University of Southern California Dissertations and Theses

Beyond spatial mismatch: immigrant employment in urban America

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Beyond spatial mismatch: immigrant employment in urban America

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BEYOND SPATIAL MISMATCH:
IMMIGRANT EMPLOYMENT IN URBAN AMERICA

by

Yang Liu

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PLANNING)

December 2008

Copyright 2008 Yang Liu

ii

TABLE OF CONTENTS

LIST OF TABLES iv

LIST OF FIGURES vi

ABSTRACT vii

CHAPTER 1. INTRODUCTION 1
1. Background and Policy Debate 2
2. Theoretical Frameworks 4
3. This Study: Question, Contribution and Organization 7

CHAPTER 2. THEORY AND RESEARCH ON SPATIAL MISMATCH
AND SOCIAL NETWORKS 13
1. The Spatial Mismatch Hypothesis 14
2. New Urban Conditions 17
3. Assimilation and Social Networks Theories 22

CHAPTER 3. A TALE OF THREE CITIES 30
1. The Urban Contexts 31
2. Geographic Definitions 35
3. Intertemporal Spatial Mismatch Index 38

CHAPTER 4. ETHNIC ENCLAVE RESIDENCE AND EMPLOYMENT
ACCESSIBILITY 43
1. Introduction 44
2. Review of Literature 47
3. Data and Methodology 51
4. Empirical Results 64
5. Conclusion 76

CHAPTER 5. ETHNIC ENCLAVE RESIDENCE AND ETHNIC NICHE
EMPLOYMENT ACCESSIBILITY 79
1. Introduction 80
2. Theory and Previous Research 84
3. Data and Methodology 90
4. Empirical Results 98
5. Conclusion and Discussion 111

CHAPTER 6. CONCLUSION AND FUTURE RESEARCH 115

iii

BIBLIOGRAPHY 120

Appendix A: Total and Foreign-born Population and Employment
(by Sector) for Three Cities, 1990-2000 131

Appendix B: First Stage Results from Regression of Log (Wage
Earnings) on Instrumental Variables and Other Variables 134

Appendix C: List of Ethnic Niches by Gender and Size of Employment
for Three Cities 135

iv

LIST OF TABLES

Table 3.1: Population and Immigrants, 1990, 2000 and 1990-2000 Growth 32

Table 3.2: Population and Employment Distribution by Area,
1990 and 2000 36

Table 3.3: Spatial Mismatch Index for Total, Immigrant and Native-born
Populations, 1990-2000 40

Table 4.1: Number of Enclaves and Percentage Reside in Enclaves 55

Table 4.2: Independent Variable Description and Sample Means 61

Table 4.3: Mean Employment Rate, Commute Time and Annual Wages of
Latinos by Neighborhood Type and Nativity 62

Table 4.4: Probit Regression Estimates of Latino Employment Status
by Nativity 65

Table 4.5: Regression Estimates of Latino Workers’ Commute Times
by Nativity 70

Table 4.6: Employment and Commute Time Model Estimates on Locational
Variables of Latino Immigrants by Gender 74

Table 5.1: Number of Niches and Percentage Niche Employment
by Gender 93

Table 5.2: Top Three Immigrant Niches by Size of Employment by Gender 94

Table 5.3: Percentage Niche Employment by Residential Location and
by Gender 98

Table 5.4: Probit Regression Estimates of Latino Niche Employment Status
by Gender 99

Table 5.5: First Stage Regression Estimates of Latino Immigrants' Log
(Wage Earnings) by Gender 106

v

Table 5.6: Regression Estimates of Latino Immigrants' Commute Times
by Gender 107

vi

LIST OF FIGURES

Figure 3.1: Racial/Ethnic Compositions in 1990 and 2000 33

Figure 3.2: Population and Employment Growth by Area, 1990 and 2000 37

vii

ABSTRACT

The rapid increase of immigrant population in metropolitan areas across the
United States brings significant changes to urban labor market. This study
locates immigrants’ labor market performance in the economic and spatial
contexts of cities and examines the role of space and spatially-constructed social
networks on their employment outcomes in both the general labor market and
the ethnically concentrated niche sectors in particular.
Building on two distinctive theoretical frameworks: the spatial mismatch
hypothesis and the social networks theories, this study examines the relative
strength of spatial accessibility to job opportunities and social accessibility to
ethnic networks in determining immigrants’ employment status and quality in
terms of wage earnings and commuting time. Particular attention is paid to low-
skilled Latino immigrants who are overly represented in the urban poor
population and are the foci of much policy debate.
Using Census statistics for 1990 and 2000, results from Chicago, Los
Angeles and Washington, D.C. indicate that suburbanization is the prevalent
trend among the immigrant population and employment. During the last
decade, jobs in general shifted away from where immigrants live while
immigrants followed jobs and resulted in minor lessening in the overall
magnitude of spatial mismatch.

viii

On the micro level, the effect of residence in ethnically concentrated
enclaves is found to vary by location and by gender. The central city is generally
associated with both dampened employment rate and longer commutes, testing
to spatial mismatch effect. Enclave residents in inner and outer ring suburbs,
while as likely to work as non-enclave counterparts, usually commute longer to
jobs, suggesting that social networks might direct them to employment outside
of the local labor market. Further identifying ethnic niche industries finds that
enclave residence in the suburbs, not central cities, increases the likelihood of
niche employment. In general, women are more enclave-disadvantaged than
men: while they rely more heavily on ethnic networks to find jobs, these jobs are
of lesser quality and accessibility. These findings underscore the importance of
the interaction between spatial accessibility and social context in shaping
immigrants’ labor market experience and the diversity of enclave and niche
effects.

1

CHAPTER 1.

INTRODUCTION

Two major trends are evident in American cities at the turn of the twenty first
century: immigration and economic and spatial transformation. The nation is in
the midst of its fourth immigration wave, with the foreign-born population
reaching 31 million in year 2000, or 11% of the total population. Immigrants are
both sustaining the U.S. labor market and are becoming an emerging dominant
force in the U.S. housing market (Myers and Liu 2005). They are involved in
every aspect of urban life and are an underlying force behind any urban change,
especially in “immigrant magnet metros” of Los Angeles, Chicago, Washington,
D.C., New York, San Francisco and Miami (Frey 2003). At the same time,
American metropolitan areas are experiencing fundamental structural changes
in their employment bases. As the country continues its shift from
manufacturing-based to service-based economy, the industrial and spatial
distribution of jobs in cities is rapidly evolving as well. At the intersection of
these two intertwining processes, it is imperative to understand how to integrate
immigrants economically in the given urban contexts.

2

1. Background and Policy Debate
Traditional views of immigrants’ socioeconomic mobility have centered on the
notion of assimilation. It is widely acknowledged that while immigrants tend to
concentrate in inner city ethnic communities and start working at low wages
upon first arrival, they tend to disperse to white suburban neighborhoods and
advance in the labor market with increased duration – the processes of spatial
assimilation (Massey and Denton 1985) and economic assimilation (Borjas 1985).
However, recent studies document that newer waves of immigrants exhibit
diverse residential mobility paths and do not necessarily move to the white
suburbs with their elevated socioeconomic attainment (Alba et al. 1999). On the
contrary, ethnic clustering can endure which can be manifested by the
emergence of high-status suburban immigrant communities (e.g. Logan, Alba,
and Zhang 2002 for Los Angeles and New York and Li 1998 for Chinese
ethnoburbs in San Gabriel Valley, Los Angeles). However, the effect of
residential segregation and diversity of ethnic neighborhoods on their
employment outcomes is not adequately addressed in the academic literature.
Employment decentralization accelerated in the second half of the 20
th

century. Recent evidence shows that a quarter of central cities experienced job
losses and more than three quarters lost their private sector employment share
to suburbs between 1993 and 1996 (Brennan and Hill 1999) and in 1996 a third of
people work more than 10 miles from the city center (Glaeser, Kahn, and Chu

3

2001). Within this general pattern, the suburbanization of manufacturing,
service and retail jobs is especially prominent and these are the sectors in which
low-skill jobs heavily concentrate. It is unclear though what effects decentralized
distribution of employment opportunities in these cities have on the economic
well-being of immigrants with varied residential locations. This dissertation
thus investigates how immigrants’ employment accessibility and quality is
shaped by the transformed spatial structure and the social environment of the
communities they live in.
The large influx of immigrants to the country’s urban areas has aroused
many policy debates regarding urban changes attributable to immigration. The
passing of the Immigration and Nationality Act (INA) in 1965 switched priority
from national origin quotas that favored European immigrants to family
reunion and skill-based criteria, opening door to immigrants from Latin
America and Asia. These two regions send 50% and 26% respectively of the total
11.8 million immigrants who arrived within the last decade (for historical
background and recent trends of immigration, see Martin and Midgley 2003).
This population features bifurcated skill sets and resources upon first arrival,
with Latino immigrants overly-represented in the low-skilled labor force. While
some maintain that they fill in vacancies at the bottom of job ladders and are an
integral part of the U.S. economy, others argue that they steal jobs from the low-
skilled native-born workers and exacerbate the employment difficulties of

4

blacks. Nowadays, with immigrants constituting an increasing share of the
urban poverty population, there comes the assertion that immigrants are
“importing poverty” to this country (Camaroda 1999). Understanding the
spatial and social constraints and preferences of low-skilled Latino immigrants
in the urban labor market beyond those generally associated with minority
workers will help solve these and other puzzles.

2. Theoretical Frameworks
Two major theoretical frameworks originated from distinctive disciplines
enlighten this discussion: the spatial mismatch hypothesis and social networks
theories. A long-standing academic tradition that examines how residential
segregation affects inner city minorities’ job accessibility is the “Spatial
Mismatch Hypothesis” proposed by Kain in 1968, and has gathered
considerable interest till this day. This hypothesis states that in the context of
economic restructuring, blacks in inner city neighborhoods are suffering from
high unemployment rate, low wages and long commutes because of their spatial
isolation from suburbanized low-skill and semi-skill job opportunities and
limited mobility to follow suburban jobs. These spatial obstacles bound
residents in job-poor neighborhoods and partly result in their prolonged
poverty and distress. Voluminous empirical studies in the past years have tested
this hypothesis and are conducted on different scales and adopted different

5

approaches. Results have been mixed and highly sensitive to the specification of
each research design (Ihlanfeldt and Sjouquist 1998 for comprehensive review).
As one testing strategy, studies that examined the intra-metropolitan
employment outcome differentials among various racial/ethnic groups (e.g.,
Raphael 1998, Stoll 1999) have established the significant effect of spatial
residential location on the unfavorable labor market outcomes of minorities as
compared to whites.
Sociologists approach the issue of immigrants’ labor market performance
and incorporation from a different perspective and emphasize the role of social
networks as an important mechanism in channeling immigrants to jobs. These
social networks connect newcomers to the settled co-ethnics and facilitate the
circulation of information regarding housing, job opportunities and social,
cultural and religious activities. As an effect, they help shape the segmentation
of the labor market along ethnic lines and the formation of ethnic niches where
immigrants from the same origin heavily cluster. Case studies are collected
across the country, from Cubans in Miami (Wilson and Portes 1980) to Chinese
in New York City (Zhou and Logan 1989), that document the operations of these
ethnic economies and ethnically-concentrated occupations and industries in
immigrant gateway cities. What lacks in the literature is identification of the
spatial context and physical basis of social networks and empirical examination
of their effects.

6

It is inconclusive which direction the above competing theories lead us in
our understanding of immigrants’ employment accessibility and is an area that
deserves much attention. On the one hand, as immigrants also tend to be
clustered in central city ethnic neighborhoods upon first arrival, they may face
the same spatial barriers to abundant suburban job opportunities as traditional
minorities. On the other hand, with less attachment to specific localities,
immigrants are more residentially and economically mobile over their life
course (Myers 1999). They also benefit from the social networks and ethnic
capital generated from living with co-ethnics in ethnic neighborhoods in
locating jobs. In this sense, they do not fit in the static stereotype of urban
“underclass” (Wilson 1987) and may be less spatially constrained. But it is
indeterminate how the spatial and social environments interact and play out in
immigrants’ employment outcomes and is the overarching objective of this
dissertation.
The few studies addressing this issue fail to converge on a common
theme. Some researchers found that immigrants are not as constrained by
spatial effects in the labor markets. Immigrants (especially Mexican) workers
consistently depict relatively high employment rate as compared to native-born
minority workers (Aponte 1996). Pastor and Marcelli (2000) also find that
individual skills matter more than "pure" spatial mismatch in Los Angeles,
especially for recent Latino immigrants. While acknowledging the role of social

7

networks in connecting immigrants to employment, they however caution about
the quality of these jobs. A more recent study on youth also finds that space has
no significant effect on first-generation immigrants’ employment outcomes as
compared to native-born whites (Painter, Liu and Zhuang 2007). Others argued
the contrary to be true. Preston, McLafferty and Liu (1998)'s results indicate the
persistence of spatial barriers faced by immigrant workers as evidenced by their
overall longer commutes than their America-born counterparts in New York.
Parks (2004a, 2004b) also analyzed the labor market outcomes of selected
immigrant groups in Los Angeles, and found that space still matters in
determining less-educated workers' employment prospects. Controversies arise
as these studies are conducted for different urban contexts and applied different
methodologies and specifications, making any cross-city comparison and
generalization hard to come by.

3. The Study: Question, Contribution and Organization
In an attempt to incorporate both veins of theories interactively, this study
extends the discussion in a number of important ways. First of all, a more
detailed division of space is adopted which consists of both the physical location
in urban spatial structure and the social location of accessibility to ethnic
networks. This is achieved by partitioning the urban areas into three rings:

8

central city, inner ring suburbs and outer ring suburbs, and within each ring,
ethnic enclaves and mixed neighborhoods.
Second, unlike previous research, this study investigates the three
interlinked aspects of employment accessibility and quality: employment status,
earnings and commuting duration, providing a more systematic assessment of
immigrants’ labor market performance given residential location.
Third, a comparative analysis of the three metropolitan areas of Chicago,
Los Angeles and Washington, D.C. is conducted in an effort to demonstrate any
similarities and differences across various types of immigrant gateways.
Applying the same methodology to metropolitan areas with varied immigrant
history and urban conditions reveals inter-metropolitan comparison of intra-
metropolitan dynamics.
Last, linking the spatial mismatch hypothesis with immigrants’ labor
market segmentation literature, this dissertation further explores the connection
between residential location (especially ethnic enclave residence in various
rings) and ethnic niche employment, and the labor market implications of
engaging in ethnic concentration both at home and at work. This is a further test
of the strength of spatially-bounded social networks in ethnic neighborhoods in
directing immigrants to jobs that their co-ethnics already disproportionately
occupy.

9

Understanding the role of space and spatially-constrained social
networks in immigrants’ economic mobility both sheds light on the theoretical
debate and carries importance in policy and planning. In an era with continued
immigrant inflow, any urban policy discussion needs to take this major
demographic shift into account. Local community development strategies
targeted at the immigrant populations and distressed ethnic neighborhoods
would benefit from the knowledge regarding the relative importance of spatial
mismatch and social networks mismatch in immigrants’ economic performance.
This would also help determine the potential effectiveness of place-based and
people-based approaches in fostering immigrants’ job accessibility. Some of the
options include resident relocation programs that move residents closer to the
jobs, economic development incentives that bring in businesses and create new
jobs, expansion of transportation options to increase residents’ mobility, as well
as social services that facilitate the acquisition of skills and flow of job
information among local residents.
This dissertation uses U.S. Census Public Use Microdata Sample (PUMS)
datasets for year 2000, complemented by summary population and employment
statistics from the 1990 and 2000 decennial census. The PUMS dataset features a
detailed list of demographic and socioeconomic variables on both the individual
and household level as well as geographic identifiers that are crucial to the
research questions proposed. Summary statistics provide important contextual

10

information regarding the relative size and growth of total population, the
foreign-born population and employment on the county level, serving as the
basis for the geographic partition of urban spatial structures. Empirical analyses
are restricted to low-skilled Latino immigrants with less than a high-school
degree. Latino immigrants constitute a prominent and growing segment of
current immigration to the United States and the low-skilled group is of
particular concern for their perceived difficulties in the labor market.
Quantitative research methods are adopted that model the selected job
accessibility indicators, namely employment status, wages, and commute times
on residential location, a vector of individual and household sociodemographic
characteristics, and some neighborhood level variables. Ethnic enclaves and
ethnic niches are identified using residential concentration quotient and
occupational concentration quotient respectively to designate locations and
occupations with disproportionately high immigrant presence.
This dissertation proceeds as follows. Chapter 2 elaborates on the
theoretical frameworks of the spatial mismatch hypothesis and social networks
theories. While the former has not been traditionally associated with
immigration research, its possible applications are discussed in relation to the
latter.
Chapter 3 documents the urban contexts of the three selected case study
metropolitan areas, especially their growth in total population, immigrant

11

population and employment by location during the ten year period between
1990 and 2000. Through calculations of the Spatial Mismatch Index, it also
portrays the existence and magnitude of spatial mismatch in aggregate terms.
The geographic partition of space is conducted and summary statistics
presented.
Chapter 4 identifies ethnic enclave neighborhoods that Latino
immigrants residentially cluster and empirically examines how residence in
various locations, especially in ethnic enclaves affects low-skilled Latino
immigrants’ employment status, wage earnings and commuting duration as
compared to native-born Latino workers. Gender differences are also
highlighted to capture the different labor market behaviors by male and female
workers.
Chapter 5 identifies the ethnic niche occupations that Latino immigrants
heavily concentrate in these areas and further explores the underlying
mechanisms of ethnic networks in connecting immigrants from ethnic enclave
residence to ethnic niche employment. It essentially asks the question: are
enclave residents more likely to participate in niche employment without
respect to spatial proximity and what is the quality of such employment? The
same indicators of employment accessibility as in Chapter 4 are examined and
results are compared.

12

This study concludes in Chapter 6 with a summary of key findings, its
contribution to the theoretical debate, and a discussion of policy implications
and directions for future research.

13

CHAPTER 2.

THEORY AND RESEARCH ON
SPATIAL MISMATCH AND SOCIAL NETWORKS

Theories from various academic traditions offer different perspectives on the
trajectories and mechanisms of immigrants’ economic incorporation into the
urban economies of the metropolitan areas they settle in. These theoretical
traditions also derive distinctive conclusions regarding the obstacles faced by
immigrants in securing job opportunities. Two such theoretical frameworks are
the spatial mismatch hypothesis which emphasizes the role of space and spatial
proximity as a determining factor in minorities’ employment accessibility, and
the social networks theories which contend that connection to ethnic networks
facilitate immigrants’ economic mobility through information circulation and
job referrals. The central point of contention lies at the effect of residential
segregation for immigrants and whether ethnic neighborhoods are pockets of
social and economic closure to opportunities or basins of rich resource sharing
and network activities among the co-ethnics.

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1. The Spatial Mismatch Hypothesis
Changing spatial structure and social relations in American metropolises in the
second half of the twentieth century prompts the exploration of the linkages
between residential segregation in the housing market and racial inequality in
the labor market. Spatial Mismatch Hypothesis was first introduced by Kain
(1968) as a spatial explanation to the relatively inferior employment outcome of
inner city blacks that adds to their existing economic disadvantages and
exacerbates concentrated poverty. Simply put, this hypothesis states that there is
a discrepancy between the concentration of minority (especially black) workers
in the central cities and the fast suburbanization of employment that
undermines their job accessibility. Kain (1968) derived this hypothesis from
evidence in Detroit and Chicago by estimating job loss due to employment
suburbanization and residential segregation, and suggested that “the continued
high levels of [black] unemployment in the full employment economy may be
partially attributable to the rapid and adverse...shifts in the location of jobs”
(p.197).
Generally speaking, there are two conditions and three outcomes implied
in this hypothesis. The two conditions are (1) housing segregation of racial
minorities in the inner cities and their inability to follow jobs to the suburbs; and
(2) the suburbanization of jobs, especially manufacturing and service sector jobs
that are suitable for low-skilled workers, which reduces the available job

15

opportunities in the central cities and increases their job search and commute
costs to access suburban jobs. The direct outcomes that follow include: (1) higher
unemployment rate in ghettos; (2) lower wages in ghettos (because of labor
surplus) as compared to the suburbs; and (3) longer commutes for ghetto
residents. Another effect, as is later elaborated by sociologists like Wilson (1987),
is the forming of an “urban underclass” in these neighborhoods with low labor
force participation. The lack of economic means and social capital in the ghettos
propels the flight of employed, higher-class blacks and perpetuates a “culture of
poverty” that further worsens their residents’ living conditions and impair their
children’s future development prospects. This hypothesis directed urban
researchers’ attention to understand “how race, location, and their interaction
affect economic opportunities” (Glaeser, Hanushek, and Quigley 2004, p.71).
Space is introduced as a structural element in addition to human capital
characteristics in explaining labor market outcomes.
Intuitively appealing, the testing of SMH has proved to be both
theoretically and empirically challenging. This is partly because of the
endogenous nature of residential location and job access as derived from a
monocentric model and partly because of the multiplicity of dimensions in the
spatially and racially segmented urban housing and labor market that renders
any single definite explanation difficult. This body of research exhibits a high
level of heterogeneity with respect to scope and methodology and the results

16

have been mixed. The standard approaches to test the SMH include: “(1) racial
comparisons of commuting times or distances; (2) correlations of wages and the
employment rate with measures of job accessibility; (3) comparisons of the labor
market outcomes of central city and suburban residents” (Ihlanfeldt and Sjoquist
1998, p. 852).
The rationale behind the third methodology is that the size and growth
rate of jobs differ across urban geographies and given the mobility constraint of
low-skilled workers, their residence in specific area serves as a proxy for job
availability in the local labor market. Studies on intra-metropolitan employment
differentials have established the significant effect of residential location on
racial employment disparities between black and white workers (e.g., Raphael
1998, Stoll 1999). One empirical challenge to this line of studies is endogeneity
between residential location and job accessibility as implied in the standard
urban economic models. In order to bypass this issue, some restrict their
samples to at-home youth assuming that the parents make the residential
choices without concern for their children’s employment prospects (e.g.,
Raphael 1998, Stoll 1999) while others uses exogenous shocks like firm
relocation as natural experiment and compares different responses of white and
minority workers (Fernandez 1994; Zax and Kain 1991; 1996).
The Spatial Mismatch Hypothesis spins off a series of other mismatches
that are created by the spatial separation between jobs and workers. Regarding

17

resident mobility, the inaccessibility of suburban job sites by public
transportation (Sanchez 1999) and the limited car ownership of low-skilled
workers (Taylor and Ong 1995; Raphael and Rice 2002; Ong and Miller 2005)
constrain their job search radius and possible commuting sphere, hurting their
employment prospects– so-called auto or transport mismatch. Information
mismatch arises as suburban employers adopt somewhat local recruiting
strategy that reach job seekers only in close-by neighborhoods (Holzer and
Ihlanfeldt 1996), and few information channels exist that inform inner city job
seekers of suburban openings (Ihlanfeldt 1997). While these two processes are
largely spatially constructed urban employment dynamics, the third one,
namely economic restructuring and the structural changes it brings to the labor
market is more an aspatial trend that gives rise to the mismatch between skills
requirement of a new information-based urban economy and the human capital
endowments of workers (see Kasarda 1988, Kasarda and Ting 1996 for excellent
accounts). Of course, a changing spatial pattern of skills structure is implicit and
is somewhat intertwined with the process of employment suburbanization,
together creating a geographic skills mismatch (Stoll 2005).

2. New Urban Conditions
While the Spatial Mismatch Hypothesis was originated from an urban era with
clear spatial division and color line, today’s cities have transformed dramatically

18

along both the spatial and demographic dimensions, calling for new theoretical
frameworks to accommodate the substantive changes. Some new schools of
theories, for example, the Los Angeles school of postmodern urbanism are
developed that challenges the Chicago school of urban thinking and calls for the
embracement and incorporation of new, post-modern urban realities, using Los
Angeles as the case study site. These conditions include flexible economic
production, multi-centric polyglot urban form, demographic diversity, and
social and cultural pluralism (Dear 2000). These urban complexities defy any
uniform generalization and emphasize the varied urban experience of specific
populations and particular places. While this theory rightfully directs the
researchers’ attention to an imminent urban agenda, a systematic framework of
analysis is yet to emerge from these observations.
New urban conditions pose three major challenges to the traditional
SMH debate: transformed urban geography, changing demographic dynamics
and changing neighborhood contexts. First of all, an evolving urban geography
of employment and residential distribution is resistant to a simple central city-
suburbs divide; rather, more nuanced spatial partition is warranted. Urban
spatial form is evolving over time. The 21
st
century witnessed continued trend of
low-density suburban sprawl, decentralization and dispersion of businesses and
residences. Vast majority of central cities all around the nation are losing jobs,
especially private sector jobs, to their suburbs (Brennan and Hill 1999). At the

19

same time, the suburb is not a uniform concept anymore and distinctions need
to be made between the older, inner suburbs and the newer, outer suburbs. Such
urban problems as were traditionally associated with central cities: deteriorated
infrastructure and old housing stock, high crime rates, low-quality public
schools, and concentration of minority and poor households are quickly
spreading to inner ring suburbs as well (Downs 1997). Given these facts, a new
partition of urban geography needs to include the central city, inner ring
suburbs and the outer ring suburbs to capture these spatial variations.
Secondly, urban unemployed and working poor population based on
black-white dichotomy is obsolete. Latino and Asian workers, many of whom
foreign born or second-generation immigrants, make up increasing shares of the
labor force in gateway metropolitan areas, and are especially overrepresented in
certain occupations and industries. Recent immigrants are also becoming
increasingly diverse in terms of their resources, skills and socioeconomic status.
They cluster on both ends of the skills hierarchy of a “new barbell economy”
(Frey 2002) which result in their overly representation in both the low-skilled
service and manufacturing jobs and the high-skill professional jobs (Smith and
Edmonston 1997). Immigrants are geographically concentrated, with more than
half of all foreign-born population living in just ten metropolitan areas (U.S.
Census Bureau), exerting strong impact on these urban labor markets. While
policy and academic debates regarding the effect of immigrants on the

20

employment rate and wages of native-born minority workers on both the
national and metropolitan level have been ongoing (e.g. Borjas 1987, Card 1997),
the intra-metropolitan dynamics in employment accessibility given residential
location has not been adequately addressed.
Thirdly, neighborhood and community contexts are changing as well.
The economic and social distress of ghettos are not replicated in barrios or Asian
neighborhoods, where the density of cultural and ethnic capital is actually
providing a nurturing environment and an informal economy that benefits their
residents. Here, it is ambiguous how residential location, as bounded by both
spatial structure and social environment, play a role in immigrants’ employment
outcomes. On the one hand, it is assumed that immigrants, as new minorities,
face the same geographic barriers as traditional central city residents, while on
the other hand, they might constitute an “exception” to the SMH (Aponte 1996)
as they are residentially more mobile and socially more connected to chase job
opportunities (Borjas 2001). In low-skilled occupations, Latino and Asian
workers are relying increasingly on social and ethnic networks in securing jobs
in immigrant niches (Waldinger 1996) and that employers are also in favor of
network hiring in recruiting immigrant workers (Waldinger 1997) . Social
connectedness, as argued by some, outweigh spatial proximity in explaining
immigrants’ relative labor market success (Waldinger and Der-martirosian
2001). Therefore, the tenants of SMH that housing segregation is detrimental to

21

labor market performance are not readily applicable to immigrant workers,
whose settlement patterns and job matching mechanisms are considerably
different from traditional minorities.
It is also worth noting that immigrant’s spatial settlement pattern
evolved through the years. Contrary to the traditional spatial assimilative path
prevalent among European immigrants from central city concentration to
suburban dispersion (Chicago school, Parks, Burgess, and McKenzie 1925),
newer immigrant cohorts are directly settling in the suburbs (Alba et al. 1999)
and are depicting a variety of residential preferences and choices. Ethnic
concentration and clustering endures with elevated socioeconomic status. It is
documented that immigrant segregation in 2000 is at its century-high (Cutler,
Glaeser, and Vigdor 2005) and while white immigrants are highly assimilated
spatially, non-white immigrants depict a greater variety in residential patterns
(Galster, Metzger, and Waite 1999). Residential segregation in ethnic
communities is argued to be a result of preference and voluntary will rather
than institutional and economic constraints as in the case of ghettos (Marcuse
1997). This is evidenced by the emergence of high-status suburban immigrant
communities (Logan, Alba, and Zhang 2002, and as an example, Li 1998 for
Chinese ethnoburbs in San Gabriel Valley in Los Angeles). This fact also
deviates from the traditional SMH stereotypes of minority/ethnic central cities
and white. Wright, Ellis, and Parks (2005) intends to “re-place whiteness in

22

spatial assimilation research” by considering metropolitan areas as
“constellations of neighborhoods”, rather than “central city-suburban
doughnuts” (p. 111).

3. Assimilation and Social Networks Theories
Social scientists have always been interested in how immigrants incorporate into
the host society. The concept of assimilation denotes a common belief that
immigrants fare by entering the American mainstream. An early view of
immigrants’ adaptation to the host society was offered by the Chicago School of
Sociology in a series of works by Park and his coauthors (Park and Burgess 1921;
Park 1952). This human ecology perspective posits a race relations cycle of
contacts, competition, accommodation, and eventual assimilation as the natural
route for newcomers. Assimilation is achieved through contacts and integration,
not segregation (Sanders and Nee 1987). Gordon (1964) provides a synthesis of
the assimilation perspective by identifying seven types of assimilation: cultural
(acculturation), structural (institutional and social participation), marital
(intermarriage), identificational, receptional (absence of prejudice), behavior
receptional (absence of discrimination), and civic (absence of value and power
conflict). Through the image of the “melting pot”, the earlier assimilation
theories necessitate integrating social and cultural heritages and conforming to

23

the American way of life (Americanization) as the path to socioeconomic
incorporation.
However, the diversity of current immigration casts doubt on this theory
that largely pertains to earlier European immigrants and makes some ponder “is
assimilation dead” (Glazer 1993). Advocates of pluralism contend that
assimilation imposes “ethnocentric and patronizing demands on minorities”
(Clark 2003, p.12) and argue ethnicity-specific social norms actually endure. The
two contending paradigms of acculturation and ethnic retention can be
reconciled (Gans 1997) upon the agreement that the process of adaptation is
non-linear and multiple. A “rethinking” and broadening of assimilation theory
still considers it as useful construct as long as it tolerates mixed and uneven
modes of incorporation with assimilation being the direction of change instead
of end result (Alba and Nee 1997). Segmented assimilation theory emerges to
denote the disparate and complex development trajectories of second-
generation immigrants (Portes and Zhou 1993; Zhou 1997).
While general assimilation theories encompass all facets of the
adaptation process, it has an overt emphasis on social and civic assimilation.
Economic assimilation denotes immigrants’ incorporation into the labor market,
as manifested by their employment status, occupations, and earnings.
Economists’ engagement with this issue has extensively followed human capital
models in the neoclassical economic tradition with its set of assumptions on

24

competitive market, individual rational choice and utility maximization. Human
capital attributes such as age, education, English proficiency, length of
residence, and years of experience are frequently found to be instrumental in
economic achievements and both initial skills set and rate of progress vary for
immigrants of different national origins (Chiswick 1978, Borjas 1985, Schoeni
1987). In terms of methodology, empirical analyses are conducted using cross-
sectional national statistics (sometimes with two or more years of observations)
and on an aggregate national scale, with little geographic and procedural
specificities.
As a different school of thinking, the new economic sociology places
emphasis on the social processes and social relationships in our society and
economy, and as the context in which economic actions are embedded. This field
focuses “on the ways in which social influences modify the assumed
maximizing behavior of individuals and lead to predictions differing from those
of conventional economic models” (Portes 1995, p.3). In the case of immigration,
one fundamental distinction is sociologists’ belief that individuals are not just
rational utility-maximizing selves, but also members of ethnic groups and larger
social structures, and that economic progress is not only individual endeavor,
but also group efforts (Granovetter 1985).
Sociology’s main contribution to economic assimilation of immigrants
lies in ethnic enclave hypothesis (Wilson and Portes 1980), which is grounded in

25

dual labor market theory (Gordon 1972) and ethnic solidarity theory (Light
1972). The former states that labor market is segmented into a primary and a
secondary labor sector. The primary sector features high-skilled, high-wage jobs
with stable work conditions and ample opportunities for promotions whereas
the secondary sector is comprised of low-skilled, low-paying jobs with
circumscribed prospects for upward mobility. For immigrants who are trapped
in this sector, their economic advancement might be severely impeded. The
latter, ethnic solidarity theory is based on the observation that the mobilization
of ethnic resources and establishment of ethnic enterprises promote collective
economic development and welfare among Japanese, Chinese and West Indian
immigrants.
With evidence from Cubans in Miami, ethnic enclave hypothesis asserts
ethnic enclave is a third labor sector and that immigrants who work for ethnic
entrepreneurs can obtain returns to human capital comparable to those
employed in the primary sector, and absent among those employed in the
secondary sector, and thus providing a “separate but equal” mode of economic
incorporation (Wilson and Portes 1980). This is because ethnic economies based
on ethnic solidarity and enforceable trust both reward immigrants for their
native languages and past experiences (Greenlees and Saenz 1999) and also open
up opportunities of career mobility and self-employment (Nee, Sanders, and
Sernau 1994). Empirical studies testing the hypothesis have been somewhat

26

controversial. Sanders and Nee (1987) found that in Florida, immigrant
entrepreneurs are as well off in or out of ethnic enclaves whereas immigrant
employees receive less returns to human capital in the enclaves. Their empirical
strategy however is rebutted by Portes and Jensen (1987) who clarified that
place of work, not place of residence, should be the defining criteria of enclave
membership, starting a debate thereafter on the operational definition of enclave
economy (Sanders and Nee 1992, Model 1992).
1

More recently the concept of ethnic niche or immigrant niche is
introduced to describe a broader type of immigrant employment concentration,
which can be both occupational and industrial, and both high-skill and low-skill
in nature (Waldinger 1994; Waldinger and Dermartirosian 2001). Immigrant
employment clustering is embedded in the historical contexts when ethnic
groups first enter certain jobs and perpetuated through the repeated actions of
social networks that connect new-comers to the same jobs as their settled co-
ethnics. Many researchers attribute immigrants’ impressively high labor force
participation given poor human capital and job accessibility to their wealth of

1
Ethnic enclaves consist of “immigrant groups which concentrate in a distinct spatial
location and organize a variety of enterprises serving their own ethnic market
and/or the general population” (Portes 1981, p.291). This is a rather narrow concept,
and loosening the spatial proximity condition produces the general term of ethnic
economy (Light et al. 1994; Logan, Alba, and McNulty 1994). In this paper, ethnic
enclave is used to refer to immigrants’ residential concentration.

27

social capital and social networks
2
, especially compared to low-skill blacks (e.g.,
Aponte 1996; Waldinger 1996). The concept of social networks originated from
Granovetter (1973), who argued that “weak ties” of acquaintances, as opposed
to “strong ties” of kinship, can be more effective in achieving individuals’
economic goals due to its wider net and more effective information flow. Social
networks comprise a source of social capital, which in turn creates human
capital (Coleman 1988) and converts into jobs and earnings by ways of (1) the
circulation of information on jobs and other resources that assist newcomers’ job
search and economic transition; and (2) ethnic network recruiting and job
training on the part of the employers (Rodriguez 2004). In short, by facilitating
information flow, social networks ease the job-matching process between
workers and employers and increased efficiency on both ends. Case studies find
that niches are formed through social networks that increase immigrants’ job
accessibility in a new labor market and expand their economic opportunities
(Munshi 2003). However, it is argued that the jobs obtained through ethnic
networks are often of inadequate quality (Waldinger 2001) and that ethnic hiring

2
Social networks refer to “sets of recurrent associations between groups of people
linked by occupational, familial, cultural, or affective ties” (Portes 1995, p. 8) and
social capital means “the capacity of individuals to command scarce resources by
virtue of their membership in networks or broader social structures” (p.12).

28

also generates the side effect of “social closure” of certain ethnic niches to ethnic
groups other than incumbent one (Waldinger 1997).
In sum, sociological perspective on immigrants’ economic incorporation
emphasizes the social structures of economic actions and the ethnic
segmentation of the labor market. It contradicts the claims of traditional
assimilation theory that ethnic segregation deters the assimilation process and
constantly finds that ethnicity plays a more important role than skills and
duration of residence for immigrants. It also tends to focus on the job channeling
process and the collective dynamics of ethnic enclaves, directly challenging the
assumptions of competitive market and individual utility maximization in
neoclassical economics. In methodological terms, descriptive statistics and
detailed ethnographic case studies are frequently used to portray specific
immigrant groups in particular industries and/or localities.
Immigrant neighborhoods, or ethnic neighborhoods, serve in many cases
as the physical base in the creation and functioning of social capital and ethnic
capital. They also constitute important linkages in the social and spatial
processes of ethnic networks between residence and work. Immigrant barrios do
not resemble the social pathologies of traditional ghettos for their rich social and
ethnic capital and vibrant informal economies (Clark 2001) and immigrant
workers are much more likely than blacks to use neighborhood contacts to
locate jobs (Elliott and Sims 2001). While it is widely agreed that neighborhood

29

context in general, and ethnic neighborhood in particular, plays a large role on
immigrants’ job acquisition and economic achievement, the direction and
magnitude of these effects remains largely uncertain. Systematic empirical
research is limited regarding the role of ethnic enclave (through social networks)
on employment and on ethnic niche employment.
This dissertation bridges these two theories: spatial mismatch and social
networks in a systematic framework in examining the employment accessibility
of low-skilled Latino immigrants. Both spatial proximity and social
connectedness will be taken into account and their relative effects evaluated.
Residential clustering in various spatial locations might have different impacts
on immigrants’ employment outcomes, demonstrating the interactive nature of
space and social environment. This framework enriches both the discussion of
spatial mismatch hypothesis and of immigrants’ social networks theories by
providing a more comprehensive perspective on the role of residential location
in shaping immigrants’ labor market experience.

30

CHAPTER 3.

A TALE OF THREE CITIES

The three metropolitan areas of Chicago, Los Angele and Washington, D.C. are
selected as comparative case study sites in this dissertation. While the three
areas are important in their distinctive ways in urban theory and policy, they
have not been subject to much comparative examination, with few exceptions
(Myers 1999, Painter and Gabriel 2008). This chapter first introduces the overall
demographic and economic contexts of the three metropolitan areas, then
conducts the geographic partition of central city, inner ring suburbs, and outer
ring suburbs based on population and employment growth patterns, and lastly
through the Spatial Mismatch Index presents the magnitude of jobs and housing
imbalance from 1990 to 2000.

31

1. The Urban Contexts
Chicago, Los Angeles and Washington D.C. are representative of America’s
metropolitan areas in many ways. Among the top five largest metropolitan areas
in the U.S. in 2000, these three metropolises all have large populations and
employment bases. They differ however, in their spatial form, industrial
structure, and demographic composition. In a job sprawl classification system,
Chicago and Washington, D.C. PMSAs are both defined as decentralized metros
and Los Angeles PMSA is classified as extremely decentralized metro based on
metro employment within certain distances of CBD (Glaeser, Kahn, and Chu
2001).
Chicago has been the stereotype of the modern industrial city, where the
Chicago School in sociology and urban studies originated (Park et al 1925). It is
also the city (together with Detroit) that Kain based his evidence on to introduce
the Spatial Mismatch Hypothesis. The very conditions and contexts that the
SMH is capturing such as the black and white color line, the concentric urban
form and reorganized industrial locations, are exemplified by the city itself.
Given its long-standing prominence in the U.S. urban history, Chicago has been
attracting immigrants from all parts of the world throughout the last century.
On the contrary, Los Angeles rises in the urban scene with the Los Angeles
School of New Urbanism (Dear 2000) which stands for a multi-ethnic and multi-
centric metropolis that is post-Chicago, post-industrial and post-modern. The

32

large inflow of immigrants after the World War II, especially from Asia and
Latin America has brought to the city real demographic and socioeconomic
diversity. Washington D.C. occupies a unique role among all metropolitan areas
as the site for federal policy making and policy makers base their urban
knowledge on their everyday experience of living in the city. Immigrants start to
settle in D.C. area from the 1980s, making it an “emerging gateway” (Singer
2004).

Table 3.1 Population and Immigrants, 1990, 2000 and 1990-2000 Growth
Population Immigrants Percentage Population Immigrants Percentage Population Immigrants Percentage
1990 8,015,923 907,506 11.3% 14,531,533 3,944,828 27.1% 4,045,388 481,525 11.9%
2000 8,904,130 1,456,185 16.4% 16,483,304 5,067,615 30.7% 4,708,479 815,221 17.3%
1990‐2000 888,207 548,679 61.8% 1,951,771 1,122,787 57.5% 663,091 333,696 50.3%
% Growth 11.1% 60.5% 13.4% 28.5% 16.4% 69.3%
Source: Calculations of 1990 and 2000 Census County and City Data Book
Chicago Los Angeles Washington, D.C.

Table 3.1 displays immigrants’ share in the total population for 1990,
2000 and population growth between 1990 and 2000 for the three cities. It shows
that immigrants’ share in the total population rises in all of the three
metropolitan areas, and while Chicago and Washington D.C. have smaller
immigrant populations overall than Los Angeles for both years, they
experienced substantive growth in the past decade. Immigrants constitute 16.4%

33

and 17.3% of the total population respectively in these two areas in 2000, as
compared to 30.7% in Los Angeles. But the immigrant populations grow by
60.5% and 69.5% respectively in the ten year period in those two areas while the
growth in Los Angeles is 28.5%. In all areas, immigrant growth contributes to
over half of the total population growth, representing a driving force behind
urban demographic changes. With their striking growth in Chicago and
Washington, D.C., and their already large base in Los Angeles, immigrants are
playing an increasingly important role in the social and economic lives of these
urban areas.

Figure 3.1 Racial/Ethnic Composition in 1990 and 2000
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990 2000 1990 2000 1990 2000
Latino
Other, Non‐Hispanic
Asian, Non‐Hispanic
Black, Non‐Hispanic
White, Non‐Hispanic
Chicago Los Angeles Washington,D.C.
Source: Census 1990 and 2000 Summary File 1 (SF1) 100Percent Data

34

While these three cities represent different types of immigrant
destinations, their racial/ethnic composition also depicts different patterns.
Myers (1999) made detailed demographic portrait of these three cities together
with New York for the 1980-1990 period. Ten years later, past trends continue in
the same direction and result in greater demographic diversity across
metropolitan areas. In all the areas, white population declined to various
degrees, now comprising of 60%, 39% and 56% of the total population in 2000
for Chicago, Los Angeles, and Washington, D.C. respectively. In Los Angeles,
white share has already fallen below half of the population. Black share of the
population largely stayed constant with slight declines for all areas, at 18.4%,
7.3% and 25.7% for the three metros in 2000. Latino, Asian, and other
3
categories
on the contrary experienced growth in share, with the Latino population
registering the fastest growth. They now represent 40% of the total population in
Los Angeles, matching the proportion of white residents, 16.4% in Chicago, and
8.8% in Washington, D.C. It has been documented that while the Latino group is
dominated by Mexicans in Los Angeles, its composition is more diverse in
Washington, D.C. area (Singer 2003).

3
The other category here needs to be interpreted with caution. As 2000 Census
started to let respondents mark two or more races in the questionnaire. Part of the
increase in the “others” category is attributable to the inclusion of multi-racial
people in this group for cross-census consistency.

35

In the face of these rapid demographic shifts brought about by
immigration and racial/ethnic composition change, it is imperative to
understand the labor market performance of Latinos, especially Latino
immigrants in urban areas. Placing their experience in the spatial and economic
structures in different metropolitan areas helps reveal how their socioeconomic
mobility is shaped by the cities and communities they live in. Insights on
common themes and diverging patterns shed light on other cities in the U.S. as
well.

2. Geographic Definitions
In this study, these three metropolitan areas refer to Chicago-Gary-Kenosha, IL-
IN-WI CMSA, Los Angeles-Riverside-Orange County, CA CMSA, and
Washington, DC-MD-VA-WV PMSA.
4
After an examination of the geographic
location, population and employment density and growth pattern of these
constitutive counties and following some previous studies (e.g. Pastor and
Marcelli 2000 for Los Angeles, Stoll 1999 for Washington, D.C), the City of

4
While largely following the county composition of these metropolitan areas in
2000, I have excluded certain counties that are either geographically remote or
cannot be separated on the Public Use Microdata Area (PUMA) level, which is the
geographic unit of analysis in my regression models. To be more specific, Kankakee,
IL PMSA and Kenosha, WI PMSA are excluded from Chicago-Gary-Kenosha, IL-IN-
WI CMSA and Berkeley County and Jefferson County, WV excluded from
Washington, DC-MD-VA-WV PMSA. For resulting counties and detailed statistics
for each metropolitan area, please see Appendix A.

36

Chicago, City of Los Angeles and District of Columbia are coded central cities,
their surrounding counties – Cook County, IL and Lake County IN, Los Angeles
County, CA, and Montgomery County, Prince George County, MD, Arlington
County, Alexandria City, VA are coded inner ring suburbs respectively, and the
rest outer ring suburbs.

Table 3.2 Population and Employment Distribution by Area, 1990 and 2000
Population ImmigrantsEmployment Population Immigrants Employment Population ImmigrantsEmployment
Central City 2,783,660 469,187 1,207,108 3,820,693 1,336,665 2,274,350 606,900 58,887 788,475
35% 52% 26% 26% 34% 32% 15% 12% 27%
Inner Ring 2,797,986 267,591 2,158,391 5,042,479 1,558,401 2,341,274 1,768,414 265,489 1,198,788
35% 29% 46% 35% 40% 33% 44% 55% 41%
Outer Ring 2,434,277 170,728 1,322,277 5,668,361 1,049,762 2,402,778 1,670,074 157,149 968,419
30% 19% 28% 39% 27% 34% 41% 33% 33%
Central City 2,896,016 628,903 1,220,040 4,057,398 1,512,720 2,160,033 572,059 73,561 756,979
33% 43% 23% 25% 30% 29% 12% 9% 22%
Inner Ring 2,965,289 461,648 2,376,859 5,522,700 1,936,724 2,293,085 1,992,592 428,770 1,307,757
33% 32% 44% 34% 38% 31% 42% 53% 38%
Outer Ring 3,042,825 365,634 1820840 6,903,206 1,618,171 2,972,918 2,143,828 312,890 1,352,559
34% 25% 34% 42% 32% 40% 46% 38% 40%
Source: Calculations of 1990 and 2000 Census County and City Data Book
Chicago Los Angeles Washington, D.C.
1990 2000

The spatial distribution of people and economic activities is uneven
across the three rings in each city and Table 3.2 presents an overview of the total
population, immigrant population and employment among central city, inner-
ring suburbs and outer-ring suburbs in the three metropolitan areas in 1990 and
2000. In all metros, central cities decrease in their proportion of metropolitan
total population, immigrant population and employment while outer ring

37

suburbs increase their shares uniformly between 1990 and 2000. The shares of
inner ring suburbs stay somewhat constant. In 2000, immigrants are distributed
relatively evenly in Los Angeles, concentrated in the central city in Chicago
(43%) and the inner-ring suburbs in D.C. (53%).

Figure 3.2 Population and Employment Growth by Area, 1990 and 2000
‐20%
0%
20%
40%
60%
80%
100%
120%
Population
Immigrants
Employment
Population
Immigrants
Employment
Population
Immigrants
Employment
Central City
Inner Ring Suburbs
Outer Ring Suburbs
Chicago Los Angeles Washington, D.C.
Source: Calculations of 1990 and 2000 Census City and County Databook

Growth statistics presented in Figure 3.2 show that the outer ring
suburbs outpace inner ring suburbs and central cities in gaining total and
immigrant population. In outer ring Washington, D.C. and Chicago, an
immigrant upsurge of around and above 100% is observed, signifying that
immigration is not only a central city, but also a suburban phenomenon. If not

38

for immigrants, population growth in central cities is either minimal or negative.
In terms of employment, total employment either decreased (Los Angeles and
Washington, D.C.) or increased very slightly (1.1% for Chicago) for the central
cities. The growth in inner ring suburbs is either negative (-2.1% for Los
Angeles) or small (10.1% for Chicago and 9.1 for Washington D.C.) compared to
the outer ring suburbs (23.7%, 37.7% and 39.7% respectively). Broken down by
industry (see Appendix A), except for one case (Washington, D.C.),
manufacturing jobs shrink in all central cities and inner ring suburbs. Job losses
in other industries are found in these areas too. Outer ring suburbs add
considerable number of jobs, especially in the service sector, followed by the
wholesale and retail trade sector. Overall, there is substantive discrepancy in job
opportunities across spatial subdivisions in these three metropolitan areas, in all
industries of employment. These variations will necessarily be reflected in the
employment accessibility of residents of different locations.

3. Intertemporal Spatial Mismatch Index
The spatial organization of both residence and employment in metropolitan
areas has evolved through the years. The relative movement of population and
jobs and their net effect can be captured through the Spatial Mismatch Index.
This index reduces the two complicated dynamics to a single number and thus
allows for inter-temporal comparison and testing of the persistence and degree

39

of spatial mismatch over time on different scales. Martin (2001) borrowed the
concept of dissimilarity index from segregation literature (Massey and Denton
1988) and found that from 1970 to 1990 as blacks’ residential mobility cannot
fully adjust to decentralized employment, the resultant combined impact
increased the disparity between the spatial distribution of employment and the
distribution of the black population by more than 20%. Raphael and Stoll (2002)
found a reverse of this trend from 1990 to 2000: there is a narrowing spatial
mismatch in the 20 metropolitan areas with largest black populations. It is
unclear however how the magnitude of spatial mismatch changed between the
location of immigrants and jobs.
The Spatial Mismatch Index (SMI) is expressed as

∑
− =
i
i i
P
P
E
E
SMI
2
1
, (1)
where i = (1,… n) and refers to each county (or other geographic sub-units) in
the metropolitan area. In this analysis, Ei and Pi are the employment and
population in a given county respectively. E and P are the employment and
population for the metro as a whole. Multiplying this proportion by 100, SMI
can be interpreted as the percentage of residents that need to be relocated in
order to achieve a perfect balance between employment and residential
distribution.

40

Table 3.3 Spatial Mismatch Index for Total, Immigrant and Native-born
Populations, 1990-2000
SMI with 2000
Employment
Distribution and Change due to Change due to
1990 Population Total employment population
1990 Distribution 2000Change shift shift
Total Population 4.3 5 4.8 0.5 0.7 -0.2
Foreign-Born 12.2 17.2 11.2 -1 5 -6
Native-Born 6 4.5 7 1 -1.5 2.5
Total Population 6.2 3.8 5 -1.2 -2.4 1.2
Foreign-Born 8.7 13.3 8 -0.7 4.6 -5.3
Native-Born 10.1 6.8 9.1 -1 -3.3 2.3
Total Population 15.1 12.6 13.8 -1.3 -2.5 1.2
Foreign-Born 22.1 21.5 21.6 -0.5 -0.6 0.1
Native-Born 15.5 16.7 13.5 -2 1.2 -3.2
Source: Calculations of 1990 and 2000 Census County and City Data Book
Chicago
Los Angeles
Washington, D.C.

This measure can be applied to test the imbalance between immigrants’
residential distribution and employment distribution in these three metros.
Table 3.3 shows the results. In each of the metros, SMI is calculated for both
years for total, foreign-born
5
and native-born populations. SMI is also obtained
with 2000 employment distribution and 1990 population distribution so as to
decompose the total intertemporal difference into changes attributable to
employment shift and population shift.
Comparatively, Washington D.C. has the highest SMI level for all
population groups among all metros. In Chicago and Washington, D.C. foreign-

5
Foreign-born and immigrant are used interchangeably in this study to denote
those people who are not born in the U.S. and are not born of American parents.

41

born populations are residentially more distant from where jobs are in both
years as evidenced by their higher SMI whereas the reverse is true for residents
of Los Angeles. In terms of change, the degree of spatial mismatch diminished
for foreign-born residents in all metros, but only for native-born residents in Los
Angeles and Washington, D.C. What’s more, while the magnitude of total
change seems to be small, counter-acting forces of employment and population
shifts are quite substantial. In both Chicago and Los Angeles, jobs are diverting
from where immigrants live during the last decade. Immigrants however are
moving towards where jobs are, and this residential mobility totally offset the
adverse effect of employment shift, resulting in a decline in spatial mismatch.
The opposite is observed for native-borns in these two metros with native-born
workers moving away from jobs and jobs following them. The pattern in
Washington, D.C. is somewhat different.
These trends illustrate immigrants’ relative lack of spatial proximity to
employment as compared to native-borns, especially in Chicago and
Washington, D.C. If geography constitutes a major barrier in the successful job
launch of immigrants, we would expect to see that immigrants, especially those
residing in concentrated enclaves to have lower employment rate and longer
commutes than native-born workers. It can also be expected that disparities in
Chicago and Washington, D.C. would be larger than that in Los Angeles.
Analyses in Chapters 4 and 5 are conducted on the intra-metropolitan level and

42

will reveal the dynamics behind these aggregate trends by examining
immigrants’ employment accessibility by various residential locations in
comparison to native-borns.

43

CHAPTER 4.

ETHNIC ENCLAVE RESIDENCE
AND EMPLOYMENT ACCESSIBILITY

This chapter examines the impact of living in ethnic enclaves in different
parts of the metropolitan area on low-skilled Latino workers’ employment
accessibility. It does so by comparing the employment status and commuting
times of Latinos living in and out of ethnic neighborhoods in central city, inner-
ring suburbs and outer-ring suburbs in Chicago, Los Angeles and Washington,
D.C. Using 2000 Public Use Microdata Sample (PUMS), this chapter finds that
enclave effect is much muted and spatial mismatch effect evident in the central
cities. But in the suburban areas, while as likely to work as non-enclave
counterparts, enclave residents tend to commute longer to jobs, suggesting the
importance of ethnic networks in enclave neighborhoods. These disparities in
commuting duration are not fully compensated for by their wage earnings or
neighborhood-level housing costs. Further distinguishing Latino immigrants by
gender shows that women are more enclave-disadvantaged than men.

44

1. Introduction
The effect of residential segregation on minorities’ economic well-being has been
a subject of much academic and policy attention. Difficulty in accessing
suburbanized job opportunities, especially low-skilled jobs, has constantly been
found to be a major obstacle for inner city minorities which results in their high
unemployment rate, long commutes and low wages (“Spatial Mismatch
Hypothesis”, Kain 1968, see Houston 2005 for review) and the concentration of
poverty in the certain neighborhoods (Wilson 1987). In the last decade,
immigrants continue to settle in large U.S. metropolitan areas and are
participating in the urban labor force on a large scale (Frey 2002). Given their
high level of residential segregation in ethnic neighborhoods (Cutler, Glaeser
and Vigdor 2005) and the continued trend of economic restructuring and
employment decentralization (Glaeser, Kahn and Chu 2001), it is imperative to
understand their employment accessibility, and how it is shaped by the spatial
structure and social environments of the cities and communities they live in.
Comparisons between Latino workers and black workers in segregated
neighborhoods have found that immigrant barrios do not resemble traditional
ghettos for their rich social and ethnic capital and vibrant informal economies
(Clark 2001) and immigrant workers are more likely than blacks to use
neighborhood contacts and networks to locate jobs (Elliott and Sims 2001).
Termed as “Ethnic Enclave Hypothesis”, it is argued that ethnic enclaves

45

provide immigrants with alternative paths to economic stability (Wilson and
Portes 1980). While it is widely agreed that ethnic neighborhood context plays a
large role on immigrants’ job acquisition and economic achievement, the
direction and magnitude of these effects remains largely unclear in the empirical
literature. Existing studies show that residence in ethnic enclave neighborhoods
has no significant effect or negative effect on the employment status of certain
immigrant women groups (Parks 2004b), or even hampers immigrants’
economic assimilation as measured by wage growth (Borjas 2000).
6

In light of these ethnic enclave effects, a key question is does spatial
proximity still matter for immigrants or social networks can overcome their
geographic barriers to employment. Recent application of the spatial mismatch
hypothesis to immigrants in Los Angeles suggests that they are not as spatially
constrained in their employment probabilities as traditional minorities (Pastor
and Marcelli 2000, Painter, Liu and Zhuang 2007). While these two lines of
literature focus on ethnic concentration and spatial mismatch respectively on
immigrants’ employment outcomes, no study has explicitly taken into account
the interaction between the two and their different implications for immigrants.
The two distinctive concepts of inner city neighborhood and ethnically
concentrated neighborhood are even sometimes mixed together. As a matter of

6
These studies measure ethnic enclave on different scales, ranging from census
tracts (Parks 2004b) to metropolitan area (Borjas 2000).

46

fact, ethnic concentration is no longer a central city phenomenon. While
traditional spatial assimilative theories suggest that dispersion is the end result
of immigrants’ locational attainment (Massey 1985), recent studies have shown
that immigrants do not necessarily move to white suburban neighborhoods as
they live in the country for a longer period of time (Alba et al. 1999). On the
contrary, ethnic clustering can endure even with their accumulated wealth and
elevated socioeconomic status, and is evident in suburban areas as well as inner
cities (Logan, Alba and Zhang 2002
7
). At the same time, the suburb is no longer
a uniform concept, as the country’s first, older suburbs differ considerably from
both the inner city and newer suburbs and has distinctive implications for its
residents (Puentes and Warren 2006).
This chapter captures these spatial contexts by partitioning metropolises
into three areas: central cities, inner ring suburbs and outer ring suburbs. Within
each area, ethnic enclaves with high concentration of Latino immigrants are
identified. It contributes to the literature by examining the interactive effects of
ethnic enclave residence with structural spatial location and testing the ethnic
enclave hypothesis and spatial mismatch hypothesis simultaneously. It
compares employment accessibility of low-skilled immigrant and native-born
Latinos in and out of ethnic enclaves in central city, inner ring suburbs and outer

7
They distinguish these two types of neighborhoods by “ethnic communities” and
“immigrant enclaves”.

47

ring suburbs in order to illustrate the distinctive impacts of different residential
choices on residents’ likelihood of obtaining a job and the travel burden if
working. Unlike other studies that look at only one indicator, this chapter
examines employment status and commuting duration together as these are the
two interlinked aspects of employment accessibility. Should inner city and
enclave residents experience longer commutes, it is further explored whether
these spatial disparities persist after possible compensations in neighborhood
housing prices and workers’ wage earnings are accounted for. Lastly, it
highlights the interaction between space and gender by analyzing Latino
immigrant men and women separately.

2. Review of Literature
Examination of the spatial separation between residence and job growth and
how it translates into labor market performances of minority workers has
gathered much research attention. The decentralization and segmentation of job
opportunities increases the job search and commuting costs of inner city
minority workers, who suffer from relatively restricted residential mobility in
the urban housing market (Kasada 1988). At the same time, the inaccessibility of
suburban job sites by public transportation (Sanchez 1999) and the limited car
ownership of low-skill workers (Taylor and Ong 1995, Raphael and Rice 2002)

48

further constrain their employment possibilities and diminish their quality of
employment.
One empirical strategy to test the spatial mismatch hypothesis is
comparing the labor market outcomes of central city and suburban residents
(Ihlanfeldt and Sjoquist 1998). A number of studies has examined intra-
metropolitan and inter-group employment outcome differentials (e.g. Raphael
1998, Stoll 1999) and has generally found significant effect of residence in central
cities, where job growth is weak or negative, on the unfavorable employment
status of blacks as compared to whites. In the transformed urban geography, the
suburbs is no longer a uniform concept, rather there are vast variations among
suburban communities. Such urban problems as were traditionally associated
with central cities – deteriorated infrastructure and old housing stock, high
crime rates, low-quality public schools, and concentration of poverty – are
quickly spreading to inner ring suburbs as well (Downs 1997). A recent study by
Puentes and Warren (2006) selected 20 American’s first, older suburbs based on
their age, location and demographic trends from 1950 to 2000. According to
them, these inner ring suburbs differ from both the central cities and the newer,
fast-growing places and are the “policy blindspots” that deserve much attention.
This paper thus goes beyond the central city-suburban dichonomy that is
prevalent in spatial mismatch literature and adopts a three-area geographic
partition of central city, inner ring suburbs and outer ring suburbs.

49

Once travel mode is controlled for, commuting duration provides a direct
indicator of the geographic mismatch between home and work for low-skilled
workers and their mobility difficulties in a spatially segmented labor market
(Taylor and Ong 1995, Gotllieb and Lentnek 2001). Longer commutes may not
only increase travel burden and job cost for workers, but may also lower their
actual wage rate and increase their unemployment probabilities (Ong and
Blumenberg 1998). Studies that compare commuting patterns of workers by
location and racial/ethnic identity have found that blacks living in the central
cities commute longer to work than their white counterparts in the 1980s (Taylor
and Ong 1995
8
). Gottlieb and Lentnek (2001) and Shen (2001) went beyond the
structural location of the central city and further established that blacks living in
low-income minority neighborhoods suffer from longer commutes.
9

Very few studies address the effect of residential segregation on
immigrants’ employment accessibility and there is little consensus in the
literature. Aponte (1996) began the inquiry for immigrants and found that
Mexican workers are an “exception” to the spatial mismatch hypothesis as they
consistently depict relatively high employment rate as compared to native-born

8
They attribute blacks’ long commutes to their reliance on slower public transit and
contends that there is more an “auto mismatch” than spatial mismatch.

9
Gottlieb and Lentnek (2001) also argued that spatial mismatch is not only a central
city problem. Blacks living in minority neighborhood in the suburbs are also faced
with spatial barriers to work.

50

minority workers, which might be attributable to their strong social networks in
job search and employers' hiring strategy. Pastor and Marcelli (2000) found that
individual skills matter more than "pure" spatial mismatch in Los Angeles,
especially for recent Latino immigrants. Also for Los Angeles, Painter, Liu and
Zhuang (2007) underscored the importance of space on Latino and second-
generation immigrant youth’s employment probabilities, but not for first-
generation immigrants.
In regards to commuting, results by Preston, McLafferty and Liu
(1998) indicate the persistence of spatial barriers faced by immigrant workers as
evidenced by their overall longer commutes than their America-
born counterparts in central New York CMSA. Parks (2004a) found that living
in ethnic enclaves shorten commute times to different extent for six immigrant
groups in Los Angeles area and claimed that “space still matters”. Controversies
arise from partial conceptualizations of residential segregation and employment
accessibility, and from focusing on a single urban area. This chapter approaches
this question with a more comprehensive view of residential location which
consists of both spatial proximity to jobs and social accessibility to ethnic
networks in locating jobs. It also considers the two interlinked accessibility
indicators of employment status and commuting duration together, as well as
comparing three different immigrant metros to explore any common patterns

51

Urban economic theories suggest that housing prices and wage rates
compensate workers for their commuting costs. That is, a worker might choose
to live farther from employment locations for cheaper housing and more
favorable neighborhood amenities. Also, in a competitive market, workers
should be compensated by higher wage earnings for their longer commutes
(Mills 1972). Gabriel and Rosenthal (1996) and Petitte and Ross (1999) have
shown that these compensation differentials lessen the disparities in commuting
duration. Also, it is acknowledged in the literature that men and women face
different spatial and social barriers to employment given their distinctive roles
within the households and in the labor market (e.g., Hanson and Pratt 1995).
This chapter takes these important issues into consideration.

3. Data and Methodology
3.1 Data and Sample
The primary datasets for this study is the 2000 Census Public Use Microdata
Sample (PUMS). These data files feature a very detailed list of demographic,
socioeconomic and commuting variables for households and individuals that
are crucial for the research questions. The smallest geographic identifier given in
PUMS is Public Use Microdata Area (PUMA): statistical areas with at least
100,000 residents. PUMA is the analytical unit of space in this study, on which
neighborhood characteristics are calculated. The limitation of using PUMA is

52

that ethnic enclaves need to be identified on a relatively large geographic scale,
as compared to such finer geographies as the census tract. However, census tract
level research would necessitate using summary statistics for demographic and
socioeconomic indicators as well, losing the rich individual and household
characteristics available from the microdata
10
. Also, PUMAs proximate the
larger local housing and labor markets, making them meaningful geographies to
measure ethnic concentration as well. As the three-area geographic partition is
conducted on the county level based on population and employment
distribution and growth as described in the previous chapter, PUMA
boundaries are matched to county boundaries in order to group individuals by
their place of residence.
11

The sample of this research is low-skilled immigrant and native-born
Latinos between the ages of 16 and 65 in these three metropolitan areas who are
in the labor force.
12
Here, low-skilled refers to those with less than a high school
degree. Those people who live in group quarters or are non-relatives of the
household head are also excluded from the sample. As previous studies show

10
Unless special arrangements can be made with the Census Bureau to access
confidential microdata with geographic identifier on the census tract level.

11
The Integrated Public Use Microdata Series (www.ipums.org) has these
correspondence tables under “Geographic Tools”.

12
Those not in the labor force include housework, unable to work, school and other.

53

that Latino workers exhibit high levels of employment rate (Aponte 1996),
placing immigrants in comparison to native-born workers in the same
residential location reveals any distinctive traits that are particular to
immigrants, especially the importance of social networks. In estimating models
of commuting time, the samples are further restricted to Latino workers who
worked outside the home last week and have a positive commute time. Low-
skilled Latino workers, especially immigrants have constrained residential and
economic mobility and are a vulnerable group in urban labor market. They are
more subject to spatial barriers than highly-educated and high-skilled workers,
who are compensated for their longer commutes by high-paying jobs.

3.2 Defining Ethnic Enclave
There exist different ways of defining ethnic enclaves, and on different
geographic scales. The majority of works define ethnic enclaves on the census
tract level, using some index of relative concentration. These include odds ratio
(e.g. Logan, Alba and Zhang 2002, and Alba, Logan and Crowder 1997, with
contiguity criterion), location concentration quotient (Park 2004b), among
others. The exact thresholds vary as well across different studies. Some
researchers have realized that immigrants’ spatial concentration pattern has
evolved with advancing transportation and communication and ethnic
neighborhoods now extend over larger geographic areas with lower density

54

than they used to be, normally comprising of many adjacent census tracts
(Logan, Alba and Zhang 2002). Measuring ethnic concentration on the PUMA
level, Wang (2006) shows that defined PUMA level ethnic enclaves largely
represent the tract-level concentration pattern, making it meaningful scale of
measuring spatial clustering. In this research, immigrant enclave dummies are
constructed on the PUMA level, based on calculations of residential
concentration quotient (RCQ) as expressed by

RCQ=
m
im
j
ij
P
P
P
P
, (2)

where j= (1,….n) and refers to the PUMA. P
ij
is the number of Latino immigrants
in a PUMA and P
j
is the total population in that PUMA. P
im
is the number of
Latino immigrants in a metro and P
m
is the total population for that metro. A
RCQ of 1 means that Latino immigrant concentration in a certain PUMA is on
par with that of the metro whereas a RCQ of greater than 1 stands for a greater
level of Latino immigrant concentration. This paper uses the threshold of
RCQ>2 for Latino immigrant enclaves indicating those PUMAs that have twice
or higher concentration of Latino immigrant population than the metro as a

55

whole.
13
By this definition, 10 out of 61 PUMAs in Chicago, 17 out of 110
PUMAS in Los Angeles and 6 out of 32 PUMAS in Washington, D.C. are
considered Latino immigrant enclaves in 2000. Of all the Latinos living in these
metropolitan areas, around half live in these ethnic communities in Chicago and
Los Angeles (53% and 47% respectively) while three quarters live in ethnic
concentration in Washington D.C. (73%).

Table 4.1 Number of Enclaves and Percentage Reside in Enclaves
Number of Enclave PUMAs
Percentage Latinos in Enclaves
source: author's calculation of PUMS data
10 17 6
53% 47% 73%
Washington, D.C. Los Angeles Chicago

3.2 Model Specifications
This study compares the likelihood for employment of immigrant and native-
born Latinos living in central cities, inner ring suburbs and outer ring suburbs

13
The identification of an ethnic enclave lacks definite quantifiable criterion in the
literature. For odds ratio, some authors set the threshold value to be 1.5 (Wang 2006)
and for RCQ, Parks (2004a) used the cut-off RCQ of 5 for Salvadoran, Guatemalan,
Chinese, Korean and Vietnamese enclaves and RCQ of 3 for Mexicans in Los
Angeles. While she based her analysis on a finer geographic scale – census tract,
relative concentration on the PUMA level is much smaller. Therefore, a cutoff RCQ
of 2 is used here.

56

and those living in ethnic enclaves versus in mixed neighborhoods. If employed,
their commuting times are examined across these locations. Should there exist
differentials in economic outcomes among groups living in various rings after
other human capital and household attributes are controlled for, it is an
indication that spatial accessibility and proximity to jobs remains an issue for
inner city residents. Otherwise if such employment disparities only exist
between enclave and non-enclave residents in the same ring, then it is more a
matter of social accessibility and social networks. While Latino immigrants are
the focus of this study, their native-born counterparts serve as important
comparisons to reveal any characteristics specific to immigrants.
Following the empirical strategy of Painter, Liu and Zhuang (2007),
which compares the employment and schooling status of minority and
immigrant youth living in different parts of Los Angeles metropolitan area,
employment status is estimated on locational effects, individual and household
characteristics using probit model. Commuting times are estimated in a two-
stage least squares (2SLS) framework by treating wage earnings as endogenous,
as explained earlier.
14
In order to bypass this simultaneity, instrument variables

14
While this paper estimates employment status and commuting time in two
separate models, some studies choose to estimate these two outcomes
simultaneously in a sample selection framework: commuting time model that
controls for employment status. However, Gabriel and Rosenthal (1996) suggests
that the selection effects are slight and insignificant.

57

are needed to identify the predicted wage variable in the commuting model. It is
suggested in the literature that household wealth, i.e. other income besides the
worker’s labor earnings and the sources of non-labor income will affect a
worker’s earning but will not directly influence the commuting duration
(Gabriel and Rosenthal 1996, Petitte and Ross 1999). Independent variables used
in the employment model will be entered in commuting time model, as well as
variables that indicate a worker’s travel mode and industry of employment, and
PUMA-level housing prices. The resulting models are expressed as:

Prob (Employment
ij
=1) = f (L
j
, X
ij
, Wealth Composition
ij
), (3)
Log (Commute Time
ij
) = f (L
j
, X
ij
, M
ij
, I
ij
, H
j
, Wage
ij
),
Log (Wage
ij
) = g (L
j
, X
ij
, M
ij
, I
ij
, H
j,
Wealth Composition
ij
), (4)

where i indexes individuals and j indexes PUMAs, Employment
ij
is binary
employment status (employed or not), Commute Time
ij
is the usual travel time
to work in minutes, and Wage
ij
is a worker’s pre-tax wage and salary income in
1999, both expressed in log linear format. L
j
are the area dummy variables of
living in central city, inner ring suburban or outer ring suburban locations
interacted with ethnic residence status to explicitly illustrate each neighborhood
type’s effect on low-skilled Latino workers’ employment accessibility. X
ij
is
composed of workers’ sociodemographic characteristics including immigrant

58

status, and for immigrants, their membership in different arrival cohorts to the
United States and English language proficiency, gender, marital status, presence
of children under 5 in the household and labor market experiences. M
ij
is
commuting mode to work by public transportation or other modes with
automobile-riders being the reference, as travel speed necessarily affects the
length of commuting. I
ij
indicates a worker’s industry of employment that
corresponds to the industrial groupings presented earlier for each metropolitan
area. In a restructured and suburbanized urban economy, the geographic
distribution and turnover rate of jobs in different industries vary substantively,
providing different levels of proximity and accessibility for low-skilled
workers.
15
H
j
represents PUMA-level median housing prices and median
monthly rents (both in loglinear forms) to capture possible neighborhood cost-
of-living variations across different types of residential locations. Lastly, Wealth
Composition
ij
is a vector of the amount and composition of household non-labor
income, including dummy variables indicating whether the household received
investment income, business income, Social Security income and welfare income
in 1999. These household wealth conditions help determine a member’s decision
to enter the labor market and the optimal amount of labor he or she is willing to

15
For example, Preston, McLafferty and Liu (1998) found that employment in
manufacturing and producer services jobs increase central city New York women’s
commutes.

59

supply for wage earnings, but they do not directly affect the travel time to work.
Therefore, they enter the employment models and serve as instrument variables
for wages in 2SLS models on commuting times. Each model is estimated for the
total sample, and is stratified by immigrant status to highlight how these factors
impact native-born and immigrant workers differently. Distinctions are further
made between male and female Latino immigrants in separate models to
explore the interaction between space and gender.

3.3 Descriptive Statistics
Descriptions of independent variables and their sample mean statistics for the
three cities are presented in Table 4.2. These statistics reveal both common
patterns and also important variations of the chosen metropolitan areas. While
D.C. inner ring suburbs are home to over half of its low-skilled Latino
population, a majority of low-skilled Latino workers in Chicago reside in the
central city. Ethnic enclave residence is evident in all areas for the three cities,
with highest proportions found in Chicago’s central city, Los Angeles’ inner ring
suburbs and D.C.’s inner ring suburbs (37%, 14% and 30% respectively). Over
three quarters of low-skilled Latino workers are immigrants in all cities and
around half speak no or limited English. However, their migration cohort
compositions are not the same. D.C. area has the highest share of new comers
(45% of 1990s arrival and 36% of 1980s arrivals) whereas both Chicago and Los

60

Angeles have more established immigrants, pointing to their distinctive
positions as immigrant destinations. Other sociodemographic characteristics
show striking similarities, in terms of percentage female workers, percentage
with children less than 5 years old in the household, percentage married, and
years of working experience.
D.C. workers rank first in other household income, followed by L.A. and
Chicago workers. Around or less than 10% of workers in all areas receive any
kind of non-labor income. In terms of commuting mode, a vast majority of
workers (around or above three quarters) in the sample use private automobiles,
with Washington, D.C., a metro with relatively extensive public transportation
system, having the lowest share. There exist certain differences in the industrial
composition of workers, with lower proportion of D.C. workers in
manufacturing and trade jobs, and more in services and public administration
jobs, compared to Chicago and Los Angeles.

61

Table 4.2 Independent Variable Description and Sample Means
Chicago L.A. D.C.
Location Variables
Central City Residence in Central City 0.480.300.11
Central City Enclave Residence in Central City Enclave 0.37 0.14 0.06
Inner Ring Suburb Residence in Inner Ring Suburb 0.26 0.36 0.52
Inner Ring Suburb Enclave Residence in Inner Ring Suburb Enclave 0.07 0.14 0.30
Outer Ring Suburb (omitted) Residence in Outer Ring Suburb 0.27 0.34 0.37
Outer Ring Suburb Enclave Residence in Outer Ring Suburb Enclave 0.13 0.05 0.10
Sociodemographic Variables
Immigrant If foreign-born (1=yes) 0.76 0.79 0.91
No or Limited English Proficienc If no or limited English Proficiency (1=yes) 0.44 0.44 0.51
Migration Cohort 1 (omitted) Arrived in the U.S. 1990-2000 0.32 0.23 0.45
Migration Cohort 2 Arrived in the U.S. 1980-1989 0.21 0.30 0.36
Migration Cohort 3 Arrived in the U.S. 1970-1979 0.17 0.20 0.07
Migration Cohort 4 Arrived in the U.S. before 1970 0.06 0.06 0.04
Female If female (1=yes) 0.36 0.39 0.38
With Child Presence of child(ren) under 5 in household 0.22 0.22 0.21
Married If married (1=yes) 0.61 0.59 0.56
Experience
a
Working Experience in years 18.16 18.90 17.03
Experience2 Working Experience Squared in years 496.03 509.31 428.55
Wealth Composition Variables
Other Household Income
b
besides wage earnings in 1999 in dollars 7297 16801 22781
Investment Income if received interest income in 1999 0.06 0.05 0.08
Business Income if received business income in 1999 0.03 0.09 0.11
Social Security Income if received social security income in 1999 0.04 0.05 0.02
Welfare Income if received welfare income in 1999 0.03 0.08 0.02
Commuting Mode Variables
c
Auto (omitted) if commute by automobile 0.82 0.81 0.75
Transit if commute on public transportation 0.11 0.12 0.18
Other Mode if commute by other means 0.08 0.07 0.07
Industry of Employment Variables
c
AMC (omitted) Employed in Agriculture, Mining or Construction 0.09 0.12 0.29
Manufacturing Employed in Manufacturing Industry 0.36 0.28 0.03
Trade Employed in Wholesale and Retail Trade 0.15 0.16 0.10
FIRE Employed in Finance, Insurance & Real Estate 0.03 0.03 0.04
Services Employed in Services 0.26 0.27 0.37
Public Employed in Public Administration, Transport or Util 0.12 0.14 0.18
Neighborhood Variables
Median House Value
b
Median House Value in PUMA of Residence
Median Rent
b
Median Monthly Contract Rent in PUMA of Residence
N 4038 41280 2626
a. Obtained by ( age - education(years) - 6) and adjusted by year last worked.
b. Natural Log is taken in model estimation.
c. Conditional on being employed.

62

Table 4.3 Mean Employment Rate, Commute Time and Annual Wages of
Latinos by Neighborhood Type and Nativity
ForeignNative- ForeignNative- ForeignNative-
All Born Born All Born Born All Born Born
Non-Enclave
Employed 0.87 0.92 0.78 0.86 0.87 0.80 0.91 0.93 0.77
Commute
a
33.82 34.44 32.33 29.89 30.49 26.46 31.95 31.31 38.30
Wages
b
17630 18882 14577 15417 15516 14849 20869 20532 24240
N 437 294 143 6635 5587 1048 122 109 13
Enclave
Employed 0.86 0.88 0.81 0.86 0.88 0.74 0.92 0.93 0.75
Commute
a
34.84 35.08 34.09 31.91 32.31 28.47 36.07 35.29 55.50
Wages
b
17863 18232 16674 13758 13916 12392 17174 16692 29233
N 1483 1116 367 5737 5051 686 175 167 8
Non-Enclave
Employed 0.90 0.90 0.87 0.86 0.89 0.80 0.91 0.92 0.88
Commute
a
23.90 24.46 22.24 26.75 27.26 25.21 31.85 32.80 23.08
Wages
b
19735 21159 15534 17531 18072 15901 16824 17504 10576
N 756 560 196 8845 6470 2375 582 523 59
Enclave
Employed 0.87 0.88 0.84 0.87 0.88 0.78 0.92 0.93 0.86
Commute
a
33.33 32.91 34.84 28.40 28.72 26.72 35.71 36.04 30.38
Wages
b
18906 20127 14532 16046 16506 13634 17466 17565 15828
N 280 218 62 5959 4904 1055 778 729 49
Non-Enclave
Employed 0.91 0.94 0.83 0.87 0.89 0.82 0.93 0.93 0.87
Commute
a
21.72 21.63 22.10 25.99 26.50 24.52 28.03 28.42 25.71
Wages
b
19735 20537 16349 16672 17001 15714 17955 19023 13180
N 561 441 120 11902 8651 3251 707 598 109
Enclave
Employed 0.90 0.92 0.77 0.90 0.91 0.81 0.95 0.96 0.80
Commute
a
25.47 26.38 18.78 27.66 28.05 23.99 32.02 32.38 21.25
Wages
b
18968 18918 19332 15440 15579 14150 16705 17034 9267
N 521 444 77 2202 1958 244 262 252 10
a. Commuting time in minutes. Figures are conditional on being employed.
b. Annual wages in dollars. Figures are conditional on being employed.
Chicago Los Angeles Washington, D.C.
Central
City
Inner
Ring
Suburb
Outer
Ring
Suburb

63

An initial view of the employment rate, commuting time and wages of
Latino workers, stratified by their residential location and immigrant status is
provided in Table 4.3. For each sample, it is almost always the case that central
city residents have the lowest employment rate and longest commutes while
outer ring suburban residents have the highest employment rate and shortest
commutes, with few exceptions. While differences in employment rate seem to
be small between enclave and non-enclave residents, enclave workers tend to
commute longer to work than comparable non-enclave workers in all but two
instances (native-born Latino workers in the outer ring suburbs of Los Angeles
and Washington, D.C.). Overall, wages exhibit a less clear pattern. Comparisons
between native-born and immigrant workers demonstrate that immigrants have
higher employment rate than native-born workers in all locations, but their
journey to work are generally longer in a majority of cases. These patterns
suggest that both spatial proximity and enclave effects are at work in
determining Latino workers’ employment accessibility, and these effects apply
differently to native-born and immigrant workers.

64

4. Empirical Results

Table 4.4 presents results for probit models of employment status and Table 4.5
displays 2SLS model estimates of commuting times, the first stage regression
results on wages can be found in Appendix B. The F-statistics for the test of the
collective significance of wealth composition variables as instruments on wages
are all quite large and significant at 0.1% level.
16
For each table, statistics are
shown for the three cities and within each city, for the stratified samples of
foreign-born and native-born workers respectively. Lastly, Table 4.6 presents the
locational variable estimates for male and female Latino immigrants separately
from both the employment status models and commute time models.

4.1 Employment Status Models
Three residential location areas (central city, inner ring suburbs and outer ring
suburbs) and ethnic enclave status are interacted to create six types of diverse
neighborhoods. Using outer ring suburbs in general as the omitted reference
group, statistics reveal the relative effects of living in other five types of
neighborhoods on Latino workers’ employment status and commutes.

16
The only exception is F-statistics for native-born Latino workers in Washington,
D.C., which is not significant. This might be due to the small sample size of this
group.

65

Table 4.4 Probit Regression Estimates of Latino Employment Status by Nativity
Intercept 0.610 *** 1.502 *** 0.492 *** 0.966 *** 1.188 *** 1.270 ***
Location Variables
Central City -0.274 -0.144 0.017 -0.103 *** -0.083 -0.043
Central City Enclave 0.051 -0.236 * -0.163 * 0.045 0.048 0.064
Inner Ring Suburb 0.162 -0.253 * -0.046 -0.044 -0.106 -0.138
Inner Ring Suburb Enclave -0.191 -0.102 -0.039 0.009 0.036 0.054
Outer Ring Suburb Enclave -0.364 -0.087 -0.023 0.149 ** 0.140 0.183
Sociodemographic Variables
No or Limited English Proficiency -0.13 -0.107 *** -0.062
Arrived in 1980s 0.052 -0.059 * -0.062
Arrived in 1970s -0.071 -0.082 ** -0.090
Arrived before 1970 -0.227 -0.155 *** -0.086
Female 0.096 -0.342 *** -0.008 -0.438 *** -0.252 -0.487 ***
With Child 0.147 -0.237 ** -0.009 0.024 0.521 -0.031
Married -0.053 -0.050 0.272 *** 0.020 -0.065 -0.024
Experience 0.050 *** 0.045 *** 0.059 *** 0.046 *** -0.005 0.059 ***
Experience2 -0.001 -0.001 *** -0.001 *** -0.001 *** 0.001 -0.001 **
Wealth Composition Variables
Log (Other Household Income) 0.016 -0.007 -0.003 0.006 ** -0.046 -0.002
Investment Income 0.382 -0.043 0.312 *** 0.110 ** -0.191 0.149
Business Income 0.039 -0.056 0.275 *** 0.175 *** 0.313 0.208
Social Security Income -0.120 -0.097 0.136 -0.191 *** 4.316 -0.721 **
Welfare Income -0.748 ** -0.303 -0.471 *** -0.496 *** -1.368 ** -0.555 *
Log Likelihood -420.5
N 965
*p<0.05, **p<0.01, ***p<0.001.
2378
Foreign
-949.6 -3942.9 -10940.5 -89.5 -554.9
3073 8659 32621 248
Chicago Los Angeles Washington, D.C.
Native Foreign Native Foreign Native

66

Results in Table 4.4 show that living in central cities and inner ring
suburban areas in all three cities lowers immigrants’ employment probability,
and this effect is significant for inner ring suburban Chicago and central city Los
Angeles. As the previous chapter presented, the outer ring suburbs outpaced
both the central cities and inner ring suburbs in employment growth in all three
cities, granting greater employment accessibility to those living in these areas. In
terms of enclave residence, Latino immigrants in central city enclaves in Chicago
are less likely to be employed than outer ring suburban residents, all else equal.
In Los Angeles’ outer ring suburbs, being in an ethnic enclave actually increases
Latino immigrants’ employment probabilities. It seems that social networks and
social connections are effective where jobs abound, that is the outer ring
suburbs, and either no enclave advantage or enclave disadvantage in job
accessibility is found for central cities and inner ring suburbs. In Washington
D.C. area, no significant spatial effect is observed. These results indicate that
spatial effects on employment status are sparse and are confined to certain
metropolitan areas for Latino immigrants. Enclave effects show different signs:
negative in central city Chicago and positive in outer ring suburban Los
Angeles, suggesting that for Latino immigrants in the labor force, enclave
residence is reinforcing the spatial disadvantage/advantage of the structural
location that they belong to. These interactions are important and one misses

67

these crucial inter-linkages by just talking about spatial mismatch effect or
ethnic enclave effect without reference to the other.
From results for pooled Latino sample (not shown here), being an
immigrant is more likely to be employed than native-born workers in Chicago
and Los Angeles. With regards to native-born Latinos, residential location does
not play a significant role in their employment outcomes, with one exception.
For native-born Latinos, living in central city ethnic enclaves with high
concentration of Latino immigrants actually dampens their employment
probability. A supply effect might be in play here, with native-born and
immigrant Latino workers competing for jobs in the area.
In terms of other variables in the model, in all instances, having no or
poor English language skills hurts immigrants’ chances of getting a job in the
labor market, and this effect is significant in Los Angeles. An important
reference in time that is not shared by native-born workers is immigrants’
duration in the United States. Assimilation theories suggest that immigrants
register socioeconomic progress and cultural familiarity in the host society as
their residential tenure endure (Gordon 1964), though the mode and pace of
incorporation can be uneven (Alba and Nee 1997). For low-skilled Latino
immigrants however, cohort effect is either not significant (Chicago and D.C.) or
negative for earlier arrivals as compared to the newest cohort who arrived
during the 1990s (L.A.). This suggests the high employment rate of Latino

68

immigrants upon their first arrival. Female immigrants are less likely to be
employed than their male counterparts in all three metropolitan areas and but
the same effect does not apply to native-born Latinos. This signifies specific
constraints and barriers that immigrant women face in successfully entering the
labor market, which are not shared by native-born women. Further research is
needed to identify these distinctive conditions. Latino immigrants with children
are less likely to work only in Chicago. Previous studies have noted that
women’s household responsibilities constrain their job search radius to smaller
geographies and limited occupations, which lowers their employment
probabilities (Hanson and Pratt 1995). In L.A. and D.C. however, they are not
adjusting their labor supply to accommodate childcare needs at home.
Experience exhibits uniformly significant effect on employment probability for
all cities and groups, with each additional year having diminishing gains in
employment rate.
Of all the wealth composition variables in the model, having social
security income and having welfare income in the household consistently
lowers the likelihood a Latino will work. Interestingly, in Los Angles, higher
household non-labor income leads to higher employment probability for
immigrants, so does having investment income and business income for both
immigrant and native-born Latinos. It looks like these Latino households view
investment and business income as complementing, rather than substituting

69

labor earnings and the more prosperous households benefit from multiple
sources of income.

4.2 Commuting Models
Turning to commuting time models, the interactions of spatial mismatch effect
and ethnic enclave effect demonstrate striking similarities across the three cities.
Central city residents uniformly suffer from significantly longer commutes than
outer ring suburban residents. Living in ethnic enclave does not make a
difference in this area. Inner ring suburban residents in Chicago and Los
Angeles also tend to commute longer, but the effects are smaller in magnitude.
One important finding is that strong enclave effects are detected for both inner
ring suburbs and outer ring suburbs, but not for central cities. Both immigrant
and native-born workers in the inner ring suburb enclaves tend to commute
longer than their non-enclave counterparts, with the exception of D.C. native-
borns. Immigrants in the outer ring suburb enclaves also experience
significantly longer journey to work than non-enclave residents in the same ring,
but the effects are not significant for native-borns.

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Table 4.5 Regression Estimates of Latino Workers’ Commute Times by Nativity
Intercept 3.472 ** 2.200 *** 3.734 *** 4.284 *** 4.254 4.766 ***
Location Variables
Central City 0.318 ** 0.414 *** 0.174 *** 0.134 *** 0.426 0.353 **
Central City Enclave 0.046 0.009 -0.009 -0.008 -0.409 -0.049
Inner Ring Suburb 0.060 0.087 0.090 *** 0.045 *** -0.092 0.079
Inner Ring Suburb Enclave 0.417 *** 0.310 *** 0.093 ** 0.063 *** 0.303 0.183 ***
Outer Ring Suburb Enclave -0.080 0.223 *** 0.056 0.088 *** -0.275 0.174 ***
Sociodemographic Variables
No or Limited English Proficiency 0.028 -0.028 *** 0.037
Migration Cohort 2 0.115 *** 0.024 * 0.020
Migration Cohort 3 0.043 -0.003 0.043
Migration Cohort 4 0.057 -0.029 0.104
Female -0.081 -0.055 -0.113 *** -0.083 *** 0.006 -0.087 **
With Child 0.023 0.002 0.049 0.002 0.119 * -0.022
Married 0.024 -0.005 0.016 -0.004 0.007 * 0.016
Experience 0.024 ** -0.003 0.021 *** 0.004 ** 0.040 ** 0.004
Experience2 -0.001 ** 0.000 0.000 *** 0.000 -0.001 0.000
Commuting Mode Variables
Transit 0.581 *** 0.337 *** 0.719 *** 0.614 *** 0.532 0.355 ***
Other Mode -0.553 *** -0.513 *** -0.420 *** -0.418 *** -0.433 -0.429 ***
Industry of Employment Variables
Manufacturing -0.160 -0.157 ** -0.222 *** -0.257 *** -0.387 -0.303 ***
Trade -0.362 ** -0.261 *** -0.313 *** -0.307 *** -0.275 -0.318 ***
FIRE -0.236 -0.304 ** -0.284 *** -0.225 *** -0.446 -0.487 ***
Services -0.306 * -0.314 *** -0.350 *** -0.291 *** -0.562 -0.420 ***
Public -0.145 -0.169 ** -0.195 *** -0.187 *** -0.324 -0.227 ***
Neighborhood Variables
Log (Median House Value) 0.137 0.090 -0.168 ** -0.124 *** -0.142 -0.405 ***
Log (Median Rent) -0.308 -0.037 0.190 * 0.068 0.145 0.554 **
Instrument Variable
Log (Wage Earnings)
a
-0.028 0.002 0.007 -0.005 -0.041 -0.035 **
Adj. R
2
0.242 0.150 0.135 0.137 0.126 0.165
N 759 2649 6592 27420 204 2131
F-statistic
b
6.81 *** 62.33 *** 145.13 *** 1709.2 *** 1 66.77 ***
*p<0.05, **p<0.01, ***p<0.001.
a. Treated as endogenous as described in the text, with wealth composition variables as instrument variables.
b. F-statistics are from tests of collective significance of the five wealth composition variables
in the first-stage regressions.
Chicago Los Angeles Washington, D.C.
Native Foreign Native Foreign Native Foreign

71

In light of these results, and referring back to results from the
employment status models, it is clear that ethnic enclaves in different rings have
varied implications for their residents. Central cities prove to be a
disadvantageous location as its residents experience both dampened
employment rate and lengthy commutes. Enclave effect is much muted in this
area and spatial mismatch effect is prevalent. While as likely to be employed as
their non-enclave counterparts (and in some cases more likely to work),
immigrant workers residing in ethnic enclaves in both suburban rings tend to
find jobs farther away from home as evidenced by their significantly longer
commutes. Again, enclave effects on employment accessibility emerge where
spatial mismatch is less an issue. It might be the case that the strong ethnic
networks in these enclaves connect immigrants to jobs without regard to spatial
proximity, and given these ethnic resources, immigrants do not tend to conduct
job search in local labor markets.
Los Angeles immigrants tend to incur longer commutes than native-born
workers, resonating Preston, McLafferty and Liu (1998)’s results from New York
City, but this effect is not significant for Chicago or Washington, D.C. 1980s
arrivals in Chicago and L.A. travel longer to work than new arrivals, implying
that immigrants expand their job search radius as their U.S. labor market
experience accumulate. Female workers’ journey to work is shorter than their
male counterparts, confirming the “Spatial Entrapment Hypothesis”, which

72

states that women’s household responsibilities restrain their commuting and job
search efforts, and thus limit their job opportunity possibilities (see Hanson and
Pratt 1995). Being married and having children does not have significant effects
on commuting duration. More working experience is associated with longer
commutes, especially for native-borns. This is because more experience opens
up one’s employment opportunities and leads to higher-paying jobs that
compensates for longer commutes. Commuters relying on public transit spend
more time in their journey to work than auto users while those bicycling or
walking to work have shorter commutes. Employment in industries other than
Agriculture, Mining and Constructing reduces immigrant and native-born
workers’ commuting time to various degrees for the three cities. For immigrant
workers, largest reductions are detected for Services in Chicago, Wholesale and
Retail Trade in Los Angeles, and Finance, Insurance and Real Estate in
Washington, D.C. For native-born workers these industries are Wholesale and
Retail Trade in Chicago, Services in Los Angeles, and Services in Washington,
D.C. These speak to the abundance and ubiquity of Service and Trade jobs and
their fast growth in these areas, as seen from the employment tables in
Appendix A. These varying commuting patterns are also attributable to the
different industrial structure and spatial organization of urban labor markets in
the selected metropolitan areas.

73

To further explore how spatial disparities on commuting time is
compensated for by neighborhood-level housing price differentials and
earnings, PUMA-level median housing price and median rent, as well as wage
earnings (all in log-linear format and wage earnings as endogenous variable
17
)
are entered into commuting time models. Their presence in the models does not
significantly change model estimates, including estimates on locational
variables, signaling that any compensating effect is slight. Living in a
neighborhood with lower median housing price incurs longer commuting, in
accordance to urban economic theories. However, contrary to expectation, high
rental cost is associated with longer commutes for certain groups. Also, Latino
workers’ longer commutes are not compensated for by higher wages. In
Washington, D.C., immigrant workers even commute longer for lower pay. This
might be due to their limited choices in the urban housing and labor market. Or,
as some argued, the value of “culture” might compensates for lower earnings
and higher rents in ethnic enclaves (Gonzalez 1998).

17
First-stage regression results are shown in Appendix B. Spatial effects on wage
earnings are only observed for Los Angeles, where native-born workers living in
central city, inner ring suburbs and outer ring suburb enclaves have higher wages.
Immigrants in outer ring enclaves, while having higher employment rate (from
Table 4), earn lower wages than their non-enclave residents in the same area.

74

4.3 The Issue of Gender

Table 4.6 Employment and Commute Time Model Estimates on Locational
Variables of Latino Immigrants by Gender
Central City 0.032 -0.486 -0.116 ** -0.093 * -0.101 0.089
Central City Enclave -0.157 -0.367 0.038 0.053 -0.065 0.207
Inner Ring Suburb -0.082 -0.607 ** -0.048 -0.044 -0.089 -0.166
Inner Ring Suburb Enclave -0.151 -0.056 -0.012 0.033 -0.038 0.139
Outer Ring Suburb Enclave 0.219 -0.533 * 0.224 *** 0.082 0.558 0.035
Log Likelihood
N
Central City 0.313 *** 0.622 *** 0.145 *** 0.105 *** 0.354 * 0.375
Central City Enclave 0.037 -0.056 -0.012 0.008 -0.086 -0.023
Inner Ring Suburb 0.044 0.173 * 0.054 *** 0.028 0.110 * 0.019
Inner Ring Suburb Enclave 0.251 *** 0.450 *** 0.071 *** 0.045 0.169 ** 0.217 **
Outer Ring Suburb Enclave 0.197 ** 0.264 ** 0.060 ** 0.144 *** 0.028 0.173 *
Adj. R
2
N
*p<0.05, **p<0.01, ***p<0.001.
756
Commute Time
0.121 0.202 0.106 0.196 0.132 0.186
1806 843 18031 9389 1375
896
Employment
-536.4 -396.9 -5606.2 -5263.8 -261.8 -282.6
2029 1044 20402 12219 1482
Chicago Los Angeles Washington, D.C.
Male Female Male Female Male Female

Results in the previous sections highlight gender differences in urban
employment and commuting. Table 4.6 presents comparable results for male
and female immigrants separately from both the employment and commute
time models. Only estimated coefficients on locational variables are reported.
Large variations exist between spatial effects for men and women. With the

75

exception of central city Los Angeles, residential location poses no spatial
barriers to Latino immigrant men’s likelihood for employment, confirming
Aponte (1996) and Pastor and Marcelli (2000)’s findings. Enclave residence has
no significant effect or even positive effect on their likelihood for employment.
However, living Chicago’s inner ring suburbs in general and outer ring
suburban enclaves actually decrease Latino women’s employment probability
and Latino men’s advantage of living in Los Angles’ outer ring suburb enclave
is not shared by their female counterparts. These results are similar to Parks’
(2004a) findings for certain immigrant women groups in Los Angeles: being in
enclaves actually has significantly detrimental effect on their employment
status. At the same time, enclave effects on commuting time, where significant,
are larger for immigrant women than they are for immigrant men in all but one
case (Los Angeles’ inner ring suburbs).
Overall, ethnic enclave residence lowers Latino women’s employment
probability in some cases and results in their larger disparity in commuting time
in comparison to non-enclave counterparts than men. While it seems clear that
women are more enclave-disadvantaged than men, the underlying mechanisms
are less apparent and deserve further study. It might be the case that in intra-
household dynamics, men make the residential location choices for the whole
family based primarily on his own job location or residential preference. Labor
market segmentation and occupational clustering by gender might also play a

76

role here (Wyly 1999). Ethnic networks in the enclaves might work differently
for immigrant men and women, directing them to distinctive spatial labor
submarkets and resulting in their different job accessibility disparities as
compared to non-enclave counterparts.

5. Conclusion
Synthesizing empirical results of this chapter underscores the diversity of effects
ethnic enclave residence in different urban spatial locations on Latino
immigrants’ employment accessibility. Unlike prior research, this analysis
explicitly and simultaneously tests the ethnic enclave hypothesis and the spatial
mismatch hypothesis and established the importance of their interactions on
Latino immigrants’ employment status and commuting duration. While results
somewhat vary for Chicago, Los Angeles and Washington, D.C., there exist
certain common patterns.
Central cities, with their continuous loss of jobs to suburban areas, prove
to be a disadvantageous location as its residents experience both dampened
employment rate and lengthy commutes. While spatial mismatch effect is
prevalent in this area, enclave effect is either muted or reinforcing the existing
spatial constraints. Inner ring suburb has no effect on Latino’s likelihood for
employment, but does lengthen their journey-to-work in some instances.
Enclave effects emerge in both suburban areas where job growth is relatively

77

strong and spatial mismatch is less a concern. In these areas, despite the fact that
enclave residents are as likely to be employed as their non-enclave counterparts
(and in some cases more likely to work), they tend to find jobs farther away
from home as evidenced by their significantly longer commutes, controlling for
other factors. They might be directed to spatially more distant jobs through
ethnic contacts and thus do not conduct job search in local labor markets. The
working process of such ethnic networks in channeling immigrants to jobs,
however, remains a question for further exploration. In an effort to solve this
puzzle, the next chapter identifies those employment niches that Latino
immigrants heavily concentrate and tests for the effectiveness of social
networks. It seeks to understand whether niche employment explains the longer
commutes of enclave residents and the important home-work linkages for this
group.
Low-skilled Latino immigrants are in general more likely to be employed
than native-borns but they tend to incur longer commutes as well. Contrary to
assimilation theories, earlier immigrant cohorts do not portray higher
employment probability or shorter commutes to work as compared to new
arrivals. It indicates the alternative paths of economic assimilation and spatial
assimilation of this group. Having automobile and employment in the fast-
growing sectors of trade and services significantly shorten immigrants’
commutes. Further distinguishing immigrant men and women reveals that

78

women in ethnic enclaves face greater spatial barriers to employment than men.
This suggests that there might be gender biases in the operation of these ethnic
neighborhoods.
This study sheds some empirical light on the different views regarding
the role of space and residential location on immigrants’ labor market
performance. In an era that immigrants’ residential mobility follows a path of
both dispersion and concentration, it is important to incorporate these new
spatial patterns into our discussion of employment accessibility. This chapter
advances this discussion by breaking the “central city enclave and suburban
mixed neighborhood” stereotype and takes into account ethnic communities in
all parts of the metropolitan area. This design also allows an evaluation of the
relative importance of spatial mismatch and social networks in immigrants’
employment outcomes. Results indicate that while both spatial proximity and
social networks play a role, their relative weight differ to different locations and
it is their interaction that determines the employment accessibility of low-skilled
Latino immigrants. Policy makers need to be mindful of these spatial, temporal
and gender variations of Latinos’ employment accessibility in order to make
effective efforts aiming at improving their economic well-being.

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CHAPTER 5.

ETHNIC ENCLAVE RESIDENCE
AND ETHNIC NICHE EMPLOYMENT

This chapter examines the impact of living in ethnic enclaves in different parts
of the metropolitan area on low-skilled Latino immigrants’ likelihood for ethnic
niche employment, as well as the quality and accessibility of niche employment
through models of earnings and commuting. Using 2000 Public Use Microdata
Sample (PUMS) for the three metropolitan areas of Chicago, Los Angeles, and
Washington, D.C., this study finds that while enclave residence is generally
associated with niche employment, this effect is most evident for women and
newest arrival cohorts. Niche effects on earnings and commuting duration are
location-specific and gender-specific. For male workers who live in central city
and inner ring suburban enclaves and work in niche occupations, their earnings
are higher despite niche employed workers’ overall lower earnings. Female
workers commute longer to niche jobs while male workers enjoy shorter
commutes. These disparities in commuting duration are not fully compensated
for by their wage earnings or neighborhood-level housing costs.

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1. Introduction
Labor market segmentation by race/ethnicity, gender and national origins has
been recognized as a prominent feature of urban labor markets in immigrant
gateway cities around the U.S. Evidence is accumulating in the academic
literature that the immigrant workforce tend to be highly specialized and are
concentrated in certain industries and occupations in metropolitan areas from
New York to Los Angeles (Waldinger 1996, Ellis and Wright 1998). Termed as
ethnic niche (Waldinger 1994) or ethnic niching (Wilson 2003), these over-
represented employment concentrations serve as important nodal points in
organizing the labor market experience of immigrants. At the same time,
immigrants also tend to cluster residentially in ethnic neighborhoods and their
residential segregation does not diminish with their longer duration in the U.S.
(Cutler, Glaser and Vigdor 2005). This chapter explores the connection between
the geography of their residential location and the probability of niche
employment, as well as the accessibility and quality of such employment.
While past studies have examined the patterns and characteristics of
ethnic niche employment (e.g. Zhou 1992, Wilson and Portes 1980) and ethnic
neighborhood residence (e.g. Logan, Alba and Zhang 2002) in different contexts
respectively, few explored the linkages between them and the effect of
residential location on ethnic labor market. These concepts are sometimes mixed
together in a more general term of “ethnic economy”. Ethnic enclave denotes

81

immigrants’ clustering in distinct spatial locations and where ethnic enterprises
abound to serve both the ethnic clientele and the larger population (Portes 1981).
Loosening the spatial proximity condition produces the ethnic economy (Light
et al. 1994, Logan, Alba and McNulty 1994). Ethnic niche or immigrant niche
depicts a broader type of immigrant employment concentration and can be both
occupational and industrial, and both high-skill and low-skill in nature
(Waldinger 1994).
While ethnic enclave and ethnic niche are sometimes considered as
overlapping spheres of both immigrants’ home and work, they are actually
different concepts. Social network theories as applied to immigrants suggest that
immigrants tend to use neighborhood contacts, especially of co-ethnics, to locate
jobs (Elliott and Sims 2001). As ethnic neighborhoods are the physical bases for
ethnic networks, this paper formally tests the connection between ethnic enclave
residence and ethnic niche employment. It also contributes to the literature by
making explicit distinction to ethnic enclaves in different parts of the city and
their possibly varying effects on niche employment. Three subdivisions in a
metropolitan area are identified: central city, inner ring suburbs, and outer ring
suburbs given the existence of immigrant clusters in both central city and
suburbs (Logan, Alba and Zhang 2002) and the distinctive characteristics of
inner ring suburbs (Downs 1997) as described in previous chapters.

82

Existing evidence is limited and mixed regarding the quality, or payoff of
niche employment (Logan, Alba and Stults 2003). Distinction needs to be made
between ethnic entrepreneurs and ethnic workers as Sanders and Nee (1987)
found that in Florida, immigrant entrepreneurs are as well off in or out of ethnic
enclaves whereas immigrant employees receive less returns to human capital in
the enclaves. Some studies discovered that results differ by gender: working for
ethnic industries benefit male, but not female immigrants (Zhou and Logan 1989
for Chinese in New York; Greenwell et al 1997 for Salvadoreans and Filipinos in
Los Angeles). Commuting duration, besides earnings, is also part of immigrants’
overall quality of employment, but is largely ignored in the ethnic labor market
discussion. The spatial mismatch hypothesis lends evidence in this regard. It is
argued that residents of central city neighborhoods and minority-concentrated
neighborhoods tend to suffer from constrained labor market accessibility due to
suburbanized job opportunities and limited mobility, as manifested by their
longer commutes and lower wage rates (Kain 1968, see Ihlanfeldt and Sjouquist
1998 for review).
How does niche employment play out in immigrants’ job accessibility
and commuting pattern is an open question that has not been adequately
addressed. For residents of ethnic enclaves in different locations, it is
inconclusive whether social networks will lead them to chase niche jobs further
away from home or residential segregation creates closure of job information

83

and results in employment in nearby locales. Linking the immigrant labor
market segmentation literature and spatial mismatch literature, this paper fills
this gap and systematically gauges the existence and magnitude of any
commuting and earnings penalty or benefit of engaging in ethnic concentrations
in both residence and work and any differential effects by residence in various
locations. As the previous chapter finds that while as likely to be employed as
non-enclave residents, enclave Latino immigrant residents tend to commute
longer to jobs and hypothesizes that social networks might be directing them to
job opportunities where coethnics have already established concentration so that
they do not necessarily search in the local labor market. Bringing in niche
employment status in the models of this chapter helps testing this hypothesis.
This chapter restricts its focus to only Latino immigrants in the three
metropolitan areas of Chicago, Los Angeles, and Washington, D.C. Of the three
case study areas, Los Angeles has attracted most attention in ethnic labor market
research (e.g. Ellis and Write 1999), followed by Chicago (Lim 2001, Waldinger
and Der-Martirosian 2001), while Washington D.C. have not been adequately
studied before. This paper identifies the ethnic niche industries in the three
areas and examines the role of residential location, especially ethnic enclave
residence in central city, inner ring suburbs and outer ring suburbs, on the
likelihood of niche employment. It further analyzes the accessibility and quality
of niche employment by various residential locations through the two

84

interlinked aspects of commuting and earnings. Gender disparities are
highlighted in the analysis to capture men and women’s different labor market
behaviors (Hanson and Pratt 1995). Tracing the effect of residential
neighborhood on ethnic employment status, as well as the earnings and
commuting implication for Latino immigrants is important in understanding the
connection between urban housing and labor markets and their underlying
dynamics.

2. Theory and Previous Research
2.1 Social Network Theories and Probability of Niche Employment
Many researchers attribute immigrants’ impressively high labor force
participation given poor human capital and job accessibility to their wealth of
social capital and social networks
18
, especially compared to low-skill blacks (e.g.,
Aponte 1996; Waldinger 1996). The concept of social networks originated from
Granovetter (1973), who argued that “weak ties” of acquaintances, as opposed
to “strong ties” of kinship, can be more effective in achieving individuals’
economic goals due to its wider net and more effective information flow. It is

18
Social networks refer to “sets of recurrent associations between groups of people
linked by occupational, familial, cultural, or affective ties” (Portes 1995, p. 8) and
social capital means “the capacity of individuals to command scarce resources by
virtue of their membership in networks or broader social structures” (p.12). Also see
Portes (1998) for further elaboration.

85

recognized that networks are important in job referrals and correcting
information problems and other market inefficiencies for immigrants by ways of
(1) the circulation of information on jobs and other resources that assist
newcomers’ job search and economic transition; and (2) ethnic network
recruiting and job training on the part of the employers (Scott 1996; Rodriguez
2004). In short, by facilitating information flow, social networks ease the job-
matching process between workers and employers. Case studies found social
networks formed in sending communities in Mexico are instrumental in
increasing job accessibility and expanding economic opportunities for their
members here in the U.S. (Munshi 2003).
Immigrant neighborhoods, or ethnic neighborhoods, serve in many cases
as the spatial contexts in the creation and functioning of social capital and social
networks. They also constitute important interlinkages in the social and spatial
processes of ethnic networks between residence and work. It is documented that
immigrant barrios do not resemble the social pathologies of traditional ghettos
for their rich social and ethnic capital and vibrant informal economies (Clark
2001) and immigrant workers are much more likely than blacks to use
neighborhood contacts to locate jobs (Elliott and Sims 2001).
Despite the importance of ethnic enclaves in sustaining social networks
among co-ethnics and channeling them into certain niche jobs, most studies
excluded the effect of residential location on examining the probability of niche

86

employment (e.g. Hudson 2002). The two exceptions are Parks (2004) who
found that in Los Angeles, living in an ethnic enclave is associated with ethnic
niche employment, especially for women, and Wang (2006) who established that
living in central city and ethnically-concentrated neighborhoods in San
Francisco increases the chance of niche employment. But both of these studies
failed to capture the spatial and social variation of ethnic neighborhoods in
different parts of the city and their possibly varying effects on niche
employment, which is a central concern of this research.

2.2 Ethnic Enclave Hypothesis and Payoff of Niche Employment
Various theories offer insights regarding the role of residential
segregation on immigrants’ labor market performance. One is the ethnic enclave
hypothesis (Wilson and Portes 1980), which is grounded in dual labor market
theory (Gordon 1972) and ethnic solidarity theory (Light 1972). The former
states that labor market is segmented into a primary and a secondary labor
sector. The primary sector features high-skill, high-wage jobs with stable work
conditions and ample opportunities for promotions whereas the secondary
sector is comprised of low-skill, low-paying jobs with circumscribed prospects
for upward mobility. The latter, ethnic solidarity theory is based on the
observation that the mobilization of ethnic resources and establishment of ethnic
enterprises promote collective economic development and welfare among

87

certain immigrants groups. With evidence from Cubans in Miami, the ethnic
enclave hypothesis asserts ethnic enclave is a third labor sector and that
immigrants who work for ethnic entrepreneurs and ethnic economy can obtain
returns to human capital comparable to those employed in the primary sector,
and absent among those employed in the secondary sector, and thus providing a
“separate but equal” mode of economic incorporation (Wilson and Portes 1980).
Empirical studies testing the hypothesis have been somewhat
controversial. On a national level and across metropolitan areas, Borjas (2002)
argued that enclave residence actually hampers the economic assimilation of
immigrants, as shown by their rate of wage growth. Wang (2007) also suggested
that job earnings of Latino immigrants suffer from their residential segregation
from White and Asian populations. On an intra-urban scale, Sanders and Nee
(1987) found that in Florida, immigrant entrepreneurs are as well off in or out of
ethnic enclaves whereas immigrant employees receive less returns to human
capital in the enclaves. A similar conclusion is drawn by Waldinger (2001) who
argued that jobs obtained through ethnic networks and in ethnic niches are often
of inadequate quality. Other studies discovered that results differ by gender:
working for ethnic industries benefit male, but not female immigrants (Zhou
and Logan 1989 for Chinese in New York; Greenwell et al 1997 for Salvadoreans
and Filipinos in Los Angeles). While all of the above studies tests for the
earnings implication of enclave residence or niche employment alone, none

88

looks at their interactive and combined effects on the earnings of immigrants
who both reside and work in ethnic concentrations. This paper fills this gap and
decomposes the broader concept of enclave economy into the home and work
elements and tests for their respective and interactive effects on immigrants’
earnings.

2.3 Spatial Accessibility and Commuting of Niche Employment
Much of the discussion on immigrants’ division of labor has neglected
the spatial dimension and the intraurban geography of enclaves and niches
(Ellis, Wright and Parks 2007). A central question here is can social networks
transcend spatial constraints and connect immigrants to jobs or does spatial
accessibility still matter and can pose barriers to immigrants’ jobs search. The
spatial mismatch hypothesis, while pertaining primarily to traditional
minorities, has emphasized the importance of residential location on accessing
job opportunities. It is argued that difficulty in accessing suburbanized jobs,
especially low-skilled and semi-skilled jobs, is a major obstacle for inner city
minorities which results in their high unemployment rate, long commutes and
low wages (Kain 1968, Ihlanfeldt and Sjouquist 1998 for review). Studies that
test this hypothesis have constantly found that residing in inner city
neighborhoods where job growth is weak hampers the employment prospects of
minorities and lengthens their daily commutes (e.g. Raphael 1998, Stoll 1999).

89

Applications of this hypothesis to immigrants have produced mixed
results. In regard to commuting, Preston, McLafferty and Liu (1998) showed the
persistence of spatial barriers faced by immigrant workers as evidenced by their
overall longer commutes than their America-born counterparts in central New
York CMSA. Parks (2004) however found that living in ethnic enclaves shorten
commute times to different extent for six immigrant groups in Los Angeles area.
Controversies arise from partial conceptualizations of residential segregation
and focus on either the central city or the ethnic enclaves. The previous chapter
takes into account ethnic enclaves located in different parts of the metropolitan
area (central city, inner-ring suburbs and outer ring suburbs), and found that for
Latino immigrants, while central city enclave residents do not commute longer
to work, suburban enclave residents actually have longer commutes than their
non-enclave counterparts, signaling that the interactive dynamics of spatial
mismatch effect and social networks effect differ by location. How does niche
employment play out in immigrants’ job accessibility and commuting pattern is
an open question that has not been addressed before. For residents of ethnic
enclaves in different locations, it is inconclusive whether social networks will
lead them to chase niche jobs further away from home or residential segregation
creates closures of job information and results in employment in nearby locales.
In sum, this chapter tests the association between enclave residence and niche

90

employment and the earnings and commuting effects of segregation in both
home and work.

3. Data and Methodology
3.1 Defining Ethnic Niches
The data employed in this chapter is the same as last chapter, while sample is
restricted to Latino immigrant workers only. Immigrant enclave dummies are
constructed on the PUMA level, indicating those PUMAs that have twice or
higher concentration of Latino immigrant population than the metro as a whole
based on calculations of residential concentration quotient (RCQ) as previously
described. This chapter continues to uses the threshold of RCQ>2 for Latino
immigrant enclaves and by this definition, 10 out of 61 PUMAs in Chicago, 17
out of 110 PUMAS in Los Angeles and 6 out of 32 PUMAS in Washington, D.C.
are considered Latino immigrant enclaves in 2000.
There are numerous approaches for defining ethnic niches in the
literature, some use occupation or industry alone (Logan, Alba and Stults 2003,
Ellis, Wright and Park 2007), while others use their combination (Wilson 2003,
Wang 2006). This chapter uses industry because while there exists division of
labor and many different occupations and job responsibilities within an
industry, ethnic networks might direct job seekers to sectors with substantive
ethnic presence, but not necessarily the same job functions. This chapter uses the

91

2000 Census detailed 3-digits industry codes.
19
Consistent with the literature,
employment niches need to meet two criteria: over-representation and
minimum restriction. Industrial Concentration Quotient (ICQ) is adopted for the
over-representation criterion, and can be expressed as
20
:
ICQ=
m
im
j
ij
E
E
E
E
, (5)
where j=(1,… n) and refers to occupations. E
ij
is the number of Latino
immigrants employed in an industry, and E
j
is the total employment in that
industry. E
im
is the employment of Latino immigrants in metropolitan area and
E
m
is total employment in metro. Similar to the Residential Concentration
Quotient, a ratio of larger than 1 means that Latino immigrants’ concentration in
a certain industry is greater than the metropolitan employment in that industry.
The minimum restriction criterion requires that the industry has above-
minimum number of workers to make a meaningful presence in the urban
economy. In this paper, an industrial sector is defined as an ethnic niche if its
Industry Concentration Quotient is larger than 3 and employs over 100

19
For Census 2000, the 1997 North American Industrial Classification System
(NAICS) is used in place of 1987 Standard Industrial Classification (SIC). The
NAICS classified industries into 20 sectors and 1,170 detailed categories for the
United States and the Census Bureau aggregated these NAICS categories into 265
detailed census categories.

20
Park (2004) and Ellis, Wright and Park (2007) use this approach. Some other
studies use odds ratio approach instead of concentration quotient to define niches
(e.g. Wang 2006). For a discussion and comparison, see Wang and Pandit (2006).

92

workers.
21
Also, as it has been consistently found that there exists labor market
segmentation by gender and men and women tend to occupy different niches
(Scott 1996, Wright and Ellis 2000), female and male niches are derived
respectively in addition to the general Latino immigrant niches.
In result, as Table 5.1 summarizes, both male and female Latino
immigrants are clustered in around 20 employment niches in Chicago and Los
Angeles. Higher proportion of female workers are engaged in ethnically
concentrated sectors than male workers (40% versus 26% in Chicago, and 36%
versus 20% in Los Angeles). In D.C. however, around three quarters of Latino
immigrant workers are highly concentrated in only 9 niche occupations, with
65% of female and 73% of male workers employed in 10 and 7 niches
respectively. This may be an indication of the strength of ethnic networks in
immigrants’ job search, especially among women and the newer arrival cohorts,
as D.C. is a newer gateway than the other two.

21
The threshold value of ICQ and minimum restriction are not uniform in the
literature and differ by research design. 1.5 is frequently used to define immigrant
niches by certain national origin (Wang 2006, Ellis, Wright and Park 2007) and 3 has
also been used to signify strong niches (Parks 2004b). As in this paper, Latino
immigrants are not distinguished by their national origin, so a higher ratio of 3 is
used. The minimum restriction threshold is arbitrary in nature and subject to
specific research designs, and some used 300 or 500 workers (Wilson 2003). This
paper chooses 100 as the sample is restricted to low-skilled workers already thus a
lower threshold is warranted.

93

Table 5.1 Number of Niches and Percentage Niche Employment by Gender
All Female Male
Number of Niches Number of Niches Number of Niche
Percentage Employed Percentage Employed Percentage Employed
21 24 20
28% 40% 26%
22 20 25
20% 36% 20%
910 7
72% 65% 73%
source: Author's calculation of census PUMS 2000
Chicago
Los Angeles
Washignton, D.C.

Top three niches by size of employed immigrants are listed in Table 5.2
for all three areas. It is observed that within each metro, low-skilled male and
female workers are concentrated in some overlapping and some disparate
industries, which confirmed the division of labor by gender. Across
metropolitan areas, while there exist some commonalities (especially
landscaping services for men), the majority of highly-concentrated industries are
specific to a given metropolitan area and reflect the industrial base of the urban
economy. Given the predominance of manufacturing industry in Chicago,
manufacturing and services in Los Angeles and construction and services in
Washington, D.C. in absorbing the low-skilled workforce (see table 4.2 in
Chapter 4), it is not surprising to see on the top of the ethnic niche list various
manufacturing industries in Chicago, cut and sew apparel and landscaping
services in Los Angeles, and construction and restaurant and other food services

94

in Washington, D.C. Detailed lists of all ethnic niches for the three metropolitan
areas are provided in Appendix C.

Table 5.2 Top Three Immigrant Niches by Size of Employment by Gender
Industry Number Employed ICQ Percentage Group in Industry
All Not specified manufacturing industries 116 4.2 9.6%
Landscaping services 107 6.7 15.3%
Plastics products 93 4.3 9.8%
Female Plastics products 54 7.8 5.7%
Not specified manufacturing industries 39 4.4 3.2%
Traveler accommodation 35 5.3 3.9%
Male Landscaping services 106 9.7 15.1%
Not specified manufacturing industries 77 4.1 6.4%
Furniture and fixtures 32 5.4 8.4%
All Landscaping services 1183 4.2 25.3%
Cut and sew apparel 938 3.2 19.3%
Furniture and fixtures 613 3.6 21.7%
Female Private households 824 7.9 16.3%
Cut and sew apparel 558 5.5 11.5%
Services to buildings and dwellings 373 4.1 8.5%
Male Landscaping services 1157 6.2 24.7%
Furniture and fixtures 493 4.4 17.5%
Crop production 382 4.1 16.3%
AllConstruction 6384.76.9%
Restaurants and other food services 310 3.2 4.7%
Services to buildings and dwellings 180 8.6 12.7%
Female Restaurants and other food services 129 3.8 2.0%
Services to buildings and dwellings 123 16.6 8.7%
Traveler accommodation 74 7.3 3.8%
Male Construction 629 7.2 6.8%
Restaurants and other food services 181 2.9 2.8%
Landscaping services 89 10.6 10.0%
source: author's calculation of census PUMS 2000
Note: Numbers and percentages are calculated from unweighted sample.
Chicago
Los Angeles
Washington, D.C.

95

3.2 Model Specification
This study compares the likelihood of niche employment of immigrant Latinos
workers in central cities, inner ring suburbs and outer ring suburbs and those
living in ethnic enclaves versus in mixed neighborhoods. It also examines the
quality of niche employment through earnings and commute times by
residential location. It incorporates the two competing theories on immigrants’
labor market segmentation by locating the social networks in its geographic
contexts and thus emphasizes spatial accessibility as well. Another contribution
is a richer conceptualization of the payoff of niche employment through the two
inter-linked aspects of earnings and commuting.
Following the previous chapter, niche employment status model is
estimated on residential location, individual and household characteristics using
probit model. Earnings and commuting time models are estimated in a two-
stage least squares (2SLS) framework by treating wage earnings as endogenous.
This is because urban economic theories suggest that housing prices and wage
rates compensate workers for their commuting costs. That is, a worker might
choose to live farther from employment locations for cheaper housing costs and
more favorable neighborhood amenities. Also, in a competitive market, workers
should be compensated by higher wage earnings for their longer commutes
(Mills 1972). In order to bypass this simultaneity, instrument variables are
needed to identify the predicted wage variable in the commuting model. It is

96

suggested in the literature that household wealth, i.e. other income besides the
worker’s labor earnings and the sources of non-labor income will affect a
worker’s earning but will not directly influence the commuting duration
(Gabriel and Rosenthal 1996, Petitte and Ross 1999).
The dependent variables for these models are binary niche employment
status, wage earnings, which is a worker’s pre-tax wage and salary income in
1999, and commute time, measured by travel time to work in minutes. Both
wage and commute times are expressed in log linear format. Independent
variables include location dummy variables of living in central city, inner ring
suburban or outer ring suburban locations interacted with ethnic residence
status to explicitly illustrate each neighborhood type’s effect on low-skilled
Latino workers’ niche employment accessibility; workers’ sociodemographic
characteristics, including immigrants’ membership in different arrival cohorts to
the United States, English language proficiency, gender, marital status, presence
of children under 5 in the household and labor market experience; as well as
wealth composition, as a vector of the amount and composition of household
non-labor income, including dummy variables indicating whether the
household received investment income, business income, Social Security income
and welfare income in 1999. These household wealth help determine a
member’s decision to enter the labor market and the optimal amount of labor he
or she is willing to supply for wage earnings, but they do not directly affect the

97

travel time to work. Therefore, they enter the employment models and serve as
instrument variables for wages in 2SLS models on commuting times. In
addition, the 2SLS models also include dummy variable indicating niche
employment, the interaction of residential location and niche employment,
commuting mode to work by public transportation or other modes with
automobile-riders being the reference, as travel speed necessarily affects the
length of commuting, as well as PUMA-level median housing prices and
median monthly rents (both in log linear forms) to capture possible
neighborhood cost-of-living variations across different types of residential
locations (please refer to Table 4.3 for detailed listing of variable definitions).
Each model is estimated for the total sample, and is stratified by gender
to explore the interaction between space and gender. Table 5.3 presents an
overview of percentage niche employment of Latino immigrant workers by
residential location in the three metropolitan areas. Comparing enclave and non-
enclave residents in the same ring, it is almost always the case that a higher
percentage of enclave residents are employed in niche occupations than their
non-enclave counterparts, with few exceptions. These disparities seem to be
larger for female than male workers. Across different rings in the same metro
however, it is hard to derive a general pattern in terms of niche employment
presence. Except for a few instances, there are a higher percentage of male
workers employed in niche occupations than female workers in the same

98

location and this difference is most evident in non-enclave communities. Among
metros, three quarters of low-skilled Latino immigrants are working in ethnic
niche industries in Washington, D.C., compared to around one quarter in both
Chicago and Los Angeles. This is probably due to the fact that D.C. is a newer
and emerging immigrant gateway with more new arrivals, among whom ethnic
niching could be particularly strong.

Table 5.3 Percentage Niche Employment by Residential Location and by Gender
Enclave Non-Enclave Enclave Non-Enclave Enclave Non-Enclave
Central City 26.6% 24.9% 26.2% 19.5% 26.7% 27.4%
Chicago Inner Ring Suburbs 29.6% 23.6% 35.8% 22.4% 27.0% 24.2%
Outer Ring Suburbs 33.7% 24.7% 29.5% 16.4% 35.7% 28.4%
Central City 24.2% 15.0% 24.3% 12.7% 23.9% 20.9%
L.A. Inner Ring Suburbs 23.2% 16.1% 24.6% 12.7% 25.8% 21.0%
Outer Ring Suburbs 20.7% 19.8% 15.1% 13.1% 31.6% 21.6%
Central City 74.0% 77.0% 76.2% 73.0% 72.4% 79.4%
D.C. Inner Ring Suburbs 71.7% 68.9% 63.1% 60.6% 76.6% 73.2%
Outer Ring Suburbs 68.4% 68.9% 57.8% 61.2% 74.8% 72.6%
source: author's calculation of census PUMS 2000
All Female Male

4. Empirical Results
Table 5.4 presents results for probit models of niche employment status and
Table 5.5 and Table 5.6 display 2SLS model estimates of earnings and
commuting times. All models are significant at 0.1% level. For each table,
statistics are shown for the three cities and within each city, for the stratified
samples of male and female low-skilled Latino immigrant workers. For each

99

group, niche employment status is determined by participating in the gender-
specific industrial sectors.

4.1 Niche Employment Status Models

Table 5.4 Probit Regression Estimates of Latino Niche Employment Status by
Gender
Intercept -1.108 *** -0.891 -0.873 *** -1.070 *** 0.166 * -0.330
Location Variables
Central City 0.006 0.128 -0.408 *** 0.167 *** 0.011 0.487 *
Central City Enclave 0.068 -0.063 -0.055 0.127 *** -0.079 -0.088
Inner Ring Suburb -0.001 -0.032 -0.242 *** -0.111 ** -0.020 -0.023
Inner Ring Suburb Enclave -0.013 0.165 0.046 0.229 *** 0.165 0.118
Outer Ring Suburb Enclave 0.262 * 0.114 -0.160 *** -0.009 0.004 0.005
Sociodemographic Variables
No or limited English Proficiency 0.192 0.299 ** 0.289 *** 0.265 *** 0.326 *** 0.343 ***
Migration Cohort 2 -0.011 0.056 -0.146 *** -0.161 *** -0.027 -0.076
Migration Cohort 3 -0.037 0.196 -0.147 *** -0.328 *** -0.182 -0.039
Migration Cohort 4 0.090 -0.243 -0.069 -0.498 *** 0.035 -0.209
With Child 0.066 -0.022 -0.029 0.035 0.149 0.138
Married 0.106 0.069 0.044 -0.033 -0.025 0.123
Experience 0.009 0.026 -0.001 0.037 *** 0.027 * 0.013
Experience2 0.000 0.000 0.000 0.000 *** -0.001 ** 0.000
Wealth Composition Variables
Log (Other Household Income) 0.016 * 0.011 0.007 ** 0.012 *** 0.019 ** 0.022 *
Investment Income -0.197 0.332 -0.051 0.005 -0.422 *** -0.413 *
Business Income -0.523 ** 0.184 0.297 *** 0.267 *** -0.135 0.110
Social Security Income -0.046 -0.018 -0.027 -0.080 -0.001 0.482
Welfare Income 0.516 * -0.001 0.033 0.073 0.389 -0.438
Log Likelihood
N
*p<0.05, **p<0.01, ***p<0.001.
-504.7
800
-809.0
1412
-6332.6
10180 18712
-591.8
898
-1028.9
1870
-8830.9
Female Male Female
Chicago Los Angeles Washington, D.C.
Male Male Female

100

Three residential location areas (central city, inner ring suburbs and outer
ring suburbs) and ethnic enclave status are interacted to create six types of
neighborhoods. Using outer ring suburbs in general as the omitted reference
group, statistics reveal the relative effects of living in other five types of
neighborhoods on Latino workers’ probability for niche employment. Results in
Table 5.4 show that for low-skilled Latino immigrants, ethnic enclave effects on
niche employment vary by city, by location, and by gender. Living in ethnic
enclaves in different parts of the city has different implications for working in
niche industries and gender plays a mediating role in the nexus between ethnic
residence and work. One striking finding is that residential location matters
most for Latino immigrants in Los Angeles and enclave effects are particularly
pronounced for female residents. For Los Angeles, female immigrants in the
central city are more likely to be employed in ethnic niche sectors than outer
ring suburban residents while male immigrants in the same area are less likely
to do so. Living in inner ring suburbs in general is associated with lower
probability of niche employment for both male and female workers while living
in enclaves in these areas increase their likelihood for niche employment,
significantly so for female workers.
This suggests that the place-based social networks and ethnic networks
that connect immigrants to niche jobs are stronger for women than men. It lends
empirical evidence to previous literature which claims that women’s social

101

networks are more residentially based than men, whose networks also contain
co-workers. Women tend to rely more heavily on job information from
neighbors and community contacts than men in locating jobs (Hanson and Pratt
1995, Fernandez-Kelly 1995). These patterns in part confirm the more general
result of Wang (2006) which finds that enclave residence is positively associated
with niche employment for Hispanics as a whole in San Francisco and Parks
(2004) which points out that enclave effects act in different directions for the
three Latino immigrant groups: Mexicans, Guatemalans and Salvadorans in Los
Angeles. Results presented here however further underscore the location-
specific and gender-specific nature of enclave effects as the location of enclave in
a city has varied implications for their residents, men and women differently.
With the successive inflow of Latino immigrants to many metropolitan areas,
the enclaves they form differ in history, size, form and demographic
composition and warrant detailed classification.
The fact that residential effects in Chicago and Washington, D.C. are not
as statistically significant as in Los Angeles might be attributable to two possible
explanations. For one, Latino immigrant populations in these two areas are
much smaller than in Los Angeles, about one tenth in size based on selected
samples. This would mean that the density and vitality of ethnic concentration
in these two areas are much lower in comparison. The scale and intensity that is
desirable for ethnic networks to be effective might not have been achieved in

102

these cities, resulting in less significant results. For the other, recall from Chapter
three that both Chicago and Washington, D.C. has a much higher percentage of
black population than Los Angeles. Some of this population hold similar skill
sets as Latino immigrants and might be competing for jobs in similar sectors.
While identifying the exact residential and employment clustering pattern of
blacks in these metropolitan areas is beyond the scope of this study, these
supply-side factors might reveal interesting dynamics concerning immigrants’
employment outcomes.
Common patterns emerge across all three cities, despite their varied
magnitude. With one exception (male in Los Angeles), living in central cities in
general increase the likelihood of niche employment for all groups examined,
and the effect is significant for female workers in Los Angeles and Washington,
D.C. In all but one case (female in Los Angeles), living in enclaves in this area is
actually associated with lower niche employment probability. While across all
three cities, living in inner ring suburbs has a negative effect on niche
employment, enclave residence in this area has a counteracting positive effect in
most cases (with the exception of males in Chicago). Enclave residents in outer
ring suburbs of Chicago and DC are also more likely to work for niche sectors
than their same-ring non-enclave counterparts. In sum, the link between ethnic
enclave residence and ethnic niche employment is most evident and in expected
positive sign in the suburban areas. Living in central city enclave has actually

103

opposite effect on niche employment. It might have to do with the
socioeconomic status of enclaves in different locations, with the prosperous
enclaves in the suburban areas having more employed residents and thus job
networks. At the same time, spatial proximity to jobs might have also played a
role. Intense competition can occur among low-skilled immigrants in central city
enclaves due to the slow growth of jobs opportunities in city centers as
compared to the suburbs. All these underscore the fact that enclaves and social
networks are rooted in and bounded by their respective spatial contexts and
exert diverse effects on the employment outcomes of their residents.
English ability exhibits a consistent and significant effect on niche
employment, with those with no or limited English proficiency more likely to be
employed in niche industries. Immigrants who lack English proficiency rely
more heavily on ethnic networks to find jobs with high concentration of
coethnics, so as to avoid participating in the formal labor market where English
language skill is necessary. Language barriers are most prevalent among the
newest arrival cohorts, which also explains to some extent the migration
duration effects. Immigrants’ duration in the United States is an important
determinant of their socioeconomic attainment. Assimilation theories suggest
that immigrants register socioeconomic progress and cultural familiarity in the
host society as their residential tenure endure and their level of segregation
would likely decrease (Gordon 1964). This does seem to be the case for the

104

population in question. Comparing to the newest arrivals (1990s arrival), all
earlier arrival cohorts feature lower probability of niche employment, and this
effect is significant for all earlier cohorts, both men and women in Los Angeles.
For female workers in LA in particular, the probability of participating in niche
employment declines with each earlier cohort. This indicates that the strength of
ethnic networks is most effective among newest arrivals who are new to the
country and lack life and work know-hows to survive. Ethnic networks are
instrumental in connecting them to jobs where co-ethnics have already
established roots. This is in line with with the longitudinal analysis conducted
by Myers and Cranford (1998) which shows that Latinas in southern California
gradually step out of low-paying factory occupations as their duration in the
U.S. increase (Myers and Cranford 1998). Contrary to the findings of earlier
studies however (Parks 2004b, Wang 2006), marital status and presence of
children do not have significant impact on niche employment. Having more
experience increase the likelihood of niche employment, implying that niche
jobs might not necessarily be a transitional springboard for many low-skilled
Latino immigrants.
Household wealth composition characteristics were unexplored in the
literature. Across three areas, it is consistent that higher household non-labor
income actually leads to higher niche employment rate. It might be the case that
households view non-labor income as complementary to, rather than

105

substituting labor earnings. Having investment income lowers the likelihood of
niche employment in Washington, D.C. The more controversial is having
business income, which has negative effect on niche employment in Chicago,
but positive effect in Los Angeles. These are Latino immigrants who are ethnic
entrepreneurs themselves or who have household members who are ethnic
entrepreneurs. It will be interesting to go into the intra-household level division
of labor and income composition to further understand the dynamics there.

4.2 Earnings and Commuting Models
Table 5.5 and Table 5.6 display model results for wage earnings and commuting
duration of Latino immigrants respectively. Dummy variables for niche
employment and five residential neighborhood types are included, as well as
their interaction terms to capture the effect between each pair of residence-niche
employment connection. As seen from the statistics, niche employment
significantly dampens wage earnings for male immigrants in Los Angeles and
female immigrants in Washington, D.C. But the effect is not found for other
groups. Interestingly, for male workers who live in central city and inner ring
suburban enclaves and work for niche occupations in Los Angeles, their
earnings are actually significantly higher while in both cases female workers’
earnings are lower than their respective comparable non-enclave suburban
workers working for non-niche jobs.

106

Table 5.5 First Stage Regression Estimates of Latino Immigrants' Log (Wage
Earnings) by Gender
Intercept 0.010 -0.397 5.524 *** 5.194 ** -3.265 8.711
Niche, Enclave and Interaction Variables
Niche 0.322 0.766 -0.348 *** 0.075 0.417 -1.051 *
Central City -0.033 0.649 0.116 -0.019 0.310 0.337
Central City Enclave 0.335 -0.498 -0.140 -0.002 0.239 0.205
Inner Ring Suburb 0.056 -0.038 0.083 0.073 0.375 0.284
Inner Ring Suburb Enclave 0.388 0.273 0.004 0.179 -0.079 0.127
Outer Ring Suburb Enclave -0.062 -0.324 -0.142 -0.090 0.370 0.332
Niche*Central City -0.793 -0.851 -0.480 ** 0.532 0.194 -0.157
Niche*Central City Enclave 0.694 0.564 0.730 *** -0.028 -0.466 -0.106
Niche*Inner Ring Suburb 0.010 -0.534 -0.053 0.379 -0.806 0.250
Niche*Inner Ring Suburb Enclave -0.186 0.891 0.467 ** -0.624 * 0.270 -0.322
Niche*Outer Ring Suburb Enclav -0.492 0.211 -0.076 -0.174 -1.102 * 1.294
Sociodemographic Variables
No or limited English Proficiency -0.419 *** -0.369 -0.285 *** -0.289 *** -0.239 -0.546 *
Arrived in 1980s -0.034 0.164 -0.210 *** 0.049 -0.025 -0.405
Arrived in 1970s -0.052 0.327 -0.317 *** 0.090 -0.379 -1.052
Arrived before 1970 -0.981 *** -0.664 -0.955 *** -0.240 -4.415 *** -2.558 ***
With Child 0.413 ** -0.054 0.193 *** -0.043 -0.050 0.146
Married 0.116 -0.144 0.205 *** -0.091 0.304 -0.035
Experience 0.182 *** 0.153 *** 0.143 *** 0.180 *** 0.140 *** 0.192 ***
Experience2 -0.003 *** -0.002 ** -0.002 *** -0.003 *** -0.002 *** -0.003 ***
Commuting Mode Variables
Transit -0.506 * -1.171 *** -0.487 *** -0.482 *** -0.318 -0.040
Other Mode -0.611 ** -0.415 -0.445 *** -0.588 *** -0.229 -0.617
Neighborhood Variables
Log (Median House Value) -0.124 -0.221 -0.210 -0.105 0.602 0.941
Log (Median Rent) 1.381 ** 1.565 0.772 *** 0.420 0.596 -1.892
Instrument Variables (Wealth Composition)
Log (Other Household Income) -0.006 -0.024 -0.022 *** -0.006 -0.018 -0.011
Investment Income 0.395 0.640 0.310 *** 0.300 * 0.126 0.170
Business Income -4.616 *** -6.233 *** -5.153 *** -4.605 *** -2.006 *** -4.986 ***
Social Security Income -0.140 0.772 -0.294 * 0.260 -0.978 1.771
Welfare Income -0.496 -0.254 -0.118 -0.894 *** 0.703 -1.852 **
Adj. R
2
N
*p<0.05, **p<0.01, ***p<0.001.
756
0.211 0.188 0.319 0.233 0.22 0.327
1806 843 18031 9389 1375
Chicago Los Angeles Washington, D.C.
Male Female Male Female Male Female

107

Table 5.6 Regression Estimates of Latino Immigrants' Commute Times by
Gender
Intercept 2.522 ** 1.649 4.589 *** 3.792 *** 5.702 *** 3.952 *
Niche and Enclave and Interaction Variables
Niche 0.074 0.133 -0.081 *** 0.089 * 0.137 -0.015
Central City 0.333 *** 0.650 *** 0.112 *** 0.103 *** 0.096 0.623 *
Central City Enclave 0.068 -0.036 -0.009 0.051 -0.075 -0.331
Inner Ring Suburb 0.049 0.160 0.011 0.031 0.104 -0.013
Inner Ring Suburb Enclave 0.260 ** 0.411 ** 0.060 ** 0.028 0.145 0.306 **
Outer Ring Suburb Enclave 0.242 ** 0.261 ** 0.030 0.153 *** 0.120 0.054
Niche*Central City -0.054 -0.101 0.055 -0.048 0.316 -0.389
Niche*Central City Enclave -0.075 -0.013 -0.055 -0.187 ** -0.021 0.461
Niche*Inner Ring Suburb 0.022 0.043 0.042 -0.065 0.014 0.066
Niche*Inner Ring Suburb Enclave -0.021 0.082 0.023 0.042 0.034 -0.159
Niche*Outer Ring Suburb Enclave -0.130 0.014 0.073 -0.047 0.091 0.211
Sociodemographic Variables
No or limited English Proficiency 0.036 0.011 -0.005 -0.041 0.039 0.058
Arrived in 1980s 0.129 ** 0.065 0.019 0.003 -0.064 0.112
Arrived in 1970s 0.072 0.005 0.011 -0.060 * 0.024 0.073
Arrived before 1970 0.055 0.061 0.031 -0.137 *** 0.054 0.178
With Child -0.024 0.056 -0.008 0.001 -0.001 -0.023
Married -0.020 0.014 0.028 * -0.034 * 0.036 0.023
Experience -0.002 0.001 0.003 0.007 * 0.011 0.010
Experience2 0.000 0.000 0.000 0.000 0.000 0.000
Commuting Mode Variables
Transit 0.263 *** 0.392 *** 0.516 *** 0.687 *** 0.150 ** 0.436 ***
Other Mode -0.494 *** -0.616 *** -0.433 *** -0.420 *** -0.371 *** -0.542 ***
Neighborhood Variables
Log (Median House Value) 0.112 -0.082 -0.199 *** -0.029 -0.490 *** -0.349 *
Log (Median Rent) -0.151 0.297 0.132 * -0.074 0.574 * 0.471
Instrumented Variable
Log (Wage Earnings)
a
-0.002 0.007 -0.003 -0.015 ** -0.069 * -0.023
Adj. R
2
N
*p<0.05, **p<0.01, ***p<0.001.
a. Treated as endogenous as described in the text, with wealth composition variables serving as instrument variables.
756
0.104 0.193 0.084 0.194 0.081 0.185
1806 843 18031 9389 1375
Chicago Los Angeles Washington, D.C.
Male Female Male Female Male Female

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These dynamics point to the complexity of ethnic enclave- niche linkages
and the differences in their payoff scales. It diverts from the conclusions of past
studies which state niche employment might be of lesser quality (Waldinger
1996) and that residential segregation harms the earnings of Latino immigrants
(Wang 2007). These results do echo previous findings that men and women
receive different returns to human capital in niche employment and that niche
employment might benefit male workers but not female workers (Zhou and
Logan 1989, Greenwell et al 1997).
In terms of commuting, niche employment also affects male and female
workers differently and its impact varies with residential location. Female
workers in Los Angeles who engage in niche employment experience longer
commutes than their non-niche counterparts, while niche male workers actually
have shorter commutes. As is found in the previous chapter, living in central
city in general, and in ethnic enclaves in both inner ring and outer ring suburbs
lengthen workers’ daily commutes and these effects are not explained away by
niche employment status. The previous proposition that the longer commutes of
suburban enclave residents compared to non-enclave residents might be due to
ethnic networks that connect them to niche employment farther away from
home and not in the local labor market is not validated. Knowing actual location
of their job sites and the urban form characteristics of the enclave communities
might help solve the puzzle. The only significant interactions are observed for

109

female immigrant workers who reside in central city enclaves and work in niche
occupations in Los Angeles: their daily commutes are significantly shorter. This
might be attributable to the concentration of niche industries in this area.
To summarize, the interactive niche and enclave effects on earnings and
commuting are location specific and gender specific, and there lacks consistent
patterns across different metropolitan areas. While it is generally true that niche
employment lowers the earnings of Latino immigrants, for male workers in
central city and inner ring suburban enclaves in Los Angeles, niches actually
boost their incomes, but the same advantage is not enjoyed by female workers.
At the same time, the accessibility of gender-specific niches differs for males and
females as well. While female niche workers in Los Angeles commute longer to
work, their male counterparts experience shorter commutes. But for female
ethnic enclave residents in the central cities, their commutes are shorter as well.
Future discussions on the quality and accessibility of niche employment needs
to take into account these important gender and spatial distinctions.
Regarding the other variables in the models, having no or limited English
proficiency is associated with lower wage earnings in jobs. This language barrier
constrains the range of jobs immigrants can possibly obtain and results in their
taking jobs on the lower end of labor market hierarchy. In all cities, men in
earlier arrival cohorts have lower earnings than the newest arrivals, contrary to
what assimilation theory would predict. This might has to do with the temporal

110

dimension of “time of arrival”, meaning that the entry wages of workers
entering the labor force in earlier periods may be less than those in later periods
(Borjas 1985). In most cases (except women in L.A.), Latino immigrants of earlier
cohorts tend to commute to jobs farther away from home, suggesting their
expanded job search area with longer stay. Married workers and workers with
children earn higher wages. Experience is consistently rewarded with higher
pay, though in a diminishing fashion. More experienced workers in Los Angeles
and Washington D.C. commute farther to jobs. Both transit and other mode
users earn significantly lower earnings comparing to automobile users in
Chicago and Los Angeles, while their daily commutes are longer and shorter
respectively in all cities.
To explore any compensating effects of housing price and earnings on
commuting, median house value, median rent on PUMA level, as well as wage
earning (all in log linear forms and wage earning as instrumented variable) are
included in the commuting models. Living in a neighborhood with lower
median housing price incurs longer commuting, in accordance to urban
economic theories. However, contrary to expectation, high rental cost is
associated with longer commutes for male workers in Los Angeles and
Washington, D.C. Also, Latino workers’ longer commutes are not compensated
for by higher wages. In the same two cities, immigrant workers even commute
longer for lower pay, which might be attributable to their limited choices in the

111

urban housing and labor market. Or as some argued, the value of “culture”
might compensates for lower earnings and higher rents in ethnic enclaves
(Gonzalez 1998). These underlying mechanisms call for more studies that
examine housing market and labor market in a holistic manner.

5. Conclusion and Discussion
This paper links immigrants’ residential segregation with their labor
market segmentation and examines the connection between ethnic enclave
residence and ethnic niche employment, as well as the existence of any earnings
and commuting penalty for workers who live and work in ethnically-
concentrated locations and industries. This comparative analysis of Chicago, Los
Angeles and Washington D.C. reveals that there is much variation in the
magnitude of locational effects across cities while individual socioeconomic
characteristics yield more consistent results. The enclave and niche effects are
pertinent to each individual city and the ethnic home-work connections are
location-specific and gender-specific. In general, living in enclaves in both
suburban areas would increase the likelihood of niche employment, but the
same effect is not applicable to enclaves in the central cities. This demonstrates
that ethnic enclaves and ethnic networks are embedded in the spatial contexts in
which they operate. Inadequate accessibility to jobs in central city location might
lessen the effectiveness of social networks in these enclaves. While not explored

112

in this study, the socioeconomic status of enclaves can make a difference as well.
Two distinctive forms of ethnic clustering exist: one out of necessity and limited
resources and the other one based on preference for ethnic amenities (Logan et
al 2002) and they necessarily have different implications on their residents.
Location effects, where significant, are more pronounced for female workers as
compared to male workers. It shows that women tend to utilize more
intensively residentially based ethnic networks and contacts to find jobs.
In terms of the quality and accessibility of ethnic niches, niche
employment usually entails lower wages as compared to non-niche industries,
but for male workers who live in central city and inner ring suburban enclaves
and work for niche jobs in Los Angeles, their earnings are actually significant
higher. Niche employment not necessarily always constitutes a less desirable
labor market sector for Latino immigrants, and for some enclave residents it
actually provides better opportunities for economic well-being. The same
advantage however is not shared by female workers, who do not have positive
earnings effect at any location.
Commuting effects also differ by gender: female workers commute
longer to niche jobs while male workers commute less in Los Angeles. But for
female workers who live in central city enclave and work for niche jobs their
commutes are shorter, probably due to the proximity of niches to enclaves in
this area. Overall, niche effects are more evident for women than for men. While

113

they tend to rely heavily on ethnic networks and locate niche jobs, these jobs are
of lower quality and accessibility from their homes. For male workers living in
ethnic enclaves, working for niche industries actually provides better quality
employment. The longer commutes of enclave residents in both suburban rings
remain and are not explained away by niche employment status, suggesting that
the location of these niche jobs might matter and desire further exploration.
Also worth noting is that ethnic networks exhibit most strength among
new arrivals and those with no or limited English proficiency, whose residence
in ethnic capital – dense enclaves is associated ethnic niche employment.
Unexplored in past studies, this chapter also finds that the longer commutes that
niche employment entails is not compensated by Latino immigrants’
neighborhood cost-of-living or earnings. Actually sometimes they experience
simultaneously higher rent, longer commutes, and lower earnings, as contrary
to what urban economic theories suggest. Further studies are necessary to
disentangle the underlying dynamics.
Results presented in this chapter contribute to our understanding of the
connection between ethnic residential segregation and ethnic labor market
segmentation of low-skilled Latino immigrants. It broadens the debate between
spatial mismatch and social networks theories and underlies the importance of
interaction between spatial location and social environment in affecting Latino
immigrants’ niche employment accessibility. It also highlights gender difference

114

in the functions of ethnic enclaves and finds that while women tend to utilize
residentially based ethnic ties to find jobs, the quality and accessibility of niche
jobs are not that satisfactory. Understanding decision making at the intra-
household level, and the spatial distribution of men and women’s respective
niche industries in relation to their place of residence can help further explain
some of these results, and thus provide directions for future research.

115

CHAPTER 6.

CONCLUSION AND FUTURE RESEARCH

The rapid increase of immigrant population in metropolitan areas across the
United States brings significant changes to urban economic and social life. This
demographic shift coincides with the industrial change and spatial
reorganization of cities and raises important questions regarding the
participation of immigrants in the urban labor markets. This study examines
how space and spatially-constructed social networks shape the employment
outcomes of low-skilled Latino immigrants. It further identifies the ethnic niche
sectors where Latino immigrants heavily concentrate and explores the linkage
between residential segregation and labor market segmentation.
This study draws upon two traditions of inquiry from different
disciplines: the spatial mismatch hypothesis and social networks theories in a
comprehensive examining of the role of residential location for immigrants.
These two research frameworks both have long tradition in their academic fields
and emphasize the importance of spatial accessibility and social networks
respectively in the job search process of minorities and immigrants. A research
agenda that incorporates both lines of thinking and considers immigrants’

116

residential location as both a spatial concept and domains for social relations has
not fully developed.
This study adopts this view and through a detailed geographic partition
identifies six types of communities: ethnic enclaves and mixed communities in
central city, inner ring suburbs and outer ring suburbs. As is presented in the
study, these three rings feature different employment levels and growth rates
and thus provide local residents varied spatial accessibility to jobs. This design
enables evaluating the relative strength of spatial mismatch effect and ethnic
enclave (social networks) effect through comparing the employment outcomes
of residents between enclaves and non-enclaves within each ring and across
rings. It also makes possible examining the effect of enclaves in various
locations. While it is found that both dispersion and concentration are the spatial
assimilative patterns of recent immigrants, and that ethnic clustering exist in
both the central city and suburban areas (Logan, Alba and Zhang 2002), research
is needed in distinguishing these different forms of ethnic communities and
their possible effects on immigrants.
While detailed findings are summarized at the end of each chapter,
several major themes can be highlighted here and possible directions of future
research are discussed along the way.
First of all, there is much variation across the three metropolitan areas of
study: Chicago, Los Angeles and Washington, D.C. in regards to spatial effects,

117

while effects on personal characteristics show striking similarity. Given the fact
that the distribution and growth pattern of population and employment across
rings in these areas are largely comparable, it points to other inter-metropolitan
and intra-metropolitan differences not captured in the study. The history and
composition of Latino immigration to these areas varies, so are the ethnic
concentrations they form in residence and at work. One example is Washington,
D.C., as a newer gateway with large immigration inflow after 1980s, has fewer
numbers of enclaves and niches than the other two cities, and greater percentage
of Latino immigrants participating in both. Supply factor might be in play as
well. Both Chicago and Washington, D.C. have higher percentage of black
residents than L.A., who are possibly competing for similar jobs as low-skilled
Latinos. Further research is needed to test these possible explanations.
Second, it is the interaction between spatial accessibility and enclave
residence status that determines Latino immigrants’ labor market performance.
Findings indicate that the spatial mismatch effect is prevalent in the central
cities, which dampens employment rate and lengthen commutes, ethnic enclave
residence does not make a difference in that area. On the contrary, enclave effect
is much pronounced in both suburban rings: while as likely to work as non-
enclave counterparts, enclave residents usually commute longer to jobs,
suggesting the operation of social networks in these areas in connecting
immigrants to jobs outside the local labor market. When it comes to niche

118

employment, the enclave residence in suburban areas is generally associated
with ethnic niche employment, but the same effect is not evident in central city
enclaves. Unexplored before, this shows that social networks are embedded in
the spatial and socioeconomic contexts and operate well in job-rich areas and
higher-status communities.
Third, gender differences are salient in all results. Women in ethnic
enclaves face greater spatial barriers to work. In addition, while they tend to rely
more heavily on residentially-based ethnic networks to find jobs, these jobs are
of lesser quality and accessibility than men. These results are similar to some
previous studies which find that immigrant women are enclave-disadvantaged
(Parks 2004a). This gender bias in the operation of ethnic enclaves might come
from the intra-household division of labor and joint decision-making process in
terms of residential location. Better understanding of the issue requires going
beyond the household and conducting detailed analysis at the individual level.
Finally, as this study examines the three inter-linked aspects of economic
success: employment status, wage earnings and commuting time, it is clear that
the labor market difficulties of low-skilled Latino immigrants lie more in finding
good and easily accessible jobs than in finding a job at all. This group features
high overall employment rate but the wage earnings and commuting duration
of their jobs are not as satisfactory. Upward mobility provided by these jobs
might be limited as well because there is no clear trend of increased income for

119

earlier arrival cohorts. This study further finds that their longer commutes are
not compensated for by neighborhood cost-of-living, especially renting costs.
These multiple faces of employment difficulty – jobless, working poor, long
commutes, and limited mobility – needs much careful identification and
examination.
This study broadens the debate between spatial mismatch and social
networks regarding immigrants’ employment in urban America and sheds new
empirical light on their economic performance in selected metropolitan areas.
Policy makers aiming at facilitating immigrants’ economic mobility and
promoting economic development in ethnic enclaves need to be mindful of the
interaction between space and social processes, and the location-specific and
gender-specific nature of ethnic networks. As immigrants continue to enter the
U.S. labor market on a large scale and settle in various urban neighborhoods,
this research agenda carries much importance into the future.

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131

Appendix A. Total and Foreign-born Population and Employment (by Sector)
for Three Cities, 1990-2000
1990 2000 Level %
Level Level Change Change
Total Population 2,783,660 100.0% 2,896,016 100.0% 112,356 4.0%
Total Foreign Born 469,187 16.9% 628,903 21.7% 159,716 34.0%
Total Employment 1,207,108 100.0% 1,220,040 100.0% 12,932 1.1%
Agriculture, Mining and Construction 51,488 4.3% 54,539 4.5% 3,051 5.9%
Manufacturing 225,30718.7%159,55413.1%-65,753-29.2%
Wholesale and Retail Trade 235,515 19.5% 146,460 12.0% -89,055 -37.8%
Services 413,693 34.3% 565,238 46.3% 151,545 36.6%
Finance, Insurance and Real Estate 110,841 9.2% 111,130 9.1% 289 0.3%
Public* 170,264 14.1% 183,119 15.0% 12,855 7.6%
Total Population 2,797,986 100.0% 2,965,289 100.0% 167,303 6.0%
Total Foreign Born 267,591 9.6% 461,648 15.6% 194,057 72.5%
Total Employment 2,158,391 100.0% 2,376,859 100.0% 218,468 10.1%
Agriculture, Mining and Construction 108,321 5.0% 119,403 5.0% 11,082 10.2%
Manufacturing 297,90113.8%277,211 11.7% -20,690 -6.9%
Wholesale and Retail Trade 498,798 23.1% 545,103 22.9% 46,305 9.3%
Services 587,51227.2%728,141 30.6% 140,629 23.9%
Finance, Insurance and Real Estate 242,570 11.2% 262,792 11.1% 20,222 8.3%
Public* 423,289 19.6% 444,209 18.7% 20,920 4.9%
Total Population 2,434,277 100.0% 3,042,825 100.0% 608,548 25.0%
Total Foreign Born 170,728 7.0% 365,634 12.0% 194,906 114.2%
Total Employment 1322277 100.0% 1,820,840 100.0% 498,563 37.7%
Agriculture, Mining and Construction 113,302 8.6% 150,204 8.2% 36,902 32.6%
Manufacturing 215,40816.3%252,876 13.9% 37,468 17.4%
Wholesale and Retail Trade 306,494 23.2% 418,827 23.0% 112,333 36.7%
Services 361,99727.4%564,358 31.0% 202,361 55.9%
Finance, Insurance and Real Estate 101,017 7.6% 152,709 8.4% 51,692 51.2%
Public* 224,059 16.9% 281,866 15.5% 57,807 25.8%
* Public category includes transportation, communications, other public utilities and public
administration.
Central City: City of Chicago
Inner Ring Suburbs: Cook County (excluding Chicago City), IL; Lake County, IN
Outer Ring Suburbs: DeKalb County, DuPage County, Grundy County, Kane County,
Kendall County, Lake County, McHenry County, Will County, IL; Porter County, IN.
Source: Calculation of 1990 and 2000 Census County and City Data Book
Central City
Inner-Ring Suburbs
Outer-Ring Suburbs
Chicago

132

1990 2000 Level %
Level Level Change Change
Total Population 3,820,693 100.0% 4,057,398 100.0% 236,705 6.2%
Total Foreign-Born 1,336,665 35.0% 1,512,720 37.3% 176,055 13.2%
Total Employment 2,274,350 100.0% 2,160,033 100.0% -114,317 -5.0%
Agriculture, Mining and Construction 83,332 3.7% 67,961 3.1% -15,371 -18.4%
Manufacturing 366,086 16.1% 259,672 12.0% -106,414 -29.1%
Wholesale and Retail Trade 259,496 11.4% 261,325 12.1% 1,829 0.7%
Services 917,674 40.3% 933,780 43.2% 16,106 1.8%
Finance, Insurance and Real Estate 187,047 8.2% 139,774 6.5% -47,273 -25.3%
Public* 460,715 20.3% 497,521 23.0% 36,806 8.0%
Total Population 5,042,479 100.0% 5,522,700 100.0% 480,221 9.5%
Total Foreign-Born 1,558,401 30.9% 1,936,724 35.1% 378,323 24.3%
Total Employment 2,341,274 100.0% 2,293,085 100.0% -48,189 -2.1%
Agriculture, Mining and Construction 122,745 5.2% 110,927 4.8% -11,818 -9.6%
Manufacturing 523,589 22.4% 396,717 17.3% -126,872 -24.2%
Wholesale and Retail Trade 294,437 12.6% 314,817 13.7% 20,380 6.9%
Services 739,535 31.6% 784,685 34.2% 45,150 6.1%
Finance, Insurance and Real Estate 134,025 5.7% 126,450 5.5% -7,575 -5.7%
Public* 526,943 22.5% 559,489 24.4% 32,546 6.2%
Total Population 5,668,361 100.0% 6,903,206 100.0% 1,234,845 21.8%
Total Foreign-Born 1,049,762 18.5% 1,618,171 23.4% 568,409 54.1%
Total Employment 2,402,778 100.0% 2,972,918 100.0% 570,140 23.7%
Agriculture, Mining and Construction 212,241 8.8% 284,732 9.6% 72,491 34.2%
Manufacturing 368,916 15.4% 410,522 13.8% 41,606 11.3%
Wholesale and Retail Trade 229,769 9.6% 306,553 10.3% 76,784 33.4%
Services 840,974 35.0% 1,010,892 34.0% 169,918 20.2%
Finance, Insurance and Real Estate 157,611 6.6% 173,631 5.8% 16,020 10.2%
Public* 593,267 24.7% 786,588 26.5% 193,321 32.6%
* Public category includes transportation, communications, other public utilities and public
administration.
Central City: City of Los Angeles
Inner Ring Suburbs: Los Angeles County (excluding Los Angeles City), CA
Outer Ring Suburbs: Orange County, Riverside County, San Bernadino County,
Ventura County, CA.
Source: Calculation of Southern California Association of Governments Employment Data
Central City
Inner-Ring Suburbs
Outer-Ring Suburbs
Los Angeles

133

1990 2000 Level %
Level Level Change Change
Total Population 606,900 100.0% 572,059 100.0% -34841 -5.7%
Total Foreign Born 58,887 9.7% 73,561 12.9% 14,674 24.9%
Total Employment 788,475 100.0% 756,979 100.0% -31496 -4.0%
Agriculture, Mining and Construction 23,924 3.0% 23,892 3.2% -32 -0.1%
Manufacturing 16,510 2.1% 12,783 1.7% -3,727 -22.6%
Wholesale and Retail Trade 40,434 5.1% 57,030 7.5% 16,596 41.0%
Services 307,701 39.0% 357,082 47.2% 49,381 16.0%
Finance, Insurance and Real Estate 47,505 6.0% 43,383 5.7% -4,122 -8.7%
Public* 326,171 41.4% 262,809 34.7% -63,362 -19.4%
Total Population 1,768,414 100.0% 1,992,592 100.0% 224,178 12.7%
Total Foreign Born 265,489 15.0% 428,770 21.5% 163,281 61.5%
Total Employment 1,198,788 100.0% 1,307,757 100.0% 108,969 9.1%
Agriculture, Mining and Construction 88,934 7.4% 90,720 6.9% 1,786 2.0%
Manufacturing 38,898 3.2% 39,393 3.0% 495 1.3%
Wholesale and Retail Trade 235,442 19.6% 230,982 17.7% -4,460 -1.9%
Services 412,406 34.4% 529,641 40.5% 117,235 28.4%
Finance, Insurance and Real Estate 101,653 8.5% 102,495 7.8% 842 0.8%
Public* 321,455 26.8% 314,526 24.1% -6,929 -2.2%
Total Population 1,670,074 100.0% 2,143,828 100.0% 473,754 28.4%
Total Foreign Born 157,149 9.4% 312,890 14.6% 155,741 99.1%
Total Employment 968,419 100.0% 1,352,559 100.0% 384,140 39.7%
Agriculture, Mining and Construction 110,290 11.4% 131,052 9.7% 20,762 18.8%
Manufacturing 48,379 5.0% 51,201 3.8% 2,822 5.8%
Wholesale and Retail Trade 205,695 21.2% 270,649 20.0% 64,954 31.6%
Services 308,443 31.9% 529,324 39.1% 220,881 71.6%
Finance, Insurance and Real Estate 85,065 8.8% 107,301 7.9% 22,236 26.1%
Public* 210,547 21.7% 263,032 19.4% 52,485 24.9%
* Public category includes transportation, communications, other public utilities and public administration
Central City: District of Columbia
Inner Ring Suburbs: Montgomery County, Prince George County, MD; Arlington County, VA,
Alexandria City, VA;
Outer Ring Suburbs: Calvert County, Charles County, Frederick County, MD; Clarke County,
Culpeper County, Fairfax County, Fauquier County, King George County, Loudoun County, Prince
William County, Spotsylvania County, Stafford County, Warren County,
Fairfax City, Falls Church City, Fredericksburg City, Manassas City, Manassas Park City, VA.
Source: Calculation of 1990 and 2000 Census County and City Data Book
Central City
Inner-Ring Suburbs
Outer-Ring Suburbs
Washington, D.C.

134

Appendix B. First Stage Results from Regression of Log (Wage Earnings) on
Instrumental Variables and Other Variables
Intercept -4.018 -0.172 4.904 ** 5.157 *** 2.385 2.039
Wealth Composition Variables
Log (Other Household Income) -0.060 * -0.011 -0.028 ** -0.020 *** -0.015 -0.016
Investment Income 0.874 0.433 * 0.727 *** 0.322 *** 0.181 0.213
Business Income -3.450 *** -5.043 *** -3.663 *** -4.846 *** -3.267 *** -3.338 ***
Social Security Income -0.429 0.191 0.337 -0.066 -0.223 -0.056
Welfare Income -0.458 -0.357 -0.726 *** -0.416 *** -0.588 -0.455
Location Variables
Central City 0.170 -0.025 0.356 * 0.044 0.300 0.208
Central City Enclave -0.206 0.241 -0.237 -0.069 0.150 0.123
Inner Ring Suburb 0.301 -0.001 0.238 * 0.063 -0.126 -0.009
Inner Ring Suburb Enclave 0.393 0.402 0.306 * 0.000 0.192 0.079
Outer Ring Suburb Enclave 0.562 -0.215 0.523 * -0.158 * 0.079 0.163
Sociodemographic Variables
No or Limited English Proficiency -0.406 *** -0.323 *** -0.437 ***
Migration Cohort 2 0.041 -0.116 ** -0.126
Migration Cohort 3 0.077 -0.201 *** -0.763 **
Migration Cohort 4 -0.898 *** -0.727 *** -3.555 ***
Female -0.727 ** -0.756 *** -0.305 *** -0.754 *** -1.106 *** -1.081 ***
With Child 0.092 0.294 * 0.393 *** 0.121 ** 0.140 0.037
Married 0.107 0.014 0.408 *** 0.082 * 0.215 0.196
Experience 0.218 *** 0.172 *** 0.225 *** 0.156 *** 0.191 *** 0.163 ***
Experience2 -0.004 *** -0.003 *** -0.004 *** -0.003 *** -0.004 *** -0.003 ***
Commuting Mode Variables
Transit -0.164 -0.699 *** -0.516 *** -0.466 *** -0.013 0.001
Other Mode -1.365 *** -0.506 ** -0.525 *** -0.484 *** 0.000 * 0.000 *
Industry of Employment Variables
Manufacturing 0.066 -0.134 0.460 ** 0.604 *** -0.067 0.009
Trade -0.338 -0.291 0.274 0.303 *** 0.543 * 0.355
FIRE 0.738 -0.149 0.506 * 0.681 *** -0.109 -0.267
Services -0.230 -0.532 ** -0.267 -0.056 -0.340 * -0.386 *
Public -0.661 -0.507 * -0.111 -0.192 ** -0.247 -0.201
Neighborhood Variables
Log (Median House Value) 0.575 -0.072 0.278 -0.085 0.495 0.739 *
Log (Median Rent) 0.706 1.396 ** -0.276 0.540 *** -0.111 -0.413
Adj. R
2
0.215 0.222 0.240 0.298 0.220 0.265
N 759 2649 6592 27420 204 2131
F-statistics
a
6.81 *** 62.33 *** 145.13 *** 1709.2 *** 1 66.77 ***
*p<0.05, **p<0.01, ***p<0.001.
a. F-statistics are from tests of collective significance of the five wealth composition variables.
Chicago Los Angeles Washington, D.C.
Native Foreign Native Foreign Native Foreign

135

Appendix C. List of Ethnic Niches by Gender and Size of Employment for Three
Cities

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
source: author's calculation of census 2000 PUMS data using unweighted sample
Chicago
FEMALE MALE
Plastics products Landscaping services
not specified manufacturing industries not specified manufacturing industries
Traveler accommodation Furniture and fixtures
Electronic components and products Miscellaneous fabricated metal products
Electrical machinery, equipment, and supplies Paperboard containers and boxes
Services to buildings and dwellings Bakeries, except retail
Drycleaning and laundry services Foundries
Miscellaneous manufacturing, Coating, engraving, heat treating etc.
Private households Animal slaughtering and processing
Seafood and other miscellaneous foods Miscellaneous paper and pulp products
Furniture and fixtures Car washes
Fruit and vegetable preserving Retail bakeries
Miscellaneous fabricated metal products Metal forgings and stampings
Animal slaughtering and processing Miscellaneous wood products
Bakeries, except retail Sugar and confectionery products
Cut and sew apparel Fruit and vegetable preserving
Miscellaneous paper and pulp products Not specified food industries
Sugar and confectionery products Beer, wine, and liquor stores
Electronic markets, agents and brokers machinery and equipment repair and maintenance
Not specified food industries
Pulp, paper, and paperboard mills
Metal forgings and stampings Toys, amusement, and sporting goods
Retail bakeries
Toys, amusement, and sporting goods

136

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
source: author's calculation of census 2000 PUMS data using unweighted sample
Los Angeles
FEMALE MALE
Private households Landscaping services
Cut and sew apparel Furniture and fixtures
Services to buildings and dwellings Crop production
Traveler accommodation Car washes
Apparel accessories and other apparel Waste management and remediation services
Plastics products Structural metals, and tank and shipping containers
Drycleaning and laundry services Coating, engraving, heat treating etc.
Crop production Fabric mills, except knitting
Textile product mills except carpets and rugs Miscellaneous wood products
Apparel, fabrics, and notions Bakeries, except retail
Fabric mills, except knitting Animal production
Soap, cleaning compound, and cosmetics Foundries
Fruit and vegetable preserving Machinery & equipment repair & maintenance
Seafood and other miscellaneous foods, n.e.c.Recyclable material
Leather tanning and products, except footwearTextile and fabric finishing and coating mills
Rubber products, except tires Paperboard containers and boxes
Animal slaughtering and processing Rubber products, except tires
Sugar and confectionery products Animal slaughtering and processing
Knitting mills Aluminum production and processing
Resin, synthetic rubber and fibers, and filaments
Nonferrous metal production and processing
Prefabricated wood buildings and mobile homes
Pottery, ceramics, and related products Miscellaneous nonmetallic mineral products
Not specified metal industries
Carpets and rugs

137

1
2
3
4
5
6
7
8
9
10
source: author's calculation of census 2000 PUMS data using unweighted sample
Washington, D.C.
Warehousing and storage
Hardware, plumbing and heating equipment
Retail bakeries
Beauty salons Crop production
Car washes Car washes
Drycleaning and laundry services Waste management and remediation service
Services to buildings and dwellings Restaurants and other food services
Private households Landscaping services
FEMALE MALE
Restaurants and other food services Construction
Traveler accommodation Services to buildings and dwellings

Abstract (if available)

Abstract The rapid increase of immigrant population in metropolitan areas across the United States brings significant changes to urban labor market. This study locates immigrants' labor market performance in the economic and spatial contexts of cities and examines the role of space and spatially-constructed social networks on their employment outcomes in both the general labor market and the ethnically concentrated niche sectors in particular.

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Asset Metadata

Creator Liu, Yang (author)

Core Title Beyond spatial mismatch: immigrant employment in urban America

School School of Policy, Planning, and Development

Degree Doctor of Philosophy

Degree Program Planning

Publication Date 11/18/2008

Defense Date 07/22/2008

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag commuting,employment,immigration,OAI-PMH Harvest,residential location

Place Name California (states), Chicago (city or populated place), District of Columbia (states), Illinois (states), Los Angeles (city or populated place), USA (countries), Washington (city or populated place)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Painter, Gary D. (committee chair), Myers, Dowell (committee member), Strauss, John (committee member)

Creator Email liuyang@usc.edu,ly_cat@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1814

Unique identifier UC1104417

Identifier etd-Liu-2340 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-131148 (legacy record id),usctheses-m1814 (legacy record id)

Legacy Identifier etd-Liu-2340.pdf

Dmrecord 131148

Document Type Dissertation

Rights Liu, Yang

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu