The Geography of Immigrant Labor Markets: Space, Networks, and Gender

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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The Geography of Immigrant Labor Markets Space, Networks, and Gender

Virginia Parks

LFB Scholarly Publishing LLC New York 2005

Copyright © 2005 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Parks, Virginia, 1970The geography of immigrant labor markets : space, networks, and gender / Virginia Parks. p. cm. -- (The new Americans) Includes bibliographical references and index. ISBN 1-59332-092-2 (alk. paper) 1. Alien labor--United States. 2. Women alien labor--United States. 3. Labor market--United States. 4. Immigrants--United States. 5. Social networks--Economic aspects--United States. 6. Discrimination in employment--United States. 7. Discrimination in housing--United States. 8. United States--Emigration and immigration--Regional disparities. I. Title. II. Series: New Americans (LFB Scholarly Publishing LLC) HD8081.A5P365 2005 331.6'2'0973--dc22 2005012796

ISBN 1-59332-092-2 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

Table of Contents Acknowledgements..............................................................................vii Chapter 1

Introduction ............................................................... 1

Chapter 2

The Shape of Immigrant Local Labor Markets: The Effects of Social and Spatial Accessibility......... 7

Chapter 3

Mapping Immigrant Residence and Work............... 31

Chapter 4

How Local the Immigrant Labor Market?............... 55

Chapter 5

Connecting Neighborhood and Home to Ethnic Labor Market Segregation ........................... 81

Chapter 6

Connecting Neighborhood and Home to Black and Immigrant Women’s Labor Force Participation .......................................................... 121

Chapter 7

Gendering the Nexus of Work, Residence, Networks, and Urban Form ................................... 153

Appendix 1

Measuring Space: Commute Data ......................... 161

Appendix 2

Measuring Space: Accessibility Indices ................ 163

Notes .................. ............................................................................... 167 References.......... ............................................................................... 171 Index .................. ............................................................................... 185

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Acknowledgements

The research for this book was generously supported by a grant from the National Science Foundation (BCS-9986877), a fellowship from the University of California Institute of Labor and Employment, and support from the California Census Research Data Center (CCRDC). I would like to thank the CCRDC staff and Pablo Gutierrez, Dale Iwai, and others at the Southern California Association of Governments (SCAG) for their able assistance. Further thanks go to Mark Ellis, David Rigby, Allen Scott, and Abel Valenzuela for their valuable input and advice. Mark Ellis deserves special commendation for setting me on my path in Geography, honing my arguments, and rigorously challenging my conclusions. Of course, all errors remain my responsibility.

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CHAPTER 1

Introduction

One of the defining features of the immigrant labor market experience is the concentration of immigrants into a few jobs. Whether occupational or industrial, sociologists describe these immigrant concentrations as immigrant niches (Model 1993). Social networks lie behind the development of these niches. Immigrants learn about jobs through other immigrants, and employers utilize current workers’ social contacts for recruitment purposes. As one worker extends a hand to friends and family, and they to others, job information spreads broadly through an ethnic network while channeling members narrowly into a few jobs (Waldinger 1996). These social networks perpetuate the ethnic division of labor and contribute to the segregation of immigrants from other workers in the labor market. While these social characteristics of immigrant labor markets have been well documented, little is known about the spatial characteristics of immigrant labor markets. We do know that immigrants tend to settle in just a few cities. The five largest immigrant populations in the United States reside in Los Angeles, New York, San Francisco, Miami, and Chicago (in order of size). Approximately 56 percent of all immigrants in the U.S. make one of these metropolitan regions their home (Waldinger 2001, p. 43). Thus, when we speak of “immigrant labor markets,” we describe a predominantly urban phenomenon. Our spatial knowledge of immigrant labor markets within these cities, at the intraurban scale, is limited. In contrast, the intraurban residential patterns of immigrants have been a constant focus of scholarly attention dating back to the Chicago School urban sociologists (Logan, Alba, et al. 1996; Logan, Alba, and Zhang 2002; Massey 1985; Park and Burgess [1925] 1967; Wright, Ellis, and Parks 1

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2005). Immigrants not only concentrate residentially in a few cities, they tend to concentrate residentially into a few immigrant enclave neighborhoods. This pattern of residential concentration may be related to immigrants’ patterns of concentration in the labor market. Put differently, ethnic residential segregation may be related to ethnic labor market segregation. This relationship may stem from spatial relationships, such as the location of the immigrant neighborhood relative to jobs. While the importance of space as a factor in the employment outcomes of groups such as native-born Blacks and women has been well documented (Brueckner and Zenou 2003; Hanson and Pratt 1995; Martin 2004; Mouw 2002; Raphael 1998), there is some question as to whether similar spatial effects exist for immigrants (Aponte 1996). For example, a central debate among scholars interested in the job prospects of disadvantaged groups such as native-born Blacks is the spatial mismatch hypothesis (Kain 1968, 1992, 2004). The movement of jobs to the suburbs, spatial mismatch proponents argue, negatively impacts Blacks who reside in inner-city neighborhoods. Blacks cannot easily relocate to the suburbs in pursuit of jobs given racial residential discrimination, and the costs of a long commute discourage them from taking suburban jobs while remaining in the inner city. Thus, a spatial mismatch between where Black workers live and where available jobs are located contributes to higher unemployment rates among these inner-city residents. Given similarities between immigrants and innercity Blacks as low-skill workers who experience residential segregation, immigrants may be impacted similarly by the effects of poor geographic accessibility to jobs. Conversely, spatial accessibility may not matter for immigrant employment outcomes given the strong reliance of immigrants on social networks when finding work. Immigrant employment sites may be located throughout the city, and immigrants may find employment in these jobs regardless of how near to or far from them they live. In this way, immigrant employment networks may override spatial accessibility constraints. No clear geographic relationship may exist between immigrant neighborhoods and immigrant employment sites. And while immigrants may be segregated residentially, their residential segregation may yield a different outcome than does residential segregation for blacks. Rather than providing immigrants with poor geographic accessibility to jobs, their segregation in residential space may be related to their segregation in the labor

Introduction

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market. The relationship between residential segregation and labor market segregation may evolve because immigrant social networks are spatially rooted in ethnic neighborhoods. The kind of neighborhood an immigrant lives in may be more important than where that neighborhood is located relative to employment. Geographic accessibility may matter little, while the place-based nature of social networks may matter considerably. Immigrants who live in ethnic neighborhoods may be more tightly connected to an ethnic employment network than immigrants who live outside such neighborhoods because local social networks may tie information about one place (work) to another place (home). Thus, the relationship between immigrant employment sites and immigrant neighborhoods may depend upon social networks embedding in places, rather than upon the pull of geographic nearness. Lastly, these processes may operate differently by gender. Feminist geographers have established that women’s labor markets function spatially differently than men’s. Because women tend to work closer to home in order to accommodate their household responsibilities, the area over which they look for available job opportunities is relatively smaller than the area over which men search (Hanson and Pratt 1991). Thus, even though women may know about jobs further afield, these jobs are essentially moot opportunities. We do not know, however, if the same gendered commuting pattern holds for immigrants. If it does, then the spatially constrained labor markets of immigrant women may help explain the emergence of female immigrant niches. Further, geographic accessibility may matter more for immigrant women than men. Social accessibility may matter most for immigrant men, regardless of spatial accessibility, while both social and spatial accessibility may influence the employment outcomes of immigrant women.

Research Questions This book explores the complicated social and spatial processes that give rise to the immigrant local labor market. The analyses contained in the following chapters consider the spatial characteristics of immigrant labor markets in light of gendered differences, ethnic networks, residential context, and the location of immigrant neighborhoods in relation to the location of immigrant employment. A

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key question underlying these analyses asks whether spatial accessibility matters for immigrant employment at the intraurban scale, or whether ethnic networks (social accessibility) override intraurban geographic constraints on job search and commuting. Of key concern are gendered differences. For example, do social or spatial accessibility matter more for immigrant women than men? The following chapters begin to paint a picture of how social and spatial accessibility, neighborhood location and neighborhood context, shape the immigrant labor market experience in Los Angeles. The book proceeds as follows. In Chapter 2, I review several literatures that address the two primary concerns of this study: how geographic accessibility influences employment outcomes for particular groups and the social processes that drive immigrant labor markets. Here I outline the theoretical precepts that guide the empirical analyses of later chapters. As a first approach to analyzing the relationship between home and work for immigrants, I map the residential and employment concentrations of immigrants throughout the Los Angeles region. These maps, contained in Chapter 3, are unique in that they portray the employment patterns of immigrants by national-origin and gender for the first time. Prior to the availability of the confidential one-in-six 1990 Census of Housing and Population, data that illustrate these patterns did not exist at the fine geographic scale of the census tract. These maps reveal the relatively tight locational correspondence between immigrant neighborhoods and immigrant employment sites. Chapter 4 is the first empirical chapter of three. In this chapter, I explore the spatial extent of the immigrant labor market and its “local” character. In particular, I evaluate the role of ethnic networks and residential context in shaping immigrant women’s spatial labor markets through an examination of their commutes and a test of the “spatial entrapment” hypothesis. Postulated by geographers, the spatial entrapment hypothesis holds that household responsibilities require women to work close to home, thus limiting their job opportunities (Hanson and Pratt 1991). Alternatively, sociologists argue that ethnic networks, rather than spatial propinquity, connect immigrants to jobs. While home may anchor and constrain an immigrant woman’s job search area, ethnic networks may expand her search area by overriding the friction of geographic distance. In this chapter, I consider whether immigrant women may be spatially entrapped as women and/or socially connected as immigrants.

Introduction

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In Chapter 5, I investigate the relationship between residential segregation and ethnic and gender segregation in the labor market. While sociologists have made a compelling case for the importance of ethnicity and nativity in channeling workers to specific jobs through ethnic networks, little work has been done to spatialize such networks. This chapter takes a first step in investigating the spatial nature of ethnic networks. I argue that these networks are rooted in ethnic neighborhoods and that residential segregation is likely to be a more important determinant of women’s employment outcomes than men’s given the highly local context of women’s lives as a result of household responsibilities. Lastly, in Chapter 6, I explore the extent to which local place effects matter for the labor force participation of disadvantaged women, such as low-skill native-born Blacks and immigrants. Central to the analysis is a concern with the effects of location—where jobs are located in relation to where women live. Does spatial job accessibility impact women’s decision to enter the labor market? Does location matter more for some women than others, such as those with children and stronger ties to home as a result? Additionally, I explore the effects of neighborhood context. Evidence suggests these effects matter, as researchers have described the locally specific nature of women’s lives—particularly women’s utilization of “place-based knowledge” when searching for employment (Hanson and Pratt 1991; Sassen 1995). These effects may impact women differently based upon race, ethnicity, and nativity. In a broader sense, this book attempts to reconcile competing claims concerning the roles of social and spatial accessibility in facilitating or hindering immigrant labor market outcomes. As a geographer, I perceive labor market processes as socio-spatial processes, and I argue throughout the book that space does matter for immigrant employment outcomes. The effects vary for different groups, however, and by gender. No specific spatial rule applies to all groups, though general tendencies emerge. Most significantly, the collective results point to the existence of local labor markets for immigrants. While globalization may compress time and space, drawing workers from throughout the world to the Los Angeles region, once here, these individuals balance home and work within the temporal constraints of a day. In the words of David Harvey, “Labor ... has to go home every night” (1989, p. 19).

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CHAPTER 2

The Shape of Immigrant Local Labor Markets: The Effects of Social and Spatial Accessibility

In this chapter, I consider the local labor market literature in geography in light of the significant presence of immigrants in domestic labor markets such as Los Angeles. My particular interest lies in synthesizing ideas from two literatures: the immigrant labor market literature in economic sociology and the local labor market literature in economic geography. While sociologists tend to emphasize social connections within the labor market exclusively, geographers focus on socio-spatial relations. Because of the strong social networks identified among immigrants by sociologists, immigrants prove an important group for which to consider the implications that geography may or may not play in the processes that shape immigrant local labor markets. I expand the analytical framework of the local labor market by bringing together sociology’s focus on social networks and geography’s focus on the labor market’s local geographic characteristics through an investigation of how these different traditions of labor market analysis bear upon the local labor market experiences of immigrants. I raise a number of empirical questions that address how social and spatial accessibility may shape immigrant local labor markets. These lay the groundwork for the book’s empirical analyses.

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Introducing the Labor Market The labor market arises from the exchange of an individual’s labor for a wage. In neoclassical economics, wages are set by the continual interplay of supply and demand within a market that tends toward a state of equilibrium based on the marginal productivity of labor. Human capital theory explains wages as a reflection of a worker’s productivity, best indicated by that worker’s set of skills. Higher levels of skill generate higher levels of productivity resulting in higher wages. Skills clutched in hand, individuals enter the labor market fully informed of all job opportunities and select the job that best compensates them for their abilities and potential productivity. In Marxian economics, the labor market arises from the exchange of the commodity labor power for the material necessities of reproducing that labor power. The labor market serves as the mechanism that transforms labor power into variable capital (Lee 1994). But this is not a lifeless, mechanical process. Workers constitute a special entity in the production process because they are sentient human beings. Workers bring to the market socially conditioned expectations and a set of social relations. These necessarily influence the wage-labor exchange, a bargaining process often beset with conflict. As such, labor is not a true commodity, but a “pseudo-commodity” that is “idiosyncratic and spatially differentiated” (Storper and Walker 1983). As socio-spatial sentient beings, workers interject a range of complexities into the labor market process. The uniqueness of labor power as a commodity produced by human beings serves as the starting point for sociologists and geographers interested in labor markets. While economists focus on the individual as an independent actor within the market devoid of social complexities, sociologists focus on the individual as a member of society, influenced by other actors. Contemporary economic sociology describes the “embeddedness” of economic activity (Granovetter 1985) in which “economic action takes place within the networks of social relations that make up the social structure” (Smelser and Swedberg, 1994, p. 18). The role of social networks in shaping labor market processes has received considerable attention in economic sociology (Granovetter 1974; Granovetter 1986; Montgomery 1991, 1992; Waldinger 1986-87). Geographers have focused on the socio-spatial relationships that influence workers’ expectations, employers’ actions, and the

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employment relation that emerges from the interaction of the two. Geographers argue that labor market processes must be understood as local contingencies produced by current and historical political and economic conditions. Lee (1994) articulates geography’s attention to the “local” in the labor market as follows: [A]lthough it is possible to speak, for example, of singular national, international or even global labour markets in terms of the forms of legislation which govern them and/or the migration of labour (always limited in relation to total labour supply) within them, all labour markets are in a sense local, shaped by the daily journey-to-work and intensified by the processes and experience of reproduction within particular localities (p. 309). Geographers define the local labor market by the historical and sociopolitical characteristics of local places, but they are concerned also with defining the geographic form and extent of the local labor market—the area that best defines the “local” labor market.

The Local Labor Market in Economic Geography While neoclassical economics views the local labor market as simply one scale at which equilibrium takes place (usually the metropolitan level), economic geographers incorporate both friction of distance and socio-spatial relations into the concept of the local labor market. While much early work on the local labor market focused exclusively on the empirical definition of the commuting shed, later research emphasized the fundamental role of social context over geographic definitions. The commuting shed’s fall from favor, however, has come at a price, as a concrete understanding of the local labor market’s on-the-ground form pivots upon workers’ daily commuting routines. Harvey (1989) describes the commuting shed’s relationship to the concept of the local labor market and the urban process: Unlike other commodities, labor power has to go home every night and reproduce itself before coming back to work the next morning. The limit on the working day implies some sort of limit on daily travel time. Daily labor markets are therefore confined within a given commuting range. The geographical

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The Geography of Immigrant Labor Markets boundaries are flexible; they depend on the length of the working day within the workplace, the time and cost of commuting (given the modes and techniques of mass movement), and the social conditions considered acceptable for the reproduction of labor power (usually a cultural achievement of class struggle). A prima facie case exists, therefore, for considering the urban process in terms of the form and functioning of geographically integrated labor markets within which daily substitutions of labor power against job opportunities are in principle possible (p. 19).

While some empirical work on commute sheds reduces space to a mere container within which the labor market functions, commuting behavior nevertheless gives geographic form to the local labor market. The local labor market and the geography of production Allen Scott’s (e.g. 1994, 1988) research on intraurban industrial processes stands as a significant contribution to the geographic literature on local labor markets. In his work, Scott ties the process of urban development and the emergence of urban form to the production apparatus of capitalist society (Scott 1988). Scott seeks to counter urban theoretical approaches that privilege the social space of the city (such as the ecological models of the Chicago School and the vast literature that developed in response) at the expense of recognizing the productive function of the city. Marxist theorists, such as Castells (1973), also overlooked the production apparatus in favor of consumption and reproduction. In Metropolis, Scott (1988) argues that the very emergence and existence of the modern metropolis depends upon its function as capitalist space: “What is crucial about the production system … is that it creates the powerful forces that, first, give rise to metropolitan agglomeration as a purely locational phenomenon, and, second, influence in many intimate ways the workaday existence of the entire citizenry” (p. 2). Thus, urban processes derive from the city’s first function as a site of economic activity. From this starting point, Scott links residential patterns, as social spaces within the city, to the city’s productive capacity through the mechanism of the local labor market. Scott (1988) argues “for a view of intraurban social space as being deeply marked in both its locational and functional characteristics by the operation of the division of labor

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and local labor markets” (p. 218). Most generally, the local labor market for Scott emerges from the “locational symbiosis” between employment sites and workers’ places of residence. In a discussion moving from commuting patterns to the locational patterns of agglomeration economies, Scott (1988) discusses the multiple but constitutive forces that give rise to the spatial form of the local labor market. Commuting patterns underlie the geographic form of the local labor market by tying together the locations of workplaces to the residences of workers. Workers trade off between the costs (and amenities) of housing and the costs of commuting in light of their wage rate when choosing their residential locations. Firms trade off between costs of land, rent, transportation amenities, and labor when choosing employment locations. Scott explains the rational of firms in choosing location sites near suitable labor pools, especially in the case of industrial agglomerations, as follows: Because of their [interlinked producers] inflated collective demand for labor and the direct impact this has on wage rates, such clusters of producers frequently locate as a body close to the geographical center of their main labor force. This locational strategy secures continued transactional efficiency while ensuring that upward pressures on wage rates are as restrained as they possibly can be (1988, p. 130). This transaction-costs perspective seeks to explain how industrial location decisions both impact and are impacted by workers’ commute costs, and how workers’ residential decisions both impact and are impacted by industrial location decisions. A thicker version of the local labor market Many workers face constraints in their residential and employment decisions. A thicker, more contextualized version of the local labor market helps us to incorporate these constraints by explicitly incorporating ethnicity, race, and gender, as well as attendant sociopolitical factors such as housing and employment discrimination. Peck (1996) argues for a socially constituted conception of the labor market that moves “beyond cartography to focus on processes” (86). According to Peck, the local labor market is a "conjunctural causal structure" which takes into account the local variability of three key

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components: the production process, the social reproduction of labor supply, and forces of social regulation, such as institutions and unions. Peck’s concept of the local labor market centers upon Harvey’s (1989) notion of the local labor market as place. As Peck explains: "His [Harvey's] concern is not with the local labor market as a space in which universal labor market processes operate, but as a place in which these processes may be channeled and modified to produce unique local outcomes'" (1996, p. 89). Thus, the local labor market gets positioned as a go-between (what Peck calls a “mid-level theoretical device”) holding in the one hand, abstract general processes, and in the other, material outcomes. While this conception of the local labor market re-orients us to the socio-political influences on the labor market and their particular local manifestations, Peck’s focus elides the geographic nature of the local labor market and the everyday consequences geographic accessibility poses for both workers and firms. While Peck argues that “no matter how accurate the commuting data or how powerful the computer system into which it is fed, the problems of delimiting the boundaries of local labor markets are insoluble” (1996, p. 88), the research he uses to support his claims rely upon exactly such data. The work of Hanson and Pratt (1995), for example, employs commute data to illustrate the spatial diversity of people’s lived daily lives and the social constructions that inform these lives (such as the social construction of work and home for women—a point Peck can emphasize given the rich empirical evidence provided by Hanson and Pratt’s work). The strength of Scott’s and Hanson and Pratt’s articulations of the local labor market lies in their rigorous attention to mechanisms that inform the geographically “local” of the local labor market. The importance of Peck’s approach, however, lies in its use as an analytical tool that enables articulation of spatial processes as locally differentiated and socially constructed phenomena. Peck’s work builds upon the significant contribution of Hanson and Pratt (discussed in the following section) in shaping our understanding of local labor markets as socially constructed heterogeneous spaces and places. Hanson and Pratt (1992) forcefully argue that Local labor markets are . . . heterogeneous because of gender, race and class-based segmentation . . . but they are also spatially segmented through the fine-scaled processes defining labor supply and demand. . . . [T]he geography of labor

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markets is far richer than simply a measure of distance. . . . Individuals knowledge of the universe of jobs available to them, their expectations about wages and benefits, and the gendering and racialization of jobs all are shaped locally. . . . Job opportunities are more than just dots on a map; they are to a considerable extent socially constructed through the interactions embodying everyday life (quoted in Peck, 1996, pp. 89-90). The work of both Peck and Hanson and Pratt necessarily problematizes a transactions-cost analysis and pushes us to probe for multidimensionality within the local labor market, such as specific racial, ethnic, class, and gendered forms the local labor market may take.

The Role of Race and Gender in Shaping Labor Markets Research that does strive for a multi-dimensional understanding of the local labor market recognizes that many workers face constraints selecting their residential and employment locations that extend beyond housing costs, commuting costs, and wages. Blacks face a housing market shaped by the historical and current effects of discrimination. Household responsibilities require that women work close to home, restricting their job search areas. Two key literatures address these issues: the spatial mismatch and spatial entrapment literatures. Both attempt to bring factors such as race and gender considered exogenous by neoclassical theory and some economic geographers into an explanation of labor market processes that treats such factors as endogenous. Spatial mismatch hypothesis First developed by Kain (1968, 1992, 2004), the spatial mismatch hypothesis explains high black unemployment as a function of both employment suburbanization and residential segregation. As jobs shift from the inner-city to the suburbs, blacks are unable to readily access the relocated jobs by moving their place of residence because of constraints imposed by housing discrimination. In turn, high commute costs discourage blacks from accepting jobs in the suburbs. As jobs

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move, blacks are left behind in a wake of rising inner-city unemployment. The claims of the spatial mismatch hypothesis remain controversial given inconsistent empirical findings, though recent research employing innovative methodology supports the spatial mismatch hypothesis (Brueckner and Zenou 2003; Martin 2004; Mouw 2000; Raphael 1998). Supporters argue that spatial mismatch partially accounts for the racial gap in unemployment.1 For example, Ihlandfeldt and Sjoquist (1990, 1991) find that job proximity explains roughly 30 percent of the gap between black and white teenage unemployment rates, and Stoll (1998) finds that job decentralization negatively affects young black men’s unemployment and duration of unemployment. Mouw (2000) finds that the decentralization of jobs away from black neighborhoods between 1980 and 1990 in Detroit accounted for one-quarter of the black-white unemployment gap. Martin (2004) finds that shifts in employment away from Black neighborhoods increased Black unemployment rates by up to 4.3 percentage points between 1980 and 1990. Detractors of the spatial mismatch hypothesis (Leonard 1987), however, argue that the gap in unemployment between blacks and whites stems from factors other than space, such as racial discrimination or transportation resources (see also Cooke 1993; Holloway 1996). In his study of black families in Chicago, Ellwood (1986) finds comparable unemployment rates between similarly educated blacks regardless of where they live within the city. From this he concludes that “race, not space” explains the persistent gap in unemployment between whites and blacks. Ong and Taylor (1995) argue that neither race nor space determine employment; rather access to a car proves the most important factor for employment. (For a review of the earlier spatial mismatch literature see Holzer 1991; see Mouw 2000 and Fernandez 2004 for reviews of more recent work). Wage effect of spatial accessibility The spatial mismatch hypothesis also has been used to explain blacks’ lower earnings. If blacks experience lower levels of job accessibility, “wage effects should occur as a larger pool of potential workers compete for nearby jobs, thus depressing their wages and creating a positive ‘wage gradient’ for jobs further away” (Holzer 1991, p. 107). Additionally, increased commuting costs for blacks who do find

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employment further from their homes will experience a decrease in their net wages as commuting costs increase (Holzer 1991). Most studies measuring the economic effects of job accessibility are studies that use a measure of job suburbanization as their accessibility measure. These studies utilize a dichotomous independent variable indicating the location of residence (or workplace) in either the central-city or the suburbs. Using this approach, Vrooman and Greenfield (1980) and Price and Mills (1985) find negative effects of central-city residence on black earnings. In contrast, Harrison (1974) finds that the distribution of weekly earnings for blacks living in central-city areas are not different from those living in the suburbs. Interestingly, studies measuring the difference in earnings by centralcity and suburban workplaces found that blacks who work in the central-city earn significantly lower wages than those employed in the suburbs (Brueckner and Zenou 2003; Danziger and Weinstein 1976; Hughes and Madden 1991; Ihlandfeldt and Sjoquist 1991; Straszheim 1980). Further research needs to expand the scope of analysis to test better the specific claims of the spatial mismatch hypothesis. The experience of different groups, such as immigrants an women, may either lend support or detract from the explanatory power of the mismatch hypothesis. For example, why do low-skill immigrants have higher rates of employment than low-skill blacks? Do immigrants experience lower levels of residential segregation? Do they live closer to jobs than blacks? If yes, then the claims of the spatial mismatch may hold. However, if immigrants experience similar levels of residential segregation as blacks, if they in fact live in the same neighborhoods as blacks, and live similar distances away from jobs as blacks, then the claims of the spatial mismatch hypothesis are questionable. It would seem, then, that the spatial proximity of a neighborhood to employment is less important than the social networks it provides. Spatial entrapment hypothesis Research in geography points to women’s household responsibilities and gendered networks as key factors in shaping the spatial distribution of women’s employment (Hanson and Pratt 1988, 1991, 1995; Johnston-Anumonwo 1992; Madden and White 1980). Women tend to work closer to home than men, a spatial employment strategy reflected in the nearly universal finding that women’s commutes are, on average,

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shorter than men’s (Gordon, Kumar et al. 1989; Hanson and Hanson 1980; Hanson and Johnston 1985; McLafferty and Preston 1991). In order to manage the demands of both work and home, women adopt complex time management strategies. Minimizing their commute is one such strategy. A shorter commute allows them more time before and after work to shop for groceries, drop off and pick up children, prepare meals, and tend to myriad other household errands. In their survey of people with commutes of less than ten minutes, Hanson and Pratt (1995) found that the reason most frequently given by women for their short commutes was “wanting to be able to get home quickly to tend to children or to respond to household emergencies” (p. 99). Transportation research on the types of trips women make for the purposes of meeting household demands also support this interpretation: women tend to make more family and household support trips and spend more time in household and family support activities than men (Hanson and Hanson 1980; Hanson and Johnston 1985; Niemeier and Morita 1996; Rosenbloom 1993). Finally, a short commute may reflect household strategies in relation to transportation costs. Because men tend to earn more than women, men’s jobs are often privileged in the household (Spain and Bianchi 1997). In the face of transportation scarcity, such as access to only one car, “rational” decisions provide the husband with the car. (This also may be the result of hierarchical and patriarchal relationships within the home.) Thus, women constrained by transportation mode choose workplaces easily accessible by foot or public transit. Because household duties limit women’s commutes, their job search is more spatially constrained. Hanson and Pratt (1991) argue that when women restrict their job opportunities spatially, they limit their wage opportunities. Women settle for lower-paying, but geographically accessible, jobs. As a consequence, women are “spatially trapped” within local labor markets that generate lower returns to skill (Hanson and Pratt 1988, 1991; Rutherford and Wekerle 1988; Villeneuve and Rose 1988). Women work close to home so they can make things work at home, but exchange higher wages in order to do so. Geographers also have examined the relationship between increased commute and income for women. Madden (1981) found that women’s commutes would be as long or longer than men’s if they received the same wages as men. Rutherford and Wekerle (1988) measure the marginal utility of additional travel in terms of additional

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income for men and women. If women trade-off lower wages for shorter commutes (Ericksen 1977), then longer commutes should yield higher wages. In regressions on income by distance traveled, Rutherford and Wekerle (1988) found that women earned only onethird of what men did for the same additional commute. The spatial entrapment hypothesis also argues that the gender division of labor is spatialized by the location of female-dominated jobs in response to this constraint, as well as by women’s inability or unwillingness to commute long distances (Hanson and Pratt 1988; Werkerle and Rutherford 1989; Villeneuve and Rose 1988). As women make decisions to work close to home, firms make decisions to locate nearby in order to improve the odds of recruiting them. In her study of San Francisco “back offices,” Nelson (1986) argues that the shift of service jobs to cheaper sites outside the city center is motivated by not only land costs but also accessibility to “captive” female labor. In this scenario, firms knowingly locate within convenient reach of desirable supplies of female workers—in Nelson’s example, educated non-militant white women living in the suburbs. A weakness of the spatial entrapment hypothesis is its basis in empirical work that analyzes women as an aggregate group and that focuses on geographic study areas where white women comprise the majority. While a few studies of commute behavior consider women of color in relation to white women (McLafferty and Preston 1991), but the study of immigrant women in the context of this debate has been largely absent (to my knowledge, only one other study considers the commutes of immigrant women—Preston, McLafferty, and Liu 1998). Given the presence of an ethnic and racial division of labor, conclusions drawn from the study of non-immigrant women may not hold for immigrant women. Spatial job search Underpinning the spatial mismatch and spatial entrapment hypotheses is the proposition that some groups, such as blacks and women, search for work from a fixed-place of residence. Blacks’ residential locations are fixed by the historical and contemporary effects of housing discrimination. Women’s residential locations are fixed in part by the gender division of household labor that constrains their commute behavior. For many women, residence is chosen in relation to their husband’s workplace.

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Particularly over the short-term, these workers face constraints in finding employment imposed by search costs as postulated by the spatial job search model (Lippman and McCall 1976). All things being equal, workers will opt for jobs closer to home in order to minimize commute costs. The extent of a worker’s spatial search is determined by the costs of a commute the worker is willing to bear in relation to his or her reservation wage. Workers with non-pecuniary constraints on their commute, such as women’s household responsibilities, will limit their job search area further to accommodate such constraints. The intensity of the search, or how long or thoroughly a worker searches, is determined by equating the marginal costs and benefits of search. Search costs include opportunity costs of postponing employment, time, travel expenses, and other resources necessary to carry out the search (such as daycare expenses for job seekers with children). Stoll (1999) provides empirical evidence for the spatial job search model, showing that access to a car and distance to search areas affects the geographic extent of job search. As Stoll and Raphael (2000) have noted, individual search behavior may undo the effects of spatial mismatch. They point out that if blacks and whites share the same search behavior and both search in job-rich areas, then blacks’ search behavior can override the effects of spatial mismatch. On the other hand, if blacks search from a fixedresidential location and limit the spatial extent of their search area because of costs, then residential segregation will limit the areas within which blacks search for jobs. Stoll and Raphael (2000) find that “racial residential segregation, coupled with spatially related job search costs, ensures that blacks, Latinos, and whites search for work in different parts of the metropolitan area and near their residential areas” (202). These areas have slower employment growth. The overall effect of black and Latino search (poorer search quality in slow-growth employment areas) contributes to underemployment and unemployment among blacks and Latinos. Relating job search to job accessibility, Johnson (2004) finds that racial differences in employment accessibility account for one-fourth of the difference between whites and blacks in successfully completing a job search.

The Shape of Immigrant Local Labor Market

19

Social Networks and Job Search Job search depends upon information about employment opportunities, a highly socially and spatially variable commodity. While the neoclassical labor market model assumes full information about jobs on the part of job searchers, sociological and geographic research points to the highly differentiated nature of job information. Information accessed by job searchers through social networks is a critical component of job search (Cohn and Fossett 1996; Granovetter 1974). A plethora of research exists testing the effects of social networks on employment outcomes, though the bulk of this research treats social networks and the information they contain as aspatial. Some research, however, considers the effects of both social and spatial accessibility. Kasinitz and Rosenberg (1996) found that blacks in Brooklyn were unable to gain employment in a local manufacturing firm because they lacked social connections to the firm. Spatial accessibility did not explain their absence among the firm’s employees. Mouw (2002), however, finds that both firm location and the use of employee referrals in the hiring process lead to greater interfirm racial segregation. Other research points implicitly to the spatial characteristics of social networks, primarily as residentially-based networks of the urban poor. Wilson (1987, 1996) argues that blacks residing in poverty neighborhoods have particularly poor social networks, especially as employed middle-class blacks have abandoned these neighborhoods for the suburbs. Studies such as Reingold (1999) assume a specific spatial context by limiting their samples to inner-city residents. The research by Stoll and Raphael (2000) discussed earlier points to the ramifications of such residentially-bound networks in terms of spatial mismatch: these racially segregated, inner-city neighborhoods tend to be located near areas of declining or slow employment growth. Lastly, Elliott (1999) finds that workers who find jobs through a nonwhite contact experience wage penalties and that these penalties increase if the contact is also a neighbor. Proximity, however, may not characterize the spatial nature of these residential networks in terms of their connections to jobs. Especially in the case of immigrants, residential networks may connect immigrants to jobs far from home. This is not to downplay the spatial nature of these connections. A spatial corridor exists between these neighborhoods and the job sites to which they are connected, though a corridor not necessarily characterized by proximity (Sassen 1995). The

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The Geography of Immigrant Labor Markets

spatial job search model, however, gives theoretical validity to the concept of proximity. Understanding how proximity is overridden or reinforced given the characteristics of particular groups of workers remains an important task for a more fully developed model of the local labor market. While the role of proximity may be minimal in the case of immigrant local labor markets, gender research points to its central role in shaping women’s local labor markets. Gendered networks Geographers have argued that women’s social networks play an important role in women’s employment outcomes while highlighting the spatial characteristics of these networks. Specifically, geographers have found that women’s networks tend to be place-specific and local in character. Hanson and Pratt (1991) found that women, when searching for jobs, rely heavily upon information from other women who are not only close friends or family but who also live nearby. In this way, women’s networks are both gender-specific, comprised of what Granovetter (1973, 1974) describes as strong ties, and spatially local. Hanson and Pratt (1991) also found that women prioritize geographic proximity as a condition of paid employment because of their domestic responsibilities, and that women search for work from a residentially-fixed location. Nearly all of the women in Hanson and Pratt’s study found work after moving to their current neighborhood, and several gave up jobs they had prior to moving. Hanson and Pratt (1991) argue that gendered networks play a part in perpetuating occupational sex segregation. Because women tend to work in female-dominated jobs, information about jobs and job contacts that circulate through women’s networks will most likely be about jobs into which women are heavily segregated. In a comparison of women employed in female-dominated occupations to women in male-dominated occupations, Hanson and Pratt (1991) found that the former relied upon job information from other women to a greater extent than women employed in male-dominated occupations. Drentea (1998) substantiates this finding. In her study, women who relied upon informal job search methods (i.e. networks) had jobs with more women in them than women who utilized formal job search methods. Sassen (1995) emphasizes the need to understand women’s networks as examples of place-based knowledge. She points to research that identifies women’s networks as primarily residentiallybased, while men’s networks tend to reach beyond the neighborhood

The Shape of Immigrant Local Labor Market

21

and focus at the workplace. Women’s networks contain more friends and family members while men’s more diverse networks contain more coworkers (Marsden 1987; Moore 1990). Such findings raise critical questions about the gendered effect of residential segregation on job search and employment outcomes. Residential segregation may matter more for women than for men in terms of finding employment and the quality of that employment. The quality of local job opportunities may affect the quality of women’s employment to a greater extent than for men, as well as point to the need for locally directed employment initiatives targeted at women. Ethnic networks and immigrant labor markets The economic sociology of immigration literature relies heavily upon the role of social networks in explaining the process of matching immigrant workers to jobs in the labor market. The general outlines of the labor market are drawn from the constitutive forces of supply and demand, usually explained in terms of labor queues and job queues. As with all workers, immigrants find work within a labor market structured by an ethnic and racial hierarchy wherein different groups work different jobs. Faced with a labor queue of available workers, employers hire as far up their hierarchy of preferred workers as possible (Thurow 1975; this process also operates by gender, Reskin and Roos 1990). This “preference” stems from a set of ideas associated with the skills and work attitudes of particular groups of workers (Kirschenman and Neckerman 1991; Moss and Tilly 1996). In a racial hierarchy, ethnic workers are ranked in relation to one another—a process that in U.S. labor markets usually positions whites at the front of the queue, blacks at the back, and immigrants in the middle (Lim 2001). On the supply-side, the shape of the queue depends upon the relative availability of different groups of workers—the demographic make-up of the labor pool. If preferred workers are in short-supply, employers hire farther down their preference queue, opening opportunities for groups such as immigrants and blacks (Waldinger 1996). Workers also have a set of job preferences, a ranking of jobs based on pay, benefits, hours, and other work characteristics. In like fashion to employers and their labor queues, workers shape the ethnic division of labor by taking jobs as far up their job queue as possible. Ethnic networks then reinforce the ethnic and racial segregation of jobs by channeling co-ethnics into similar jobs and the same

22

The Geography of Immigrant Labor Markets

workplaces (Granovetter 1974; Tilly 1990). Immigrant networks “facilitate job search, hiring, recruitment, and training because they fulfill the needs of workers and employers, furnishing reliable, low-cost information about the characteristics of jobs and workers” (Waldinger 1994, p. 27; see also Bailey and Waldinger 1991). Thus, the concentration of immigrants into a few jobs, or niche jobs (see Model 1993), reflects immigrants’ wealth of social capital (Massey, Alcaron et al. 1987). Immigrants rely upon social networks to find employment like other workers, though the literature describes immigrant/ethnic networks as qualitatively different. Immigrant networks are marked by “bounded solidarity and enforceable trust” between co-ethnics (Portes 1998; Portes and Zhou 1992). These are tightly bound networks, governed by the expectations of a larger community. Expectations of reciprocity are high, maintaining and deepening network relationships. Although ethnic networks provide immigrants easy access to employment information, these networks tend to be characterized as “strong ties.” As such, they contain little to no employment information beyond the immigrant’s immediate social sphere. In this way, immigrants may become “socially trapped” into ethnic niche jobs.

Placing Immigrants in the Local Labor Market The position of immigrants vis-à-vis local labor market theory raises key questions about the relationship between social and spatial accessibility in generating labor market outcomes. Sociologists have largely focused on the role social networks play in connecting immigrants to jobs, though attempts have been made to understand some spatial aspects of immigrant labor markets in discussions that stem from research on ethnic enclave economies. The assumption is often made, however, that geographic factors generate few if any effects on immigrant labor market outcomes precisely because of immigrants’ status as migrants. The reasoning usually follows that if immigrants initially undertake a migration journey thousands of miles long, comparatively small distances at the intraurban scale can have little relevance on their employment outcomes. I argue that such a characterization conflates two distinct “journeys” (the migration journey and the journey-to-work) into too simple a notion of spatial labor market processes. Rather, these are two

The Shape of Immigrant Local Labor Market

23

processes governed by very different factors and decision-making processes, including geographic factors. I believe this challenges us to understand the job search as a multi-stage process. First, immigrants decide whether to migrate to another country in search of work. Second, upon arrival at their destination, immigrants engage in the next stage of the job search process—finding a job and a residential location that fit within the spatial-temporal constraints of daily life. Labor must go home every night, thus the relevance of geographic local labor market processes for immigrants. Bringing in/locating the ethnic enclave While geographers have focused on the effects of space on the gender division of labor, sociologists have theorized the effects of space on the ethnic division of labor through the “ethnic enclave” debate. The ethnic enclave hypothesis argues that coethnics enjoy greater returns to human capital when employed within the enclave, understood as a spatial concentration of coethnics at some scale (Portes and Jensen 1989; Wilson 1980). The ethnic enclave hypothesis claims that these higher returns to skill stem from the rewards of “bounded solidarity and enforceable trust” enjoyed by workers employed by fellow coethnics (Portes and Zhou 1992). While ethnic networks may constrain immigrants’ job searches to enclave employment, the outcome is seen as beneficial in terms of returns to skill—a dramatically different theorized outcome than that of the spatial entrapment hypothesis for women. In its original formulation, the ethnic enclave hypothesis draws upon labor market segmentation theory and addresses the effects of the place of work on workers’ returns to skill (Wilson 1980). Discussions of labor market segmentation theory within the immigration literature have focused on immigrants’ position within the secondary sector and the mechanisms that serve to trap immigrants in these secondary jobs (Piore 1979). Wilson (1980) and Portes and Jensen (1987) challenged this conclusion with the ethnic enclave hypothesis. They argue that immigrants have a third sector available to them in addition to the primary and secondary sectors—the enclave labor market associated with immigrant-owned firms and the employment of coethnics. This enclave sector mimics the primary sector by generating higher returns to skill than in the secondary sector and by providing immigrants a path toward entrepreneurship through on-the-job training and access to credit within the enclave. These benefits rest upon ethnic solidarity and

24

The Geography of Immigrant Labor Markets

reciprocal obligation. In empirical tests of the hypothesis, Portes and Bach (1985) found that although Cubans employed in Miami’s ethnic enclave received lower returns to skill than Cubans employed in the primary sector, they received higher returns to skill than Cubans employed in the secondary sector. Empirical studies since Portes and Bach’s initial work have generated mixed results. Sanders and Nee (1987) argue that the case study nature of Portes’ research (the Cuban enclave in Miami) does not adequately allow comparisons to non-enclave participants. In their study of Chinese and Cuban enclave participants and non-participants (measured by residence in and out of San Francisco and Miami respectively), Sanders and Nee find that enclave employees were at a significant labor market disadvantage compared to those who lived outside the enclave. The Sanders and Nee paper initiated a heated methodological debate. Jensen and Portes (1992) criticized Sanders and Nee for using place of residence data as a proxy for place of work. The primary problem facing researchers has been a lack of available data that can be used to test Portes’s results from his longitudinal study of Cubans in Miami, such as data on place of work and ethnicity of owner. The theoretical implications of the enclave for immigrant women are unclear. In light of recent research indicating the gendered nature of immigrant networks (Hagan 1998; Hondagneu-Sotello 1994), it seems ethnic solidarity may be gendered as well. The enclave may not generate the same theorized benefits for women as for men, particularly if employers are men. On the other hand, gendered networks may channel immigrant women into enclave jobs that generate higher returns to skill as theorized for men. Empirical findings point to the disadvantaged position of women in the enclave. In a study examining Dominican and Colombian women employed in Hispanic-owned firms in New York City, Gilbertson (1995) found that enclave employment conferred upon women low wages, few fringe benefits, and limited opportunities for advancement. This corroborates Zhou and Logan’s (1989) finding that Chinese women employed in New York’s Chinatown had no measurable increased earnings-return to skill. These studies, however, are at odds with Portes and Jensen’s (1989) results reporting increased returns experienced by Cuban women employed in Miami’s Cuban enclave.

The Shape of Immigrant Local Labor Market

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If enclave employment is generally exploitative of women (Gilbertson 1995), then ethnic networks that channel immigrant women into enclave jobs may serve as an example of “the weakness of strong ties” (Granovetter 1973). If networks are sufficiently strong, they may “trap” immigrant women into enclave jobs. And while immigrant women who are employed in an employment enclave may not be spatially entrapped in terms of being limited to employment that is close to home, they may be spatially entrapped if enclave employment limits them to certain places of employment, i.e. workplaces within the enclave. On the other hand, both processes may operate simultaneously for immigrant women. Ethnic enclave formation in the workplace may respond to women’s spatial entrapment stemming from household responsibilities. Immigrant women with few skills who rely heavily on gendered networks in obtaining employment may be the least able to engage in long commutes. Ethnic enclave formation may respond in kind, and these jobs may be most closely located to immigrant women’s places of residence. Geography of production and ethnic neighborhoods Scott (1988) explains the emergence of ethnic neighborhoods as a response to the productive forces of the capitalist U.S. city: Ethnicity in the American metropolis is thus preeminently a contingent outcome of local labor market pressures and needs. This involves the continual recreation of pools of cheap and malleable labor (including women and adolescents) suitable for employment in the disintegrated complexes of laborintensive manufacturing and service industries that cluster within the metropolis. In this specific sense, urban ethnicity is at once a durable phenomenon, and yet it is also largely transient insofar as any particular group is concerned. With the notable stubborn exception of Blacks, groups with subordinate cultural identities in the American city have fairly consistently been assimilated over the course of three or four generations into the mainstream of urban life (Rodgers, 1981; Zunz, 1981). Thus, the socialization processes and upward mobility characteristic of American society have continually undercut the conditions under which cheap exploitable labor at the bottom of the employment ladder can be internally

26

The Geography of Immigrant Labor Markets reproduced. The concomitant vacuum has invariably been filled by new rounds of immigration, new rounds of ethnic neighborhood formation, and new rounds of social and political fragmentation (226).

While Scott argues that this explanation is a “far cry indeed from the purely ecological theories of moral order and social solidarity that have so far seemed to dominate the literature on urban ethnicity” (1988, p. 226), I would argue that this explanation applies to the first stage of the immigrant location process—the migration journey of economic migrants. This explains why migrants journey to U.S. cities and accounts for the immigrant presence in U.S. urban labor markets. It does not, however, explain where immigrants locate residentially within the urban area once they have arrived. For this explanation, Scott relies upon his general theory of geographical local labor market processes: the location of immigrant neighborhoods evolves as part of the reciprocating effect of workers moving near potential jobs and firms locating near potential labor pools (Scott 1988). Both immigrants and their employers seek low-rent districts, placing low-skill workers and low-wage jobs near one another in the city. As Scott (1988) explains, “Typically, these ethnic groups form dense segregated neighborhoods close to centers of employment where unskilled low-wage jobs abound” (p. 226). An extensive historical literature documents this spatial relationship between ethnic, particularly immigrant, neighborhoods and industries employing these residents (Hershberg 1981; Ward 1968, 1971). But it seems that the persistence of ethnic residential segregation in most U.S. cities, even among moderate- and high-skilled immigrants, demands a more nuanced approach, perhaps even one incorporating ideas of “social solidarity.” Sociologists have made convincing claims to the importance of ethnic networks, bounded solidarity, and close ties among immigrants. Ethnic neighborhoods provide such resources within a geographically proximate cultural safe-haven. Scott (1988) acknowledges this, commenting that immigrant “neighborhoods are held together as geographical units by the tight social networks (built up around idiosyncrasies of language and culture) that develop within them” (p. 226). Whether these neighborhoods necessarily emerge in close proximity to immigrant worksites or persist after these worksites disappear (with historical location decisions shaping present-day geographies) remains in question for contemporary urban geographies.

The Shape of Immigrant Local Labor Market

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Emerging Questions Concerning the Immigrant Local Labor Market The location of immigrant neighborhoods in relation to immigrant employment opportunities remains a question to which we have an unsatisfactory empirical response. Though ethnographic research of the immigrant enclave (e.g. Zhou 1992) seems to indicate strong patterns of ethnic jobs clustering near ethnic neighborhoods, quantitative work on the enclave either defines the enclave as a metropolitan area (Borjas 2000; Logan, Alba, and Stults 2003; Zhou 1992), focuses on place of work (at a large geographic scale) independent of place of residence (Portes and Jensen 1989), or focuses only on place of residence (Sanders and Nee 1987). The latter two approaches analyze residential patterns in isolation from employment patterns while the former disallows any notion of a local labor market smaller than the metropolitan area. As Scott has forcefully argued, this ignores the importance of the residential-workplace relationship and the key role of production in generating urban form. While Scott’s research (1984, 1988) does attempt to explore the interrelated patterns of workplace and residence, his small survey samples disallow generalization.2 As a result, many questions concerning the place of immigrants within the U.S. urban geographic context stand as empirical questions demanding exposition. The extent to which immigrant neighborhoods and work places cluster together remains somewhat unclear for contemporary metropolitan areas. A contemporary process that spatially links ethnic neighborhoods to ethnic employment may not function as in the past due to ethnic networks and transportation trends. Sociologists argue that having information about a job, no matter how far from home, generally trumps a blind search for a job close to home (Waldinger 1996). Facilitated by the contemporary ubiquity of the car (despite its relative expense, 80% of all immigrants in Los Angeles commute to work by car), these networks may render a relationship of propinquity between ethnic neighborhoods and ethnic employment sites unnecessary. Networks may connect immigrants to jobs far from their places of residence, greatly expanding an immigrant’s spatial job search area. Immigrant neighborhoods may have initially emerged in response to jobs nearby, but residential inertia may take hold, pulling in new immigrants even if these (formerly nearby) jobs move. Social information may be behind this pull. Immigrants may move into ethnic

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The Geography of Immigrant Labor Markets

neighborhoods for purposes of “social solidarity,” a byproduct of which is information about employment opportunities. Though these employment opportunities may not be located geographically nearby, an important socio-spatial relationship exists between the ethnic neighborhood and the employment site. Sassen (1995) calls this a spatially circumscribed “activity space,” but not one necessarily characterized by geographic proximity. In this way, neighborhoods provide information about jobs that may or may not be located nearby. Nevertheless, these residential-workplace information networks function as spatially circumscribed activity spaces connecting two spatial endpoints together. Such spaces may be gendered, and residentially-based employment information may be more significant for immigrant women than men. Research has pointed to the importance of residentially-based ties for women (Hanson and Pratt 1991) and to the importance of family and community employment ties for immigrant women compared to men (Tienda and Glass 1985; Fernandez-Kelly 1995). Such gendered networks may have a greater effect on where immigrant women work and what they do, thus tightening the boundaries of their local labor market “activity spaces.” One empirical test of these processes would examine whether women who live in ethnic neighborhoods are more likely to work in ethnic niche jobs. This approach, however, leaves unexamined the role of geographic proximity as a component of this relationship. If an analysis of geographic proximity identified a lack of geographic clustering of ethnic jobs near ethnic neighborhoods, this would seem to indicate the importance of social over geographic accessibility at the intraurban scale. Conversely, evidence of immigrant jobs clustering near immigrant neighborhoods would indicate the importance of geographic accessibility to jobs for immigrants. Immigrants may find themselves within both socially and spatially segmented labor markets. Further, geographic accessibility may partially explain the emergence and development of some immigrant niche jobs. If immigrant neighborhoods are located within close proximity to industries within which immigrants concentrate, then geographic proximity may partially explain this industrial concentration. Again, these patterns may be more or less pronounced by gender. If immigrant women experience spatial constraints because of household responsibilities and are limited in their travel to a greater

The Shape of Immigrant Local Labor Market

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extent than men because of household allocation of transportation resources, then gendered employment networks may connect immigrant women to jobs over a more constrained area than do men’s networks. As a result, jobs located near to immigrant neighborhoods may serve to both establish an “ethnic niche” of immigrant women workers and sustain the niche as social and spatial connections mutually “lock-in” this flow of workers. Such questions highlight the importance of approaching local labor markets as socially constructed activity spaces that center upon what Sassen (1995) terms the workplace-community/workplace-household nexus. This analytical approach requires consideration of race, ethnicity, gender, nativity, and household characteristics as endogeneous to labor market processes. Sassen (1995) concludes her essay, “Immigration and Local Labor Markets” with the following statement: Thinking about labor markets as activity spaces determined or specified in part by the workplace-home link or the spatial dependency of employers and workers introduces a series of variables into the analysis that are typically seen as exogenous. Most important to us are information about the market and formation of preferences. Both information and preferences can be shown to be at least partly—but often in good measure—place-specific, internal to the activity space or ‘economic subsystem’ under consideration. Framing labor markets as activity spaces also allows us to detect or reconstruct how gender, race, and nationality can shape information channels in the labor market and thus shape individual expectations. This can be inferred to have a strong reproductive effect for existing patterns and contributes to explaining labor market segmentation. Local experience or place-based knowledge can be seen as central to the spatial segmentation of labor markets (1995, pp. 115-116). Unraveling the interplay of these constitutive influences on immigrant local labor markets requires an approach informed by an understanding of social networks gleaned from economic sociology and an understanding of socio-spatial processes gleaned from economic geography.

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CHAPTER 3

Mapping Immigrant Residence and Work

Available data have limited the investigation of fine-grained spatial processes that connect home and work. Most public data sets exchange fine-scale geographic information for individual-level information, or vice versa. Researchers must choose between aggregate data at a fine spatial scale (such as aggregate demographic data at the census tract level) or disaggregate, individual-level data at a much larger spatial scale (such as the MSA). Rarely are individual-level data geocoded below what the Census defines as a Public Use Microdata Area, or PUMA—a geographic area comprised of at least 100,000 persons. Such an areal unit is often much too large to capture fine-scale sociospatial patterns, such as ethnic neighborhood clustering. These neighborhood characteristics are necessary when examining the effects of place and location on individual labor market outcomes, and labor market analysis is difficult to carry out without information on individual characteristics, such as nativity, years of education, or marital status. Additionally, information on the geographic place of work for individual-level data is particularly difficult to obtain. Scholars studying the spatial employment patterns have relied largely upon the Census Transportation Planning Package that provides employment data at the tract level, though in aggregate form (for example, see Hanson and Pratt 1988). Simultaneously considering a worker’s place of residence and her place of work provides a key to analyzing the spatial dimension of the ethnic and gender division of labor. This requires, of course, a data set with both tract of residence and tract of 31

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The Geography of Immigrant Labor Markets

work information. Until recently, no public data set has provided such information, and scholars have had to construct such data sets from their own data collection (see England 1993; Hanson and Pratt 1995). The U.S. Census Bureau now makes available a unique 1990 Census of Housing and Population data set that contains tract of residence and tract of work information for individuals. Made available to researchers under controlled circumstances, this confidential data set contains the full sample of the 1990 long form responses (the 1-in-6 sample). This data set allows the user to identify an individual and her individual characteristics (such as nativity status, education level, English proficiency, etc.), match this information to the characteristics of the neighborhood she lives in (as well as locate this neighborhood geographically), and finally to match all of this information to information about the characteristics of her workplace tract (as well as locate this workplace tract geographically). Unless otherwise noted, I utilize this data set throughout the study to analyze the workplace-neighborhood relationships of immigrants in Los Angeles. To ensure large enough cell sizes for modeling, the study focuses on the Los Angeles consolidated statistical metropolitan area (CSMA)—the five counties of Los Angeles, Orange, Riverside, San Bernardino, and Ventura. This regional view is significant, however, for more than simply statistical purposes. In a sprawling urban area such as Los Angeles, the labor market ignores the geographic boundaries of municipal entities such as cities and counties. Further, the prototypical urban/suburban divide has little conceptual or geographic relevance for an urban region historically developed on a pattern of suburbanization. The high rises of Downtown Los Angeles were nonexistent before the 1970s, and while much of the typical urban economic functions associated with downtown development (city government, financial services, etc.) do occur in Downtown Los Angeles, they also occur throughout the multinodal landscape of the Los Angeles region. The outlying counties of the Los Angeles region, therefore, cannot be viewed as residential suburbs with little employment activity. Finally, the well-known car culture and well-developed freeway system of the Los Angeles region are both cause and result of a multinodal urban landscape. Therefore, many people live in one county and work in another, dictating the importance of a regional view given the limited relevance of politically delineated boundaries. Local lab

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market processes may operate at a much finer scale than the region or even the city for many groups, but these processes must be identified from a larger-scale perspective in order to avoid arbitrarily delimiting the boundaries of local labor markets. This book examines the six largest low-skill immigrant groups in the Los Angeles region: Mexicans, Salvadorans, Guatemalans, Chinese, Koreans, and Vietnamese, representing, respectively, three Latino and three Asian groups. While Filipinos also constitute a very large immigrant group in the region, a large proportion of Filipino workers are relatively high skilled. My analysis is primarily interested in investigating the effects of geographic accessibility for low-skill workers, as the higher wages of high-skill workers do much to overcome the obstacles of geographic accessibility (Simpson 1987). Accordingly, I have selected the Vietnamese as my third Asian-origin group.

Mapping Immigrant Residence And Work Descriptive maps (see Figs. 3.1-3.18 at end of chapter) illustrating immigrant places of residence in relation to their places of employment provide a first look at the local character of immigrant labor markets and the spatial interdependence of home and work. I use a residential concentration quotient to reveal the clustering of immigrants into ethnic neighborhoods: RCQj = (Pij / Pj) /(Pim / Pm)

(3.1)

where RCQj is the residential concentration quotient for residential tract j, Pij is the population of group i in residential tract j, Pj is the total population of residential tract j, Pim is the population of group i in metro area m, and Pm is the total population of metro area m. The RCQ measures a group’s share of a neighborhood’s population relative to the group’s share of total population in the Los Angeles region. A quotient equal to 1 represents parity in a tract; that is, the group’s population share in the tract is equal to its share in the region as a whole. Anything above 1 reflects a disproportionate concentration of a group in a tract; below 1 represents an underrepresentation. For example, a group with a quotient value of 5 in a particular tract is represented at

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The Geography of Immigrant Labor Markets

five times its expected share of the tract’s population if the group were evenly distributed across the region.1 The exception is Mexicans. Because Mexicans comprise such a large portion of the Los Angeles population (12 percent), even neighborhoods with a RCQ=1 have a high percentage of Mexican residents (12 percent). As a result of this scale effect, only 1.11 percent of all Mexicans live in neighborhoods with a RCQ >=5. I have adjusted the enclave cut-off for Mexicans to three (RCQ >=3); 35% of all Mexicans live in enclave neighborhoods by this definition. Black concentrated neighborhoods are also those neighborhoods defined as having a concentration of blacks five times greater than their expected share (RCQ >=5). The work maps are generated using a similar measure of employment concentration: ECQj = (Pij / Pj) /(Pim / Pm)

(3.2)

where ECQj is the employment concentration quotient for employment tract j, Pij is the total employment of group i in employment tract j, Pj is total employment in employment tract j, Pim is the total employment of group i in metro area m, and Pm is total employment in metro area m. Maps of immigrant residence and work While the three Latino groups (Figs. 3.1, 3.4, and 3.7) share much overlap in neighborhood location, a sharp pattern of neighborhood segregation is evident when comparing these groups to the three Asian groups (Figs. 3.10, 3.13, and 3.16). Mexicans, Salvadorans, and Guatemalans are concentrated near Downtown Los Angeles, in East L.A., to the southeast in cities such as Bell Gardens and Huntington Park, in the San Fernando Valley communities of Pacoima and Van Nuys. The Central American groups share an important enclave in the Pico Union area, and Mexicans have a greater presence in Orange County communities such as Santa Ana. The three Asian groups, in contrast, find their enclave neighborhoods in very different areas of the Los Angeles region. Further, they share much less overlap among themselves than do the three Latino groups. The Chinese are primarily located to the north and east of Downtown Los Angeles in Chinatown and the “Chinese suburbs” of Monterey Park and Hacienda Heights. Smaller

Mapping Immigrant Residence and Work

35

concentrations are evident in Cerritos and such exclusive communities as Palos Verdes and Cowan Heights. Koreans are most heavily concentrated in and around Koreatown, but also reside in enclaves in south Los Angeles (Torrance, Gardena, Carson), Cerritos, and Orange County’s Garden Grove. While the Vietnamese have established enclaves near the Chinese in places such as Chinatown and Monterey Park, their largest presence is found in Orange County, such as in the Little Saigon neighborhood of Westminster. Moving from the maps of residence to the maps of employment, we see the relatively tight correspondence between home and work, as well as the gendered spatial segregation of work. The latter is strikingly revealed in the greater spatial concentration of women’s employment among several groups, especially Mexicans (Figs. 3.2 and 3.3), Salvadorans (Figs. 3.5-3.6), and to a lesser extent, the Chinese (Figs. 3.11 and 3.12). The contrast between Salvadoran men and women is most notable on this account (Figs. 3.5 and 3.6). Not only are immigrant women, such as Mexicans and Salvadorans, much more segregated by employment industrially than their male counterparts (explained further in Chapter 5), they are also much more segregated spatially. That is, immigrant men’s employment tends to be more dispersed across the Los Angeles region while immigrant women’s employment is concentrated into fewer areas. A second, significant pattern is the relatively strong geographic correspondence between immigrant neighborhoods and immigrant places of work. While employment is always more dispersed than residence, the maps reveal that each group’s immigrant enclave neighborhoods serve as anchor points in the maps of work for both men and women. The Mexican (Figs. 3.1-3.3), Chinese (Figs. 3.9-3.11), and Vietnamese (Figs. 3.16-3.17) maps reflect this pattern most strikingly, each group displaying the key spatial characteristic of the classic ethnic enclave economy: the coterminous location of residence and employment. In the case of the Vietnamese, this pattern also extends beyond the ethnic enclave economy (such as Little Saigon in Orange County). A high concentration of both Vietnamese men and women are employed in the large tract near Seal Beach just to the west of many of their enclave neighborhoods, the site of Rockwell International. This is most likely the location of Vietnamese niche industries such as electrical machinery and computer manufacturing. A slight deviation from this pattern is found in the map of Salvadoran women’s employment (Fig. 3.6). Many of the heaviest

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The Geography of Immigrant Labor Markets

concentrations of Salvadoran women’s employment are located apart from their residential concentrations. This is largely due to Salvadoran women’s high concentration in domestic services (one in five women are employed in this industry) and the location of this employment in upper-middle class, usually white, homes in areas such as Brentwood, Beverly Hills, and Encino or the newly gated communities of Orange Park Acres and Cowan Heights (though not majority white, decidedly upper-middle class). This map, however, must be interpreted cautiously. The relatively low employment density of these hill community tracts can be deceiving; their relatively large size and dark shading make them appear more significant than they are. Plenty of Salvadoran women work in the geographically smaller, but much denser, tracts near Downtown and Salvadoran neighborhoods such as Pico Union. While these maps visually illustrate the relatively strong colocation patterns of immigrant residence and employment, the analyses in the following chapters explicitly model the effect of residential and employment location on immigrant employment outcomes.

37 Figure 3.1: Residential concentrations of Mexicans in Los Angeles, 1990

38 Figure 3.2: Work concentrations of Mexican men, Los Angeles, 1990

39 Figure 3.3: Work concentrations of Mexican women, Los Angeles, 1990

40 Figure 3.4: Residential concentrations of Salvadorans, Los Angeles, 1990

41 Figure 3.5: Work concentrations of Salvadoran men, Los Angeles, 1990

42 Figure 3.6: Work concentrations of Salvadoran women, Los Angeles, 1990

43 Figure 3.7: Residential concentrations of Guatemalans, Los Angeles, 1990

44 Figure 3.8: Work concentrations of Guatemalan men, Los Angeles, 1990

45 Figure 3.9: Work concentrations of Guatemalan women, Los Angeles, 1990

46 Figure 3.10: Residential concentrations of the Chinese, Los Angeles, 1990

47 Figure 3.11: Work concentrations of Chinese men, Los Angeles, 1990

48 Figure 3.12: Work concentrations of Chinese women, Los Angeles, 1990

49 Figure 3.13: Residential concentrations of Koreans, Los Angeles, 1990

50 Figure 3.14: Work concentrations of Korean men, Los Angeles, 1990

51 Figure 3.15: Work concentrations of Korean women, Los Angeles, 1990

52 Figure 3.16: Residential concentrations of the Vietnamese, Los Angeles, 1990

53 Figure 3.17: Work concentrations of Vietnamese men, Los Angeles, 1990

54 Figure 3.18: Work concentrations of Vietnamese women, Los Angeles, 1990

CHAPTER 4

How Local the Immigrant Labor Market?

Introduction The spatial extent of the immigrant labor market has been the subject of some debate. Arguing against the effects of the friction of distance, many sociologists challenge the applicability of hypotheses such as spatial mismatch to immigrants. Ethnic networks, they assert, rather than spatial propinquity, connect immigrants to jobs (Sassen 1995; Waldinger 1996). In contrast, geographers have claimed that the friction of distance does spatially delimit local labor markets, especially for women and minorities (see especially the work of Hanson and Pratt 1991, 1995). For example, the spatial entrapment hypothesis postulates that because household duties limit women’s commutes, women search for work over a smaller geographic area than do men. For an immigrant woman, then, home may anchor and constrain her job search area, but ethnic networks may expand her search area by overriding the friction of geographic distance. Immigrant women may be spatially entrapped as women and/or socially connected as immigrants. Residential context also may influence where immigrants work, particularly given ethnic residential segregation. Immigrant-owned businesses may locate near ethnic neighborhoods, providing immigrants with easy access to job opportunities. Immigrant women who live in such neighborhoods may have short commutes, but no shorter than their male counterparts. In this case, women’s shorter commutes may not be the result of spatial entrapment as much as a reflection of the location of enclave jobs. This chapter tests whether spatial entrapment holds for immigrant women and explores the role of ethnic networks and residential context in shaping immigrants’ labor markets and their gendered patterns. 55

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Gender, Networks, Space and the Local Labor Market Both networks and space sort workers into jobs, and gender mediates both processes. Tightly circumscribed social networks among groups such as immigrants channel workers into a few jobs, contributing to the ethnic division of labor (Model 1993; Waldinger 1996). The prevalence of ethnic networks in matching immigrants to jobs stems from the fact that networks work well for workers and employers; both parties find them a reliable source of low-cost information (Waldinger 1994). Given the gendered nature of social life, men and women’s social networks tend to be comprised of mostly men or women, respectively. These gender-segregated networks connect men and women to different jobs, a process that both emerges from and contributes to occupational sex segregation (or the gender division of labor). As a result, immigrant women often find themselves directed to jobs that are largely female and immigrant. Space plays a role in sorting workers, particularly women, into the labor market as well. Research in geography points to women’s household responsibilities and gendered networks as key factors in shaping the spatial distribution of women’s labor (Hanson and Pratt 1988, 1991, 1995; Johnston-Anumonwo 1992; Madden and White 1980). Women tend to work closer to home than men in order to manage the demands of both work and home, a spatial strategy reflected in the nearly universal finding that women’s commutes are, on average, shorter than men’s (Gordon, Kumar et al. 1989; Hanson and Hanson 1980; Hanson and Johnston 1985; McLafferty and Preston 1991). Consequently, many women settle for lower-paying, but geographically accessible, jobs and become “spatially trapped” within local labor markets that generate lower returns to skill (Hanson and Pratt 1988, 1991; Rutherford and Wekerle 1988; Villeneuve and Rose 1988). Criticisms of the spatial entrapment hypothesis point to its basis in empirical work that analyzes women as an aggregate group, that focuses on certain geographic areas that may lend themselves well to the assertions of spatial entrapment, that reflects temporally specific socio-spatial patterns, or that looks primarily at white women (e.g. see England 1993). A few studies of commute behavior consider women

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of color in relation to white women (McLafferty and Preston 1991), but the study of immigrant women in the context of this debate has been largely absent (to my knowledge, only one other study considers the commutes of immigrant women—Preston, McClafferty, and Liu 1998). Given the presence of an ethnic and racial division of labor, conclusions drawn from the study of native-born women may not hold for immigrant women. Hanson and Pratt (1988) argue that we need to consider the effect of ethnicity in spatial distributions of employment; specifically how “ethnic residential segregation might be linked to the spatial segregation of women’s employment” (1988, p. 199). The recent availability of individual data (sufficient to identify nativity) at a fine geographic scale now makes it possible to disentangle these relationships.

Gendering the Local Interplay of Work and Home: Immigrant Women and the Ethnic Enclave The spatial expressions of the gender and ethnic divisions of labor arise primarily from the constitutive forces of residential and industrial location. The mutual processes of spatial supply, generated through workers’ residential decisions, and spatial demand, created through industrial location, give form to the local labor market. For example, the spatial entrapment hypothesis argues that the gender division of labor is spatialized by women’s inability or unwillingness to commute long distances from home (supply) and by the location of femaledominated jobs in response to this constraint (demand) (Hanson and Pratt 1988; Villeneuve and Rose 1988; Werkerle and Rutherford 1989). While the generalization of this claim to all women has been contested (England 1993), location near an available workforce plays an important role in firm and industrial location decisions and the development of local labor markets. As Scott (1988) argues, the development of local labor markets occurs in dynamic fashion with firms and workers locating near one another. Recent research on the ethnic enclave economy makes a similar claim. Portes (1995) defines ethnic enclave economies as “spatially clustered networks of businesses owned by members or the same minority. They are not dispersed among other populations, … but emerge in close proximity to the areas settled by their own group” (p.

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27). Usually these businesses initially emerge to meet needs of the immigrant community, providing culturally specific goods and services. In turn, these businesses offer job opportunities to co-ethnics who may face difficulty in obtaining employment outside the enclave because of language and/or skill deficiencies. In fact, some scholars argue that these jobs generate higher returns to skill than jobs outside the enclave (Portes and Jensen 1989; Wilson 1980). However, the extent to which immigrant neighborhoods and work places cluster together remains somewhat unclear for contemporary metropolitan areas. In contrast to the concept of the ethnic enclave economy (Portes and Bach 1985), Light and Gold (2000) argue that “the concept of the ethnic economy is agnostic about [locational] clustering” (p. 10). The absence of any clustering of ethnic jobs near ethnic neighborhoods would likely indicate that social rather spatial accessibility determines the boundaries of the immigrant local labor market. This would support the sociologists’ claim that having information about a job, no matter how far from home, generally trumps a blind search for a job close to home (Waldinger 1996). Ethnic networks, however, may have different spatial characteristics for men and women. If immigrant women experience commuting constraints because of household responsibilities, then gendered employment networks may connect immigrant women to jobs over a more geographically constrained area than men’s networks. “Spatial entrapment” may introduce spatial drag into the otherwise frictionless operation of ethnic job networks. Additionally, employers likely play a role in shaping the spatial extent of immigrant labor markets, especially for women (Hanson and Pratt 1992). Location near a preferred workforce may serve to both establish an ethnic niche of immigrant women workers and sustain the niche as social and spatial connections mutually “lock in” this flow of workers. Evidence of short commutes for immigrant women employed in niche jobs compared to their male counterparts and their female counterparts who work outside of niche jobs would suggest this process. In this chapter’s analysis of immigrant commuting patterns, I evaluate how gender, ethnic networks, and residential context mediate the spatial extent of immigrant labor markets (Wyly 1999). Three questions drive this analysis. First, do we see evidence of spatial entrapment among immigrant women? Second, what is the relationship between ethnic networks and employment location? Third, what effect

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does living in an immigrant enclave neighborhood have on commute distance?

Commuting in a Segregated Labor Market As discussed above, the spatial entrapment hypothesis posits that women’s shorter commutes contribute to occupational sex segregation in the labor market (Hanson and Johnston 1985; Hanson and Pratt 1991, 1995). What happens, though, when occupational sex segregation intersects with the ethnic division of labor? Ethnic networks may mitigate the constraints of spatial entrapment, and women employed in niche jobs may actually commute further to work. Conversely, spatial entrapment constraints may limit the spatial reach of ethnic networks. Among immigrants, Table 4.1 displays both the rule and the exception to women’s shorter commutes.1 Across four of the six groups, women engage in significantly shorter commutes than their male counterparts. The difference is largest between Chinese men and women (four minutes) and smallest between Guatemalan men and women (less than a minute). Striking, however, are the differences between Guatemalan and Salvadoran men and women. Guatemalan women’s average commutes are the same as men’s, and Salvadoran women travel significantly longer than Salvadoran men. The dramatic occupational segregation of Guatemalan and Salvadoran women may largely explain their long commutes: 20 percent of these women work as domestics, cleaning homes of other Angelenos throughout the region. This is an industry marked by a particular spatial form—at once relatively disperse, though tending toward concentration in select neighborhoods. I return to this characteristic later. Employment within an ethnic niche provides a proxy for ethnic networks (Wright and Ellis 2000). Social contacts through co-ethnics provide a key link between immigrants and jobs, and ethnic niches reflect highly refined systems of these ethnic employment networks. Niche employment, then, may equalize men and women’s commutes if ethnic networks override the constraints of spatial entrapment for immigrant women. Commute times to niche employment, then, will not significantly differ between men and women. If, on the other hand, spatial entrapment holds for immigrant women, then the ethnic networks that tie them to niche employment will be more sensitive to

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distance than men’s networks. In this case, commutes to ethnic niche employment will be shorter for women than for men. TABLE 4.1 Commute Time in Minutes by Gender

Mexican men Mexican women

Mean 25.26 21.95

Std dev 18.00 17.65

t value

Salvadoran men Salvadoran women

29.08 30.33

19.32 32.31

-2.28**

Guatemalan men Guatemalan women

29.53 28.75

20.76 23.97

1.00

Chinese men Chinese women

26.87 22.67

20.16 18.42

61.74***

Korean men Korean women

26.80 24.29

17.15 17.75

4.55***

Vietnamese men Vietnamese women

26.40 24.28

17.04 16.91

3.66***

18.49***

Data: 1990 5-percent PUMS *p<.10, **p<.05, ***p<.001 I use an industry concentration quotient to identify an industry as an ethnic niche industry. An industry concentration quotient is determined for each civilian industry (identified using 3-digit census industry codes) with at least 1,000 workers in the Los Angeles region (235 total) as follows: ICQj = (Eij / Ej ) /(Eim / Em)

(4.1)

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61

where ICQj is the industry concentration quotient for industry j, Eij is the employment of group i in industry j, Ej is the total employment in industry j, Eim is the employment of group i in metro area m, and Em is the total employment in metro area m. An industry is identified as an ethnic niche for a group if the industry concentration quotient is equal to or greater than 3 for that group.2 For example, if a Mexican woman works in an industry where Mexican women comprise three times their expected share of the industry’s total employment (ICQ = 3), then this woman is coded as working in an ethnic niche industry. (See Table 5.2 in Chapter 5 for each group’s top industrial niches.) Do women employed in ethnic niche jobs have longer or shorter commutes than women employed in non-niche jobs? That is, do ethnic networks expand women’s labor markets relative to employment not connected to networks? Table 4.2 presents limited evidence in support of this claim. For all groups except Koreans, women employed in niche jobs have longer commutes. However, the difference is only statistically significant for Chinese, Salvadoran, and Guatemalan women. For Mexicans, Koreans, and Vietnamese women, commutes to niche and non-niche jobs are no different, statistically speaking. For some immigrant women, then, ethnic networks do in fact expand their spatial labor market area. But what of gendered differences? Do ethnic networks override the constraints of spatial entrapment for women, thus equalizing the spatial extent of men and women’s local labor markets? Comparisons of commutes between immigrant men and women employed in niche jobs tell different stories for different groups (Table 4.3). Spatial entrapment seems to limit the spatial scope of ethnic networks for Mexicans, Chinese, and Vietnamese women—their commutes remain shorter than men’s commutes to niche jobs. In contrast, ethnic ties seem to equalize commutes for Korean and Guatemalan men and women—their commutes to niche jobs are not statistically different. Once more, extreme occupational segregation explains the pattern of Salvadoran women’s commutes to niche employment: they travel significantly further to their niche jobs than do their male counterparts. As mentioned above, this is largely explained by occupational sex and ethnic segregation. These immigrant women are disproportionately employed in personal services as domestics. Explanation lies not only with what these women do, however, but where they do it.

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Salvadoran and Guatemalan domestics’ spatial pattern of employment mirrors the pattern of residential segregation in Los

TABLE 4.2 Commute Time (Minutes) to Niche/Non-niche Employment for Women

Mexican non-niche Mexican niche

Mean 21.78 22.31

Std dev 17.16 18.17

t value

Salvadoran non-niche Salvadoran niche

27.68 34.17

20.96 25.87

-7.16***

Guatemalan non-niche Guatemalan niche

26.12 31.74

20.25 27.29

-4.35***

Chinese non-niche Chinese niche

22.50 25.24

18.36 19.01

-13.85***

Korean non-niche Korean niche

24.67 23.15

18.26 16.06

1.57

Vietnamese non-niche Vietnamese niche Data: 1990 5-percent PUMS *p<.10, **p<.05, ***p<.001

24.26 24.32

17.30 15.76

-0.05

-1.66

Angeles. Because these women’s work sites are other people’s homes, and because these employers are predominantly white, the spatial separation of work and home for Salvadoran and Guatemalan women reflects racial and ethnic residential segregation patterns. Whites not only live in different neighborhoods than Salvadoran and Guatemalan immigrants, they live relatively far away.

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The experience of Salvadoran women, as well as Guatemalans, requires careful interpretation in light of their severe occupational

TABLE 4.3 Commute Time (Minutes) to Niche Employment by Gender

Mexican men Mexican women

Mean 24.24 22.31

Std dev 16.79 18.17

t value 5.66***

Salvadoran men Salvadoran women

28.48 34.17

18.12 25.87

-4.65***

Guatemalan men Guatemalan women

30.12 31.74

19.24 27.29

-1.00

Chinese men Chinese women

28.08 25.24

20.36 19.01

9.09***

Korean men Korean women

25.22 23.15

16.62 16.06

1.8

Vietnamese men Vietnamese women Data: 1990 5-percent PUMS *p<.10, **p<.05, ***p<.001

27.26 24.32

15.76 16.62

2.55**

segregation. They may well be faced with a choice of either employment as domestics or no employment at all; such a narrow decision frame may well trump any constraints of spatial entrapment. Given a somewhat wider set of job opportunities, ethnic networks for these women may well respond to the pressures of spatial entrapment.

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The Geography of Immigrant Labor Markets

Testing the Spatial Entrapment Hypothesis for Immigrants In order to disentangle the multiple influences on commute distance, I estimate a linear regression model in which commute distance is a function of niche employment (a proxy for ethnic networks), residential enclave status, and individual characteristics. Three questions drive this analysis. First, do we see evidence of spatial entrapment among immigrant women? Second, what is the relationship between ethnic networks and employment location? Do ethnic networks mitigate the effects of spatial entrapment for women? Even if networks expand immigrant women’s labor market areas, is the spatial reach of such networks to employment still constrained by the effects of spatial entrapment for immigrant women compared to immigrant men? And lastly, what effect does living in an immigrant enclave neighborhood have on commute distance? Are immigrant jobs located geographically close to immigrant neighborhoods? I estimate the following model separately for six national-origin groups (Mexicans, Salvadorans, Guatemalans, Chinese, Koreans, Vietnamese) in order to capture all group interaction effects:

Commute = b 0 + biI + b1Niche + b2 Enclave

(4.2)

where commute is the estimated SCAG commute distance (in minutes) from the individual’s tract of residence to his or her tract of employment, I is a vector of individual characteristics, niche is a dummy variable indicating employment in a gender-specific ethnic niche industry (female niche industry for women, male niche industry for men), and enclave a dummy variable indicating the enclave status of the individual’s tract of residence (see Table 4.4 for a list of all regressors used in the models). Using the 1990 Census of Housing and Population data matched to the SCAG travel data, I calculate estimated SCAG commute times (commute) for each individual from their tract of residence to their tract of work.3 The SCAG travel data are used to overcome the problems inherent to self-reported commute data.4 Testing the effect of gender provides key evidence for or against the spatial entrapment hypothesis. If, controlling for other factors, the effect of gender is negative and significant, then immigrant women

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TABLE 4.4: Regressors Used in Models Variable Definition Dependent: Scagtime Estimated SCAG travel time between tract of residence and tract of work Independents: SEX 0=male, 1=female NI3 Employed in ethnic- & gender-specific niche job (1=yes) NI3_SX Interaction term between niche and sex (NI3*SEX) RS2 Residence in moderately concentrated ethnic neighborhood (1=yes) RS3 Residence in extremely concentrated ethnic neighborhood (1=yes) RS2_SX Interaction term between RS2 and SEX RS3_SX Interaction term between R32 and SEX RS2_NI3 Interaction term between RS2 and NI3 (niche) RS3_NI3 Interaction term between RS3 and NI3 (niche) COH2 Cohort-of-arrival, 1980-85 (1=yes) COH3 Cohort-of-arrival, 1975-80 (1=yes) COH4 Cohort-of-arrival, 1970-75 (1=yes) COH5 Cohort-of-arrival, pre-1970 (1=yes) ED Years of education WKEXP Work experience (age-educ-6) WKEXP2 Quadratic term for WKEXP HRS Average hours worked per week HRS_SX Interaction term between HRS and SEX MAR Marital status (1=married) MAR_SX Interaction term between marital status and sex BUS Bus as mode of transportation to work (1=yes) WALK Walk as mode of transportation to work (1=yes) OTHMODE Carpool, motorcycle, bicycle, rail (1=yes) Note: Reference groups are RS1, 1985-90 for cohort-of-arrival, car for mode of transportation.

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have shorter commutes than men. Most likely, such a result indicates that immigrant women experience spatial constraints in their search for work stemming from household responsibilities. The effect of gender, however, may depend upon other factors. These possible interaction effects are discussed below. Employment within an ethnic niche provides a proxy for ethnic networks. Sociologists argue that ethnic niches reflect a process of “occupational closure” resulting from informational networks “bounded by ethnic ties” (Waldinger 1996). Thus, ethnic niches represent the operation of highly refined systems of ethnic employment networks. If these networks mitigate the friction of geographic distance in connecting workers to jobs, then the expected effect of employment in an ethnic niche will be positive (lengthening a worker’s commute). If the effect is negative, this suggests that immigrants may niche in industries located close to their places of residence. Correspondingly, ethnic networks contain localized information about jobs; that is, information about jobs that tend to be located more closely to immigrants’ places of residence. If the effect of gender is negative and the effect of niche employment positive, then ethnic networks help mitigate the effects of spatial entrapment for women. However, a negative gender effect coupled with a negative niche effect points to women’s highly localized employment networks. A female niche may develop in response to the constraints of spatial entrapment. Further, the effect of networks may depend upon gender. A significant interaction between niche employment and gender would indicate this. Networks may expand men’s spatial search areas (a positive main effect), but only slightly expand women’s (a smaller, but negative, effect). Or women’s networks may actually further constrain women’s search areas even as networks expand men’s areas (a positive niche effect and a larger, though negative, interaction effect). Because immigrants are concentrated residentially within Los Angeles, living in an ethnic enclave neighborhood may affect access to employment and commutes. I use a residential concentration quotient to identify immigrant enclave neighborhoods as defined in Chapter 3 (Equation 3.1). Neighborhoods are coded according to an ordinal scale representing degree of ethnic concentration. Individuals living in a mildly concentrated ethnic neighborhood (RCQ=0-3) serve as the comparison group. Individuals who live in a moderately concentrated ethnic neighborhood (RCQ=3-5) are coded as rs2=1. Individuals living

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in extremely concentrated ethnic neighborhoods (RCQ=>5) are coded as rs3=1. Immigrants residing in enclave neighborhoods may have short commutes if these neighborhoods are located within or near the spatial bounds of an ethnic enclave economy, thus providing immigrants easy access to jobs (Portes and Jensen 1987). Additionally, firms not part of the enclave economy but that prefer to hire immigrants (or discriminate against other workers) may locate near these neighborhoods in order to increase their chances of hiring these workers. Positive and significant interaction effects between niche and enclave residence would indicate that immigrants niche in jobs located near their residential concentrations. Alternatively, a high degree of ethnic residential segregation could lower immigrants’ overall access to jobs. On average, immigrants living in ethnic enclave neighborhoods may need to commute further to employment in order to overcome the effect of this residential segregation. Further, if networks connect immigrants to jobs without regard to spatial propinquity, then we would expect a positive and significant interaction effect between niche employment and residence in an ethnic enclave. Here, again, gender could complicate the picture. If immigrant men largely choose residential locations—we know they often migrate before their female partners—and choose these locations as close to their employment as possible subject to affordability and segregation, then these locations may not be as well-suited for women vis-à-vis their job opportunities. Searching for work from a fixed residential location, particularly one that is spatially segregated within the city, may force women into longer commutes. This scenario may only hold for immigrant women’s niche jobs, if this employment takes on a spatial distribution significantly apart from the location of immigrant neighborhoods (domestic service in white neighborhoods, for example) or is spatially disperse (service jobs such as manicurists or home health care aides). Lastly, a set of individual characteristics is included as controls. These include cohort-of-arrival, education, potential work experience, hours worked last week, marital status, and travel mode. Time in the United States (cohort) will likely affect the number of employment contacts a worker has, thus increasing that worker’s job search area and likely increasing his or her commute distance. Commutes tend to increase with higher levels of education. As workers increase their

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skills, the number of jobs that match their level of qualification drops. These workers must engage in a wider search to find jobs matched to their higher skill level. As wages increase with skill, workers with higher skills can more easily offset the costs of long commutes than can low-skill workers (Simpson 1987). Likewise, potential work experience tends to increase a worker’s commute. Not only do workers with more experience tend to have more information about jobs gained through their employment experience, they also tend to earn more, thus offsetting the costs of a longer commute. Part-time workers tend to have shorter commutes than full-time workers. Marital status may lengthen a commute for men, but shorten it for women if marital status proxies household responsibilities for women. Travel mode controls for individual variations in available transportation resources. Commute Model Results Table 4.5 (see end of chapter) contains the parameter estimates and robust standard errors (to account for the clustered nature of the data) from the above-defined regression model that estimates the effect of gender, ethnic networks, and ethnic enclave residence on commute time. Interaction terms were tested and dropped from the models if insignificant. Strikingly, the effect of gender is negative and significant for all groups—strong support for the applicability of the spatial entrapment hypothesis to immigrants (women’s commutes are shorter than men’s). This is at odds with Preston, McClafferty, and Liu’s (1998) finding the immigrant women in New York commute equally long as their male counterparts. This main effect of gender, however, must be interpreted with some caution given the presence of significant interactions between gender and other variables for many groups. Before discussing the remaining key variables of interest—ethnic niche employment and ethnic enclave residence—and their interaction effects, a few comments on controls are of note. Strikingly, commute distances for workers riding the bus were shorter than commute distances for workers who drive a car in only three of the six groups. Of these groups, the difference between commute distances was no larger than 1.7 minutes. While public bus transit tends to be a much slower form of transportation than the car in Los Angeles, workers who depend upon the bus do not necessarily limit the distance they travel to work as a result. It is highly likely, however, that these workers take

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more time to cover this same distance than workers who commute by car. (Recall that the commute time used in these models is the estimated travel time between two tracts by car.) Cohort effects reveal a somewhat unexpected pattern. These indicate that immigrant workers are not adjusting their residential location in relation to their employment over time. That is, immigrants who have been in the U.S. for the longest (such as those who arrived before 1975—cohorts 4 and 5) have longer commutes than more recently arrived workers. This points to the fact that other factors determine the residential decisions of immigrants over time, such as the quality of housing, schools, and other amenities. Finally, education has the expected positive and significant effect on commute distance, as does the effect of being married. For several groups, however, the effect of married does not depend upon gender. In order to more easily examine the combination of significant effects present in these models, I present adjusted predicted commute times in Table 4.6 generated from the models’ parameters. These are predicted commutes for recently arrived (1985-90), unmarried workers who commute by car; all other variables are mean-centered. Though these graphs depict a complex story, several general patterns clearly emerge. Firstly, and significantly, these graphs depict strong evidence for the applicability of the spatial entrapment hypothesis to immigrant women. In nearly all cases, immigrant women work closer to home than do men. While ethnic networks may attenuate or intensify the disparity between men and women’s commute distances, women’s commutes are nonetheless shorter than men’s. Salvadoran women employed in niche jobs and who live in ethnic enclave neighborhoods prove the exception. They commute the same distance to work as their male counterparts. Secondly, for all national-origin groups, residence in an ethnic enclave neighborhood lessens the disparity in commutes between all workers—male and female, those employed in niche jobs and those not. Immigrant workers who live in ethnic enclave neighborhoods tend to work relatively close to home. Thirdly, the effect of ethnic networks varies between groups and by gender. A highly group-specific phenomenon, no general pattern describes where immigrant niche jobs are located relative to where immigrants work. For Koreans, Salvadorans, and Guatemalans, the interaction effect of niche and sex is significant and positive, indicating that ethnic niche employment lengthen these women’s commutes

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relative to other factors. The main effect of niche employment for these groups is negative—employment in a niche industry reduces men’s commutes. Taking the multiple effects of gender, niche employment, and the interaction of the two together, we see that being female reduces one’s commute, as does employment in an ethnic niche industry. However, being female and being employed within an ethnic niche industry reduces the shortening effects of either gender or niche employment alone. This reveals that Korean, Salvadoran, and Guatemalan women’s ethnic employment networks do expand women’s local labor market areas in face of other constraints. The effect of niche employment (and its attendant operation of ethnic networks) does not differ by gender for Mexicans or the Vietnamese. For the Vietnamese, both men and women’s niche jobs tend to be located closer to home. For Mexicans, niche jobs are located slightly further away than non-niche jobs (but the effect is minimal). Niche employment has no significant effect on the commutes of Chinese men or women. For the purpose of delving more deeply into the complexities of the model results, I discuss the results for Salvadorans (see Table 4.6) in some detail here. These comments provide a guide for interpreting the specific results of the remaining groups. Among Salvadorans, the model results support the spatial entrapment hypothesis for women who reside outside of ethnic enclave neighborhoods. Whether employed in niche or non-niche jobs, these women work closer to home than their male counterparts. The pattern, however, does not hold for Salvadoran women living in ethnic enclave neighborhoods. Women who commute from these neighborhoods to niche jobs have the same commutes as their male counterparts. Salvadoran non-enclave neighborhoods are located closer to both Salvadoran men and women’s niche jobs than to their non-niche jobs. The difference is most dramatic for men: their niche jobs are located nearly five minutes closer than their non-niche jobs. The difference for women, however, is less dramatic (just under a two minute difference). Both niche employment and enclave residence have an equalizing effect on gendered commute patterns. In fact, the gender commute gap disappears between male and female niche workers living in ethnic neighborhoods; they travel equal distances to their niche jobs. The equalizing effect of niche employment is also present among nonenclave workers, though the gender gap is not overcome in this case— women still commute shorter distances to niche jobs than men. Of

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note, however, is the finding that Salvadoran women living in enclave neighborhoods must travel slightly further to their niche jobs that to their non-niche jobs. This may reflect a spatial mismatch between the location of Salvadoran women’s niche jobs (such as domestic jobs in white neighborhoods) and the location of Salvadoran neighborhoods. Model results for Salvadorans are at odds with the expectations drawn from descriptive statistics using self-reported commute times. The model points to Salvadoran women’s shorter or equal commutes compared to men’s while descriptive statistics using self-reported commute times indicate that Salvadoran women have longer commutes than their male counterparts. Further, the model indicates that women employed in niche jobs have longer commutes than their female counterparts employed in non-niche jobs, but only for enclave residents (and the difference is not great). The self-reported commute times show Salvadoran women with significantly longer commutes to niche employment than to non-niche employment. I believe several reasons account for this incongruity. First, I did not cut the self-reported commute time data by enclave residence for the purposes of the earlier reported descriptive statistics. The models reveal that residential context is an important determinant of women’s commutes. Second, standardized commute distances reduce the large variations found in self-reported commute times. These variations emerge from the extreme speed differentials among transportation modes and even public transportation routes, especially in Los Angeles. A job five miles away may take ten minutes by car or thirty minutes by bus. Third, although Salvadoran women are severely segregated into private household services, they are also concentrated in many other industries, though to a lesser degree. These niche industries tend to concentrate in the same areas of the region as Salvadoran neighborhoods (primarily the Los Angeles downtown area). Even Salvadoran women’s niche in “services to buildings” (as janitors) reflects the likelihood that these are janitorial jobs in downtown office buildings. The extremely long commutes of women employed as domestics, however, likely exert a strong influence on the average commute time of Salvadoran women employed in niche jobs, pulling the average up. This skews our interpretation of the effects of ethnic networks on Salvadoran women’s spatial employment patterns. Methodologically, a standardized commute time better accounts for the

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geographic extent of the local labor market for particular groups than does commute time. Lastly, comparisons of standardized commute times with selfreported commutes present a rich opportunity of analysis. The extremely long self-reported commute times of Salvadoran women should not to be dismissed as “poor data” as they point to these women’s very real travel burdens. If comparisons show that these women take longer to travel the same distance as their male counterparts, this would indicate that these women rely upon slower means of transportation, possibly bus commutes punctuated with long walks and wait-times between transfers, and that these women may be engaging in other household tasks during their commutes, such as grocery shopping or dropping off children at school or day care.

Gender and the Immigrant Local Labor Market The findings of this chapter reveal that immigrant women experience the same pressures as other women to work close to home, as postulated in the geography literature by the spatial entrapment hypothesis. For some immigrant women (Mexicans, Koreans, Salvadorans, and Guatemalans), ethnic networks work against the constraints of spatial accessibility. In general, however, the spatial boundaries of immigrant women’s local labor markets relative to residence are more constrained than the boundaries of immigrant men’s local labor markets. Perhaps most striking are the similar commute times among immigrant workers who live in ethnic neighborhoods. These immigrants, whether male or female, work very close to home. Immigrants who have immigrated most recently (1985-90) work closest of all. The jobs filled by these enclave residents may be jobs within the ethnic economy (jobs provided through immigrant-owned businesses). They may also be jobs within firms that “prefer” immigrants as a labor force and have located near these ethnic neighborhoods. This analysis, however, cannot determine if immigrants tend to niche in particular jobs because these jobs are located near ethnic neighborhoods. This analysis reveals that immigrants in enclave neighborhoods commute similar distances to niche and non-niche jobs. The ethnic enclave economy may offer jobs to immigrants that are ethnically specific only within the enclave economy’s geographic borders, rather than across the region as a whole. Niche jobs

How Local the Immigrant Labor Market?

73

(ethnically specific at the regional level) may also be located nearby, or they may be located closer to immigrants who live outside the enclave (the models show evidence of this for some groups). A measure of spatial accessibility to all jobs within an ethnic niche industry needs to be calculated from enclave and non-enclave residential tracts. This would establish whether or not immigrants niche in nearby industries. If women tend to niche in industries located closer to home than men, this would point to further consequences of spatial entrapment. Immigrant women’s niches could be explained partly by the fact that they are located closer to these women’s homes; firm location may in fact respond to these women’s limited commuting behavior. Questions concerning the consequences of spatial entrapment demand further research. Although past research has established a relationship between women’s limited job search areas and women’s lower pay, this may not apply to immigrant women (Hanson and Pratt 1995). Local jobs and shorter commutes for these women may be a convenience without associated wage penalties (particularly enclave economy and/or ethnic niche jobs). However, we do have evidence that ethnic niche and ethnic enclave economy jobs pay poorer wages to women than non-niche or non-enclave economy jobs (Gilbertson 1995; Zhou and Logan 1989; see also Chapter 5 in this book). Ethnic networks may matter for getting a job, but they may lock immigrant women into relatively poor quality jobs. As a consequence, these women may be “socially trapped” within poor job networks. If better, non-niche jobs are located further from home, then spatial accessibility may exacerbate the effects of poor social accessibility—spatial entrapment may magnify social entrapment. Such questions build from the foundations established in this chapter.

TABLE 4.5, part 1: Parameter Estimates (Standard Errors) of Regressors on Commute Time

74

Regressor Mexicans cons 24.610*** (0.434) sex -5.986*** (0.572) ni3 0.864** (0.292) ni3_sx --

Salvadorans Guatemalans Chinese Koreans 27.588*** 27.804*** 22.796*** 23.465*** (0.913) (1.225) (1.277) (1.354) -5.825*** -4.735*** -2.271*** -2.200** (0.611) (0.806) (0.706) (0.803) -4.869*** -1.172 0.556 -5.708*** (0.907) (0.655) -(0.606) (1.084) 3.188*** 2.852** -4.120*** (0.785) (0.914) (1.005) rs2 -2.958*** -3.621*** -2.611*** -0.583 -0.539 (0.162) (0.498) (0.652) (0.489) (0.550) rs3 -7.714*** -4.721*** -3.681*** -2.145*** -4.187*** (0.748) (0.506) (0.668) (0.455) (0.591) Data: Confidential 1-in-6 sample of the U.S. Census of Housing and Population p<.05*, p<.01**, p<.001***

Vietnamese 19.542*** (1.268) -2.002*** (0.421) -1.650** (0.491) --1.445** (0.536) -5.629*** (0.506)

TABLE 4.5, part 2: Parameter Estimates (Standard Errors) of Regressions on Commute Time Regressor rs2_sx

75

Mexicans Salvadorans Guatemalans Chinese 0.565* 2.919*** 1.004 -(0.271) (0.781) -1.004 rs3_sx 2.144 2.651** 2.739** --1.277 (0.798) (1.011) rs2_ni3 3.433*** 4.654*** --(0.318) (0.944) rs3_ni3 -0.161 4.641*** --(1.330) (0.932) coh2 -0.047 -0.348 0.566 2.324*** (0.177) (0.365) (.494) (0.541) coh3 -0.039 0.697 0.411 2.208*** (0.185) (0.453) (625) (0.599) Data: Confidential 1-in-6 sample of the U.S. Census of Housing and Population p<.05*, p<.01**, p<.001***

Koreans --

Vietnamese --

--

--

3.034** (1.238) 4.633*** (1.309) 0.676 (0.567) 2.754*** (0.601)

--2.242*** (0.625) 2.782*** (0.634)

TABLE 4.5, part 3: Parameter Estimates (Standard Errors) of Regressions on Commute Time Regressor coh4

76

Mexicans Salvadorans Guatemalans Chinese 1.211*** 2.097** 2.224** 1.890** (0.200) (0.612) (0.781) (0.670) coh5 2.303*** 1.371 4.892*** 2.684*** (0.228) -0.817 (0.909) (0.635) ed 0.043* 0.091* -0.060 0.175** (0.018) (0.041) -0.055 (0.058) wkexp 0.068*** 0.003 -0.034 -0.117*** (0.018) -0.017 -0.023 (0.020) wkexp2 -0.002*** ---(0.0003) hrs -0.030*** -0.024 -0.026 0.039* (0.008) (0.015) (0.020) (0.015) Data: Confidential 1-in-6 sample of the U.S. Census of Housing and Population p<.05*, p<.01**, p<.001***

Koreans 1.770** (0.659) 2.486** (0.895) 0.263*** (0.074) -0.024 -0.021 --

Vietnamese 0.702 -1.434 1.542 -2.039 0.143* (0.066) -0.086*** (0.023) --

-0.009 (0.014)

0.110*** (0.017)

TABLE 4.5, part 4: Parameter Estimates (Standard Errors) of Regressions on Commute Time Regressor hrs_sx

77

Mexicans Salvadorans Guatemalans Chinese 0.055*** ---(0.013) mar 1.401*** 0.904** 0.559 3.049*** (0.161) (0.299) -0.410 (0.631) mar_sx -1.581*** ---2.011** (0.259) (0.838) bus -1.799*** -1.170** -1.105* -1.670 (0.194) (0.378) (0.509) (0.919) walk -16.830*** -19.475*** -19.031*** -19.308*** (0.260) (0.750) (0.939) (1.000) othmode -8.880*** -11.960*** -13.887*** -18.810*** (0.278) (0.705) (0.856) (0.948) [N] [114155] [15368] [8168] [11160] Data: Confidential 1-in-6 sample of the U.S. Census of Housing and Population p<.05*, p<.01**, p<.001***

Koreans --

Vietnamese --

2.366** (0.707) -2.228* (0.922) -1.258 (1.390) -18.298*** (1.496) -16.970*** (1.271) [9884]

1.578** (0.463) --0.247 (1.122) -17.030*** (1.647) -9.581*** (1.345) [8911]

TABLE 4.6, part 1: Predicted Commute Times Using Model Parameters By Niche Employment and Enclave Residence Mexicans Niche Non-Niche

Salvadorans Niche Non-Niche

78

Men: 18.0 17.3 23.4 23.7 Enclave 25.8 25.0 23.5 28.4 Non-Enclave Women: 16.3 15.6 23.4 20.5 Enclave 22.0 21.2 20.9 22.6 Non-Enclave Data: Confidential 1-in-6 sample of the U.S. Census of Housing and Population

Guatemalans Niche Non-Niche 25.8 29.5

24.1 27.8

23.8 24.7

22.1 23.1

TABLE 4.6, part 2: Predicted Commute Times Using Model Parameters By Niche Employment and Enclave Residence Chinese Niche Non-Niche

Koreans Niche Non-Niche

79

Men: 22.9 22.4 25.9 22.9 Enclave 25.1 24.5 21.4 27.1 Non-Enclave Women: 20.7 20.1 22.1 20.7 Enclave 22.8 22.2 23.3 24.9 Non-Enclave Data: Confidential 1-in-6 sample of the U.S. Census of Housing and Population

Vietnamese Niche Non-Niche 18.0 25.8

17.3 25.0

14.9 20.5

16.5 22.1

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CHAPTER 5

Connecting Neighborhood and Home to Ethnic Labor Market Segregation

While sociologists have explained the job segregation of immigrants as a result of ethnic networks that channel workers into specific jobs, few geographers have queried the spatial and place-based contexts in which immigrant job segregation takes place. Most likely a strong relationship exists between social networks and their spatial and placebased characteristics. Hanson (1992) argues that, “the spatial bias of place-based social networks helps to create labor market segmentation in space” (p. 581). A key source of “spatial bias,” particularly for immigrants, is the ethnic enclave neighborhood. Examining the effect of ethnic residential segregation on ethnic labor market segmentation provides a window upon one dimension of the place-based relationship between home and work—are workers who live in ethnic neighborhoods more closely tied to ethnic jobs? Feminist geographers have convincingly demonstrated that placebased social networks, especially employment networks, are gendered (Hanson and Pratt 1988, 1995). Given women’s stronger ties to home as a result of the household division of labor and women’s more locally-rooted social networks, household and residential circumstances are likely to be more important determinants of immigrant women’s employment outcomes, such as job segregation, than men’s. Further, the geographic characteristics of a neighborhood, such as its accessibility to employment opportunities, is likely significant for women’s employment outcomes. Geographic location of

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neighborhoods and jobs may play an important role in creating and sustaining labor market segmentation among immigrant women. In this chapter, I examine the extent to which immigrants find employment in niche jobs because of the type of neighborhood they live in or because of where that neighborhood is located in relationship to jobs. Is the effect of either neighborhood context or spatial job accessibility more pronounced for women than men? This provides an important look at the relationship between residential context, residential location, and ethnic and gender segmentation in the labor market. Understanding the socio-spatial relationship between residence and work is central to revealing patterns of urban growth and development (Burgess [1925] 1967; Ward 1968; 1971; Scott 1988), but becomes even more critical when the relationship leads to and/or perpetuates inequality (Kain 1968; Wilson 1987; Massey and Denton 1993). In the case of immigrant women, employment in an ethnic niche is associated with lower wages and a further depreciation of wages as the niche becomes increasingly dominated by coethnics (Zhou and Logan 1989; Catanzarite 2000; 2002). Elliott (1999) describes wage penalties experienced by workers who find jobs through a nonwhite contact (often a co-ethnic) and the even greater penalty when this contact is a neighbor. He articulates this latter process as one of “labor market insulation”: the use and development of “informal networks within and through locally constrained communities” (p. 213). In this way, placebased social isolation exacerbates labor market inequality.

Immigrants’ Gendered Networks Research on immigrant labor markets highlights the role of social networks in connecting immigrants to jobs (Model 1993; Waldinger 1994). As networks channel immigrants into specific jobs, the increased concentration of coethnics gives rise to an ethnic niche. Sociologists argue that ethnic niches reflect a process of “occupational closure” resulting from informational networks “bounded by ethnic ties” (Waldinger 1996). Immigrants in niche jobs recruit their friends, family, and neighbors into jobs, a process helpful and often welcomed by employers (Johnson-Webb 2002). In this way, ethnic networks provide a key link between immigrants and jobs, and the ethnic niche emerges as the most visible manifestation of these networks.

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Ethnic networks channel both men and women to ethnic niche jobs. Once employed in these jobs, the ethnic niche functions similarly for men and women as well. Bailey and Waldinger (1991) argue that the ethnic niche is “characterized by an external, informal training system that shapes the employment relationship and increases the availability and quality of information for workers and employers” (p. 432). Both Hondagneu-Sotelo (1994) and Mattingly (1999) find evidence of just such a training system among immigrant women employed in domestic service (a classic immigrant niche). New immigrant domestic workers rely upon subcontracting from more experienced immigrants as a means of entry into work, providing, as Hondagneu-Sotelo (1994) points out, “an important apprenticeship and springboard into independent contracting” (p. 56). In contrast to its gender-neutral functions, the ethnic niche takes a highly gendered form. Ethnic networks tend to be gender segregated, directing immigrant men and women into very different labor market positions and ethnic niches (Hiebert 1999; Wright and Ellis 2000). Hanson and Pratt (1991) find that gendered networks, in general, play a part in perpetuating occupational sex segregation. Because women tend to work in female-dominated jobs, employment information that circulates through women’s networks will most likely be about jobs into which women are segregated. In a comparison of women employed in female-dominated occupations to women in maledominated occupations, Hanson and Pratt (1991) find that the former relied upon job information from other women to a greater extent than women employed in male-dominated occupations. Similarly, Mattingly’s (1999) study of female domestic workers (a highly femaledominated occupation) in San Diego finds that most job referrals came from other female domestic workers who were relatives or friends. Researchers also have identified the highly familiar and local context of women’s social networks. Women’s networks contain more friends and family, while men’s contain more coworkers (Marsden 1987; Moore 1990). Hanson and Pratt (1991) find that women rely heavily upon job information not only from women who are close friends or family, but who also live nearby. In similar fashion, sociologists such as Sassen (1995) and Fernandez-Kelly (1995) describe women’s networks as a form of “place-based knowledge” and as “toponomical,” respectively. In her study of inner-city young women in Baltimore, Fernandez-Kelly (1995) argues that neighborhood

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context centrally defines the social networks of individuals with few ties outside their residential environment. Such findings raise critical questions about the gendered effect of residential segregation on job search and employment outcomes. If women’s networks are locally circumscribed, then residential segregation may matter more for women’s employment outcomes than for men’s, especially among groups that experience relatively high levels of residential segregation such as immigrants. We have no empirical evidence as of yet to substantiate this claim. Although Mattingly (1999) argues that social networks are “easy for some immigrant women to access, particularly if they live in ethnic enclaves” (p. 66), she provides no evidence to support this claim.

The Immigrant Enclave Neighborhood The immigrant neighborhood plays an important role in immigrant life. Viewed as an initial place of reception for new immigrants, the immigrant neighborhood serves as a cultural safe-haven that provides a wealth of resources. Sociologists argue that “concentrated immigrant settlement areas arise and are maintained because they meet newcomers’ needs in such areas as affordable housing, family ties, familiar culture, and help in finding work” (Logan, Alba, and Zhang 2002, p. 299). Immigrant neighborhoods, then, provide a delimited physical space within which ethnic networks circulate. To the extent that ethnic employment networks are rooted in residential neighborhoods, we should expect to see a relationship between living in an immigrant enclave neighborhood and working in an immigrant niche job. To date, little empirical work has demonstrated such a connection. Logan, Alba, and Zhang (2002), an exception, show an association between working in an ethnic industrial sector and living in an immigrant enclave, though they predict living in an immigrant enclave as a function of working in an ethnic industrial niche. They find limited significance in their predictive coefficients across all groups, and for Filipinos in New York and Chinese in Los Angeles the effect is in the opposite direction (working in an ethnic niche is negatively associated with living in an immigrant enclave). The theorized relationship, however, likely operates in the opposite direction as that tested by Logan, Alba, and Zhang (2002). Immigrants,

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particularly new arrivals, move into an enclave neighborhood that provides them with information about jobs and access to ethnic employment networks. Utilizing these neighborhood resources, immigrants then look for work. Immigrants may well learn of housing opportunities from their fellow workers, but this is not the direction of the theorized relationship between immigrant neighborhoods and immigrant jobs. No other research, to my knowledge, examines whether these associations are gendered. Residentially-based employment information may be more significant for immigrant women than men. The research discussed in the above section points to the importance of residentially-based ties for women (Hanson and Pratt 1991) and to the importance of family and community employment ties for immigrant women compared to men (Fernandez-Kelly 1995; Tienda and Glass 1985). Such gendered networks may have a greater effect on where immigrant women work and what they do, thus tightening the boundaries of their local labor market opportunities. A first step toward examining these relationships proceeds by asking whether women who live in ethnic enclave neighborhoods are more likely to work in ethnic niche jobs. This examines one dimension of the placebased relationship between work and home—are workers who live in ethnic neighborhoods more closely tied to ethnic niche jobs? Neighborhood location relative to jobs The question posed in the above section ignores the role of geographic proximity as a component of the ethnic neighborhood/ethnic job relationship. Immigrants may find employment in ethnic niche jobs simply because these jobs are located close-by. Geographers have long purported the geographic relationship between immigrant neighborhoods and immigrant jobs (Hershberg 1981; Scott 1988; Ward 1968, 1971). In this chapter, I analyze the relationship between living in an immigrant enclave and working in an immigrant employment niche while controlling for neighborhood proximity to niche jobs. Controlling for the location of an immigrant neighborhood relative to niche jobs is necessary to capture the contextual effects of an immigrant neighborhood on niche employment. This allows me to isolate the extent to which simply living in an immigrant neighborhood may tie an individual to niche jobs apart from the effects of the location of that neighborhood.

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The Geography of Immigrant Labor Markets

The gendered nature of these effects is of primary interest. Are immigrant women’s ties to ethnic employment more reliant on residential context than men’s? Is residential context significant for immigrant job segregation once accessibility to immigrant jobs is controlled for? Is the effect of either neighborhood context or spatial job accessibility more pronounced for women than men? Answering these questions helps unpack the complex socio-spatial interplay of immigrant residential and labor market segregation and aids in identifying structural forces underpinning urban inequality. When labor market segregation contributes to the weaker economic position of immigrants, we need to understand the multiple mechanisms that perpetuate labor market segregation. If residential segregation is a factor, then we need to evaluate the beneficial functions of the immigrant neighborhood in light of its more limiting effects.

Enclave Neighborhoods and Niche Industries I use a residential concentration quotient to identify immigrant enclave neighborhoods as defined in Chapter 3 (Equation 3.1). A tract is identified as an ethnic enclave neighborhood for a group if the concentration quotient is equal to or greater than 5 for that group. This is similar to Logan, Alba, and Zhang (2002) odds-ratio cut-off of 5 used to define an immigrant enclave. The exception is Mexicans. Because Mexicans comprise such a large portion of the Los Angeles population (12 percent), even neighborhoods with a RCQ=1 have a high percentage of Mexican residents (12 percent). As a result of this scale effect, only 1.11 percent of all Mexicans live in neighborhoods with a RCQ >=5. I have adjusted the enclave cut-off for Mexicans to 3 (RCQ >=3); 35% of all Mexicans live in enclave neighborhoods by this definition. Black concentrated neighborhoods are also those neighborhoods defined as having a concentration of blacks five times greater than their expected share (RCQ >=5). I use an industry concentration quotient to identify an industry as an ethnic niche industry as defined in Chapter 4 (Equation 4.1). Because I am interested in the employment outcomes of job seekers, I focus on industries in which a group is overrepresented as workers, not as owners, and thus exclude the self-employed (see Logan et al. 2000 for a distinction between an entrepreneurial and a labor niche). An industry concentration quotient is determined for each civilian industry

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(identified using 3-digit census industry codes) with at least 1,000 workers in the Los Angeles region (235 total). An industry is identified as an ethnic niche for a group if the employment concentration quotient is equal to or greater than 3 for that group. For example, if a Mexican woman works in an industry where Mexican women comprise three times their expected share of the industry’s total employment (concentration quotient = 3), then this woman is coded as working in an ethnic niche industry. As can be seen in Table 5.1, women across all immigrant groups, with the exception of Koreans, are more segregated in the labor market than men. This difference is greatest between Salvadoran and Guatemalan men and women. While only 12 percent of Salvadoran men find employment in a niche industry, nearly 40 percent of

TABLE 5.1 Percent Men/Women Employed in Niche Industries Men

Women Mexicans 16.1 26.3 ** Salvadorans 12.0 39.8 ** Guatemalans 17.5 46.2 ** Chinese 9.8 21.8 ** Koreans 18.6 17.6 Vietnamese 21.0 28.1 ** Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p< .05. ** p< .01 Salvadoran women do. Similarly, 17.5 percent of Guatemalan men are employed in niche industries compared to over 46 percent of Guatemalan women. Though not as extreme, this gender difference is significant for other groups as well. Just over 26 percent of Mexican women are employed in a niche industry, compared to 16 percent of men. While fewer than 10 percent of Chinese men hold employment in niche industries, nearly 22 percent of Chinese women do.

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Table 5.2 reflects the top industrial niches, ranked by number employed, for each group and the average hourly wage of group members in each industry. Women share more industrial niches across groups than men. Traditionally an immigrant employer, the apparel industry serves as a top industrial niche for five of the six female groups (Vietnamese women do not concentrate in apparel). Domestic employment (personal service in private households) provides niche employment for women in the three Latino groups—Mexicans, Salvadorans, and Guatemalans—and is a top niche for all three. Mexican, Salvadoran, and Korean women all share a top industrial niche in laundry and garment services, as do Korean and Vietnamese women in beauty shops (manicure shops being the specific niche of Vietnamese women). Unlike any other immigrant female group, Vietnamese women find most of their top niches in light manufacturing industries. In total, Mexican, Salvadoran, and Guatemalan women share six niche industries. Of these, three are also shared with Koreans (yarn and fabric mills, apparel, dressmaking shops). Chinese women share two niche industries with Mexicans and Salvadorans (confectionary products and apparel), only apparel with Korean women, and only apparel wholesale trade with Vietnamese women. In addition to beauty shops, Korean and Vietnamese women share a niche in dressmaking shops. Dressmaking shops also serve as a niche industry for Mexicans, Salvadorans, and Guatemalans. Table 5.3 reveals that niche employment offers all immigrant women significantly lower wages than non-niche employment. These descriptive data uphold the findings of Zhou and Logan (1989) that the ethnic enclave economy tends to trap immigrant women in lower wage jobs with fewer returns to skill. Immigrant men do not share as many industrial niches across groups as women. The Latino groups share the most: Mexican, Salvadoran, and Guatemalan men share niches in four industries (bakery products, yarn and fabric mills, misc. furniture goods, automobile parking and carwashes and), Salvadorans and Guatemalans share four more between them (e.g. automotive repair and service to buildings), Mexicans and Salvadorans three more (e.g. dyeing and finishing textiles), and Mexicans and Guatemalans additionally share automotive repair. Chinese men share no niches with any of the three Latino groups. They share three with Koreans (e.g. wholesale trade) and one with Vietnamese men. Koreans share two textile industries as

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89

niches with the three Latino groups, automotive repair and services to buildings with both Salvadorans and Guatemalans, and one additional industry each with Salvadorans and Guatemalans. Koreans share one niche with Vietnamese men (not specified machinery). Vietnamese men share one niche with Salvadorans and Guatemalans (dressmaking shops) and two additional niches with Salvadorans (not specified machinery and retail bakeries). The negative wage consequences of niche employment are not as stark for immigrant men as for women (see Table 5.3). Wages are statistically lower in niche jobs for men in only three groups (Mexicans, Salvadorans, and Koreans), and the difference in niche and non-niche wages are smaller for men than for women. Table 5.4 shows the relationship between enclave residence and niche employment. Across all groups, men and women who live in enclave neighborhoods have higher rates of niche employment than men and women who live outside of enclave neighborhoods (Korean men excluded). The difference, however, is not statistically significant for all men, while it is for all women. Further, the rates of niche employment for women who live in enclave neighborhoods are much higher than for men. The sharpest gender contrasts are found between Salvadoran and Guatemalan men and women. While approximately 14 percent of Salvadoran men live in enclave neighborhoods and hold employment in niche industries, over 48 percent of Salvadoran women do. The numbers are 22 percent and 55 percent for Guatemalan men and women, respectively. The greatest difference in rates of niche employment between women of the same national origin who reside in enclave neighborhoods and those who do not are found among Mexican, Salvadoran, and Guatemalan women. The percentage of these women who are employed in niche industries is approximately 14 points higher for those who live in enclave neighborhoods than for those who do not. Calculating spatial job accessibility The accessibility index presented in Appendix 2 (see Equation A2.1) can be tailored to particular groups, thus capturing segmentation in the labor market. If women and men work different jobs, accessibility should be constrained to reflect this fact. Accessibility can also be measured to particular kinds of jobs, such as ethnic niche jobs for immigrants. I calculate a separate accessibility measure to niche jobs

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The Geography of Immigrant Labor Markets

for men and women of the same immigrant group for each residential tract (see Equation A2.3 in Appendix 2). Measuring accessibility to jobs that are 1) jobs in ethnic niche industries, and 2) held by group members, captures the locally specific nature of niche employment. Narrowing the accessibility index in this way captures the fact that ethnic concentrations evolve at both the neighborhood/tract and industry level. These highly localized labor markets emerge as employers locate in close proximity to desirable labor pools, engage in local recruiting strategies, and make use of employee recruitment networks. Employees, likewise, have preferences for short commutes and make use of personal contacts and ethnic networks to find a job (Hanson and Pratt, 1992). The final hiring decisions of employers solidify the match between worker and job, ultimately putting the ethnic niche in place (Waldinger, 1994). Table 5.5 shows that across all groups women have higher levels of accessibility to niche jobs than men; that is, women’s niche jobs are located closer to women’s tracts of residence. Among men, niche jobs are located closer to enclave neighborhoods than non-niche jobs, except for Salvadorans and Guatemalans. Women who live in enclave neighborhoods also have higher levels of accessibility to niche jobs than women who live outside enclave neighborhoods, except Vietnamese and Guatemalans. No difference exists between Vietnamese enclave and non-enclave residents, while Guatemalan women who live outside of enclave neighborhoods have better accessibility to niche employment. This is likely due to the fact that Guatemalan women are so highly concentrated in domestic service jobs, many of which are located in the wealthier hill communities of Los Angeles far from Guatemalan enclave neighborhoods. In this case, the map of ethnic residential segregation is the map of ethnic labor market segregation: white neighborhoods are the tracts of work for Guatemalan and Salvadoran women.

Modeling the Ethnic Enclave and Ethnic Niche Relationship To analyze relationships between labor market and residential segregation, I model the effects of immigrant enclave residence and geographic accessibility on ethnic niche employment using logistic regression. The model takes the following form:

Connecting Neighborhood & Home to Labor Market Segregation logit(P) = b 0 + b1Enclave + b2 Access + biH + bjI

91

(5.4)

where P is the probability of employment in a gender-specific ethnic niche industry (female niche industry for women, male niche industry for men), enclave is a categorical variable indicating whether an individual lives in a residential enclave or not, access is a logged continuous measure of accessibility to respective gender-specific ethnic jobs (women’s jobs for women, men’s jobs for men) for the individual’s tract of residence, H is a vector of household characteristics, and I is a vector of individual characteristics. To account for differing effects of enclave residence within a group by sex, enclave is interacted with sex. All other interactions are tested, but dropped if insignificant. See Table 5.6 for a list and description of variables used in the models. Table 5.7 presents means and standard deviations of the model regressors. The model is run separately on six immigrant groups to account for interaction effects between national-origin group and all other explanatory variables. The six immigrant groups are: Mexicans, Salvadorans, Guatemalans, Chinese, Koreans, and Vietnamese. The sample includes employed respondents between the ages of 18 and 64 and excludes the disabled, those living in group quarters, and the selfemployed. Ethnic niche employment proxies access to an ethnic network, as ethnic employment niches are created by, and therefore point to, the operation of ethnic employment networks. An individual is coded as working in an ethnic niche industry if the employment concentration quotient of that individual’s industry of employment is equal to or greater than 3 for the individual’s sex and national-origin group (see Equation 4.1). For example, if a Chinese woman is employed in the apparel industry, then she is coded as working in a niche industry as the concentration quotient for Chinese women in the apparel industry equals 5.5. Niche industries are identified separately for men and women to capture the dual effects of the ethnic and gender division of labor. Testing for an ethnic neighborhood effect on ethnic niche employment provides a means by which to evaluate one dimension of the connection between ethnic residential segregation and ethnic labor market segregation. Such a connection may reveal place-based characteristics of ethnic employment networks. If the probability of

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The Geography of Immigrant Labor Markets

niche employment increases with residence in an immigrant enclave, this may be because ethnic networks exhibit a place-based nature. Testing for interaction effects allows investigation into the gendered nature of such place effects. If the interaction term between enclave residence and gender is significant and positive, then women’s employment networks may be more spatially local than men’s networks. Immigrant women’s employment outcomes, then, may depend upon residential context to a greater extent than immigrant men’s. Including a measure of geographic job accessibility allows us to differentiate between the locational and social effects of a particular neighborhood on employment outcomes. For example, a Salvadoran woman may find employment in a niche industry simply because she lives in a neighborhood that is readily accessible to niche employment (these jobs may be located across the street, for example) rather than because her neighborhood provides ready access to ethnic employment networks. Because geographers have hypothesized the geographic colocation of immigrant neighborhoods and immigrant employment, this locational factor is important to control for if we are interested also in testing for the social effects of neighborhood, such as access to ethnic networks. A set of household variables are included and interacted with gender to reveal the gendered nature of these effects. Being married may increase one’s set of “strong ties”—close friends, neighbors, and family members—thus increasing one’s probability of finding employment in an ethnic niche job (Granovetter 1973; 1974). This effect may depend upon gender; married women may be more likely to find employment through their husbands—a strong tie—as Mattingly (1999) found in her study of female domestic workers. Because many people are involved in significant relationships without being officially married, I classify individuals as members of a couple or not rather than as married or not. I do this by searching through household records and categorizing individuals with a spouse or partner present as a member of a couple. Individuals involved in a relationship with a member of their own national-origin group have a potentially stronger set of ethnic ties than an individual whose partner is a member of a different ethnic group. I identify same- and mixed-ethnic couples and include this information with the variable hh_mix (0=couple of same national-origin group, 1=mixed couple).

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Immigrants living in households with other adults present have more individuals within their immediate social network and more contacts to employment. This is particularly true of multigenerational or extended family households common among immigrants. Because these family contacts are strong ties, increasing numbers of other adults present in the household will likely increase one’s probability of finding niche employment. A set of individual characteristics is used to control for variations between workers. Women may be more likely to find employment in niche industries if their employment networks are characterized by strong ties to a greater extent than are men’s networks (Marsden 1987; Moore 1990; Hanson and Pratt 1991). New arrivals may rely more heavily upon ethnic networks in their search for work, tying them more tightly to employment in ethnic niches. Workers with more work experience may be less likely to rely upon ethnic networks in their employment search as they work more jobs and compile their own set of employment networks that extend beyond the bounds of ethnicity. On the other hand, workers with more work experience may remain in jobs that they originally found through ethnic networks if employment in such jobs isolates them from other sources of non-redundant job information. Individual efforts to improve one’s employment prospects, such as education, most likely decrease the likelihood of niche employment. Additionally, network characteristics vary by education. Less-educated workers are more likely to have networks characterized by strong ties (Burt 1990). As immigrants make use of ethnic networks to overcome barriers to employment such as language ability, poor English will likely increase the likelihood of working in a niche. Mode of transportation used to travel to work may influence whether workers find niche employment or not. Of particular interest is the effect of carpooling. Are immigrants who need to rely upon others for transportation more likely to find employment where other co-ethnics work? Further, mode of transportation introduces an important control as access to a car may increase one’s employment opportunities, as it potentially extends one’s geographic search area. Results Table 5.8 presents results as log odds for models run separately on each immigrant group. Robust standard errors are reported to correct for the

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effects of clustered data (by census tract). I note similar patterns of effects among groups and highlight significant differences by gender. The most consistent predictor of ethnic niche employment across all groups is time-of-arrival. Recently-arrived immigrants are more likely than earlier arrivals to find employment within a niche industry. This effect is more pronounced for women than for men among Mexicans, Salvadorans, and Guatemalans. For Koreans and Guatemalans, enclave residence dampens the assimilating effect of increased tenure in the United States. Women are more likely to be employed in ethnic niche industries than men. The effect is statistically significant for all groups except Koreans and is most pronounced among Chinese and Mexicans (odds of niche employment for women are fifteen times greater than for men). Salvadoran women are seven times more likely than Salvadoran men to be employed in a niche industry, and Guatemalan women four times more likely than Guatemalan men. The effect of living in an immigrant enclave varies by group, by gender, and by cohort, but is statistically significant in some way for all groups. For both Mexican men and women, residence in an enclave is positively associated with niche employment, though the effect is less pronounced for women than for men. The association is also positive for Salvadoran women, but negative for Salvadoran men. Among Koreans and Guatemalans, the effect of enclave residence depends upon time-of-arrival. For those Koreans who live outside of the enclave, increasing tenure in the U.S. is associated with a negative and decreasing likelihood of niche employment. While this trend holds for enclave residents as well, the effect of living in an enclave mitigates the tenure effect; that is, the negative effect of increasing tenure in the U.S. on the probability of niche employment is lessened by enclave residence for all Koreans except those who arrived before 1970. For example, Koreans who arrived between 1970 and 1980 and who live in enclave neighborhoods are more likely than their non-enclave residing counterparts to find employment in an ethnic niche. Similarly among Guatemalans, enclave residence dampens the increasingly negative effect of tenure on the probability of niche employment. Guatemalans differ from Koreans, however, in that the association between enclave residence and niche employment is positive for the oldest Guatemalan arrivals. For this Korean cohort, the association is strongly negative.

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Among the Chinese and Vietnamese, the effect of enclave residence is statistically significant only for select female cohorts. For Chinese women who arrived before 1970, living in an enclave neighborhood doubles the odds of niche employment. Enclave residence also doubles the odds of niche employment for Vietnamese women who arrived between 1980 and 1985. In order to more easily assess the effects of enclave residence by cohort for Chinese and Vietnamese women, Table 5.9 shows predicted probabilities for women from different cohorts who live inside and outside of enclave neighborhoods, but who otherwise share the same statistical traits (speak poor English, live with a spouse or partner of the same national-origin, commute by car; all other means are group centered). While the probability of niche employment tends to decline for earlier cohorts, Chinese women who are the earliest arrivals (pre-1970) and live in an ethnic enclave neighborhood are more likely than all other Chinese women to be employed in an ethnic niche (pr=.38). Similarly, Vietnamese women who arrived between 1980 and 1985 (the peak years of Vietnamese migration) and who live in an enclave neighborhood are most likely among Vietnamese women to be employed in a niche industry. These women likely rely heavily upon residentially-based ethnic employment networks that connect them to ethnic niche jobs. This strong connection between enclave residence and niche employment may indicate a historic residential and labor market isolation that has solidified over time. Geographic accessibility to niche jobs exerts some kind of significant effect on niche employment for all groups except Koreans. Among Salvadorans, a higher level of geographic accessibility to niche jobs is associated with a greater likelihood of niche employment for both men and women. Among the remaining groups, accessibility depends upon gender. For Mexican women, greater geographic accessibility to niche jobs is weakly associated with a higher probability of niche employment. The effect is opposite for Mexican men. Chinese men and women experience a pattern similar to Mexicans. Among Guatemalans and the Vietnamese, both men and women share a positive accessibility effect, though a negative accessibility and gender interaction somewhat mitigates the effect for women. To better understand the effects of increased accessibility on niche employment, I compare predicted probabilities of niche employment at

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the 25th percentile of accessibility (relatively low geographic accessibility to niche jobs) and the 75th percentile of accessibility (relatively high geographic accessibility to niche jobs). Presented in Table 5.10, these probabilities are calculated for recently arrived immigrants (all groups except Koreans, for whom accessibility is not statistically significant) who live in an enclave neighborhood who speak poor English, live with a spouse or partner of the same nationalorigin, and commute by car. All other means are group centered. The difference between the probabilities of niche employment at low and high levels of accessibility is substantive for all groups, except Mexican women. Most striking are the effects of increased accessibility among Chinese women and Vietnamese men. Increasing accessibility from the 25th percentile to the 75th percentile raises the probability of niche employment by nearly ten points for Chinese women (pr=.29 to pr=.38) and by six points for Vietnamese men (pr=.18 to pr=.24). These findings indicate that geographic accessibility matters for immigrant employment outcomes. For some groups (Chinese and Mexicans), geographic accessibility is a more important determinant of niche employment for women than for men, possibly revealing evidence of “spatial entrapment” among these immigrant women (Hanson and Pratt, 1988; England, 1993). Nearby jobs may become female niche jobs partly due to the commuting constraints of women. As immigrant women begin to occupy these jobs due to location initially, ethnic networks may then take hold to further channel immigrant women into these jobs. The cumulative and reinforcing effects of spatial and social accessibility perpetuate the stream of particular applicants to these jobs, and a female immigrant niche evolves. Education is significant and negative for four of the six groups. Increasing levels of education enable immigrants to expand their employment prospects beyond immigrant niche employment. However, the effect of education is not significant for Koreans or the Vietnamese. This may likely reflect the high level of entrepreneurial activity among Koreans and the Vietnamese. Though the selfemployed are excluded from this sample, workers with relatively high levels of education may find employment in these entrepreneurial endeavors. This overlap of an entrepreneurial and labor niche is described by Portes and Jensen (1987) as the “ethnic enclave economy.”

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English language proficiency is significantly associated with niche employment for all groups, though the effect depends upon gender. Among all immigrant women, speaking English well decreases the likelihood of ethnic niche employment to a greater extent than for men. For Chinese and Vietnamese men, better English is positively associated with ethnic niche employment. This may be due to niche employment among these men that requires greater interaction with the public or the wider business community. (As I have dropped the selfemployed from the sample, the greater need of business owners to speak English does not explain this positive association.) Except for the Chinese, all groups share the expected and significant effect of potential work experience. At lower levels of potential work experience, the likelihood of niche employment is positive. At a certain point, the likelihood of niche employment decreases with increasing levels of potential work experience (captured in the quadratic term). Immigrants accumulate employment contacts and labor market knowledge as they gain experience in the labor market, thereby expanding their employment opportunities beyond the constraints of the ethnic niche. The effect is not statistically significant for the Chinese. Household characteristics exert varying influences by group and by gender. Involvement in a couple-relationship (a broader definition than “married”) has a statistically significant effect for Mexicans and the Chinese. Chinese immigrants in a couple-relationship are less likely to be employed in an ethnic niche. Among Mexicans, the effect depends upon gender. Mexican women in a couple-relationship are less likely to be employed in an ethnic niche while Mexican men in a couplerelationship are more likely. The effect of being in a mixed-couplerelationship (not of the same national-origin group) is statistically significant for Mexicans and the Vietnamese. For both groups, being in a relationship with someone from a different national-origin group reduces one’s likelihood of ethnic niche employment. This likely reveals the contact to non-redundant job information obtained through one’s partner of a different ethnicity—though a strong tie, this person is connected to a different set of ethnic networks and, thus, to jobs beyond the individual’s group’s niche industries. For all groups except Salvadorans, the number of adults in the household exerts a significant effect on niche employment. For Guatemalans, Koreans, and the Vietnamese, each additional person in the household increases an individual’s likelihood of niche

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The Geography of Immigrant Labor Markets

employment. This reflects the effect of strong ties in channeling coethnics to similar jobs. For Mexican and Chinese workers, the effect depends upon gender. For both Mexican men and women, the likelihood of niche employment increases with each additional adult in the household, though the effect is less pronounced for women. For Chinese workers, the effect is positive for women but negative for men. This may reflect Chinese women’s greater dependence upon family employment. Carpooling is positively associated with niche employment for all groups except the Vietnamese. While this finding makes intuitive sense—immigrant workers in niche jobs work at the same job sites, facilitating carpooling—the direction of the causal arrow cannot be determined. Do workers choose employment because a carpool is available to them? Does the carpool reflect a set of ethnic employment networks that tie an immigrant to niche employment? Or do workers find employment first and then establish a carpool? Of particular interest is the finding that home-based work is strongly associated with niche employment for the three Latin American groups. For example, 65 percent of all Mexican women who work at home are employed in a niche industry. Though causality cannot be determined, this finding raises important questions about the characteristics of work into which immigrants are segregated. These workers are not “telecommuting” from home. Home-based Mexican, Salvadoran, and Guatemalan workers are primarily engaged in lowwage, exploitative work such as garment piece-work.

Home, Ethnic Networks, and the Link between Residential and Labor Market Segregation In a comparison of local labor markets in Worcester, Massachusetts, to those of unskilled male workers in mid-Victorian London described by Jones (1971), Hanson and Pratt (1992) found striking similarities: labor markets in both places were highly localized and rooted in particular neighborhoods, a result of workers’ short commutes and use of social networks to find work. Hanson and Pratt (1992) concluded, “We have seen in this study of contemporary Worcester that the situation Jones depicted is neither a historical relict [sic] nor a description limited to male working-class life” (p. 403). In late

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twentieth century Los Angeles, a city that differs dramatically from Worcester and mid-Victorian London in many ways, I too have found evidence that immigrants circulate within highly localized labor markets that are rooted in ethnic enclave neighborhoods. Hanson and Pratt (1992) also found that “[s]tructured housing and labor markets are mutually reinforcing” (p. 403). Even in diverse polyglot Los Angeles, my study similarly points to a structured association between residential segregation and labor market segregation. This relationship is fashioned from what are typically understood as social and spatial processes, though both are best understood as socio-spatial processes. My finding that living in an ethnic enclave is generally associated with ethnic niche employment reveals the embeddedness of ethnic employment networks in particular places (such as immigrant enclave neighborhoods), thus highlighting the socio-spatial context in which labor market segregation occurs. Immigrants living in ethnic enclave neighborhoods have ready access to immigrant social networks and the information they carry, such as information about jobs. These are the social networks that give rise to and maintain the ethnic niche. I also find that geographic accessibility to jobs plays an important role in sustaining labor market segregation among immigrants. Excepting Koreans and Chinese and Mexican men, immigrant men and women who live closer to immigrant niche jobs are more likely to be employed in these jobs. In addition, the maps of immigrant employment contained in Chapter 3 reveal a close geographic correspondence between immigrant neighborhoods and places of work, reinforcing the finding in Chapter 4 that immigrants who live in enclave neighborhoods have the shortest commutes. The localized nature of immigrant labor markets in Los Angeles points to socio-spatial processes that are likely at work in generating ethnic niches, in contradiction to the sociologist’s claim that ethnic niches are purely a function of social processes. Jobs may become and remain ethnic niche jobs because of the supply of immigrant workers living nearby, and (“voluntary”) residential segregation may persist because of this locally available supply of jobs. Alternatively, immigrants may remain “stuck” in these niche jobs when moving is difficult due to (“involuntary”) residential segregation.2 In either case, research has shown that employers also play a part in creating these socio-spatial linkages by (re)locating their establishments so as to more readily tap certain ethnic labor pools (Hanson and Pratt, 1992).

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While many scholars have argued for the importance of gender in dividing the labor market, fewer have linked gender to spatially localized processes that divide the labor market (the most important exceptions, again, being Hanson and Pratt, 1988, 1991, 1995; and McClafferty and Preston, 1991, 1992). Because the distinctive characteristics of ethnic networks theoretically could exempt immigrant women from the constraints of geography, I aimed to empirically establish the interplay of networks and space for immigrant women’s employment outcomes. One of my most significant findings is the gendered difference in the severity of labor market segregation. With the exception of Koreans, immigrant women are more likely (for many groups, much more likely) than their male counterparts to find employment in ethnic niche industries. This dual ethnic and gender segregation may be explained partly by women’s greater use of ethnic networks when finding employment. This provides an important caveat to the wider literature on ethnic networks, ethnic niche employment, and immigrant labor markets. While ethnic networks and their connection to ethnic niche employment is a salient fact of immigrant life, it seems women are more likely to participate in this aspect of the immigrant experience and are possibly more dependent upon it. Though gender emerges as an important mediator of the effects of space and place on immigrant employment outcomes, I find that these effects do not exhibit sharply gendered contrasts across all immigrant groups. Geographic accessibility generally has a positive effect on niche employment for both immigrant men and women, with the exception of Mexican and Chinese men. Place-based context appears as important to many immigrant men as immigrant women for their employment outcomes, as living in an ethnic enclave generally tends to be associated with ethnic niche employment for both men and women. In general, however, women who live in ethnic enclave neighborhoods have a higher rate of niche employment than men who also reside in these neighborhoods. Finally, while I find immigrant labor markets rooted in immigrant neighborhoods, they are also rooted in households. Immigrants who live in households with other immigrants are more likely to find employment in niche industries, revealing the additional operation of very strong ties on employment outcomes. This household effect is most pronounced for women among many immigrant groups, likely illustrating that immigrant women’s employment networks are

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characterized by highly specific and hyper-local forms of place-based knowledge. These findings highlight the importance of approaching local labor markets as socially constructed activity spaces that center upon what Sassen (1995) terms the workplace-community/workplace-household nexus. Fundamental to this nexus is the role that residential segregation plays in perpetuating labor market isolation and its deleterious effects among immigrant workers, especially women. The role of the ethnic enclave revealed here is particularly interesting in helping us to understand the dynamics of place-based inequality. Though the benefits of the ethnic enclave are numerous, it is also tied into a set of socio-spatial labor market practices that abet inequality.

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The Geography of Immigrant Labor Markets TABLE 5.2, part 1: Top 5 Industrial Niches for Each Ethnic and Gender Group (ranked by number employed) % group in Avg. CEQ industry wage

Mexican men Landscaping Furniture Agricultural, crops Automobile parking & carwashes Yarn, thread, and fabric mills Mexican women Apparel Personal serv. in private households Agricultural, crops Misc. plastic products Laundry & garment services Salvadoran men Automotive repair Not spec. manufacturing Serv. to buildings Automobile parking & carwashes Bakery products Salvadoran women Personal serv. in private households Serv. to buildings Apparel Laundry & garment services Misc. fabricated textiles Data: 1990 5-percent PUMS

5.8 4.1 5.1 3.9 3.2

4.0 3.0 3.0 10.0 5.5

$6.79 $8.83 $7.75 $5.90 $9.35

6.2 5.3 3.3 3.7 3.8

9.7 5.6 1.9 1.8 1.5

$5.80 $6.03 $7.27 $6.71 $5.85

4.6 3.0 4.1 8.1 3.6

4.0 3.5 2.7 2.1 0.7

$8.14 $6.43 $6.03 $6.06 $6.78

19.9 6.4 7.0 3.1 4.0

22.0 4.2 10.9 1.2 0.7

$5.58 $5.65 $5.83 $6.12 $5.44

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103

TABLE 5.2, part 2: Top 5 Industrial Niches for Each Ethnic and Gender Group (ranked by number employed) % group in CEQ industry Guatemalan men Apparel Automotive repair Service to buildings Furniture Automobile parking & carwashes Guatemalan women Personal serv. in private households Apparel Service to buildings Hotels & motels Misc. fabricated textiles Chinese men Not spec. wholesale trade Engineering Computer manufacturing Service incidental to transpo. Radio, TV, computer stores Chinese women Apparel Banking Service incidental to transpo. Not spec. wholesale trade Accounting & bookkeeping Data: 1990 5-percent PUMS

3.9 4.8 4.6 3.1 7.5

Avg. wage

6.0 $5.68 4.1 $10.64 3.0 $6.34 2.3 $7.68 1.9 $6.78

26.3 4.3 7.3 3.2 3.9

28.0 8.3 4.8 3.4 0.7

$4.71 $5.44 $8.15 $5.88 $4.50

5.2 3.5 3.7 3.0 3.5

2.4 2.4 2.2 2.0 1.4

$18.31 $19.84 $19.87 $14.72 $12.37

5.9 3.5 3.3 5.0 3.1

9.2 $6.00 7.3 $11.57 2.1 $11.90 2.3 $9.89 1.8 $14.27

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The Geography of Immigrant Labor Markets TABLE 5.2, part 3: Top 5 Industrial Niches for Each Ethnic and Gender Group (ranked by number employed) % group in CEQ industry

Korean men Automotive repair Liquor stores Not spec. wholesale trade Religious organizations Laundry & garment services Korean women Apparel Apparel stores Laundry & garment services Beauty shops Jewelry stores Vietnamese men Computer manufacturing Electrical machinery n.e.c., manuf. Machinery n.e.c., manufacturing Not spec. electrical machinery, manuf. Radio, TV equip, manufacturing Vietnamese women Beauty shops Electrical machinery n.e.c., manuf. Medical instruments, manufacturing Not spec. electrical machinery, manuf. Computers, manufacturing Data: 1990 5-percent PUMS

Avg. wage

3.4 20.5 4.7 3.1 3.9

2.9 2.6 2.1 2.0 1.5

$8.88 $13.07 $14.66 $11.58 $14.61

5.5 4.6 4.8 3.8 4.8

8.5 3.6 1.8 1.8 0.7

$7.82 $9.11 $5.49 $7.72 $7.86

3.4 4.6 4.1 5.9 3.4

5.1 5.1 3.6 3.3 1.7

$12.63 $11.87 $11.20 $13.26 $13.58

12.0 4.9 8.6 6.4 3.9

5.6 $7.14 5.4 $9.04 3.6 $8.13 3.6 $8.16 2.4 $13.75

TABLE 5.3: Mean Hourly Wage in Niche and Non-niche Industries Mexicans

Salvadorans

Guatemalans

Chinese

Koreans

Vietnamese

$6.99 ** $8.70

$7.22 $7.88

$17.45 $17.09

$13.07 $15.67

$7.12 ** $5.66

$5.31 ** $7.66

$9.43 ** $13.54

$7.91 ** $14.23

Men 105

Niche $7.41 ** Non-niche $8.93 Women Niche $6.19 ** Non-niche $8.01 Data: 1990 5-percent PUMS *p< .05. ** p< .01, b p<.10

b

$12.23 $13.07 $8.58 ** $10.08

TABLE 5.4: Percent Men/Women Employed in Niche Industries by Enclave Residence

106

Mexicans Salvadorans Guatemalans Chinese Koreans Vietnamese MEN: Inside enclave 18.6 ** 13.6 ** 22.3 ** 10.1 18.4 22.1 Outside enclave 14.9 11.1 14.5 9.6 18.7 20.1 WOMEN: Inside enclave 30.8 ** 48.3 ** 55.3 ** 28.4 ** 20.2 * 30.7 ** Outside enclave 24.0 34.9 41.4 16.9 16.6 26.3 Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p< .05. ** p< .01.

TABLE 5.5: Mean Accessibility to Niche Jobs by Enclave Residence by Gender

107

Mexicans Salvadorans Guatemalans Chinese Koreans Vietnamese MEN: Outside enclave 0.0889 0.1236 0.1285 0.1083 0.1068 0.1037 Inside enclave 0.1002 0.1197 0.1163 0.1200 0.1091 0.1053 WOMEN: Outside enclave 0.1319 0.2599 0.2866 0.1902 0.1708 0.1749 Inside enclave 0.1598 0.2736 0.2489 0.2417 0.1834 0.1749 Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population Note: Higher values indicate higher levels of accessibility.

108

The Geography of Immigrant Labor Markets TABLE 5.6: Regressors Used in Models Variable Definition Dependent: NICHE Probability of ethnic niche employment (1=yes) Neighborhood-level independents: ACCESS Spatial accessibility to niche jobs ENCLAVE Residence in ethnic enclave (1=yes) Household-level independents: COUPLE Living with spouse or partner (1=yes) Spouse or partner is different ethnicity HH_MIX (1=mixed household) LNADULTS Number of adults in household (logged) Total number of children age 18 or younger LNKIDS (logged) Individual-level controls: SEX 0=male, 1=female ED Years of education ENG English language ability (0=not at all, not well; 1=well, very well) WKEXP Potential work experience (age-educ-6) WKEXP2 Quadratic term for WKEXP COH2 Cohort-of-arrival, 1980-85 (1=yes) COH3 Cohort-of-arrival, 1975-80 (1=yes) COH4 Cohort-of-arrival, 1970-75 (1=yes) COH5 Cohort-of-arrival, pre-1970 (1=yes) CAR Drive alone to work (1=yes) CARPOOL Carpool to work (1=yes) BUS Bus to work (1=yes) WALK Walk to work (1=yes) OTHERMODE Other mode to work--bicycle, etc. (1=yes) HOME Work at home Note: Comparison group for cohort-of-arrival is 1985-90

TABLE 5.7, part 1: Means (std. dev.) of Model Regressors Mexicans .3157

Salvadorans .4268

Guatemalans .3966

Chinese .4782

Koreans .4876

Vietnamese .4319

a

a

a

a

a

a

.3346

.3705

.3650

.4230

.3173

.4051

a

a

a

a

a

a

0.1144 0.2036 0.2009 0.1702 0.1438 (0.05427) (0.0888) (0.1010) (0.1008) (0.0786) english 0.4967 0.5016 .5005 0.7896 .6920 (0.4999) (.5) (.5000) (0.4076) (.4617) ed 8.3017 8.7597 8.6719 14.0239 13.6207 (4.3579) (4.3725) (4.4323) (3.9344) (2.9749) wkexp 17.4652 17.5584 17.4754 18.2626 16.7334 (11.7073 (10.8878) (10.8499) (11.869) (11.5201) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population

0.1487 (0.0767) 0.7718 (0.4197) 12.3563 (3.5999) 15.1051 (10.8498)

sex enclave access 109

a

suppressed by the Census Bureau, b cohort 3 for Vietnamese includes all migrants who arrived before 1980

TABLE 5.7, part 2: Means (std. dev.) of Model Regressors Mexicans Salvadorans Guatemalans Chinese Koreans 442.0917 426.8333 423.0949 474.3825 412.6977 (535.0006 (488.9693) (484.3739) (551.297) (485.5463) couple 0.3943 0.3816 0.3618 0.5917 0.5725 (0.4887) (0.4858) (0.4805) (0.4915) (0.4948) hh_mix 0.1179 0.1757 0.1983 0.2402 0.1052 (0.3225 (0.3806) (0.3987) (0.4272) (0.3069) adults 3.9787 3.5522 3.727 4.2785 3.7084 (5.0736 (1.9709) (2.2323) (24.5351) (17.6532) kids 2.2376 1.7340 1.6123 1.6574 1.3639 (2.7746) (1.5086) (1.5046) (13.2816) (9.114) coh1 0.2768 0.2763 0.3787 0.2294 0.2446 (0.4474 (0.4472) (0.4851) (0.4205) (0.4299) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population wkexp2

110

a

Vietnamese 345.8688 (448.4852) 0.3894 (0.4876) 0.0897 (0.2857) 4.7347 (19.0349) 1.9067 (9.9316) 0.1565 (0.3633)

suppressed by the Census Bureau, b cohort 3 for Vietnamese includes all migrants who arrived before 1980

TABLE 5.7, part 3: Means (std. dev.) of Model Regressors

coh2 coh3 111

coh4 coh5

Mexicans 0.2019 (0.4014 0.2007 (0.4006)

Salvadorans 0.4163 (0.493) 0.1914 (0.3934)

Guatemalans 0.3071 (0.4613) 0.1578 (0.3646)

Chinese 0.2789 (0.4485) 0.1848 (0.3882)

Koreans 0.2838 (0.4509) 0.2363 (0.4249)

Vietnamese 0.3832 (0.4862) 0.4603 (0.4985)

0.1671 (0.373

0.0768 (0.2662)

0.0893 (0.2852)

0.1268 (0.3328)

0.1664 (0.3724)

b

0.0689 (0.2533) 0.7214 (0.4483) 0.2053 (0.404)

b

0.1536 0.0393 0.067 0.1799 (0.3605 (0.1942) (0.25) (0.3842) car 0.4836 0.4554 0.4245 0.7233 (0.4997) (0.498) (0.4943) (0.4474) carpool 0.2967 0.2261 0.2179 0.1601 (0.4568) (0.4183) (0.4129) (0.3667) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population a

0.7585 (0.428) 0.1688 (0.3746)

suppressed by the Census Bureau, b cohort 3 for Vietnamese includes all migrants who arrived before 1980

TABLE 5.7, part 4: Means (std. dev.) of Model Regressors Mexicans 0.1172 (0.3216)

Salvadorans 0.2366 (0.425)

Guatemalans 0.2525 (0.4345)

Chinese 0.0554 (0.2287)

Koreans 0.0298 (0.17)

Vietnamese 0.0381 (0.1914)

walk

0.0566 (0.2311)

0.0385 (0.1924)

a

0.03422 (0.1818)

a

0.0155 (0.1235)

othermode

0.0359 (0.1861)

0.0242 (0.1536)

0.0297 (0.1699)

0.0123 (0.1101)

a

0.0134 (0.1151)

home

0.0099 (0.0991)

0.0192 (0.1373)

a

0.0147 (0.1205)

0.0129 (0.1128)

0.0058 (0.076)

[6444]

[7751]

bus

112

[N] [103855] [14058] [7433] [8884] Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population a

suppressed by the Census Bureau, b cohort 3 for Vietnamese includes all migrants who arrived before 1980

TABLE 5.8, part 1: Model Parameters (Std. Errors) Predicting Niche Employment

113

Mexicans Salvadorans Guatemalans Chinese Koreans sex 2.72** 1.8979** 1.4235** 2.7259** -0.0137 (.1062) (.1149) (.1753) (.3116) (.1118) access -0.4431** 0.2034** 0.3673** -0.0998b 0.0539 (.0767) (.0355) (.0748) (.0595) (.0468) a a -0.2385** 0.5608** acc_sex 0.4872** (.0261) (.09) (.0805) enclave 0.3052** -0.0096 0.1012 0.198 -0.4071** (.0836) (.0874) (.0988) (.1413) (.1213) a a -0.0875 enc_sex -0.1643** 0.2169* (.0547) (.1038) (.166) english -0.3726** 0.0559 -0.0226 0.7273** -0.2237* (.0328) (.0758) (.1155) (.1912) (.11) eng_sex -0.3333** -0.5452** -0.4172** -1.4073** -0.5366** (.0423) (.0953) (.1340) (.2048) (.1447) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01, a not statistically significant and dropped, b p<.10

Vietnamese 0.4531b (.262) 0.4269** (.0669) -0.3168** (.0912) 0.0364 (.105) -0.1919 (.1586) 0.3888** (.105) -1.1499** (.1344)

TABLE 5.8, part 2: Model Parameters (Std. Errors) Predicting Niche Employment

ed wkexp wkexp2 114

couple

Mexicans -0.0453** (.0029) 0.0227** (.0028) -0.0002** (.0001) 0.1791** (.0268)

Salvadorans -0.0219* (.0105) 0.0632** (.0077) -0.0009** (.0002) -0.0589 (.051)

Guatemalans -0.0142 (.0116) 0.0412** (.0094) -0.0005* (.0002) -0.0428 (.078)

Chinese -0.0218** (.0064) -0.0034 (.0098) 0.0001 (.0002) 0.1541* (.0768)

a a a -0.1887** (.0416) hh_mix -0.244** -0.1284b -0.1371 0.0205 (.0358) (.0683) (.0905) (.0783) lnadults 0.249** 0.0367 0.1686* -0.3573** (.0338) (.0524) (.0726) (.1002) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01, a not statistically significant and dropped, b p<.10

couple_sex

Koreans -0.0212* (.0097) 0.0327** (.0114) -0.0005** (.0002) -0.1326 (.0816)

Vietnamese -0.0045 (.0092) 0.0854** (.0099) -0.0017** (.0002) 0.0892 (.0692)

a

a

-0.1396 (.1289) 0.2214 (.0683)

-0.4002** (.1226) 0.1296* (.0631)

TABLE 5.8, part 3: Model Parameters (Std. Errors) Predicting Niche Employment

lnadults_sex lnkids lnkids_sex 115

coh2 coh3 coh4 coh5

Mexicans

Salvadorans

Guatemalans

-0.2099** (.0432) 0.0203 (.0229)

a

a

Chinese

Koreans

Vietnamese a

0.0303 (.0642)

0.3446** (.1173) -0.0061 (.065)

a

-0.0503 (.0432)

0.0473 (.0649)

-0.1254* (.0605)

a

a

a

a

a

0.0406 (.0323) -0.0115 (.0393)

-0.116 (.0906) -0.095 (.1133)

-0.2324** (.0819) -0.4655** (.1131)

0.0208 (.0923) -0.0532 (.105)

-0.1139 (.1324) -0.2889* (.1304)

0.2672** (.0833) 0.1144 (.1298) -0.377** (.0894)

-0.1014* (.0412)

-0.4749** (.0992)

-0.3956* (.2051)

-0.1996b (.1124)

-0.4705** (.1729)

-0.1596** -0.3112 -0.7188* -0.3421b -1.1948** (.049) (.2386) (.2813) (.1871) (.3055) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01, a not statistically significant & dropped, b p<.10, d coh3=migrants who arrived before 1980

d

d

TABLE 5.8, part 4: Model Parameters (Std. Errors) Predicting Niche Employment Mexicans

Salvadorans

Guatemalans

Chinese

Koreans

Vietnamese

a

0.3325* (.1519)

-0.1247 (.1568)

116

coh2_sex

-0.1681** (.0528)

-0.3064** (.1146)

a

coh3_sex

-0.323** (.0573)

-0.322* (.1379)

a

a

a

a

coh4_sex

-0.2825** (.0597)

-0.6459* (.2709)

-0.5492* (.2313)

a

0.3001 (.2039)

d

coh5_sex

-0.5506** (.0643)

a

-0.6577* (.2831)

-0.0845 (.2233)

0.8065* (.378)

d

coh2_enclave

a

a

a

a

0.3845* (.1719)

-0.405** (.1464)

coh3_enclave

a

a

0.3586* (.1498)

a

0.489* (.1975)

a

coh4_enclave

a

a

a

a

0.5241* (.243)

d

Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01, a not statistically significant & dropped, b p<.10, d coh3=migrants who arrived before 1980

TABLE 5.8, part 5: Model Parameters (Std. Errors) Predicting Niche Employment

coh5_enclave

Mexicans

Salvadorans

Guatemalans

Chinese Koreans

Vietnamese

a

a

0.8022** (.2636)

a

d

a

a

a

-0.5532b (.2994)

coh2_enc_sex (.2263) d 0.7495* (.3711) carpool 0.543** 0.1671b 0.4599** 0.2027* 0.3327** 0.1166 (.0287) (.0921) (.1103) (.0874) (.0795) (.0757) bus 0.2565** 0.3284** 0.3991** 0.4183** 0.6694** -0.4149b (.0361) (.0603) (.0884) (.1201) (.169) (.1564) walk 0.0756 0.0368 0.7195** 0.091 -0.3555 -1.011** (.0482) (.2226) (.2223) (.1768) (.2648) (.3006) othermode 0.4077** -0.0964 0.337 -0.6067b -0.541 -1.2188* (.0495) (.1666) (.2276) (.3207) (.4294) (.6028) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01, a not statistically significant & dropped, b p<.10, d coh3=migrants who arrived before 1980

coh5_enc_sex

a

117

TABLE 5.8, part 6: Model Parameters (Std. Errors) Predicting Niche Employment Mexicans 1.5111** (.0766)

Salvadorans 1.829** (.1571)

Guatemalans 1.8107** (.187)

Chinese -0.2029 (.2695)

-0.3373** (.0449)

-0.3534** (.1157)

-0.4298** (.1455)

a

a

a

othermode_sex

a

a

a

a

a

1.5261* (.6981)

walk_sex

a

a -0.7748** (.2637) -1.5952** -2.4708** (.2452) (.2988)

a

a

-3.2478** (.1992)

-0.5111* (.2552) -2.1889** (.1741)

-1.3275** (.2388)

-1.3088** (.2491)

[103855]

[14058]

[6444]

[7751]

82.0%

75.9%

home carpool_sex

118

constant

[N]

[7433]

Koreans Vietnamese -0.3293 -0.8651* (.3357) (.3833)

[8884]

Correctly classified 81.1% 79.6% 76.2% 85.2% Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01, a not statistically significant and dropped, b p<.10

TABLE 5.9: Predicted Probability of Niche Employment by Enclave Residence Inside enclave Predicted 95% conf. Interval probability

119

Chinese women 1985-90 1980-85 1975-80 1970-75 pre-1970 Vietnamese women 1985-90 1980-85 pre-1980

Outside enclave Predicted 95% conf. Interval probability

0.3107 0.3152 0.2994 0.2696 0.3836

(.2550, .3724) (.2639, .3714) (.2453, .3597) (.2136, .3341) (.2388, .5525)

0.2875 0.2918 0.2768 0.2484 0.2085

(.2417, .3381) (.2469, .3412) (.2279, .3315) (.2008, .3031) (.1582, .2697)

0.4362 0.5024 0.3467

(.3659, .5091) (.4325, .5722) (.2865, .4121)

0.4747 0.4722 0.3827

(.4081, .5422) (.4067, .5386) (.3249, .4440)

Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population Note: Probabilities estimated for women who speak poor English, live with a spouse or partner of the same national-origin, commute by car; all other means are group centered.

TABLE 5.10: Pred. Probability of Niche Employment by Accessibility Level Low accessibility

High accessibility

Pred. prob. 95% conf. interval Pred. prob. 95% conf. interval Men 0.1964 (.1736, .2213) 0.1557 (.1441, .1681) Women 0.3727 (.3340, .4131) 0.3792 (.3517, .4076) 0.1068 (.0882, .1289) 0.1202 (.0996, .1443) Salvadoran: Men Women 0.5182 (.4723, .5638) 0.5511 (.5055, .5961) 0.1308 (.1056, .1609) 0.1679 (.1362, .2053) Guatemalan: Men Women 0.5051 (.4478, .5623) 0.5308 (.4681, .5926) Men 0.072 (.0486, .1053) 0.066 (.0453, .0951) Chinese: Women 0.2867 (.2324, .3481) 0.3819 (.3203, .4475) 0.1781 (.1423, .2205) 0.2438 (.2013, .2921) Vietnamese: Men Women 0.3217 (.2044, .4668) 0.3445 (.2343, .4744) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population Note: Probabilities estimated for immigrants who speak poor English, live with a spouse or partner of the same national-origin, commute by car; all other means are group centered. Mexican:

120

CHAPTER 6

Connecting Neighborhood and Home to Black and Immigrant Women’s Labor Force Participation

While much attention has focused on the broad structural and political reasons behind women’s increased participation in the labor force, less attention has been directed toward the local context of women’s lives and the interconnected effects of home, neighborhood, and employment location on women’s decision to work. Poverty scholars have positioned neighborhood effects as key determinants of employment outcomes, and feminist geographers have long indicated the importance of local job accessibility for women’s employment. Yet research specific to women’s employment in relation to neighborhood effects lags behind research on men’s employment, and few researchers have directly investigated the effect of employment location on women’s labor force participation. To my knowledge, no research has tested simultaneously the effects of neighborhood context and job location on women’s labor force participation. Further, most research that explores the dramatic rise of women’s labor force participation over the past half-century focuses on women in the aggregate, ignoring key sites of difference, such as race, ethnicity, and nativity. The intersection of women’s employment and immigration—a second, equally dramatic, change in the labor force— demands explication. Comparisons of native-born minority women and immigrant women are particularly significant when investigating the

121

122

The Geography of Immigrant Labor Markets

effects of neighborhood context on women’s labor force participation, as black and immigrant neighborhoods have been theorized to exert radically different influences on employment outcomes. Residents of black “ghetto” neighborhoods are described as both socially and spatially isolated, limiting their opportunities and connections to employment (Massey and Denton 1993; Wilson 1987, 1996). Conversely, immigrant “enclave” neighborhoods are described as rich in social capital, providing residents with ample contacts to employment (Logan, Alba, and Zhang 2002; Massey, Alcaron et al. 1987; Zhou 1992). Geographers have described immigrant neighborhoods as advantageously located near employment opportunities (Scott 1988). This chapter explores the effects of household characteristics, neighborhood context, and spatial job accessibility on low-skill nativeborn black and immigrant women’s labor force participation. The analysis queries the extent to which local place effects matter for women’s employment. Central to the analysis is a concern with the effects of location—where jobs are located in relation to where women live. Does spatial job accessibility impact women’s decision to enter the labor market? Does location matter more for some women than others, such as those with children and stronger ties to home as a result? Additionally, this chapter explores the effects of neighborhood context. Evidence suggests these effects matter, as researchers have described the locally specific nature of women’s lives—particularly women’s utilization of “place-based knowledge” when searching for employment (Hanson and Pratt 1991; Sassen 1995). These effects may impact women differently based upon race, ethnicity, and nativity. Lastly, these effects have been theorized to matter most for lesseducated women, therefore the analysis focuses on low-skill women. Using detailed geographic census data, this chapter models the probability of being employed for low-skill native-born black and immigrant women as a function of household characteristics, neighborhood context, and residential spatial accessibility to low-skill jobs.

Connecting Neighborhood & Home to Labor Force Participation

123

Neighborhood Context and Employment Outcomes Researchers studying poverty demonstrate the difficulties residents of poor, segregated neighborhoods face in gaining employment (Massey and Denton 1993; Wilson 1987, 1996). Inner-city neighborhoods are often located far from suburban manufacturing jobs (Kain 1968; Zax and Kain 1996), and the absence of middle-class role models adversely affects the future prospects of young ghetto residents (Wilson 1987). Massey and Denton (1993) describe a “culture of segregation” wherein a rising concentration of black poverty leads to an environment where joblessness, welfare dependency, and other negative social conditions become the norm. The implication of some of these theoretical claims for women is unclear, and few empirical analyses address gender explicitly in relation to employment outcomes. Johnson, Bienenstock, and Farrell (1999) argue that the “difficulty [encountered in the labor market by female residents of ‘ghetto environments’] is related, at least in part to the fact that the [sic] women are more likely to have truncated social networks, which diminishes their capacity to gain access to resources controlled by larger social networks ” (p. 11). Johnson, Bienenstock, and Farrell (1999) find that Black and Hispanic women whose social networks contain at least one person who resides outside their neighborhood are more likely to be employed than those without a “neighborhood bridge” contact. Neighborhood-bound contacts, then, are less helpful for minority women looking for work, though the analysis of Johnson, Bienenstock, and Farrell (1999) lacks important neighborhood controls (for example, in what types of neighborhoods are neighborhood contacts less beneficial?).1 In the immigration literature, emphasis has also been placed on neighborhood context, but of a different cast. Immigration research points to the important role immigrant neighborhoods play in defining the immigrant experience, particularly economic attainment. Unlike the malevolent ghetto, the immigrant enclave is often championed as a cultural safe-haven—a place that offers employment contacts to the newcomer, affordable housing, linguistic familiarity, culturally specific goods and services, and a set of ethnic networks and relationships that help facilitate the newcomer’s adaptation to the unfamiliar receiving

124

The Geography of Immigrant Labor Markets

society. Though perhaps poverty stricken, the immigrant enclave neighborhood is viewed as a vibrant locale rich in social capital. If immigrant enclave neighborhoods provide abundant employment networks to their residents, then immigrant women who live in such neighborhoods may be more likely to participate in the labor force. Ethnic networks grease the wheels of employment search, thus reducing the friction new entrants often encounter when first looking for work. The probability of labor force participation is likely higher when job search obstacles are essentially eliminated for workers who “fall into” a job through an informal contact, an occurrence that Hanson and Pratt (1991) found common among the women they interviewed about job search methods. The theoretical claims made regarding the immigrant neighborhood differ dramatically, however, from those made within the poverty literature concerning the “ghetto” neighborhood. The former is described in positive terms, as harboring a wealth of social capital made easily available to its residents. The latter is described in negative terms, as isolating its residents from beneficial contacts to employment beyond the tight bounds of the neighborhood. Thus, we would expect neighborhood context to have opposite effects on nativeborn black women and immigrant women. For both native-born minority women and immigrant women, neighborhood context is likely an important influence, whether positive or negative, on their employment decisions. Neighborhood is an environment that shapes both expectations and social networks. In a study of female ghetto residents, Fernandez-Kelly (1995) describes the spatial nature of social networks as follows: Because people derive their knowledge from the physical spaces where they live, they also anticipate that which is probable in their nearby environment, and they recognize as reality that which is defined as such by members of their interpersonal network occupying proximate spheres of intimacy. For that reason, social and cultural capital are toponomical, that is, dependent on physical and social location (p. 215). Key here is the highly local nature of socio-spatial interactions. While Fernandez-Kelly does not explicitly articulate a language of scale, her theoretical arguments implicitly adopt a perspective informed by scale.

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125

Because class, race, and ethnicity shape the range of physical and social locations across which individuals live their daily lives, the scale at which social and cultural capital take their toponomical form differs for different groups. The critical argument of Fernandez-Kelly and other poverty scholars is that poor, ghetto residents live lives embedded in spatially hyper-local contexts. The scale, or scales, at which social and cultural capital take their toponomical form is also a function of gender. Research indicates that women’s lives are more local, in a variety of ways, than men’s. Hanson and Pratt (1991) found that women, when searching for jobs, rely heavily upon information from other women who are not only close friends or family but also who live nearby. Hanson and Pratt (1991, 1995) also found that women prioritize geographic proximity as a condition of paid employment because of their domestic responsibilities, and that women search for work from a residentially fixed location. Nearly all of the women in Hanson and Pratt’s (1991) study found work after moving to their current neighborhood, and several gave up jobs they had prior to moving. Sassen (1995) emphasizes the need to understand women’s social networks as examples of place-based knowledge. She points to research that identifies women’s networks as primarily residentially-based, while men’s networks tend to reach beyond the neighborhood and focus at the workplace.

Neighborhood Location Neighborhood location relative to jobs constitutes an important component of the neighborhood context debate. Analysis of location effects has established its firmest foothold in the research surrounding the spatial mismatch debate. This research explores the extent to which some groups experience negative labor market outcomes as a result of poor spatial accessibility to jobs, such as that created by residential segregation (Holzer 1991; Kain 1968, 2004; Zax and Kain 1996). Though the effect of poor spatial job accessibility has been hotly contested, recent research employing innovative methodology supports spatial mismatch (Brueckner and Zenou 2003; Martin 2004; Mouw 2000; Raphael 1998).2

126

The Geography of Immigrant Labor Markets

Most research, however, has focused on men. Thus, the extent to which spatial mismatch holds for women demands more analysis. A few scholars have addressed the question of whether a spatial mismatch affects women’s employment. McLafferty and Preston (1992) find that African American and Hispanic women engage in significantly longer commutes than their white counterparts in New York City.3 They conclude that this indicates the effects of a spatial mismatch; job opportunities are not located nearby for these minority women. However, they do differentiate between the effects of mismatch for African-Americans and Latinas. Poor spatial job access is greatest for African-Americans, whereas a lack of access to well-paying jobs is the primary problem for Latinas. In a study of the effects of spatial accessibility on unemployment among less-educated women in Los Angeles, Parks (2004) finds that better spatial accessibility to jobs is associated with lower unemployment among native-born black, foreign-born Mexican, and foreign-born Vietnamese women. No association was found for four other immigrant groups. A few studies investigate the effect of spatial job accessibility on women’s labor force participation. Thompson (1997) finds that spatial mismatch has an impact on the labor force participation of women regardless of race, though minority women face higher degrees of mismatch. Allard and Danzinger (2002) find that women who live within greater proximity to job opportunities are more likely to work and have a higher probability of leaving welfare. Feminist geographers have also theorized and tested the effects of neighborhood location on women’s employment outcomes through research addressing the spatial entrapment hypothesis. At its core, the spatial entrapment hypothesis argues that women have limited time available for commuting as a function of household responsibilities. Shorter commutes constrain women’s job search areas, thereby restricting the range of women’s job opportunities. Research has found a relationship between shorter commutes and lower wages, that women often are underemployed as a result of commuting constraints, and that female-dominated jobs tend to be located closer to women’s places of residence (Hanson and Johnston 1985; Hanson and Pratt 1991, 1995). The research of Hanson and Pratt (see particularly 1988) has shown a strong relationship between the spatial distribution of jobs (relative to women’s residence) and occupational sex segregation. Underpinning the spatial mismatch and spatial entrapment hypotheses is the proposition that some groups, such as blacks and

Connecting Neighborhood & Home to Labor Force Participation

127

women, search for work from a fixed-place of residence. As a result, these workers face constraints in finding employment imposed by search costs as postulated by the spatial job search model (Lippman and McCall 1976). All things being equal, workers will opt for jobs closer to home in order to minimize commute costs. The intensity of the search, or how long or thoroughly a worker searches, is determined by equating the marginal costs and benefits of search. Search costs include opportunity costs of postponing employment, time, travel expenses, and other resources necessary to carry out the search (such as daycare expenses for job seekers with children). Thus, the spatial accessibility of local job opportunities may be a critical factor in determining whether or not disadvantaged workers, such as low-skill minority women, participate in the labor force or not.

Spatial Accessibility and Women’s Labor Force Participation While much of the spatial entrapment research has focused on women’s commutes and wages, little has evaluated the relationship between spatial job accessibility and women’s decision to work. Women who live in residential areas that are relatively inaccessible to jobs face a long commute. These women may want to work, but may decide against employment given the commute. Available jobs may not compensate them for a long commute, and/or the demands of household responsibilities may not make a long commute possible. The decision may be further impacted by transportation availability. Some residential areas may be relatively accessible to jobs by car, but not by public transportation. Transit-dependent women may forego employment if they live in such residential neighborhoods, whereas, ceterus paribus, they would choose employment if they lived in a neighborhood with greater transit accessibility to jobs. The effect of spatial job accessibility on the decision to work is a key corollary to the spatial entrapment hypothesis, but one that remains relatively untested at a fine spatial scale. The research that does address the relationship between employment context and women’s labor force participation is hampered by a lack of fine-scale geographic data. Most analyses rely

128

The Geography of Immigrant Labor Markets

upon a mix of individual-level data and MSA-level measures of employment opportunities, such as percentage employment in services (McNabb 1977; South and Xu 1990; Ward and Dale 1992; see Hanson, Kominiak, and Carlin 1997 for a methodological review). Finely scaled spatial data are critical for a test of the effect of employment accessibility on women’s labor force participation, as significant research indicates that distinct intraurban labor markets exist and that individuals search for work at a scale well below the metropolitan level (Hanson and Pratt 1992; Stoll 1999; Stoll and Raphael 2000). In a test of the effects of location on women’s employment outcomes using fine-scaled employment data, Hanson, Kominiak, and Carlin (1997) analyze the relationship between accessibility to jobs in female-dominated occupations on the probability of being employed in a female-dominated occupation. They find that residential location is important for one group of women—college-educated part-time workers with young children. They stress that local context matters differently for different women. Though Hanson, Kominiak, and Carlin (1997) do not find significant effects for other women, they indicate that their results may have been limited by sample size. Hanson, Kominiak, and Carlin (1997) also test the effects of local job availability on unemployment among women. Using discriminant analysis, they test the effect of availability of female-dominated jobs on the probability of being employed versus being unemployed. Their tests do not reveal significant effects, though they indicate that the small sample size of unemployed women likely hinders the analysis. Like Johnson, Bienenstock, and Farrell (1999), however, Hanson, Kominiak, and Carlin (1997) examine the effects of employment accessibility on the likelihood of unemployment, not labor force participation. This chapter’s analysis examines the effects of household characteristics, neighborhood context, and spatial job accessibility on low-skill native-born black and immigrant women’s labor force participation. Central to the analysis is a test of neighborhood context (for example, whether a neighborhood is an immigrant enclave or a black “ghetto”) and neighborhood location in relation to job opportunities. Immigrant enclave neighborhoods may provide immigrant women with ready access to ethnic employment networks. Easy access to information about jobs may increase a woman’s likelihood of employment. Additionally, these neighborhoods may be located near immigrant jobs, thereby increasing the likelihood of

Connecting Neighborhood & Home to Labor Force Participation

129

employment as a function of geographic proximity. If a job is located nearby, a woman may be more inclined to take it given that she can more easily balance commuting time with household responsibilities. Alternatively, black women who reside in poor black neighborhoods may be isolated from beneficial employment contacts, thereby decreasing the likelihood of their labor force participation. These neighborhoods may also be relatively inaccessible to jobs, further reducing the likelihood of participation in the labor force. Household context most likely influences labor force participation for both immigrant and native-born black women as well. Women who live in households with more employed adults have more strong ties (close friends and family) to draw upon for employment information, and children may likely dampen the probability of labor force participation (Granovetter 1973).

Modeling the Effects of Home, Neighborhood, and Accessibility Using detailed geographic census data, I model the probability of labor force participation for low-skill native-born black and immigrant women as a function of neighborhood context, neighborhood location (spatial accessibility), and household characteristics. The logistic regression model takes the following form: logit(P) = b 0 + b1R + biH + bjI

(6.1)

where P is the probability of labor force participation; R is a vector of residential characteristics, including whether the neighborhood is an ethnic enclave or not and the spatial accessibility of the neighborhood to employment; H is a vector of household characteristics; and I is a vector of individual characteristics.4 Interactions between independent variables are tested, but dropped if insignificant. The model is run only on low-skill women (high school education or less) and separately on six immigrant groups and native-born blacks. The immigrant groups chosen are those with the largest low-skill female populations in Los Angeles: Mexicans, Salvadorans, Guatemalans, Chinese, Koreans, and Vietnamese. The model is run also on native-born black women as the

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The Geography of Immigrant Labor Markets

effects of living in a black concentrated neighborhood are theorized in opposition to the effects of living in an immigrant concentrated neighborhood. Theory leads us to expect that the effect of living in a concentrated neighborhood on labor force participation would be negative in the model for native-born blacks and positive in the models for foreign-born women. See Table 6.1 for a list and description of variables used in the models. I limit my sample to low-skill women because education exerts a strong influence on women’s decision to work and provides a “bridge” to networks beyond the neighborhood. For low-skill women with limited labor market opportunities, local contacts (such as those in the household or the neighborhood) may be their primary sources of information about job opportunities and the experience of work in general. Spatial job accessibility likely matters more for low-skill women as well. High-skilled workers are compensated for longer commutes to high-skill, high-wage jobs (Simpson 1987). Low-skill women may decide against employment if available job opportunities require a lengthy and costly (in terms of both time and money) commute. Further, the motivation to work likely varies dramatically between high-and low-skill women. Women’s rising educational attainment has contributed to women’s greater desirability to work. Women with higher levels of education are more likely now than in the past to work and less likely to leave work when they have children (Spain and Bianchi 1997). Conversely, falling household income among less educated married women has contributed to their increased labor force participation. As the wages of their male counterparts have stagnated or declined, these women have moved into the labor force in order to bolster household income and maintain consumption patterns once supported by men’s “family wages” (Fortin 1995; Myers 1985; Spain and Bianchi 1997). I use a residential concentration quotient to identify immigrant enclave neighborhoods as defined in Chapter 3 (Equation 3.1). A tract is identified as an ethnic enclave neighborhood for a group if the concentration quotient is equal to or greater than 5 for that group. The exception is Mexicans, for whom an enclave is defined as greater than or equal to 3 (see the explanation of Equation 3.1 in Chapter 3 for more details). Black concentrated neighborhoods are also those neighborhoods defined as having a concentration of blacks five times greater than their expected share (RCQ >=5).

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If enclaves provide resources in the form of social capital, specifically to ethnic employment networks, then immigrant women who live in immigrant enclave neighborhoods may be more likely to participate in the labor force given easy access to information of jobs. Further, ethnic enclaves may provide jobs to immigrant women in businesses that service the immigrant community (Portes and Jensen 1989). These jobs may not require English and may be much easier to obtain for the immigrant woman than jobs outside the enclave. Such characteristics reduce cultural barriers to employment that may keep immigrant women out of the workforce. Lastly, I include average neighborhood household income to control for class effects. “Ghetto” neighborhoods are poorer neighborhoods, and the underclass debate centers on the dual effects of segregated neighborhoods and concentrated poverty. Therefore, negative neighborhood effects may only take hold in poor black concentrated neighborhoods. Additionally, “cultural” effects captured by the ethnic enclave may depend upon the relative wealth of that enclave. The effect of living in a poor Vietnamese enclave may differ from the effect of living in a wealthier Vietnamese enclave. Employment accessibility For each group, I tested measures of spatial accessibility to different kinds of jobs. These accessibility measures are calculated as described in Appendix 2 (Equation A2.1), but differ by the kinds of job to which accessibility is measured. First, I use a measure of accessibility to all low-skill jobs (jobs held by individuals with a high school education or less).5 This allows for a measure of accessibility to skill-appropriate job opportunities for low-skill workers. Accessibility to high-skill jobs does not provide a true measure of accessibility, as low-skill women cannot qualify for these jobs. Accordingly, high-skill finance jobs in downtown Los Angeles are not counted as spatially “accessible” to low-skill women living nearby in Pico Union or South Central Los Angeles. Second, I employ a measure of accessibility to all low-skill jobs held by women.6 This further refines the accessibility measure to account for sex segregation in the labor market. Women are more likely to qualify for jobs held by other women. Third, I utilize a measure of accessibility to all low-skill jobs held by members of the respondent’s national origin or racial group.7 This refines the measure to account for the ethnic division of labor. Workers are more likely to

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be hired into jobs held by members of the same national-origin group given the operation of immigrant employment networks (Waldinger 1986-87). Fourth, I use a measure of accessibility to all low-skill jobs held by female members of the respondent’s national origin or racial group.8 Narrowing the accessibility measure in this way captures both ethnic and sex segregation in the labor market. Lastly, as a replication of Hanson, Kominiak, and Carlin’s (1997) study, I include a measure of accessibility to all jobs identified as female-dominated occupations.9 Larger values of the accessibility index reflect greater geographic accessibility to employment. If geographic accessibility to jobs influences women’s decision to participate in the labor market, then accessibility should exert an upward pressure on the probability of labor force participation. Household variables Family context provides a critical determinant of women’s labor force participation because of women’s domestic social roles. A number of household factors may influence a women’s decision to exchange time spent on household tasks for outside employment. A key factor is children. The significant responsibility that women bear as primary childcare providers proves a major obstacle to labor force participation, though less than formerly (Apter 1993; Connelly 1992; Leibowitz and Klerman 1995). However, Stier and Tienda (1992) find that young children do not exert a negative effect on Hispanic immigrant wives’ labor force participation. The standard labor force participation explanations may not hold for immigrants, though effects may differ by national-origin group. I include two variables to capture the effect of children: a dummy indicating presence of young children (age three or younger) and a continuous variable providing the count of all children age 18 or younger in the household. The presence of other adults in the household may help offset the costs of entering the labor market for women through an intrahousehold substitution of labor (Osterman 1993; Tienda and Glass 1985; Yoon and Waite 1994.) Other adults may provide childcare (presumably at very little or no cost) and carry out other household tasks such as cooking, cleaning, and shopping. I include two variables to capture these effects. First, I include a count of all adults (age 19 or older) in the household. Second, I include a dummy indicating the presence of an unemployed adult female in the household (femhelp). Given gender-typing of domestic roles, a women is most likely to carry out

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household tasks. An unemployed woman supplies a considerable amount of flexible time to the household that may free other women from household responsibilities, allowing them to enter the workforce more easily. The presence of such female help may be particularly important in the context of immigrant households that tend to include extended family members, such as grandparents and other relatives. I also include a count of all employed adults in the household.10 While the presence of other adults in the household may allow an intrahousehold substitution of labor, employed adults cannot easily absorb additional household production tasks. Additional employed adults may allow a woman to stay home because of the income they provide to the household, offsetting the positive effect of total adults. On the other hand, the presence of other working adults may represent employment contacts that facilitate a woman’s entry into the labor market. These strong ties grease the wheels of jobs search and ease the transition of a woman into the workforce. Finally, I include a dummy indicating whether a woman lives with her spouse or partner. Married women (or by my definition, “partnered” women) tend to have lower rates of labor force participation than their single counterparts (Jacobsen 1998, p. 39). This is primarily due to the income a spouse provides. However, if the earnings of a spouse or partner are not sufficient to support the household, women may enter the workforce out of economic necessity. I include a measure of other household income, excluding the respondent’s wages, to capture this effect. Increased household income most likely exerts a downward pressure on the probability of women’s labor force participation. Individual controls Individual controls include cohort-of-arrival (for immigrants), English language ability (for immigrants), and potential work experience. Years spent in the U.S. likely influence immigrant women’s labor force participation, though the direction of the effect is unclear. Recent migrants may be more likely to work out of economic necessity, though they may also harbor stronger cultural beliefs about the primacy of women’s domestic roles. Long (1980) found a negative effect of time in the U.S. on married immigrant women’s labor force participation. He postulates that their husband’s initial low earnings necessitate these

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The Geography of Immigrant Labor Markets

women’s employment. As their husbands’ earnings increase over time, these women are able to drop out of the work force. English proficiency likely facilitates access to employment, thereby increasing the likelihood of labor force participation for those immigrant women with good English language skills. Bean and Tienda (1987) find that the effect of English proficiency on labor market participation among Hispanics is stronger among women than men. They argue that this is because of the type of jobs that are sex-typed for women—often clerical or other “soft skill” occupations that require personal interaction with customers. Potential work experience is included for both immigrants and nativeborn blacks. Women with more potential experience in the labor market may be more likely to participate in paid employment as their past experience increases their employability and their knowledge of the labor market facilitates their transition into employment. Increased experience also increases a woman’s opportunity cost of staying at home (Mincer and Polachek 1974). Finally, I run separate models by national-origin group and nativity to account for differences along national-origin and ethnic lines. Nelson and Tienda (1985), among others, have argued that nationalorigin is a proxy for distinct modes of immigrant incorporation that reflect such factors as access to resources, context of reception (e.g. state-sanctioned refugee groups or not), home country circumstances of departure, ascribed characteristics, and historical factors that shaped the immigration experience for different groups. Cultural factors also vary along national-origin and ethnic lines.

Findings: The Effects of Home, Neighborhood, and Accessibility These empirical results are based on a sample of women ages 19-64 living in the five-county Los Angeles CMSA drawn from the confidential one-in-six sample of the 1990 U.S. Census of Housing and Population.11 The sample was restricted to women with a high school degree or less schooling, who are not enrolled in school, and who are not living in group quarters. Table 6.2 presents summary statistics of labor force participation rates by national origin group.12 Considerable heterogeneity is present

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among groups. Women from the two Central American groups have the highest rates of labor force participation with 68.4 percent of all Salvadoran women and nearly 64 percent of all Guatemalan women in the labor force. Native-born black women participate in the work force at the next highest level with a rate of 60.7 percent. While African American women have higher rates of labor force participation than whites, they also experience greater unemployment (Jacobsen 1998, p. 460; Spain and Bianchi 1997). This is an important example of labor market inequality to bear in mind, as racial discrimination may influence black women’s decisions to enter the labor force, contributing to the discouraged worker effect. While the focus of this study is on the determinants of labor force participation, not unemployment, the effects of living in a concentrated black neighborhood may be important for black women’s joblessness. Vietnamese and Chinese women participate in the labor market the least, at 50.3 percent and 51.5 percent respectively. While Koreans show the highest rate of labor force participation among Asian immigrants (57.1 percent), this group sits at the middle of the distribution of all groups. Mexican women participate in the work force at a much lower level than their Central American counterparts, with a participation rate fourteen points lower than Salvadorans. Among women with young children, Koreans have the lowest participation rate overall (36.5 percent). Koreans also exhibit the greatest drop-off of participation for women with young children from the average—a difference of more than 20 percentage points. The next largest drop-off is among native-born blacks. The participation rate for native-born black women with young children falls 15 points below their average rate, from 60.7 to 46 percent. The gap is smallest among Mexican women, dropping approximately six points below the average for women with young children. Even with young women, Salvadoran and Guatemalan women continue their very high participation levels, at 59.4 percent and 54.4 percent respectively. For all immigrant groups, labor force participation rates increase with time spent in the U.S. This is likely due to women’s acclimation to the expectations of the U.S. labor market and perhaps due to changes in traditional attitudes about women’s domestic and economic roles in the family. However, for all groups except Salvadorans, the labor force participation rate drops for the cohort that arrived before 1975.13 This is

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most likely explained by an age effect. Workers in this group are older and entering retirement. Comparisons of the models using the five different accessibility measures reveal that the effect of accessibility either does not exert a significant effect regardless of the type of accessibility measure used or, if the effect of accessibility is significant, accessibility depends upon access to group- and gender-specific jobs. Separate models were run on each group to test the sensitivity of the effect of accessibility when measured to different types of jobs. Five different models were run, each substituting in a measure of accessibility to the following types of jobs: all low-skill jobs, all low-skill jobs held by women, all low-skill jobs held by a member of the respondent’s national-origin group, all low-skill jobs held by a female member of the respondent’s national-origin group, and all jobs typed as a female-dominated occupation. The results of the significance of the accessibility coefficient in each model are summarized in Table 6.3. No accessibility measure is statistically significant for the Mexican, Korean, Vietnamese, or nativeborn black models. For the remaining groups (Chinese, Salvadorans, and Guatemalans), the model results appear to reflect the effects of the ethnic and gender division of labor. Although all the accessibility measures are restricted to skill-appropriate jobs, this seems to matter less than who holds the jobs to which accessibility is measured. For Chinese, Salvadoran, and Guatemalan women, higher levels of accessibility to jobs held by members of their own group increases the likelihood of labor force participation. This likely reflects the ethnic division of labor. Jobs are easier to obtain when they have already been “typed” for an individual’s ethnic and/or national-origin group, reflecting the constitutive processes of ethnic employment networks, employer preferences, and discrimination. The level of significance for the accessibility effect increases slightly from the model using accessibility to all group-specific jobs to the model using accessibility to all group- and gender-specific jobs for all groups. This result seems to capture the simultaneous effects of the ethnic and gender division of labor. Accessibility to jobs held by female members of their national-origin group increases the likelihood of labor force participation for Chinese, Salvadoran, and Guatemalan women. I report the coefficients from the group models using this measure of accessibility in Table 6.4.14

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To better understand the effects of increased accessibility on the labor force participation of Chinese, Salvadoran, and Guatemalan women, I compare predicted probabilities of labor force participation at the 25th percentile of accessibility (relatively low geographic accessibility to jobs) and the 75th percentile of accessibility (relatively high geographic accessibility to jobs). Presented in Table 6.5, these probabilities are calculated for recently arrived immigrant women who live in an enclave neighborhood who speak poor English, live with a spouse or partner, have young children, and have no female help available in the household. All other means are group centered. Though the effect is statistically significant, increased accessibility raises the probability of labor force participation by only one point for these ideal types of Salvadoran and Guatemalan women. For this ideal female Chinese type, however, increased accessibility raises the probability of labor force participation by nearly four points (from pr=0.22 to pr=0.26). The effect of living in an immigrant enclave neighborhood or black concentrated neighborhood (for native-born black women) is a statistically significant effect for Mexican, Salvadoran, and Vietnamese women. For Mexican women, living in an immigrant enclave neighborhood increases their likelihood of participating in the labor force. For both Salvadoran and Vietnamese women, the effect of living in an enclave neighborhood depends upon the average wealth of the neighborhood. Among Salvadorans, increasing levels of average neighborhood income mitigate the positive enclave effect. Salvadoran women who live in wealthier enclave neighborhoods are less likely to participate in the labor market than their counterparts in poorer enclave neighborhoods. The effects are opposite for Vietnamese women. Residents of wealthier Vietnamese enclave neighborhoods are more likely to participate in the labor force than their counterparts who live in poorer Vietnamese enclave neighborhoods. In order to more easily compare the effect of enclave residence on labor force participation, I predict probabilities for Mexican, Salvadoran, and Vietnamese women who live inside and outside of immigrant enclave neighborhoods. Presented in Table 6.6, these probabilities are calculated for recently arrived immigrant women who speak poor English, live with a spouse or partner, have young children, and have no female help available in the household. All other means are group centered. At average levels of neighborhood income, the

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probability of participating in the workforce is three points higher for Mexican women who live in enclave neighborhoods compared to Mexican women who live outside of such neighborhoods. Salvadoran women who live in enclave neighborhoods also have a higher probability of labor force participation than their counterparts outside the enclave, while Vietnamese enclave residents have a slightly lower probability of workforce participation than non-enclave residents. For both Salvadoran and Vietnamese women, however, the effect of enclave residence depends upon the average wealth of the neighborhood. To capture this interaction effect, I predict the probability of labor force participation for enclave residents in both relatively poorer enclaves and relatively wealthier enclaves (at the 25th and 75th percentiles of average neighborhood income, respectively). These probabilities are reported in Table 6.7. Among Vietnamese women who live in poorer enclave neighborhoods, the probability of labor force participation is approximately 0.21. This probability rises to 0.27 for Vietnamese women who live in relatively wealthier neighborhoods. The effect is reversed for Salvadoran enclave residents, though less pronounced. Salvadoran women who live in relatively poor enclave neighborhoods have a higher probability of participation (pr=0.57) than their counterparts in wealthier neighborhoods (pr=0.54). In addition to the models for Salvadoran and Vietnamese women, average neighborhood income is statistically significant for Mexican, Guatemalans, and native-born blacks. In these three cases, the effect of tract income is positive. That is, women of these groups who live in better-off neighborhoods are more likely to participate in the workforce. The effects of household and family context are generally consistent with prior expectations and other research on women’s labor supply. For all groups, living with a spouse or partner reduces the probability of labor force participation, as do the number of children, presence of young children, and total adults in the household. Of particular interest is the effect of additional employed adults in the household. For all groups, each additional employed adult exerts a strong and statistically significant positive effect on the probability of women’s labor force participation. The presence of a non-working adult female in the household exerts a statistically significant and positive effect on the probability of working for women with children. This interaction effect is significant for all groups except for the

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139

Chinese. In this case, the presence of a non-working female in the household increases a woman’s likelihood of employment regardless of the presence of children or not. Lastly, other household income is negative and statistically significant for all groups except the Chinese and Guatemalans. As other household income rises, women are less likely to work. For all groups, the effect of potential work experience is positive and statistically significant. Among immigrants, the effect of English language proficiency is positive and statistically significant for all groups except Koreans and Salvadorans. For Koreans, this finding may reflect the high rate of employment of Korean women in family businesses where English is not a necessary prerequisite for employment. For Salvadorans, the lack of an effect may be due to these women’s high rate of segregation into jobs where they work with other Latino immigrants and Spanish is the primary language spoken on the job. Among the Latino immigrant groups and the Vietnamese, the effect of time in the U.S. is statistically significant and linearly related to the probability of labor force participation. As women spend more time in the U.S. and become more acclimated to the U.S. labor market, they tend to participate in the workforce more. This is at odds with Long’s (1980) finding of a negative effect of time in the U.S. on the labor force participation of married Hispanic women. Further, Stier and Tienda (1992) found that recently arrived Mexican wives were more apt to participate in paid employment than earlier arrivals. The differences between these and my results may be due to two factors. First, my sample is not restricted to wives. Second, my sample examines “recent arrivals” from a later wave of immigration than either of the above-mentioned studies, as my data are from 1990 compared to 1980 or 1970. These later migration streams of women may be characterized primarily by the pull factor of family reunification rather than economic migration.15 The cohort effects among Chinese and Korean women are statistically significant though no clear pattern emerges for these effects. For Chinese women, all later arrivals are more likely to participate in the workforce than the most recently arrived (1985-90), though the effect of time in the U.S. declines and then rises again for women who arrived before 1970. Among Koreans, all later arrivals are also more likely to participate in the workforce than the most recently

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arrived (1985-90), though the effect does not increase linearly with time in the U.S.

The Significance of Local Place Effects on Women’s Employment In this chapter, I examine the effects of home, neighborhood, and employment location on immigrant and native-born black women’s labor force participation. My key task was to test simultaneously, but independently, the effects of a neighborhood’s geographic accessibility to jobs and its social attributes. Comparisons of native-born black women and immigrant women are of particular interest, since nativeborn blacks and immigrants have been contrasted on the capacities of their social networks and the social characteristics of their neighborhoods. In brief, immigrant enclave neighborhoods have been described as beneficial while the effects of black “ghetto” neighborhoods have been understood as deleterious. I find that the significance and direction of neighborhood effects differs for different groups. While the effect of living in a black concentrated neighborhood is negative in the models for native-born black women, the effect is insignificant. This finding casts some doubt on the theorized relationship between black concentration effects at the neighborhood level and the labor force participation of low-skill black women. Living in an immigrant enclave neighborhood, however, influences some immigrant women’s labor force participation. For Mexican women, living in an immigrant enclave neighborhood increases their likelihood of participating in the labor force. For both Salvadoran and Vietnamese women, the effect of living in an enclave neighborhood depends upon the average wealth of the neighborhood. Salvadoran women living in poorer enclave neighborhoods are more likely to be in the workforce than their counterparts living in wealthier enclaves, while the effect is the opposite for Vietnamese women. Geographic accessibility to employment also matters differently for different groups. No evidence indicating a spatial mismatch effect on the probability of labor force participation for native-born black women was found. Bringing jobs closer to these women’s home will not necessarily increase their likelihood of entering the workforce. The

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structural relationships of the household are more important in determining native-born black women’s probability of entering the workforce than neighborhood or job location effects. Conversely, geographic accessibility was positively related to Chinese, Salvadoran, and Guatemalan women’s labor force participation—living close to jobs increased their likelihood of entering the workforce. Importantly, however, the types of jobs that are geographically accessible matter. For these women, accessibility to skill-appropriate jobs held by other women of their national-origin group matters most. This reflects the ethnic and gender division of labor. A Salvadoran woman, for example, is more likely to find employment in a job that has been typed a “Salvadoran woman’s job” through ethnic networks, employer preferences, and discrimination. While the effects of neighborhood context and location are mixed, this study bears out the importance of local place effects for women’s employment outcomes. For all women in this study, the hyper-local effects of home are paramount in determining whether they participate in the workforce or not. Among all women, the number of employed adults exerts a strong upward pressure on women’s probability of labor force participation. I speculate that this reflects in part the role that strong ties and place-based knowledge play in connecting women to employment. Women who live in households with other employed adults have access to a larger set of employment contacts that may help facilitate their entry into the workforce. Other household effects are also surprisingly consistent among groups. Particularly noteworthy is the finding that the presence of a non-working adult woman in the household increases the likelihood of labor force participation for women with children. This most likely reflects an intrahousehold substitution of labor, allowing women to trade time spent on household production for employment. Why neighborhood effects differ between groups necessitates further explanation, as do the differences by national-origin group and ethnicity in labor force participation described in Table 6.2. Labor market theory is silent on the latter, while sociological explanations of neighborhood effects, especially those of ethnic enclaves, do not hold for all groups in this study. Fully accounting for these group differences is beyond the scope of this study, as this task demands information that reflects ethnic differences in cultural factors, such as attitudes about women’s domestic and economic roles in the household,

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and ethnic differences in the employment networks available to residents of different ethnic enclaves. Further, differences in the labor force participation rates of women likely stem from disparities in social and economic resources between groups. This presents an important direction for future research.

Connecting Neighborhood and Home to Labor Force Participation 143 TABLE 6.1: Regressors Used in Models Variable Dependent: LFP

Definition Probability of labor force participation

Neighborhood-level independents: ACCESS Spatial accessibility to jobs of type k ENCLAVE Residence in ethnic enclave or concentrated black neighborhood (1=yes) TRACTINC Average household income in neighborhood tract ($1,000's) Household-level independents: COUPLE Living with spouse or partner (1=yes) LNKIDS Total children age 18 or younger (logged) KIDS3 Presence of children age 3 or younger (1=yes) FEMHELP Presence of nonworking female in household other than respondent (1=yes) LNADULTS Number of adults in household (logged) LNEMPADULTS Number of employed adults in household (logged) OTHINC Other household income, excluding respondent's wages ($1000's) Individual-level controls: COH2a COH3

Cohort-of-arrival, 1980-85 (1=yes); immigs Cohort-of-arrival, 1975-80 (1=yes); immigs

COH4b COH5 ENG WKEXP WKEXP2

Cohort-of-arrival, 1970-75 (1=yes); immigs Cohort-of-arrival, pre-1970 (1=yes); immigs English ability (0=poor, 1=good); immigs Potential work experience (age-educ-6) Quadratic term for WKEXP

a

Comparison group for cohort-of-arrival is 1985-90; As so few Vietnamese migrated to the U.S. before 1975, only three Vietnamese cohorts are defined: 1985-90, 1980-85, and pre-1980.

b

TABLE 6.2: Labor Force Participation Rates

Mexicans 54.5

Chinese 51.5

Koreans 57.1

Salvadorans 68.4

Guatemalans 63.9

Vietnamese 50.3

NB Blacks 60.7

48.0

41.1

36.5

59.4

54.4

41.3

45.9

48.0 55.7 57.7 56.7 55.3

43.6 53.3 59.3 56.1 50.3

46.3 59.2 65.7 63.9 51.7

65.3 67.8 69.5 73.5 70.2

61.1 65.2 66.3 65.0 64.6

41.8 47.5 61.0 a

b

b

b

48.1

58.7

[N] [54236] [2591] [2765] [7660] [3880] Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population Note: a For the purposes of this table, the Census Bureau requested that I collapse the 1970-75 and pre-1970 cohort groups used in the models into one pre-1975 cohort; b not applicable for the group.

[3205]

[13067]

Labor force participation rate

144

LFP with young children (3 years or younger) LFP by time-of-arrival:a 1985-90 1980-85 1975-80 Pre-1975 LFP in enclave neighborhood

b b

TABLE 6.3: Statistical Significance of Accessibility Measures in Group Models

145

Accessibility to: All low-skill jobs All female low-skill jobs All group low-skill jobs All female group low-skill jobs Female-dominated occupations a

Mexicans Chinese Koreans

Salvadorans Guatemalans Vietnamese NB Blacks

n.s.a n.s. n.s. n.s. n.s.

n.s. n.s. sig.b sig.b n.s.

n.s. sig.b sig.c sig.c sig.c

n.s. n.s. n.s. n.s. n.s.

n.s.=not significant; b significant at the .05 level; c significant at the .01 level.

n.s. sig.c sig.b sig.b sig.b

n.s. n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s. n.s.

TABLE 6.4, part 1 Model Parameters (Standard Errors) of Effects on Women's Labor Force Participation Salvadorans Guatemalans Vietnamese Native-Born Black 0.0682* 0.0995* 0.0009 0.0185 (.0328) (.0456) (.0749) (.0287) enclave 0.7841* 0.0965 -0.8441* -0.0271 (.2745) (.0988) (.3345) (.0583) tinc 0.009** 0.0082** 0.0077 0.0086** (.0025) (.003) (.0047) (.0024) enc*tinc -0.0197* b 0.0179* b (.0092) (.0073) coh2 0.5594** 0.5542** 0.5337** 0.4046** 0.3484** 0.4408** d (.0357) (.1392) (.1371) (.0797) (.1096) (.1139) coh3 0.7811** 0.5427** 0.6931** 0.4825** 0.504** 0.7112** d (.0343) (.1692) (.1674) (.0987) (.1399) (.1234) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01 a access to all low-skill jobs held by female members of the group, b not significant, c p<.10, d not applicable.

accessa

146

Mexicans -0.0141 (.0367) 0.1472** (.0376) 0.0057** (.0017) b

Chinese 0.2075* (.0692) -0.1826 (.1244) -0.0077 (.0042) b

Koreans 0.0155 (.0742) -0.1694 (.1193) -0.0029 (.0026) b

TABLE 6.4, part 2 Model Parameters (Standard Errors) of Effects on Women's Labor Force Participation

147

Mexicans Chinese Koreans Salvadorans Guatemalans coh4 0.8997** 0.5073* 0.6498** 0.7334** 0.2146 (.0392) (.2190) (.1856) (.1199) (.1491) coh5 0.9226** 0.6570** 0.4829 0.8994** 0.7618** (.0458) (.2414) (.2618) (.168) (.199) eng 0.2921** 0.5881** -0.0178 0.1036 0.3071** (.0259) (.1303) (.1247) (.0695) (.0936) wkexp 0.0378** 0.0287 0.087** 0.0652** 0.0527** (.0034) (.0215) (.0209) (.009) (.0134) wkexp2 -0.0011** -0.0009** -0.0024** -0.0016** -0.0012** (.0001) (.0003) (.0004) (.0002) (.0003) Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01 b not significant, c p<.10, d not applicable.

Vietnamese d

Native-Born Black d

d

d

0.6618** (.1002) 0.0672** (.0163) -0.002** (.0003)

d 0.0594** (.0078) -0.0015** (.0002)

TABLE 6.4, part 3 Model Parameters (Standard Errors) of Effects on Women's Labor Force Participation

148

Mexicans Chinese Koreans Salvadorans Guatemalans couple -0.956** 0.0879 -0.2535 -0.8786** -1.005** (.0256) (.1214) (.156) (.0634) (.085) lnkids -0.3138** -0.3096** -0.5948** -0.4907** -0.5415** (.0240) (.1049) (.1259) (.0761) (.0969) kids3 -0.3378** -0.4892** -0.7914** -0.426** -0.364** (.0236) (.1505) (.1464) (.0642) (.0902) lnadults -2.6819** -2.9873** -3.7884** -2.2366** -2.5382** (.0808) (.2782) (.2673) (.1661) (.2023) lnempad 4.7769** 5.6447** 6.6982** 3.9084** 4.2716** (.0958) (.3980) (.3370) (.1699) -0.2421 Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01 b not significant, c p<.10, d not applicable.

Vietnamese -0.4873** (.1069) -0.9532** (.1096) -0.4697** (.1108) -1.7166** (.1945) 3.8432** (.2298)

Native-Born Black -0.3859** (.0612) -0.587** (.0609) -0.2807** (.0647) -1.5016** (.0988) 4.3364** (.1021)

TABLE 6.4, part 4 Model Parameters (Standard Errors) of Effects on Women's Labor Force Participation

femhelp kids*femhelp othinc 149

constant

Mexicans Chinese Koreans Salvadorans Guatemalans Vietnamese -0.3077** 0.3386* 0.1144 -0.8623** -0.7245** -0.1852 (.0639) (.1413) (.2111) (.1469) (.195) (.1945) 0.4143* 0.1387** b 0.6583** 0.4085** 0.3356c (.0455) (.2404) (.1232) (.1746) (.1722) -0.018** -0.0118 -0.0079** -0.003** -0.0033 -0.0121** (.0017) (.0079) (.002) (.001) (.0021) (.0036) -1.4581** -1.7735** -2.1536** -0.8747** -0.9144** -1.2641** (.0973) (.4401) (.3642) (.1884) (.2562) (.3369)

N 54236 2591 2765 7660 Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population *p<.05, **p<.01 b not significant, c p<.10, d not applicable.

3880

3205

Native-Born Black -0.2986** (.1073) 0.2785** (.1066) -0.0231** (.0022) -0.7727** (.1321) 13067

TABLE 6.5 Predicted Probabilities of Labor Force Participation by Accessibility Level Chinese Salvadorans Guatemalans Pred prob 95% conf. interval Pred prob 95% conf. interval Pred prob 95% conf. interval

150

Low accessibility High accessibility

0.2176

(0.1542, 0.3114)

0.5479

(0.4958, 0.5989)

0.4933

(0.4297, 0.5572)

0.2550

(0.1870, 0.3375)

0.5580

(0.5065, 0.6083)

0.5094

(0.4466, 0.5719)

Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population Note: Probabilities estimated for recently-arrived (1985-90) women who speak poor English, live in an enclave neighborhood with a spouse or partner, have young children, and have no female help available in the household; all other means are group centered.

TABLE 6.6 Predicted Probabilities of Labor Force Participation by Enclave Residence Mexicans

Salvadorans

Vietnamese

151

Pred. prob. 95% conf. interval Pred. prob. 95% conf. interval Pred. prob. 95% conf. interval Inside enclave Outside enclave

0.3056

(0.2852, 0.3268)

0.5496

(0.4977, 0.6004)

0.2456

(0.1920, 0.3085)

0.2753

(0.2609, 0.2902)

0.5263

(0.4834, 0.5689)

0.2539

(0.1962, 0.3219)

Data: Confidential 1-in-6 sample of the 1990 U.S. Census of Housing and Population Note: Probabilities estimated for recently-arrived (1985-90) women who speak poor English, live with a spouse or partner, have young children, and have no female help available in the household; all other means are group centered.

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CHAPTER 7

Gendering the Nexus of Work, Residence, Networks, and Urban Form

The collective results of this study point to the importance of home, neighborhood, and geography in shaping the local labor markets of Los Angeles’s immigrant community. Gender manifests as a significant mediator of space, place, and employment outcomes, though the reciprocal relationship between residential and labor market segregation holds for immigrant men and women. Both spatial and social accessibility matter in connecting immigrants to jobs, indicating that immigrant local labor market processes need to be conceived as socio-spatial processes. Our knowledge of immigrant labor markets gleaned from sociological studies must be matched by geographic research that situates these processes in space and place. By examining both the effects of spatial and social accessibility together, we can begin to decipher the multiple forces that shape and give rise to urban form.

Bringing Gender and Networks into an Understanding of Urban Form While the appeal of the power of social networks to overcome the constraints of space (especially considering the initial transnational migration journey of the immigrant) in the job matching process has proven nearly irresistible to many sociologists, the results of this study indicate that caution is in order. This study finds strong support for the 153

154

The Geography of Immigrant Labor Markets

importance of geography in creating and sustaining labor market segregation among immigrants and points to a reciprocal and reinforcing relationship between ethnic residential segregation and ethnic labor market segregation. Space does matter, though it is strongly conditioned by social networks. Conversely, social networks appear strongly conditioned by space and place. The city functions neither as a pure Granovetterian system nor as a rigid spatial cost model. The relationships between job and worker, employment site and neighborhood, evolve from the interplay of production and social production, spatial and social propinquity. These relationships may be viewed as contributing to the evolution of urban form in the immigrant metropolis. At once a site of production and social reproduction, the geography of the city may be explained through an evolutionary process driven by three interlocking conditions: employment, residence, and recruitment networks. The salience of the ethnic division of labor and the high degree of ethnic residential segregation are related processes, each giving shape to the other. The interplay of these processes in space gives rise to the dimensions of the immigrant urban landscape, connecting nodes of immigrant neighborhoods to nodes of immigrant employment sites. Propinquity, however, may neither be a necessary nor sufficient condition of this system. Geographic proximity may initially establish a connection between immigrant neighborhood and immigrant employment site, but ethnic recruitment networks maintain and sustain the connection. These networks are the ties that bind immigrant neighborhoods to immigrant employment sites. As a result, however, geographic propinquity between work and home is maintained as a defining feature of the immigrant urban landscape. This study bears this fact out, especially for residents of ethnic enclave neighborhoods. The ethnic enclave neighborhood serves as a pivotal feature of the immigrant metropolis. While the ethnic enclave neighborhood serves as a cultural safe-haven for the recent arrival, it also provides a locally proximate supply of labor to particular industries and firms. Whether immigrants initially locate in close proximity to these employment sites, or whether these firms seek out locations near the residence a preferred immigrant labor force, this mutual co-location takes hold and may set into motion a mutually reinforcing co-location process. Once the match takes place, ethnic recruitment networks connecting these employment locations to these places of residence deepen and develop, “locking in” the employment match between certain kinds of jobs and

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155

certain groups of workers. While geography facilitates this employment match and nudges it along a particular path, lock-in probably does not take hold through geography alone. In fact, geography may only provide the conditions through which the initial, somewhat accidental, matching of workers to firms occurs. Once the match takes place, ethnic recruitment networks may likely solidify and perpetuate the relationship between firm, industry, group, and neighborhood. Ethnic recruitment networks reinforce the ethnic division of labor by channeling co-ethnics into similar jobs and the same workplaces (Granovetter 1974; Tilly 1990). Immigrants rely upon social networks to find employment like other workers, though the literature describes ethnic networks as qualitatively different (Portes 1998). Immigrant networks are tightly bound networks, governed by the norms of a larger community. Expectations of reciprocity are high, maintaining and deepening network relationships. In this way, connections between immigrant employment sites and immigrant neighborhoods can be particularly strong, locking in a job matching process along a path dependent trajectory. The inertia of the system propels it forward, resilient to most disruptions short of radical demographic change or production re-organization. In some cases, however, the inexorable force of the ethnic and gender division of labor and the social networks that enforce it can override geographic friction and connect immigrants to jobs far from their homes. The classic example presented in this study is that of female Central American domestic workers. These women, with few skills and poor English, find work in the private homes of the middle and upper classes. The high rates of residential segregation in an urban metropolis such as Los Angeles dictate that these women’s places of work—typically the homes of well-off whites—are located far from these women’s places of residence—the immigrant enclave neighborhood. As a result, these women engage in long commutes to jobs that do not, in economic terms, compensate for such long commutes. This is a story, however, of the most marginalized workers searching for work within a highly segmented labor market. Few jobs are available to these women, so they take whatever they can find. Further, the incredibly strong gravitational pull of social networks within these networks serves to overcome the friction of geography.

156

The Geography of Immigrant Labor Markets

Place, however, plays an important role in this job matching process. These immigrant women know of these jobs not because they are nearby, but because an individual who knows about the job is located nearby. In this way, ethnic recruitment networks are rooted in neighborhoods and serve as a form of place-based knowledge. As demonstrated in Chapter 5, immigrants who live in ethnic enclave neighborhoods are employed at a significantly higher rate in ethnic niche jobs than their non-enclave counterparts. This is particularly true for immigrant women, especially Central Americans. Among all immigrants, Salvadoran and Guatemalan women who live in enclave neighborhoods have the highest proportional rates of niche employment. Geography, then, matters in the guise of place and the socio-spatial embodiment of social networks. The evidence of this study points to the importance of the interplay of space, place, and social networks for two immigrant groups in particular: recent arrivals and immigrant women. Recent arrivals are more likely to live in ethnic enclave neighborhoods, to be employed in ethnic niche jobs, and to have shorter commutes than earlier arrivals. For these immigrants, the geographic connections between employment and residence are close. While it is this spatial pull that likely initiates and facilitates this match, ethnic recruitment networks bind the connection. These workers depend upon ethnic networks and informal recruitment among firms, thus further strengthening the employment, residence, and network connection. Gender proves a salient feature of the immigrant urban landscape. Immigrant women’s experiences as women in the labor market and as women at home and the multiple gendered expectations and discriminations they face in both spheres strongly shape their urban geographies. A key defining feature of immigrant women’s labor market experience is their high propensity to concentrate, or niche, in a limited number of industries. For most immigrant groups, women niche to a significantly higher degree than immigrant men. This may arise from a number of factors, including spatial influences. First, as women, immigrant women face a labor market already acutely segregated by gender. This fact limits the number of viable job opportunities available to these women. They enter a labor market already tending toward the over-concentration of women in gendertyped jobs. Second, the use of ethnic networks and of strong ties is stronger for immigrant women than for men. This phenomenon, layered upon an already gender-segregated labor market, further limits

Work, Residence, Networks, and Urban Form

157

the scope of immigrant women’s labor market experience by channeling them into both gender- and ethnic-specific jobs. Third, this study finds that geographic accessibility matters for immigrant women’s niche employment. For some immigrant groups, geographic accessibility is a more important determinant of niche employment for women than for men, possibly revealing evidence of spatial entrapment among these immigrant women. Nearby jobs may become female niche jobs partly due to the commuting constraints of women (resulting from the demands of household responsibilities). As immigrant women begin to occupy these jobs due to location initially, ethnic networks may then take hold to further channel immigrant women into these jobs. Given the choice of two nearby jobs, ethnic networks make one more obtainable than the other. Social and spatial connections work in concert to mutually lock in this flow of immigrant female workers. Thus, a female immigrant niche evolves. This research illustrates how the gendered landscape of the immigrant labor market cumulatively builds from individual, to household, to neighborhood, to city. The shape of the labor market represents the coalescence of these multiple scales. Women’s domestic roles at home strongly condition their labor market experiences, influencing their initial decision to work to what kinds of jobs they take and where. Immigrant women have shorter commutes than immigrant men, evidence of spatial entrapment among these women. The demands of home likely reduce the time available for commuting, limiting the spatial area over which immigrant women search for work. The number of adults in the household significantly increases their probability of finding work in an immigrant niche job, indicating immigrant women’s use of job networks characterized by strong ties and hyper-local contacts. Lastly, the relationship between enclave neighborhood residence and immigrant niche employment is strong for both immigrant men and women, but stronger among women for many immigrant groups. These household and neighborhood effects likely indicate that immigrant women’s employment networks are characterized by highly specific and hyper-local forms of place-based knowledge. These findings also highlight the importance of approaching local labor markets as socially constructed activity spaces that center upon what Sassen (1995) terms the workplace-community/workplacehousehold nexus. This analytical approach requires consideration of

158

The Geography of Immigrant Labor Markets

race, ethnicity, gender, nativity, and household characteristics as endogeneous to labor market processes. For immigrants, and immigrant women especially, the workplace-household nexus fundamentally shapes their labor market experiences and contributes most visibly to their experience within segmented labor markets. In beginning to develop a conceptual understanding of urban form as an evolutionary process that drives the geography of the city through the interplay of employment sites, residence, and recruitment networks, we must understand this as a process mediated by gender. In fact, these processes may be both more strongly conditioned and acutely experienced by women. The connections between nodes of immigrant neighborhoods and nodes of immigrant employment sites seem more tightly bound for immigrant women as a result of their more geographically local labor markets and place-based recruitment networks. While the long commutes of immigrant women domestic workers prove an exception to the usual close geographic connection between women’s employment sites and residences, these women’s employment experiences point to the operation of strong recruitment networks that connect these women’s places of social interaction (often their neighborhoods) with their places of work. Past research on urban form, such as that by Scott (1988), has failed to position gender centrally. This research begins to address this lack, though much more work needs to be done to fully flesh out an understanding of urban form as an evolutionary process built upon the relationships between production, residence, and social networks. Most significantly, such a conceptual idea demands historical exposition and longitudinal analysis.

Future Research Steps This study constitutes a small contribution to a complex research agenda concerning the role of social and spatial accessibility in shaping immigrant labor markets that demands the attention of geographers, a group well-placed to address such questions. Future research needs to expand beyond the geographic study area of Los Angeles, as well as document the interplay of industrial and demographic shifts and their effects on questions of accessibility for disadvantaged groups. Most importantly, key comparisons need to be made between immigrant and minority native-born groups.

Work, Residence, Networks, and Urban Form

159

Further, geographers need to pay close attention to the scale at which labor market processes operate. This study provides strong evidence that the neighborhood matters, but how place-based relationships build at the regional scale remains somewhat unexplored. For example, do ethnic industrial niches operate at the sub-regional level as well as the regional level? Lastly, more work needs to be done to connect immigrant labor market studies to policy questions. An important finding of this research is that both social and spatial accessibility matter for employment outcomes. Immigrants do not travel effortlessly through urban space on a magic carpet woven of ethnic networks. Commutes and job location matter, particularly for immigrant women with children. Similarly, local economic development does not necessary deliver jobs to nearby residents. Workers are connected, as well as closed off from, jobs through social recruitment networks. The combination of both social and spatial propinquity, however, partly explains the perpetuation of immigrant industrial niches in Los Angeles, particularly for many new arrivals and women. Such findings raise critical questions about the gendered effect of residential segregation on job search and employment outcomes, particularly for immigrant women. Residential segregation may matter more for women than for men in terms of finding employment and the quality of that employment. The quality of local job opportunities may affect the quality of women’s employment to a greater extent than for men, as well as point to the need for locally directed employment initiatives targeted at women.

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APPENDIX 1

Measuring Space: Commute Data

To determine commuting times between tracts, I matched the confidential census data to detailed travel data made available by the Southern California Association of Governments (SCAG). This data set contains peak a.m. private auto travel times between every traffic analysis zone (TAZ) to every other TAZ in the Los Angeles region. This temporal measure of distance is superior to a geographic measure of distance given the heavy traffic patterns and topography of the Los Angeles region. If a worker must travel along heavily congested roads to her place of work, the fact that only five miles separates home from work is irrelevant. Five miles in one direction may require thirty minutes of travel, while five miles in the opposite direction may only require fifteen minutes. Thus, the latter destination is “closer” in the way most people experience distance—by time. Further, two mountain ranges bisect the region. The Santa Monica Mountains are the most significant as they separate the San Fernando Valley from the rest of the City of Los Angeles. Few passageways access the Valley, greatly increasing travel times as cars pile up through these freeway bottlenecks. Geographic distance for workers who live on one side of the Santa Monica Mountains and work on the other means little in terms of the “length” of their daily commute. I matched the SCAG data and the census data geographically by census tract. A TAZ is usually smaller than a census tract but is always wholly contained within a census tract. When necessary, I aggregated TAZ data up to the census tract level. This convenient geographic relationship between TAZ boundaries and Census tract boundaries, however, was not in place until 1997 when SCAG redesigned its TAZ system. Previously, the typical TAZ was larger than a census tract and

161

162

Appendix 1

did not share a similar geography to the tract. This makes 1990 SCAG travel data very difficult to use in conjunction with census data and the accurate correspondence between TAZ network patterns and a particular census tract nearly impossible to achieve. I trade this rather glaring inaccuracy for what I deem a more accurate measure by using 1997 travel times instead of 1990 travel times. If the travel network improved between 1990 and 1997 (as it may have done due to highway improvements and the addition of the 105 freeway), then travel times will be lower in the 1997 data, thereby underestimating the effects of poorer spatial accessibility that would be evident in the 1990 data. This makes for conservative estimates of the effects of spatial accessibility. Further, the SCAG region was much smaller in 1990, disallowing analysis of major portions of the five-county region that now fall within the 1997 SCAG region.

APPENDIX 2

Measuring Space: Spatial Accessibility Indices

In Chapters 5 and 6, I utilize a measure of accessibility to capture the effects of an individual’s geographic accessibility to job opportunities given their residential location in the Los Angeles region. Modeling the effect of spatial job accessibility on employment outcomes requires a single parameter measuring an individual’s relative access to a set of job opportunities. Two measures have commonly been used to measure accessibility: 1) gravity-like measures that weight jobs at different distances, and 2) isochronic measures that calculate the absolute number of opportunities (jobs) available at given distances (Cervero, Rood, and Appleyard 1999). Studies that utilize isochronic measures introduce subjective and sometimes arbitrary commuting radii when determining the local labor market (i.e., Ellwood 1986). The spatial boundaries imposed may be too large or too small, thus misestimating the effect of spatial accessibility. In transportation studies, gravity-like models are preferred measures of accessibility because they account for all possible opportunities (jobs) in the metropolitan area, weighting these opportunities by distance based on real commuting patterns. The resulting measurement is a single index of accessibility. Following Raphael (1998), Cervero, Rood, and Appleyard (1999), and Mouw (2000), I calculate a gravity-like measure of accessibility as follows:

163

164

Appendix 2 N

Ai =

∑ E × exp(−γˆd ) j

ij

(A2.1)

j=1

where Ai is the accessibility index for residential tract i, Ej is the number of workers in tract j, N is the total number of tracts, γˆ is an empirically-derived distance-decay coefficient (a weight of jobs at different distances from tract i), and dij is the distance (in minutes) on the highway network between tract centroids for all i-j pairs. Larger values of the accessibility index reflect greater geographic accessibility to employment. I empirically derive the distance-decay parameter to be directly input into the above equation by estimating the gravity model: Tij = κLαi E βj exp(−γdij )

(A2.2)

where i indexes all residence tracts (origins), j indexes all employment tracts (destinations), Tij is the count of workers that live in tract i and work in tract j, Li is the count of workers living in tract i, Ej is the count of workers (jobs) employed in tract i, dij is the distance between tracts i and j measured in minutes by private commute time in the SCAG data, and α, β, γ, and κ are parameters to be estimated. Using a negative binomial count model, I estimate γˆ = -0.058. This weights jobs at k distance from tract i by: 0 minutes=1; 5 minutes =.75; 10 minutes =.56; 20 minutes=.31. Accessibility index used in Chapter 5 For the analysis in Chapter 5, I tailor a spatial accessibility index for particular groups, thus capturing segmentation in the labor market. If women and men work different jobs, accessibility should be constrained to reflect this fact. Accessibility can also be measured to particular kinds of jobs, such as ethnic niche jobs for immigrants. I calculate a separate accessibility measure to niche jobs for men and women of the same immigrant group for each residential tract:

Appendix 2

165 N

AGi =

∑ (E

Gj) × exp(−γˆdij )

(A2.3)

j =1

where AGi is the accessibility index for group G in residential tract i to niche jobs, and EGj is the number of workers in group G employed in niche jobs in employment tract j. Because an accessibility index is non-dimensional, I normalize it by dividing EGj by the total number of all niche jobs in the metro region held by workers in group G. In this way, accessibility measures are comparable; otherwise, smaller groups would always have lower accessibility measures as they hold fewer jobs (e.g. women would always have lower accessibility measures as they hold fewer jobs than men). This also allows comparisons across immigrant and gender groups.

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NOTES

Chapter 2 1. Other recent studies not discussed here that support spatial mismatch include: Bruekner and Zenou 2003; Holzer et al. 1994, Holzer and Ihlanfeldt 1996, McLafferty and Preston 1996, Raphael 1998. 2. Though Scott (1988) states, “Most of the [Latina and Asian] workers in the [dress] industry live in residential districts close to the center of the center city, where they have immediate access to their places of employment” (p. 94), this comment is not substantiated empirically. Chapter 3 1. See Allen and Turner (1997) for similar maps, though generated using a different measure of concentration and not differentiated by nativity. Chapter 4 1. Statistics in this section derive from the 1990 PUMS 5-percent sample, as the Census Bureau prefers not to disclose mean scores from data analysis run on confidential data. 2. The threshold criterion for defining a niche is somewhat arbitrary. Waldinger (1996) uses 1.5. I use 3 as I am interested in selecting industries in which ethnic networks are particularly strong. Further, for large immigrant groups such as Mexicans, a threshold value of 1.5 selects an extensive set of industries as niche industries. 3. Please see Chapter 2 for a description of the census data and Appendix 1 for a description of the travel data. 4. While self-reported commute data contribute a rough picture of intraurban labor market patterns, they are a biased proxy for job proximity. Mode differences and inaccurate reporting obscure the spatial relationship between home and work. Mode differences pose particular problems. Is a ten-minute walk half as close as a 20-minute car trip? How much of an individual’s public

167

168

Notes

transit commute consists of walking to a bus stop or train station, and how much actually occurs on public transit? Evidence of inaccurate reporting arises in the form of “data bunching.” Self-reported commute times tend to bunch at five-minute intervals, revealing that individuals tend to round their commute times up or down to the nearest five minutes. Immergluck (1996) finds that commute time varies by race, even after controlling for spatial job access, skills, and other factors. Chapter 5 1. Please see Chapter 2 for a description of the data. 2. See Logan, Alba, and Zhang’s (2002) paper on the difference between immigrant enclaves and ethnic communities and the difference between voluntary and involuntary residential segregation. Chapter 6 1. Additionally, Johnson, Bienenstock, and Farrell (1999) do not test explicitly the effects of neighborhood and neighborhood bridge contacts on women’s labor force participation; rather, they test these effects on women’s joblessness. The dependent variable of their model is the probability of being employed versus being unemployed, as opposed to participating in the labor market or not. 2. For a review of the earlier spatial mismatch literature see Holzer 1991; see Fernandez 2004, Raphael 1998, and Mouw 2000 for a review of more recent work. 3. Research by Johnston-Anumonwo (1995) and McLafferty and Preston (1996, 1997) substantiates this finding. 4. Labor force participants include employed and unemployed workers. These individuals have made a decision to be in the workforce. Non-participants are not actively seeking employment. 5. The formula for this accessibility measure follows: N

Aki =

∑ (E

kj ) × exp(−γˆdij )

j =1

where Aki is the accessibility index for residential tract i to jobs of type k (lowskill), and Ekj is the number of jobs of type k (low-skill) in employment tract j.

Notes

169

6. The formula for this accessibility measure follows: N

∑ (E

AFki =

Fkj ) × exp(−γˆdij )

j =1

where AFki is the accessibility index for residential tract i to jobs of type k (lowskill) held by women (F), and EFkj is the number of low-skill jobs held by women in employment tract j. 7. The formula for this accessibility measure follows: N

AGki =

∑ (E

Gkj ) × exp(−γˆdij )

j =1

where AGki is the accessibility index for residential tract i to jobs of type k (lowskill) held by group G, and EGkj is the number of workers in group G employed in low-skill jobs in employment tract j. 8. The formula for this accessibility measure follows: N

AFGki =

∑ (E

FGkj ) × exp(−γˆdij )

j =1

where AFGki is the accessibility index for residential tract i to jobs of type k (low-skill) held by female (F) members of group G, and EFGkj is the number of female workers in group G employed in low-skill jobs in employment tract j. 9. Given the highly variable nature of local labor markets, I define occupations that are more than 70 percent female in the Los Angeles region as femaledominated occupations for this study. The formula for this accessibility measure follows: N

Aki =

∑ (E

kj ) × exp(−γˆdij )

j =1

where Aki is the accessibility index for residential tract i to jobs of type k (those identified as female-dominated occupations), and Ekj is the number of workers employed in jobs identified as female-dominated occupations in employment tract j.

170

Notes

10. All count variables are logged to correct for heteroscedasticity or skewness. Because it is possible for households to have zero employed adults, the variable lnempadults takes the value log(1+employed adults). 11. Please see Chapter 2 for a description of the data. 12. The Census Bureau allows limited disclosure of summary statistics. Therefore, no other summary statistics of the variables included in my analysis are presented here. 13. As so few Vietnamese migrated to the U.S. before 1975, only three Vietnamese cohorts are defined: 1985-90, 1980-85, and pre-1980. 14. Model parameters remain stable when the different measures of accessibility are substituted in. 15. This is not to argue that these women are not part of a larger household economic migration. This is simply a speculation that recently arrived women may not be migrating primarily for the purposes of finding immediate employment.

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INDEX

72-73, 84, 99, 122124

Census data, 31-32 Chicago School, 1, 10

Ethnic niche, 1, 22, 59, 72-73, 82-83, 99. See also employment concentration by industry

Commuting, 9-11, 17-18 gender patterns, 16-17, 59-63 data, 161-163 urban form and, 15, 154155 urban process and, 14

Fernandez-Kelly, M. Patricia, 83, 124

Concentrated poverty, 123

Fixed residence, 22, 127

Employment concentration, calculating by place of work, 33 calculating by industry, 60-61 immigrant examples by industry, 88-89 maps by place of work, 38-39, 41-42, 44-45, 47-48, 50-51, 53-54

Gender division of labor, 56, 59, 154-155

Ethnic division of labor, 21, 56, 59, 154-155

Hanson, Susan, 12-13, 16, 24, 98, 128. See also Commuting, gender patterns; Occupational sex segregation; Spatial entrapment

Gender queue, 21 “Ghetto” neighborhoods, 122-124, 131 Granovetter, Mark, 8 “strong ties,” 20, 22, 25, 92, 98, 129

Ethnic economy, 58 Ethnic enclave, economy, 23-25, 57-58 gender and, 58 neighborhood, 25-28, 66,

185

186 Harvey, David, 9-10 Household context, 100, 132-133, 141 Housing discrimination, 13, 17 See also Residential segregation Immigrant neighborhood, see Ethnic enclave neighborhood

Index Monterey Park, 34 Pacoima, 34 Pico Union, 34 Santa Ana, 34 Westminster, 35 Neighborhood effects, 122, 140 Occupational sex segregation, 21, 56 Peck, Jamie, 11-13

Journey-to-work, see Commuting Labor force participation, 127 immigrant rates, 135 native-born Black rates, 135 Labor market embeddedness of, 8, 19 local, 5, 9-13, 22, 26, 99 immigrant, 21-22, 27-31, 157 marxist approach to, 8 neoclassical approach to, 8, 19 segregation, 2-3, 81, 99100 Labor queue, 21 Los Angeles region, 36 Beverly Hills Chinatown, 34 East L.A., 34 Huntington Park, 34 Koreatown, 35

Portes, Alejandro, see Ethnic enclave economy Pratt, Geraldine, see Hanson, Susan Residential concentration, 33. See also Ethnic enclave neighborhood, Residential segregation calculating, 33 maps, 37, 40, 43, 46, 49, 52 Residential segregation, 2, 67, 81, 99. See also Housing discrimination, Residential segregation gender and, 81, 101 Sassen, Saskia, 25, 28-29

Index

187

Scott, Allen, 10-11, 25

Toponomical knowledge, 124

Social accessibility, see social networks

Transportation, 16, 27, 127 mode, 98

Social networks, 19, 124 ethnic, 21-22, 72-73, 8283 gendered, 20-21, 56, 81, 83

Wages, ethnic niche and, 88, 89 spatial mismatch and, 1415 Waldinger, Roger, 22, 27, 56

Spatial job accessibility, calculating, 89-90, 131-132,163-165 Spatial entrapment, 12, 15-17, 55-57, 64, 125-126 Spatial job search, 17-18 Spatial mismatch, 13-15, 125-126