Social Determinants of Immigrant Selection: The United States, Canada, And Australia (The New Americans: Recent Immigration and American Society)

The New Americans Recent Immigration and American Society

Edited by Steven J. Gold and Rubén G. Rumbaut

A Series from LFB Scholarly

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Social Determinants of Immigrant Selection The United States, Canada, and Australia

Yukio Kawano

LFB Scholarly Publishing LLC New York 2006

Copyright © 2006 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Kawano, Yukio, 1968Social determinants of immigrant selection : the United States, Canada, and Australia / Yukio Kawano. p. cm. -- (The new Americans) Includes bibliographical references and index. ISBN 1-59332-135-X (alk. paper) 1. Emigration and immigration--United States--Mathematical models. 2. Emigration and immigration--Canada--Mathematical models. 3. Emigration and immigration--Australia--Mathematical models. 4. Immigrants--United States--Statistics. 5. Immigrants--Canada-Statistics. 6. Immigrants--Australia--Statistics. 7. Vocational qualifications--United States--Statistics. 8. Vocational qualifications-Canada--Statistics. 9. Vocational qualifications--Australia--Statistics. I. Title. II. Series: New Americans (LFB Scholarly Publishing LLC) JV6201.K39 2006 331.6'2--dc22 2006014618

ISBN 1-59332-135-X Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

Contents

List of Tables .......................................................................................vii List of Figures.......................................................................................ix Acknowledgements...............................................................................xi Introduction............................................................................................1 Economic and Social Selection............................................................11 Immigration History and Policy...........................................................47 Modeling Immigration Processes.........................................................75 Determinants of Immigrant Skills...................................................... 101 Immigrant Selection in a New Context.............................................. 143 Appendix ......................................................................................... 151 Bibliography ...................................................................................... 155 Index ...........................................................................................169

v

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List of Tables

Table 1-1: Population and Immigration in the Three Countries ............5 Table 1-2: Education and Earning by Nativity, 1980s and 1990s..........6 Table 3-1: Admission to the U.S. by Classes, 1989-2000....................66 Table 3-2: Planned and Actual Immigration to Canada, 1979-2000....67 Table 3-3: Immigration to Canada by Entry Category, 1984-2000......69 Table 4-1: List of Four Model Settings and Units ...............................79 Table 4-2: Estimated Relative Earnings by Entry Cohorts ..................86 Table 4-3: Standardized Education Differentials by Entry Cohorts.....89 Table 5-1: National Origin Characteristics of U.S. Immigrants......... 102 Table 5-2: U.S. Variables................................................................... 104 Table 5-3: Ethnic Group Variables .................................................... 105 Table 5-4: Regression of Relative Earnings – Base Model................ 108 Table 5-5: Regression of Relative Earnings at U.S. National Level .. 110 Table 5-6: Regression of Education at U.S. National Level .............. 114 Table 5-7: Number of MSA Entry Group for Each National Origin . 116 Table 5-8: Regression of Earnings at MSA Level – Base Model ...... 117 Table 5-9: Regression of Earnings at MSA Level ............................. 118 Table 5-10: Regression of Education at MSA Level ......................... 122 Table 5-11: Relative Earnings of Immigrant Cohorts by Continent .. 126 Table 5-12: Relative Education of Immigrant Cohorts by Continent 127 Table 5-13: Explanatory Variables by Five Continents ..................... 128 Table 5-14: Regression of Earnings by 5 Continents of Origin ......... 130 Table 5-15: Regression of Education by 5 Continents of Origin ....... 132 Table 5-16: Relative Earnings of Immigrants at National Level ....... 135 Table 5-17: Immigrants’ Education Relative to Natives’ .................. 136 Table 5-18: Regression of Earnings by 3 Hosts and 5 Origins .......... 139 Table 5-19: Regression of Education by 3 Hosts and 5 Origins ........ 141 vii

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List of Figures

Figure 2-1: Image of Immigrant Catch Up .................................19 Figure 2-2: Cohort Effects and the Cross-Section Age-Earnings Profile of Immigrants .........................................................21 Figure 2-3: Relative Wages of Immigrants Who Arrived When They were 25-34 Years Old ...............................................23 Figure 2-4-a (Left): Mobility from Equal to Unequal Country ..24 Figure 2-4-b (Right): Mobility from Unequal to Equal Country 24 Figure 3-1: Historical Immigration Flow in the USA and Proportion to the Total Population, 1790 – 2000................54 Figure 3-2: Historical Immigration Flow and Proportion to the Total Population in Canada, 1867-2000 .............................57 Figure 3-3: Historical Immigration Flow and Proportion to the Total Population in Australia..............................................60 Figure 3-4: Immigration to the United States by Regional Origin, 1901-2000 ..........................................................................64 Figure 3-5: Immigrant Stock in the U.S. 1900-2000 ..................65 Figure 3-6: Immigration to Canada by Regional Origin, 19562000....................................................................................68 Figure 3-7: Foreign Born Residents in Canada 1981-1996 ........69 Figure 3-8: Immigration to Australia by Regional Origin 19451999....................................................................................71 Figure 3-9: Foreign Born Residents in Australia 1901-2000 .....72 Figure 4-1: Estimated Relative Wage Concept (Image).............84

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Acknowledgements

This research project would not have been completed without the support provided by so many individuals in my five years at Johns Hopkins University. I am greatly indebted to Dr. Lingxin Hao, who taught me practically everything from basic statistics to working discipline. Her incessant encouragement and criticism were essential to the development of this book. I am also very grateful to Christopher Chase-Dunn. He not only showed me the essence of the historical comparative perspective, but also provided me with the space to enjoy life outside school, which is often missing from young scholars’ lives. At Johns Hopkins, Professors Melvin Kohn, Andrew Cherlin, Katrina Bell McDonald, Beverly Silver and Giovanni Arrighi, and librarians Sharon Morris and Jim Gillispie offered me invaluable lessons and training. Professor Siew-Ean Khoo at the Research School of Social Sciences in the Australian National University provided me with full support in using Australian census data. Professor Masaki Takenouchi at Tokyo University has mentored my research for nearly a decade. I received generous financial support in the form of a Fulbright scholarship in my first two years at Johns Hopkins. I also received a research grant from the National Science Foundation, Grant No. 0100832. Any opinions, findings, and conclusions or recommendations expressed in this material are solely those of the author and do not reflect the views of the Fulbright Commission or the National Science Foundation.

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CHAPTER 1

Introduction

The resurgence of international migration in the last few decades of the twentieth century was comparable to the great wave of migration in the early 20th century. The immigrant stock in the world more than doubled, from 79 million in 1960 to 175 million in 2000 (United Nations 2002). This change calls for attention in terms of its demographic, economic, cultural, and political causes. First, world population growth is not only exponential but also uneven. According to the UN projection, as much as 93 percent of population growth between the years 2000 and 2025 will take place in countries in the South (Hammer and Thomas 1997). As a result, the aging population is growing in rich countries while the young population is increasing in poor countries, which creates demand for young labor force on one hand and surplus labor on the other. This labor power imbalance creates international migration pressure between poor and rich countries. Secondly, the widening income gap between countries, as well as inequality within them, motivates individuals to leave their home countries for better opportunities. The income that migrants expect to enjoy after migration is very high due to the increasing betweencountry inequality and cheapened mobility cost thanks to mass transportation technology and market competition. This fact enables some immigrants to borrow initial transportation and settlement costs from relatives or agencies and to repay them after making enough money at the destination. 1

2

Social Determinants of Immigrant Selection

Thirdly, advanced technology and infrastructure have enabled instant long-distance communication for many people, which allows potential migrants to not only gain information, but also more generally familiarize themselves with the culture of their destinations. In another aspect, the cultural diffusion of the world has engendered a large-scale consumer market, which has intensified international competition and thus increased demand for cheap labor – possibly immigrants. This cultural diffusion in labor and commodity is also unevenly distributed in the world, and so is migration pressure. Lastly, liberal immigration reforms in the 1960s and 70s abolished discriminatory admission policies. In other words, it is no longer feasible for developed countries to select immigrants on the basis of race, ethnicity, or nationality. In addition, prolonged relative peace in the world secures migration flows, and small-scale ethnic conflicts such as those in the Eastern Europe after the collapse of communist regimes allow more people to leave their countries. SELECTIVE MIGRATION AND RECENT POLICY TREND All these conditions have contributed to the dramatically increasing number of immigrants, particularly from the South to the North, or from the third world to the first world. Consequently, in countries with significant numbers of immigrants, there are growing concerns over the selection of the immigrant labor force in terms of skills and raceethnicity. As Thomas Sowel said, “Migrations tend to be selective, rather than random, in terms of skills and ambition, as well as in origins and destinations” (Sowell 1996: 4). Immigrants’ skills and ambition are now reportedly declining. The “declining skill of immigrants” has been one of many controversial topics of immigration study since the 1980s, argued over mainly in the United States. Perhaps the following statement by the National Academy of Sciences summarizes the issue, although not all observers would agree with it: Once in the United States, the foreign-born workers on average earn less than native workers. This gap between foreign-born and native workers has widened recently. Among both men and women, those who have arrived most recently and those who come from Latin America earn the lowest

Introduction

3

wages. Even though recent new arrivals are better educated than their earlier counterparts, the education of the native-born has improved even more, so that the gap in skills, and thus in wages, has widened. This relative decline in immigrant skills and wages can be attributed essentially to a single factor – the fact that those who come most recently have come from poorer countries, where the average education and skill levels are below those in the United States. (Smith and Edmonston 1997: 7)1 Since unskilled workers are more likely than skilled ones to be a fiscal burden, this perception of current trends leads to the proposition of restrictive immigration policy that screens out the less skilled. One way to restrict immigration is to set an upper limit to family reunion visas; another way is to admit immigrants based on evaluation of their skills, such as the point systems used in Canada and Australia. Because limiting family reunion may not be a popular policy in the U.S. for humanitarian reasons, some researchers and policy makers are advocating the point system. Screening immigrants by skills, such as education, training, and other qualification may be neutral and at least not as racist as the Immigration Act of 1924, but some nationality groups would be disproportionately advantaged and others disadvantaged since educational levels are structurally different between the countries of origin. It would clearly limit Mexican immigrants. They were singled out by the National Academy as a group whose wage level never converges with native-borns’ (Smith and Edmonston 1997). Before jumping to any conclusion, it is important to know how immigrant skills have been changing and what determines their skill selection. It is also beneficial to compare the U.S. immigration experience with that of other major immigration countries using the point system, such as Canada and Australia.

1

Following their definition, “native” in this book means any native-born person and is not limited to Native American or Australian aboriginal people.

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Social Determinants of Immigrant Selection

COUNTRIES OF IMMIGRATION: THE UNITED STATES, CANADA, AND AUSTRALIA The three countries share common characteristics. They are countries populated by immigrants and their descendants – immigrants who came mainly from Europe and were followed by multiple non-European ethnic groups. Their major language is English; they have similar political and economic systems; and they are at the most advanced level of economic development. Their differences start with geography: the U.S. is located in the most advantageous climatic and ecological situation, not having the arctic tundra of Canada or the large inland desert of Australia. The U.S. and Canada are located across the Atlantic from Europe, whereas Australia is rather close to Asia. The most distinctive feature of the U.S. is that it has a large Latin American population, especially Mexican, in the neighborhood. These geographic differences are clearly manifested in the historical development of the three countries as well as in their current immigration policies. In terms of demography, there are great differences in their population sizes as a result of the different geographies and historical processes. As Table 1-1 shows, the United States has a population almost ten times the size of Canada’s and nearly fifteen times the size of Australia’s. In fact, Canada’s population is about the size of California’s, and Australia’s population the size of New York State’s. As the population densities indicate, Canada and Australia are sparsely populated, and the empty spaces are not likely to be filled by natural population growth since the fertility rates are under the replacement level. The United States, exceptionally for a developed country, maintains its natural growth rate slightly above replacement level. Canada and Australia need immigrants more than the United States does in order to maintain moderate population growths. Although the U.S. has the largest number of immigrants, in proportion to the total population, the U.S. (10%) has relatively fewer immigrants than Canada (15%) or Australia (24%). This is a cumulative result of the different levels of annual admission in the post-World War II period, in which Canada and Australia have been receiving 0.5 to 1 percent of their population, whereas the U.S. has been receiving 0.2 to 0.4 percent. The table also shows the number of immigrants by their continents of origin. The significant presence of Latin American immigrants is

Introduction

5

unique to the U.S., where more than half of all immigrants are Latinos, but Asian immigrants also constitute a significant minority in all three countries. The decline of European immigrants is most obvious in the U.S. and much less so in Canada and Australia. Nevertheless, the majority of the native-born populations are of European descent, and the increasing non-European components may cause friction in all three countries. Table 1-1: Population and Immigration in the Three Countries USA* Canada* Land Area (sq thousand km) 9,159 9,094 Persons per sq km 30.7 3.2 Fertility Rate (birth per woman) 2.06 1.6 Total Population (thousands) 281,422 29,672 % Immigrants 9.8 15.1 27,624 4,467 Total Immigrants (thousands, %) (100.0) (100.0) 4,355 2,332 Europe (15.8) (52.2) 7,246 1,563 Asia (26.2) (35.0) 701 229 Africa (2.5) (5.1) 147 49 Oceania (0.5) (1.1) 14,477 49 Latin America (52.4) (1.1) 698 245 North America (2.5) (5.5)

Australia* 7,618 2.5 1.77 19,157 23.6 3,908 (100.0) 1,935 (49.5) 728 (18.6) 160 (4.1) 24 (0.6) 75 (1.9) 353 (9.0) 634 Other ** (16.2) * National characteristics such as land area are taken from the World Factbook 2002 (www.cia.gov); U.S. population data is from the 2000 census statistics; Canadian and Australian data are from the 1996 census. The numbers given for Canadian immigrants do not include temporary residents. ** “Other” immigrants in Australia may belong to one of the above categories. The Australian census does not specify the countries from which only small numbers of immigrants had come.

Since immigrants are clustered in the working age strata of the population, their additional labor force will help maintain the ratio between active workers and the dependent population (overall dependency ratio), which will relieve the fiscal burden on native

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Social Determinants of Immigrant Selection

workers. According to Statistics Canada (Badets and Chui 1994), the low dependency ratio of immigrants (29.8%) compensated for the high ratio of natives (52.9%), reducing the total ratio to 48.1 percent in 1991. A similar age structure is found in the United States and Australia.2 Table 1-2: Education and Earning by Nativity, 1980s and 1990s U.S.A Census year

Canada

Australia

80

90

81

91

81

91

12.5

12.9

11.8

12.3

10.0

11.5

11.4

11.2

12.2

12.4

9.9

11.5

-8.58

-12.86

3.50

0.87

-1.51

0.37

Education (years) Native Immigrant % difference

Median/Mean*Earnings (national currencies) Native

17,942 18,000 23,672 23,070 21,195 21,255

Immigrant

17,088 16,520 23,401 22,349 20,993 20,559

% difference

-4.76

-8.22

-1.15

-3.13

-0.95

-3.27

Age 25-64 in Total population (%) Natives

46.7

50.2

45.5

51.1

42.9

45.7

Immigrants

55.1

64.0

65.4

67.6

67.0

68.5

Source: multiple census samples of the three countries compiled by the U.S. Bureau of Census, Statistics Canada, and the Australian Bureau of Statistics. Educational attainments are calculated based on the sample of all males and females, aged 25-64 years old. The earnings for U.S. persons are based on wage and salary income including self-employment income of males and females aged 25-64, and those who worked during the previous year. * Mean income is used for Australia because a reliable median earning cannot be calculated from the Australian census. All incomes are adjusted by CPI.

2

Foreign born persons over-represent the young to middle-aged population of the sending countries. This is partly because many of them migrate when they are young adults, married or single, and have children in the destination countries rather than taking them along. Those native-born children are citizens of the receiving countries by law. In the Appendix, the population pyramids of immigrants and natives show that foreign-born children comprise a very small part of the total foreign-born population in these three host countries.

Introduction

7

IMMIGRANT SKILLS IN THE THREE COUNTRIES Immigrants contribute to receiving countries by providing a workingage population. The native populations of these countries are getting older and their birth rate too low to reproduce a sufficient work force to support their dependent population such as children and the elderly. However, such demographic benefits of immigration may not last long if these immigrants are more likely to be sick, disabled, or unemployed than natives. If they cannot support themselves, public costs will eventually exceed the temporary relief afforded to the host country by the immigrants. If immigrant skills are declining, it is necessary to increase the skilled component of the immigrant flow, as restrictionists argue, or to improve the skills of those already in the host countries and facilitate their adaptation process. Table 1-2 compares educational attainments and earnings between immigrants and natives in the three countries in question using two censuses for each country. The U.S. experienced a significant relative decline of immigrant education, part of which was due to the Immigration Reform and Control Act (IRCA), which came into effect in 1989.3 In Canada, relative immigrant education also declined, but the 3

1990 is a difficult year for which to discern accurate trends about immigrants in the U.S., because immigrant statistics in 1990 might be biased by the legalization of “undocumented” immigrants by the IRCA of 1986. The IRCA program, which took effect in 1989 and lasted until the year 2000, legalized 480,000 immigrants in 1989 and 880,000 in 1990 (INS, various years). Probably, at least 700,000 persons, mostly Latin American immigrants, were legalized before the Census Day (April 1). If an illegal immigrant in 1980 and an immigrant legalized by the IRCA in 1990 had the same likelihood of responding to census questionnaires, the estimates should have little bias. However, it is more realistic to think those recently legalized in 1990 were more likely to respond to the census than the illegal migrants in 1980. Even those not-yet-legalized illegal immigrants could have responded to the 1990 census. The bottom row of Table 2 shows the proportion of the age group between 25 and 64 in all immigrants. The dramatic increase (9 points) in this segment of the population suggests that the increase was due to the legalized immigrants. To support this observation, Enchautegui and Zimmerman (1994), using the 1980 census, have already pointed out that the declining educational attainment of immigrants was due to the illegal immigrants. This does not, however, disprove the fact that the education level of immigrants as a whole is declining in the United States.

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Social Determinants of Immigrant Selection

difference in education is small because the immigrants had been more educated than natives were. In Australia, in contrast, the average education level of immigrants not only increased over the ten years, but also slightly exceeded the natives’ level. Although accurate international comparison is impossible due to the difference between education systems, the relatively low level of native education in Australia may have contributed to the relative improvement of immigrant education. The educational levels of the Canadian and Australian groups have approached convergence, whereas the American situation seems to be characterized by divergence. Overall, the three countries have diverse trends in immigrants’ relative education levels. Unlike education, relative earnings had quite similar trends in all three countries. Immigrants had always earned less than natives had, and the gaps were widening in all three countries. The gaps widened most dramatically in the United States, and less so in Canada and Australia. On average, immigrants in Canada and Australia were as well-educated as natives but earned less than natives. This was a gain for these countries since their immigrant labor forces were less costly but better educated. In the U.S., however, the level of immigrant education did not keep up with the rapid rise of the native education level. This observation is consonant with the National Academy’s report on the “declining skills of immigrants” (Smith and Edmonston 1997). PURPOSE OF RESEARCH Advocates of immigration restriction use the relatively lower earnings of immigrants as evidence of immigrants’ low ability in terms of hardto-observe skills such as diligence, motivation, and even luck, which would hinder their assimilation. However, the relative decline of immigrant earnings or the slow rate of their adaptation cannot be

Number of IRCA Admission Year Legalized Year Legalized

1989 478,814 1995 4,267

1990 1991 1992 880,372 1,123,162 163,342 1996 1997 1998 4,635 2,548 955

Source: INS Annual Reports, various years.

1993 24,278 1999 8

1994 6,022 2000 421

Total 2,688,824

Introduction

9

attributed exclusively to their lack of skills at the individual level. There are many other factors such as the ethnic characteristics of the receiving community, political and societal reception from mainstream groups, and governmental policies toward immigrants. Now that the transition of the immigrant composition from European to Asian and Latin American is under way, what is really at stake is the interaction between these new race-ethnic groups and their recipient societies, in which individual skills are only one of many determinant factors. The low earnings of immigrants are not due solely to the lack of their individual ability. Historically, recourse to nativism has always emerged whenever major ethnic components of immigrant flows have changed. It is new today that the incorporation of non-white racial and ethnic groups into the mainstream is not only a matter of democratic principle, but also a matter of absolute numbers that may potentially change the map of power politics. This research examines the issues of immigrant skills from quantitative and comparative sociological perspectives, asking why some immigrant groups have better skills than others. Chapter 2 argues that the individual rational choice assumption is not sufficient to answer this question. The question needs to be addressed using sociological conceptual tools to explain the selection of immigrant skills. These conceptual tools are based on macro- and meso- level elements: the former pertain to national- and international-level theories and the latter to theories of group action, inter-group relations, and community. In Chapter 3, histories of immigration in the U.S., Canada, and Australia are examined. The purpose of this macrohistorical comparison is to illuminate the long-term trends of raceethnic diversification, which occurred in each wave of immigration, along with the repeated nativist responses in the three countries. Chapter 4 presents the research strategy and methods. It develops conceptual and empirical models based on the theoretical discussion and derives hypotheses. After discussing data sources and limitations, it describes the measurement of two dependent variables: the earnings differential and the educational attainment differential. The major independent variables are also introduced. Especially discussed in detail are the estimations of the dependent variables based on individual-level census data. The four analytical settings are defined in terms of the origins and destinations of immigrants. This is a strategic compromise between data and conceptual models. In Chapter 5, eight

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Social Determinants of Immigrant Selection

sets of multivariate regression analyses are performed for the two dependent variables in four analytical settings. Each setting reexamines the economic models and then moves on to sociological models. A model adjusted for selection bias is also included in each setting. The four hypotheses are tested separately in the most appropriate analytical settings. Chapter 6 summarizes the previous chapters and highlights the most important findings. The results of the statistical analyses are used in a historical analysis of differences between the three countries and of policy implications. This last chapter also discusses the limitations of this research and future tasks.

CHAPTER 2

Economic and Social Selection

This chapter reviews economic and sociological theories that explain immigrant selection in general and selection based on skills in particular. Immigrants are “selected” in a two-fold sense: on one hand, they are selected by immigration laws and subsequent implementation of national policies of receiving, and sometimes sending, countries – selection at borders. On the other hand, they select themselves by making the decision to move, because not everyone in sending countries is motivated or compelled to leave their country. Since these selections are not at random, many characteristics of immigrants differ from those of the general population in sending countries. We focus on the labor market skills such as knowledge, experience, expertise, diligence, and motivation. There are two types of selection in terms of skills: positive selection is observed when immigrants come from the upper half of the population in terms of skill distribution in their sending countries, and negative selection when they come from the lower half. It is usually assumed in the literature that positive selection is more desirable than negative selection because positively-selected immigrants are more likely to contribute to the society of the receiving countries and less likely to become public charges. It must be noted that the selection of immigrant skills is one of the many aspects of immigrant selection in general. Immigrant selection involves much broader issues such as the selection of national identity, racial and ethnic politics, the majority-minority division of the society, and international coalition and competition. Any arguments about skill 11

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Social Determinants of Immigrant Selection

selection are meaningless without situating the issues in the larger geopolitical and historical context. However, it is beyond the scope of this book to deal with such structural determinants in their entireties. This theoretical overview, therefore, is limited to only a small part of all the theories. CLASSICAL THEORIES OF MIGRATION Migration theories in classical political economy were drawn from the perspectives of colonial masters. In the seventeenth and eighteenth centuries, European empires were expanding their colonies in the “new world.” Surplus population at home and population shortage in the colonies were one of the chronic problems faced by colonial nations. Selection of migrants in this context was based on the question of who and how many should “emigrate” as seen from the point of view of managing the colonies. In the early colonial period, the majority of migrant laborers were selected by publicly funded charity organizations from the pool of the poor or criminals under the welfare systems of colonial nations to be servants or indentured workers for a handful of capitalists and colonialists. Even after the voluntary and independent migration of workers began due to reduced migration costs and enrichment of some workers, free flow of migrants was never recommended by contemporary theorists. Mercantilists, who dominated classical political economy from the fifteenth to the early eighteenth century, maintained that the emigration of manpower was harmful to national interest and it was necessary to control the flow of migrants as well as other flows of goods and capital. This protectionism was undermined in the late eighteenth and nineteenth centuries by the emergence of classical economics and its free-trade doctrine backed by the rapid industrialization of the time. However, as exemplified in J. S. Mill’s Principles of Political Economy (1848), classical theorists did not recommend laissez faire for migration policy. Instead, they argued that exportation of labor and capital from old to new countries is beneficial to national interest and, if used with proper political control, can be a means to counteract the tendency of profits to fall. This idea of planned labor exportation was advocated by E. G. Wakefield (1833) as systemic colonization to expand markets, relieve population pressures, and promote foreign investments. In his plan, the

Economic and Social Selection

13

government would set an artificial price of land at which workers would be able to buy it only after years of hard work. In Wakefield’s system, the income from this land sale would be used as a fund for additional labor migration. Similarly, Mill argued in favor of emigration as long as the government could prevent migrants from becoming landowners soon after their arrival in new lands. This significant reservation to laissez faire in the classical economic thought was highlighted by Thomas as dualism between free trade outside Britain’s economic empire and controlled migration within the empire (Thomas 1954: Ch. 1). Gallagher and Robinson (1953) used the term ‘imperialism of free trade’ to describe the coexistence of a free trade policy and political interventions in Britain in the same period. Marx vigorously attacked this (especially Wakefield’s) dualism of freedom and control which was taken for granted by the classical school. In chapter thirty-three of Capital, volume 1, he pointed out selfcontradictions in the essential dogma of classical thought: laissez faire, private property, and the law of supply and demand. That is: emigration to the “new worlds” was not free; private ownership was restricted; and land price was artificially set by the colonial governments. It was, according to Marx, a manipulation of primitive accumulation of capital, i.e., separation of means of production such as land and tools from the direct producer, which denies the producer’s rights to private property under the free economy. For Marx, migrants who were uprooted from their homeland and excluded from land ownership should be seen as the proletariat. Marx’s insight into this systemic exploitation of migrants in the receiving economy deserves as much contemporary attention as his controversial views regarding surplus population. Unlike adherents to the classical school, Marx did not perceive colonial expansion as alleviation of population pressure, but as part of the new international division of labor4 which produces surplus-population on one part of the earth and surplus capital on the other. This “supernumerary” population

4

“A new international division of labour, a division suited to the requirements of the chief centers of modern industry, springs up and converts one part of the globe into a chiefly agricultural field of production, for supplying the other part which remains a chiefly industrial field” (Marx 1867: 425). This idea of a new international division of labor, called NIDL, has reemerged in a group led by Folker Fröbel.

14

Social Determinants of Immigrant Selection

is not a product of Malthusian natural human instinct, but is a systematic outcome of modern industry, which constantly creates surplus labor by innovations and the up-scaling of production. As to the question of who benefits from immigration, Marx held the view that migration benefits the capitalist class but harms the working class. Although there were already various discussions of migration in classical political economy, their attention was centered almost exclusively on the economy. The first major departure from economycentrism was accomplished by Weber’s interpretive sociology. He recognized that migration is a social action and cannot be fully understood by economic rationality alone. In Economy and Society, Weber stated that “War and migration are not in themselves economic processes, though particularly in early times they have been largely oriented to economic considerations” (1978/1922: 70). This interpretation of migration as a social action had stemmed from his earlier study of the labor relations in east Elbian Germany in the early 1890s.5 In eastern Germany, the transition of agriculture from feudalism to commercial mass-production under the pressure of the world market created great demand for seasonal day laborers. The need was filled by massive Polish migration and not by German farm laborers, whose economic conditions consequently deteriorated. Weber posited, “The prime cause appears to be the difference in the level of wages” (Weber 1924:174-5). However, his data “show that where such differences, or related factors, do not arise, migration still takes place” (ibid.). Therefore, he concluded that not economic reasons alone, but a “combination of economic and psychological factors explains this” (ibid.). He argued that Polish migrants undoubtedly wanted higher wages, but a sense of freedom from feudal constraints and prospects of the future social mobility was as, if not more, important as economic reasons:

5

Upon the request of Verein für Sozialpolitik, the Association for Social Policy, Weber evaluated their farm survey conducted in 1890 and reported first in 1892. See Bendix (1962: 38-47) and Käsler (1979: 51-63). The problem of migrant workers was “the most pressing concern in his treatment of the German eastern regions” (Käsler 1979: 60).

Economic and Social Selection

15

It is possible to argue about such elementary movements, which gave expression to the tremendous and purely psychological magic of “freedom.” In good measure this is a grand illusion, but after all a man and so also the farm laborers do not live “by bread alone.” The efforts and aspirations of the farm laborers make just this evident to us, that the “bread and butter question” is of secondary importance. (Weber 1892: 797-798, quoted in Bendix 1962:46) Because of these psychological as well as economic reasons, migrant workers tolerate poor living conditions and work even harder than they used to at home. In The Protestant Ethic and the Spirit of Capitalism, Weber took the example of Polish migrant workers: That the simple fact of a change of residence is among the most effective means of intensifying labour is thoroughly established. The same Polish girl who at home was not to be shaken loose from her traditional laziness by any chance of earning money, however tempting, seems to change her entire nature and become capable of unlimited accomplishment when she is a migratory worker in a foreign country. (1904-5: 191n) In today’s terms, Weber analyzed non-economic determinants of the migration decision and positive adaptation by diligence to the host society. In the contemporary application of the Weberian perspective, Piore (1979) explained that migrants accept bottom-rank jobs despised in host countries because any jobs there carry a sense of modernity in the values of their origin countries. The theory of relative deprivation also takes into account the subjective meaning of social action. Studies of immigrant assimilation and ethnic identity, as we shall review later, also follow Weberian lines. A little earlier than Weber’s first report on East Elbe, the British statistician Ravenstein formalized the idea of the migration decision in “The Laws of Migration” (1885; 1889). Although he was principally interested in internal migration and his “laws” were not really laws but rather patterns in today’s sense, his formulation still deserves some attention because he was probably the first to apply individual rational choice, maximization of utility, to migration theory.

16

Social Determinants of Immigrant Selection

Bad or oppressive laws, heavy taxation, an unattractive climate, uncongenial social surroundings, and even compulsion (slave trade, transportation), all have produced and are still producing currents of migration, but none of these currents can compare in volume with that which arises from the desire inherent in most men to “better” themselves in material respects. It is thus that the surplus population of one part of the country drifts into another part, where the development of industry and commerce, or the possibility of procuring productive land still in a state of nature, call for more hands to labour. (Ravenstein 1889: 286) Three quarters of a century later, Everett Lee (1966) reformulated Ravenstein’s “laws” into positive and negative factors and intervening obstacles of migration. Lee’s article was reprinted so many times, and its famous illustration (1966: 50) is seen everywhere, because it concisely integrated existing immigration theories – economics and sociology – under the assumption of rational choice, although theoretical items in the article are obsolete even for the 60s. Lee did not use the term “push-pull” throughout his article. He used signs such as “+” and “-” and terms such as “positive” and “negative factors” that “hold” and “repel” migrants, but not “push” or “pull.” Perhaps he chose not to use those terms deliberately in order to distinguish himself from the Chicago School. For many contemporary sociologists in the 60s, the “push-pull” concepts were nothing new. Stouffer in 1940 said “concepts like ‘push’ and ‘pull’ are used frequently” (Stouffer 1940: 846). Another earlier article by Heberle (1938) also used the concepts to explain rural-urban migration in Germany. Dorothy S. Thomas (1941) used these terms to explain migration of Swedes to the United States.6 Lee also followed D. S. Thomas et. al. (1938) in investigating the question of migrant selectivity regarding how different migrants are

6 Perhaps this was the first time the “push-pull” concepts were used for international migration. I suspect these conceptual tools were originated directly or indirectly in Thomas and Znaniecki (1918-1920).

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from non-migrants, 7 and attempted to answer it with an individual rational choice paradigm. After the banal statement that “migrants are not a random sample of a population at origin,” Lee explained it with an opportunistic truism: The reason why migration is selective is that persons respond differently to the sets of plus and minus factors at origin and at destination, have different abilities to overcome the intervening sets of obstacles, and differ from each other in terms of the personal factors. (1966:56) All this tells us is that different individuals have different reasons. To distinguish positive and negative selection, Lee (1966) said that migrants responding primarily to “plus” factors at destination are positively selected, and those responding primarily to “minus” factors at origin negatively selected, which explains practically nothing theoretically or empirically. He actually contradicts himself because “plus” or “minus” is always a relative evaluation of a factor in comparing origins and destinations: e.g., high wage and low wage, good climate and bad climate. If a refugee has escaped from political turmoil to come to a politically stable country, is this person responding to a “minus” factor (instability at origin) or a “plus” factor (stability at destination)? Neoclassical economists tend to translate any factors of migration into costs and benefits, assuming that individuals make decisions based on such calculations in order to maximize their material well being. For example, in the Harris-Todaro model of rural-urban migration (Harris and Todaro 1970), critical factors are expected wages and probabilities of employment in both sending and receiving areas. A slightly more sophisticated model adds to it forgoing costs such as transportation, living costs between jobs and psychological cost, and opportunity costs such as deportation. 8 One can elaborate forever in this direction by adding all conceivable factors into the equation, but this cannot explain why some immigrants are “selected.” 7

By the time of the Lee article, a substantial number of studies had been produced on selective internal migration enquiring whether qualified people are more mobile or not. See for example Bayer (1968) for further references. 8 See Massey et. al. (1993) for a concise review.

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Social Determinants of Immigrant Selection

Portes and Walton denounced the “push-pull” analysis by pointing out that “Nothing is easier than to compile lists of such “push” and “pull” factors and present them as a theory of migration” (1981: 25). Twenty-eight years before Lee’s article, D. S. Thomas et. al. (1938) had already provided better testable hypotheses. With recognition that migrants “are not a random sample of the parent population,” they stated: It is probable that the strength of the selective factors operating to produce migration differentials will vary depending on the types of communities to and from which the migration takes place, on the distance spanned in the migration, and on the time at which the migration occurs. (Thomas et. al. 1938: 7) Though the statement is a-theoretical, these structural and semistructural factors – community, distance and time – are some of the most important sociological variables of migration. As discussed above, Lee’s version of the “push-pull theory” survived as a simple schematic presentation of the various factors of immigration decisions, but it did not leave us any useful analytical tools because all it does is to list up “push-pull” conditions. Some of its newer variants, however, incorporated researchable theories into the rational choice framework. One direction is to consider differential opportunity structures depending on which different rational choices – leave or stay – are possible, and that explains migrant differentials. Another is to assume non-individual rationality that may or may not conflict with individual rationality. The latter is an approach toward meso-structural level theory and will be discussed later. The former still treats an individual as an economic man but takes heterogeneous groups into consideration. One of these new developments, and one that will be a theoretical basis of this research, is self-selection theory. SELF-SELECTION THEORY: THE ROY - BORJAS MODEL The debate over immigrant self-selection started in the mid-1980s in the U.S. when the 1970 and 1980 census data became available. The two censuses captured the impact and new trends engendered by the

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1965 reform of the Immigration and Naturalization Act. A little earlier, Chiswick (1978) observed in his study based on the 1970 census that the earning growths of white immigrant men are on average higher than those of their native counterparts. He attributed this difference to the positive effect of labor market adjustment (measured in years since immigration), which over time compensates for immigrants’ disadvantages upon arrival and eventually lets immigrant earnings exceed native earnings. Why does an immigrant not only achieve the level of a comparative native, but also eventually overtake him? It was posited that immigrants have unobserved qualities that are higher than those of natives: e.g. they are more able and motivated than their native counterparts. In other words, immigrants are positively self-selected. Figure 2-1: Image of Immigrant Catch Up

Country A

2

3

1

Country B

20

Social Determinants of Immigrant Selection

Figure 2-1 presents a simple image of labor market transition of immigrants. The two bell-shaped curves are images of national distribution of, say, normalized earnings. When an immigrant moves from country B to country A, her earning position temporarily drops from point 1 to point 2, and it takes a while before she adapts herself to country A and regains the equivalent position 3. The earning at point 2 may not be less than the earning at 1 in terms of international price, but it is certainly less than what this immigrant expects to receive in return for her skills. For example, a doctor from country B would expect to receive as much as any comparable doctor in country A would receive, but she has to learn skills specific to country A before she reaches parity with native doctors. If she receives more than her counterpart does after all the adjustment has taken place, she must have had some additional traits that give her the extra earning potential. Theoretically, this positive selection is induced by the cost of migration relative to the income at origin: the higher the relative cost, the higher the selfselection.9 However, this theory is not directly tested because of the lack of information at migrants’ origin, but indirectly implied by the convergence of earnings. Empirically, using years since migration (YSM) to indicate the adaptation period, Chiswick (1978) observed that immigrants have less earnings than natives when they enter the U.S. (YSM=0) but the positive effect of YSM allows their earnings to catch up and eventually exceed native earnings, controlling for other factors in the earning

9

The theory is expressed as difference in returns to migration. Let wol, wdl, woh, wdh, be earnings of unskilled workers (sub-l) at origin (sub-o) and destination (sub-d), and those of skilled workers (sub-h) at origin and destination, respectively. Migration costs are forgoing earning in the transition period as a proportion of origin earning pwol, pwoh, and out-of-pocket cost D. Also, a skilled worker can earn (1+k) times greater than an unskilled one. The rates of return rh and rl are:

rl = ( wdl − wol ) /( pwol + D) and rh = ( wdh − woh ) /( pwoh + D) = (1 + k )( wdl − wol ) /[(1 + k ) pwol + D] = ( wdl − wol ) /[ pwol + D /(1 + k )] .

Thus, rh > rl so far as D >0. The larger the D and k, the greater the difference in return and the more motivated the skilled workers are to move.

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profile. 10 Based on this observation, he concluded that earnings of white immigrant men cross over the levels of their native counterparts within 10 to 15 years after immigration. The implication of this for immigrant selection is that “the higher earning may therefore be a consequence of a self-selection in migration in favor of high ability, highly motivated workers …” (Chiswick 1978: 920). Therefore, he confirmed that white immigrants are positively self-selected, especially those who come from a country where the cost of mobility is great. Figure 2-2: Cohort Effects and the Cross-Section Age-Earnings Profile of Immigrants Wage

C’ P’

1950 Cohort

Q’ 1970 Cohort P R’ 1990 Cohort Q

R 20

40

60

Age

C Source: Borjas, George. 1994. “The Economics of Immigration.” Journal of Economic Literature 32: 1674. 10

His statistical model can be expressed as:

log Ei = BX i + δ Ai + γ 0 I i + γ 1Yi + ε i , where Ei is worker i’s earning, X is a vector of controls such as education, region of residence, rural-urban area, weeks worked in the survey year (1969), and marital status; A is age or labor market experience (age – education – 5); I is a dummy variable set to 1 if the worker is an immigrant; and Y is the years since migration and it is set to zero for natives. Chiswick observed that γ0 is negative, meaning the earning difference between immigrants and natives is negative when Y is zero, and that γ1 is positive, meaning that, in every additional year of assimilation, immigrants’ earnings improve relative to those of natives. This equation represents the model in table 2 in Chiswick (1978), but I adopted the simpler expression used in Borjas (1994: 1671).

22

Social Determinants of Immigrant Selection

Borjas (1985) questioned this hitherto widely believed theorem of positive immigrant selection by pointing out that previous crosssectional analyses misinterpreted the economic progress of different cohort groups. His cohort analysis revealed that the apparently rapid economic progress of immigrants was misleading because it interpreted cross-cohort differences as if they were the temporal progress of a single group. In fact, within-cohort earnings of the most recent entry cohort grew little or even declined slightly relative to those of their native counterparts. Figure 2-2 shows a hypothetical comparison of cross-sectional (Chiswick 1978) and cohort (Borjas1994) interpretations of immigrant wage growths. It draws three age-earning profiles PP’, QQ’, and RR’ for three arrival cohorts (1950, 1970, and 1990), supposing they arrive at age 20, they have the same wage growth rates, and entry wage levels are declining in newer cohorts. We need at least two time points to determine these cohort-specific growth rates. In cross-sectional data, however, we cannot determine these slopes because one cohort has only one time point. As a result, the regression line CC’ connects different cohorts at different ages as if the 20-year-old from the 1990 cohort will earn 20 years later as much as the 40-year-old from the 1970 cohort is currently earning. The regression line of cross-sectional data, indicated as CC’, is steeper than any of the cohort specific slopes, making it look like the growth is faster than it actually is. In his later study, Borjas (1999) actually observed age-earning profiles of different age-cohort groups. As shown in Figure 2-3, earlier cohorts had not only higher entry wages, but also steeper relative wage growths. The latter fact makes the fallacy of age-earning profiles based on cross-sectional data even more obvious. As a result of his contribution, many immigration studies dealing with trends and dynamics started to use data from multiple time points. To further advance the idea that immigrants are not necessarily positively selected, Borjas’s studies (1987; 1989; 1991; 1994) applied A. D. Roy’s theory of occupational mobility (Roy 1951). Roy theorized that when the gap in average earnings between two sectors triggers a worker’s occupational transfer, she makes a decision in conjunction with her skill endowment and position in the earnings

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distribution.11 In other words, given the income gap and individual rationality, the incentive for job transfer is unevenly distributed by skill levels, resulting in the self-selection of a particular group. Highlyskilled workers transfer to the occupation in which returns to their skills are greatest – where within-dispersion of returns is wider; Figure 2-3: Relative Wages of Immigrants Who Arrived When They were 25-34 Years Old 10

Percent

0

-10

-20

-30 1960

1970

1980

1990

2000

Year

Source: Borjas, G. 1999. Heaven’s Door: Immigration Policy and the American Economy. Princeton: Princeton University Press. P.30. The figure is made from 1970, 80, 90 census and CPS.

11

The Roy model is based on sorting of workers in the uneven return to skills: provided that a worker will choose a job that rewards larger return to his or her skill than another, skilled workers will be concentrated in an occupation with greater skill requirements, and therefore a few workers can produce high output. On the other hand, the unskilled will be concentrated in an occupation with rather replaceable skill requirements, and therefore they will earn less. The distribution of earnings within an economic section will be more dispersed in the skilled section and more concentrated in the unskilled section. When the average returns to the skilled increase, and if the skill is transferable, the more highly skilled end of the unskilled workers will choose to transfer to the other section. If the average returns to the unskilled increase, the lower end of the skilled workers will transfer to the other section.

24

Social Determinants of Immigrant Selection

unskilled workers also choose an occupation which offers the greatest returns to their skills, but because of the replaceable nature of unskilled jobs, they will choose to go where within-dispersion of return is rather narrower. Figure 2-4-a (Left): Mobility from Equal to Unequal Country Figure 2-4-b (Right): Mobility from Unequal to Equal Country

YB

YA

Greater Stream from Upper Part of Distribution

YB

YA

Greater Stream from Lower Part of Distribution

Borjas applied this theory to the self-selection of immigrants. If a labor market rewards skills with high returns and less ability with less return, it encourages the migration of the skilled; if the market rewards the skilled and unskilled similarly, it encourages the migration of the less skilled. In other words, relative differences in variance of returns to skills between sending and receiving countries determine how migrants are self-selected.12 A wide variance in economic returns means that the wealth of a country is unequally distributed in the population, and a narrow distribution means rather equal distribution. Therefore, it is expected that skilled migrants flow from countries with relative 12

It should be noted that self-selection in observed and unobserved ‘quality’ may but do not necessarily correlate with each other. Immigrants may be the least productive among the most educated in the home country, or the most productive among the least educated.

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equality to countries with relative inequality, and unskilled migrants in the opposite direction, if all other conditions are equal. Figures 2-4-a and 2-4-b illustrate this idea. In Figures 2-4-a and 2-4-b, the normal curve indicates the distribution of skills and rewards to the skills; wider distribution indicates more unequal, narrower distribution indicates less unequal distribution; the arrows indicate immigrant streams – the width indicates the size of the flow; YA and YB indicate the means. Given the gap in the mean reward, the immigrant stream flows from country B to country A. According to the self-selection theory, the ‘quality’ of the stream is determined by different levels of inequality in the two countries. As Borjas defines the terms: 1. Positive Selection occurs when the migrants have aboveaverage skills. The migrant flow from i to j is positively selected when the destination offers a higher rate of return to skills. The migrants are then drawn from the upper tail of the skill distribution because region i, in a sense, “taxes” skilled workers and “insures” less-skilled workers against poor labor market outcomes. 2. Negative Selection occurs when the migrants have below average skills. The migrant flow is negatively selected when the source region offers a larger payoff to skills. Few skilled workers will then want to move from region i. In short, as long as regional income differences (net of migration costs) are large enough to induce migration, highly skilled workers will naturally gravitate to those regions where the rate of return to skills is high. In the optimal sorting of workers to regions, highly-skilled workers live in regions that offer high rates of return to skills and less-skilled workers live where the rate of return to skills is relatively low (Borjas 2000a: 5). In the empirical study using the 1970 and 1980 U.S. censuses, Borjas (1987) focused on two indicators of immigrant skills: estimated earning growths and educational attainments. The former indicates “unobserved” skills controlling for individual human capital, and the

26

Social Determinants of Immigrant Selection

latter “observed” skills (details will be discussed in Chapter 4). On educational attainments, his analysis of 60 groups (15 countries-oforigin times four entry cohorts) revealed that relatively low returns to education in countries such as Chile, Venezuela, and Kenya led to emigration of highly skilled workers, and relatively high returns in countries such as Korea, Malaysia, and Mexico led to emigration of relatively unskilled workers. On unobserved “quality,” Borjas’s estimation based on 164 groups (41 countries-of-origin times four entry cohorts) revealed that inequality in the home countries negatively affects immigrants’ relative earnings. On both observed and unobserved skills, he found that skilled immigrants come from less unequal countries. It was also found that recent immigrants from developed countries (with greater GNP per capita) are positively selfselected, but those from the less developed countries of Asia and Latin America are negatively self-selected. For example, immigrants from Taiwan earn about 16-34 percent less than comparable white natives in terms of lifetime earnings (Borjas 1987: 48). This observation stands in contradiction to the conventional image of immigrants, especially of Asian immigrants being hard-working and highly motivated relative to comparable natives. Some other empirical evidence is supportive of the self-selection theory. A study of self-selection of Puerto Rican migrants to and from the United States (Ramos 1992) found that the Puerto Rican case was consistent with the Roy-Borjas theory in both observed and unobserved skills. Another study of immigrant women in the United States (Cobb-Clark 1993) found supportive evidence that positive selection is associated with relatively high GDP, low income inequality, and low return to education in home countries. However, the applicability of the theory has been questioned (Chiswick 1986; 2000). It is less clear especially when more countries are considered. Cross-national comparison between the U.S., Canada, and Australia by Borjas (1991) revealed that the skills of immigrants in Canada and Australia are higher than those in the U.S. although the U.S. should have the best immigrants because it has the most unequal distribution amongst the three. Borjas attributed this to a difference in immigration policies. Source country mix also may have biased the conclusion. Jasso and Rosenzweig found that the negative selection of immigrants to the United States disappeared when they extended the number of sending countries from 41 (Borjas 1987) to 107 (Jasso and Rosenzweig 1990). They concluded that the systematic omission of

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poorer countries from Borjas’s sample caused bias in his analysis. In his response, Borjas (1990) posited that their results are unreliable due to very small sample sizes of some national origin groups. Jasso, Rosenzweig and Smith (2000) also found that the recent trends did not agree with “declining skills of immigrants.” Based on INS data and occupational income estimates, they observed that the average occupational income of legal immigrants has increased since the late 1980s, after the decline in the 1970s and 80s. Fix and Passel (1994), based on the 1990 U.S. census, found that the decline could be directly attributed to the increasing numbers of illegal immigrants and refugees who have in general lower skills than legal immigrants. It was also implied that many undocumented immigrants legalized by the IRCA after 1989 contributed to the decline of immigrant skills. Most recently, Duleep and Dowhan (2002) observed in their study based on the Current Population Survey (CPS) and Social Security Administrative data (SSA) that foreign-born men in almost all entry cohorts had a higher earnings growth than do natives. The self-selection theory is interesting in its association with inequality, but it misses theoretically and methodologically crucial points. As mentioned at the beginning of this chapter, immigrants are selected not only by self-selection but also by politico-legal and historical-structural factors, but the self-selection theory and its applications eliminated most of these factors from their frameworks. The self-selection theory is based on the “international labor market” framework, which assumes individual rational choice and a free labor market across national borders. In this hypothetical market, migrants’ decisions are not interfered with or influenced by anything but their economic rationality. Practically, however, many people are motivated but do not actually migrate. In such a situation, economic rationality may induce migration, but greater motivation is not necessarily a primary “determinant” of actual migration: about 70 percent of all immigrant admission to the U.S. is based on the family reunion visa, and Canadian and Australian point systems also emphasize families and relatives who are already in the host countries. Economic gain could motivate anyone, but admission decision favors family connection regardless of the skills of applicants. In other words, there are many whose economic motivations are very high but who cannot enter any of those host countries because they lack family linkages. Self-selection analysis does not take into account how, or how much,

28

Social Determinants of Immigrant Selection

this family linkage affects the positive or negative selection of immigrants. As Jasso and Rosenzweig (1995) observed in the INS administrative data, family reunification immigrants are only slightly less, if not as much, skilled than immigrants admitted in the occupation category. Another shortfall of the ‘international labor market’ assumption is that it does not take into account other individuals – it is always between individual and market. 13 Besides the family linkages mentioned above, various types of supports from family, relatives, friends, community and other institutions always matter for both decision-making and successful immigration. Migrant networks provide pipes through which money, information, and emotional support are conveyed, which not only facilitate the mobility of people, but also inspire more people to participate in the mobility. Some people could be more motivated than others to move if they are regularly exposed to migration activities. This is a question of “relative deprivation.” Facilitation of select members who belong to networks – in this case by reducing migration costs – is a question of “social capital.” Both of these theories are discussed in detail in the next section. Another problem is methodological as well as theoretical. Chiswick and Borjas both rely heavily on the hypothetical association between skill selection and assimilation. Assimilation is very narrowly defined in economics as convergence of income (earning or wage) differentials between immigrants and natives: more precisely, it is a differential in income growth rates. Thus the level of positive or negative selection defined above depends very much on how soon an immigrant adapts to the new environment after entry. In this sense, inter-personal factors that affect adaptation at group, community, and other higher levels must be taken into consideration.

13

“The action, or behavior, of the system composed of the actors is an emergent consequence of the interdependent actions of the actors who make up the system” (Coleman 1986: 1312). For more on collective behavior in sociology, see Granovetter (1983); Coleman (1987; 1990: ch. 9).

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MIGRANT NETWORKS AND SOCIAL CAPITAL FOR ADAPTATION A migrant network is defined as the “sets of interpersonal ties that connect migrants, former migrants, and non-migrants in origin and destination areas through ties of kinship, friendship, and shared community of origin” (Massey et. al. 1993: 448). 14 Cross-border extension of social networks connects people in origins and destinations. A social network is not an organization of economically rational individuals, but it consists of reciprocal and redistributive social organizations such as family, kin, lineage, neighborhood, and other local organizations (Portes 1995). At any level, networks assemble groups that have distinctive goals and modus operandi, of which the general function is to control access to limited resources. The access to material, psychological, and informational resources distributed to the members of social networks is called social capital (Coleman 1988). In the case of migrant networks, members take advantage of networks to find jobs, accommodate travel, finance business, share information, etc., and in return they abide by its rules and reciprocate favors. Migrant networks affect migrant mobility through three channels: motivation, facilitation, and adaptation (or assimilation). Networks motivate people to migrate by providing information about successful fellow migrants, to varying degrees depending on the type and amount of the information. Motivation for migration is not evenly distributed in the society of origin because its people have different perceptions of their situation and of themselves. The theory of relative deprivation explains why this is so and specifies the implications of this for the selection of immigrants. Networks also facilitate the movement by reducing the costs of migration. The type and the resource endowment of the network certainly affect the level of support and thus differentiate the skills of workers coming to the host countries. The relative deprivation theory explains that motivations for social actions are not necessarily the maximization of utility.15 According to 14

Massey et. al. (1987) provide several cases of Mexican migrant-network formation in which established immigrant men (pioneer immigrants) recruited relatives, friends, and then members of their source community. 15 The relative deprivation theory was pioneered by Stouffer et. al. in The American Soldier (1949) and later formalized by Runciman. Its rule of sum is:

30

Social Determinants of Immigrant Selection

this theory, social actions are motivated by one’s perception of deprivation in comparison with a reference group. A reference group is a group with whom one can identify in terms of cultural and socioeconomic characteristics such as race, ethnicity, nationality, locality, religion, ideology, occupation, status groups, etc (Merton and Kitt 1950). A person would decide to migrate by comparing him or herself with successful migrants abroad, or successful return migrants. Using the concept “relative deprivation,” Williams elaborated an idea of social distance. “The less the social distance between two unequally rewarded segments of a society, the more likely it is that comparisons will be made and that such comparisons will result in perception of relative deprivation or relative subordination or both [italic sic]” (Williams 1975: 363).16 The transnational migrant networks would become a reference group, and a sense of relative deprivation would develop when the social distance between migrants and potential migrants is short. It is thus plausible that migration incentive varies depending on the density, quality and type of migrant networks. If referencing behavior is different among skilled and unskilled people, it will influence their decisions to migrate. Granovetter (1973; 1983; 1985) stated that dense networks of kinship and close friendship are more prevalent among unskilled workers, whereas diffuse contacts of acquaintances are more prevalent among the skilled immigrants. Unskilled workers are more likely to find family and relatives as their reference group, and skilled workers use family as well as occupational or educational groups for reference. Another implication is that origin societies may affect the choice of reference group. If the segmentation in a modernized society is based not so much on race and ethnicity as on socioeconomic status, reference behavior in such a society is very different from that in a traditional society. The wider range of referencing behavior leads to “the greater likelihood of relative deprivation” [italic sic] (Williams “A is relatively deprived of X when (i) he does not have X, (ii) he sees some other person or persons, which may include himself at some previous time, as having X (whether or not this is or will be in fact the case), (iii) he wants X, and (iv) he sees it as feasible that he should have X (Runciman 1966: 10). 16 “Social distance” refers to the degree of intimacy that prevails between groups and individuals. “The degree of intimacy ... measures the influence which each has over the other” (Park 1950: 257).

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1975: 364), because there are very many to be compared. Generally, those who were born in Western industrialized countries have less difficulty in comparing themselves with natives of the U.S., Canada, or Australia. Professional immigrants, e.g. engineers, doctors, and scientists, would compare themselves with comparable occupational groups regardless of racial and ethnic origins. The “new economics of migration” applied the relative deprivation theory to migration study and argued that the migration decision is not necessarily based on utility maximization but rather on relative improvement of a person’s life compared with that of members of reference groups (DeJong and Gardner eds. 1981; Stark and Bloom 1985; Katz and Stark 1986; Stark and Taylor 1989; 1991). Stark and others generalized the reference group concept as one’s position in the income distribution of a country, expecting that those in the lower tail feel more deprived than those in the other tail. Empirical testing found significant effects of this indicator controlling for the absolute income gap between two countries. Another function of migrant networks is to facilitate migration through reducing costs and risks incurred by migration. Massey et. al. (1987) studied network formation of Mexican migrants in which pioneer immigrants, having established their settlement, recruited relatives, friends, and then members of the sending community (chain migration).17 Once networks are established, it reduces migration cost for succeeding migrants by providing travel cost, temporary shelter, jobs, residences, etc. They also provide emotional support by sharing ethnic cultural features such as food and music. The networks also provide an informal safety net to reduce risks incurred by working in a foreign country such as unemployment, childcare, sickness, stress, etc. Because the low costs and lessened risks inspire further incentive for migration, migrant networks have a self-propelling mechanism that increases the number of migrants who belong to the same social networks. Empirically, “the size of the migrant stock was the most important predictor of immigrant location” (Massey et. al. 1994: 729). Taylor (1987) found that the odds of a Mexican’s migrating to the U.S. increase significantly if this person has close relatives already living in the host country. 17

For other examples, see Massey (1988), Prasartkul et. al. (1985) and Watson (1975).

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Social Determinants of Immigrant Selection

Massey et. al. (1993; 1994) also suggest that as networks expand, immigrant flow becomes less selective and more representative of the population in sending societies. In other words, persons with all levels of skills will migrate when migration becomes easy. Over time, therefore, the selective role of migrant networks would decrease: the newer the network, the more selective it would be. On the other hand, Khoo (2003) found in the case of Australia that immigrants with family sponsors are more likely to settle permanently and also more likely to be skilled workers. Migration research has not reached a consensus on the effects of social capital. Borjas (2000b) found a slightly negative correlation between wage growth and immigrants’ geographic clustering. However, some studies of 1990 U.S. Census data (Karas 2002; Kawano 2000, 2002) revealed that the effect of social capital on an individual immigrant’s economic outcome differs depending on the nature of the social networks. According to Granovetter (1973; 1983), skilled workers tend to take advantage of resources from weak ties as well as strong ties, while the unskilled tend to rely heavily on strong ties. This expectation is empirically supported by the finding that family reunification immigrants in the United States have relatively fewer skills than those admitted in the occupation category although the gap is decreasing (Jasso and Rosenzweig 1995). Migrant networks provide information, legal, financial, mental, and other support to its members, which facilitates not only migration itself, but also their socioeconomic adaptation. By accelerating the earning growth of immigrants, migrant networks provide just what the self-selection theory attributed to the unobserved ability of immigrant individuals. There are more than human capital and labor market conditions that determine rapidity or slowness of immigrant adaptation. Migrant networks are one of the factors. However, the effects of networks on adaptation are not monotonic, and there is still a controversy over whether ethnic communities of new immigrants are positively affecting themselves and native communities. As discussed earlier, the self-selection theory uses economic assimilation to represent self-selection, but the measurement can be biased if it ignores many factors other than individual ability and labor market condition that determine immigrants’ income growths. An immigrant in a supportive host community may experience faster economic progress than others would in an unsupportive community. If

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a new arrival finds her job through networks, she can start to accumulate the destination-specific skills immediately upon entry without forgoing costs. A lack of connections leads directly to greater costs. The other side in the debate would say, however, that if a migrant community is so large that all its members are migrants or their descendants, a new immigrant does not have to “assimilate” to the outside society. Language skill is a good example. An immigrant with tight community support may not have to learn the language of the host country because she can use her mother tongue on most occasions of her daily life: working, shopping, dining, etc. She would have rapid income growth in a short period after arrival, but it would stagnate later depending on the capacity of the ethnic economy. On the other hand, another immigrant with no communal help may have to learn the host language and her initial output may be little, but after she masters the language, her opportunity will be greater than that of her counterpart confined in the smaller ethnic economy. Clearly, the economic progress of immigrants in the host country is determined not only by their “inner ability” but also by the environment and the organization in which they are incorporated. In order to understand how individual immigrants are incorporated into host societies, we need to review the concept of “assimilation” and alternative concepts proposed by those who are critical of assimilationist perspectives. Assimilation means homogenization of culturally distinct persons into one indistinguishable group. Abramson defined it as “the process that leads to greater homogeneity in society” (1980:150). According to Park and Burgess, whose works have long been guidance to so many researchers in race-ethnic relations, assimilation is “the process by which the culture of a community or country is transmitted to an adopted citizen” (1921: 734).18 It is the final stage of the “race-relations cycle” at which persons arrive after contact, competition, conflict, and accommodation. Drawing upon Georg Simmel’s theory of conflict-

18

Another definition by Park and Burgess: “Assimilation is a process of interpenetration and fusion in which persons and groups acquire the memories, sentiments, and attitudes of other persons or groups, and, by sharing their experience and history, are incorporated with them in a common cultural life” (Park and Burgess 1921: 735). For more discussion of assimilation by Park and others, see Gordon (1964: Ch. 3).

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accommodation oscillation, they hypothesized that in the dynamics of social interaction, disequilibrium caused by a conflict inevitably leads to equilibrium, and harmony is accomplished by one group’s subordination to the other. This harmonious perspective does not explain the everlasting disequilibrium between whites and blacks. 19 One’s submission to the other may result in a temporal state of symbiotic subordination, but not long-term equilibrium or harmony. This dualism between Anglo-conformity and racism has long been causing ambiguity and confusion in the Chicago school.20 The problem of the Chicago-style assimilation concept was its lack of a clear identity of the receiving society: to what society are immigrants assimilating?21 According to Gordon (1964), there are three ideologies of assimilation: Anglo-conformity, melting-pot, and cultural pluralism. Anglo-conformity or “Americanization” means assimilation to a single image of American-ness that is of the White Anglo-Saxon protestant (WASP) culture. The melting pot is an amalgamation of multiple cultures rather than the imposing of one culture upon another. Cultural pluralism indicates retention of “old” cultural traits while partially accepting a single “American” culture. Since the 1950s and 60s, pluralism and multiculturalism have been gaining ground in sociological paradigms, but nativism has recurrently been revived within assimilation perspectives. The Chicago school’s one-way assimilation perspective had been challenged many times from the 1930s through the 1950s.22 Beyond the Melting Pot, by Glazer and Moynihan (1963), was therefore the culmination of the past attempts to depart from the Chicago school, but at the same time the beginning of multiculturalism (Kazal 1995). They pointed out that cultural pluralism may last forever even after a minority group has reached socioeconomic parity with the majority. After this work, criticisms of assimilation began to be energized, and 19

“Although Park hoped for the eradication of racial differences through full assimilation in the very long run, he did not think of it as a process that had much relevance to the analysis of race relations in his America” (Coser 1977: 360). 20 Sollors wrote that the terms assimilation and pluralism have been “enmeshed in the most confusing and paradoxical interpretations of American identity” (Sollors 1996: xxv). 21 Persons 1987: 85; Kazal 1995: 442; Abramson 1980: 150. 22 See Kazal (1995) for more detailed literature history.

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alternative theories have emerged. Ruben Rumbaut, for example, wrote that the seemingly simple enough proposition of assimilation is: value laden with arrogant presumption of ethnic superiority and inferiority and fraught with the bitter baggage of the past – and the politics of the present … tarred with the suspicion that an Anglo-conformist demand hid within it, like an ideological Trojan Horse (Rumbaut 1997: 926-7). As the immigrant stock in the United States diversified after the 1960s, immigration theories turned their emphasis from monolithic assimilation to multicultural and pluralist perspectives. Researchers are increasingly aware that locality, ethnicity, and group-individual interaction crucially influence immigration processes. Therefore, many studies of immigrant adaptation are now focusing on heterogeneous social contexts such as ethnic communities in which adaptation of individual immigrants takes place. In her studies of Asian immigrants, Bonacich describes immigrants’ socioeconomic position as “middleman minority” because they are located between mainstream producers and consumers. The “middleman minority,” formed in response to societal hostility and ethnic solidarity, benefits from the middle position so long as it retains its identity as distinctive from the mainstream society (Bonacich 1973; 1976; Bonacich and Modell 1980). Light’s analysis of ethnic and immigrant entrepreneurship extends Bonacich’s concept from smallscale trade businesses to any self-employment businesses (Light 1984). In this model, ethnic entrepreneurs benefit from both ethnic and class resources in the forms of physical, cultural, and human capital (Light and Bonacich 1988). Waldinger (1996) uses the term “ethnic niche” to explain his observation that immigrant concentrations are not limited to trade and self-employment but extend to particular economic sectors such as the garment and construction industries. According to Waldinger, ethnic “concentration means that ethnics employed in niches may do better than their counterparts who work in industries of lower ethnic density” (Waldinger 1996: 95). Waters (1999) reports a case in which ethnic network hiring resulted in colonization of restaurant jobs by Caribbean immigrants. This, however, does not necessarily mean immigrants are taking over all of the niche industry. As Rosenfeld and Tienda (1999) point out, the creation of an

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occupational niche may not completely wipe out native employment but rather push natives upward in the occupational stratification which is often queued by nationality and ethnicity. Portes and his associates derived the “ethnic enclave“ model from the case of Cubans in Miami to embrace an even wider variety of immigrants and coethnics. Ethnic enclaves include primary and secondary laborers as well as entrepreneurs in multiple industries that cater to each other to develop a socioeconomic unit relatively autonomous from mainstream society (Wilson and Portes 1980; Portes and Jensen 1989, 92; Zhou 1992). Their conclusion is that ethnic enclaves provide immigrants with upward mobility without their assimilation to the mainstream. This is controversial because from the assimilationist perspective, racial and ethnic de-segregation is one of the major upward paths through socioeconomic status (Massey and Denton 1988). It was pointed out that the ethnic enclave concept is not generally applicable because the Cuban enclave in Miami is rather unique and exceptional (Logan, Alba, McNulty 1994). Sanders and Nee (1987, 1989) tested the Portes-Jensen hypotheses using the census data on Cubans in Miami and Chinese in California, and concluded that, contrary to Portes et al., ethnic enclaves trap the mobility of immigrant workers, while they benefit employers by enabling them to exploit the cheap labor of coethnic workers (this is called the ethnic mobility trap). A follow-up study of enclave economy by Zhou and Logan (1989) found positive effects among Chinese in New York City. Another study by Logan and Alba (1999) focusing on five ethnic groups in New York City and Los Angeles, however, found a negative effect of enclaves on annual income for most groups – Koreans and Chinese in the two cities, Blacks and Puerto Ricans in New York, and Mexicans in Los Angeles, except for Blacks in Los Angeles, who had zero effect. This finding may support the assimilation perspective by equating enclaves with lack of assimilation, but it cannot explain why many immigrants choose to live in enclaves if the choice actually depresses one’s income. An alternative explanation to the negative effect of immigrant ethnic groups on individuals emerged in the discussions of “embeddedness.” According to Portes and Sensenbrenner, “the same social mechanisms that give rise to appropriable resources for individual use can also constrain action or even derail it from its original goal” (1993: 1338). For example, resourceful ethnic

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communities have a free rider problem, suffer from the lack of connection to the “outside,” or impose “leveling pressure” on ambitious activities. In this perspective, negative outcomes are due not to the lack of assimilation but to the characteristics of an enclave itself. Overall, the debate over the positive or negative effect of ethnic enclaves has not been settled. In such an ambiguous situation, it is important to specify circumstances in which positive and negative effects of ethnic groups take place. Portes and his associates conceptualized the modes of incorporation as combinations of societal reception and group characteristics (Portes 1981; 1995; Portes and Bach 1985; Portes and Böröcz 1989; Portes and Rumbaut 1997). According to them, the combination of friendly or antagonistic reception – political and economic – and class characteristics of an ethnic group determine what type of ethnic community it is and how it affects its members. It is helpful to consider the group-individual interaction as a balancing of different rationales (McMahon 2001). Individuals settle in a co-ethnic community because they consider not only economic, but also social, ethical, and cultural conditions. The priority for communities, on the other hand, is not to maximize output from minimum input but to retain members and improve their acceptance in mainstream society. Collectively, therefore, slow growth by many, rather than rapid growth by a few, is the ideal goal to pursue. It then benefits individuals when they go out of their communities because such general improvement helps eliminate disadvantages inherent to discriminatory perceptions of the group. For example, a successful small business owner who has decided to stay in his/her community is balancing individual and group-level rationality by prioritizing improvement of the group as a whole. The assimilation controversy now involves a debate over the “segmented assimilation” of second-generation immigrants (Gans 1992; Portes and Zhou 1993; Alba and Nee 1997), where Portes and others argue that assimilation does not necessarily promise a positive outcome because adaptation to a lower class may lead to downward or stagnant socioeconomic mobility. Although the segmented-assimilation concept usually focuses on the second generation, it is applicable to the first generation as well. As a matter of fact, new immigrants share characteristics with those already in the host country, second and later

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generations included, and they tend to find jobs in a narrow range of industries. MACRO DETERMINANTS: SUPPLY AND DEMAND Micro level approaches like the self-selection theory tend to limit their temporal and spatial scope to available data and policy objectives. The series of Borjas’s study on self-selection also limits itself to the trends in the past few decades and immigration policy reform. It is a sensible research strategy to limit the range of causal analysis and propose policy reforms based on the limited findings, but conclusions drawn from such methods tend not to stand in larger pictures. For example, the new composition of immigrant nationalities undoubtedly affects their “declining skills,” and having Mexican immigrants in the country obviously distinguishes the U.S. from Canada and Australia, but all international differences are attributed to the different immigration policies in Borjas’s studies. Just as assimilation is not caused by selfselection alone, international differences in immigrant skills cannot be attributed to the policy difference alone. However, he still ignores this and other structural factors because immigration policy alone would not change any of the structural conditions – e.g., U.S. immigration policy by itself cannot curtail the great supply of Mexican immigrants, and neither it is possible to legislate any restrictions directly targeting one nationality group. Structural conditions may not provide an easy-fix policy, but they explain how national- and international-level factors affect immigrant skill selection. In his empirical model, Borjas (1991 and others) used GDP, democracy, political regime change and distance in addition to inequality. Further conceptualization is needed on what differentiates those who move and those who stay in the home countries and how these two groups are different. Macro theories of international migration are of roughly three types: theories that explain differential supplies of immigrants in relation to host countries; those that focus on temporal transition of the supply; and those that focus on demands for immigrants in the industrial society. Some would misunderstand that supply and demand of immigrants are synonymous to push and pull in the micro level, but they are fundamentally different. Macro-level factors indicate the structural causes of potential immigration but do not necessarily confirm the flow

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– they are exogenous factors. “Push-pull” theory is, as in Portes and Walton’s criticism (1981), simply a set of individual reasons, obtained post factum, that repelled migrants from their homes or attracted them to their destinations – that is why it has to be based on individual rational choice to integrate all ad hoc reasons. Macro theories may assume individual rational choice, but since they set frameworks in which migration is imperative, mobility could be induced by institutional enforcement, organizational arrangement, or any other form of initiative until migrant networks create their own momentum. Of course, this momentum can be a problem when host countries want to reduce the flows of immigrants. In his theory of “unlimited supplies of labor,” Arthur W. Lewis (1954) applied the Marxist notion of primitive accumulation in a classical economics framework. He assumed two sectors in an economy, the subsistence sector and the capitalist sector, and applied them to an open-economy model in which these two sectors exist in each country with different productivity or levels of development. According to Lewis (1954), whether migration from the subsistence sector to the capitalist sector happens or not depends entirely on decisions made in the capitalist sector when it comes to shortage of native labor: When capital accumulation catches up with the labour supply, wages begin to rise above the subsistence level, and the capitalist surplus is adversely affected. However, if there is still surplus labour in other countries, the capitalists can avoid this in one or two ways, by encouraging immigration or by exporting their capital to countries where there is still abundant labour at a subsistence wage (176). Thus the capitalists have “unlimited” supplies of labor because they tap one developing area after another for additional labor. But does it last forever? We see that some migrant-export countries have become rich and stopped emigration and have even started receiving immigrants. Some theorists have concluded that such transition from a labor export country to a labor import country is the norm for any country under economic development and proposed the ‘inverted U-curve’ theory (Åkerman 1976) and the ‘mobility transition’ theory (Zelinsky 1971; also see Appleyard 1992a; 1992b; Hatton and Williamson

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1994;1998). They have observed that emigration rates are highest in the initial stage of national development and taper off as their home countries “catch up” with the destination countries. However, this pattern was seen only in the European countries and Japan. Historically, the deceleration of the first migration wave – consisting of southern European and Asian immigrants – was caused not by “catchup” but by the decline of the world economy leading toward the world wars and the series of anti-immigrant policies pushed by xenophobic native sentiments. It is thus premature to conclude that the current migration wave will decline due to economic development and not due to drastic political disruptions. These macroeconomic theories assume that automatic mechanisms are governing immigrant supplies, but in reality international migrant flows are initiated by non-economic interventions such as colonization, slavery, war, international aid, etc. (Cohen 1987; Portes and Bach 1985). These historical events often initiate international migration and then the flow may have its own momentum, as discussed earlier. This is called path dependence because events in the initial condition constrain current situations. Some political events such as the Bracero program are straightforward initiations of international migrant flow, but others are not necessarily obvious. For example, trade and direct investment cause international exchange of goods and industry, which carry other economic and cultural information. Such exchanges increase the degree of familiarity with cultural, social, and economic systems in the minds of potential emigrants. In one phrase, these exchanges increase the sense of affinity connections. Other routes to increasing the connections would include international human rights activities, mass media, advertisement, tourism, education system, and so on (Sassen 1988). In previous research, Jasso and Rosenzweig (1986; 1988) used the Voice of America broadcasts as an indicator of international information flow. Yang (1995) used the number of visitors from receiving countries to countries of origin to indicate the degree of cultural familiarity that the nationals of the various countries had with each other. Both found significant effects of these affinity factors (the former on earnings and latter on the numbers of U.S. immigrants). Once migration has been initiated and affinity has expanded, expansion of migrant networks accelerates further expansion. Such a self-perpetuating process of migration is called the cumulative

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causation 23 by Massey (1988; 1990) and Massey et. al. (1993). He distinguishes migration-inducing factors and perpetuation factors and explains that, because of the latter factors, migration continues even after the initial causes disappear. Although wage differentials, relative risks, recruitment efforts, and market penetration may continue to cause people to move, new conditions that arise in the course of migration come to function as independent causes themselves: migration networks spread, institutions supporting transnational movement develop, and the social meaning of work changes in host societies. The general thrust of these transformations is to make additional movement more likely, a process known as cumulative causation (Massey et. al. 1993: 448). Eventually, the theory predicts a saturation of migrant networks and the end of migratory flows. Once begun, international migration expands in and of itself ... In the long run, however, the interrelated process of economic growth, rural-urban migration, and emigration ... gradually weaken the forces making for continued migration (Massey 1988: 402). The macro processes discussed above are applied to immigrants in general, which sets a basis for other theories that explain differential migration. Segmented (dual) labor market theory 24 and 23

The term “cumulative causation” is first used by Myrdal. As I understand it, Myrdal’s use of this term is different from Massey’s because Myrdal did not see cumulative causation as self-regulating and self-balancing: “in the normal case there is no such tendency toward automatic stabilization in the social system. The system is by itself not moving toward any sort of balance between forces but is constantly on the move away from such a situation. ... Because of such circular causation a social process tends to become cumulative and often to gather speed at an accelerating rate" (Myrdal 1957: 13). 24 When Bonacich (1976) uses “segmented,” it means the use of the “segmented work” in Gordon and Edwards (1982), but the “dual” labor market in Piore (1979) is somewhat wider, including the Weberian interpretation of immigrant psychology.

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“peripheralization at the core” explain why host countries need specific types of immigrants rather than a random sample of origin populations. The development of an industrial economy creates segmented labor markets in which skilled service workers and manual or unskilled service workers are in shortage and must be complemented by immigrant workers (Bonacich 1976; Piore 1979). Native workers are unwilling to take immigrants’ jobs because their jobs are often dirty, dangerous, and difficult (3-D). Immigrants would accept the jobs because they are essentially temporary target earners who detach their means of making money from job prestige. Their prime motive is to send or take as much money as possible back home. On the other hand, the immigrants may see these jobs as rather high-status for the underdeveloped areas (Piore 1979). According to another thesis called the “peripheralization at the core,” urbanization in host countries produces demand for migrant workers in both skilled and unskilled occupations (Sassen 1982). Labor demand structure in host countries can be a crucial determinant of immigrant skills. STRUCTURAL IMBALANCING AND BRAIN DRAIN According to Portes and his colleagues, migration is a part of the selfperpetuating process of “structural imbalancing” driven by capitalist (or market) penetration into the peripheral societies. This penetration creates imbalances between sectors and institutions of the subordinate unit, which lead eventually to labor displacement. Imbalances are induced from the outside, but become internal to the structure of the weaker societies. These internal imbalances ... are what underlie sustained process of labor migration (Portes and Walton 1981: 31; also see Portes 1976; Portes and Bach 1985). Capitalist penetration today takes many forms: foreign direct investment, offshore production and implantation of export-oriented agriculture, etc. This view replaces the perception of receiving countries as passive receivers of migrants with the focus on bilateral relationships in which receiving countries actively participate in the migration process. Capitalist penetration creates potential migrant pools

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and plays an active role in maintaining and controlling migration streams (Sassen 1988; 1995; 1996). Emigration of skilled workers is a brain drain for the country of origin. The structural imbalancing theory does not attribute it to income difference but to the technological hegemony of the core countries. The logic of brain drain is that even if a country has relatively high- or middle-level income, the lack of infrastructure and local industrial levels do not match the skill of highly trained professionals – they cannot practice their skills, or there is very little need for them (Portes 1976). Such a lack of economic bases is associated with unequal distribution of capital because the location of capital investment is determined by profitability and not by necessity. Bornschier and ChaseDunn (1978) found a positive relationship between foreign investment and internal inequality. According to Portes, state arrangements toward a market economy lead to further impoverishment of labor in order for local or transnational capital to compete internationally. The idea of “brain drain” from the third world as an outcome of structural imbalancing (Portes 1976) faded away in the 1980s, but it reemerged because core countries began to concern themselves about securing skilled workers (Salt 1997). The world-systems perspective explains historical dynamics of the world in terms of interaction between core, semi-peripheral, and peripheral countries or regions (Wallerstein 1974). Usually, European migration to the “new worlds” is perceived as a movement from core to periphery, and the new waves of migration after World War II are movements from periphery to core. 25 It is also possible, however, to assume that human movements are always from periphery to core, because European migrants in the 19th century were not any better educated or particularly more motivated than “new” immigrants today. Ethnic stratification in the host countries made the old white immigrants the “core” of the national population and “new” immigrants the peripheral population. Their numbers – majority and minority – and a kind of seniority privilege contributed to the stratification and poor treatment of the newcomers. Historically, therefore, we may assume

25

See, for example, the distinction between “colonizing migration” and industrial labor migration in Portes (1981 23-25). For others, see Elizabeth M. Petras (1981), and Piore (1979).

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that the old European immigrants moved from periphery to core and new non-European immigrants from periphery to periphery. The implication of this historical perspective for immigrant selection is similar to that of segmented assimilation. European immigrants are inherently privileged in European-dominated formercolonial countries such as the U.S., Canada, and Australia, and naturally, they achieve rapid assimilation regardless of their skills. This rapid assimilation indicates positive selection, but that is because the self-selection theory does not distinguish minorities from mainstream migrants. Assimilation of non-European immigrants is a matter of dealing with structural obstacles – cultural and institutional discrimination – that make them appear to be negatively selected regardless of their individual ability. Undoubtedly the needs in the host economy for flexible and cheap labor influence its immigration policy and border control, which in turn shape the volume and composition of migration. However, the view that the state can and does control international migration26 wrongly dismisses the fact that the migration policies are affected by immigration itself as well as by many other economic and political factors: they are endogenous to the causal system. As Papademetriou warned, 27 although labor demand as a structural condition and immigration policies as intervening factors deserve much attention, labor-demand reductionism or state determinism in the literature needs more balancing. HYPOTHESES The first focus of this investigation is to re-examine the self-selection theory using newer data and additional sociological factors. Secondly, this study focuses on the role of migrant networks in determining 26

“Migratory pressures do not automatically result in massive migrations, because border control usually intervenes as a determinant factor. Independently of other conditions, it is state actions with respect to these borders that determine whether any international migration will take place at all” (Zolberg 1989). 27 “Exclusive adoption of the view of international migration as part of the international process of capital accumulation thus risks reducing a multifaceted process to one which serves only needs and strategies of capital” (Papademetriou 1991: 12).

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immigrant skills, with particular attention to the effects of ethnic characteristics on skill selection. Thirdly, it investigates the selection effects of structural factors such as international relations and national development. Based on these foci, four general hypotheses are derived (two from the last research focus). 1. Self-selection theory predicts that, at destination, inequality attracts skilled and equality unskilled workers. Conversely, equality at origin sends out skilled emigrants and inequality unskilled emigrants. As a result, the quality of immigrants in host countries improves if these countries have greater inequality and declines if they have less inequality. 2. A migrant network contributes to the decline of immigrant skills by reducing migration costs, but weak ties and social capital of the network improve the level of skill selection by affecting different parts of the population pool. Immigrants are more skilled where an ethnic group is not highly clustered, having resourceful business ties. 3. International relationships such as trade, finance, and tourism improve the transferability of skills between sending and receiving countries, which makes it quicker for immigrants to catch up with natives in terms of earnings. Thus, the more open and accessible the origin-host relationship, the more skilled are immigrants from these origins, and the greater is their affinity. 4. Structural imbalancing induces emigration of skilled workers. When the national economy experiences uneven development, great demand for skilled workers coexists with the lack of well-paid positions. Due to this structural condition, skilled workers leave their countries of origin and improve the skill selection of immigrants. Before testing these hypotheses, we first reexamine the historical backgrounds of the three host countries in the next chapter. Then we discuss data and measurements of variables as well as their expected effects in detail in Chapter 4.

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CHAPTER 3

Immigration History and Policy

This chapter reviews the history of immigration and evolution of immigration policies in the three countries in order to comprehend their long-term, macro-historical trends. It sets a framework for the following analyses. Böhning once said “Mankind has witnessed migration since time immemorial” (1984:3). The history of immigration began for the U.S. and Canada in the 17th century and for Australia in the 18th century. The four hundred years of immigration history can be divided into three periods. The first period is the time of colonization in the 16th and 17th centuries in which immigrants were mainly white Europeans. The earliest components were from the old sea powers such as Spain and Portugal, followed by British, French, and Dutch immigrants, along with a significant presence of Africans in the United States; they were joined later by Germans and Irish. The second period was the time of high immigration between the 1820s and 1920s, often called the great century of immigration, followed by a selective and restrictive period after World War I. In the nineteenth century, a massive wave of western European immigrants occurred in the first phase, and southern and eastern Europeans in the next, with an intermittent but significant inflow of Asian immigrants. The last period of de-regulation began in the 1960s and 1970s. In this period, immigration policy reforms took place in one country after another to demolish the racist provisions in their admission and naturalization policies. Today, all three countries have been experiencing a resurgence of great migrant waves and shifting compositions of the foreign-born population from European to Asian and Latin American origin. 47

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The following historical review emphasizes two broad points. First, the succession of immigrant ethnicities is indeed an irreversible historical process that has taken place in the three host countries over the last four hundred years. Antagonism toward “new” immigrants has also always existed. What is different now is that new immigrants are non-Europeans whose migration and assimilation patterns can be quite different from those of their white European predecessors. Secondly, the three host countries have distinctive historical characteristics. The U.S. is unique in that it has a large number of people of African descent and another group with Latin American origins – both are legacies of unique historical events: slavery and the U.S.-Mexican War. Compared to the U.S., Canadian and Australian governments have maintained more control over immigrant flows with their goals to improve national welfare, which is rooted in their political and cultural ties with Britain over their histories. Its history has left explicit footprints on each country’s immigration policy. Boyd (1976) characterizes immigration policies of Canada and Australia as having been made from a “manpower development” perspective, whereas U.S. policy has been numeric regulation because it presupposes that the best of all migrants will prefer it to other potential destinations. The U.S. has never concerned itself about retention of immigrants, though it did try to maintain its race-ethnic order in the past and perhaps is doing so now. On the other hand, in Canada and Australia, immigration has been always a “net migration,” and the policy objectives are not only to attract desirable immigrants, but also to retain them. Therefore, immigration policies of the U.S. are more race-ethnicity oriented than in the other two countries, which, by necessity, make concessions between racism and “manpower development.” FROM COLONIZATION TO IMMIGRATION The 15th century was a time of mercantilism in which European merchants often used vandalism to obtain trading goods. The most dominant sea powers were Spain, Portugal, and Holland, whose conquistadors vandalized Central and South American natives for gold. The British and French Empires were not able to tap this quick wealth, but they set up West Indian Companies and established plantations in the Caribbean islands. In the shift from the initial mercantilism to

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colonialism, the first settlements for immigration began in North America a century after the “discovery” of the Americas. Because of its mild climate and proximity to the Caribbean, the North American immigration quickly became large-scale and multi-racial. Immigration to what is now Canada by French settlers started about half a century after immigration to what is now the U.S. The sub-arctic climate prevented Canadian settlers from plantation-type agriculture and from slavery. Compared to the U.S., the multi-racial encounter was not so significant in Canada because the number of native residents was rather small. Australia came even later to this colonization race, but European settlement started immediately after its “discovery” in 1770. This short period of colonization resulted in the small number of “old” immigrants compared to the “new” European and non-white immigrants in Australia. In this colonial period, the British, French, and other European governments controlled and organized immigration. The approximately one hundred years between the 1820s and 1920s was the first great century of immigration. Relative peace in Europe and the U.S., except for the Civil War period, stabilized the passage across the Atlantic. In the 1840s, steamships started to navigate across the ocean, carrying more passengers at lower cost and in about half the time (Archdeacon 1983). At the same time, steam locomotives and railways dramatically improved land transportation, which accelerated rural-urban migration as well as migration from overseas. In Europe, just as Weber witnessed in eastern Prussia, industrialization, urbanization, and commercial agriculture spread to eastern and southern Europe, engendering a great number of voluntary migrations to the newly independent United States and to the British colonies of Canada and Australia. The devastating potato famine in Ireland in the 1840-50s, the Prussian War in central Europe in the 1860s, and the Franco Prussian war in 1870-71 caused the emigration of many destitute Europeans. Selection and transportation were organized by charitable societies subsidized by the government. In effect, when Britain was infested by depression and famines, those colonies were forced to become receptacles of the paupers. In response, all the colonial as well as the independent governments attempted to control admissions in order to resist such human dumping. It was also an important development that China entered a long turmoil from the mid-19th century, resulting from population growth, religious rebellions, foreign invasions, and wars. There is a clear connection

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between the advancement of Western industrialization and the mass emigration of Chinese: massive wealth in one part of the world was plundered and transferred to another part of the world where the wealth needed to be realized by someone’s labor through construction, cultivation, mining, housework, etc. for further accumulation. On the receiving sides, the African slave trade was abolished in one country after another in this period. After the British Empire finally gave up the slave trade in 1833, the labor shortages in the British and other European plantations were filled by Asian Indians and Chinese indentured coolies. From the 1830s to the 1850s, urged by the population pressure and famines, the British government also promoted emigrations of its residents to these colonies with passage and settlement supports. In the mid-19th century U.S., the annexation of Mexican territory, the gold rush, the U.S. Homestead Act, and construction of trans-continental railways attracted massive international inflow as well as internal westward migration in the 1880s and 90s. State governments and private industries mounted campaigns in Europe to attract migrants to their lands. Canada and Australia were in need of additional population as much as the U.S. was, but they were geographically and economically disadvantaged in the competition for immigrants. The most desirable settlers, especially those with capital, migrated to the U.S., and less desirable settlers went to the other two countries. The sensitivity of Canada and Australia toward screening of immigrants may have originated already in this period. Achievements of earlier fellow migrants were also an important factor. Remittance of tickets from the U.S. to families and relatives back in Ireland or England helped many emigrants. The advertisement competition peaked in the late 1870s and quickly dissolved as restrictionist sentiment grew as new ethnic groups began to arrive on the host countries’ shores. In the midst of this period, gold rushes occurred almost simultaneously in the three countries. Those who rushed to goldmines were numerically not so significant compared with the total immigration, but the gold rushes were very important in immigration history in the sense that they happened in non-traditional areas of colonization and attracted for the first time a significant number of Chinese immigrants. The first Chinese immigrants used a “credit ticket” system to pay for the passage, an advance system paid by brokers or merchants on the condition that immigrants pay the advance

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off including interest after arrival. In all three countries, Chinese presence led to anti-Asian sentiments and legislation. By the end of the 19th century, migrant flows from Northwestern Europe dwindled, and new European migrants such as Italians and Poles started to fill the vacancy. Besides this shift in the immigrants’ ethnic composition, this century witnessed another shift in immigrant occupations from mainly agricultural to urban-type labor. It was not just the rapidly increasing number of Chinese, but a combination of their concentration and the declining supply of western European immigrants that led to the anti-immigration acts, some targeting Chinese directly. Antagonistic reaction to Chinese immigrants started in California right after their arrival in the early 1850s and soon spread to Australia and later to Canada. European-American earlycomers, particularly gold miners, played a leading role in these antiChinese movements. The antagonism was partly based on the fear among mining workers that Chinese immigrants would depress wages, but mostly on the intolerance of white settlers and native-born whites toward unfamiliar people and cultures. Riots and protests occurred in many places. A large number of foreign nationals also raised a territorial concern. During this first wave, restrictions took a form of benign admission control and taxation by local government. 28 In a sense, the heat of ethnic antagonism was restricted to local levels until the 1870s. The anti-Asian actions at a national level spread rapidly in the 1880s. Through one legislation after another, white-only policy was established in all three countries. The anti-immigrant sentiment had temporarily declined as the number of immigrants decreased in the world depression in the 1890s, but it resurfaced as soon as southern and eastern Europeans arrived in great numbers in the First World War period. Nativist immigration policies were enacted in the 1920s in the U.S. and Canada. Particularly in the U.S., new European immigrants were subject to discrimination and a restrictive admission system, but they were not completely barred from the country as Asian immigrants were. Australia completed its “great white wall” (Price 1974) earlier because of its historically strong adherence to Britain. In order for Australia to secure British immigrants 28

The U.S. already had federal and state governments in place, while Canada was just going to be federated in 1867 and Australia in 1901. Thus, “local government” here means state or colonial government.

52

Social Determinants of Immigrant Selection

as opposed to other “unsuitable” persons, the government needed both racial and qualitative regulation when the number of British immigrants declined. The U.S. had discarded its attachment to Britain a long time ago, and Canada had abandoned its preference for British immigrants since about 1890 (Adelman et. al. ed. 1994). Note that this is the period in which the basis of assimilation theory – the race relations cycle – was developed. The theory was with regard to the new European white immigrants in the period of declining immigration. According to this theory, discrimination against new Europeans was going to disappear as they assimilated culturally and economically. Such a course of assimilation is not applicable to today’s immigrants because racial discrimination is persistent and new supplies of immigrants keep renewing ethnic culture and identity (Waters 2005). THE UNITED STATES The first series of immigration laws in the U.S. were enacted between the end of the 18th and the beginning of the 19th century under the emerging patriotism during the Revolutionary era. The laws were intended only for “free white” immigrants, and focused on the condition of naturalization, but paid almost no attention to admission. The number of European immigrants declined in this period because the war between England and France made transatlantic passage difficult. At least 4.5 million Irish, 5.9 million Germans, and 2.1 million Scandinavians (Norwegians, Swedes, and Danish) crossed the Atlantic to the United States between 1820 and 1920. Irish immigration reached its peak in the late 1840s and 1850s when the potato famine devastated Ireland. Although immigration was disrupted by the Civil War (1861-5), German immigrants also increased in this period and became the largest immigrant group in the 1880s, 27.7 percent of all immigrants. The number of Scandinavians, though smaller, also increased after the Civil War. Their flows declined after the depression in the 1890s and in World War I. Figure 3-1 shows historical trends of immigration flow. A significant Asian presence began in California during the gold rush of 1849. About 300,000 Chinese arrived in the three decades following the discovery of gold until the Chinese exclusion act of 1882 (Kitano and Daniels 2001; Daniels 1988). The state legislature passed a law in 1850 requiring all alien (who were ineligible for citizenship – mainly Chinese) miners to pay for mining licenses at 20 dollars per

Immigration History and Policy

53

month. In 1852, taxations on alien miners and alien passengers of arriving ships were imposed. Native miners sometimes took violent action to expel Chinese from goldmines. As Chinese immigrants spread across the country along with the railway construction sites, anti-Chinese sentiment also revived and spread. In 1882, the congress passed the Chinese Exclusion Act, which suspended the entry of Chinese “laborers” for ten years. The barring of “laborers” included most unskilled workers but excluded merchants, professionals, officials, students, and tourists. It also allowed current Chinese residents to re-enter the U.S. after going back to China. This restrictive law, which was, however, generous to those Chinese already in the U.S., was followed by a series of general immigration laws: excluded classes included prostitutes and criminals, persons with bad health, persons likely to become public charges, and workers with work contracts. In addition, 50 cent tax per immigrant was collected. Nativists in the west coast were dissatisfied with the act because it did not reduce the high number of Chinese – so they said – already living in their states.29 This anger erupted in anti-Chinese riots in the 1880s. In response, certificates of re-entry were invalidated (1888) and the suspension extended anther ten years (1892). In 1902, congress extended it indefinitely. This was clearly an elimination policy in the long run because Chinese immigrants were predominantly male and they lost the source of any additional immigration from China. Japanese immigration to the U.S. west coast area started in the 1890s, partly because they were needed to fill the vacancy left by the Chinese, partly because their country opened its border for emigration about that time. The rapidly increasing numbers of Japanese immigrants were received by an anti-Japanese campaign just like in the case of the Chinese. The “Gentlemen’s Agreement” between the U.S. and Japanese governments in 1907-8 stopped labor migration of Japanese. Before the 1924 Immigration Act, about 300,000 Japanese had emigrated to the United States. In 1890, immigration to the U.S. reached one million a year, a level never exceeded in the entire U.S. history except for the IRCA years. The major component of this flow was no longer Irish, Germans, or Asians, but those from southern and eastern Europe. Over 4.1 29

From 1860 to 1880, about 9 percent of California’s population was Chinese.

Figure 3-1: Historical Immigration Flow in the USA and Proportion to the Total Population, 1790 – 2000 2,000

1.80 immigration

1,800

1.60

% 1,600

1.40

1,400

1.20

1,200 1.00 1,000 0.80 800 0.60

600

0.40

400

year

1994

1987

1980

1973

1966

1959

1952

1945

1938

1931

1924

1917

1910

1903

1896

1889

1882

1875

1868

1861

1854

1847

0.00 1840

0 1833

0.20

1826

200

Source: INS. 2002. 2000 Statistical Yearbook of the Immigration and Naturalization Service.; U.S. Census Bureau Historical National Population Estimates: July 1, 1900 to July 1, 1999.; Statistical Abstract of the United States: 2006.

Immigration History and Policy

55

million Italians had arrived between 1880 and 1920. Greek and Turkish immigrants, though on a much smaller scale, were also significant groups from southeastern Europe. Major groups from eastern Europe were Poles, Jews, and Hungarians. Over one million Poles migrated to the U.S. between 1900 and 1920.30 The federal government imposed literacy tests on immigrants in 1917 under the increasing nationalism that occurred during the First World War. The inadmissible “class” was expanded to include those who had physical and mental illness, who were ideologically or religiously unfit, who came with credit tickets, etc. In addition, another law set up an “Asiatic barred zone” to ban all Asian immigrants except Japanese and Filipinos. The Japanese were already limited by the “Gentleman’s Agreement,” and the Filipinos were American citizens as a result of the Spanish-American War. The first comprehensive immigration restriction was established by the Immigration Act of 1924, which combined the restriction codes of the 1917 Act and the quota system that had been experimented with in the preceding few years. This system assigned each sending county a quota (3%) based on the population of the nationality group already residing in the country. The baseline statistics were initially set according to the 1910 census when the new system was first enacted in 1921 and were supposed to be renewed after every census. In the amendment of 1924, however, the baseline statistics were set according to the 1890 census, obviously in order to cut quotas assigned to eastern and southern Europeans, whose population had greatly increased in the previous decades. After 1927, the total immigrant admission quota was reduced from 350 thousand to 150 thousand, and the nationality quota was reduced from 3% to 2% of the benchmark statistics, which were brought back to the populations based on the 1920 census. This immigration law and system lasted until 1952. CANADA The French were the first to establish a permanent settlement in Canada. In the middle of the 17th century, only about 3,000 settlers lived in

30 My rough calculation from the 1900 and 1920 censuses, using 65.9% return rate estimated by Archdeacon (1983), yielded the number of some 1.15 million Poles over the two decades.

56

Social Determinants of Immigrant Selection

New France in Quebec.31 With more than 10,000 French immigrants, farmers, traders and indentured servants, and by natural growth, the population of Quebec had increased to 20,000 by 1712, 70,000 by 1760, and 140,000 by the mid-1780s. On the other hand, a few British lived in British North America in Nova Scotia until the Independence War of the United States. During and after the war, 40,000 to 50,000 British Loyalists fled the U.S. for Canada. Among the Loyalists were more than 3,000 free blacks, a third of whom emigrated to West Africa by the end of the 18th century, but the rest remained. Also, the presence of about 3,600 African and Native slaves was recorded in 1796. Some thousand German mercenaries hired by the British government were granted land and settled in Upper Canada. Until the final breakup between the British government and the United States in the War of 1812, the majority of European immigrants came to Canada through the U.S. territories for political or religious reasons. The European settler population in 1791 was a little over 250,000 in Canada. After the independence of the U.S., the British government stopped promoting immigration from the U.S. to Canada and set up institutional assistance for emigration directly from Britain to Canada from 1828. In the earlier period, land was granted for free to colonial officials and colonialists conditional on land development and subdivision to settlers. This system was criticized by Wakefield and others as creating only idle lands. After the 1830s, the British government started selling the undeveloped land to individual bidders. However, private agricultural settlement was not successful in Canada. The Canadian economy experienced agricultural depression in the mid-1840s due mainly to the liberalization of the wheat market by Britain. This discouraged agricultural settlers, mainly Scottish, whom the country badly needed. The economy was hit again in 1857 by a financial depression. In the mid-19th century, therefore, Canada did not have the capacity to receive destitute immigrants who were too poor to start new settlements. The Irish immigrants in Canada during the potato famine exceeded 100,000 in 1847, but a third of them died in the same 31

This and the following information on early Canadian immigration was provided by Kelley and Trebilcock (1998).

Figure 3-2: Historical Immigration Flow and Proportion to the Total Population in Canada, 1867-2000 6.00

450,000 immigrants 400,000

% population

5.00

350,000 300,000

4.00

250,000 3.00 200,000 2.00

150,000 100,000

1.00 50,000 0.00 1867 1872 1877 1882 1887 1892 1897 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997

0

Source: Immigration and Citizenship Canada, Annual Reports. Various years.

58

Social Determinants of Immigrant Selection

year, and most of the survivors later moved to the United States. Nevertheless, the number of Irish immigrant arrival reached 500,000 during this century (Kelley and Trebilcock 1998). Immigration from the British Isles reached its peak in 1848-54. The total inflow became as small as approximately 6,000 immigrants a year in 1860. After the Confederation in 1867, the Canadian government played a more active role in recruiting and regulating immigration. Quebec promoted immigration from France, Belgium, Switzerland and Germany. The newly enacted Homestead Act of 1872 allowed immigrants to acquire 160 acres of land with a $10 registration fee on condition of residence and cultivation. These European farmers, attracted by this generous land distribution, contributed to the 100,000 annual immigration flow in the early 1880s. Figure 3-2 shows the trends beginning in 1867. The gold rush did not significantly change the total immigrant flow, but its qualitative impact was immeasurable. In Canada, gold mines were discovered in 1857 in the Fraser River Valley. Thousands of men, including several thousand Chinese and several hundred blacks, migrated to Canada for gold (Kelley and Trebilcock 1998). The gold rush and the subsequent railroad construction boom increased the numbers of Chinese immigrants as well as anti-Chinese sentiment fueled by American miners from California. Unlike in the other two countries, however, anti-Chinese actions were not so successful in Canada. For example, a law proposed to charge a license fee to Chinese miners did not pass the local assembly in 1864 (Price 1974). Chinese immigration to Canada increased significantly in the 1880s when the Canadian Pacific Railway created a great labor demand. Some 3,000 Chinese per year entered Canada in 1881-85 (Price 1974). In 1885, the federal government imposed a $50 head tax on Chinese immigrants and limited admission of Chinese to one person per fifty tons of ship carriage.32 The head tax was raised to $100 in 1900 and to $500 in 1903. Despite the heavy tax, an increasing number of Chinese and Japanese immigrants kept arriving on Canada’s west coast. The Immigration Act of 1910, amended in 1919, finalized the basis of the White Canada policy, authorizing prohibition of entry to any 32

The British Columbia government enacted a $40 head tax for Chinese in 1878; taxes of $10 per Chinese head and $50 per immigrant were legislated in 1884.

Immigration History and Policy

59

immigrants who were unsuitable or inassimilable to Canada based on race, nationality, culture, customs, occupation, etc. Based on the 1919 amendment, the Chinese Immigration Act of 1923 barred all Chinese immigrants except for diplomats, Canadian-born Chinese, merchants, and students. In the same year, an order-in-council prohibited all immigrants of “Asiatic race” with the exception of wives and children of those already in Canada, but later in 1931 all exceptions were removed, and Asian immigrants were completely shut out. Another important aspect of the White Canada policy is the denial of the franchise. British Columbia denied Chinese the right to vote from 1885 and denied all Asian persons that right ten years later. Eventually all Asian immigrants lost their right to vote both at federal and provincial levels. Their lack of political leverage must have contributed to their vulnerable position in Canadian society. They had to wait until 1947 before the government started to repeal prohibitions against Asian voting and restored the voting rights. AUSTRALIA Right after its “discovery” in 1770, in 1788 the British government chose this scarcely populated continent island for its penal colony because the United States had discontinued accepting convicts after its independence (Rickard 1996). Over the next eighty years, Australia received some 162,000 convicts who were mostly British and about 6,000 who were Irish. Soon after the first shipment of convicts, free immigrants from the British Empire started settlements to develop plantations using those convicts. Initially, Australia was not favored by free immigrants because it was too far and they had many other alternative destinations at much closer distances. Later, the number of free settlers increased as British surplus-population increased under the Industrial Revolution and famines and as the British government provided assistance for passage and settlement. Meanwhile, the shipment of convicts had declined by the 1830s. In 1821, the white population totaled 35,000 (Price 1974). In Australia, in response to the intensifying competition for European immigrants, assisted passage became a major scheme of immigration between the 1830s and 1880s. The geographic disadvantage made the cost of land and land development expensive,

Figure 3-3: Historical Immigration Flow and Proportion to the Total Population in Australia

250

2.50 Settler Arrivals % population

200

2.00

150

1.50

100

1.00

50

0.50

0

0.00 1999-00

1997-98

1995-96

1993-94

1991-92

1989-90

1987-88

1985-86

1983-84

1981-82

1979-80

1977-78

1975-76

1973-74

1971-72

1969-70

1967-68

1965-66

1963-64

1961-62

1959-60

Source: Australian Bureau of Statistics. Community Profile of Australian Immigration. Various years.

Immigration History and Policy

61

which made Australia less attractive to potential settlers and increased the practice of assisted passage. Until the mid to late 19th century, only a small number of Indian and Chinese coolies, and virtually no African slaves, were imported to Australia. There are several reasons for the limited expansion of nonwhite immigrants in the case of Australia. The colonies already had the British convicts for cheap labor (Willard 1967); the white colonists felt hostility against slavery or slavery-like labor trade (Campbell 1969; Price 1974); therefore, the British government eventually banned the coolie trade to Australia. Although it is impossible to practically distinguish coolies, indentured workers, and credit-ticket workers, there were ethnic differences between them: most Chinese who came as coolies were Hokkien Chinese from Amoy, and those who later came with credit tickets were Cantonese from Hong Kong (Willard 1967; Price 1974). The discovery of gold in 1851 attracted British middle-class and American gold diggers to Victoria, New South Wales, and a little later to Queensland. The gold fields brought between 50 and 60 thousand Chinese immigrants to Australia. In Victoria in 1857 there were 25,421 Chinese males and just 3 females; in 1859, Chinese men in Victoria represented 20 percent of the male population (Daniels 1990: 241; Rickard 1996: 36). They worked not only in goldmines, but also in cattle fields and in railroad construction. Chinese immigrants and restrictions against them spread like wild fire. In 1855, the colonial government of Victoria passed an act limiting the number of Chinese arrivals to one for every ten tons of ship cargo and imposing an entry tax of 10 pounds per person (repealed in 1865). South Australia passed a similar act in 1857, which was repealed in 1861. New South Wales also passed a similar bill in 1861, while other colonies were already repealing their restrictions, and repealed it in 1867. As additional goldmines were discovered in Queensland after 1867, thousands of Chinese immigrants moved into this northern colony. Since this northern tropical province was very scarcely populated, Chinese residents in some areas outnumbered white settlers until they declined after a regulation bill similar to Victoria’s was passed in 1877 (Price 1974). In 1888, Colonial governments had a conference to coordinate their efforts to prevent precipitous Chinese and non-white immigration.

62

Social Determinants of Immigrant Selection

They agreed upon rejection of Chinese immigration and naturalization, which in effect considerably decreased the number of Chinese (Hawkins 1989). Immediately following the federation in 1901, the Australian parliament passed the Immigration Restriction Act, making it an official adaptation of the white Australia policy. The law ensured that “with very few exceptions, non-whites would not be permitted to settle, work, or live temporarily or permanently in Australia” (ibid.: 14). The law required a language test to screen out non-English speaking persons. The following year, the parliament passed another act denying the franchise of all non-white persons. POSTWAR LIBERALIZATION As a consequence of their restrictive immigration policies, all three countries experienced long-term cutbacks of immigration until the end of the Second World War. Australia, which did not receive the wave of Italian and Polish immigrants, received a net inflow of only 8,300 immigrants per year between 1912 and 1921. 33 In the same period, Canada received 147 thousand a year and the U.S. 566 thousand a year (these are gross inflow numbers). After the great depression, however, Canada received only 14 thousand a year in 1932-41, and the U.S. 48 thousand, less than a tenth of their intakes in the peak period. In these three countries, the lack of immigrants significantly affected their demographic structures, as discussed in Chapter 1. It slowed down population growth and accelerated the aging of the population, while birth rates were declining. Reopening the doors to immigrants was one solution to the problem, which was most strongly felt in Australia, whose population growth and density were the lowest among the three countries. International politics under the cold war also affected the immigration policies of the three countries. They all engaged in refugee programs for humanitarian reasons and accepted immigrants from less preferred countries. The attack on racism by the Civil Rights movement had an important impact on the liberalization of immigration policies.

33

This is the author’s calculation from the total overseas-born population in 1911 and 1921. Source: Department of Immigration and Multicultural Affairs 2001. Flow statistics for Australia in this period are not available to the author.

Immigration History and Policy

63

THE UNITED STATES The 1965 Amendment to the Immigration and Naturalization Act abolished the nationality-based quota system which had been disproportionately favoring white European immigrants. With this and some other minor amendments that followed, the U.S. immigration policy shifted its emphasis from race and nationality to family unification. The reform set a maximum annual issuance of 20,000 visas per country for Eastern Hemisphere immigrants, with a total limit of 170,000 visas. For the first time, a total limit of 120,000 was imposed on the Western Hemisphere, but there was no limit for each country in the region so that one country’s unused quota could be used for another country (a product of compromise with nativists). The same national ceiling of 20,000 was applied to both hemispheres in 1976, and the separate hemispheric quotas were integrated into a single overall ceiling of 290,000 in 1978. The legal equality of immigration, therefore, was not completed until more than ten years after the 1965 amendment. The 1965 reform addressed the practical problem of labor shortage by introducing a “family reunification” category for immediate relatives of U.S. citizens. This category was not subject to any annual quota. The quota system also emphasized the preference category of distant relatives such as adult children, parents, and siblings of citizens or permanent residents. As a result of this kinship-oriented immigration policy, more or less 70 percent of all immigration throughout 1980s and 90s occurred under either the immediate or less immediate family preferences (INS Annual Reports). Today’s immigration policy is based on two basic categories: numerically unlimited and limited immigrations. The former applies to immediate relatives (spouse, minor children and parents) of U.S. citizens; the latter further includes remote relatives, employment-based immigrants, and diversity immigrants. 34 The current overall annual limit set in effect since 1995 is 675,000, which can be exceeded if the number of immediate family applicants exceeds the flexible maximum of approximately 400,000. As a result, at least 71 percent of all visa allotment is given to either immediately or remotely related familysponsored immigrants. In fact, more or less 70 percent of all 34

The diversity category established in 1992 admits up to 55,000 immigrants a year by lottery.

64

Social Determinants of Immigrant Selection

immigrants throughout the 1980s and 90s were admitted under either the immediate or remote family categories.35 The major consequence of the abolishment of the quota system was the new inflow from Asia and Latin America. Figure 3-4 presents the change in the composition of immigrant flow by the region of origin in every decade over the past one hundred years. While the share of European immigrants dropped from 93.6 to 17.1 percent, the share of Asian immigrants increased from 3.7 to 30.7 percent, and that of Latin American and Caribbean immigrants from 2.1 to 47.2 percent. In terms of foreign stocks, this new wave of immigrants has fundamentally changed the ethnic composition of immigrants in the United States. As Figure 3-5 shows, the number of European-born residents declined steadily beginning in 1920 from 12 million to 4.4 million, declining in its share in the total foreign-born population from 86 to16 percent. Figure 3-4: Immigration to the United States by Regional Origin, 1901-2000 (millions in ten-year interval) 10 9 Latin America & Caribbean

8 7 6

Asia

5 4 Europe & Canada

3 2

Other

1 0 1901- 1911- 1921- 1931- 1941- 1951- 1961- 1971- 1981- 199110

20

30

40

50

60

70

80

90

2000

Source: See Fig. 3-1. 35

INS Annual Report, various years. The percentage is calculated excluding immigrants whose undocumented status was adjusted by the 1986 Immigration Reform and Control Act (IRCA). See Footnote 3 for details about the IRCA. (INS Triennial Comprehensive Report on Immigration 1999).

Immigration History and Policy

65

Figure 3-5: Immigrant Stock in the U.S. 1900-2000 (% to total foreign born) 90 80 70 60 50 40 30 20 10 0

Europe Asia Africa Oceania (Years: 1900, 10, 20, 30, 60, 70, 80, 90, 2000)

America

America

Latin

Northern

Source: Gibson, Campbell J. and Emily Lennon. 1999. “Historical Census Statistics on the Foreign-born Population of the United States: 1850-1990” Population Division working paper no. 29, U.S. Bureau of the Census. Table 2.

Meanwhile, their Asian and Latin American counterparts increased rapidly: the number of Asian residents in the year 2000 is estimated at 7.2 million or 26 percent, and Latin Americans at 14.5 million or 52 percent. Because the first generation immigrants have close kinship ties to their home countries, they will bring in more coethnic immigrants under the family-oriented immigration policy and accelerate the ethnic re-composition of immigrants and eventually of the American population in general. The 1986 Immigration Reform and Control Act (IRCA) contributed to the growth of Latin American (particularly Mexican) immigrants by allowing legalization of undocumented alien residents. The IRCA very quickly legalized almost 2.7 million immigrants in the 4 years after its enactment in 1989 (see Table 3-1 and footnote 2). Of the 2.7 million legalized immigrants, approximately 2.4 million or 92 percent were Latin American and Caribbean, and 74.7 percent were Mexican (INS 1999b). Total annual inflow reached the historical height of 1.8 million in 1991. The 1990 Immigration Act, effective after 1992,

66

Social Determinants of Immigrant Selection

placed an overall cap of 700,000 (then 675,000 in 1995 and after) and increased the allotment for employment-based immigrants from the previous quota of 27,000 to the current 140,000. The record in the 1990s suggests that, as long as the U.S. government maintains the overall cap, with exception of IRCA-type amnesty, immigrant inflow will hover around 700,000 per year in the coming decades. Table 3-1: Admission to the U.S. by Classes, 1989-2000 Immediate Year Relative of U.S. citizen 1989

20

Employ

IRCA

Family -ment Refugee Legalization Diver- Other Preference Preference Asylee dependent sity 20

5

8

44

NA

3

1990 15 14 4 6 1991 13 12 3 8 1992 24 12 12 12 1993 28 25 16 14 1994 31 26 15 15 1995 31 33 12 16 1996 33 32 13 14 1997 40 27 11 14 1998 43 29 12 8 1999 40 34 9 7 2000 41 28 13 8 Source: INS Statistical Yearbook. 2000. Table A.

57 61 22 9 5 1 1 z z z z

NA NA 3 4 5 7 6 6 7 7 6

4 3 4 4 2 1 1 1 1 1 4

CANADA In Canada, the “White Canada” immigration policy was pronounced dead by the introduction of a series of new Immigration Acts from 1962 through the 1970s. Under the previous law, Canada admitted mostly white Europeans and Americans and restricted non-white immigrants based on race, nationality, customs, and other obscure criteria that allowed immigration officials’ discretion in admission decisions. The new act implemented three (two first, and another added in 1967) admission categories. They are: 1) sponsored immigrants; 2) independent immigrants; and 3) nominated immigrants. Beginning in 1967, immigrants entering in the last two categories became subject to the point system. Thenceforth, the annual limit and sub-categorical

Immigration History and Policy

67

allotments have been revised annually in regard to the country’s economic needs and humanitarian consideration, and the total cap has been maintained at one percent of the total Canadian population. Table 3-2: Planned and Actual Immigration to Canada, 1979-2000

(thousands) Year 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Lower * * 130 130 105 90 85 105 115 125 150 * * * * * 190 195 195 200 200 200

Planned Level Higher 100 120 140 135 110 95 90 115 125 135 160 200 220 250 250 250 215 220 220 225 225 225

Actual 112 143 129 121 89 88 84 99 152 162 191 216 233 255 257 224 213 226 216 174 190 227

* No lower limit was set in these years. Source: Badets and Chui 1994; completed by author.

As shown in Figure 3-2, annual immigration to Canada after the 1960s shows no obvious trend but only fluctuation between one and two hundred thousand. This equilibrium is even clearer in the proportion to the population, which reflects Canada’s immigration policy of pegging total annual inflow to one percent of its population.

68

Social Determinants of Immigrant Selection

Within this limit, the government has accommodated the economic and humanitarian needs by adjusting the admission and point system. The 1976 amendment to the Immigration Act (put into force in 1978) emphasized family reunification and revised the categories into four: 1) independents, 2) family, 3) assisted relatives, and 4) humanitarian. From then to the mid-80s, when the country’s economy was in depression, more emphasis was given to family immigrants and refugees, and the total number of admissions declined. In the upturn after the mid-80s, the planned total immigration was increased from 90,000 in 1985 to 250,000 in 1994. Particularly after the 90s, the allotments to independent economic immigrants were significantly expanded. This shift in Canadian immigration policy is in sharp contrast with the U.S. policy that maintained its emphasis on family immigration. Figure 3-6: Immigration to Canada by Regional Origin, 1956-2000 (thousands)

1400 1200

Latin America & Caribbean

1000 Asia

800 600

Europe & USA

400 200

Other 0 1956- 1961- 1966- 1971- 1976- 1981- 1986- 1991- 199660

65

70

75

80

85

90

95

2000

Source: F. H. Leacy Ed. 1999. Historical Statistics of Canada. 11-516-XIE, A350, Statistics Canada (http://www.statcan.ca/bsolc/english/bsolc?catno=11-516-X).

Immigration History and Policy

69

Figure 3-7: Foreign Born Residents in Canada 1981-1996 (% to total immigrants) 80 70 60 50 40 30 20 10 0

Europe

Asia

Africa

America

& other

Central & South

Oceania

USA

(Years: 1981; 91; 96) Source: Citizenship and Immigration Canada (CIC). Citizenship and Immigration Statistics, 1981, 91, 96. (http://www.cic.gc.ca/english/pub/index-2.html#statistics)

As Figure 3-6 clearly shows, in 1956, the size of European immigration was 146 thousand or 88 percent of all immigrants; in the year 2000 it was only 43 thousand or 19 percent. Asian immigration in the same period grew from 3.5 thousand to 139 thousand; its share expanded from 2 to 62 percent. Latin American immigration to Canada increased moderately, but it was still very small compared to that in the United States. Canada is also experiencing the ethnic re-composition of its immigrant population. In terms of foreign-born stock, Europeanborn residents still comprise the majority as shown in Figure 3-7. However, the number of Asian-born residents had increased to five percent of the total Canadian population by 1996. While the foreign-born population in Canada was about 16 percent of the total population in the 80s and 90s, it was 6 to 10 percent in the United States. Despite this larger proportion of immigrants, or rather because of it, the Canadian authorities have been practicing selective screening and an economically oriented visa allocation system (see Table 3-3). Expansion of the independent immigration category has

70

Social Determinants of Immigrant Selection

increased Asian immigrants without family ties, but the chain-reaction inflow of families and relatives may not happen in Canada because family category visas are limited. European immigrants are decreasing, but they are still the majority of the immigrant population stock. Because of the lack of Latin American immigrants, non-European immigrants may not replace Europeans as quickly as they have in the United States. Table 3-3: Immigration to Canada by Entry Category, 1984-2000 (%)

Year

Family

Refugee& designated

Assisted Relative

Other Independent & Economic

1984

49.7

17.4

9.3

23.7

1985

45.7

19.9

8.8

25.7

1986

42.5

19.3

5.9

32.2

1987

35.2

14.1

8.1

42.6

1988

31.7

16.6

9.6

42.2

1989

31.7

19.2

11.2

37.9

1990

34.2

18.6

11.8

35.4

1991

37.3

23.2

9.6

29.9

1992

39.5

20.5

7.9

32.1

1993

43.8

11.9

9.0

35.2

1994

41.9

9.1

12.3

36.7

1995

36.3

13.4

13.8

36.5

1996

30.2

14.2

12.8

42.8

1997

27.7

12.8

11.8

47.7

1998

29.2

14.6

8.6

47.7

1999

29.1

13.4

7.8

49.7

2000

26.6

13.4

8.1

51.9

Source: B.C. Stats. British Columbia, Canada. (http://www.bcstats.gov.bc.ca/)

Immigration History and Policy

71

AUSTRALIA Immediately after the Second World War, Australia realized that it needed a larger population to defend itself, maintain the aging population with the low birth rate, and respond to the demands of the international community. It began a program to increase the immigration level to one percent of the current population but keep the nationality composition as it was, meaning 90 percent British. Soon they realized that Britain could not provide a sufficient supply of immigrants, and so they started to admit European refugees. The government also made bilateral agreements with European countries to invite those non-British whites in favorable conditions. At this time, however, non-whites were still strictly prohibited from entering Australia. Even Australian soldiers could not take their Asian wives back home (Price 1993). British and some other white European refugees had the right to sponsor families and relatives, often with financial assistance for their passage and settlement, but none of these benefits were available for non-Europeans. Figure 3-8: Immigration to Australia by Regional Origin 1945-1999 (thousands) 180 All Asia 160 140 120

Middle Easst & Africa

100 80 Europe & former USSR

60 40 20

Others

0 1945- 1949- 1954- 1959- 1965- 1970- 1975- 1980- 1985- 1990- 199549 54 59 65 70 75 80 85 90 95 99

Source: Australian Bureau of Statistics. Community Profile of Australian Immigration, Various Years.

72

Social Determinants of Immigrant Selection

100

Figure 3-9: Foreign Born Residents in Australia 1901-2000

90 80 70 60 50 40 30 20 10 0 Europe

Asia

Africa & ME

Latin America

North America

Oceania

(Year: 1901; 47; 54; 61; 71; 81; 91; 2000) Source: Australian Bureau of Statistics. (http://www.abs.gov.au/ausstats/)

The government slowly opened the door to non-whites in special circumstances – those who had distinguished skills, Chinese who could not go back to communist China, etc. The Immigration Restriction Act was replaced by the Migration Act in 1958, but the White Australia policy itself continued. The 1971 census shows that 83 percent of immigrants are still of European origin. The Labor Party finally dismantled the White Australia policy in 1973 when it took a majority of seats in the congress. The new policy put in place after 1973-74 treated all immigrants equally regardless of race, religion, or country of origin. It set up the three categories of immigrant admission: 1) sponsored, immediate family, 2) sponsored, less immediate relatives and friends, and 3) unsponsored immigrants. Modeled on the Canadian system, the first point system of immigrant selection, called NUMAS (the Numerically Weighted Multifactor Assessment System), was implemented in 1979. In the beginning, it placed more emphasis on skills and English proficiency than on family reunification, and there was significant room for discretion by government officials with regard to the “personal suitability” of applicants. Soon after, the system was revised in 1982 to make the Australian point system of today very similar to Canada’s. For example, the system includes exemption of close family members,

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73

more emphasis on kin support, and less discretion by officials. These changes opened the door to Asians and other non-Europeans, but the total intake was rather reduced after the reform, perhaps because the government was reluctant to admit Asian refugees (see Figure 3-8). Asian immigrants increased their share from 6% in 1965-70 to 21% in 1970-75, and further to 45% in 1990-95. European immigrants decreased their share from 65% to 40% and then to 28% in the same period. The shift is even clearer in terms of foreign-born stock. As Figure 3-9 indicates, all of the net increase in the foreign-born population has been due to non-European immigrants since the 1970s. In the total population, however, the white population is still overwhelmingly larger than that of Asians: there were 92% whites and 7% Asians in 2001.

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CHAPTER 4

Modeling Immigration Processes

This chapter develops conceptual and empirical models, derives hypotheses, and describes data sources and measurement of variables. The first section operationalizes the concepts discussed in Chapter 2 in order to build concrete testable hypotheses. The second section describes data sources, their merits, and their limitations. The third section explains the ways in which variables are measured. Particular attention is paid to the measurements of immigrant skills because they involve several temporal factors such as the time of arrival, age, and duration of residency in the host countries as well as individual-level data. Some strategic decisions have been made when it comes to the empirical modeling, compromising between the conceptual model and the available data. As a result, the research sets up four combinations of different data sets and analytical units: one using only the U.S. data with detailed national origin and geographic categories, and another making reference to international comparison with rough categorization of immigrants by continent and by specific national origin. Ideally, the method used for the U.S. would be applicable to Canada and Australia, but due to the small sample size and limited immigrant identification,36 it is necessary to use different data settings and geographic units.

36

Small immigrant groups are often aggregated into a larger category, such as “other South East Asian,” to maintain confidentiality of the respondents. As a result, only large groups can be identified independently.

75

76

Social Determinants of Immigrant Selection

ECONOMIC MODELS This chapter first introduces an economic model, and then integrates social and structural factors into the model as it shifts from the individual level to larger levels of analysis. The formal economic model is based on Borjas (1991: 31-40), in which immigrant skills are estimated as a function of origin and host country characteristics at the time of immigration. The generic empirical model is: (1) Qijt = α X it + β X jt + ε ijt where Qijt is the skills of immigrant groups arriving from origin i at destination j in year t in relation to the skills of comparable white natives, and Xit and Xjt are the sets of characteristics of the country of origin i and the destination country j in year t. The value of Qijt is the immigrant-native skill differential controlling for other group characteristics, and it indicates positive/negative selection. The measurement of this dependent variable is discussed in the later section of this chapter. Selection is positive if an immigrant group has on average more earning or education than natives after controlling for individual human capital and demographic characteristics; it is negative if they have less than natives. In the economic theories, Xit and Xjt capture (1) the difference between mean incomes on both sides of migration, (2) migration costs, (3) deviation of income or skill (inequality), and (4) the similarity of reward systems between the two sides of migration.37 The returns to education are added to the function for educational attainment. Indicators used by Borjas are, corresponding to the four items above, (1) the GNP differential, (2) distance, (3) the inequality ratio 38 , and (4) political freedom, communist government, recent regime change, and group-level English fluency39, controlling for pre-and post-1965 immigration reform. 37

This specification was derived from the “migrant market” and “selfselection” theories. See Borjas (1987; 1991) for detailed explanations for why these are essential factors of immigrant skill selection. 38 The ratio of household income of the top 10 percent of the households to the income of the bottom 20 percent of the households in about 1970 (Borjas 1991: 51). 39 The fraction of 1975-80 cohort of immigrants who speak English well or very well (Ibid.).

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Estimation from this model might be biased because migrants are selected from their home countries at different emigration rates (Heckman 1979). Due to the truncation of the samples of those who do not migrate, the model based only on migrants underestimates the effects relating to the self-selection of immigrant skills (Berk 1983). To adjust for this selectivity bias, Borjas (1987; 1991) used the inverse Mill’s ratio, usually indicated as λ, representing the likelihood of a person to be selected (emigrate), and multiplicatively introduced it into the following equation: (2) Qijt = (α X

'

it

+ β X ' jt )λijt + ε ijt .

The measurement of λijt is discussed in a later section. Here, the X’ excludes variables that affect migration scales, since the λ directly adjusts for the change in selectivity through the size of migration (the scale effect). The variables remaining in X’ are therefore factors that determine positive or negative selectivity. In other words, some factors such as inequality affect the selection of skill itself, but others such as migration costs affect the skills through the scale of migration. Recall that the self-selection theory assumes migrants to be selected from top or bottom end of the normal distribution in their country of origin.40 According to this assumption, a smaller emigration rate means more highly positive or negative selectivity, and a greater emigration rate means the average skill of a migrant group comes close to the population mean. Borjas (1987; 1991) called equation (1) a reduced form and equation (2) a structural form because equation (2) eliminated endogenous factors by the interactions with λ. The rate of return to education (percent increase in earnings for every year of additional education) is used for the regression on educational attainments, assuming that highly educated workers are less likely to emigrate from countries that offer high return to education. He found that the mean education had a positive effect, the return rate to education had a negative effect, and GNP per capita of origin as a fraction of

40 When the selection is negative, it is practical to assume that emigrants are not from the very bottom of the distribution but from somewhat higher because they need to be able to afford migration costs.

78

Social Determinants of Immigrant Selection

destination (USA) GNP per capita had a negative effect on immigrants’ selectivity (ibid.). SOCIAL DETERMINANTS This research retests the economic models introduced above as a point of departure. Then it moves on to assess the effects of network and structural factors on immigrant skills. Let us indicate the network factors as Xijt and revise the equations into: (3) Qijt = α X it + β X jt + δ X ijt + ε ijt , and (4) Qijt = (α X 'it + β X ' jt + δ X 'ijt )λijt + ε ijt where Xijt is the characteristics of ethnic groups or migrant networks to which immigrants belong for origin country i, destination country j in year t, and again X and X’ are distinguished in terms of scale and selectivity effects. The structural-level concepts such as affinity connection and structural imbalancing will have their indicators included in Xi. The indicators of affinity are the number of tourists and Foreign Direct Investment. The indicator of the imbalancing is the return rate to the education. (Detailed explanations of these choices are given in the measurement section.) To measure the quantitative aspect of migrant networks, clustering of coethnic groups and residential density were used. For the quality of migrant networks, the ethnic group characteristics were measured with regard to two aspects: communal types and resource endowments. This research uses newness and occupational networks of the ethnic groups for communal types, and educational and financial resources in these groups for resource endowments (again, detailed discussion is given in the measurement section). Scale and selectivity effects also exist in the ethnic group level variables. For example, migrant networks reduce mobility costs and increase the migration scale. Social capital in migrants and ethnic networks increase selectivity by facilitating the adaptation of newcomers and speeding up their earnings growth. The system affinity derived from international exchanges affects selectivity by providing better transferability of skills, which decreases the initial drop of income upon arrival and shortens the time needed to cross over native

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levels. The structural imbalancing that leads to brain drain also increases selection, while the development process itself has a scale effect as well by creating a large pool of surplus labor. UNITS OF ANALYSIS The units of analysis are defined by the subscripts ijt, and there are several approaches to this model depending on the definition of these units. For i, this research uses country-of-origin information because national statistics are often publicly available at many data sources. The subscript t represents arrival cohorts categorized in every five years. The unit of receiving side j can be countries or local areas. However, with the exception of the U.S., the other two host countries do not have a sufficient number and variety of immigrant groups in their data, which makes it difficult to have many i and many j at the same time. These conditions necessitate splitting the empirical work into four parts (see Table 4-1): the first two settings (I and II) take countries of origin for i and use only the United States for j; the other two settings (III and IV) use a smaller number of origin areas for i and use all the three host countries for j. In setting II, receiving area j in the U.S. is further divided into smaller local areas in order to address the effects of social factors. In the latter two, j represents the U.S., Canada, and Australia. Since the available nationalities of immigrants’ origin i is limited in Canada and Australia, i must be aggregated to continents or a limited number of large-size immigrant groups. Table 4-1: List of Four Model Settings and Units

Settings I: II: III: IV:

i 16 countries of origin 16 countries of origin 5 continents of origin 5 countries of origin

j The United States as a whole Sub-areas of the United States 3 host countries 3 host countries

Dependent variables are constructed according to these combinations of different units. In setting I, the dependent variables are estimated for each country of origin group in the United States and further divided by arrival cohorts, while in setting II, the dependent variables are estimated for each national origin group in each local area (again further divided by arrival cohorts). Some independent variables

80

Social Determinants of Immigrant Selection

need to change their unit definition accordingly, and others need no alteration. For example, the ethnic group or network characteristics (ij variables) must be measured at national and local levels. Model specifications are maintained as much as possible to retain comparability across the settings. Geographic unit j in setting II represents a Metropolitan Statistical Area (MSA) instead of a country of destination as a whole. An MSA is defined by the Federal Office of Management and Budget to delineate large geographic units that contain the regular social and economic activities of their residents. An MSA must be an area that includes at least one city or county and surrounding outlying counties that are integrated in terms of commuting and other socioeconomic relationships. A city with over 50,000 residents, or an urbanized area of 50,000 residents with a total metropolitan area population of at least 100,000 (75,000 in New England), qualifies to be an MSA. Conventionally, we call it just an MSA, but to be exact, a generic term for this geographic unit is an MA (Metropolitan Area) that is either an MSA or a CMSA (Consolidated MSA). The term CMSA is reserved for a few very urbanized areas and is further divided into PMSAs (Primary MSAs). To further complicate the situation, this system began with the 1990 census, and it was referred to altogether as SMSA (Standard MSA) in the 1980 census, which was integrated into the MAMSA/CMSA-PMSA system. To avoid confusion, this research calls it just an MSA, but it means either an SMSA, an MSA or a PMSA. There are 272 MSAs identified in the 1990 U.S. census, and most of them have corresponding SMSAs in the 1980 census. The focus on locality is not very new in immigration study. Encahutegui (1992) used county-level data to analyze the impact of immigrants on local employment. Tienda and Wilson (1992) used SMSAs to study earnings of Hispanics. More recently Bean, Van-Hook, and Fosset (1999) studied the effects of immigrant concentration at MSA-level on native employment. Gurak and Kritz (2000) constructed state-level contextual variables to explain interstate migration of immigrants. This research resembles that of Tienda and Wilson in terms of dependent variables and uses an approach similar to that of Gurak and Kritz, which focuses on both individual and contextual level determinants, but it is unique in including as many as 16 ethnic groups.

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81

DATA SOURCES AND SAMPLE This research analyzes a series of quantitative statistical models using individual data of migrants in the receiving countries and aggregated national data of sending and receiving countries. Samples of immigrants and comparable natives are selected from multiple censuses of the U.S. (5%, 1980 and 1990), Canada (2% in 1981, 3% in 1991), and Australia (1% in 1981 and 1991). These sample data are obtained with permission from the U.S. Bureau of Census, Statistics Canada, and the Australian Bureau of Statistics, respectively. From these sample data, individual cases with the following characteristics were selected for analysis: immigrant males aged between 25 and 64 years old, who worked and earned more than $1,000 in the previous year, were not living in group quarters, were not enrolled in school, and were not in the armed forces. It includes both wage/salary earners and selfemployed persons. Earning is defined as the sum of wage or salary income and farm or non-farm self-employment income, adjusted to regular annual earnings using hours and weeks worked in the previous year. Additional data on the U.S. land area is obtained from PL94-171 data (one of the census products for redistricting) published by the Census Bureau. The United Nations and related international organizations provide comprehensive information on migrants’ countries of origin such as national income and income distribution measures as well as other social and economic indicators. World Development Indicators (World Bank) compiles income distribution indicators and other indicators such as national population, trade, investment, and growth rates. The Human Development Reports by UNDP (the United Nations Development Program) provide life expectancy, infant mortality rates, school enrolment, labor force, central government expenditure, etc. Publications by the International Labor Organization (ILO) on poverty and income distribution (1996) are also used for a measurement of inequality. These UN data are supplemented by some private research on international indicators such as Comparative National Time-Series Archive (CNTSA) by Arthur Banks at the Inter-university Consortium for Political and Social Research (ICPSR), which was updated and supplemented by Barro and Lee (1994 Data Set for a Panel of 138 Countries). The Penn World Tables (PWT, University of Toronto) also

82

Social Determinants of Immigrant Selection

bring together some unique data on political systems and social welfare that the UN data lack. The International Data Base (IDB) made by the U.S. Census Bureau also collected national net migration, race and ethnicity, literacy, and labor force data from as many as 227 countries. The Bureau of Economic Analysis (BEA) has detailed data on international economic transactions of the United States. The World Income Inequality Database (WIID) of the United Nations University maintains an income distribution database. MEASUREMENT OF RELATIVE EARNINGS Immigrants’ unobserved skill is measured as estimated relative earnings. It measures earning differentials between immigrants and their native counterparts controlling for all observable human capital characteristics and labor market experiences. By estimation, it compares a hypothetical immigrant worker who entered a host country at the age of 20 and spent 30 years there with a native worker who is of the same age, worked for the same period, and has the same observed human capital characteristics such as education. The wage model for both immigrants and natives is: (5)

ln w = (1 − M ) β n X i + M ( β m1 X i + β m 2Yi + β m 3Ct ) + βπ ij + ε i

where lnw is natural-logged individual wage or earning, M is a dummy set to one if a person is an immigrant; X is a set of individual characteristics such as age, age-squared, education, marital status, disability, and urban residence; Yi indicates the years since immigration and its square term; C is a set of dummy variables indicating entry cohort group; and π is a dummy variable for the survey year set to one if the data is from the 1990 or 91 censuses. The migrant dummy M allows immigrants and natives to have different slopes (βn and βm1). However, the effect of π is not allowed to vary in this equation; thus it is assumed that natives and immigrants received same impact from, say, the economic boom in the 90s on their wages. This assumption is technically necessary in order to avoid multicollinearity in the immigrant equation. Because age, host-residency, arrival year, and survey years are in a linear relationship (when any three values are known, the other value is automatically given), these effects are not

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83

identifiable and it is necessary to borrow other information from the native equation (Borjas 1991). This equation (5) was repeatedly run for each origin country, each destination country (and area), and every arrival cohort, making dependent variables at different levels. It is possible that racial and ethnic discrimination are confounded with native-migrant difference in Equation (5), in the migrant effect βm1. In order to net out discrimination effects, the native reference groups are chosen from comparable native ethnic groups. The choice of reference group is not always simple and has to be somewhat arbitrary, especially when not enough native minority groups are observed in the sample. For example, German immigrants may have native whites as their reference group; but how about such new groups as Cambodians who do not have many native born coethnics? Sometimes pan-ethnic categories such as Asians or Caribbeans are used in such cases. After this estimation, however, only white natives are compared with immigrant groups. The period of residency Y captures whatever latent ability an immigrant possesses to adapt to the new labor market – greater effects of this variable on wages means that this immigrant more quickly catches up to the wage level of the comparative natives who have equivalent quality in all other observed characteristics. Figure 4-1 illustrates the image of the relative income growth. In the Figure, the three lnw-s indicate the wage growths of two immigrants by the solid lines, and that of one native by the dotted line. We can estimate these curves of wage growths by estimating someone who enters the labor market at age 20 and earns most at age 50, holding other factors in Equation (5) constant. Although the two immigrants started with the same entry wage, by their age 50 the wage of immigrant “1” exceeds the wage of the comparative native in the course of 30 years, but immigrant “2” does not catch up to the native level. Whether an immigrant group exceeds natives in terms of mean wages can be calculated by taking the fraction between immigrants and natives at age ˆ ij − lnwˆnj , but this does not take into account value discount 50 by lnw over the 30 years. The present value differential of an immigrant cohort t (Qijt) relative to the comparative white native, therefore, takes the discount rate into calculation:

84

(6)

Social Determinants of Immigrant Selection

Qijt = [ln wˆ ij 0 − ln wˆ nj ] +

gijt − g nj r

ˆ ij0 is the mean entry wage of an immigrant group; lnwˆ nj is where ln w the estimated mean wage of natives whose individual characteristics are equivalent to those of the ij immigrant group; r is a discount rate; and gijt and gnj indicate wage growth rates of immigrants and natives over the following 30 years. These two “g-s” are calculated from estimated wages at age 20 and 50 by fixing ages and host-country residency Y in Equation (5); then growth rates are calculated by: (7) g ijt = [(ln wi1 | X , age1 , Y1 , Ct ) − (ln wi 0 | X , age0 , Y0 , Ct )] / T (8)

g n = [(ln wn1 | X , age1 ) − (ln wn 0 | X , age0 )] / T Figure 4-1: Estimated Relative Wage Concept (Image)

Immigrant 1

lnŵij1 lnŵnj lnŵij2

Native Immigrant 2

lnŵij0 Entry at age 20

age 50

Modeling Immigration Processes

85

where wages are estimated conditional on mean X characteristics, ages, residency Y, and for each entry cohort C. Subscript 0 and 1 indicate time: 0 for age 20 and 0 years of residency, and 1 for age 50 and 30 years of residency. T is the time passed or 30 in this case. This age setting (20 and 30) is following Borjas (1991). In Equation (8), natives are given X characteristics, which are equivalent to the mean immigrant characteristics. This study chose 16 countries of origin in 8 cohorts from the immigrants in the U.S. in 1980 and 1990. These origin countries were chosen from three major migrant-sending regions of the world: Europe, Asia, and Latin America. Each group tends to have a large immigrant size because small groups are excluded to maintain large enough sample sizes. On one hand, this constrains the representativeness of the immigrant sample, as already pointed out in Jasso and Rosenzweig (1990). On the other hand, to treat immigrants as one big group obscures the heterogeneity of immigrants. In order to address this issue, the first part of this research concentrates on the 16 countries of origin, and the international comparisons in the later chapter expand the scope to include all immigrants in order to capture more general characteristics of immigrants. Q igt in equation (6) is calculated using estimates from the regression equation (5) for each country of origin and cohort group. Earnings are adjusted for inflation using the Consumer Price Index for the survey year. For the 16 groups of U.S. immigrants, with 8 cohorts each, 128 such estimates are calculated. Results are shown in Table 42. The scores indicate the proportional differences that each cohort group had as against comparable native whites in terms of the present value of their lifetime earnings, controlling for human capital and demographic characteristics. In general, European immigrants seem to have positive scores, and Asian and Latin American immigrants have more negative scores than positive scores. For example, immigrants from the United Kingdom are expected, at the point of entry in 198589, to earn 39 percent more than comparable white natives would earn. In contrast, the most recent Filipino immigrants are expected to make 25.8 percent less than comparable natives would after 30 years of adaptation. Since education and other observable individual characteristics are controlled for, it is assumed that the difference is due to an ability that affects economic adaptation over the period of residence in the host country. Based on this assumption, the most

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Social Determinants of Immigrant Selection

Table 4-2: Estimated Relative Earnings by Entry Cohorts Year of Arrival Country of Origin

<1950

51-59

60-64

65-69

70-74

75-79

80-84

85-89

United Kingdom

-.149

-.062

-.016

.088

.131

.274

.271

.390

Germany

.105

.103

.105

.106

.030

.022

-.024

-.068

Italy

.098

.152

.157

.165

.153

.046

.024

.023

Canada

.035

.044

.076

.139

.126

.176

.160

.204

Poland

.011

.022

.062

.131

.113

.063

.054

-.056

China

-.215

-.091

-.086

-.076

-.080

-.090

-.112

-.158

Japan

.011

.017

.112

.089

-.067

-.143

.033

.111

Korea, Rep.

-.071

-.032

-.006

.063

-.002

-.107

-.130

-.181

India

-.232

.023

.022

.047

.043

-.002

-.039

-.032

Philippines Dominican Rep.

.013

-.128

-.141

-.088

-.124

-.157

-.204

-.258

-.304

-.226

-.163

-.098

-.028

.030

.011

.150

Mexico

-.014

-.025

-.033

-.035

-.043

-.101

-.194

-.243

Jamaica

-.326

-.215

-.169

-.015

.101

.097

.129

.122

El Salvador

.222

.009

-.121

-.174

-.280

-.277

-.347

-.358

Guatemala

-.255

-.088

-.093

-.070

.007

-.048

-.101

-.053

Colombia .022 Weighted Means Europe & Canada .011 Asia -.015 Latin America -.009

.067

.024

-.055

-.099

-.132

-.153

-.262

.036 -.009

.031 -.010

.032 -.010

.014 -.014

.014 -.026

.013 -.037

.020 -.043

-.010

-.019

-.022

-.025

-.062

-.106

-.102

.016

.001

.001

-.025

-.074

-.130

-.125

Total

-.013

Source: 5% PUMS from 1980 and 1990 U.S. Census, Bureau of Census. Author’s estimation. The mean of the relative earnings is weighted by the estimated population. The Regional scores are not averages but the partial scores weighted by total population so that sum of all regions makes total average scores.

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87

recent cohorts of Japanese, Dominican and Jamaican immigrants are positively self-selected, while the rest of Asian, Latin American, and Caribbean immigrants are negatively self-selected. The table also shows trends in cohort qualities. The expected earnings of Jamaican immigrants who entered before 1950 is 33 percent less than that of natives, but their cohort quality has improved in newer cohorts, and those who entered after 1970 can earn about 10 percent more than natives. Among Chinese immigrants, on the other hand, the 1965-69 cohort can earn about 8 percent less than comparable white natives would, and the newest Chinese cohort will make 15.8 percent lower earnings. The general trend is more clearly observed in the population-weighted means at the bottom of the table. The 1951-64 cohorts were positively selected, but after 1965, the selection turned negative and became increasingly so in the newer cohorts. When total averages are decomposed into regions, it is clear that the increasing relative size of Latin American immigrants contributed to the overall decline of immigrant quality. In the most recent cohort arriving between 1985 and 89, immigrants on average earn 12.5 percent less than native whites but the greatest part of this difference, 10.2 percent, is attributed to Latin Americans. This estimated indicator means the rate of economic adaptation rather than the “unobserved” quality itself. In other words, “unobserved” qualities are implied as part of the many determinants of economic adaptation, and these non-individual differences are still implied in the between-group differences of this “quality” indicator. Comparing these estimates with Borjas’s (1991: Table 1.3) estimates based on the 1970-80 data, where national origin and cohort overlaps between the two studies, about 80 percent of the estimates have the same directions, indicating continuity of trends from 1970-80 to 198090. 41 The remaining 20 percent suggests that the relative earnings 41

In 128 immigrant-cohort groups commonly selected in the two studies, 81% of cell signs and 76% of cohort quality changes matched between the two studies. There are some minor technical differences in the two procedures. I applied a more rigorous definition of immigrant groups including ethnicity in addition to country or birth, and used ethnically comparable native groups in equation (9) rather than the rough racial categories used by Borjas. The native white base in this study was estimated using British, Irish, German, and Italian natives and not miscellaneous native whites. These differences could have altered estimates, but they might just improve the accuracy of the estimation.

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Social Determinants of Immigrant Selection

growth could change dramatically over the 10 years. For example, in Borjas (ibid.), German immigrants showed a positive growth relative to natives, but this new estimation shows that their growth declined in the 80s. For another example, all but one out of six Japanese cohorts had negative scores in Borjas (ibid.), but in the new estimation, only two cohorts had negative and six had positive scores, due probably to the Japanese economic boom in the 80s. MEASUREMENT OF RELATIVE EDUCATION Immigrants’ educational attainment is measured by the years of schooling. It is a simple measurement, but attention should be paid to age and cohorts. The earlier immigrant cohorts are more likely to be older, and the older cohorts are less educated. This structural relation should be taken into account in measuring immigrants’ educational levels. By definition, age structures of consecutive cohorts are such that the bottom (youngest) part of the population pyramid disappears by the time of observation. For example, an entry cohort of 1960 must be at least 30 years old in 1990. If we measure educational attainment of these immigrants at age 30 and over, relative to natives at all ages, the measurement could be biased because of the inverse correlation between age and educational attainment. The survey years also need to be taken into consideration because accessibility to education changes over the ten-year interval. A simple way to control away these systematic factors is to use regression estimation for each cohort. Assuming a linear relationship between education and the related factors, (9) Eijt =

β 0 + β a age + Qijts (M ij Ct ) + β tπ + ε i ,

where Qs is the differentials in schooling for immigrant group ijt (origin, host, cohort), and M and C are dummy variables to indicate ij group and cohort t compared with the natives who have zero value in the set of dummy variables. Because the adult population between 25 and 64 years old is sampled, it is assumed that other current demographic status such as marital and health status does not affect their education. Table 4-3 shows the results for United States immigrants. Those from the United Kingdom, for example, have .57 year greater education than natives do in the earliest cohort, and the advantage increased to

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89

1.15 years in the most recent cohort, after controlling for age and period. For some Asian immigrants, the change before and after the 1965-69 cohort is striking. Korean immigrant cohorts before 1969, for example, had more than 3 years more education than natives, but this difference rapidly declined after the 1970s. Obviously, the immigration restrictions until 1965 against non-white immigrants allowed only very highly educated persons from Asia to enter the United States, but this selectivity quickly eroded after the 1965 reform. Latin American groups were unaffected by the policy but show a steady decline of education. Their contribution to the total mean education is clearly seen in the population-weighted means by regional origin in the bottom three rows of the table. The decline in the relative levels of education of Latin American immigrants, as well as their large numbers, indicates their overwhelming dominance in determining the average immigrant education. Table 4-3: Standardized Education Differentials by Entry Cohorts Country of Year of Arrival Origin <1950 51-59 60-64 65-69 70-74 75-79 80-84 85-89 United Kingdom .57 .72 .81 1.27 1.04 1.42 1.05 1.15 Germany 1.11 -.19 -.07 -.48 -.96 .30 .25 1.01 Italy -1.51 -2.14 -2.82 -3.35 -3.73 -2.30 -1.32 -.52 Canada -.46 -.42 -1.05 -.64 .01 1.13 .45 .77 Poland .10 -.37 -.99 -.81 -.89 -.20 -.11 -.39 China -.32 1.52 1.69 1.34 .63 -.28 -.64 -.49 Japan .87 1.29 1.65 1.39 .86 1.81 1.05 1.79 Korea, Rep. 3.66 3.85 3.30 3.32 1.69 .63 .11 .05 India 2.97 4.33 4.34 3.94 3.21 2.04 1.14 .79 Philippines -2.16 .91 1.36 1.14 1.30 .57 .46 .43 Dominican Rep. -.86 -1.95 -3.06 -3.49 -3.69 -3.93 -3.76 -3.52 Mexico -4.65 -4.38 -5.10 -5.75 -6.45 -6.70 -6.28 -5.96 Jamaica -.89 -.47 -.65 -1.14 -1.78 -1.67 -1.94 -2.37 El Salvador .75 -.28 -1.43 -2.12 -3.33 -4.82 -5.10 -6.44 Guatemala .63 .28 -.79 -2.65 -3.70 -4.32 -4.94 -5.64 Colombia 1.96 .96 -.15 -.94 -1.71 -1.52 -2.00 -2.12 Weighted Means Europe & Canada -.23 -.41 -.41 -.28 -.16 .03 .03 .06 Asia -.06 .23 .41 .56 .50 .20 .07 .13 Latin America -1.20 -1.28 -1.85 -2.26 -3.37 -3.77 -3.17 -2.74 -1.50 -1.46 -1.85 -1.98 -3.04 -3.55 -3.08 -2.54 Total Source: 5% PUMS from 1980 and 1990 U.S. census, Bureau of Census.

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Social Determinants of Immigrant Selection

COUNTRY-OF-ORIGIN AND DESTINATION CHARACTERISTICS Distance To reflect on migration cost, this research uses the average distances to capitals of the world’s 20 most major exporters in 1000 kilometers, weighted by values of bilateral imports (Barro and Lee 1994). Past studies used physical distance from countries of origin to destinations to approximate migration cost (Yang 1995). In my examination (Kawano 2002), this variable, derived from Fitzpatrick and Madlin (1986), does not have good association with either education or earnings of immigrants. Borjas (1991) also found insignificant the effect of this variable on earnings. Raw distance itself does not fully represent migration cost unless it takes into account the convenience and price of transportation. Alternatively, Barro-Lee’s variable of distance not only represents distance with convenience of transportation, but also accounts for economic proximity of countries of origin to the world’s metropolises, which ideally reflects the “social distance” concept. It is expected that the skills of immigrants increase when social distance is greater. Remote social distance limits the scale of immigration, and thus it takes more motivation and resources to overcome such distance. FDI Foreign Direct Investment is a net balance of payment inflow in the countries of origin and indicates the affinity with major capitalist countries. It is a mediator of economic as well as cultural-ideological penetration of capitalism, through which potential emigrants build up connections as well as familiarity with host countries. It is expected that this variable has a scale effect and has a negative impact on immigrant skills. Since FDI differs by national size, it is standardized as a proportion to GDP. The data source is WDI. National Wealth Immigrants are attracted by the wealth of rich countries, but persons in very poor countries may not be able to overcome

Modeling Immigration Processes

91

the cost of migration even though they have great motivation. Nevertheless, people in host countries are generally richer than those in origin countries. Rather questionable is the scale effect of the income gap on migration: neoclassical economics assumes that the wider the income gap, the larger the number of people who are motivated to actually migrate to other countries.42 Sociologically, income gap is one of the necessary conditions, but maximization of income is not necessarily a prime motive. According to the dual labor market theory, immigrants from poorer countries may receive less than natives do because they accept the lower standard. National wealth is represented in this study by GDP per capita of the country of origin in U.S. dollars, adjusted by GDP per capita of host countries. In calculation, Purchase Power Parity (PPP) is used to adjust for surface differences in currency exchange rates (Balassa 1964). This national account data is obtained from Barro and Lee (1994), which compiled GDP data from the Penn World Table. Origin Population Size One percent of Chinese population equals about 150 percent of Guatemalan population. Even if exactly the same proportion of the population emigrates from each country, the consequences can be very different: one is regarded as massmigration while the other is not, and the former quickly creates a number of sizable communities in destinations while the latter takes a long time to create one. Although the economic theory of selection does not recognize the population size of a country as a factor of skill selection, population sizes indicate the sizes of potential migrant pools and must affect selection. Logged national origin population is added to the equation to control for a potential scale effect on immigrant group quality since a large country may naturally 42 Massey et. al. (1993) summarized this problem, stating that “In general, the only prediction that can be offered is that human capital should somehow be reliably related to the likelihood of international movement, but the strength and direction of the relationship is impossible to know in the absence of historical information about the countries involved” (456).

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send out more emigrants. This data is obtained from Barro and Lee (1994). International Tourism and Trade Another indicator of “social distance” is the scale of international tourism. It measures cultural affinity that reduces transition cost and increases the scale of immigration. Therefore, the international tourism, which is the number of international tourist arrivals (from WDI), is expected to reduce the level of skill selection. Alternatively, economic relation should be measured by trade, which is the ratio of the sum of export and import to GDP (Barro and Lee 1994), but this measurement overlaps with the “distance” variable and cannot be used together with it. Political Freedom and Stability The political statuses of countries of origin are related to selective migration in two senses. To the extent that a competitive democratic system determines the lives of the people, immigrants from countries with more freedom will have less difficulty adjusting to the democratic systems in the host countries. When their skill transfer is not very difficult, their beginning income will not be depreciated very much, and it should not take a long time before their income level catches up with that of comparative natives. On the other hand, if a country of origin has experienced political changes such as war, a coup, or revolution, its emigrants are likely to be of the refugee type. If that is the case, political systems in the origin and destination countries will be very different, and immigrants may take a longer time to adjust to the new environment. The index of political freedom, with scores 1 to 7, was compiled in Barro and Lee (1994). The number of Revolutions and Coups per year from 1960 to 84 is also in the Barro-Lee dataset. Inequality: Inequality is a major explanatory variable in the self-selection theory predicting that greater inequality at origin results in more unskilled migrants, and less inequality at origin in more

Modeling Immigration Processes

skilled migrants. There are several indicators of income inequality: the Gini coefficient, logarithmic variance, etc. This study uses Gini coefficients from WIID and the ratio of household income of the top 10% to the bottom 20% population of the income distribution, calculated from the table made by Milanovic (2002) and supplemented by Thomas et. al. (2000). Except for a few countries, this inequality information is available only for one time point. Its over-time change is estimated using the growth in government expenditure assuming more public spending leads to more equality. This variable is measured at both the country of origin and the country of destination. In the U.S.-MSA analysis, inequality indicators are calculated at the local MSA level using a 5% sample of the total population. Returns to Schooling: This variable, defined as the ratio of lifetime income to investment in education, is measured at the country of origin. If the return is greater, highly educated workers find it reasonable to stay home, and this reduces the educational level of immigrants from that country. The effect of this on immigrant earning relates to the brain drain theory. Return rates to education are usually greater in developing countries where opportunity for higher education is still limited. In developed countries, the rates are low for highly educated workers because education has high cost, but absolute monetary return is much greater than in developing countries. By migrating to developed countries, these skilled persons may achieve the highest return. Updated estimations of the returns to education are available in the literature (Psacharopoulos 1975; 1994; Psacharopoulos and Arriagada 1986). Lambda or Inverse Mills Ratio Lambda is calculated for all immigrant groups in each host country at the time of migration. Emigration rate Pijt is a proportion of the origin population that leave their countries

93

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for immigration.43 Subscript i is an origin country, j indicates the country of destination, and t is a five-year arrival interval assuming that selectivity varies depending on time and destination. Because some entry cohorts had been in the host country for a long time, there is a problem of attrition. To avoid this attrition problem as much as possible, pre-1980 cohort counts are based on the 1980/81 census and cohorts after that on the 1990/91 census. With observed migration rates Pijt, we can obtain λijt by first calculating standardized truncation point zijt using cumulative density function Φ :

Pijt = 1 − Φ ( zijt ), zijt = Φ −1 (1 − Pijt ) . From z, using the standard normal density function

λijt = φ ( zijt ) / Pijt

φ

,

is obtained for each group unit (Green

2000: 901). The variable lambda will adjust for under- or over-estimation of population parameters caused by sample self-selection (Long 1997). Levels of Education in Countries of Origin: The average years of schooling in a country of origin directly affects the level of immigrant education. The average education is a predictor of the educational attainments of immigrants. At the ethnic group level, greater mean education indicates not only greater individual education, but also richer human capital endowment in the country of origin. The data on average schooling is obtained from World Bank-related publications (Barro and Lee1996; Thomas, Wang, and Fan 2000).

43

In a simple equation,

Pijt =

PijtM PitT

where PT indicates total national

population, and PM indicates migrant population from country i to j at time t.

Modeling Immigration Processes

Host Country Unemployment From a microeconomic perspective, the host country unemployment rate is a risk that immigrants take into account when they make migration decisions. In this sense, higher risk will raise the level of selection through increasing the emigration of those who are more motivated and reducing that of those who are not. In a macro point of view, however, less job opportunities directly reduce skill-based immigration since having job is often a precondition of this type of admission, and in turn, it relatively increases the number of those entering in the family category. Therefore, though it contradicts the micro-level expectation, this research expects that higher unemployment rates in the host country lead to lower skill levels of immigrants. Unemployment rates in the three host countries are obtained from the Bureau of Labor Statistics (BLS). Post-65 Reform (U.S. Law) This is a dummy variable to indicate whether an immigrant cohort entered the U.S. before or after the 1965 Immigration and Naturalization Act. This variable was used in Borjas (1989; 1991) to distinguish pre- and post-1965 immigrants to the United States with the expectation that the selectivity of immigrants to the United States declined after this reform because it led to the admission of more people from the third world countries. He found that this variable had a significantly negative effect on the selection in “unobserved” skills. It is puzzling, however, that the post-65 immigrants were less motivated than previous arrivals, because in that period European immigrants were declining, and there were a large number of non-European (third world) immigrants on the long waiting list. If the decline is due to “observed” characteristics such as education, it is easily acceptable because public education was not well developed in those non-European countries. However, they must have been at least as highly motivated as European immigrants after controlling for observed factors. What he captured then was probably the decline in assimilability of immigrants to the mainstream society, which is rather a matter of race and culture as

95

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Lieberson (1980) showed in comparing assimilation of the new European immigrants and that of blacks. Duleep and Dowhan (2002) suggest that post-1965 immigrants actually increased skills because a number of that period’s nonEuropean cohorts, having no family members in the U.S., had to fit into the job-based visa allotment that requires skills. Considering these points, it is expected in this research that the post-1965 immigrants are not different from those who entered earlier in terms of skills. MIGRANT NETWORKS AND SOCIAL CAPITAL Variables in the sociological category are most appropriately identified in the U.S.-MSA model. As the distance variable discussed above, space is an elusive and multi-layered concept which defies simple quantification. The size of migrant networks ranges from a small neighborhood to a global diaspora depending on one’s research focus. The cost reduction aspect of the networks is most likely a national or metropolitan level issue because legal, financial, and other supports do not require much proximity of residence. People can make phone calls, transfer money, and send documents from anywhere. Concentration of ethnic minorities to which immigrants belong will further reduce the cost via institutional supports from social agencies such as expatriate organizations and travel, legal, charity, and religious organizations. Coethnic employers may provide visas for those in the network. On the other hand, there are communal aspects to the ethnic groups of immigrants, which facilitate socioeconomic adaptation of new immigrants by providing guidance, opportunity, capital, etc. This type of support requires more dense and regular interaction between members and is therefore not well represented by MSA level measurement. Nevertheless, some implications for the types of socioeconomic resources shall be drawn from the local unit consisting of coethnic residents. In addition to the cost-reduction effect of migrant networks, local coethnic groups are also expected to affect the economic progress of immigrants by assisting the migration process after arrival. It is expected that where the community ties are dense and oriented to traditional values, economic adaptation of immigrants is limited, but where socioeconomic resources are rich and capitalistoriented, the group will accelerate adaptation.

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Note that the geo-ethnic level variables are constructed based on the entire sample including both sexes and all ages, or appropriately selected age groups, and weighted to obtain population estimates. Since these variables indicate characteristics of the ethnic community receiving coethnic immigrants, they do not use cohorts but the two survey periods (the 80s and 90s) in order to obtain the community status at the time of survey. These variables will not cause a severe multicollinearity problem in multivariate models because they are based on a different population universe than the individual samples. Density and Clustering: Density is the ethnic population per square kilometer; Clustering is the ratio of group population to the general population living in the same area. Physical density and relative clustering of the immigrant coethnic population is used to measure density of migrant networks. It is assumed that physical concentration of an ethnic group increases its members’ chances to contact with each other, which leads to greater functions of community. A community maintains its members’ values and attitudes carried from the countries of origin through social and economic agents (churches, restaurants, etc.). It is therefore expected that greater density and clustering reduce immigrant selectivity by reducing migration cost and by maintaining traditional non-capitalist norms and values. The census samples provide total and ethnic population counts estimates, and the PL94-171 data provide the land area sizes. Again, this variable is available only in the U.S. Group Communal Characteristics As discussed in relation to social capital and “embeddedness,” some ethnic communities support the economic progress of immigrants and others do not. It depends on how traditional and conservative the communities are. 44 Communal type is measured in terms of group history. Assuming that immigrants 44

It is also implied in Piore (1979) that immigrants with values of their home country tend to be satisfied with less than what a native-born would aspire for, creating a downward pressure on the immigrants’ adaptation to the mainstream.

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and following generations experience assimilation as a group, the “newness” of a group is an indicator of the residual traditionalism that they carried from their home countries. In other words, the longer the presence of a group in the host country, the more they share the values of the mainstream society, which affects positively on immigrants’ earning growths. In terms of the social network, a younger ethnic community would consist more exclusively of co-ethnic and close kin relationships than older groups that over time develop “weak ties” with the mainstream society (Granovetter 1973; 1983). Average Residency in Host Country One of the measurements of migrant community is the average residency of immigrants in host countries. The variable is assumed to capture the degree of group level assimilation to the host societies. It is assumed that the values in an immigrant community dissolve into those of the host societies as they coexist for a longer time. It is unknown, however, if the dissolution of community affects individual adaptation positively or negatively. It may lead to greater “weak ties” and support adaptation, or it may lead to weaker communal support and slow down adaptation of immigrants. A positive effect of this variable will support assimilation theory, and a negative effect will support ethnic enclave theory. Ratio of the First Generation This variable is the ratio of the first (foreign-born) generation to the second and later (native-born) generations in an ethnic group. A greater value of this variable indicates that the group is new or receiving many new immigrants. According to Piore’s segmented labor market theory, newness of a group will negatively affect earnings because a new group maintains the values of their national origins. It also supports assimilation theory if this variable negatively affects immigrant skills. However, if ethnic enclave theory is right, newer groups have stronger communal ties that support collective adaptation.

Modeling Immigration Processes

Labor Force Ratio in Ethnic Population The ratio of labor force to total ethnic population shows concentration of the working-age population in an ethnic group. Since the demographic assimilation of immigrants leads to a relative decline of the working-age population through increasing the dependent population, a greater labor force ratio should mean less adaptation and thus a negative effect on earnings. Professional and Blue Collar Workers These two variables are the proportions of the total labor force that are professional and blue collar. They intend to capture the endowments of occupation-specific resources such as job information. Professional and managerial workers fall into the professional and manual workers fall into the blue-collar category. It is expected that the concentration of professionals positively affects earnings by providing more job opportunity to immigrants, while the concentration of blue-collar workers does not increase earnings as much but still positively affects income – having occupational concentration should be better than having nothing. Self-Employed Persons A greater proportion of the total population that is selfemployed implies a greater chance for immigrants to be employed in an ethnic economy. An extensive ethnic economy may promote (ethnic enclave theory) or deter (embeddedness or lack of assimilation) the economic progress of immigrants. Jobs through network hiring could be exploitative and have less opportunity of status mobility than other jobs, but having low-paid jobs is still better than having no jobs at all. Communal Resource Endowment As discussed earlier with regard to social capital theory, in order for social capital to function, both social networks and resource pools are necessary. Potentially, more resources lead to more successful adaptation of immigrants. There are three types of resource endowment: economic resources, human capital, and social relations.

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Welfare Recipient Rate and Poverty Rate The former is the proportion of the ethnic group population that is on welfare. Welfare recipients are defined as those who reported welfare income in the census. The poverty rate is the ratio of persons under 130% of the poverty threshold to the ethnic population to which they belong. The greater rates indicate lack of resources in the ethnic community. The welfare and poverty information is available only in the U.S. data. Educational Attainment A pool of highly educated persons is expected to have a positive impact on the earning of immigrants. Since family immigrants tend to share the characteristics of sponsoring members, a more educated and high-earning group would sponsor skilled immigrants. Its indicators are the average educational attainment and the proportion of the ethnic population that has education beyond high school. English Fluency This variable is defined as the proportion of the total immigrants that speaks English (or French in case of Canada) fluently. Borjas (1989; 1991) found that it positively affects immigrant earnings. With English fluency, socio-economic adaptation would be quicker than without it. However, considering the difficulty of overcoming the language barrier, motivation can just as well be higher among non-English speaking immigrants. If we control enough assimilation effects, selection for English should be negative because not learning English reduces migration cost. Race & Ethnicity Race/ethnicity dummy variables are introduced into the model to account for any unobserved effects attributed to ethnicity. The remaining differences between racial groups would imply labor market discrimination, controlling for all observable group variables.

CHAPTER 5

Determinants of Immigrant Skills

This chapter analyzes determinants of immigrant earnings growth and educational attainment in a series of multivariate regressions. The chapter is divided into four sections, one for each of the four model settings (I through IV). In each setting, relative earnings and educational attainments are analyzed. The numbers of observation are listed below in the parentheses – in all cases each group was further divided into six entry cohorts in every five-year period from 1960 to 1990 (see Chapter 4 for details): I: II: III: IV:

16 national origin groups in the U.S. (96) 16 national origin groups in MSAs of the U.S. (3169) 5 continental origin groups in the U.S., Canada, and Australia (90) 5 national origin groups in the U.S., Canada, and Australia (90)

U.S. NATIONAL LEVEL ANALYSIS (SETTING I) In this section, the target population is the sixteen major country of origins of immigrants in the United States. These sixteen groups account for about 60% of the total U.S. immigrants in 1990. Table 5-1 shows the summary of country-of-origin variables. As noted in the last chapter, the units of observation of these variables are defined by combination of i=country of origin, j=host country, and t=arrival cohort or time of arrival. Here, j is not indicated because there is only one host country in this setting. The second row of the table indicates the it units. 101

Table 5-1: National Origin Characteristics of U.S. Immigrants Distance FDI GDP Tourism Trade Pop. Free Rev. Return Educ. Gini Ineq. i it it it it it it i i it it it United Kingdom 2.93 .936 .660 9.25 .422 10.93 6.0 .240 .068 8.13 .265 3.7 Germany 2.32 .225 .757 8.46 .523 11.02 6.0 .080 .049 8.66 .310 2.5 Italy 2.61 .183 .626 17.91 .287 10.92 5.9 .040 .023 6.12 .372 3.3 Canada 2.43 1.002 .886 10.29 .456 10.04 6.0 .000 .052 9.60 .314 3.3 Poland 2.32 .006 .267 2.26 .222 10.41 1.3 .000 .029 8.50 .247 2.2 China 8.79 .022 .088 2.76 .033 13.74 .5 .040 .050 3.85 .288 1.9 Japan 8.79 .011 .639 1.23 .243 11.63 5.5 .000 .065 8.12 .328 2.0 Korea, Rep. 6.66 .122 .182 .75 .312 10.48 2.6 .400 .106 7.76 .346 3.7 India 7.14 .011 .055 .98 .059 13.37 5.0 .120 .049 3.52 .313 3.9 Philippines 8.23 .103 .108 .86 .156 10.70 2.9 .440 .080 5.83 .487 5.0 Dominican Republic 4.13 .603 .126 .31 .248 8.56 4.7 .360 .094 3.66 .445 9.3 Mexico 4.77 .265 .322 10.11 .105 11.06 3.2 .000 .141 4.19 .496 13.6 Jamaica 4.43 .986 .168 .33 .553 7.63 5.4 .000 .288 3.47 .494 6.5 El Salvador 4.83 .130 .126 .07 .268 8.31 4.1 .560 .097 3.17 .507 14.9 Guatemala 4.71 .422 .147 .22 .179 8.75 3.3 .507 .149 2.43 .502 22.6 Colombia 6.68 .222 .173 .40 .120 10.12 4.9 .000 .140 4.08 .528 10.9 Total 5.11 .328 .333 4.14 .262 10.48 4.2 .174 .093 5.69 .390 6.8 * Distance is in 1000km; FDI is its ratio to GDP; GDP is the per capita GDP of origin country/GDP of U.S.A.; Population is logged at 1000; Tourism is in millions; “Free” is the political rights index ranging from 0 to 6 where 6 is most free (the original index was reversed); “Rev.” is the annual average number of Revolution or Coup between 1960 and 84; “Return” is the ratio of the return to a year educational investment; “Educ.” is the mean education in years; “Gini” is Gini coefficient ranging 0 to 1; and “Ineq.” is the ratio of the income of top 10 percent of population to the income of bottom 20 percent, which can be 0 to infinity. Source: see the measurement section of Chapter 4. Various Units*

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The variables with only i are time-constant, and those with it are timevariant. Over-time averages are shown in the case of time-variant variables. Asian countries are in general disadvantaged in terms of “distance” which indicates travel cost or inconvenience. This is because there are more major metropolises in Europe and North America than in Asia, implying it is more costly and inconvenient to migrate from Asia than from Europe and Latin America. In terms of FDI relative to national production, Canada and Jamaica received foreign investment nearly as great as their GDP, while Japan and India received only one percent of their total GDP. The GDP per capita differentials shows that, on average from 1960 to 1990, Canadian and German productions were as much as 75 to 89 percent of American production, while that of India was only 5.5 percent and that of China 8.8 percent. The “tourism” variable may not accurately measure affinity with the host country (the U.S. in this case) because it includes tourists not only from the U.S., but also from all other countries. Tourists to Canada and Mexico are largely American, but tourism in other countries such as Italy must include many non-American tourists. The “tourism” variable is at best a measurement of “openness to western tourism and culture” rather than bilateral connection. The “trade” variable has the same problem: it includes trade with all countries. We need to regard “trade” also as an indicator of general economic openness and not of bilateral connection. The natural logarithm of population is used because the extremely large numbers in China and India skew the distribution. “Free,” for political freedom, indicates the level of the competitive party system and gives the best scores to the U.K., Germany, and Canada, while it gives the lowest scores to communist China and Poland. Its greater scores indicate more democratic political systems. “Revolution” indicates political instability using the number of revolutions and coups. Korea, Philippines, El Salvador, and Guatemala had frequent outbreaks of revolution or coups, while some other countries experienced no such incidents in 1960-85. The “return to education” is high in Latin American countries, especially in Jamaica, reflecting on their low average education (the “Educ.” variable). European and Asian countries show generally high average education and low return except for China and India, while Latin American countries show less education and high return. The Chinese-Indian pattern of less education and low return indicates underdevelopment in which both human and physical capital investments are insufficient for

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their huge populations. The Latin American pattern indicates imbalance between industrialization and educational infrastructure. The two indicators of inequality show generally consistent measurements, although there are minor alternations in the rank order. The communist countries have the lowest inequality, and the underdeveloped Latin American countries have the highest inequality. Table 5-2: U.S. Variables Year 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 Total

Gini .349 .357 .367 .371 .386 .396 .371

Inequality 4.72 4.54 4.57 4.89 5.67 5.78 5.03

Unemployment. .054 .032 .048 .063 .083 .061 .057

Source: WIID; Bureau of Labor Statistics (www.bls.gov).

Thus far, we have reviewed variables describing characteristics of the countries of origin. Variables related to countries of destination in this section are based on U.S. data. Most of these variables are timeconstant, but some vary over time. Table 5-2 shows the summary of time-varying variables at the host country. These variables are not expected to have much explanatory power because they have small variations. The next table (Table 5-3) introduces ethnic group level variables constructed from the total sample of the two census data sets. These variables are fractions or average characteristics of particular groups constructed from individual sample data. The “cluster” variable is the proportion of the total population that belongs to each ethnic group. It is easy to imagine that British and German stock (both foreign-born and native-born) are the largest, about 16 and 15 percent, respectively, but it is new to find out that Mexican ethnic stock (5.3%) is already larger than Italian (5.1%) or Polish (3.1%) groups in 1980-90. The Asian population is increasing, but it are still very small in terms of individual national origin groups. This variable may not capture the network nature at this national level of analysis; regional differences in

Table 5-3: Ethnic Group Variables Cluster

Prof.

Blue

Self. Migrant Resident Elderly Labor English Welfare Low ed. High ed. Av. Ed.

United Kingdom .158 .200 .237 .055 .023 20.1 .202 .636 .997 .027 Germany .149 .224 .233 .058 .033 24.3 .166 .675 .966 .018 Italy .051 .204 .224 .055 .087 24.5 .187 .671 .818 .022 Canada .004 .233 .223 .057 .272 20.7 .249 .648 .988 .027 Poland .031 .219 .233 .051 .078 22.9 .204 .668 .840 .020 China .006 .263 .148 .062 .658 10.5 .106 .704 .668 .030 Japan .004 .253 .149 .065 .308 12.8 .113 .743 .748 .014 Korea, Rep. .003 .180 .215 .101 .795 7.3 .048 .654 .705 .021 India .003 .363 .130 .055 .743 7.9 .083 .666 .923 .015 Philippines .005 .215 .174 .031 .674 10.3 .112 .658 .925 .032 Dominican Republic .002 .057 .381 .023 .785 10.2 .051 .704 .501 .091 Mexico .053 .076 .346 .028 .298 11.5 .061 .601 .502 .038 Jamaica .002 .139 .209 .030 .797 10.9 .085 .730 .996 .031 El Salvador .001 .051 .361 .027 .857 7.5 .029 .757 .507 .027 Guatemala .001 .061 .368 .029 .833 8.3 .031 .727 .567 .025 Colombia .001 .118 .329 .042 .803 10.6 .044 .740 .662 .030 Source: U.S. Census 1980 and 1990 PUMA. Author’s Estimation. See Chapter 4 for definitions of variables.

.346 .174 .224 .243 .212 .267 .137 .207 .162 .175 .573 .562 .329 .690 .637 .356

.654 .826 .776 .757 .788 .733 .863 .793 .838 .825 .427 .438 .671 .310 .363 .644

14.3 14.5 13.7 14.1 13.9 14.3 14.6 15.2 16.1 15.1 11.3 12.4 13.9 10.8 11.1 13.4

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clustering at MSA level that betters operationalization of the concept of network will be analyzed in the next section. The next three variables are proportions of professionals, bluecollar workers, and self-employed workers in the labor force. European ethnic groups have very similar tendencies: 20 percent professional and slightly more blue collar, and about 5 percent self-employed. Except for Koreans, Asian ethnic groups are more concentrated in professional occupations. Koreans have an exceptionally high concentration of the selfemployed: one out of ten Koreans in the U.S. is self-employed. Indians have the highest concentration of professionals: 36 percent of their labor force is professional. Chinese show the next highest proportion, 26 percent. Latin America and the Caribbean have in general fewer professionals and more blue collar workers. Only 5.7 percent of Dominicans are professional, while as much as 38 percent of them are blue collar workers. “Migrant” is the proportion of ethnic population that is foreign born. European groups have generally low scores, less than 9 percent, reflecting the decline of new inflows, and Asian and Latin American groups have high scores, about 70 to 80 percent. Exceptionally, Canadians, Japanese, and Mexicans have the medium-range scores of about 30 percent.45 The “resident” variable, which indicates the average U.S. residency, ranges from 7 to 25 years. The residency is shortest among Korean and longest among Italian immigrants. European immigrants stayed over 20 years, about twice as long as the rest of the ethnic groups. Koreans, Indians, Salvadorans, and Guatemalans are relatively new among the sample groups. The newness or oldness of ethnic groups also can be seen in the proportion of their elderly members. While as many as a quarter of Canadian residents in the U.S. are over 65 years old, only 3 percent of Guatemalans and Salvadorans are elderly. Here we also observe a clear 45

The three countries fell into the 30% range for different reasons: in the Canadian group, there are very few U.S.-born Canadians simply because they are not clearly an “ethnic” group – this thus diminishes the denominator. Japanese have lower scores than other Asian groups because their new inflow did not increase in the past decades, but the rate is higher than that of European groups because the native born population is still relatively small. Mexicans, on the other hand, do have a large native-born base, and the number for the new native-born population is very large.

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107

contrast between regional origins in terms of elderly rates: in Europe the rate is 17 to 25 percent, in Asia other than Korea it is 8 to 11 percent, and in Latin America and the Caribbean it is 3 to 9 percent. The proportion of the labor force doesn’t show as much variation as expected, but it is clear that newer groups have higher concentration of labor force, e.g. Salvadorans are 76 percent labor force as opposed to the U.K.’s 64 percent. Mexicans have an anomalously low proportion of labor force, which is probably attributed to their high fertility rate and thus large number of children not in the labor force. The remaining variables are communal resources. Poles and Italians have the highest English fluency among those from nonEnglish speaking countries, reflecting the length of their stay in the U.S. Among Dominicans, Mexicans, and Salvadorans, only half of the ethnic population speaks English fluently. Dominicans are most dependent on welfare (9 percent). As a group, Japanese have the highest concentration of highly educated persons (83 percent), but in terms of average years of education, Indians have strikingly long education (16 years). When we take the difference between the ethnic average education and the home country average, Indians have 12.5 years more than their national average, Chinese 10.5, Jamaicans 10.4, Colombians 9.3, and Filipinos 9.2 years. EARNING: AN ECONOMIC MODEL This section examines a series of multivariate analyses using the statistical software STATA. First, it is informative to revisit Borjas’s (1991) model using newer – 1980 and 90 instead of 1970 and 80 – data. However, this is not an exact duplication of his model. Compared to his data and model, this study included two newer cohorts and has selected a smaller number of national origin groups (16 as opposed to 41). In the estimation of relative skill differentials, this research used detailed classification of ethnic groups, while Borjas used broader race-ethnicity categories (non-Hispanic White, non-Hispanic Black, Asian, and Hispanic). Some other variables are also different although attention was paid to operationalizing the concepts of original variables as much as possible. Table 5-4 shows the results of regression estimation on the earning differentials. These models are based on equations (1) and (2) in Chapter 4. On the left-hand side is the “reduced form equation” using variables related to both scale and selection on immigrant skills. Political freedom positively affects the skill selection, and political

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instability negatively affects it, which is consistent with Borjas’s previous reports. The greater inequality in the country of origin negatively affects the level of selection, which confirms the self-selection theory. The effects of English and GDP are also consistent with Borjas’s analysis. The immigration reform (“U.S. law”) has a negative but non-significant effect (it was significant in Borjas’s analysis). This implies that the distinction between pre- and post-1965 entry cohorts in terms of immigrant skills had disappeared during the 1980s. The effect of host country unemployment is negative, which is not consistent with Borjas’s previous study. According to his explanation, unemployment in the host country has a positive effect because it reduces immigration of less-skilled workers particularly, but skilled workers are more likely to find jobs even during economic downturns. Such a relationship could have reversed during the 1980s, or perhaps it was not so definitive to begin with. Table 5-4: Regression of Relative Earnings – Base Model Reduced Equation Variables Free Rev. & Coup Gini Unemployment US Law English Distance GDP Constant

Coef.

t

.015 -.103 -.400 -1.356 -.007 .150 -.015 .016 .134

(2.14) (-1.71) (-3.48) (-1.91) (-.26) (2.12) (-3.30) (.30) (1.54)

Structural Equation* Coef. ** * *** *

t

.009 -.043 -.231 -.245 -.013

(5.80)*** (-1.99)* (-5.03)*** (-1.28) (-1.64)

.239

(3.85)***

** ***

R-squared .533 .419 Notes: The variable λ interacts with the variables in the structural model. Presented are estimates using the 1980 and 1990 censuses. For results using 1970 and 1980 census, see Borjas (1991: 52, Table 1.5).

The “distance” variable has a negative and significant effect on the skill selection of immigrants, which not only disagrees with the previous study, but also seems counter-intuitive. It implies that less-

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109

skilled workers are willing and able to migrate from distant countries where migration cost would be greater, and more-skilled workers are discouraged from moving from those countries. This could be a result of the country-mix of this data set, which does not include as many European countries as the previous study. Alternatively, it might result from the distance variable’s itself taking a different meaning when Barro-Lee adjusted the physical distances with bilateral trade relationships, which could have made Europe closer and reversed the effect of this variable. On the right-hand side of the Table 5-4 is the “structural equation” in which scale effects are replaced by λ. Practically, λ positively multiplies selectivity factors according to the likelihood of selection so that selection biases in coefficients are adjusted. Therefore, the significant effects in the “reduced equation” become even more significant in the “structural equation” (but the magnitudes become smaller). It should be noted that λ is a positive variable and it enlarges those explanatory variables in different degrees. Except for the unemployment and distance, the re-analysis of Borjas’s (1991) model using a decade newer data suggests that those findings in the previous study persisted over the 1980s as far as this economic model specification is concerned. EARNING: A SOCIOLOGICAL MODEL This research further examines sociological models that take into account migrant networks and historical structural factors in addition to the economic self-selection model. With exactly the same sample size as in the last analysis, four more models are shown in Table 5-5. The first model estimates the total effects of being Asian or Hispanic compared to European immigrants. Asian groups earn 16 percent and Hispanic groups earn 18 percent less than the relative earnings of European immigrants. Model 2 introduces national-origin and host characteristics with distance effects decomposed by interactions with race-ethnicity. The interaction terms show that distance positively affects immigrant earnings only among Europeans, while it affects negatively or has close to zero effect among Asians and Hispanics. Besides that, the negative effect of political instability and the negative effects of national-origin inequality confirm the findings in the previous economic analysis.

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Social Determinants of Immigrant Selection

Table 5-5: Regression of Relative Earnings at U.S. National Level Model 1 Model 2 Model 3 Coef. Coef. Coef. (t-score) (t-score). (t-score) -.163 *** .811 ** -1.339 Asian (-6.43) (2.59) (-3.11) -.189 *** .747 ** -.425 Hispanic (-6.69) (2.63) (-.68) … .011 .006 Free (1.46) (.51) … -.280 *** .525 Rev. (-2.88) (2.61) … .014 -.039 FDI (.32) (-.98) … -.008 .207 GDP (-.11) (1.46) … -.309 * -.009 Gini (-1.89) (-.03) … .048 3.988 Return (.12) (4.55) … -.023 .020 Pop. (-1.64) (.76) … -.865 -1.209 Unemployment (-1.10) (-1.92) … -.564 -1.225 Host-Gini (-.52) (-1.26) … .256 ** .173 Distance*Europe (2.57) (1.72) … -.029 080 Distance*Asian (-1.54) (1.97) … -.045 * -.042 Distance*Hispanic (-1.84) (-.67) … … -2.429 Clustering (-5.24) … … -.471 Migrant (-4.35) … … 3.109 Professional (4.76) … … .663 Blue Collar (.70) … … -2.776 Self-Emp (-1.42) (Cont.)

Model 4* Coef. (t-score) *** -.107 *** (-2.87) -.164 *** (-3.65) .005 ** (2.60) ** .000 (.00) … …

***

*

* *

-.072 (-.96) .768 *** (3.95) … -.381 * (-1.96) .474 * (1.95) … … …

*** *** ***

-.393 *** (-4.51) -.112 *** (-3.96) .286 *** (2.68) .223 (1.55) -.077 (-.28)

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111

(Cont.) Table 5-5: Regression of Relative Earnings at U.S. National Level Model 1 Coef. (t-score) … Residency

Model 2 Coef. (t-score). …

Elderly

…

…

English

…

…

Welfare

…

…

Low ed.

…

…

Costant

.104 *** (5.7)

.013 (.03)

R-square

.365

.603

Model 3 Coef. (t-score) -.033 (-3.75) -2.535 (-4.20) -1.073 (-2.92) 5.834 (4.35) -1.609 (-3.15) 1.518 (1.99) .805

Model 4* Coef. (t-score) *** -.005 *** (-2.74) *** -.170 * (-1.86) *** … *** *** *

1.157 *** (2.94) .041 (.41) -.374 ** (-2.63) .665

* The variables in Model 4 interact with λ. Model 2 also adds national origin and host characteristics. Revolution and the Gini coefficient in the origins have significant results. The effects of host country conditions (unemployment and host-Gini) are unclear perhaps due to their small variations. According to this result, the longer distance, therefore greater inconvenience, makes immigrants from distant areas more able and motivated only in the case of European immigrants. This hypothesis does not apply well to Asian and Hispanic groups, probably because the inconvenience for them is not just some obstacle to overcome, but it even disables those potential migrants from emigrating: no matter how motivated they are, they won’t be able to come to the U.S. when migration cost is too high. Model 3 adds ethnic group variables to the equation, although the measurements of these variables are not very reliable at national level. As a result, the effects of Hispanic and Gini coefficients become nonsignificant, while that of the return to education become positive and very significant. Immigrants’ earning differentials improve 4 percent as the return rate to education in their home countries increases one percent. To the extent that the return rate variable appropriately measures the concept of “structural imbalancing” development, this model implies that the countries with high return to education are experiencing the brain drain – positive selection – on “unobserved”

112

Social Determinants of Immigrant Selection

skills. This means that, after netting out individual human capital, more able and more motivated workers tend to migrate to the U.S., particularly from those countries offering high returns to education. Model 4 introduces the selection variable λ into the equation. Following equation (4) in Chapter 4 (see p.78), variables assumed to be scale factors are omitted from the model, but those variables in the model that reduced their significance after the introduction of λ, i.e. revolution, elderly and education, could also be scale factors. Some network variables are suspected to have both scale and selection effects, and thus were included in Model 4. After adjusting for the scale effects by λ, the effects of Hispanic and political freedom become significant, and the number of revolutions and coups turned to be completely irrelevant to immigrant earnings. Overall, Models 3 and 4 show that the effects of the two most important variables in Borjas (1991), GDP and inequality, are explained away by the ethnic group characteristics. To address the group measurement issues more appropriately, it is necessary to use smaller geographic units such as Metropolitan Statistical Areas (MSAs). EDUCATION: AN ECONOMIC MODEL In the model for educational attainment, re-examination of Borjas’s model (1991) produced outcomes similar to his results, even though this research uses relative education controlling for age and observation year, and his research used unadjusted educational attainment. The estimation of relative education Qs (superscript “s” for schooling) in the U.S. is:

Q s = −1.21 + .58µ s − 7.77δ s − 2.53π , R 2 = .232

(-1.23) (3.54) (-1.75) (-1.86) where µs is a mean education at origins, δs is a return to education, and π is the origin country’s GDP per capita standardized by host GDP per capita. Though this estimation has smaller R2 and somewhat less significant coefficients than Borjas’s, all directions of the coefficients match his results. As far as this specification is concerned, the selection mechanism on an “observed” skill has continued through the 1980s. However, when we try the “structural equation” model, which he didn’t try, the return to education becomes insignificant and only the mean

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113

education remains accountable for the selection of educational attainment:

Q s = −3.43 + .185µ s λ + 1.99δ s λ , R 2 = .295

(-3.30) (5.58) (1.04) . This result raises a question of whether the return to education is a selectivity factor. If it is, introduction of λ should have increased its significance. Another set of alternative models based on sociological theories may address this question. EDUCATION: A SOCIOLOGICAL MODEL As shown in Model 1 of Table 5-6, Asian immigrants in general have 1.7 years more education than European immigrants and Hispanics 3.0 years less. Country-of-origin and host characteristics as well as interaction terms are introduced in Model 2. Just like the last models on earnings, the dramatic changes in the effect of race-ethnicity dummies are compensated for by the effects of interaction terms and the intercept. Contrary to expectation, a greater number of revolutions and coups increases educational attainment, which is probably because more highly educated people are able to leave their countries under political instability. This variable has a negative effect on earnings in the last analysis, which suggests that political instability positively selects “observed” skills but it negatively selects “unobserved” skills. It is understandable that refugee-type immigrants are not very motivated because they were forced to leave their countries. It is also likely that adaptation to the U.S. is difficult for immigrants from politically unstable countries because their social systems are different from the U.S. system. Tourism and FDI, the indicators of system affinity, increase the education of immigrants. Clearly, where the reward system is similar, highly educated persons find assurance that their educations are also appreciated in the host country. The negative effect of the GDP differential indicates that educational attainments of immigrants decline as origin GDP increases relative to host GDP. This is inconsistent with the previous research by Borjas (1991). As far as immigrants from the sixteen countries of origin are concerned, better-educated workers come from less wealthy countries. The effect of inequality (the Gini coefficient) shows negative effect, and it is consistent with the self-selection theory: the greater the

114

Social Determinants of Immigrant Selection

Table 5-6: Regression of Education at U.S. National Level

Asian Hispanic Free

Model 1 Coef. (t-score) 1.687 *** (5.02) -3.027 *** (-7.50) …

…

Model 2 Coef. (t-score) -13.384 (-2.95) -21.354 (-5.23) .642 (6.86) 4.223 (3.60) .138 (1.14) .075 (1.71) .978 (3.57) -4.272 (-3.40) -17.402 (-5.60) 17.647 (5.46) -4.926 (-.54) -48.231 (-4.31) -3.706 (-2.96) .792 (2.85) 1.941 (6.93) …

…

…

…

…

Rev.

…

Mean Educ

…

Tourism

…

FDI

…

GDP

…

Gini

…

Return

…

Unemployment Host-Gini

…

Distance* Europe Distance* Asian Distance* Hispanic Clustering

…

Professiona l Low ed. Constant R-square

…

… …

-.333 (-1.34) .627

*** *** *** ***

* *** *** *** ***

*** *** *** ***

28.521 *** (4.72) .868

Model 3 Coef. (t-score) -9.620 (-2.51) -5.814 (-1.18) .262 (2.23) 7.894 (7.71) .453 (3.86) .134 (4.00) .791 (3.02) -3.073 (-2.59) -14.541 (-6.49) 7.427 (1.38) -12.586 (-1.71) -84.572 (-7.16) -1.611 (-1.39) .904 (4.27) 1.133 (2.98) -4.572 (-1.64) 18.129 (5.14) -10.045 (-2.30) 32.348 (6.19) .908

* The variables in Model 4 interact with λ.

**

** *** *** *** *** ** ***

* ***

Model 4 Coef. (t-score) -.242 (-1.71) -.842 (-3.00) .032 (1.43) 1.045 (4.23) .084 (2.40) -.038 (-4.58) .174 (2.02) …

…

***

…

** ***

***

*** ** *** **

.829 (1.25) 6.826 *** (4.78) -6.711 *** (-2.92) 4.745 ** (2.65) …

***

***

*

-1.169 (-1.66) 3.648 *** (3.27) -.352 (-.47) -11.121 *** (-7.94) .875

Determinants of Immigrant Skills

115

home country inequality, the less skilled the immigrants from that country. However, the effect of host inequality is also negative where it should have been positive if the self-selection theory were right. Since the Gini coefficient of the U.S. had increased over time, it is negatively correlated with the relative educational attainments that had declined in newer arrival cohort (see Table 4-3 on p.89). The return to education in Model 2, unlike that in the economic model, has positive effect on immigrants’ educational attainments: every ten percent increase in return to education increases about 2 years of relative education. Just adding race-ethnicity dummies to Model 1 caused this reversal from a negative to a positive effect, though the model is not shown here. From a micro perspective, greater return to education should have decreased selectivity (a negative effect) because skilled workers tend to stay in such situation. From a macro perspective, however, the positive effect makes sense because “structural imbalancing” induces emigration of highly skilled workers. The decomposed interaction terms for distance indicate that a greater migration cost improves educational attainment of Asian and Hispanic but not that of European immigrants. The effect of host inequality is very significantly negative, but since this variable is constant across countries of origin, it needs more examination using smaller units of observation. Model 3 introduced ethnic network characteristics, but the results with these variables are not very reliable because the measurements of these concepts are difficult at the national level. Finally, adjustment of selectivity is examined in Model 4 using λ. Directions of coefficients remain generally the same except for tourism and the Gini coefficient. The important finding here is that the effect of the return to education remains positive and becomes more significant, supporting the structural imbalancing rather than the self-selection hypothesis. In general, at this analytical setting, the main variables of selfselection theory, inequality and GDP lose their accountability after other national origin variables and ethnic group variables are introduced. This suggests that structural factors and migrant networks explain immigrant earnings and educational attainments better than self-selection theory does. As repeatedly mentioned, however, this analytical unit – one host, sixteen national origins and six entry cohorts – has its limits in accounting for local, within ethnic group differences,

116

Social Determinants of Immigrant Selection

and thus it is important to examine geographically more detailed units before reaching final conclusions. U.S. MSA-LEVEL ANALYSIS (SETTING II) As described in the previous chapter, there are 272 MSAs in 1980 and 1990. From these areas, any of the 16 ethnic groups that have greater than 30 foreign-born population within an MSA are selected, from which dependent variables are estimated based on a restricted sample (male, age 25 to 64, etc – see Chapter 4) and local ethnic group variables are constructed based on a full 5% sample. The number of ethnic groups per area varies from one area to another, and the number of areas per ethnic group varies from one group to another. They depend on how concentrated an immigrant group is in an MSA and on how wide-spread they are in terms of entry cohorts. Altogether, the combination of national origins, entry cohorts, and destination MSAs make up 3,654 group units to be analyzed. Table 5-7 shows the number of observation by national origin. These numbers indicate spatially and temporarily spread settlement patterns of immigrants. In the 1980s and 90s, Mexicans are already the most widespread of all the 16 groups, and probably of all the immigrant groups. Chinese immigrants are ranked third after the German immigrants, indicating their significant presence in the 1980s and 1990s. Due to the decline of new entry, European immigrant groups are less likely to be identified than those new groups. Table 5-7: Number of MSA Entry Group for Each National Origin U.K.

Germany

Italy

Canada

Poland

China

297

448

299

125

232

349

Japan

Korea

India

116

180

374

Jamaica El Salvador Guatemala

Philippines Dom. Rep. Mexico 251

68

Colombia

119 90 79 124 Source: U.S. Census PUMS 1980 and 1990, 5% samples.

503

Determinants of Immigrant Skills

117

EARNING: AN ECONOMIC MODEL Because of the large sample size, regression results become more significant for all the explanatory variables. However, it should be cautioned that the number of MSA-ethnic groups affects the results as frequency weights. Therefore, the widespread groups have greater influence on the outcomes than other groups do. The economic models including reduced and structural equations were re-examined again at this level, giving results that are, as shown in Table 5-8, almost identical with the results in the national-level analysis shown in Table 5-4. The consistency of the results confirms the previous interpretations. Table 5-8: Regression of Earnings at MSA Level – Base Model Variables Free Revolution & Coup Gini Unemployment US Law English Distance GDP Constant R-squared

Reduced Equation Coef. (t-score) .019 *** (23.22) -.074 *** (-6.10) -.403 *** (-19.83) -1.163 *** (-9.19) -.017 *** (-4.07) .103 *** (7.88) -.009 *** (-9.34) -.002 (-.19) .060 *** (3.65) .392

Structural Equation* Coef. (t-score) .008 *** (39.36) -.023 *** (-5.68) -.225 *** (-27.57) -.231 *** (-7.17) -.013 *** (-11.30) … … … .178 (16.11) .351

***

* The variable λ interacts with all the variables in this model. For the original model using the 1970 and 1980 censuses, see Borjas (1991: 52, Table 1.5). EARNING: A SOCIOLOGICAL MODEL Table 5-9 shows the results of the sociological analysis that takes into account migrant networks and historical structural factors. Due to the

118

Social Determinants of Immigrant Selection

Table 5-9: Regression of Earnings at MSA Level

Revolution & Coup FDI

…

GDP

…

Gini

…

Return

…

Pop.

…

Distance

…

Unemp (J)*

…

Unemp (j)*

…

Model 2 Coef. (t-score) -.222 (-12.43) -.209 (-14.66) .022 (22.64) -.060 (-3.28) .079 (7.89) -.107 (-7.87) -.467 (-15.03) .796 (8.52) .005 (1.86) .021 (9.08) -.246 (-1.88) …

Unemp (ij)*

…

…

Host-Gini (J)* Gini-MSA (j)* Gini-group (ij)* Clustering

…

Asian Hispanic Free

Model 1 Coef. (t-score) -.132 *** (-32.09) -.159 *** (-30.37) …

…

…

*** *** *** *** *** *** *** *** * *** *

-1.951 *** (-12.16) …

…

…

…

…

Density

…

…

Migrant

…

…

Professional

…

… (Cont.)

Model 3 Coef. (t-score) -.327 (-15.38) -.384 (-18.14) .029 (21.11) -.103 (-5.20) .068 (6.78) -.163 (-9.63) -.493 (-14.88) 1.182 (10.74) -.004 (-1.32) .038 (12.92) -.071 (-.56) -.020 (-.44) .108 (3.28) -1.175 (-6.53) -.165 (-2.59) .113 (2.31) -.113 (-4.34) -.000 (-1.19) -.103 (-6.85) .090 (3.73)

*** *** *** *** ***

Model 4* Coef. (t-score) -.057 (-14.05) -.109 (-19.49) .005 (16.30) -.038 (-7.75) …

***

…

*** ***

-.079 (-7.23) .562 (14.70) …

***

…

*** *** ** ** ***

*** ***

-.231 (-6.87) .014 (.93) .028 (3.06) .183 (4.97) .040 (2.17) .014 (1.06) -.050 (-5.70) -.000 (-1.38) -.015 (-3.06) .041 (6.40)

*** *** *** ***

*** ***

***

*** *** **

***

*** ***

Determinants of Immigrant Skills

119

(Cont.) Table 5-9: Regression of Earnings at MSA Level Model 1 Coef. (t-score) Blue Collar … Self-Emp

…

Resident

…

Elderly

…

English

…

Welfare

…

Low ed.

…

Costant R-square

.031 *** (9.36) .280

Model 2 Coef. (t-score) …

Model 3 Coef. (t-score) .064 (2.44) … -.348 (-5.11) … -.005 (-6.53) … .070 (2.78) … -.045 (-1.95) … .336 (2.92) … .232 (6.44) .692 *** .565 (10.69) (7.38) .514

.566

Model 4** Coef. (t-score) ** .034 (4.25) *** -.154 (-7.47) *** -.002 (-8.89) *** .046 (6.28) * … *** *** ***

.218 (5.88) .068 (6.54) -.266 (-8.45)

*** *** *** ***

*** *** ***

.493

* Upper case J indicates national level and lower case j indicates MSA level, and ij indicates ethnic groups in MSAs. ** The variables in Model 4 interact with λ as described in Model (4).

large sample size, the coefficients are stable across models: most signs remain unchanged from Model 2 to Model 3. The positive effects of FDI and the return to education support the structural imbalancing hypothesis contending that qualified workers come places from where the imbalance between economic development and high return to education coexist. The negative effect of origin inequality (the Gini coefficient) was insignificant in the previous analysis but they become significant at this level, confirming the expectation of the self-selection theory. Because the “distance” variable shows a clearly positive effect, unlike the previous analysis (Table 5-6), it is not necessary to test for interaction between distance and race-ethnicity. The new focus here is the geo/ethnic destinations. As shown in the table, unemployment and host inequality are measured at three levels: national (the U.S.), MSA,

120

Social Determinants of Immigrant Selection

and ethnic groups within MSAs. Model 3 includes MSA and ethnicMSA levels as well as other group level variables (indicated by i and j). The self-selection theory expected a positive effect of unemployment, but in this analysis, a positive and significant effect appears only with regard to the local ethnic group to which an immigrant belongs (indicated by ij). This suggests that unemployment rates selectively affect skilled and unskilled workers only within MSA-ethnic groups and not at higher levels. According to the self-selection theory, inequality at destination improves selectivity, but Model 3 found a positive effect only at the ethnic group level in the reduced-form model. However, after controlling for selectivity using λ in Model 4, the effect of inequality becomes positive at all three levels. In this case, the interaction between λ and the inequality measurement switched the relationship between skills and inequality from negative to positive.46 Another new variable in this model is the physical density of the co-ethnic group measured by population per square kilometer. The density variable was expected to complement the “clustering” variable in representing the presence of ethnic networks, but it has only a weak negative effect. Other group-level effects generally support our expectations. For example, at the MSA level, a 10 percentage-point increase in “migrant” leads to 1 percent lower earnings. The 10 percentage-point increase in professionals increases immigrant earnings by 0.9 points. EDUCATION: AN ECONOMIC MODEL The economic model for educational attainments by Borjas (1991) is re-examined using this MSA-level data. The result is as follows:

Q s = −1.16 + .49µ s − 22.2δ s − 4.59π , R 2 = .229

(5.69) (14.0) (-22.3) (-16.3) 46

The correlation coefficient between earnings differentials and host-Gini coefficients is -.110 when unadjusted. When the Gini is adjusted by lambda, the correlation coefficient turns positive to .188. This reversal is due to the positive correlation between relative earnings and lambda, .214. Therefore, the relation between earnings and emigration rate turns negative – the higher the emigration rate, the lower the earning differentials of immigrants.

Determinants of Immigrant Skills

121

where Qs is the immigrant-native differential in educational attainment (years) controlling for age and survey year, µs is a mean education at the origins, δs is a return to education, and π is the origin country’s GDP per capita relative to host GDP per capita in the year of entry. The result is almost identical with the previous national-level analysis except for the large negative effect of the return to education (it was negative 7.77 at the national level). In the next result, when the selection bias is adjusted, the t-score for the return rate is reduced significantly from –22 to –9, whereas the t-score for the mean education remains at the same level:

Q s = −.337 + .110µ s λ − 4.47δ s λ , R 2 = .145

(-1.33) (13.2)

(-9.08) .

EDUCATION: A SOCIOLOGICAL MODEL In the national-level analyses, the effect of the return rates reversed from negative to positive after controlling for race-ethnicity. As shown in Table 5-10, sociological models at MSA level produced results quite similar to the results at the national level. The estimated coefficients are stable due naturally to the large number of observation, and almost all of them are consistent with the results of national level analyses. It is confirmed that the return to education (“Return”) has a positive effect on immigrants’ educational attainment. As in Model 3, for every ten percentage-point increase in the rate of return to education, educational attainment increases by 0.65 years. This finding supports the structural imbalancing theory because it suggests “brain drain” of highly educated workers from countries providing greater return to education but probably lacking suitable positions. If the selfselection theory were right, that effect should have been negative because highly educated workers could have found more opportunities in such sending countries. However, the self-selection theory is correct about the negative effect of origin inequality (the Gini coefficient). The effects of host unemployment and inequality on educational attainments are tested at multiple levels. The self-selection theory found supportive evidence in MSA unemployment, but the coefficient is negative at national and ethnic group levels (the effect at the latter level is insignificant). This implies that greater unemployment at

122

Social Determinants of Immigrant Selection

Table 5-10: Regression of Education at MSA Level Model 1 Model 2 Model 3 Variables Asian Hispanic Free Revolution & Coup Mean Education Tourism FDI GDP Gini Return Distance Unemployment (J)* Unemployment (j)* Unemployment (ij)* Host-Gini (J)* Gini-inMSA (j)* Gini-ingroup (ij)*

Coef. (t-score)

Coef. (t-score)

1.519 *** -2.197 *** (.10) (-5.73) -3.997 *** -4.375 *** (.11) (-12.24) … .430 *** (11.92) … .388 (1.43) … .135 *** (2.75) … -.083 *** (-5.41) … .735 *** (7.21) … -2.663 *** (-5.17) … -10.656 *** (-12.05) … 8.173 *** (7.08) … .678 *** (10.35) … -11.122 *** (-3.52) … .860 (.73) … -5.468 *** (-6.40) … -34.167 *** (-7.38) … -3.260 ** (-2.23) … 5.100 *** (4.03) (Cont.)

Model 4**

Coef. (t-score)

-2.091 (-5.26) -1.508 (-3.63) .200 (5.86) 2.787 (10.36) .242 (5.26) -.002 (-.12) .446 (4.03) -2.002 (-4.17) -8.451 (-10.48) 6.546 (4.87) .511 (7.86) -12.915 (-4.54) 4.014 (3.83) -.280 (-.36) -48.222 (-11.37) -1.887 (-1.45) 1.630 (1.41)

Coef. (t-score)

*** *** *** *** ***

*** *** *** *** *** *** ***

***

-.479 (-8.07) -.955 (-8.69) -.015 (-2.35) .898 (10.60) .055 (6.15) -.035 (-9.78) .155 (4.46) …

*** *** ** *** *** *** ***

1.003 *** (4.21) 6.663 *** (11.46) … -6.458 (-8.24) 1.792 (5.71) .065 (.28) 3.087 (4.88) -.310 (-.78) .919 (2.85)

*** ***

***

***

Determinants of Immigrant Skills

123

(Cont.) Table 5-10: Regression of Education at MSA Level Model 1 Model 2 Model 3 Model 4**

Clustering Density Migrant Professional Blue Collar Elderly Welfare Low ed. Constant R-square

Coef. (t-score)

Coef. (t-score)

Coef. (t-score)

Coef. (t-score)

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

-.429 (-.98) -.001 (-.92) 1.387 (5.32) 10.066 (16.83) 3.108 (5.26) 2.524 (5.30) -9.960 (-4.08) -6.819 (-8.53) 14.999 (9.60) .702

-.184 (-1.31) -.000 (-.81) .377 (4.84) 2.880 (16.66) 1.030 (5.40) .823 (6.50) -1.890 (-8.06) -2.582 (-3.30) -9.637 (-17.90) .712

1.162 *** (.07) .464

14.750 (8.97) .614

*** *** *** *** *** *** ***

*** *** *** *** *** *** ***

* See notes for Table 5-9. ** The variables in Model 4 are interacted with λ as described in Model (4).

destination areas affects highly educated workers less severely (or even positively) than it affects less educated workers, because the highly educated have more flexible ability and resilient demand for their occupations. At national and group levels, however, the effect of unemployment is negative or insignificant. The implication from this multi-level comparison is that differentiation by unemployment is favorable to educated workers only at the level of local labor markets, and at the other levels unemployment discourages highly educated workers from migrating. The Gini coefficient at destination is also split into three levels in which only the group level inequality confirms the self-selection theory. In Model 4, as in the earning analysis, the effect of national level inequality reversed from negative to positive.

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Social Determinants of Immigrant Selection

Adjusting for the selection bias, the greater host-inequality increases the educational attainments of immigrants. The effects of ethnic group characteristics are stable. The larger sample size allowed more variables in the model. Migrant network variables (clustering and density) are insignificant in determining the educational attainment of immigrants. The communal type factors (migrant, professionals, blue collar, and elderly) tend to select educated immigrants from the countries of origin. The greater relative size of the first generation (“migrant”) increases migration of educated workers, which is because sponsors of family migration tend to invite immigrants whose skills are comparable to their own qualifications. This logic also explains why blue-collar workers have a less positive effect than the professionals do. The greater proportion of elderly also increases immigrants‘ education probably because elderly members of an ethnic group may not sponsor as many family immigrants as younger members would, which results in more skill-based immigrants. The lack of communal resources indicated by welfare and a loweducated population reduces the educational attainment of immigrants, which also implies selectivity under family immigration. FINDINGS FROM THE LAST TWO MODEL SETTINGS The MSA-level analysis elucidated two general points. On one hand, it found that political freedom, GDP and origin inequality affect earnings and educational attainments of immigrants in the way that the selfselection theory expected. FDI and the return to education have positive effects as structural imbalancing theory expected. On the other hand, host characteristics examined at multiple levels showed contradictory outcomes between different geographic levels as well as before and after adjusting for selection bias by the lambda variable. The effects of unemployment at the three geographic levels are consistent between models 3 and 4, but inconsistent at different levels. For earnings the ethnic group-level unemployment has a positive effect, and for education the local MSA-level unemployment has a positive effect, supporting the self-selection theory. The national level unemployment, however, has consistently negative effect, which implies that general condition of economy affects selection and adaptation of immigrants. The effects of host-inequality reversed between Models 3 and 4. Model 4 shows generally positive effects of host-inequality, which is consistent with the self-selection theory. At the ethnic group level, it is

Determinants of Immigrant Skills

125

also consistent and positive. Because the adjustment by λ corrects for the different selectivity by national origins, the outcome in Model 4 is more credible when it contradicts that of Model 3. Another important finding is that the cost reduction effect of ethnic clustering (migrant networks) is effective only in determining earnings and not education. For education, type, and resource endowment of the receiving ethnic group determine the educational attainment of immigrants who belong to the group. A twist is that disadvantageous types and resources of the ethnic community can increase immigrants’ unobserved skills precisely because of the effect of greater obstacles, as seen in the models for earnings. Nevertheless, it is clear that sociological elements are playing very important roles in the process selecting immigrant skills. The next sections explore the same scheme at the international level. THREE HOSTS AND FIVE CONTINENTS OF ORIGIN (SETTING III) The first international comparison uses five continental origins of immigrants and six entry cohorts, which makes 30 observations for each host country. While this strategy obscures national origin characteristics by taking their averages at the continental level, it is expected to provide credible accounts for the effects of destination on immigrants in general. Because the five continents include most of the immigrants in the host countries except for those from Oceania, this strategy improves upon those in the previous sections in terms of the coverage of the immigrant population. Relative earnings and standardized educational attainment differences of immigrants are estimated according to this categorization. The values shown in Tables 5-11 and 5-12 are not the averages of estimates in the last sections, but are separately calculated based on the mean characteristics within hostcontinent entry groups. Nevertheless, the aggregation cancels out contrasts and the distributions of dependent variables become narrower than those of the variables in the previous model settings. EARNING: AN ECONOMIC MODEL Table 5-11 shows that the American and Australian immigrants in general had been experiencing a decline in relative earnings in the past decades: about a 9% drop in both countries. In contrast, Canada had an

Table 5-11: Relative Earnings of Immigrant Cohorts by Continent Host United States

Canada

Australia

Origin Sub-total* Europe Asia Middle east, Africa Latin America, Caribbean North America Sub-total* Europe Asia Middle east, Africa Latin America, Caribbean North America Sub-total* Europe Asia Middle east, Africa Latin America, Caribbean North America

Total*

* Weighted by population size.

60-64 .009 .057 .017 .038 -.038 .068 -.097 -.057 -.364 -.275 -.302 .043 -.063 -.057 -.074 -.214 -.162 .127 -.020

65-69 .010 .106 .033 .127 -.075 .137 -.080 -.023 -.211 -.232 -.201 -.094 -.078 -.071 -.120 -.105 -.208 -.225 -.037

Entry Cohort 70-74 -.004 .126 .010 .144 -.076 .125 -.056 .051 -.132 -.146 -.158 -.042 -.054 -.022 -.051 -.338 -.045 -.161 -.020

75-79 -.027 .151 -.061 .192 -.120 .226 -.048 .034 -.102 -.070 -.102 -.104 -.061 -.018 -.073 -.282 .067 -.048 -.032

80-84 -.082 .181 -.093 .184 -.179 .255 .021 .012 .045 .017 -.033 .049 -.106 -.060 -.106 -.343 .184 -.213 -.076

85-89 -.071 .203 -.086 .250 -.220 .285 -.035 -.275 .101 -.116 .016 -.034 -.144 -.023 -.185 -.365 .018 -.097 -.077

Table 5-12: Relative Education of Immigrant Cohorts by Continent* Host United States

Canada

Australia

Total**

Origin Sub-total** Europe Asia Middle east, Africa Latin America, Caribbean North America Sub-total** Europe Asia Middle east, Africa Latin America, Caribbean North America Sub-total** Europe Asia Middle east, Africa Latin America, Caribbean North America

60-64 -.26 -.22 2.89 2.36 -1.37 .41 .14 -.40 2.87 3.13 2.07 2.56 -.51 -.74 1.47 1.36 1.83 2.79 -.21

65-69 -.88 -.99 2.81 1.97 -2.60 .47 .94 .32 2.67 3.11 1.37 3.14 -.17 -.27 .87 -.18 .52 1.98 -.33

* Scores are standardized by age. ** Weighted by population size.

Entry Cohort 70-74 -1.58 -1.49 2.42 1.44 -3.91 .90 .74 -.36 2.00 2.24 .59 3.13 .11 -.27 1.17 .09 .00 3.04 -.95

75-79 -1.45 .14 1.08 1.60 -4.47 1.65 .63 .82 .27 2.03 .43 2.71 .72 .93 .51 -.08 .44 2.87 -1.03

80-84 -1.58 .85 .51 1.51 -3.86 1.54 .51 1.34 -.09 1.83 -.08 2.88 .20 .55 -.25 .33 -1.36 1.18 -1.29

85-89 -1.20 1.10 .92 1.14 -3.81 1.67 .47 .43 .58 1.29 -.39 2.10 .98 1.07 .84 1.18 .30 1.76 -.69

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Social Determinants of Immigrant Selection

Table 5-13: Explanatory Variables by Five Continents* Middle Latin Europe Asia East and America Africa Gini .343 .308 .226 .496 Mean Education 6.92 3.94 1.57 4.09 Tourism 9.86 1.59 .410 2.57 FDI .296 .044 .092 .395 GDP .633 .120 .106 .270 Return .042 .060 .035 .128 Clustering .825 .027 .083 .031 Migrant .114 .717 .452 .485 Labor Force .652 .685 .653 .658 Professional .185 .198 .158 .124 Residency 22.0 9.66 11.5 13.6 English .773 .551 .876 .477 High Education .594 .640 .668 .575

North America** .366 10.9 15.9 2.44 1.11 .094 .034 .261 .620 .126 20.1 .625 .447

* The numbers are the averages across entry cohorts and host countries. Units are all proportions except for Mean Education (years), Tourism (million) and Residency (years). ** Mexico is included in Latin America.

upward trend from the 1960s until the most recent cohort, who had a small drop: about a 12% jump and a 3.5% drop. Combining all these, the general trend in relative earnings of immigrants is a decline as shown in the bottom row of the table. Since these totals are weighted by population size, the scores of large-size immigrants pulled the trends. In the U.S., The negative scores of Latin American and Asian immigrants affected the total trend because these groups have increased their shares in the U.S. immigrants to about 70 percent of the most recent cohort. In Canada, the growing numbers of Asian immigrants and their improving relative earnings had contributed to the upward trend. In Australia, the newer cohorts of Asian immigrants are also replacing European immigrants, but their relative earnings had declined and thus the total trend is downward. Only the Latin American group has a positive score in the recent Australian cohorts, but their share in the total immigrants is only 2-3 percent.

Determinants of Immigrant Skills

129

Table 5-12 introduces immigrants’ educational attainment differentials controlling for age and survey year in the three host countries. The general trend of all three host countries is downward because the large number of the U.S. immigrants affects the whole. Looking at host countries separately, a large number of less-educated Latin American immigrants dominate the U.S. trend, while immigrants’ relative educational levels are consistently positive in Canada and increasingly positive in Australia. This contrast suggests that Canada and Australia have successfully creamed off highly-educated immigrants. However, it does not necessarily guarantee that the two countries successfully select highly motivated immigrants. Compared to the first two settings, the explanatory variables reduced variations as a result of aggregation (Table 5-13). The aggregation also produced values that are very different from the averages of the 16 selected groups (in setting II) because in this section nearly all the immigrants are included. For example, the mean education of the six Asian countries was 5.8 years in the previous section (Table 5-1), but it is 3.9 years for the overall Asian continent. Another anomalous case is ethnic clustering. The clustering of Europeans is as dense as 82.5 percent in the total population because the indicator includes the native-born population. This variable indicates majority-minority status rather than migrant networks. The host Gini coefficients are, on average over time, .37, .31, and .34 for the U.S., Canada, and Australia, respectively; unemployment rates are 5.7, 6.7, and 3.7 percent, respectively. Again, the specification in Borjas (1991: Table 1.14) is examined. He used a very simple empirical model for his three-country analysis probably due to the small number of observations (48). The reexamination of his model using 1980-90 data is the following:

Q = −.815 − .015β + .849δ − .103γ 0 + 2.11γ 1 + .110π , R2 = .247

(-4.37) (-.40) (1.20) (-.63) (3.99) (2.91) , where β is “U.S. law” (post-1965 cohort), δ is unemployment, γ0 is origin inequality, γ1 is host inequality, and π is GDP per capita of origin country relative to the host country‘s GDP per capita. The positive effects of unemployment and host inequality, in addition to the negative effect of origin inequality confirm the self-selection theory.

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Social Determinants of Immigrant Selection

This and Borjas’s results not only have the same signs for all variables except for the intercept, but also have significant effects on the same variables – host inequality and origin GDP. Table 5-14: Regression of Earnings by 5 Continents of Origin Model 1 Model 2 Coef. Coef. (t-score) (t-score) -.495 -.156 Canada (-4.68) *** (-3.95) -.175 -.748 Australia (-5.24) *** (-6.54) … -.928 US*Gini (-4.20) … -.076 Canada*Gini (-.34) … .658 Australia*Gini (2.97) … -.586 Host Gini (-.63) … .320 Unemployment (.41) … .054 US law (1.64) .120 … GDP (4.00) … … Clustering Migrant

…

…

Labor Force

…

…

Professional

…

…

Residency

…

…

English

…

…

High Education Constant R-square

… .066 (2.78) *** .276

… .504 (1.52) .547

*** *** ***

***

***

Model 3 Coef. (t-score) -.267 (-1.93) -.549 (-4.52) -.898 (-3.86) -.771 (-2.20) .405 (1.97) -.642 (-.81) .197 (.27) .053 (1.87) .309 (6.38) .125 (2.37) -.178 (-1.50) 1.901 (2.97) -1.228 (-3.34) -.011 (-2.76) -.230 (-2.25) .512 (3.27) -.569 (-1.24) .699

* The variables in Model 4 are interacted with λ.

Model 4* Coef. (t-score) .013 * (.30) -.068 *** (-1.56) .120 *** (1.03) .042 ** (.60) .155 * (2.74) *** .469 (2.06) ** -.143 (-.65) .014 * (1.64)

… *** **

*** *** ***

.054 (2.56) ** -.060 (-1.79) * .169 (1.37) -.179 (-1.63) -.001 (-1.37)

… ** ***

.030 (.73) -.952 (-2.97) *** .559

Determinants of Immigrant Skills

131

EARNING: A SOCIOLOGICAL MODEL The model above, however, does not take into account unobserved factors that belong to the host countries, such as immigration policy, geo-political position, and historical paths. An alternative sociological model here uses host country dummies to take into account such between-host differences. As shown by the first model in Table 5-14, immigrants’ relative earnings in Canada and Australia are 16 to 18 percent lower than immigrants‘ earnings in the United States without any controls (immigrants in the U.S. are the reference group). The effect of origin inequality (the Gini coefficient) is insignificant when it enters the equation without decomposition (not shown). However, in Models 2 to 4, interaction of the inequality with three destination countries reveals that the origin inequality has a negative effect in the U.S., but it has a positive effect in Australia and a somewhat negative effect in Canada (Models 2 and 3). The U.S. immigrants show the relationship between origin inequality and selectivity predicted in the self-selection theory. The relationship is less significant in Canada and is reversed in Australia: the relative earnings are greater for the immigrants from countries with greater inequality. The effect of host inequality is significantly positive after adjusting for selection bias in Model 4 as the self-selection theory expects. The effect of GDP is positive and consistent with the observation by Borjas (1991). EDUCATION: AN ECONOMIC MODEL Immigrant education has not been analyzed across the three host countries. Borjas (1987, 1991) did not extend his analysis of immigrant education from the U.S. to the other countries. Here, the applicability of his economic model is reexamined using the three host countries with the continent level data:

Q s = 1.57 − .111µ s − 14.9δ s + 2.09π , R 2 = .162

(3.58) (-.76) (-3.12) (1.72) where Qs is the differentials in the years of education between immigrants and natives, µs is a mean education at origins, δs is a return to education, and π is the origin country’s GDP per capita. The return

132

Social Determinants of Immigrant Selection

Table 5-15: Regression of Education by 5 Continents of Origin Model 1

Variables Canada

Coef. (t-score)

Coef. (t-score)

Canada*Gini

…

Australia*Gini

…

Host Gini

…

Mean Education

…

Tourism

…

FDI

…

GDP

…

Return to Educ.

…

Clustering

…

-4.284 (-4.09) -6.241 (-6.28) -32.820 (-10.06) -18.578 (-5.72) -14.177 (-4.37) -10.197 (-1.49) -.011 (-.08) .083 (1.27) -.124 (-.84) .308 (.36) 36.373 (4.44) …

Migrant

…

…

Professional

…

…

English

…

…

High Education

…

…

Constant

.221 (.79)

6.876 (4.54)

R-square

.106

.704

Australia US*Gini

1.266 *** (3.21) .576 (1.46) …

Model 2

*** *** *** *** ***

***

**

Model 3

Model 4*

Coef. (t-score)

Coef. (t-score)

-6.343 (-5.43) -7.656 (-7.21) -17.702 (-2.82) .860 (.13) 3.060 (.47) -4.298 (-.73) -.358 (-1.93) .185 (2.66) -.369 (-2.46) 2.625 (2.04) -10.456 (-.53) -2.817 (-2.66) 1.105 (1.18) 9.336 (2.77) .441 (.43) -3.925 (-3.01) 9.866 (3.74) .799

* The variables in Model 4 are interacted with λ.

*** *** ***

* ** ** **

***

***

-.578 (-1.43) -1.150 (-2.90) -3.603 (-1.67) -.572 (-.26) .798 (.37) 5.389 (3.18) .066 (2.25) -.013 (-.65) -.002 (-.25)

***

*

*** **

… -3.027 (-.47) -.731 * (-1.98) -.478 * (-1.91) 3.283 *** (3.08)

… ***

***

-.369 (-1.02) -2.588 (-.89) .689

Determinants of Immigrant Skills

133

to education has a negative effect, and it is consistent with the theoretical expectation because highly educated workers would stay in their home country if the return to their education were considerable at home. However, unlike the analysis with regard to the U.S. alone, the effect of the mean education is insignificantly negative and the effect of the origin GDP positive. This inconsistency is due to adding Canadian and Australian data into the data set and does not result from additional immigrant groups or aggregation by continents. To confirm this, the regression results of the U.S. data alone (n=30, not shown) are similar to those in the previous sections. As far as this model specification is concerned, the three countries do not have similar patterns. Examination of more elaborated models based on sociological theory is necessary. EDUCATION: A SOCIOLOGICAL MODEL As seen in Models 2 and 3 in Table 5-15, the effects of the return to education become positive after controlling for unobserved host-effects. Decomposition of the return rates revealed that greater return to education in countries of origin positively affects immigrants’ educational attainment, particularly in Canada and Australia. 47 In Models 3 and 4, the return to education does not have significant results. The effects of origin and host inequalities match the selfselection theory, though host inequality is not significant. The effect of FDI is positive and significant. The measurements of communal characteristics are not very reliable here because too many immigrant groups are aggregated by continent. These variables only maintain model-comparability.

47

The table below shows the effects of decomposed “return to education.” The base is Model 2 in Table 5-15. Other results are compressed: Variable Return*USA Return*Canada Return*Australia

Coefficient 21.29 50.11 37.68

t-score 1.76 4.15 3.12

p * *** ***

134

Social Determinants of Immigrant Selection

THREE HOSTS AND FIVE COUNTRIES OF ORIGIN (SETTING IV) Unlike the very inclusive sample used in the last section, this section uses a more restricted sample in which only five countries of origin are included. The shortcoming of the last section was that the continentlevel grouping obscured the variations of variables. This section, therefore, attempts to clarify the national-origin effects by selecting immigrant groups observable in the three host countries with considerable numbers. In Canada and Australia, where immigrant populations are not so large, many national origins of immigrants are not disclosed in public data. National origins are often lumped together into regional categories such as Southeast Asia and Middle East to secure confidentiality of the respondents. Because of these restrictions, only five countries of origin are specified for this section: Great Britain, Germany, Italy, Poland, and China. Immigrants from these countries exist in all three host countries, and their sample sizes are large enough to produce reliable estimations. Obviously, these five countries do not represent all of the immigrants or provide a balanced combination of race and ethnicity. Since this selection tends to favor groups that have accumulated their ethnic presence in the host countries, the five groups have to come from the earlier waves of mass migration. As a result, newer groups were excluded even though they have had sizable inflows recently: the data set has only one Asian group and no Latin American group. European groups are from many parts of Europe, but the data set consists of predominantly white immigrants. Inference from this section, therefore, must be limited to these five countries – and even within the five, interpretation of the results must take into account the inclination to the old white immigrant groups. The grouping categories are five countries of origin, six entry cohorts, and three host countries, which adds up to 90 observations. Table 5-16 shows the earning differentials used as dependent variables in this section. As far as these five immigrant groups are concerned, their earnings relative to natives declined in the United States but increased in Canada and Australia between the 1960-64 cohort and the 1985-89 cohort. In the U.S., British immigrants show an upward trend across the entry cohorts controlling for individual characteristics such as age, education, and the period they spent in the

Table 5-16: Relative Earnings of Immigrants at National Level Host United States

Canada

Australia

Entry Cohort 70-74 75-79 .025 -.024 .131 .274 .030 .022 .153 .046 .113 .063 -.080 -.090 -.030 -.006 -.002 .030 -.181 .024 -.074 -.215 .039 .092 -.048 -.022

Origin Sub-total* U.K. Germany Italy Poland China Sub-total* U.K. Germany Italy Poland China

60-64 .072 -.016 .105 .157 .062 -.086 -.103 -.068 -.096 -.108 -.220 -.358

65-69 .071 .088 .106 .165 .131 -.076 -.090 -.061 -.001 -.105 -.212 -.227

Sub-total*

-.119

-.123

-.065

U.K. Germany Italy Poland China Total*

-.088 -.027 -.187 -.067 -.758 -.047

-.061 -.130 -.172 -.375 -.587 -.093

.002 -.121 -.227 -.203 -.402 -.028

* Weighted by population size

80-84 -.050 .271 -.024 .024 .054 -.112 .078 .205 -.061 .017 -.047 .082

85-89 -.080 .390 -.068 .023 -.056 -.158 .048 .141 .008 .024 -.290 .179

-.014

-.047

.120

.077 -.022 -.201 -.261 -.131 -.015

.050 -.139 -.215 .012 -.160 -.030

.160 .009 -.226 -.093 .206 .012

Table 5-17: Immigrants Education Relative to Natives Host United States

Canada

Australia

Origin Sub-total** U.K. Germany Italy Poland China Sub-total** U.K. Germany Italy Poland China

60-64 -0.57 0.81 -0.07 -2.82 -1.05 1.69 1.07 2.29 1.81 -2.05 0.33 1.85

65-69 -0.71 1.27 -0.48 -3.35 -0.64 1.34 1.60 2.32 2.00 -1.80 1.68 2.56

Sub-total**

-0.26

-0.20

U.K. Germany Italy Poland

0.2 0.78 -2.36 0.73

0.13 0.86 -1.21 0.99

China Total**

Entry Cohort * 70-74 -0.86 1.04 -0.96 -3.73 0.01 0.63 1.65 1.99 1.22 -1.23 1.62 1.84

75-79 -0.20 1.42 0.30 -2.30 1.13 -0.28 1.08 2.02 1.97 -0.04 2.44 -0.09

80-84 -0.34 1.05 0.25 -1.32 0.45 -0.64 0.68 2.11 1.21 -0.46 2.01 -0.68

85-89 0.02 1.15 1.01 -0.52 0.77 -0.49 1.03 1.41 0.38 -0.91 1.10 1.00

0.00

0.86

0.46

0.98

0.22 0.71 -1.32 1.12

1.12 1.59 -1.05 1.88

0.43 1.61 -0.65 1.46

1.32 1.35 0.08 1.43

3.6

1.62

1.06

0.37

-0.04

0.72

0.13

0.04

0.32

0.50

0.00

0.54

* All scores are standardized by age ** Weighted by population size

Determinants of Immigrant Skills

137

host countries; other groups, particularly Chinese immigrants, have declining trends. In Canada, trends are generally upward except for that of Polish immigrants. In Australia, all but Italian immigrants are experiencing slow improvement of cohort earnings, and Chinese in particular have had rapid “catch up” to cross over the earnings of comparative natives. Table 5-17 shows another dependent variable. Observed relative educational attainments of immigrants declined in Canada about one year from the 70s to the 80s, but in the U.S. and Australia, the trends of immigrants‘ relative education is generally upward: more recent cohorts have greater educational attainments relative to comparable white natives. In all host countries, educational levels of British, German, and Polish cohorts are 1 to 2 years above the native levels controlling for age and survey year. Italian immigrants had levels 2 to 3 years below that of natives in earlier cohorts but came close to parity in later entry groups. Chinese cohorts have quite the opposite trend: their earlier cohorts have very high levels of education in all three countries, but the level declined in newer cohorts. Chinese immigrants show a clear decline in educational attainment in the post-1965 entry cohort, though the trend reversed in the most recent cohort. It should be noted that these trends do not indicate a significant difference by host country. Educational levels of Chinese immigrants had declined in all three countries obviously as a result of the liberalization of the immigration policy in the 1960s. The effect of the point system in Canada and Australia, however, is not obvious from the table. These trends in the three countries look similar, although the immigrant-native differential is negative in the U.S. and mostly positive in Canada and Australia after the 1975-79 cohort. EARNING: AN ECONOMIC MODEL The simple economic model for the continent-level analysis is reexamined. Again, its result matches quite well with the previous empirical study using the 1970-1980 censuses (Borjas 1991):

Q = −.841 − .033β + 2.83δ − .211γ 0 + 1.84γ 1 + .185π , R2 = .319

(-3.81) (-.77) (3.60) (-.60)

(3.12) (3.23) ,

138

Social Determinants of Immigrant Selection

where β is “U.S. law” (the post-1965 cohort), δ is unemployment, γ0 is origin inequality, γ1 is host inequality, and π is GDP gap between origin and host country. Probably because this model directly uses national origin characteristics and does not dilute variations, t-scores and Rsquare have generally improved. This re-examination again confirmed the credibility of the data set based on the 1980-90 censuses and its comparability with previous research based on the 1970-80 censuses. However, the results of the economic model do not remain comparable when sociological factors in host countries are taken into account. Table 5-18 shows regression results of sociological models including host country dummies. As Models 2 and 3 show, origin inequality (the Gini coefficient) has a positive and significant effect on immigrants’ relative earnings: the earning differential improves when the Gini coefficient in the origin increases. This result contradicts what the self-selection theory predicts. The effect of origin GDP (relative to destination GDP) is significantly negative and it also contradicts with the self-selection theory. These results indicate that skilled workers (in terms of the relative earnings) come from places where inequality is greater and GDP is smaller: the predictions of the self-selection theory are reversed as far as these five country-of-origin groups are concerned. The distance variable has a negative and significant effect, which is also unexpected because long distance means higher migration cost and therefore higher selectivity. In this case, since the distance for European countries are more or less the same, probably the long distance of China and low earnings of Chinese immigrants affected this result. The significantly positive effect of the return to education in Model 3 indicates that earning differentials are .8 percent greater for every ten percent increase in the return to education at origins, supporting the structural imbalancing hypothesis. Network variables were measured at the national level and are therefore not reliable, although they were introduced in Model 3. Finally, Model 4 adjusts the selection bias by λ, and the t-score for the Gini coefficient declined, but host inequality increased its significance. The negative effect of inequality at origin and the positive effect of inequality at destination match the self-selection theory.

Determinants of Immigrant Skills

139

Table 5-18: Regression of Earnings by 3 Hosts and 5 Origins Model 1 Model 2 Coef. Coef. (t-score) (t-score) Variables -.107 ** -.076 Canada (-2.64) (-1.02) -.102 -.199 *** Australia (-2.14) (-4.94) … 2.057 Gini (3.80) … .442 Host Gini (.41) … 8.712 Return (5.07) … 2.039 Unemployment (2.28) … .063 US law (1.66) … -.422 GDP (-3.03) … -.066 Distance (-4.61) … … Clustering Migrant

…

…

Labor Force

…

…

Professional

…

…

Residency

…

…

English

…

…

High Education

…

…

Constant R-square

.055 * (1.92) .219

-.805 (-1.97)

** ***

*** **

*** ***

*

Model 3 Coef. (t-score) .084 (.86) -.304 (-3.29) 2.025 (3.48) .912 (.87) 8.386 (3.88) 2.629 (2.92) .056 (1.56) -.387 (-2.43) -.075 (-3.73) -.696 (-2.25) .089 (.49) .398 (.71) -.962 (-2.45) -.007 (-1.04) .315 (2.55) -.176 (-.82) -.973 (-1.70)

.569

* Variables in Model 4 are interacted with λ.

.653

*** ***

*** ***

** *** **

**

**

*

Model 4* Coef. (t-score) -.048 *** (-2.72) -.056 ** (-2.48) .169 * (1.94) -.410 (-1.62) 1.195 *** (3.52) .556 ** (2.25) .019 * (1.88) … … -.161 ** (-2.12) .021 (.42) -.212 * (-1.76) -.223 ** (-2.24) .002 (1.56) … .042 (.68) .524 *** (2.92) .599

140

Social Determinants of Immigrant Selection

EDUCATION: AN ECONOMIC MODEL The economic model of educational attainment is re-examined using the data of five origin countries. As shown below, this data setting produced much more significant results than at the continent level. The effects of mean education and GDP are now consistent with the findings in the U.S. analyses, but the return to education has an opposite, positive effect on educational attainments:

Q s = −2.67 + .282µ s + 49.3δ s − 1.93π , R 2 = .423

(5.14) (3.86) (6.93) (-4.02) Ten percent greater return to education in the country of origin, according to this model, leads to about five years of additional education. Better-educated workers come from countries where education is highly appreciated. This positive effect of return rate on education is consistent with the structural imbalancing argument and with most of the sociological models in the previous sections and contradicts with the self-selection theory. Sociological models further confirm this observation. EDUCATION: A SOCIOLOGICAL MODEL As shown in Model 3, Table 5-19, the rate of return to education maintains its positive effect on education after controlling for other effects in country-of-origin, host, and ethnic groups. Its coefficient declined only slightly to 3.7 years of education for a 10 percent increase in return-rate. Origin and host inequalities (Gini and Host Gini) show results that support the self-selection theory: inequality at home reduces and inequality at destination increases immigrants’ skills. The ethnic group variables do not have significant results in this model setting. All t-ratios are very low compared with those of other analytical settings in the earlier sections. Probably the selection of origin countries, which are mostly “old” sources of European immigrants, did not produce large enough variations in these variables. The Chinese group would provide some variation, but the number of Chinese is too small, one-fifth of the sample, to balance the estimations. Nevertheless, all five groups have a long history in each host country, and their communal characteristics would not have much determinant power on immigrant characteristics any longer.

Determinants of Immigrant Skills

141

Table 5-19: Regression of Education by 3 Hosts and 5 Origins Model 1 Model 2 Model 3 Model 4* Coef. (t-score)

Coef. (t-score)

Coef. (t-score)

Coef. (t-score)

.395 (3.96) .235 (1.65) .072 (3.17) 11.810 (4.92) .052 (2.53) -1.533 (-1.81) 1.550 (1.24) …

Variables

…

1.223 (2.84) .636 (2.02) .132 (.85) 32.080 (2.36) -.439 (-2.92) -9.163 (-2.35) 2.908 (.45) 3.435 (2.70) .118 (.74) …

…

…

…

…

…

…

…

…

Constant

-.280 (-1.18)

-1.343 (-.54)

1.058 (1.60) 1.197 (1.94) -.013 (-.07) 37.438 (2.33) -.528 (-3.40) -8.508 (-2.09) 2.920 (.44) 3.981 (3.10) -.035 (-.17) 2.139 (.96) .291 (.26) -.747 (-.29) -.907 (-1.05) 2.985 (1.97) -1.587 (-.63)

R-square

.131

.707

.733

Canada Australia Mean Education Return Free Gini Host Gini GDP Distance Clustering Migrant Professionals English High Education

1.276 *** (3.81) .905 *** (2.70) … … … … … … …

*** **

** *** **

***

* Variables in Model 4 are interacted with λ.

*

** *** **

***

***

*** *** ** * ***

… 1.038 * (1.94) -.051 (-.20) .046 (.08) … *

-.216 (-.56) -4.553 *** (-3.52) .729

142

Social Determinants of Immigrant Selection

A limitation of this last analytical setting is the number of countryof-origin groups. The selection of only five sample groups from hundreds of nationality groups makes general inferences impossible. The next and final chapter will integrate the findings from all four analytical settings to draw meaningful conclusions.

CHAPTER 6:

Immigrant Selection in a New Context

The population is quickly aging in the advanced countries, while it is rapidly growing in the less developed countries. These circumstances have fundamentally changed migration both in its race-ethnic composition and in the reduced skills of recent immigrants. It is increasingly difficult for the host countries to obtain cheap and skilled workers through immigration, and the competition among receiving countries for qualified immigrants has become one of the most important social and political issues facing them. The concern over the declining skills of immigrants fueled the debate over the implementation of restrictive immigration policy in the advanced countries. In particular, the United States recently expanded its job-based migration quota, and some in that country are even advocating the implementation of a point system as a method to cream the best workers off the migrant pool. Obviously, the restrictive immigration policy may screen immigrants based on observable criteria such as education, training, experience, and English fluency. However, such policy cannot do anything about unobserved skills such as diligence, motivation, ambition, etc. In fact, the incentives of Western European immigrants are declining as their countries offer increasingly greater economic opportunities. 143

144

Social Determinants of Immigrant Selection

DETERMINANTS OF SKILL SELECTION The current literature is divided over the decline of immigrant skills and the causes of skill selection. This research has critically applied sociological theories to the economic analysis of immigrant selection introduced by George Borjas (1985, 1987, 1989, 1991) because his methods and findings are quite influential as well as controversial in immigration studies. The economic theory of self-selection assumes that positive and negative selections of immigrant skills result from dissimilar labor market conditions, particularly inequality, to which individuals with different skills respond differently. Sociological theories disagree with this juxtaposition of individuals and markets, and assume mechanisms that operate between and beyond them. Specifically, the theories of migrant networks and segmented assimilation propose meso-level determinants of immigrant selection and adaptation that differentiate group outcomes. The levels of capitalist penetration determine system affinity between sending and receiving countries, which affects transferability of immigrant skills. The theory of structural imbalancing explains the relationship between peripheral development and brain drain. In short, the first purpose of this research has been to test these sociological theories of immigrant skill selection, which would explain what economic theory left unexplained; and the second purpose is to re-examine the self-selection theory, which has not been tested for the 1980-90 census data – the previous research used data from ten years earlier. Immigrant skills were measured in terms of “unobserved” and “observed” skills at different group units according to the combinations of origins and destination units. Then analyses were performed at four analytical settings. The economic models introduced by Borjas (1991) were tested at all of the analytical settings. Although some variables were not perfectly duplicated, the results revealed that his findings based on 1970-80 data could be replicated using 1980-90 data. At the same time, this re-examination indicated the reliability of the measurements and general data quality of this research. According to the first hypothesis, inequality in origins decreases and inequality in destinations increases positive selectivity of immigrant skills. This research focused on the two inequality variables: the Gini coefficients in countries of origin and destinations. The effects of national origin characteristics should be most appropriately

Immigrant Selection in a New Context

145

accounted for in analytical setting I because these 16 identified countries include the majority of immigrants in the U.S., and they equally represent three major race-ethnic groups (European, Asian and Hispanic). In this setting, the effect of origin inequality was negative on both earning and education of immigrants except for a model of education with selectivity adjustment (in which the effect was slightly significant and positive). Since the effect of inequality should be unbiased in the latter model, the self-selection theory was not supported for the effect of origin inequality on education, though it was very well supported on earnings. On the other hand, the effect of host inequality is most appropriately estimated in setting III because the host country dummies in the models account for unobserved nonrandom differences between host countries and improves the estimation of other host effects. On both equations for earnings and education, the host inequality variable obtained positive and significant effects. These results support the first hypothesis about the effect of host inequality on immigrant skills. Generally, the self-selection theory has found not perfect but general support in this research. The second hypothesis on migrant networks predicted a negative effect of ethnic clustering and a positive effect of relatively new, highly skilled and resourceful ethnic groups on immigrants’ skills. The measurements of these ethnic-group variables are most reliable in the small geographic units of analytical setting II. First, the ethnic clustering had negative effects on both earnings and education, proving that migrant networks negatively affect immigrant skills through reduction of migration costs in the U.S. Secondly, the newer an ethnic group, the better the skills of new immigrants who belong to the group. Thirdly, a highly skilled ethnic group improves the skills of its new members. Lastly, communal resources improve immigrant skills except for the English fluency of the group. The English fluency of ethnic groups negatively affects the immigrant skills probably because it reduces the migration costs and enables less skilled workers to migrate. In short, it was concluded that migrant networks and English fluency induce less skilled workers to migrate, but some types of ethnic groups induce the migration of highly skilled workers and improve their adaptation to the host economies. In the third hypothesis, greater system affinity between origin and host countries increases the skill selection of immigrants by improving

146

Social Determinants of Immigrant Selection

the international transferability of skills. Since the variables in focus are at the national level, analytical setting I is appropriate. The results were contradictory. While political freedom positively affected both skills, tourism had no effect on earnings and a negative effect on education after adjusting for selectivity. Other indicators of system affinity, such as trade and FDI, were also inconsistent. Altogether, the third hypothesis was not supported. In the fourth hypothesis, the structural imbalancing measured by the return to education improves immigrant skill selection by inducing brain-drain emigration. Settings I and IV provide the most appropriate framework to test this hypothesis. The effects of the return to education on both earning and education were positive in the two settings. In Borjas’s educational attainment model, this variable was an indicator of reward distribution, and he obtained negative effects for it. This research, however, used it as a measurement of structural imbalancing and obtained positive effects. The negative effect in economic models reversed to positive when additional controls were introduced into the equation. Because the positive effect was significant and stable across the two analytical settings and different specifications, its positive effect is robust. This result supports the fourth hypothesis on the braindrain effect of structural imbalancing. Nevertheless, how well the return to education represents the structural imbalancing is still a subject of scrutiny. If uneven national development and uneven job opportunity causes brain-drain migration, the solution is not just national development but a balanced development of economic as well as human capital resources. HISTORICAL PATTERNS AND IMMIGRATION POLICIES The effects of host country dummies have interesting implications. These dummies represented any between-host variations that were not accounted for by other observed factors. The major variations are policy differences and geo-historical conditions. The dummies for Canada and Australia held significant and negative coefficients through all specifications in setting III. This means that immigrants in the United States were more skilled than those in Canada and Australia, all else being equal. When national origin groups were limited to the five mostly European countries in setting IV, the dummies for Canada and Australia had positive effects on education but not on earnings.

Immigrant Selection in a New Context

147

Therefore, Canada and Australia select better-educated immigrants, but their immigrants’ skills are (in relation to native skills) less than those in the U.S., as far as the limited five national origin groups are concerned. One of the proposals in the U.S. immigration policy reform is to screen out unskilled immigrants by the point system, but there is no guarantee that that will improve immigrant skills. In a milder way, the U.S. is recently trying to increase the proportion of employment-based immigration by expanding admission through the H-1 (temporary worker) visa. These ad hoc remedies to the “declining skills of immigrants” may temporarily improve “observed” skills of immigrants, but in the long-run, the supply of highly-educated immigrants can diminish if an appropriate adaptation policy does not accompany the restrictive policy; and they have no control over “unobserved” skills. A substantial and long-term approach to this issue should be based on a comprehensive understanding of the current world migration situation and race-ethnic relations in the host countries. If the point system is implemented in the U.S., its consequence will be different from the consequences of those in Canada and Australia for two reasons. First, the U.S. has Mexico and other Latin American countries as major migrant sources, which makes implementation of a point system in the U.S difficult. The legalized IRCA immigrants will sponsor family-reunification immigrants in the coming decades and increase the unscreened immigrant population. Asian immigrants did not benefit from the IRCA, and they will be major targets of screening if the point system is implemented. That is why the balance between family-based admission and job-based admission is more important than implementation of the point system. Secondly, the issue of race-ethnic relations in the U.S. is significantly different from that in Canada and Australia. The historical differences with relation to this issue, as discussed in Chapter 3, is persistent in the current situation.48 The race-ethnic stratification and 48

The table below shows four regression results based on the three-hosts and five-continents data (using setting III). Asia and Latin America are dummy variables to indicate the continents of origin. Intercepts are compressed. Asian and Latin American immigrants are significantly disadvantaged only in the United States; in Canada, the disadvantage is insignificant, and in Australia, the difference is even somewhat positive.

148

Social Determinants of Immigrant Selection

segregation are fundamental to the U.S society, and implementation of multiculturalism does not achieve substantive integration and economic parity of different race-ethnic groups. In the analyses of the U.S. immigrants, the negative effects of Asian and Hispanic dummies were never explained away in any model specifications. That implies that the decline in relative earnings of the U.S. immigrants is a matter of socioeconomic adaptation as much as it is a matter of selection of “unobserved” skills. As far as the predicted earnings from age 20 to 50 are concerned, Asian and Latin American immigrants do not catch up with comparable natives for the reasons specific to their race or ethnicity. The fundamental problem of the self-selection theory is that it attributes this structural disadvantage to the lack of individual skills. On the other hand, Asian and Latin American immigrants in Canada and Australia earned almost as much as European and North American immigrants, controlling for human capital characteristics. 49 Since the point system cannot screen immigrants by “unobserved” characteristics, the U.S. will not be able to equalize the earning disadvantages of Asian and Latin American immigrants by the point system alone. Nevertheless, a great number of Asian and Latin American immigrants keep coming to the U.S. despite the fact that they are disadvantaged in this host country. This is because the U.S. provides the greatest outcomes in absolute terms. As the brain-drain argument reveals, highly educated migrant workers tend to leave their home countries even though those countries provide high return rates to their educational investment. In countries where advanced education is cheap, such as in China and India, it is quite rational for highly educated individuals to migrate to a country where the absolute return

All Hosts USA Canada Australia 49

Origins Asia Latin America Asia Latin America Asia Latin America Asia Latin America

See previous footnote.

Coef. -.064 -.074 -.189 -.277 -.041 -.060 .038 .115

t-score -1.58 -1.84 -5.95 -8.73 -.71 -1.04 .64 1.94

P>|t| .117 .070 .000 .000 .487 .306 .526 .063

R-squared .052 .772 .046 .123

Immigrant Selection in a New Context

149

to education is greatest in international prices even though the price is relatively lower than what natives would receive. It is just as Piore (1979) proposed in his dual labor market theory. Since the relatively low price of skilled workers benefits their employers, this new braindrain migration will expand in the coming years. This book has re-examined and reconfirmed the economic models of self-selection based on 1980/81 and 1990/91data. This is an empirical achievement because no studies have re-examined Borjas’s (1991) study, which was based on 1970/1 and 1980/1 data. More importantly, this research has made a theoretical achievement by pointing out the limitations of economic models and integrating sociological theories and economic theories into the models of immigrant earnings and educational attainments. The results from statistical analyses support hypotheses based on theories of migrant networks, migrant social capital, and structural imbalancing. Lastly, this study has provided some insights into immigration policy reform based on a historical and statistical comparison between the United States, Canada, and Australia. Further research should improve upon several limitations of this book. The public census data of Canada and Australia are limited in terms of the number of immigrant samples and the disclosure of national origins information. The census variables are taken for generic purposes and lack specific information on migration. Pooling multiple cross-sectional data is also problematic if we need to assess changes over time because the identity of a unit (person or group) may change dramatically from one time point to another. Longitudinal surveys specifically designed for immigration research would be the ideal solution to these problems. Such longitudinal surveys seem to be in operation now in all three host countries. Estimation of immigrant earnings growth should be significantly improved by these data once they become publicly available. The analytical method can be improved by making use of multilevel estimation methods. For example, the hierarchical linear models or structural equation models can model the variation among higherlevel units such as groups and countries as well as the growth curve of individual earnings. The author (Kawano 2002) used a hierarchical model to account for individual wage growth of U.S. immigrants as a function of higher-level factors. Since geographic variations of social

150

Social Determinants of Immigrant Selection

characteristics are receiving more attention in sociology, these methods should be used more often in the studies of immigration. Lastly, the conceptualizations of self-selection and adaptation were confounding in this research. For studies of immigrant earnings, theoretical and methodological clarifications of these concepts are necessary. Although the interaction between economics and sociology has been very fruitful, the sociology of immigration would need to develop formal sociological models. There is a limit to extending the economic “individual rational choice” assumption to the higher levels such as community. That is, such groups may not act to maximize economic outcomes, but to achieve different goals. At the individual level, for example, sociology can evaluate family reunion of immigrants as a somewhat positive outcome even though it does not create any economic benefit. Similarly, groups could act for noneconomic purposes such as stability, social status, cultural identity, etc. Hopefully, this research will encourage theoretical endeavors to explain such non-economic processes of immigration.

Appendix Population Pyramids of the U.S. Canada and Australia USA 1980 90-101

Age group

75-79 60-64 45-49 30-34 15-19 0-4 -15

-10

-5

0

5

10

15

10

15

Population % Natives

Immigrants

USA 1990 90-101

Age group

75-79 60-64 45-49 30-34 15-19 0-4 -15

-10

-5

0

5

Population % Natives

151

Immigrants

152

Appendix

Canada 1981 90-101

Age groups

75-79 60-64 45-49 30-34 15-19 0-4 -15

-10

-5

0

5

10

15

10

15

Population % Natives

Immigrants

Canada 1991 90-101

Age groups

75-79 60-64 45-49 30-34 15-19 0-4 -15

-10

-5

0

5

Population % Natives

Immigrants

Appendix

153

Australia 1981 90-101

Age group

75-79 60-64 45-49 30-34 15-19 0-4 -15

-10

-5

0

5

10

15

10

15

Population % Natives

Immigrants

Australia 1991

75-79

Age group

60-64 45-49 30-34 15-19 0-4 -15

-10

-5

0

5

Population % Natives

Immigrants

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Index

community, 18, 32-37, 91, 97 discrimination, 44, 51-52, 83, 100 dual labor market, 91, 149 earning differentials, 82, 107, 111, 120, 134, 138 educational attainment, 7-9, 25-26, 76-77, 88, 94, 101, 112-115, 120-121, 124-125, 133, 137, 140-146, 149 emigrant, 40, 45, 49-50, 77, 90-94 emigration, 26, 39, 40-45, 50, 53, 77, 93, 95, 120 ethnic economy, 33, 99 ethnic enclave, 36-37, 98-99 ethnic identity, 15 ethnic network, 35, 78, 120 ethnicity, 2, 30, 35-36, 48, 82, 87, 100, 107-109, 113-115, 119-121, 134, 148 foreign-born, 2, 27, 47, 64, 69, 73, 98, 104, 116 Hispanic, 80, 107-115, 118, 122, 145, 148, 167

adaptation, 7-8, 15, 20, 28-32, 35-37, 62, 78, 85-87, 96100, 113, 124, 144-150 anti-immigrant, 40, 51 Asian immigrant, 4, 26, 40, 47, 51, 55, 59, 69, 89, 128, 147 assimilation, 8, 15, 21, 28-29, 32-38, 44, 48, 52, 95, 98100, 144, 155-156, 159-161, 164-167 segmented assimilation, 37, 44 blue collar, 99, 106, 124 Bonacich, 35, 41, 42, 156, 161 Borjas, 21-28, 32, 38, 76-77, 83-87, 90, 95, 100, 107-108, 109, 112-113, 117, 120, 129-131, 137, 144-146, 149, 156-157, 160, 165 Chinese, 36, 50-53, 58-61, 72, 87, 91, 103, 106-107, 116, 137-138, 140, 157-158, 161, 167 Chinese immigrant, 50, 53, 5861, 137 Chiswick, 19-22, 26-28, 157 co-ethnic, 37, 96-98, 120 169

170

human capital, 25, 32, 35, 76, 82, 85, 91, 94, 99, 112, 146148 illegal immigrants, 7, 27 immigrant selection, 21, 29, 44, 72, 144 immigration Act, 55, 65, 68 Immigration and Naturalization Act, 19, 63, 95 Immigration Reform and Control Act, 64-65, → IRCA inequality, 1, 25-27, 38, 43-45, 76-77, 81, 92, 104, 108-109, 112-115, 119-124, 129-133, 138-140, 144 IRCA, 7-8, 27, 53, 64-66, 147 Japanese, 53-55, 58, 85, 88, 106-107, 156-158, 161 kinship, 29-30, 63-65 Korean, 36, 89, 106 laissez faire, 12-13 Latin America, 2-9, 26, 47-48, 64-65, 69-70, 85-89, 103107, 126-129, 134, 147-148 Latin American, 69-70, 85-89, 103, 106, 128-129, 134, 147-148 mainstream, 9, 35-37, 44, 9598 Marx, 13, 39, 162 Mexican, 3-4, 29-31, 36-38, 48-50, 65, 104-107, 116, 163-165 migrant networks, 28-32, 3941, 44-45, 78, 96, 124-145 Mill, 12 modes of incorporation, 37 MSA, 80, 93, 96, 101, 106, 112, 116-124

Index

native-born, 3-6, 51, 97-98, 104-106, 129 nativism, 9, 34 negative selection, 11, 17, 25, 28, 76-77, 95, 100, 144 negative-selection, 26 occupation, 22-23, 27-32, 36, 42, 51, 59, 78, 99, 106, 123, 160 occupational mobility, 22 Portes, 18, 29, 36-43, 163-168 positive selection, 11, 20-22, 25, 26, 44, 95, 111 professionals, 43, 53, 99, 106, 120, 124 push-pull, 16-18, 39 race, 2, 9, 30, 33-36, 40, 48-49, 52, 59, 63, 66, 72, 82, 95, 107-109, 113-115, 119-121, 134, 143-147, 158, 168 rational choice, 9, 15-18, 27, 39, 150 reference group, 30-31, 83, 131 relative deprivation, 15, 28-31 segmented labor market, 42, 98 selection bias, 10, 109, 121, 124, 131, 138 self-selected, 19, 24-26, 87 self-selection, 18-27, 32, 38, 44-45, 76-77, 92-94, 108109, 113-115, 119-124, 129133, 138-140, 144-145, 148150 skill selection, 3, 12, 28, 38, 45, 76, 91-92, 107-112, 125, 134, 144-148 social capital, 28-32, 45, 9799, 149 social networks, 29-32, 99

Index stratification, 36, 43, 147 undocumented immigrants. → illegal immigrants unskilled, 20, 23

171 Wakefield, 12-13, 56 Weber, 14-15, 49, 156, 160, 168