Labour and Employment Statistics. The Bureau of Agricultural Statistics Integrated Agricultural Survey covers daily wages of farm workers by type of crop. These series are all published in the Yearbook of Labour Statistics (Bureau of Labour and Employment Statistics, various years). BIBLIOGRAPHY Abrera-Mangahas, A. and Aguila-Bautista, L. (1990) Profiling Filipino Worker Families Through a Socio-Economic Survey Module, Manila: Department of Labour and Employment (DOLE) and ILO/ARTEP. Alonzo, R.P. (1993) ‘The economics of household-operated activities in the Philippines: macro and micro perspectives’, submitted to ILO/ARTEP (mimeo). Bureau of Labour and Employment Statistics (various years) Yearbook of Labour Statistics, Manila: Department of Labour and Employment, Bureau of Labour and Employment Statistics. Bureau of Women and Young Workers (undated) Regional Statistics on the Employment of Women and Young Workers 1980, 1984 and 1989, Manila: Department of Labour and Employment, Bureau of Women and Young Workers. Canlas, D.B. (1978) ‘A quantitative study of fertility and wife’s employment in the Philippines, 1973’, Philippine Economic Journal, XVII:88–121. Castro, J.S. (1982) ‘The Bataan Export Processing Zone’, Asian Employment Programme Working Paper, September. Cortes, I. (1982) ‘Discrimination against women and employment policies’, NCRFW Research Monograph No. 1. Costello, M.A. and Ferrer, P.L. (1993) Projected patterns of labour force participation for the year 2000: the Philippines and its administrative regions, DOLE and ILO/ARTEP, June. del Rosario, R.S. (1985) Life on the Assembly Line: an Alternative Philippine Report on Women Industrial Workers, Manila: Aklat Pilipino. Encarnacion, J. (1973) ‘Family income, educational level, labour force participation and fertility’, Philippine Economic Journal, XII:536–549. Gill, I.S., Sedlacek, G.L. and Nayar, R. (1993) ‘Gender and employment: implications of household responsibilities for access and rewards to market work’, Washington DC: World Bank (mimeo). Institute for Labour Studies (various years) Employment Report, Manila: Department of Labour and Employment, Institute for Labour Studies. ILO (International Labour Organization) Yearbook of Labour Statistics, Geneva: ILO, various years. Ministry of Labour and Employment (MOLE) (1984) The Bataan Export Processing Zone, Manila: MOLE. National Commission on the Role of Filipino Women (NCRFW) (1989) Philippine Development Plan for Women, 1989–1992, Manila: NCRFW. National Economic Development Authority (NEDA) (1989) Philippine Statistical Yearbook 1989, Manila: National Economic Development Authority.

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WOMEN IN THE LABOUR MARKET: PHILIPPINES Reyes, E.A., Milan, E. and Sanchez, M.T. (1989) ‘Employment, productivity and wages in the Philippine labour market: an analysis of trends and policies’, PIDS Working Paper Series No. 89–03. Santos, A.M. and Liamzon, C.M. (1985) Too Little, Too Late: an Alternative Philippine Report on Government Initiatives for Women, Manila: Aklat Pilipino. Tidalgo, R.L.P. (1988) ‘Labour markets, labour flows and structural change in the Philippines’, in Pang Eng Fong (ed.) Labour Market Developments and Structural Change: the Experience of ASEAN and Australia, Singapore: Singapore University Press. Tidalgo, R.L.P. and Esguerra, E.F. (undated) Philippines Employment in the Seventies, Manila: Philippine Institute for Development Studies. World Bank (various years) World Development Report, Washington DC: World Bank.

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8 CHANGES IN WOMEN’S ECONOMIC ROLE IN THAILAND Mathana Phananiramai

THE MACROECONOMY During the past two decades, Thailand has undergone tremendous social and economic changes. In terms of social changes, the population growth rate has slowed down, from an annual rate of over 3 per cent in early 1970 to a rate of below 1.5 per cent in 1990. This deceleration has important implications for the quality and quantity of labour. Following the trend in population growth, labour supply in Thailand used to increase at an annual rate of 3–4 per cent, but the increase has now slowed down to approximately 2 per cent. The high growth rate of population and labour in the past necessitated the allocation of enormous resources to train new members, hence little effort could be spared to improve the quality of population and labour. In this respect, Thailand shared the characteristics of many developing countries and was a country abundant with cheap unskilled labour. However, with recent rapid economic development and the slowdown in the population growth rate, the problem of labour surplus may soon evolve into one of labour shortage. Meanwhile, Thailand has also experienced rapid structural economic changes. Until about 1960, the Thai economy was mainly based on agriculture. The launch of the first five-year Social and Economic Plan in 1961 could be marked as the first step in the industrialization effort. After a short period of basic infrastructure expansion, early industrialization policy involved import substitution, which aimed at reducing imports and increasing employment opportunities within the country. In order to achieve that objective, manufactures were heavily protected with tariffs, and tax incentives were available for imported capital goods. However, the policy had limited success because imports of capital goods increased rapidly, and employment generation was limited because most industries used imported capitalintensive technology. Therefore, around 1972, industrialization policy shifted from import substitution to export promotion. With abundant cheap labour, several export274

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oriented industries which were labour intensive, such as textiles and garments, food processing, electronic goods and gem cutting, expanded rapidly. These developments have caused tremendous changes in the sectoral contribution to GDP. The share of the agricultural sector was reduced from 31.5 per cent in 1975 to 12.4 per cent in 1990. Within the non-agricultural sector, the manufacturing and service sectors saw the most rapid expansion. Structural economic changes have also caused employment restructuring. In 1970, more than 75 per cent of the labour force are employed in the agricultural sector, but this was reduced to less than that in 1990. Home-based economic establishments have given way to establishments separated from home, such as firms or corporations. Thus the percentage of self-employed or unpaid family workers has declined while the percentage of employees has increased. Amid the process of social and economic change outlined above, the role of women has also changed. The importance of the economic role of women can be traced back several hundred years in Thai history. In the past, when prime-age males were forced to be away from their families either to work for the crown or to join the national defence forces, women were left behind to take full responsibility for the family farms. Therefore there were never any cultural or religious barriers which prevented women from participating in economic activities. In fact it is traditionally assumed that women take responsibility for housework as well as economic activities while men take responsibility for economic and political activities. The practice that men and women work side by side in economic activities has been carried over to modern society. Hence it is not surprising that the female labour force participation rate in Thailand has been one of the highest in the world, at approximately 70 per cent. However, economic activities in modern societies are in greater conflict with housework, which remains the primary responsibility of women. This creates new challenges and opens up new opportunities for women to participate in the process of social and economic development of the country. The objective of this chapter is to examine the changing economic role of women in Thailand. The section beginning on p. 275 examines women’s labour force participation rates. The section on pp. 281–286 focuses on changes in women’s employment status. Women’s working hours and their earnings relative to men are discussed in the section beginning on p. 286, and the section on pp. 299–302 investigates the legal and institutional aspects which affect women’s employment. Conclusions and policy implications are given in the final section (pp. 302–303). WOMEN’S LABOUR FORCE PARTICIPATION RATE The labour force participation rate is usually measured by the ratio of persons who are in the labour force to all potential workers. Persons in the labour force include those who are employed or unemployed but are available for work, where the 275

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definition of availability varies from country to country. In industrialized countries, actively looking for a job is usually used as an indication of availability. In Thailand the definition of availability used by the National Statistical Office has changed from time to time. Previously, as in the case of industrialized countries, the act of looking for a job was used as an indication of availability. But more recently, the available work force was defined to include also those who had not been looking for work because of illness or belief that no suitable work was available. (See further details on data sources and the definition of labour force participation rate in the Appendix.) The labour force participation rates of men and women classified by location (as reported by various publications from the Labour Force Survey conducted by the National Statistical Office) are given in Table 8.1. Slightly different definitions of the labour force participation rates were used during these periods. (For more details, see the Appendix.) The figures show that labour force participation rates fluctuated from year to year, especially for women. On average, the labour force participation rate

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in municipal (urban) areas is lower than in non-municipal (rural) areas. This is true for both men and women. As in most countries, women’s labour force participation rate is lower than men’s, and the difference is greater in municipal than in nonmunicipal areas, reflecting differences in the nature of economic activities performed. In non-municipal areas, women work mainly in agriculture or else as unpaid workers in small family owned enterprises. Hence women can participate in economic activities while performing their basic responsibilities within the household. However, in municipal areas, most economic activities take place outside the household. Therefore, it is not possible for women to participate in economic activities as well as taking care of household responsibilities. Hence the division of labour between women and men is more evident, namely, men specialize in market activities while women specialize in non-market activities. Over time, the labour force participation rate of the Thai population seems to increase slightly among men, and more rapidly among women. However, the increase in the labour force participation rate may be just a reflection of the change in the age structure towards having a higher percentage of persons in the prime age group. Labour force participation rates by broad age groups in 1980 and 1989 are given in Table 8.2. Analysed by age group, the labour force participation rate of prime aged men (age 25–60 years) changed very little over time. The participation rates of prime aged women increased slightly over time in municipal areas, but declined in non-municipal areas. The increase recorded in municipal areas is related to higher educational attainment and lower fertility. The decline in nonmunicipal areas is the result of the changing economic environment, whereby more and more economic activities are shifted from home based to non-home based institutions. Such a change in economic environment produces greater conflicts between market work and household activities and results in a lower female labour force participation rate. For persons aged under 25, the labour force participation rate is affected by the proportion of the age group attending secondary and college education. As more persons continue their education beyond the primary level, the labour force participation rate should be lower. No downward trend in the labour force participation rate for persons aged under 25 years was observed in Thailand between 1980 and 1989. The labour participation rate for this group was rather high and even showed some increase. This observation is consistent with the fact that school enrolment ratio in the secondary and tertiary levels remained very low in Thailand and no significant improvement could be observed in that period. In 1988 the ratio of secondary school enrolment to the population aged 12–17 was 0.28, and the proportion of tertiary school enrolment to the population aged 18–24 was 0.04. The ratio for the tertiary level was 0.11 if enrolments in open university programmes (distance education teaching) are included in the 277

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numerator. However, most students enrolled in open university programmes are simultaneously participating in the labour market. The labour force participation rate of persons aged above 60 declined over time for both men and women. The declining trend prevailed in both municipal and non-municipal areas. Greater wealth accumulation as a result of economic development could be one reason for this declining trend. Also, as the country develops, more people work in the formal non-agricultural sector which has a fixed retirement age of 60. The labour force participation rates by educational attainment are given in Table 8.3. Education is classified into three broad categories: primary, secondary and tertiary. The number of years of formal education 278

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corresponding to these three levels are respectively six, six and four. In order to concentrate on the effect of education in isolation from the effect of age, only the labour force participation rates of the population aged 25–60 years are considered. The effect of educational attainment on male labour force participation rates is unclear. However, it is clear that education increases the labour force participation rates of women. This effect is more significant among women in municipal areas than those in non-municipal areas. That education has a positive effect on women’s labour force participation is in accordance with the microeconomic theory of household production. As a woman’s education increases, the relative marginal product of her time in market activities increases, hence she (or her household) can achieve higher utility if she allocates more time to market activities.

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The labour force participation rates of persons classified by marital status are given in Table 8.4. Again, in order to abstract from the effect of age, we consider only the labour force participation rates of persons aged between 25–60 years. Since male labour participation rates are relatively high regardless of their marital status, we will only highlight the effect of marital status on women’s labour force participation. In 1989, the participation rates of single (never married) and divorced women were higher than those of women in the other three marital status categories. It is difficult to state precisely the causal relationship. It may be out of necessity that single and divorced women work in order to earn their living, or it may be that women who possess sufficient earning ability choose to be single or are prone to end a marriage contract by divorce once disagreement occurs within the marriage. It is interesting to note that in 1980, by comparison, married women had participation rates close to those of women of other marital statuses. This is in part attributable to the higher share of the rural population in the earlier year.

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WOMEN’S EMPLOYMENT BY INDUSTRY, OCCUPATION AND WORK STATUS The size of the labour force increased from 22.5 million in 1980 to 27.3 million in 1989, implying an average annual growth rate of 2.1 per cent. In 1980, the agricultural and mining sector absorbed approximately 70 per cent of the work force, while manufacturing, commerce and the service sector each absorbed approximately 8 per cent of the total work force (see Table 8.5). In 1989, the agricultural and mining sector remained the largest sector in terms of labour absorption, but the percentage was

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reduced to 56 per cent. Labour absorption in each of manufacturing, commerce and the service sector increased to approximately 13 per cent of the total. It is worth noting that in 1980, female workers out-proportioned male workers in agriculture and commerce. However, in 1989, female workers out-proportioned male workers in manufacturing, commerce and the service sectors. In 1980, 47.5 per cent of the total work force was female, which decreased to 44.1 per cent in 1989. Defining a female labour-intensive industry as an industry in which the proportion of female labour is higher than the proportion of female labour in total employment, then, in 1980, industries which were female labourintensive included agriculture, manufacturing of textiles, footwear and wearing apparel, and paper; wholesale and retail trade; and personal services. In 1989, female labour-intensive industries consisted of all the above mentioned industries plus the non-metallic mining and precious stone industry; manufacturing of food, tobacco, wood, leather and leather products, rubber, public services, amusement services and activities inadequately defined (see details in Table 8.6). In 1989, approximately 7.1 million women worked in the agricultural sector and 4.3 million worked in the other fifteen female labour-intensive industries listed above. These women accounted for 93 per cent of total female employment (agriculture alone absorbed 56 per cent). The most female labour-intensive industries are the textile, footwear and garment industries. Approximately three quarters of employees in these industries are women. Personal services is the next most female labour-intensive industry where two thirds of the employees are women. In food manufacturing, tobacco manufacturing, wood manufacturing and other manufacturing, wholesale and retail trade and public services, approximately half of the workers are female. Female labour-intensive industries generated 47.7 per cent of GDP in 1989. Many of these industries are also Thailand’s major exporting industries such as textiles, footwear and wearing apparel, and food manufacturing. Thus women workers make a crucial contribution to the export earnings of the country. Not only did female employment shift from agriculture to the manufacturing and service sectors between 1980 and 1989, but employment status also shifted. When women were employed in the agricultural sector, the majority were classified as unpaid family workers. As more women were employed in the manufacturing, commerce and service sectors, the proportion of women employees and ownaccount workers increased, while the proportion of unpaid family workers decreased (see Table 8.7). This change in work status applied to men as well as to women, but the change was more rapid among women. The recent rapid growth in the manufacturing and service sectors which generate a high demand for female workers is one of the reasons for such a rapid change. In 1989, 282

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whereas the majority of women employed in rural areas were unpaid family workers, only one-fifth of women in urban areas were in this employment status. Work status also varies by age as shown in Table 8.8. For both males and females aged 15–24 years old, unpaid family workers form the largest group. Probably many of these work part-time or are temporarily employed in family businesses. With increased age, men apparently gradually change status from unpaid family to own-account workers, sometimes with an interim period as an employee. Therefore, the proportion of men who are own-account workers and employers increases rapidly with age, while the proportion of employees is largest at age 25–34. Female

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work status also changes with age, but the shift from unpaid family worker to ownaccount worker or employer is not as marked. Many women remain in the status of unpaid family workers throughout their working lives. This pattern of changing employment status by age prevails in both rural and urban areas, except that in urban areas the proportion of unpaid family workers and own-account workers is lower and the proportion of employees is higher in all age groups (tabulations available from author). Employment by occupation is given in Table 8.9. In 1980, the three most popular occupations among female workers, in descending order, were agricultural worker, sales worker, and craft or production worker. For male workers, these three occupations were also the most popular, but the order was agricultural worker, craft or production worker, and sales worker. In 1989, the proportion of both men and women who worked as craft or production workers came second, next only to agricultural and mining workers. White collar workers (professional and technical workers, administrative, executive and managerial personnel and clerks) constituted 5.5 per cent of total employment in 1980, which increased to approximately 8.1 per cent in 1989. Within this occupational group, the proportion of male workers in the category of administrative, executive and managerial personnel was four times higher than female workers (2.0 per cent versus 0.5 per cent) in 1980. In 1989, although the proportion of female white collar workers increased significantly, the proportion of male workers in administrative, executive and managerial positions was still almost three times higher than that of female workers (2.2 per cent versus 0.8 per cent). There were marked differences in the structure of employment by industry and occupation among workers in municipal and non-municipal areas. The structure in non-municipal areas was similar to the structure for the whole country described above. However, in municipal areas, employment was mainly concentrated in the service, commerce and manufacturing industries; and most individuals worked as salespeople, craft or production workers, or in service occupations. Over time, the proportion of white collar workers increased rapidly. Again, female labour tended to concentrate in a few industries and occupations. This is an indication that occupational choice is more limited for women than for men. WOMEN’S WORKING HOURS AND EARNINGS In 1980, average hours worked per week were 56.7 for men and 55.1 for women. The average hours worked were shorter in 1989 at 54.3 and 51.5 for men and women, respectively (see Table 8.10). Thus, Thai people on average work almost nine hours per day and six days per week. Women work slightly fewer hours in the market, but considering that other household chores remain mainly the responsibility of women, Thai women are 286

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indeed hard working. Average working hours also vary slightly with age. For men, average working hours increase with age and peak at ages 35–55. For women, average working hours have two modes, at ages 15–24 and ages 35–44. The slight decline in average working hours for women at ages 25–35 corresponds to the time when most women have young children which interferes with women’s employment. In rural areas, the pattern of women’s working hours declined continuously after ages 15–24. But in 1989, the pattern in rural areas was more similar to urban areas, with a small dip in working hours at ages 25–34, then an increase at ages 35–44. The dissolution of the extended family and the changing nature of work in rural areas is causing work in the labour market to conflict more with women’s other responsibilities. In 1980, average working hours in rural areas were longer than those in urban areas. But as average working hours in rural areas have fallen over time, whilst average working hours in urban areas remained constant, the difference in working hours between urban and rural has been reduced. However, this difference must be interpreted carefully. A smaller proportion of women in urban areas participate in the labour market

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compared to those in rural areas. These women are most probably full-time and permanently employed in the labour market just like men, therefore their average working hours are equal to or even longer than men’s. Those women who can not afford to work full-time may have been forced to exit the labour market. Working environments may be quite different from those in rural areas where part-time employment is widely available. Therefore, for urban women, the distinction between those who specialize in market and home activities is more evident, whereas almost all rural women participate in both market and home activities. Average nominal monthly wage rates of employees classified by sex, location, and broad age groups, for 1980 and 1989, are given in Table 8.11. In 1989, on average, female employees earned 20 per cent less than male employees. This earnings gap between male and female employees varied by age group, but the gap seemed to increase with age. For example, female employees aged 15–24 earned only 8 per cent less than male employees in the same age group, the gap was 15 per cent for the age group 45–54, and for the age group 55 or over, women earned less than half what men did. This implied that on the average, women’s 289

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earnings not only started lower, but the rate of increase was also lower than that of men. The earnings gap between men and women was more evident in municipal areas, as women in municipal areas earned 25 per cent less than men in 1989. However, the gap seems to be narrowing with time, since in 1980, women earned 28 per cent less than men. The pattern of male-female wage differentials by age and residential areas did not change significantly over the decade. One might suspect that the gap in male and female earnings reflects differences in their education. However, the gap remains even if educational attainment is controlled for (see Table 8.12.). In 1989, on average female employees with primary education earned 30 per cent less than male employees with the same level of education. This gap was 40 and 29 per cent, respectively, in municipal and nonmunicipal areas. The wage rates of women with secondary and college education were also lower than those of men with corresponding education, by 20 and 25 per cent, respectively. Thus in terms of male-female wage differentials, the gap seems to decrease with educational attainment, but seems to be wider in municipal than in non-municipal areas, except for employees with secondary education. Over time, the male-female wage gap seems to be narrowing among employees with primary education (women’s wage rate was 30 per cent lower in 1989 versus 40 per cent lower in 1980), but widening for employees with secondary education (20 per cent lower in 1989 versus 14 per cent lower in 1980). The gap for college educated employees also increases slightly over time (women’s wage rate was 25 per cent lower in 1989 versus 20 per cent lower in 1980). The average monthly wage rates of male and female employees controlling for industry are compared in Table 8.13. In 1989, women earned less than men across all industries. The gap varied by industry and location. In urban areas, the gap was narrowest in the public utility industry, but in rural areas, the gap was narrowest in the construction industry. However these are the two industries in which women were the most under-represented. In 1980, the average wage rate of female employees in the public utility and transport industries was higher than that of male employees. However, female employment in these two industries constituted only 1.5 per cent of total female employees. The proportion of female employees in these two industries increased slightly over time to 2.4 per cent in 1989, and the average women’s wage rate became lower than men’s. Therefore, sex discrimination in terms of employment restriction seems to decrease over time, but was accompanied by an increase in discrimination in terms of pay. From the discussion above, it can be seen that there is sex discrimina-

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tion in terms of employment and pay in Thailand, because women are under-represented in high paying industries, and receive lower wages even after some selected characteristics such as age (which is used as a proxy for experience) and educational attainment are controlled for. However, with tabular analysis, it is not yet possible to conclude whether the difference is due to the difference in the characteristics of men and women or due to wage discrimination. Therefore, using data from the Labour Force Survey in 1980 and 1989, four wage equations, two for men and two for

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women were estimated from employees (the only group of workers with observed wages). Estimated regression equations using OLS are given in Table 8.14. Table 8.15 gives probit equations for the probability of employment, and Table 8.16 gives selection-corrected earnings regressions. 292

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Eight probit estimates for the probability of being an employee (for two years, 1980 and 1989; the two sexes, male and female; and two areas, municipal and non-municipal) are estimated and shown in Table 8.15. The overall performance of the model is quite good, as the pseudo R2 (the proportion of correctly predicted cases by the model) is above 75 per cent in municipal areas and almost 90 per cent in non-municipal areas. The most important factors determining the probability of being an employee are the work status of household head, own age and own educational attainment. If the household head’s work status is employee, it is highly likely that household members will also be employees. Persons with high education are more likely to be employees. The likelihood initially increases with age and peaks before the middle age groups. Marital status seems to be more important a determinant of women’s than of men’s work status. Single women are more likely to be employees than women in other marital status categories whereas the opposite is true for men. Eight semi-log wage equations were estimated by OLS with selectivity bias corrections, and the results are shown in Table 8.16. The overall performance of the model is satisfactory, with R2 ranging from 44 to 66 per cent. Age and education are positively related to wage rate in all cases. In municipal areas, married men and women on average earn more than those who are single. Divorced or separated men’s earnings are not significantly different from single men in both years. But divorced or separated women, on the other hand, earned less than single women in 1980. The difference was not significant in 1989. In non-municipal areas, married men again earn more than men in other marital status categories, but women’s marital status does not significantly affect their earnings. The coefficients of Lambda are quite interesting. With the exception of males in 1989, all coefficients of Lambda are statistically significant and negative in municipal areas. This implies that omitted factors which drive a person to become an employee also cause a downward bias on the person’s wage rate. The implication may be that employees are on the average less capable or they may wish to avoid financial risk and hence are satisfied with a lower average earnings with smaller variances. In non-municipal areas, with the exception of the male equation in 1980 which is positive and significant, the coefficients of Lambda are not statistically significant. Probably in non-municipal areas, the chance of being an employee is more limited and those who have greater abilities choose to migrate to cities. The employment status for those who remain is thus randomly selected after age, education and marital status are controlled for. The positive significant coefficient for men in 1980 may be because of the large share of government employees in the total (26 per cent) who may have higher ability than agricultural employees. The share of government employees among total rural employees fell in 1989 (to 22 per cent) at which time the coefficient on Lambda became less significant. 295

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The mean characteristics used to decompose wage differentials are presented in Table 8.17. In 1989, female employees were on average three years younger than male employees. Employees in municipal areas were two years older than those in non-municipal areas. Obviously, this is because municipal residents or males generally remain in school longer, hence they start work later. The average educational attainment of male employees was slightly lower than that of female employees (8.7 versus 9.1 in municipal areas and 6.3 versus 6.5 in non-municipal areas). These figures show that the educational attainment of employees is generally higher than that of all workers (the figures for all male and female workers respectively were 8.2 versus 7.1 in municipal areas and 5.3 versus 4.5 in nonmunicipal areas). That the education of female employees was higher than male employees also contrasted with the fact that for all workers males’ education was 297

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higher. The explanation here is that the labour force participation rate for males in the prime age group is very high regardless of their education, whereas the labour force participation rate of women increases with educational attainment. Moreover, educated women are more concentrated in the formal sector and their work status is likely to be employee, while less educated women are over-represented in the informal sector and many are self-employed or unpaid family workers. The labour force participation rate of women and their work status are also affected by marital status, as single women are most likely to participate in market activities, particularly as employees. But due to differences in the nature of work, the effect is stronger in municipal than in non-municipal areas. This explains why 50 per cent of total female employees in municipal areas are single but only 40 per cent in non-municipal areas are. According to the decomposition shown in Table 8.18, all the differences in characteristics between male and female employees account for less than half of total male-female wage differentials. The difference between In (WAGE) for men and women implies that in 1989, female employees earned 28 per cent less than male employees in municipal areas.1 This difference can be decomposed into two parts. The first part is due to differences in characteristics such as age, education and marital status. Female employees are on the average younger, have higher education and a higher proportion are single. Given females’ characteristics, if they were paid according to the male wage structure, they should earn only 9 per cent

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less than males on average. In other words, only 32 per cent of the total wage differentials could be explained by characteristic differences. The remaining 68 per cent of the differentials is not explained by differences in characteristics between men and women. These other sources may be sex discrimination in pay, or in employment, as well as unmeasured differences in characteristics. In non-municipal areas, although females earned only 21 per cent less than males, the sources of the differences are similar to those in municipal areas, namely, characteristic differences accounted for only 31 per cent (Table 8.18). The remaining 69 per cent was due to differences in the pay structures and availability of employment choice for male and female employees, and unmeasured differences in characteristics. According to the 1980 wage structure, the calculated female wage is 31 and 32 per cent lower than the male’s in municipal and non-municipal areas respectively. Characteristic differences account for approximately 39 and 43 per cent respectively in municipal and non-municipal areas. This means that over time, the percentage differences in wage rate between male and female have become narrower. Moreover, the proportion of wage differentials due to differences in characteristics seems to decline, while the proportion due to discrimination seems to increase over time. The implication here is that merely accelerating the education of women cannot close the wage gap between males and females. LEGAL AND INSTITUTIONAL ASPECTS OF WOMEN’S EMPLOYMENT The Ministry of the Interior is empowered to regulate the employment of labour in general, such as fixing normal hours of work, overtime, rest periods, etc., which includes the regulation of employment of women and children. Much of the current regulation dates from 16 April 1972 from an announcement by the Ministry of the Interior. The announcement does not apply to central, provincial and local government officials, or employees of other establishments as specified by the Ministry of the Interior, or to domestic workers. One chapter in the announcement is devoted to women’s employment. The objective of such an announcement is mainly to protect women from types of work which are considered to be too heavy or too dangerous for them. According to the announcement, no employer is allowed to employ women in the following activities: • Work related to cleaning machinery or motors in motion, work in construction too high above the ground, mining involving underground work, work connected with the manufacture or transport of explosives or inflammable materials, etc. • Work which is too heavy for women. Thus, the law specified the maximum weight which women are allowed to carry, lift, haul or push.

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• Work in places which are not safe for women. For example, unmarried women under eighteen years of age are not allowed to work in night clubs, dance halls, dance studios, or places where liquor is sold. • Work between midnight and six a.m., unless by nature or by conditions the works must by performed during such time. However, these restrictions are not strictly observed, especially in small establishments. The second half is devoted to the welfare of women related to maternity. A woman is entitled to take maternity leave, in addition to thirty days of annual sick leave, for a period of sixty days including holidays. If a woman has been employed for not less than 180 days, she is also entitled to receive wages during her leave at her current wage rate for a period not exceeding thirty days. For a woman who has been employed for less than 180 days, the leave is without pay. Moreover, the employer has to consider the request of any woman to change work temporarily before or after her confinement, if she has a certificate from a first class medical doctor, showing that she is unable to be employed in her present work. While the benefits described in the previous paragraph are still applicable, maternity leave has recently been extended to a maximum of ninety days. A woman who has been employed for not less than 180 days is entitled to leave with pay for the whole period of ninety days. The employer is responsible for paying her current wage rate for forty-five days, and the Social Security Office is responsible for paying for the remaining forty-five days. Because of this financial arrangement, the extension is applied only to employees in establishments which are under the coverage of the Social Security Act. In the past, establishments with at least twenty employees were covered by the act, but since June 1993, establishments with at least ten employees have been brought under its coverage. In the public sector, there are a few positions which can be filled by men only. These positions usually involve working in the field, working with male prisoners, or positions which involve frequent moves from one locality to another. However, it is the policy of present government administration to reduce employment discrimination against women, and therefore several jobs have been re-specified so that employing women in those previously prohibited positions is allowed. Although the formal prohibition against employing women in these government positions has been removed, in practice it will probably be a long time before these positions are actually filled by women. For example, the position of provincial governor is open to both men and women. At present, however, there is only one female governor and she has been in that position for less than half a year. A similar situation exists with the position of headman at the district or sub-district level. Although nowadays no regulation prohibits women taking this position, only a tiny proportion of headmen are women. 300

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In terms of pay discrimination, Section 26 of Chapter 4 of the announcement stated that: ‘Where the work is of the same nature, quality and volume, the fixing of wages, overtime pay, and holiday work pay, shall be equal regardless of the sex of the employee’. Thus pay discrimination is not allowed by law. The government has ratified the 1988 UN Convention on discrimination against women, in principle, although with several reservations. But in reality, discrimination against women is practised widely. Discrimination against women in terms of promotion exists and is evident in both private and public employment. Sex-segregated job advertisements in newspapers are tolerated. Not only does discrimination against women still exist, but there is also discrimination against married women, whether explicit or implicit. Marriage bars persist in some occupations. Some job advertisements specify ‘single women are preferred’. Furthermore, it is relatively difficult for unskilled women to be employed if they have young children. Soonthorndhada (1991) surveyed several large and medium factories around Bangkok and its periphery in 1990 and found that very few establishments provide facilities for married women. Most dormitories for employees are arranged for single women. No in-plant child-care services are provided for employees. Hence, workers with young children have to rely on relatives or hired workers for such services. But the expense tends to discourage married women with young children, except for those working in higher positions. The recent expansion of employment in industrial sectors has led to the supply of domestic workers becoming more scarce and their wage rate has been pushed up. In fact, discrimination against women occurs even before entering the labour market. Traditionally, daughters are prepared to play the role of mother and follower, while sons are prepared to play the role of leader. Thus, when constrained by resources, parents tend to invest less in a daughter than in a son. Women are welcome to participate in the labour market for economic reasons, but working as a means to establish self esteem or fulfilment is not yet widely accepted. Women’s roles as wives and mothers remain important factors in measuring their success. Thus, women are still given second priority for education, training on the job and promotion to high-ranking positions. These attitudes towards the traditional segregation of roles of the two sexes prevail and are difficult to change, even among women. One reason which makes Thailand seem to lag behind in this social aspect of sex discrimination may be that Thailand lacks institutions which are strong enough to push the issue. Thailand does not lack institutions or persons who are interested in women’s issues, such as NGOs, social workers, academics and unions. However, these institutions or persons usually deal with specific problems or with a specific group of under-privileged women. The cooperation among the institutions is not strong enough to create unanimous political will to solve the problem. Only recently (June 1991) was the National Commission on Women’s 301

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Affairs established under the Office of the Prime Minister. The commission is chaired by the prime minister or deputy prime minister, and includes the heads of several government and non-government agencies dealing with women’s issues. The tasks of the commission are: 1 To present to the cabinet policies and plans for promoting the activities, roles and status of women. 2 To design a master action plan for the policies approved by the cabinet which must be in harmony with the National Social and Economic Plan, and to monitor and evaluate the action plan. 3 To promote activities related to women’s development in both public and private agencies. 4 To advise the prime minister of any changes in law and regulations which might interfere with the promotion of the activities, roles and status of women. 5 To report to the cabinet at least twice a year, about the situation of women in general. 6 To consider any other tasks as given by the cabinet. The commission is authorized to question, and have access to necessary documents from public agencies for its consideration. At present, the commission has established several sub-committees to study various issues related to women. Although it is too soon to assess the performance of the commission, it is expected that some major changes aimed at reducing discrimination against women could be pushed forward through this office. SUMMARY AND CONCLUSION This chapter examines changes in various aspects of women’s economic roles between 1980 and 1989. Women’s labour force participation rates have been very high during the decade under examination. Women’s labour force participation rate in municipal areas was lower, but increasing over time, while the rate in nonmunicipal areas was higher, but declining. Women’s employment by industry, occupation and work status is not evenly distributed. In 1980, women were concentrated in the agricultural sector, textiles, footwear and paper handicrafts, trade and personal services industries. In 1989, female labour-intensive industries expanded to include also food manufacturing industries, cutting precious stones, and leather and rubber handicrafts. These female labour-intensive industries are also Thailand’s major exporting industries which implies that female labour makes a crucial contribution to export earnings. Women’s work status changes with the industry in which they work. Women employed in the agricultural sector mainly work as unpaid family workers. Women in manufacturing, commerce and service sectors are likely to work as employees. 302

WOMEN’S ECONOMIC ROLE IN THAILAND

Thus the proportion of women working as employees has increased over time. In terms of occupation, women are under-represented in professional, administrative and technical work. Male employment in these occupations was four and three times that of female employment in 1980 and 1989, respectively. Women participating in the labour market, on average, worked fifty-five hours a week in 1980, which reduced to fifty-two hours in 1989. Working hours in rural areas were higher but declining over time causing the difference in working hours between rural and urban women over time to diminish. Women’s working hours were only slightly less than those of men. If these women also have other household responsibilities, they are indeed hardworking. Among employees, women’s monthly earnings were 72 per cent and 80 per cent of men’s in 1980 and 1989 respectively. Thus the gross difference in earnings narrowed. Approximately 40 per cent of the wage differential in 1980 could be explained by differences in the average age, education and marital status of men and women. But these characteristic differences by gender became less pronounced and could explain only 30 per cent of wage differentials in 1989. The study confirms that discrimination against women in terms of pay and employment still exists in Thailand. However, with the exception of some protective legislation, there are few legal restrictions limiting women’s employment. This means that any attempt to reduce discrimination against women must go deeper than changing legal aspects. Traditional values of segregated roles between sexes must be changed. And only strong political will and strong co-operation between various involved institutions can accelerate such change. APPENDIX: LABOUR FORCE DATA The Labour Force Survey in Thailand, which is the main data source used in this paper, has been conducted by the National Statistical Office (NSO) since 1963. Beginning in 1971, two rounds of the survey were conducted annually. The first enumeration round was held from January to March which coincided with the non-agricultural season and the second round was held from July to September which coincided with the agricultural season. Since 1984, another round of the survey has been conducted in May. All Labour Force Surveys are nationwide, covering both urban and rural areas. The definition used to count persons in the labour force changed slightly over time. According to the 1980 definition, all persons aged 11 and over who were employed and unemployed were included in the labour force. Employed persons were all persons of 11 years of age and over who: 1 worked for wages, salaries, profits, dividends or any other kind of payment during the survey week; or

303

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2 did not work at all but had jobs or businesses from which they were temporarily absent because of illness, vacation or holiday, or for other reasons; or persons who did not work at all during the survey week and were not looking for work because they were waiting to be called in for new job assignments or waiting to be recalled to their former job within thirty days from the day of interview; or 3 worked without pay in enterprises or on farms owned or operated by household heads or members to whom they were related by kinship or marriage or through adoption, and worked at least twenty hours during the survey week, or worked less than twenty hours but wanted to work more. Unemployed persons were those persons of 11 years of age and over, who during the survey week did not work at all, but wanted to work and were able to do so. Persons in this category include those who, during the survey week: 1 did not work at all but were looking for work; or 2 did not work and were not looking for work because of illness, but would have been looking for work had they not been ill; or 3 did not work at all and were not looking for work because of the belief that no work was available. In the 1989 definition, the total labour force included all persons of 13 years of age and over, who during the survey week were either in the current labour force or the seasonally inactive labour force. Except for the difference in age range, the definition of the current labour force in 1989 is comparable to the definition of the labour force used in 1980, namely the current labour force includes all persons of 13 years of age and over who, during the survey week, were either employed or unemployed. However, there were some minor modifications, as follows. First, in the 1989 definition, employed persons included those who worked for at least one hour without pay in business enterprises or on farms owned or operated by household heads or members, whereas in the 1980 definition, persons who worked without pay had to work at least twenty hours during the survey week, or worked less than twenty hours but wanted to work more. Second, those who were waiting to be called up to new job assignments were classified as employed in the 1980 definition, but were classified as unemployed in the 1989 definition, except that those who were waiting to be recalled to their former job within thirty days would be classified as employed. These modifications did not significantly alter the labour force participation rate but the inclusion of the seasonally inactive labour force did, especially the labour force participation rate of the rural population. In 1980, those who were waiting for the appropriate farming season were not included in the labour force. But in 1989, persons of 13 years of age and over, who usually worked without pay 304

WOMEN’S ECONOMIC ROLE IN THAILAND

on farms, or in business enterprises owned or operated by the head of the household or any other member of the household, but who during the survey week were economically inactive because of waiting for the appropriate season, would be included in the labour force. In the second round of the 1989 survey, the seasonally inactive labour force constitutes about 0.1 per cent and 6.1 per cent of the total labour force in urban and rural areas respectively. The effect of the changing definition on the labour force participation rate is more significant among women and the young population than among men and the adult population. NOTE 1

The estimated percentage difference in male-female wage rate using ln(WAGE) was higher than the actual percentage at 25 per cent since the distribution of wages is somewhat skewed.

BIBLIOGRAPHY Ashakul, T. (1989) Migration Trends and Determinants, Human Resources and Social Development Program, Thailand Development Research Institute, September. Bauer, J., Naohiro, O. and Poapongsakorn, N. (1989) Forecasts of Labor Force and Wages for Thailand 5, Honolulu, Hawaii, June. Chalamwong, Y. and Sussangkarn, C. (1989) Determinants of the Number of Surviving Children: A Recent Experience in Thailand, Human Resources and Social Development Program, Thailand Development Research Institute, September. Cheung, P., Cabigon, J., Chamratrithirong, A., Mcdonald, P.F., Syed, S., Sherlin, A. and Smith, P.C. (1985) Cultural Variations in the Transition to Marriage in Four Asian Societies, International Union for the Scientific Study of Population, International Population Conference, Florence, June. Human Resources and Social Development Program (1989) Number of Surviving Children and Schooling Continuation Rates in Thailand in the 1980s: Some Further Results, Thailand Development Research Institute, revised November. Hutaserani, S. (1989) Determinants of Household Structure in Thailand, Human Resources and Social Development Program, Thailand Development Research Institute, September. ——(1992) Managing the Urban Informal Sector in Thailand: A Search for Practical Policies Based on the Basic Minimum Needs Approach, Human Resources and Social Development Program, Thailand Development Research Institute, March. Hutaserani, S. and Sussangkarn, C. (1989) Determinants of Assortative Mating in Thai Marriage Markets and Implications for the Stability of Intact Households (Preliminary Draft), Human Resources and Social Development Program, Thailand Development Research Institute, December. Knodel, J., Debavalya, N., Chayoan, N. and Chamratrithirong, A. (1984) ‘Marriage Patterns in Thailand: A Review of Demographic Evidence’, pp. 31–68 in A.Chamratrithirong 305

WOMEN AND INDUSTRIALIZATION IN ASIA (ed.) Perspectives on the Thai Marriage, Publication No. 81, Nakorn Pathom: Institute for Population and Social Research, Mahidol University. Limanonda, B. (1983) ‘Marriage patterns in Thailand: rural-urban differentials, Paper No. 44, Bangkok: Institute of Population Studies, Chulalongkorn University. Mason, A., Phananiramai, M. and Poapongsakorn, N. (1987) Households and Their Characteristics in the Kingdom of Thailand: Projections from 1980 to 2015 Using HOMES No. 1, Honolulu, Hawaii, November. Ministry of the Interior (1989) Report on Labor Policies (in Thai), Planning Division, Department of Labor. Phananiramai, M. (1991) The Extended Structure of Families in Thailand (First Draft), Human Resources and Social Development Program, Thailand Development Research Institute. Phananiramai, M. and Chalamwong, Y. (1988) ‘A demographic-economic model for Thailand’, Demographic-Economic Models and Policy Simulations for Malaysia, The Philippines and Thailand: a Comparative Study, Economic and Social Commission for Asia and the Pacific Bangkok, Thailand. Phananiramai, M., Chalamwong, Y. and Pattamakitsakul, P. (1989) The Female Labor Force Participation Rate and Surviving Children, Human Resources and Social Development Program, Thailand Development Research Institute, November. Phananiramai, M. and Hutaserani, S. (1989) Some Implications of Household Structure in Thailand (First Draft), Human Resources and Social Development Program, Thailand Development Research Institute, December. Phananiramai, M. and Pattamakitsakul, P. (1990) Household Structure and Labor Force Participation Rate in Thailand, Human Resources and Social Development Program, Thailand Development Resource Institute, February. Phananiramai, M. and Sussangkarn, C. (1991) ‘Promotion of analysis and consideration of population consequences of development planning and policy in Thailand’, Toward an Integrated Economic-Demographic Model (First Draft), 22–24 February. Poapongsakorn, N. (1988) Household Projections and Housing Needs Thailand (No. 6), Honolulu, Hawaii, June. Pongsapich, A., Kataleeradabhan, N, Sirisawat, P. and Jarubenja, R. (1989) Women Homeworkers in Thailand, Social Research Institute, Chulalongkorn University, October. Pramualratana, A., Havanon, N. and Knodel, J. (1984) ‘Explaining the normative basis for age at marriage in Thailand’, pp. 181–204 in A.Chamratrithirong (ed.), Perspectives on the Thai Marriage, Publication No. 81, Nakorn Pathom: Institute for Population and Social Research, Mahidol University. Soonthorndhada, K. (1991) ‘A study on employment and fertility of female migrant workers in the manufacturing industry of Bangkok and periphery area.’ Ph.D. thesis, National Institute of Development Administration, Bangkok, Thailand.

306

INDEX

Abrera-Mangahas, A. and Aguila-Bautista, L. 272 Abdullah, T. and Ziedenstein, S. 53, 78 Acharya, S. and Jose, A.V. 70, 78 Acharya, S. and Panwalkar 55, 78 Acharya, S. and Papanek, G.F. 46, 78 Acharya, S. and Shah, I. 53, 54, 78, 79, 80 added worker effect 249 adjusted earnings ratio, Malaysia 229, 232; see also relative earnings/pay adjusted relative earnings 153; see also adjusted earnings ratio; relative earnings/pay age-specific participation 4, 10, 11, 12; in India 13, 47–9; in Indonesia 13, 88–94, 100–2; in Japan 14, 139–41, 154; in Korea 14, 168–9; in Malaysia 15, 211– 14, 240; in Philippines 15, 246–7, 249; in Thailand 16, 277–8; see also patterns of participation age-participation profile; see age-specific participation Ali, Z. 209, 241 Alonzo, R.P. 272 allocation of women by industry, Malaysia 217 Anand, S. 219, 241 Anata, A., Alatas, S. and Tjiptoherijanto, P. 83, 132 Anderson, L.H. and Hill, M.A. 160, 162 Anker, R. 43, 78 Anker, R. and Hein, C. 2, 41 Anker, R. and Khan, M.E. 43, 78 Anker, R.Khan, M.E. and Gupta, R.B. 38, 41 Ariffin, J. 209, 210, 238, 239, 241, 242 Ashakul, T. 305

attrition rate, in Malaysia 236 Bai, M.K. and Cho, W.H. 198, 205 Bai, M.K. and Park, J.Y. 205 Bakker, I. 3, 4, 5, 7, 36, 42 Bardhan, P.K. 78, 80 bargaining power 26 Bataan Export Processing Zone 269 Bauer, J.Naohiro, O. and Poapongsakorn, N. 305 Bazargan, A. 85, 131, 132 Becker, G. 152, 162 Behrman, J.R. and Deolalikar, A.B. 115, 120, 132 Berma, M. and Shahadan, F. 210, 242 Beteille, A. 53, 78 Blau, F.D. and Beller, A.H. 152, 162 Bluestone, B. 179, 205 Booth, A. 83, 132 brain drain, Philippines 245 brawn drain, Philippines 245 Bremen, J. 63, 78 Bureau of Labour and Employment Statistics (Philippines) 271, 272 Bureau of Women and Young Workers (Philippines) 245, 246, 252, 267, 271, 272 bumiputras, Malaysia 208, 209 Byron, R.P. and Takahashi, H. 115, 131, 132 Canlas, D.B. 249, 272 Castro, J.S. 269, 272 CEDAW (1988 Convention on Elimination of Discrimination Against Women) 240; see also UNCDAW 307

INDEX Chalamwong, Y. and Sussangkarn, C. 305 changes in distribution of employment in Korea: by employment status 170; by industry 171; by occupation 176; see also employment patterns changes in structure of female labour supply in Korea 166 Chapman, B.J. and Harding, J.R. 219, 242 Cheung, P., Cabigon, J., Chamratrithirong, A., Mcdonald, P.F., Syed, S.Sherlin, A. and Smith, P.C. 305 Chia, S.Y. 209, 242 childcare 10, 36, 37; in Japan 135; in Korea 196–7; in Malaysia 207, 238, 240; in Philippines 246; in Thailand 301 Cho, E. 198, 205 Cho, W.H. 179, 186, 193, 205 Chong, 209 Cohn, S. 5, 22, 42 Cook, A. and Hayashi, H. 139, 162 Corcoran, M. and Duncan, G. 152, 162 Cortes, I. 269, 272 compositional effect 27 contribution of female earnings to household income, Indonesia 142 Costello, M.A. and Ferrer, P.L. 249, 272 Cremer, G. 85, 99, 132 Datta, R.C. 70, 78 daycare see childcare days of work, India 69 del Rosario, R.S. 268, 269, 272 definitional changes/problems 38–41, 43, 44, 130, 251, 254–5, 271, 276, 303–4; see also Labour Force Surveys definitions, earnings 41; employment status 77, 130; participation 39, 76, 130–1, 161, 202, 241, 303–4; see also Labour Force Surveys Department of Statistics, Government of Malaysia 208, 213, 215, 241, 242 differential earnings see earnings gap differences in endowments 6 discrimination see discriminatory practices discriminatory practices 35; in India 74; in Indonesia; in Japan 127–9; in Korea

183, 186–, 195–6; in Malaysia 239; in Philippines 256; in Thailand 299–303; see also employment discrimination; pay discrimination; sex discrimination dispossessment, of land 55 Dixon, R. 43, 78 double-peaked see patterns of participation (double-peaked) dualist theories (of labour markets) 179 dualistic economy, of Malaysia 207 Duncan index/indices 5, 17, 20, 22, 59, 62, 69 earnings behaviour (micro analysis) 70 earnings definitions see definitions, earnings earnings differential see earnings gap earnings discrimination see pay discrimination; see also discriminatory practices; employment discrimination; sex discrimination earnings gap 6, 31, 32, 37; in India 71, 72, 75; in Indonesia 82, 121, 124, 125, 129; in Japan 134, 135, 145, 147, 149, 150– 4, 157, 161; in Korea 165, 174, 175, 178, 179, 186–92; in Malaysia 209, 225, 229, 236; in Philippines 265, 270; Thailand 289, 290, 297–9, 303; see also relative earnings/pay earnings ratio see relative earnings/pay; see also earnings gap Economic Planning Board (EPB), Korea 173, 204, 205 Economically Active Population Survey, Korea 201, 204 education-specific participation rate see labour participation by education Edwards, L.N. 159, 160, 162, 163 Elwell, C. 134, 163 Employment Act, Malaysia 239; see also legislation employment discrimination: in internal labour market 188; in Korea 183, 184, 187, 188, 196, 197; in Philippines 267; in Thailand 290–1, 299, 300, 303; by occupation 188; at port of entry 186,

308

INDEX 188; see also discriminatory practices; pay discrimination; sex discrimination employment patterns 3, 4, 16; in India 44, 59; in Indonesia 82, 83, 110, 113, 129; in Japan 134, 150; in Malaysia 207, 213, 215, 240; in Philippines 244, 246, 251, 269–70; in Thailand 275, 281–6, 302–3; by industry 4, 16–19, 21, 37 (in India 43, 46, 50, 55, 60, 63–7; in Indonesia 82, 87, 112, 113; in Japan 134, 135, 139, 142–5; in Korea 171–4; in Malaysia 213, 215–19; in Philippines 251–3, 257, 258, 269–70; in Thailand 275, 281–3, 286, 290, 302–3); by occupation 5, 16, 22–5, 37 (in India 46, 53, 62, 68, 69; in Indonesia 87; in Japan 142, 146, 147; in Korea 176–9; in Malaysia 207, 210, 213, 217–18, 220; in Philippines 246, 254, 255, 257, 269– 70, 281; in Thailand 286–7, 302–3); by employment status 5, 16, 26–8, 37 (in India 43–6, 55, 59–61, 63; in Indonesia 85, 90, 95–9, 110, 111, 113, 114; in Japan 134, 135–7; in Korea 170–1; in Malaysia 215–16, 217, 218, 221; in Philippines 255–8, 269; in Thailand 275, 281, 282, 284–6, 303); by public/ private status 6, 37 (in India 53, 62, 73; in Japan 142–5; in Korea 198; in Malaysia 215; in Philippines 246, 270; in Thailand 290, 295, 300) employment restructuring 275 employment status: in India 59, 89; in Indonesia 95–9, 110–11, 113–14; in Japan 142, 152; in Korea 170; in Malaysia 215, 218; in Philippines 257, 258, 263, 269; in Thailand 275, 281–2, 284–6, 295, 298, 302–3 employment status definition see definitions (employment status) Employment Structure Survey, Korea 202 Encarnacion, J. 249, 272 endowments 265 Equal Employment Opportunity Law, Korea 195–7, 200, 205; see also legislation (equal employment opportunity)

Equal Renumeration Act/Law, India 74; see also legislation (equal pay/wage) Equality in Employment Law see Equal Employment Opportunity Law equity-promoting legislation see legislation (equal employment opportunity) expenditure-saving activities 55; see also market-oriented job export industries 68, 210, 240; see also light export industries feminization of employment 5, 17, 37, 211 fertility: declines in Japan 135, 158, 160; rates (Indonesia 110; Malaysia 209) First Malaysia Plan (1966–70) 208 Fong, M.S. 209, 242 gender gap see earnings gap gender-specific job advertisement see sexsegregated job advertisement Goldin, C. 3–6, 16, 17, 22, 42, 86, 88, 91, 132 GOI (Government of India) 43, 44, 78, 79 government of Korea 205 Gill, I.S.Sedlacek, G.L. and Nayar, R. 257, 272 Hanami, T. 162, 163 Hashimoto, M. 135, 152, 162, 163 Hashimoto, M. and Raisian, J. 162, 163 Hausman tests 120 Harris, J.Kannan, K.P. and Rodgers, G. 44, 78 Hayashi, H. 139, 163 Heckman, J. 71, 78 Heckman selection correction variable 71, 116–20, 233, 263, 296; see also selection bias Hill, M.A. 152, 162, 163 Hirschman, C. and Aghajarian, A. 209, 242 Horton, S., Kanbur, R. and Mazumdar, D. 2, 42 hours of work see working hours Hugo, G., Hull, T., Hull, V. and Jones, G. 85–7, 132 human capital 158; endowment 55, 158; formation 198; theory 75, 152, 194 309

INDEX Human Resources and Social Development Program 305 Hutaserani, S. 305 Hutaserani, S. and Sussangkarn, C. 305

job ladders 6 Jones, G. and Manning, C. 85–8, 99, 132 Jones, G. 209, 242 Jose, A.V. 43, 56, 79

ILO Convention No. 89 34; see also ILO Conventions; ILO Convention No. 100 ILO Convention No. 100 34, 35, 127, 265; see also ILO Conventions; ILO Convention No. 89 ILO Conventions 265; see also ILO Convention No. 89; ILO Convention No. 100 ILO Yearbook of Labour Statistics 2, 4, 18, 19, 23, 24, 26, 27, 37, 38, 39, 40, 42, 205, 242, 251, 272, 295 IMF International Financial Statistics 21, 25, 28, 42 income and substitution effects 108 industrial dualism 179 industrial restructuring in Korea 197 industrial segregation 176, 178, 179, 257 industrialization 1, 2, 38, 251, 269, 274 informal sector: in Japan 139, 140; in Malaysia 238; in Thailand 298 Institute for Advancement of Malaysian Women (IKWAM) 238 Institute for Labour Studies, Philippines 272 institutions 32, 34, 37; in India 53, 73–5; in Japan 135; Korea 193–5; Malaysia 207, 236, 238, 240; in Philippines 244, 265; in Thailand 275, 301–3 internal labour market discrimination 188; see also discriminatory practices; employment discrimination; pay discrimination; sex discrimination inverted U-shape participation profile see patterns of participation (‘U-shaped’) see also age-specific participation IRRI (International Rice Research Institute) 44, 78, 132, 133

Knodel, J., Debavalya, N., Chanyoan, N. and Chamratrithirong, A. 306 Korea Women’s Development Institute 197, 205 Korns, A. 85, 110, 113, 132 Krishnamurty, J. 44, 47, 79 Kwok, K.K. 209, 242 Kwon, T.W. 167, 205

Japanese Government-General of Korea 186, 205 Japan Institute of Labour 134, 162, 163 Japan Statistical Bureau 162, 163

labour absorption 46, 281, 282 labour intensive 43, 179, 200, 238, 268, 275, 282, 283, 302 labour force participation 46, 47, 86, 99, 105, 135, 209, 225, 238, 240, 244, 246; see also participation rate; age-specific participation; labour force participation by education; patterns of participation labour force participation by age see agespecific participation labour force participation by education: in India 50, 51, 55; in Indonesia 95–9, 108; in Japan 158–9; in Korea 169–70; in Philippines 265; Thailand 278–9, 297–8 Labour Force Surveys, discussion of methodology 38–41, 44–5, 47, 49, 75– 8, 84–5, 130–1, 161, 201–4, 241, 270– 2, 276, 303–5 labour market earnings, Indonesia 113 labour market participation see labour force participation labour market regulations see regulations; see also legislation labour participation by age see age-specific participation labour participation by education; see labour force participation by education Labour Standards Law, Japan 139; Korea 195–6; see also legislation; regulations Lee, K.H. and Sivananthiran, A. 209, 238, 242 Lee, W.D. 198, 205

310

INDEX legislation: in Indonesia 127; domestic 36; equal employment opportunity 3, 34, 35; in India 74; in Indonesia 127; in Japan 160; in Korea 195–7, 200; in Malaysia 239; in Philippines 266–9; equal pay/wage 3, 34–5 (in India 74; in Indonesia 127–8; in Japan 139; in Malaysia 239); protective 3, 34 (in Indonesia 128–9; in Japan 160; in Korea 195–7; in Malaysia 238, 240; in Philippines 268; in Thailand 299, 303); see also minimum wages; maternity leave; institutions; regulations light export industries 1, 22; see also export industries Lim, A.C. 242 Lim, L. 209, 238, 242 Lim, L.L. 3–6, 10, 42, 208, 209, 242, 243 Limanonda, B. 306 Lluch, C. and Mazumdar, D. 85, 86, 132 Locher-Scholten, E. and Niehof, A. 86, 132, 133 low-wage industry/firms 174–5, 180, 186, 188, 192–3, 197–8 ‘luxury’ unemployment 101 M-shaped distribution of participation see patterns of participation; see also agespecific participation macroeconomy: of Malaysia 207; of Philippines 244; of Thailand 274 male education 225 manufacturing sector see organized sector market-oriented job 55; see also expenditure-saving activities marital status: in Indonesia 110; in Japan 135, 142, 159–60; in Malaysia 210, 238; in Philippines 257–60; in Thailand 280, 295, 298; marriage bars 3, 6, 10, 34, 35; in Korea 195; in Philippines 268; in Thailand 301 Mason, A., Phananiramai, M. and Poapongsakorn, N. 306 Maternity Benefit Act, India 74; see also legislation (in India); maternity leave maternity benefits see maternity leave

maternity leave 35; in India 73–4; in Indonesia 128; in Korea 195; in Malaysia 239; in Philippines 267–8; in Thailand 300 Mazumdar, D. 10, 42, 208, 210, 213, 217– 19, 236, 243 Mehmet, O. 208, 243 Mehran, F. 85, 131, 132 Mincer, J.A. 152, 163 Mincer, J.A. and Polachek, S. 152, 163 Mincer model 70, 71 minimum wages 36; in India 74; in Indonesia 114, 144; in Malaysia 238–9; in Philippines 269, Minimum Wages Act see legislation (in India); see also minimum wages (in India); regulations Ministry of Education, Korea 205 Ministry of the Interior, Thailand 299, 306; see also legislation; regulations Ministry of Labour, India 77 Ministry of Labour, Korea 182, 205 Ministry of Labour and Employment (MOLE), Philippines 268, 272 Mitra, A. 44, 79 Moir, H. 85, 132 Nagraj, K. 46, 79 National Commission on the Role of Filipino Women (NCRFW) 266, 268–9, 272 National Economic Development Authority (NEDA), Philippines 251, 254, 272 National Social and Economic Plan, Thailand 274, 302 NEP see New Economic Policy, Malaysia New Development Plan see Sixth Malaysia Plan New Economic Policy (NEP), Malaysia 207–10, 213, 220, 240 Oaxaca decomposition 32, 33, 71–3, 75, 121, 123, 189, 236–7, 265–6; see also relative earnings/pay Oaxaca, R. 71, 79 occupational discrimination see employment discrimination 311

INDEX occupational distribution see employment patterns (by occupation) occupational segregation see industrial segregation Occupational Wage Survey, Korea 178, 186, 203 Oey-Gardiner, M. 108, 132 O’Neill, J. 152, 163 organized sector 63, 74, 171, 175 Osman Rani, H. and Jomo, K.S. 209, 243 Osawa, M. 152, 163 Papanek, H. 55, 79 Papola, T.S. 63, 79 Park, J.D. 167, 205 Park, S.I. 188, 205 participation by age see age-specific participation participation by education see labour force participation by education participation definitions see definitions (participation) participation patterns see patterns of participation participation rate 3, 7, 10, 17, 22; in India 44; in Indonesia 86, 87, 108, 124; in Japan 135–9; in Korea 166–70; in Malaysia 210, 211, 219, 236; in Philippines 249, 265; in Thailand 275– 80, 302, 305; see also labour force participation ; patterns of participation patterns of employment see employment patterns patterns of participation 3, 4, 7, 36–7;in India 46–7, 50, 53, 56, 75; in Indonesia 88, 95, 99, 129; in Japan 139, 140; in Malaysia 209, 240; double-peaked 4, 10, 29 (in Indonesia 86, 93; in Malaysia 211; ‘M-shaped’ in Japan 140; in Korea 168); single-peaked 4, 10 (in Malaysia 211); plateau 4, 10, 29;‘U-shaped’ 3, 7, 36 (in India 47, 50;in Indonesia 106; in Philippines 265) pay differential see earnings gap pay discrimination 6; in Indoesia 114, 127– 8; in Japan 160; in Korea 165, 186, 197; in Malaysia 239; in Philippines

267; in Thailand 290–1, 299, 301, 303; see also discriminatory practices; earnings gap; employment discrimination; relative earnings/pay; sex discrimination permanent employees 135 Phananiramai, M. 306 Phananiramai, M. and Chalamwong, Y. 306 Phananiramai, M. and Hutaserani, S. 306 Phananiramai, M. and Pattamakitsakul, P. 306 Phananiramai, M. and Sussangkarn, C. 306 Phananiramai, M.Chalamwong, Y. and Pattamakitsakul, P. 306 Philippine Development Plan 268; for Women (NCRFW) 246, 268–9 Piore, M.T. 179, 206 plateau profile see patterns of participation purdah (seclusion) 52, 53, 57 Poapongsakorn, N. 306 Polachek, S. 68, 79 Pongsapich, A., Kataleeradabhan, N., Sirisawat, P. and Jarubenja, R. 306 Pramualratana, A., Havanon, N. and Knodel, J. 306 Psacharopoulous, G. and Tzannatos, Z. 2, 3, 5, 6, 22, 36, 42 regional variation in labour participation, India 50, 52, 55, 56, 86 Ramachandran, V.K. 44, 79 Rao, M.J.M. and Datta, R.C. 70, 79 Ravallion, M. and Huppi, M. 84, 132 regulations: in India 74; in Indonesia 127– 9; in Malaysia 239in Thailand 299–302; see also legislation relative earnings/pay 3, 6, 29, 30, 34, 36, 37; in Indonesia 87, 113–14, 124; in Japan 135, 148–50, 152–4; in Korea 177; in Malaysia 207, 219–20, 225–7, 236, 240; in Philippines 255–7, 270; in Thailand 275; see also adjusted earnings ratio; earnings gap; Oaxaca decomposition; pay discrimination Republic Act (Philippines) 267; see also discriminatory practices; legislation

312

INDEX Reyes, E.A., Milan, E. and Sanchez, M.T. 246, 273 rice farming 86 Rodosho Fujinkyoku 138, 147, 159 Roh, M.H., Kim, T.H.Kim, Y.O. Yung, S.J. and Moon, Y.K. 178, 206 Routh, G. 5, 22, 42 rural-urban: differences in Philippines 249; division 3; migration 7; in Korea 167; shifts 7; Ryotwari system 53, 56 Sajogyo, P. 86, 132, 133 SAKERNAS survey 40, 84, 85, 88, 95, 103, 111, 114, 115, 124, 125, 130–1 Santos, A.M. and Laimzon, C.M. 268, 273 Schneider, M. 201, 206 Schultz, T.P 2, 3, 4, 5, 42 seclusion see purdah selection bias 32, 140, 265, 296; see also Heckman selection correction variable selection effect see selection bias self-employment 60 Sen, G. 52, 79 seniority, in determining wages in Korea 186 seniority-merit wage system 135 sex discrimination: in Korea 188, 196, 197, 198; in Thailand 290–1, 299, 301 sex-segmented job advertisement see sexsegregated job advertisement sex-segregated job advertisement 35; in India 74; in Japan 160; in Korea 195, 196; in Malaysia 239; in Philippines 268; in Thailand 301 sex-specific job advertisement see sexsegregated job advertisement single-peaked participation profile see patterns of participation (single-peaked) Sixth Malaysia Plan (1991–5) 208 Smith, J.P. 208, 209, 215, 220, 243 Smith, J.P. and Ward, M.P 152, 163 socio-cultural reasons 52 social and institutional aspects 73 social security 35, 300 Sodhy, S. 209, 243 Soemantri, S. 85, 86, 133

Soonthorndhada, K. 301, 306 Standing, G. 2, 5, 6, 42, 179, 206 status-production activities 55 Stoikov, V. 162, 163 structural changes in female labour supply in Korea 166 subsidiary workers 45, 46, 50, 53, 76 Sugeno, K. 160, 163 Sulaiman, H. 209, 243 Summers and Heston 21, 25, 28, 42 Sundram, K. 50, 79 surveys see Labour Force Surveys supply-side logic 50 SUSENAS survey 40, 84, 85, 88, 99, 103, 110, 114, 115, 124, 125, 130–131 Suwa, Y. 162, 163 Tan, H. 153, 163 temporary workers 135 Terrell, K. 71, 79 Tham, A.F. 209, 242 Thurow, L. 179, 206 Tidalgo, R.L.P. 245, 254, 273 Tidalgo, R.L.P. and Esguerra, E.F. 270, 271, 273 Tilak, B.J. 70, 79 two-peaked participation profile see patterns of participation (doublepeaked) two-track employment system 160 U-shape pattern of participation see patterns of participation (‘U-shaped’) Uh, S.B 178, 206 Umemura, M. 135, 163 UNCDAW (UN Convention on Elimination of Discrimination Against Women) 1988 34, 35, 128, 266, 301; see also CEDAW UNESCAP 3, 4, 10, 42, 243 Unnvehr, L. and Stanford, M.L. 52, 79 urbanization 3, 10, 36, 218, 240, 247 US Department of Commerce 162, 163; Department of Labor 159, 162, 163 vacancy rates 180–2 Visaria, P. and Minhas, B.S. 44, 79 313

INDEX Visaria, P. 59, 79 Wachtel, H.M. and Betsey, C. 179, 193, 206 wage differential see earnings gap wage discrimination see pay discrimination; see also discriminatory practices; employment discrimination; sex discrimination wage gap see earnings gap White, B. 86, 87, 133 White, M.133 Williams, L.B. 110, 133 Women in Development and Nation Building Act, Philippines267

Wood, A. 5, 42 working hours, Indonesia 103–6; Philippines 255, 257–8; Thailand 286, 288–9, 303 World Bank 44, 79, 83, 84, 110, 133, 208 World Development Report 1, 2, 42, 244, 245, 246, 273 Yahye, R.S. 210, 243 Yashiro, N. 152, 163 Yatim, M.M. 209, 243 Yeoh, S.P 209, 243 Zamindari system 53, 56

314