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Journal of Housing Research • Volume 10, Issue 1 Mae Foundation 1999. All Rights Reserved.

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Neighborhood Opportunity Structures and Immigrants’ Socioeconomic Advancement George C. Galster, Kurt Metzger, and Ruth Waite*

Abstract This article explores immigrants’ socioeconomic success consequential to their choice of neighborhood. We describe and analyze seven aspects of socioeconomic success during the 1980s for 14 immigrant groups in five metropolitan areas. Exposure indices measuring aspects of the census tracts in which these groups lived in 1980 are calculated and analyzed. Multiple regression explores the degree to which 1980s neighborhood context explains socioeconomic advances of pre-1980 immigrants during the 1980s, controlling for group starting position in 1980 and metropolitan area of residence. Findings support the notion that a neighborhood of poorly educated, welfare-assisted, nonworking residents retards educational, professional, and employment prospects of immigrants. We also find evidence that a higher incidence of residential exposure to other members of one’s immigrant group leads to higher rates of poverty and, perhaps, lower gains in employment during the subsequent decade. These findings should be interpreted cautiously, however, because of data limitations, specification shortcomings, and ambiguities in interpreting causation. Keywords: immigration; socioeconomic advancement; neighborhoods; ethnic enclaves; assimilation

Introduction There is considerable debate over reasons for the differential rates of socioeconomic advancement observed historically for different immigrant groups in the United States.1 This debate recently has taken on increased political significance as states and the federal government have considered and sometimes enacted legislative initiatives that would restrict immigration or limit types of social benefits to immigrants already living in the United States. The popular justification for these initiatives is that many immigrants “don’t pull their own weight” and thus create “fiscal burdens.”2 Some argue that the differences can be attributed primarily to intergroup variations in individual attributes such as human capital or values conducive to economic advancement. Others suggest the importance of factors well beyond the control of individual immigrants, such as variations in labor-market discriminatory barriers, political power structures, industrial restructuring, and the macroeconomic health of the metropolitan areas into which they migrated. Still others indicate that we

*George C. Galster is the Clarence Hilberry Professor of Urban Affairs at Wayne State University, Detroit, and a Senior Affiliate of the Urban Institute in Washington. Kurt Metzger is the Director and Ruth Waite is a manager in the Michigan Metropolitan Information Center in the Center for Urban Studies of Wayne State University. The authors thank Michelle Porter and Anne Zobel for their excellent research assistance. The helpful comments of Pat Simmons, Amy Bogdon, and three anonymous referees are also gratefully acknowledged. 1

For a recent review, see Preston, McLafferty, and Liu (1998).

2

For a recent review of the issues and corresponding research, see James, Romine, and Zwanzig (1998).

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George C. Galster, Kurt Metzger, and Ruth Waite

merely need patience; as for waves of immigrants before them, the passage of time will permit most new immigrants to assimilate smoothly into the mainstream. Only recently has there been consideration in this debate of the local spatial context of immigrant neighborhoods and its potential effect on systematically structuring opportunities. The research reported here builds on the work of George Galster (Galster and Killen 1995; Galster and Mikelsons 1995) on the metropolitan opportunity structure (MOS). In brief, the opportunity structure concept argues that there are vast intrametropolitan spatial disparities in markets, public resources, local social systems, and information about opportunities, and that perceptions of these disparities strongly affect the decisions people make regarding education, work, and crime. Many of these disparities are manifest on small geographic scales. Thus the neighborhood provides an additional explanatory factor for differences in achieved socioeconomic status, independent of family background and personal characteristics such as immigrant status. This article explores the empirical applicability of this framework to a sample of 14 immigrant groups that moved into five U.S. metropolitan areas before 1980. The objectives of our investigation are three-fold: 1.

Describe and compare various measures of the average socioeconomic progress achieved by these pre-1980 immigrant groups in the five case-study sites during the 1980 to 1990 period.

2.

Describe and compare the average neighborhood-opportunity contexts confronting different immigrant groups in the five sites in 1980.

3.

Determine the extent to which a group’s neighborhood-opportunity context in 1980 is associated with its average socioeconomic progress during the subsequent decade, controlling for other factors.

To accomplish these objectives, we quantitatively compare the census tract environments confronting different immigrant groups and the groups’ aggregate socioeconomic progress in the two major metropolitan areas that have witnessed the most U.S. immigration during the past decade: New York City and Los Angeles. Moreover, we provide similar comparisons in three other large metropolitan areas that have seen less immigration during the 1980s but have substantially different patterns of growth and decline: Atlanta, Philadelphia, and Washington, DC. Both intra- and intermetropolitan area comparisons and intergroup comparisons are analyzed to determine the variation in neighborhood-opportunity contexts and explore its role on socioeconomic outcomes for immigrants. We find support for the proposition that various aspects of neighborhood context independently affect the ability of immigrants to advance economically in several dimensions.

Determinants of Immigrants’ Socioeconomic Advancement: A Review of Spatial and Nonspatial Factors Seven distinct theories about the determinants of immigrants’ success in the United States can be identified (Hirschman 1996; Portes and Zhou 1992; Preston, McLafferty, and Liu

Neighborhood Opportunity Structures and Immigrants’ Socioeconomic Advancement

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1998). Four of them—cultural assimilation, human capital, economic restructuring, and ethnic enterprise—place no significance on the neighborhood context in which immigrants find themselves. There is considerable, if not wholly convincing, empirical support for each of these theories, although none escapes from several empirical and conceptual conundrums. For a review of the arguments, see Portes and Zhou (1992) and Portes (1996). The other three theories—ethnic enclave, segmented assimilation, and ethnic capital—focus explicitly on the role of spatially clustered business enterprises or demographic groups with which immigrants come into contact. Given the neighborhood focus of this article, these three theories will be reviewed in more detail.

Nonspatial Determinants of Immigrants’ Economic Success Cultural Assimilation Theory. This theory conceptualizes foreignness primarily as differences in values and behaviors and thus anticipates that, over time, immigrants will advance as they acquire mainstream values and habits. Each new immigrant group must find ways to become normatively acceptable to the mainstream before economic progress can follow (Warner and Srole 1945). Some groups assimilate faster than others because they come to the United States with highly functional cultural norms and traditions such as emphasis on hard work and assigning special status to education and certain high-paying occupations (Sowell 1978). Associated with this cultural assimilation is increased residential dispersion among native-born populations (Massey 1985), but this is seen typically as a consequence of increases in immigrants’ socioeconomic status, not as a cause. Human Capital Theory. This theory posits that immigrants’ success is affected primarily by the amount of educational credentials, skills, and experience they offer to prospective employers. Though typically there is an adjustment period during which immigrants acquire appropriate American human capital, such as English language proficiency, school credentials, and social and job contacts, thereafter their economic achievement rises rapidly (Chiswick 1978, 1979). Economic Restructuring Theory. This theory claims that international forces of technological, political, and economic competition have fundamentally changed the nature of work in those central cities that traditionally have been ports of entry for immigrants. This change has reduced the demand for the unskilled labor brought by many immigrants, limiting their prospects for economic success (Fry 1996; Gans 1992). Ethnic Enterprise Theory. This theory focuses on coethnic employment and self-employment as an avenue for immigrants’ advancement. As articulated by Portes and Zhou (1992), immigrant laborers can expand opportunities for other members of their group by intensively recruiting them into the same firms, thereby colonizing these firms and transforming them into ethnic enterprises (Waldinger 1996). Immigrant entrepreneurs can take one of three paths to prosperity: (1) serving in niches abandoned by mainstream businesses as commercial and financial middlemen for impoverished domestic customers who are not of their own group; (2) becoming international traders between the origin and host nations; and (3) operating in ethnic enclaves characterized by a concentration of integrated ethnic firms in which much of the workforce is composed of members of their own group. The last path has a clear spatial dimension to it and thus will be discussed more fully.

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Spatial Determinants of Immigrants’ Economic Success Ethnic Enclave Theory. This theory of immigrant socioeconomic advancement has been most fully developed by Portes. He defines an enclave as a “spatially clustered network of businesses owned by members of the same minority [immigrant] group” (Portes 1995, 27). These enclave economies may serve the cultural demands of immigrants from the same group and provide goods and services to the general population. Typically they are economically diverse and institutionally complete. Classic illustrations of ethnic enclaves include Little Havana in Miami, Chinatown in New York and San Francisco, Koreatown in Los Angeles, and the Dominican Village in the Washington Heights neighborhood of New York City. Enclaves can stimulate local economic activity on the supply side through social capital and networks and on the demand side by concentrated preferences for ethnic goods (Light and Rosenstein 1995). The success of the enclave, measured either by wages, employment, or profits, depends on the size of the ethnic community, its stock of human skills, and the availability of capital (Portes 1995; Portes and Zhou 1992). The literature typically discusses ethnic enclaves in terms of their positive features. For example, enclaves have been seen as encouraging: 1.

Social capital formation (Portes and Sensenbrenner 1993; Portes and Zhou 1992; Sanders and Nee 1996; Smith 1995; Waldinger 1995)

2.

Informal on-the-job training and business apprenticeships (Bailey and Waldinger 1991; Portes and Zhou 1992)

3.

Character loans (loans based on personal familiarity with the borrower) (Portes and Zhou 1992; Smith 1995)

4.

Conduits for investments from nations of the immigrants’ origin (Portes and Zhou 1992; Tseng 1995; Yoon 1995)

5.

Higher productivity by clustering same-language workers (Waldinger 1996, 1997)

6.

More ethical business behaviors, because of threat of social ostracism (Portes and Zhou 1992)

7.

Denser networks for sharing job information (Aponte 1996; Bailey and Waldinger 1991; Smith 1995)

8.

A more efficient evaluation of prospective employees’ foreign educational credentials (Light et al. 1993; Nee, Sanders, and Sernau 1994)

However, others have raised a variety of cautions that suggest some potential disadvantages from enclave economies: 1.

If personal contacts are limited to members of a class-homogeneous group from immigrants’ own country of origin (own group), social isolation can result (Waldinger 1996).


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2.

If the enclave completely serves all social and institutional needs, new immigrants may have less motivation to assimilate and develop host-country language and other skills (Massey and Denton 1987).

3.

Workers may be more likely to accept exploitative situations (Gilbertson 1995; Waldinger 1997).

Segmented Assimilation Theory. Another theory with a spatial dimension, segmented assimilation, has been espoused by Portes and Zhou (1993) and Waters (1996a, 1996b). It contends that how the first generation of immigrants adapts to life in the United States creates different opportunities for the second generation. Immigrant groups that maintain strong ethnic ties and resist acculturation (typically by residing in a closely-knit residential enclave) provide better opportunities for the second generation, it is contended, through the creation of ethnic social capital. In contrast first-generation immigrants who have inadequate social capital face extreme discrimination, and residing in close proximity to American minorities (who also have traditionally faced such discrimination) will provide fewer opportunities for their offspring to succeed. Their segment of the second generation is more likely to adopt the attitudes and behaviors of an oppositional culture that takes an adversarial stance against the perceived discriminatory barriers and mainstream society. The consequences are said to be stunted aspirations, educational achievements, and employment prospects.3 Ethnic Capital Theory. The concept of ethnic capital was developed by Borjas (1992, 1995, and 1998). He defines ethnic capital as the average amount of human capital present in the preceding generation of the ethnic group, and effective ethnic capital as the weighted average of ethnic capital possessed by various groups residing in the neighborhood. He hypothesizes that children of immigrants will enjoy increased chances of economic success when they grow up in neighborhoods having larger amounts of ethnic capital and, to a lesser degree, effective ethnic capital. The ethnic spillovers that such co-location patterns provide in the Borjas formulation include intergenerational transmissions of social and human capital, norms for educational attainment, educational and job information, and employment opportunities.

Metropolitan Opportunity Structure and the Socioeconomic Advancement of Immigrants Ethnic enclave, segmented assimilation, and ethnic capital have been the only extant theories of immigrant economic success focusing on spatial issues, but the consideration of urban space should not stop there. We believe that the MOS concept, as formulated by Galster (1993) and Galster and Killen (1995) offers a broad framework within which to understand immigrants, neighborhoods, and socioeconomic advancement. The MOS is defined as the array of markets, institutions, social and administrative systems, and networks that potentially offer resources promoting socioeconomic advancement.4 The quality and quantity of resources offered by the MOS varies as one moves across a metro3

4

There clearly is debate on this point; for more information see Rumbaut (1997) and Branigin (1998).

Note that this definition is considerably more encompassing than that for the identical term used by Waldinger, Aldrich, and Ward (1990), who used the term essentially to mean only market conditions.

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politan area (Galster and Mikelsons 1995), although the spatial scale at which variances become significant depends on the dimension of the MOS being considered. For instance, skill requirements for a particular industry or occupational category probably do not vary much across an entire metropolitan area; however, since public school quality varies across school districts and a variety of social norms, the vibrancy of residential enclaves and the availability of ethnic capital may vary across neighborhoods. Against this tableau of a spatially variant MOS is superimposed a decision maker who is confronting crucial choices such as education, labor force participation, or criminal activity, all of which influence future socioeconomic status. The MOS model posits that revealed choices will reflect the feasibility and relative payoffs of the alternatives (as perceived by decision makers) from the perspective of immigrants’ places of residence. These perceptions, of course, will be influenced by the local manifestations of the MOS, as filtered through a variety of (potentially biased and value-laden) information-conveying media, including local social networks. There is ample, sophisticated empirical research that supports the implications of the MOS framework. This research indicates that many features of the neighborhood environment are highly correlated with decisions about schooling, substance abuse, crime, and labor force participation. For recent reviews, see Ellen and Turner (1997); Briggs (1997); and BrooksGunn, Duncan, and Aber (1997). The MOS model offers an overarching framework within which most of the nonspatial and spatial concerns in the literature can be seen as a subset; that is, the MOS framework suggests that researchers should not confine interest in neighborhood context solely to the density of own-ethnicity population—that might reflect a degree of neighborhood ethnic capital or possibly support development of an enclave economy— or to immigrants’ exposure to native blacks or whites—that might reflect widely divergent paths of assimilation. Rather, it asks us to consider (consistent with the human capital perspective) why certain immigrant groups obtain less human capital than others, and suggests that the answer may lie in the group’s perceptions of the quality of the suppliers of human capital that are available in their neighborhood and the accessibility to jobs for those in their locale with more human capital. Similarly, economic restructuring may have especially devastating impacts on immigrants in a particular neighborhood if a proximate factory closes and emerging job opportunities are far distant. As formulated by Wilson (1987, 1996), the resulting concentrations of poor, unemployed residents who become dependent on welfare and the informal economy create an environment of social isolation that decreases the advancement opportunities for succeeding generations raised there. Thus, immigrant residence in socially isolated neighborhoods should be of importance. Finally, from an assimilationist perspective, the opportunity to learn mainstream mores and behaviors is premised on social contact between immigrants and natives. From the MOS view, this suggests that residential contact between immigrants and native white residents in the neighborhood becomes a key determinant of assimilation. Despite these clear theoretical predictions, thus far there has been no consensus about the sorts of conditions that most strongly affect socioeconomic advancement of immigrants in the neighborhoods to which they gravitate (Allen and Turner 1996; Preston, McLafferty, and Liu 1998). This is because only a few empirical studies have investigated directly the impacts of neighborhood attributes on immigrant socioeconomic mobility.


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Borjas’ (1995) analysis of immigrants in the National Longitudinal Survey of Youth revealed that the percentage of population in the census tract that was of the same ethnicity as the observed persons when they were youths (i.e., in 1979) had a strong positive correlation with their 1990 educational attainment and wages, controlling for age, gender, first- or secondgeneration immigrant status, and parental skill levels. Follow-up work (Borjas 1998) identified a positive correlation between an immigrant child’s eventual educational attainment and that of adult neighbors, both those of the same ethnic group and, to a lesser degree, those in different ethnic groups. Mollenkopf, Kasinitz, and Waters’ (1997) pilot study of young adults in New York who were born in the United States to post-1965 immigrant parents offers some intriguing preliminary findings. They compared offspring of Anglophone West Indian, Dominican, and Chinese immigrants to those of native-born blacks, whites, and mainland-born Puerto Ricans. Not surprisingly, when the immigrant parents brought greater social status and human capital, their children had more favorable labor market outcomes. Many offspring of immigrants and native-born whites and blacks evinced “oppositional culture” traits; although, surprisingly, these traits proved less a barrier to employment than had been anticipated. Finally, the authors tentatively concluded that ethnic residential concentration produced the advantage of ethnic job niches, but also the disadvantage of weak schools and public services. Waters (1996b) follows up on that conclusion by examining the impacts of residential segregation on black West Indian immigrants and their offspring in Brooklyn during the 1990 to 1992 period. Despite the growing importance of education for gaining good-paying jobs in the emerging economy, getting a good education proved extremely difficult for her study group. Schools available to children of Brooklyn West Indian immigrants had a multiplicity of problems, including concentrations of poorly prepared children, weak teachers, and high levels of violence. Graduation rates were minuscule. Her interviews led her to conclude that segregation dashes immigrant families’ hopes that their children will acquire the skills needed for economic success in the United States. Compared with previous research, this study investigates a wider breadth of the indicators of neighborhood-opportunity context—indicators that according to the hypotheses of those earlier theories influence the socioeconomic success of immigrants. This article investigates impacts of own-group immigrant concentrations (which arguably are associated with ethnic capital and might also support development of enclave economies) and looks at exposures to neighbors of various racial characteristics (associated with particular paths of assimilation). Moreover, it employs proxies for social isolation and role models, job information networks, job accessibility, and local public service quality. It compares relationships between these neighborhood contexts and a group’s subsequent socioeconomic progress (on average) over a decade, both across groups within a single metropolitan area and across metropolitan areas for a single group. Several measures of socioeconomic advancement are employed, including income, occupational status, educational attainment, and self-employment.

Research Questions This article investigates the following questions: 1.

For immigrants entering prior to 1980, what degree of socioeconomic advancement during 1980 to 1990—measured by various indicators—has been evinced by different

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foreign-born groups in the New York, Los Angeles, Atlanta, Philadelphia, and Washington, DC, metropolitan areas? 2.

What are the typical neighborhood-opportunity contexts—in multidimensional terms— confronting various immigrant groups in the five metropolitan areas in 1980?

3.

Are these neighborhood contexts different: Among immigrant groups? Among immigrant groups and general population groups defined by race and ethnicity (non-Hispanic blacks, whites, Asians, and Hispanics)? Across the five metropolitan areas within a single group?

4.

For immigrants entering the United States before 1980, is there any association between dimensions of their neighborhood context in 1980 and their socioeconomic advancement during 1980 to 1990, controlling for other factors?

Sample Selection Metropolitan Area Study Sites We selected five metropolitan areas to investigate immigrants’ neighborhood characteristics and socioeconomic advancement: Atlanta, Los Angeles, New York, Philadelphia, and Washington, DC. These five areas were chosen to represent archetypes of metropolitan areas categorized by the extent and type of immigration, racial and ethnic composition, and overall changes across the decade in economic well being.5

Data Sources All data for operationalizing neighborhood indicators in this research were obtained from the Census Bureau’s 1980 and 1990 STF-4 census tract files. Data were used from all tracts in all counties fitting our definitions of metropolitan areas. To operationalize the mean socioeconomic characteristics of the various groups, we aggregate individual observations of each group in the Census Bureau’s 1980 and 1990 Public Use Microdata Sample (PUMS) files for each of the five metropolitan areas. Aggregated estimates of population characteristics from PUMS were checked against aggregations produced from STF-4 as a quality control. Reliance on STF-4 for this study has advantages and disadvantages. It is clearly the richest, publicly available database containing economic, demographic, and physical attributes of small geographic areas (census tracts) that approximate common conceptions of neighborhood. For a multidimensional portrait of neighborhood conditions confronting immigrant groups distinguished by place of birth it is unsurpassed. However, STF-4 does not provide critical demographic or economic information, such as year of arrival, age, or income, about

5 The geographic definitions of these study sites do not correspond precisely with Census Bureau definitions of the corresponding metropolitan areas, but, rather, are constructed to facilitate consistent comparisons between the years 1980 and 1990. Details of the counties that constitute each metropolitan area are available from the second author. Details are also presented in Galster, Metzger, and Waite (1999).


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the various immigrant groups on the neighborhood level. Hence we are forced to use aggregate, metropolitan area–wide estimates of these characteristics of our 14 immigrant groups (generated from PUMS) and compare them with metropolitan area–wide aggregate indicators of neighborhood conditions faced by each of these groups. Clearly it would be desirable, for many reasons, to analyze a longitudinal database consisting of multiple observations of individuals’ personal and neighborhood characteristics, but no such database exists with adequate numbers of immigrants in general, much less with various foreign-born groups in specific metropolitan areas.

Groups Analyzed Table 1 (New York City panel) shows the 14 foreign-born groups that are analyzed in this study. These groups represent the largest four foreign-born groups in each metropolitan area.6 Several additional groups were selected to provide a variety of continental origins and average socioeconomic status in the United States. In all calculations involving PUMS data, a minimum sample size of 100 was specified for a group to be included. Thus, in each metropolitan area portrayed in table 1 (with the exception of New York) one or more groups are not listed; 56 group-area pairs are analyzed. In all analyses, only immigrants who arrived in the United States before 1980 are considered. The socioeconomic indicators are traced for this arrival cohort between 1980 and 1990 where this is feasible with available data. We stress that this does not constitute a true cohort, since groups observed in 1990 in the given metropolitan area as pre-1980 immigrants are not identical to those observed in 1980 because of attrition (death and out-migration) from the latter group and additions to the pool through in-migration to the metropolitan area during the decade. Despite this shortcoming, we believe this arrival cohort is far superior to a comparison of all members of a group with given birthplace in both 1980 and 1990, since the 1990 figure would be influenced not only by changes in the pre-1980 cohort but also by characteristics of those who immigrated during the 1980s. It is also important to note that our arrival cohort is an open-ended designation, combining groups that potentially could differ greatly in their period of immigration before 1980, thereby introducing another potential source of temporal bias.7 Some of the pre-1980 immigrants are members of groups that arrived primarily after 1965 (such as Koreans), whereas others are in longer-established groups (such as Italians). Not only does tenure within the United States differ but so does the demographic composition of these two sorts of groups, with the latter having (1) more retirees and (2) fewer children who immigrated before 1980 and entered the labor force after 1980. Finally, it is worth noting ambiguities in the interpretation of socioeconomic advancement for an immigrant group as a whole. Because we are taking aggregate snapshots of an arrival cohort group in a metropolitan area in two different periods and are not tracking birth

6

There is one exception: Salvadorans are a large immigrant group in Washington, DC, but unfortunately were not categorized separately in the 1980 census STF-4.

7

We are indebted to an anonymous referee for this point.

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Table 1. Socioeconomic Status Measures for Pre-1980 Immigrants by National Origin and Metropolitan Statistical Area, 1980–90

1990

n

% College Graduate 1980

1990

% Professional Occupationb n

Immigrant Place of Birth

1980

1980

Canada Germany Korea United Kingdom

24.14 9.09 115.05 30.17 43.59 13.42 47.13 10.58 7.28 13.31 29.10 30.85 1.75 39.88 14.49 14.74 0.25 36.23 33.23 13.01 21.05 10.27 5.21 15.07 24.66 30.92 6.26 43.97

n

1990

% SelfEmployedc 1980

n

1980

1990

1980

1990

n

3.32 0.39 9.33 4.27

64.93 66.27 69.51 71.79

84.96 20.04 5.26 1.12 14.15 28.09 76.42 10.15 6.23 2.78 13.45 23.59 75.81 6.30 19.66 7.49 112.16 18.41 81.62 9.83 8.12 3.25 14.87 25.50

55.22 48.21 49.89 54.24

27.13 24.61 31.48 28.74

1980 1990

n

Mean Income ($ thousands)e

n

1990

51.51 4.38 6.90 10.21 41.14 1.26 7.74 8.12 26.60 5.55 15.79 25.12 42.76 11.21 8.62 12.89

% Persons in Povertyd

% Employed

Los Angeles % No High School Diplomaa n


n

Immigrant Place of Birth 1980

1990

Canada China Germany India Italy Jamaica Korea Mexico Philippines Poland Soviet Union United Kingdom Vietnam

21.30 16.21 14.82 21.50 6.68 33.49 1.65 33.32 33.51 0.19 16.98 15.81 18.88 22.75 3.87 9.30 12.78 62.34 64.68 2.34 51.40 17.06 8.04 13.86 5.81 21.32 13.05 23.75 27.78 4.03 14.17 14.07 37.47 42.01 4.53 75.87 12.84 2.41 2.48 0.06 12.00 16.23 47.83 48.94 1.12 38.53 16.48 12.82 20.27 7.45 32.78 114.23 16.61 23.80 7.18 16.12 15.65 15.62 19.12 3.50 28.14 18.50 13.41 25.05 11.64

27.50 31.85 22.79 12.08 58.45 24.38 18.24 78.71 18.23 45.01 47.01 21.77 36.64

1980

% Professional Occupationb

% SelfEmployedc


% Employed

1980

1990

n

1980

1990

n

39.24 40.57 33.56 58.49 25.22 28.99 25.56 5.41 29.39 39.53 33.28 39.17 19.64

46.11 47.13 44.26 59.78 43.68 46.87 36.55 9.08 39.83 49.26 39.16 45.03 28.42

6.87 6.55 10.70 1.29 18.46 17.88 10.99 3.67 10.44 9.73 5.89 5.86 8.79

11.54 15.10 12.43 9.63 19.96 5.07 18.87 3.31 3.32 28.68 19.48 12.03 3.79

15.40 3.86 70.37 73.65 3.28 5.88 5.32 10.56 15.85 0.75 71.84 77.14 5.31 11.85 7.09 14.76 14.03 1.60 71.07 73.28 2.21 7.17 5.79 11.38 13.97 4.34 71.85 76.31 4.45 8.59 4.77 13.82 22.07 2.11 66.20 73.91 7.71 9.03 6.27 12.77 9.12 4.05 76.50 80.32 3.81 7.87 5.15 12.72 29.93 11.06 65.40 70.43 5.03 14.74 8.95 15.79 6.34 3.03 64.61 67.07 2.46 23.73 17.49 16.24 4.91 1.59 79.65 84.13 4.48 5.08 2.90 12.18 25.57 13.11 67.53 67.32 10.21 5.66 8.17 2.51 24.31 4.83 60.05 64.80 4.75 18.33 8.62 19.71 14.60 2.57 70.37 74.07 3.71 6.46 5.16 11.31 8.22 4.43 46.41 65.50 19.08 43.61 14.36 129.25

1980

1990

n

1980

1990

n

Mean Income ($ thousands)e 1980

1990

n

23.54 24.01 22.40 27.15 18.02 21.29 21.01 15.52 26.20 20.16 16.02 21.61 14.89

48.22 54.81 49.72 75.51 41.10 41.01 56.95 32.65 58.23 39.13 36.87 46.40 49.80

24.67 30.80 27.31 48.36 23.08 19.72 35.94 17.13 32.03 18.97 20.85 24.79 34.92


Atlanta % No High School Diplomaa

New York % No High School Diplomaa n


n

1980

1990

n


1990

Canada China Dominican Republic Germany India Italy Jamaica Korea Mexico Philippines Poland Soviet Union United Kingdom Vietnam

21.41 17.39 21.78 32.01 10.23 46.13 50.15 4.01 51.61 10.43 21.89 22.30 0.41 24.75 31.53 6.78 60.43 112.11 3.08 6.14 3.06 7.11 15.44 8.33 29.27 17.49 15.73 21.21 5.48 35.83 43.29 7.46 8.18 13.70 63.84 69.02 5.18 59.43 56.76 12.68 63.69 18.95 5.01 7.40 2.39 14.91 21.81 6.91 32.00 15.33 9.79 17.55 7.75 16.83 28.14 11.31 13.61 11.53 49.26 51.43 2.17 39.62 39.27 10.35 54.47 10.35 12.16 13.84 1.68 17.75 18.30 0.54 7.40 13.44 67.31 65.80 11.51 52.53 53.40 0.87 48.04 19.98 10.75 13.67 2.91 27.36 33.26 5.90 37.60 114.13 17.33 23.24 5.92 36.86 38.17 1.31 20.79 18.47 15.24 21.98 6.74 39.23 47.76 8.54 34.82 11.73 19.31 24.52 5.21 33.98 26.23 17.75

28.79 52.04 72.54 36.76 11.88 72.64 37.32 15.14 54.82 10.84 58.02 51.73 29.26 36.55

1980


% SelfEmployedc

% Employed n

1980

n

1990


1980

1990

1980

8.40 6.90 2.86 10.41 5.30 10.73 3.04 17.12 4.14 3.65 12.29 12.52 7.39 2.91

9.11 0.71 64.60 73.49 8.90 7.27 8.71 1.81 76.53 76.73 0.20 14.78 6.85 4.00 55.76 56.11 0.35 31.38 11.42 1.01 69.05 74.25 5.20 6.56 7.01 1.72 72.29 80.33 8.04 6.13 10.66 10.07 64.94 68.28 3.33 9.29 5.02 1.97 70.94 79.50 8.56 13.70 16.68 10.44 62.15 73.25 11.10 10.28 4.52 0.37 66.53 74.35 7.81 17.82 6.37 2.72 80.70 85.69 4.99 4.37 13.79 1.50 68.53 71.55 3.02 9.65 13.64 1.11 59.21 70.83 11.62 19.90 8.75 1.36 64.91 75.08 10.17 7.58 8.17 5.26 51.76 72.44 20.68 42.35

1990

n

6.71 10.57 11.41 13.37 29.00 12.38 5.40 11.16 2.71 13.42 6.60 12.68 9.53 14.17 8.00 12.28 16.68 11.13 2.18 12.19 7.48 12.18 12.62 17.27 5.41 12.17 14.44 127.91


1990

n

23.86 18.87 11.93 21.56 28.07 17.61 17.66 23.08 18.06 28.94 18.62 14.21 22.29 13.40

51.71 44.20 25.91 48.33 80.16 42.45 41.39 57.52 38.73 73.60 38.17 34.25 51.45 46.15

27.84 25.33 13.99 26.77 52.09 24.84 23.72 34.45 20.67 44.65 19.55 20.05 29.17 32.75

Philadelphia % No High School Diplomaa

% College Graduate


1990

Canada China Germany India Italy Jamaica Korea Philippines Poland Soviet Union United Kingdom Vietnam

15.69 25.20 26.21 7.38 63.76 32.10 16.33 10.71 55.68 44.08 27.07 30.11

19.51 10.06 16.91 12.91 112.74 10.86 17.34 15.08 112.38 114.95 17.74 122.76

29.44 47.94 16.80 67.52 4.35 6.15 32.44 57.49 8.63 12.51 15.76 12.64

36.62 7.17 49.17 56.46 7.29 4.55 8.88 4.33 68.14 76.62 8.47 5.38 2.09 13.28 24.31 54.81 30.50 44.15 13.79 48.18 56.36 8.19 8.76 17.87 9.11 70.59 75.92 5.33 14.35 19.06 4.71 22.80 55.24 32.44 21.65 4.85 30.95 37.21 6.27 7.31 8.07 0.76 65.72 75.30 9.59 7.65 5.93 11.72 20.24 44.27 24.03 73.08 5.56 67.32 63.73 13.59 5.06 4.67 10.39 76.26 83.51 7.25 3.16 0.53 12.64 28.28 72.13 43.85 6.60 2.25 14.40 22.50 8.10 14.66 15.26 0.60 65.37 68.78 3.40 8.39 8.67 0.28 16.38 33.45 17.07 10.85 4.70 11.03 23.41 12.38 8.82 3.00 15.83 61.93 78.93 17.00 19.29 8.79 110.50 15.71 34.61 18.90 45.61 13.17 33.67 37.43 3.76 14.29 24.00 9.71 60.99 67.61 6.62 11.83 12.29 0.46 21.09 57.40 36.31 61.45 3.96 53.72 56.65 2.93 2.13 6.03 3.90 75.49 81.39 5.89 3.49 3.25 10.25 28.71 70.52 41.81 14.26 5.63 23.55 29.20 5.65 18.43 15.98 12.45 71.00 70.58 10.42 10.62 8.48 12.14 16.62 33.88 17.26 19.60 7.09 33.24 38.36 5.13 16.03 11.81 14.22 61.63 64.37 2.74 15.19 10.64 14.55 15.22 31.97 16.75 21.72 5.97 36.12 47.54 11.42 6.84 6.80 10.04 64.97 75.68 10.70 7.02 6.35 10.67 20.72 38.71 17.99 31.36 18.72 25.86 37.04 11.17 6.90 1.27 15.62 45.90 72.14 26.23 39.45 16.50 122.95 14.02 43.83 29.81

1990

1980

1990

1980

1990

n

1980

1990

n

105

1980

1980

n

Mean Income ($ thousands)e

n

1990

n


1990

1980

n

% Employed

Immigrant Place of Birth 1980 25.20 25.26 33.12 10.29 76.50 32.96 23.66 15.79 68.06 59.03 34.80 52.87

n

% SelfEmployedc


Table 1. Socioeconomic Status Measures for Pre-1980 Immigrants by National Origin and Metropolitan Statistical Area, 1980–90 (continued)

106

Table 1. Socioeconomic Status Measures for Pre-1980 Immigrants by National Origin and Metropolitan Statistical Area, 1980–90 (continued)


1990

n


1990

% Professional Occupationb n

1980

1990

n

% SelfEmployedc 1980

1990

% Employed n

1980

1990


n

1980 1990

n


1990

n

Canada

15.65

8.45

17.20 33.95 45.75

11.80 55.56 65.18

9.62

8.15

6.34 11.81 70.86 79.23

8.38

3.51 2.10

11.40 33.82 65.46 31.64

China

25.24 23.71

11.53 46.69 54.03

7.35 44.07 57.64

13.57

7.20

3.96 13.25 73.25 85.87 12.62

8.02 5.67

12.35 27.66 64.23 36.56

Germany

9.49

9.53

0.04 34.84 38.46

3.63 43.61 47.88

4.27

5.78

7.23

1.45 71.93 80.50

8.57

3.90 3.10

10.79 27.37 53.82 26.45

India

9.79

6.83

12.96 69.14 75.25

6.11 52.75 60.25

7.50

6.23

5.85 10.38 74.79 84.09

9.30

3.25 1.13

12.11 30.05 75.55 45.50

Italy

46.08 36.03 110.05 17.41 27.00

9.59 31.11 43.00

11.89

3.74

Jamaica

31.88 18.78 113.09 13.54 22.42

8.88 20.48 29.80

9.33

Korea

17.60 14.83

Mexico

28.40 33.37

Philippines

14.01

Poland

30.33 27.06

Soviet Union

32.71 21.26 111.45 38.79 41.86

3.08 62.11 78.74

United Kingdom

11.59 10.27

11.32 32.90 36.70

3.80 51.78 52.76

0.98

6.85

8.37

Vietnam

23.57 17.11

16.46 20.53 35.96

15.42 23.72 36.61

12.89

6.05

4.77 11.27 59.34 81.67 22.33 28.88 3.60 125.28 18.10 57.67 39.57

9.94

12.77 38.78 36.41 12.37 25.60 32.37 3.05 33.22 46.15

13.27 39.34 32.26 17.08 63.77 69.30

5.69 74.78 78.52

5.76 1.70

14.06 25.48 57.54 32.06

0.00

4.91 72.76 88.09 15.34 13.16 3.17

19.99 19.31 47.86 28.55

4.83 70.50 76.96

16.75 25.31 57.89 32.57

4.91

6.78 12.50 17.33

4.98 34.57 25.11 19.46 31.25 26.25 15.00 14.07 50.27 53.32

7.22 12.91

6.46 10.36 3.61

10.97 24.39 42.44 18.05

4.69

7.46

2.77 70.65 90.35 19.69

9.09 8.12

4.75

6.61

1.86 79.22 86.23

7.01

1.68 3.21

1.53 26.60 65.38 38.78

5.53 14.49 13.52 10.98 75.00 78.44

3.44

1.60 3.13

1.53 30.20 52.34 22.14

12.93

16.64 12.63

9.76 12.87 71.96 82.30 10.33 14.35 3.98 110.36 22.25 55.09 32.85 1.52 71.53 78.65

7.12

3.81 3.79

10.02 26.85 57.39 30.54

Source: Tabulations of 1980, 1990 PUMS. Note: Only immigrant groups having at least 100 observations in both 1980 and 1990 PUMS are shown. a Universe: Persons 25 years of age and over. b Professional occupations include the following subcategories: executive, administrative, and managerial occupations; professional specialty occupations. Universe: employed persons 16 years of age and over. c Universe: Employed persons 16 years of age and over. d Poverty status was determined for all persons except institutionalized persons, persons in military group quarters and in college dormitories, and unrelated individuals under 15 years of age. e Household Income.


Washington, DC % No High School Diplomaa


107

cohorts, intra- and intergenerational changes are blurred (Myers and Cranford 1998).8 Adults of one generation observed at the beginning of the period may have significantly altered their well-being during the ensuing decade by retraining, promotion, or retirement, for example. On the other hand, many immigrant children constituting a younger generation that was not yet in the labor market at the beginning of the period (the 1.5 generation) may have moved into their first income-earning stages during the period.9 This younger generation might have different characteristics, such as educational attainment, than workers from older birth cohorts. Our inability to distinguish whether observed changes in a group’s well-being are due to changes within or across generations is unfortunate since it complicates the interpretation of changes in group socioeconomic status during the decade. In principle, the PUMS might be used to define narrower cohorts of immigrants and track specific birth cohorts, thereby avoiding the temporal biases described above. Such an approach was not adopted here for two reasons. First, there are limited sample sizes in the PUMS database for many foreign-born groups in all metropolitan areas; thus, to further stratify observations would cost excessive degrees of freedom in our ultimate analyses. Second, given the limitations of the STF-4 data, we have no way of observing the neighborhood conditions of any more narrowly defined subsets of immigrants. Thus, the more finely grained the specification of the immigrant group, the less reliably its neighborhood condition will be measured. Tables 2 and 3 present corresponding 1980 census tract data for 14 immigrant groups and 4 mutually exclusive race and ethnicity groups specified by the census as non-Hispanic whites (whites hereafter), non-Hispanic blacks (blacks), non-Hispanic Asians (Asians), and Hispanics. These latter groups provide a useful benchmark against which immigrant groups can be compared. We emphasize, however, that the census tract data for race and ethnicity do not permit distinction between U.S.-born and foreign-born groups. Thus, these racial and ethnic categories include foreign-born residents as a subset and do not represent a purely distinctive, mutually exclusive basis for comparison. One further caveat regarding the data must be noted. It is not possible to identify a 1980 group for the above four racial and ethnic categories comparable to the pre-1980 arrival cohort, since the 1990 PUMS does not delineate 1980 place of residence for the various racial and ethnic groups. As a consequence, changes in socioeconomic status are not presented for these groups in table 1.

Socioeconomic Advancement of Immigrant Groups, 1980 to 1990 Socioeconomic advancement during the 1980s for each of the pre-1980 arrival cohorts studied in each of five metropolitan areas undoubtedly can be operationalized over several dimensions of intrinsic interest. Thus, we employ seven alternative dependent variables, all measured for the whole group as changes from 1980 to 1990. The variables are: (1) mean (nominal) household incomes; (2) percent of persons in poverty; (3) percent ages 16 to 64 8

9

We are indebted to an anonymous referee for this point.

Myers and Cranford (1998) find that the intergenerational effect predominates for Latina workers in Southern California from 1980 to 1990.

108

Table 2. Immigrant Exposure Rates to Census Tract Demographic Characteristics, by Place of Birth and Metropolitan Statistical Area, 1980


Los Angeles

New York

% In Own Immigrant Group

% White

% Black

% Hispanic


% White

% Black

% Hispanic


% White

% Black

% Hispanic


0.48 0.54 0.38 0.47 0.54 0.46 0.40 0.70 0.93 0.36 0.53 1.14 0.48 1.22

92.03 85.99 77.15 88.38 85.04 88.82 55.12 85.73 76.97 80.42 89.86 86.85 88.38 81.31

5.38 10.10 19.02 8.98 11.12 7.65 42.84 10.59 17.92 17.33 6.63 7.81 9.26 12.30

1.41 1.91 2.38 1.42 1.77 2.07 1.04 1.73 3.30 0.97 2.19 3.17 1.33 3.42

1.38 4.20 0.28 0.94 0.70 0.83 0.64 3.71 23.34 4.27 1.99 4.85 1.25 3.64

76.06 48.22 55.44 75.60 68.07 70.04 36.37 55.69 30.37 50.66 78.01 70.57 76.97 50.10

3.21 5.00 6.81 3.63 4.76 2.92 38.46 6.10 9.32 6.78 4.09 4.55 3.42 4.85

15.01 27.77 30.45 14.98 17.84 20.50 18.17 24.10 54.62 29.28 12.29 17.84 14.00 31.33

0.63 12.55 13.64 2.13 1.87 6.05 6.05 1.86 1.07 1.93 3.62 5.20 1.14 1.08

83.60 52.37 26.70 84.00 68.83 81.97 25.25 71.83 55.13 67.07 79.70 75.19 81.86 61.64

6.75 7.29 19.43 5.32 9.22 5.48 56.90 6.41 14.05 11.08 7.09 9.81 8.38 13.17

7.07 16.33 49.95 8.08 14.65 10.16 15.58 13.64 27.40 15.20 10.34 11.54 7.29 16.08

All residents White, not Hispanic Black, not Hispanic Asian, not Hispanic Hispanic

89.07 74.84 1.66 2.30

89.07 23.64 84.70 74.30

8.60 74.84 11.41 22.03

1.25 1.02 1.86 2.30

75.77 55.58 13.24 46.29

75.77 20.75 53.97 40.26

3.06 55.58 6.48 7.29

15.77 19.34 25.26 46.29

86.45 60.57 11.97 36.75

86.45 21.70 63.78 39.00

4.80 60.57 9.17 21.04

6.55 15.95 14.74 36.75


Atlanta

Table 2. Immigrant Exposure Rates to Census Tract Demographic Characteristics, by Place of Birth and Metropolitan Statistical Area, 1980 (continued)


Washington, DC


% White

% Black

% Hispanic


% White

% Black

% Hispanic


0.54 3.15 0.43 1.08 1.00 3.45 0.89 1.36 1.22 1.09 1.50 3.80 0.78 1.35

89.47 79.83 57.50 88.09 84.22 86.74 38.26 78.57 78.79 75.69 86.98 84.91 89.23 66.19

7.40 9.57 20.43 8.19 10.39 9.45 57.00 14.99 13.04 15.99 9.21 10.63 7.74 25.90

1.75 2.68 19.83 2.34 1.99 2.75 2.90 2.83 6.54 5.29 2.58 2.69 1.69 3.54

0.78 1.20 1.13 1.01 1.24 0.71 1.90 1.79 0.48 1.33 0.65 1.25 0.93 2.18

82.14 73.64 50.69 80.17 76.23 74.01 41.98 77.57 73.07 71.29 79.21 69.33 81.86 72.06

10.42 15.77 36.68 12.49 13.48 18.79 50.17 12.39 17.34 20.31 12.30 21.52 11.07 14.28

3.63 4.28 8.93 3.49 4.52 3.33 4.57 3.98 5.47 3.68 4.12 4.73 3.44 6.12


90.49 66.96 3.86 20.68

90.49 28.05 78.67 51.63

6.54 66.96 14.29 26.16

1.76 3.82 2.91 20.68

82.19 68.00 5.52 5.62

82.19 27.95 75.10 70.45

11.20 68.00 14.40 19.41

3.12 2.15 4.36 5.62

Source: 1980 census STF-4.


Philadelphia

109

110

Table 3. Immigrant Exposure Rates to Census Tract Socioeconomic Characteristics, by Place of Birth and Metropolitan Statistical Area, 1980


Los Angeles

New York

% Pub. Asst.a

% NonEmployed

% No HSb

Central City

% Pub. Asst.a

% NonEmployed

% No HSb

Central City

% Pub. Asst.a

% NonEmployed

% No HSb

Central City


2.60 4.31 2.97 3.39 4.39 2.61 5.13 3.33 7.26 4.06 2.80 3.08 3.51 4.27

26.68 27.36 25.56 27.04 28.05 26.13 27.01 27.20 29.18 28.90 25.81 26.88 27.15 25.31

18.44 22.36 19.21 22.59 22.30 17.33 24.35 23.73 33.75 29.12 15.91 16.45 21.75 26.34

10.67 12.87 23.62 14.86 15.75 32.17 18.54 9.09 34.69 12.95 38.62 34.48 13.18 32.94

6.60 9.84 11.19 6.69 6.70 7.70 13.34 8.33 15.36 9.69 7.06 9.23 6.44 11.82

30.18 31.65 34.63 30.55 29.16 30.25 34.84 30.40 36.94 31.63 30.05 30.90 29.92 32.67

21.77 30.55 35.67 21.56 21.53 25.66 29.46 26.76 51.74 30.67 22.69 27.62 20.75 36.15

24.58 43.96 31.86 28.44 27.28 34.48 55.97 46.49 35.55 38.09 53.92 58.32 26.99 28.30

5.79 10.23 22.25 5.82 7.84 8.24 16.30 6.85 15.11 8.76 8.09 9.46 6.13 10.50

32.11 34.42 43.12 32.92 32.63 34.80 38.72 32.48 38.79 33.83 34.72 35.88 32.20 34.83

26.92 43.89 55.12 29.20 31.74 39.74 39.54 29.72 43.24 33.56 36.26 36.00 27.79 36.43

35.64 82.93 85.75 40.12 48.27 48.13 78.87 58.98 60.71 48.17 58.86 72.41 34.94 49.19


3.68 14.31 4.00 6.11

27.47 37.90 27.58 29.21

26.00 43.29 24.44 27.56

10.63 60.42 16.66 26.74

6.98 20.61 8.95 13.34

30.87 40.99 31.13 35.48

22.60 37.62 28.41 44.44

20.22 47.69 34.80 29.57

5.78 22.00 8.78 21.11

32.80 43.81 33.64 43.30

29.06 45.05 35.21 50.38

30.79 62.97 62.95 68.75


Atlanta

Table 3. Immigrant Exposure Rates to Census Tract Socioeconomic Characteristics, by Place of Birth and Metropolitan Statistical Area, 1980 (continued)


Washington, DC

% Pub. Asst.a

% NonEmployed

% No HSb

Central City

% Pub. Asst.a

% NonEmployed

% No HSb

Central City


4.58 6.88 19.06 5.42 5.47 8.64 15.59 6.90 6.36 9.14 7.66 7.66 5.25 10.99

33.62 35.79 50.84 34.17 32.85 36.43 41.16 34.88 37.44 38.98 36.17 35.52 33.53 38.38

21.20 27.30 46.06 26.11 23.37 38.37 37.76 26.26 22.12 32.34 37.60 33.28 25.04 29.68

17.80 39.59 45.99 27.35 25.86 39.86 50.84 27.14 23.57 42.04 46.72 67.97 23.44 47.84

2.82 3.74 5.57 2.90 3.32 3.24 6.65 3.25 3.25 3.17 2.93 4.02 2.75 4.01

27.39 27.27 28.51 28.67 26.37 27.47 29.26 27.53 28.23 29.22 26.58 26.32 27.82 26.06

13.65 16.34 22.11 14.05 15.20 17.01 26.55 15.45 16.20 15.82 14.19 17.92 13.92 16.63

12.31 13.19 48.08 10.36 5.89 13.98 38.09 1.71 22.69 10.94 13.50 19.18 13.81 4.35


5.38 23.51 7.33 21.30

34.01 47.57 36.03 48.24

26.43 46.15 26.67 47.96

22.33 62.77 33.84 43.60

3.01 10.87 3.38 4.01

28.20 34.01 27.69 28.35

16.19 34.02 15.48 17.35

7.84 52.17 7.63 18.85

Source: 1980 census STF-4. a Receiving public assistance. b No high school diploma.


Philadelphia

111

112


who are not employed; (4) percent ages 16 and older who are self-employed; (5) percent ages 25 and older with no high school diploma; (6) percent ages 25 and older with college degree; and (7) percent employed in managerial and professional (professional hereafter) occupations.10 Descriptive statistics for these indicators of socioeconomic advancement are shown in table 1. Table 1 shows some remarkable patterns of socioeconomic change across the sample. Some groups showed consistent patterns of inferior or superior performance on a given indicator across sites, while other groups had much more site-specific outcomes. Often, different indicators tell distinct stories about changes in the socioeconomic status of a group, further reinforcing our emphasis on a multidimensional approach to indicators. In discussing some of the outstanding findings in terms of relative ranking of immigrant groups’ performance, we omit Atlanta, since only four groups were analyzed there because of limited sample sizes from the PUMS. Indian immigrants to the United States clearly surpassed all other groups studied, in all four sites, in terms of growth in (nominal) mean income during the 1980s, typically by a wide margin. Of course, Indians also began the decade ranked in the top three groups by mean income in each metropolitan area, although this certainly does not fully explain their rapid and continued earnings growth.11 Surprisingly, other indicators revealed less sanguine pictures of progress. Indeed, Indians did not score among the top three groups in any other indicator. In fact, they evinced some of the lowest relative growths in professional occupations in Los Angeles, New York, and Philadelphia, although in each case they began the decade with a much higher percentage of professional workers than any other group. Filipino immigrants also performed well as a group in the 1980s, as measured by growth in mean income. As did the Indian group, they began the decade in privileged income positions among immigrants in Los Angeles, New York, and Philadelphia. Filipino income growth was secured even though the group had some of the lowest increases in the percent of collegeeducated adults of any group. And the portrait of change takes on considerable cross-site variations: Filipinos ranked in the top three groups for growth in number of professionals in Washington, DC, and increase of the self-employed in New York, but ranked in the bottom three for increases in number of professionals in Philadelphia and number of self-employed in Los Angeles. Koreans, arguably, demonstrated even more remarkable income advancement than Indians or Filipinos. They were in the top three groups for mean income growth in New York, Los Angeles, and Philadelphia; and in the latter two metropolitan areas, they move considerably farther up the rankings from 1980 to 1990 than Indians or Filipinos. But how Koreans apparently found success varies considerably across our study sites. In Los Angeles, Koreans ranked in the top three groups according to growth in both professionals and selfemployment, but in New York they ranked among the lowest three groups in these same categories. In Philadelphia they ranked in the bottom three groups in growth of professionals but in the top three in growth of self-employment. 10 We employ the standard census codification: managerial, executive, administrative, and professional specialty occupations. 11

Duleep and Regets (1996) find, for example, an inverse relationship between immigrants’ income gain over the decade and level of income at the beginning of the decade.


113

Vietnamese immigrants are noteworthy for their dramatic rates of reducing poverty within the group. In all four sites, their poverty rates fell during the 1980s from 23 to 29 percentage points. This is at least double the poverty escape rate shown by the second-highest group in each site. Vietnamese immigrants made this amazing leap upward by succeeding on a variety of fronts. They had the largest increase in college graduates observed in Los Angeles, Philadelphia, and Washington, DC. They ranked in the top three groups in growth in professionals in Philadelphia and in self-employment in Los Angeles and New York. Other immigrant groups showed much more modest indicators of socioeconomic success during the 1980s. But some predominantly black groups, such as Jamaicans and Dominicans, who are stereotyped with low achievement, proved to have a distinctly mixed set of indicators.12 Although showing moderate mean income increases, Jamaicans had the secondhighest rate of poverty reduction in New York and Philadelphia. They also had the secondhighest growth of college graduates in New York and the first- or second-largest growth of professionals in Los Angeles, New York, and Philadelphia. Similarly, despite having the lowest observed income growth of any New York group, Dominicans showed the third-highest increase in professionals and second-highest growth in self-employment. White immigrant groups from Eastern and Central Europe often demonstrated consistently weak indicators of progress. For instance, Poles ranked in the bottom three groups in mean income growth in all four sites, had a similar rank for self-employment growth in Los Angeles and Philadelphia, and had the least reduction in poverty in Washington, DC. Soviet immigrants’ performance was amazingly varied across the four sites. In Los Angeles, they ranked second highest among all groups in self-employment growth and reductions in poverty, but also among the lowest in professional growth. In New York, they evinced the third-lowest mean income growth. In Philadelphia, they had the lowest income growth and the third-highest drop in self-employment. In Washington, DC, they were second highest in reductions of poverty but second lowest in self-employment growth.

Neighborhood Context Faced by Immigrant Groups, 1980 Measuring Neighborhood Context through Exposure Indices All neighborhood (census tract) data are summarized in the tables below in the form of the well-known exposure (P) index. The residential exposure of immigrant group x to some other group y across i census tracts within the given metropolitan area is given by: x

Py 4 R (xi / X) (yi / ni ) i

(1)

where xi is the number of immigrants in group x residing in tract i; X is the total population in immigrant group x in the metropolitan area; yi is the population of another group (defined 12

Butcher (1994) documents disparities between native-born whites and black Caribbean immigrants in 1979. Native-born whites’ average annual earnings were $14,371, with 93 percent employed, and 22.9 percent college graduates. Jamaican immigrants’ average annual earnings were $10,115, while 87 percent were employed and 14.4 percent were college graduates. Corresponding figures for other Caribbean immigrants were $9,525, with 84 percent employed and 17.4 percent college graduates.

114


by a certain demographic or economic characteristic, such as those who are employed or who have no high school diploma) residing in tract i; and ni is the total population residing in tract i. The intuitive interpretation of any exposure index is the average percentage of group y members living in a group x immigrant’s neighborhood in the metropolitan area. Alternatively, it can be interpreted as the probability that a randomly drawn immigrant group x member shares a census tract with a member of demographic/economic group y (Massey and Denton 1988). This exposure index has a lower limit equal to the minimum value of yi / ni observed across tracts in a metropolitan area, and an upper limit equal to the maximum value of yi / ni observed among tracts occupied by any members of group x.13

Variables and Measurement Issues The STF-4 data source permits operationalizing numerous elements of the 1980 neighborhood-opportunity-context dimension of the MOS. We stress, however, that some of the available measures fall well short of the theoretical construct implied by the previous theories, so results should be interpreted with care. The degree to which any given immigrant group is embedded within its own enclave economy is measured here by the exposure of members of that group to neighbors from the same immigrant group.14 This variable is a crude proxy for the underlying concept. First, it does not measure the clustering of immigrant business activity, as opposed to residences.15 Second, if many immigrants in a group live widely scattered among other groups, with a significant core remaining concentrated in one multifunctional neighborhood, the importance of the residential concentration may be understated by the exposure rate. Finally, the owngroup exposure rate does not capture potential class differentiation that proves essential for the quality and diversity of social contacts used for advancement (Kelly and Schauffler 1996). Despite these limitations, the own-group exposure index embodies at least two aspects of immigrant residential concentration that might support formation of enclave economies. First, concentrated demand for specialized consumer goods and services within an ethnic neighborhood could support the development of an enclave economy, although such consumer markets will not necessarily engender the division of labor and differentiated entrepreneurial class that ultimately characterize such an economy. Second, intragroup contacts within the neighborhood setting constitute yet another dimension of the multifaceted social networks, including the family and coworkers, that promote social capital formation and are essential to enclave economies.16

13 For more information on the properties of this index, see Massey and Denton (1988) and Galster, Metzger, and Waite (1999). 14 We also experimented with a variable measuring exposure to neighbors of the same ancestry as the given immigrant group, whether they were born in the United States or not. This operationalization of ethnic enclave proved statistically insignificant in all regressions. 15 As noted by an anonymous referee, Mexican immigrants are highly exposed to their own group in Los Angeles compared with Cuban immigrants in Miami, yet the latter likely have a more defined enclave economy. 16

We are indebted to Pat Simmons for these observations.


115

The most direct measure for Borjas’ notion of ethnic capital would be the exposure to neighbors with particular educational attainments, weighted by the number of neighbors of the same ancestry as the given immigrant group. Unfortunately, cross-tabulations of education by ancestry are unavailable in STF-4. Nevertheless, we can devise a reasonable aggregate proxy for own-group ethnic capital (or lack of) by interacting the exposure to own-group immigrants and the mean percent of that group over age 25 with no high school diploma. As for effective ethnic capital provided by other groups in the neighborhood, we employ exposure to all neighbors over age 25 with no high school diploma. Using the segmented assimilation theory, assimilation into the mainstream instead of an oppositional culture is measured here as the exposure of an immigrant group’s members to non-Hispanic white neighbors.17 Unfortunately, STF-4 data do not provide a racial and ethnic breakdown by nativity; thus researchers must choose one or the other to delineate more assimilated immigrant groups. Our position is that exposures to mainstream (and oppositional) cultures in the domestic population are worthy of testing and can only be distinguished by the racial and ethnic distinctions of neighbors, with the concomitant sacrifice in knowing nativity of these neighbors. Social isolation associated with concentrated poverty neighborhoods spawned by deindustrialization is measured by three exposure rate variables: neighbors on public assistance, those not employed, and those with no high school diploma. These elements have been identified as key correlates of “underclass” neighborhoods (Ricketts and Sawhill 1986). The fraction of employed neighbors had been widely used as a proxy for availability of role models (Wilson 1987, 1996) and sources of information about jobs (O’Regan and Quigley 1995, 1996). Finally, the percent of an immigrant group residing in the central city serves as a proxy (albeit an imperfect one) for the group’s perceptions of low-quality public education and limited accessibility to, and information about, expanding job opportunities where additional human capital potentially could pay off. In addition, the percent of neighbors without a high school diploma may also measure the former perception. We recognize, however, that this variable glosses over potential variations in these dimensions, across both central city and suburban neighborhoods, and thus its interpretation is ambiguous.

Results Data on immigrant groups’ exposure rates to these various neighborhood context variables are presented in tables 2 and 3, with table 2 focusing on demographic characteristics of neighbors. Table 2 shows that in Atlanta, Philadelphia, and Washington, DC, there was a uniformly low level of exposure to other members of one’s own immigrant group: never more than four percentage points. In marked contrast, this variable showed sizable variations across immigrant groups in the other two sites. In Los Angeles, Mexican immigrants typically had 23.3 percent own-group (Mexican) immigrants in their neighborhoods, while Soviet immi-

17

We experimented with exposure to other immigrant groups as another possible measure of assimilation or, perhaps, intergroup competition, but it was never statistically significant.

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grants (second in high own-group exposure) had only 4.9 percent. In New York, Dominicans were exposed to 13.6 percent, and Chinese immigrants were exposed to 12.6 percent of their own group; but there was considerable drop-off in these exposure rates for other groups. Not surprisingly, table 2 shows that immigrant groups presumed to comprise overwhelmingly whites—Canadians, Poles, Germans, Italians, and British—were exposed to other whites at virtually identical rates as whites in the population at large. Other groups presumed to comprise predominantly blacks—Dominicans and Jamaicans—typically had the lowest exposure to whites in all five sites (never exceeding 57.5 percent); they were especially low in New York. Nevertheless, immigrant blacks in all sites were considerably more exposed to whites than to blacks, reinforcing what White (1992) observed in Los Angeles. Across all sites, Mexican immigrants showed some of the lowest exposures to whites of any immigrant groups, but only in Los Angeles was it lower than the white exposure rate for Hispanics as a whole. Finally, Asian immigrant groups varied dramatically in their exposure to whites within any given metropolitan area. Indian groups and Korean immigrants always demonstrated higher exposure rates to whites than the other groups; and among Asian immigrants, the Chinese evinced the lowest exposures to whites in Los Angeles and New York. Finally, table 2 shows immigrants’ exposures to black and Hispanic neighbors. In all study sites, Jamaicans had by far the highest exposure to blacks, ranging from 38 to 57 percent and exceeding the exposure of the next-highest group by at least 13 percentage points. Dominican, Mexican, and Filipino immigrants also had substantial exposure to blacks in all five metropolitan areas. Immigrants from the Dominican Republic had the highest exposures to Hispanics in New York, Philadelphia, and Washington, DC, with substantial exposures in Los Angeles. Mexicans had by far the greatest exposure to Hispanics in Los Angeles and were second- or third highest in all other metropolitan areas. More surprisingly, Vietnamese immigrants had the highest or second-highest exposures to Hispanics in three locations (Atlanta, Los Angeles, and Washington, DC). In contrast, immigrants from Canada and Western Europe generally had the lowest exposures to blacks and Hispanics. Table 3 reveals that in 1980 there also were marked differences among immigrant groups in the kinds of socioeconomic conditions they confronted in their typical neighborhoods. For several groups, these differences were remarkably consistent across our sample of metropolitan areas: Jamaicans, for example, experienced relatively disadvantageous neighborhood conditions across multiple indicators in all sites; Canadians experienced relatively advantageous conditions in all sites. For other groups, such as Dominicans and Italians, there was considerable cross-sectional variation. One way to see these patterns is to rank the immigrant groups in each metropolitan area by exposures to the four unambiguous indicators of detrimental neighborhood conditions: percent of residents on public assistance, percent not employed, percent with no high school diploma, and percent residing in the central city. The following rankings are for 1980: 1.

Jamaican immigrants ranked within the top five groups (typically first or second) on exposure to all neighborhood indicators of disadvantage in all sites, except for nonemployment in Atlanta and for having no high school diploma and residing in the central city in Los Angeles.


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2.

Dominican immigrants ranked within the top five groups (typically first or second) on exposure to all indicators of disadvantage in all sites except Atlanta; they had dramatically less favorable neighborhood conditions compared with other immigrants in New York and, to a lesser degree, Philadelphia.

3.

Mexican immigrants ranked within the top five groups on exposure to all indicators of disadvantage in all sites except Philadelphia; they had worse neighborhood indicators in Atlanta than any other group studied there.

4.

Vietnamese immigrants ranked within the top five groups on exposure to public assistance in all sites, but their rank on other indicators (especially neighborhood nonemployment rates and central city residence) varied considerably across sites.

5.

Soviet immigrants were highly concentrated in the central city, except in Atlanta, but often exhibited low exposures to other indicators of neighborhood disadvantage.

6.

Chinese immigrants ranked within the top five groups on the public assistance indicator in four sites and the nonemployment rate indicator in three sites; other indicators were less consistent across sites.

7.

Canadian immigrants typically had the most favorable rankings for the array of neighborhood indicators across all sites; immigrants from the United Kingdom and Germany usually ranked highly across the board as well.

Another set of insights about relative neighborhood conditions can be gained by comparing immigrants’ exposure rates to those of various racial and ethnic groups in the population at large. Table 3 presents neighborhood-condition data for those population groups in the lower panel. It first shows that, with one exception, immigrants in 1980 were not exposed to disadvantageous neighborhood socioeconomic conditions as extreme as were black residents of the same metropolitan area. The exception was Dominican immigrants in New York, who faced several neighborhood situations worse than black populations there in general. Next, table 3 shows that any conclusions about the neighborhood conditions of immigrants presumed to be white (Canada, Germany, Italy, Poland, Soviet Union, and United Kingdom) relative to all whites depend on the indicator and the metropolitan area. All or most of the white immigrant groups in Los Angeles, Washington, DC, and especially Atlanta lived in neighborhoods that were more advantaged than those of whites in rates of public assistance, employment, and high school diplomas. The presumably nonwhite immigrant groups can also be compared with all whites in terms of neighborhood conditions. Again, in Atlanta and, to a lesser degree, Washington, DC, even nonwhite immigrant groups in 1980 often occupied neighborhoods having more favorable indicators for public assistance, employment, and high school completion rates than whites. On the contrary, in Los Angeles, Philadelphia, and especially New York, nonwhite immigrant groups rarely, if ever, occupied neighborhoods with more favorable conditions than those of whites. The neighborhoods in which Asian immigrants (China, India, Korea, Philippines, and Vietnam) lived can be compared with all Asians’ neighborhoods, shown in the bottom of table 3.

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Indian and Korean immigrants’ neighborhoods in 1980 proved more advantaged than those of Asians in general on most indicators across all sample sites (with the exception of Indians in Atlanta). Conversely, Vietnamese immigrants’ neighborhoods proved less advantaged than those of Asians in general on all four indicators in all five metropolitan areas. Finally, table 3 permits an analogous comparison for immigrant Hispanics and all Hispanics. The relative 1980 position of Mexican immigrants’ neighborhoods compared with those of all Hispanics was boosted in New York and Philadelphia by the especially inferior neighborhood conditions experienced by a large population of Dominicans, many of whom were identified as Hispanic.18 Thus, in 1980, there appeared to be considerable variation in neighborhood conditions facing immigrants across three dimensions: metropolitan areas, immigrant groups, and often metropolitan areas for a given immigrant group. Although a general hierarchy of neighborhood conditions on multiple indicators was revealed (with Dominicans and Jamaicans at the less advantaged end and Canadians at the more advantaged end), relatively few generalizations beyond that can be made. This variability suggests the potential for identifying empirically an independent relationship between an immigrant group’s socioeconomic advancement during the 1980s and its initial neighborhood context in 1980.

The Empirical Relationship between Immigrant Groups’ Neighborhood Context and Their Socioeconomic Advancement The Model The significant variation described above in both pre-1980 immigrant groups’ socioeconomic advancement and their neighborhood contexts during the 1980s is but prelude to an empirical exploration of their interrelationship. This investigation takes the form of a set of straightforward multiple regressions, with the set of seven indicators of socioeconomic progress across the decade (PROGRESS), listed in table 1, serving as dependent variables and the set of 1980 neighborhood demographic and socioeconomic context variables (NEIGHBORHOOD, listed in tables 2 and 3,) serving as explanatory variables.19 Two additional sets of variables are specified as controls. The first simply consists of four dummy variables (METROPOLITAN AREA) denoting the Los Angeles, New York, Philadelphia, and Washington, DC, metropolitan areas; Atlanta is the excluded reference area. These variables measure fixed effects associated with each metropolitan area, such as idiosyncrasies in regional economic growth, industrial restructuring, labor market functioning, educational facilities, and other factors that may similarly affect all immigrant groups within the metropolitan area. 18

Puerto Ricans in New York also tend to experience disadvantageous neighborhood conditions (Rosenbaum et al. 1998). 19 The exception is that the set of exposures to various racial and ethnic groups of neighbors—white, black, and Hispanic—proved highly collinear, so only the exposure to whites appears in the reported results. Caution in interpreting this variable’s coefficients is thus advised.


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The second set of control variables (GROUP SES) is composed of mean 1980 socioeconomic characteristics of each immigrant group in the sample. The variables employed correspond to those listed in table 1 under the 1980 columns, with the addition of percent of households with children that are headed by a female. Collectively, these variables are designed to control for a wide variety of human capital and other, unmeasured characteristics of groups at the beginning of the decade—characteristics that are hypothesized by the theories outlined earlier to be related to groups’ subsequent economic progress. Symbolically, the seven exploratory regressions to be estimated take the form: [PROGRESS] 4 a ` [NEIGHBORHOOD][b] ` [METROPOLITAN AREA][d] ` [GROUP SES][f] ` e

(2)

where: a, [b],[d],[f] are parameters to be estimated by ordinary least squares, e is a random error term with the usual assumed properties, and the vectors of variables are defined above. The sample employed for the analysis consists of the 56 observations of immigrant groups (with sufficient PUMS observations) across five metropolitan areas that are presented in table 1.

Methodological Caveats Since methodological limitations are critical for the interpretation and evaluation of the study’s results, we begin with a discussion of these limitations. First is the obvious potential for the ecological fallacy. The MOS theory clearly suggests that regression models ideally be estimated with individuals as the unit of analysis, and these individuals’ socioeconomic progress modeled as a function of their neighborhood contexts and their individual and family characteristics. Unfortunately, data limitations force us to estimate relationships for aggregations of individuals and neighborhood conditions at the metropolitan level. The second area of caution concerns measuring the causal premise of our MOS framework: Neighborhood has an independent effect on the life chances of individuals (or, when aggregated, their groups) and hence on their socioeconomic performance observed subsequently. There are three possible reasons why empirically the independent causal effect of aggregate neighborhood characteristics on its constituent population (measured at some point) may be obscured: 1.

An identity problem where the given group’s characteristics contribute mathematically to the aggregate tract characteristics observed, in degree corresponding to the proportionate size of the group in the tract population

2.

A causal direction problem where the group’s pre-1980 presence in the tract has led other tract residents to respond such that the 1980 observed characteristics of the tract are the result of the dynamic triggered earlier (and perhaps still ongoing) by the given group being there

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3.


A behavioral or self-selection problem where an unobservable characteristic(s) of the members of the given group led them to locate in the tract with particular characteristics in the first place (Smith 1995)

The effects of these three elements can best be understood in the context of the multiple regression methodology employed. The primary effect of the first two potentially confounding factors above is to make for higher correlation between the two sets of independent variables: 1980 neighborhood-condition exposure indices and 1980 group socioeconomic status measures. The implied multicollinearity could, in principle, create econometric problems (e.g., make it less likely to find statistically significant coefficients). Indeed, multicollinearity did prove to be a problem in the current analysis. Using the test proposed by Belsley, Kuh, and Welsch (1980), several condition indices had worrisome values, and neighborhood exposures to whites, nonemployed persons, and those with no high school diploma were highly correlated with one or more measures of immigrants’ 1980 socioeconomic status.20 The third factor is more fundamental because it means that the regression may not be controlling for a prime characteristic of the group that actually is responsible for any decadal changes in their socioeconomic status but is only spuriously correlated with neighborhood conditions. However, we believe it reasonable to assume that, having controlled for various 1980 socioeconomic status characteristics, any unobservable predictors of success will be held constant within the given group across all five metropolitan areas (though certainly not necessarily constant across groups within any metropolitan area). If true, observed crossmetropolitan area variation in neighborhood conditions for a given group should be reflective of differences in the MOS that independently may affect observed changes in that group’s socioeconomic status from 1980 to 1990. Cautions in interpretation follow to the degree that this assumption is not valid. Additional research might productively explore the feasibility of using two-stage estimation methods to correct for this potential problem.

Results Regression results are presented in table 4. Overall, the regressions did an admirable job explaining changes in immigrant group employment rates, poverty rates, and mean incomes, with adjusted R-squares exceeding 0.75 in all three cases. The regressions did relatively little, however, to reveal the correlates of the remaining four indicators of socioeconomic advancement: high school and college attainment, professional occupations, and especially self-employment. The adjusted R-squares for these regressions were no larger than 0.29, and few coefficients proved statistically significant. We began by conducting an F-test on the null hypothesis that the set of neighborhood variables did not add to the explanatory power of each of the seven regressions; that is, that all their coefficients were zero. This hypothesis could be rejected at the 1-percent level for both the employment-rate and poverty-rate change equations, but we failed to reject it at the 5-percent level for all other equations.21 20 Belsley, Kuh, and Welsch (1980) claim that multicollinearity problems arise when the condition index exceeds 30; we had six condition indices with such values. 21

The formula for conducting this test is presented in Johnston (1984, 219).

Table 4. Exploratory Regression Results: Correlates of Immigrant Socioeconomic Progress, 1980–90 n % College Graduate

n% Professional Occupations

n % SelfEmployed

n % Ages 16 to 64 Employed

n % Persons in Poverty

n Mean Income ($ thousands)

B

Standard Error

B

Standard Error

B

Standard Error

B

Standard Error

B

Standard Error

B

Standard Error

B

1980 neighborhood characteristics Exposure to: % Own immigrant group % White, not Hispanic % Persons on public assistance % Persons ages 16 to 64 not employed % Persons 25` with no high school diploma % In central city

0.14 10.07 10.40 0.93 0.17 10.13

0.30 0.14 0.96 0.80 0.33 0.08

0.02 10.01 1.29 11.57 10.37 0.11

0.30 0.14 0.97 0.80** 0.33 0.08

0.05 10.20 1.07 10.68 10.81 10.04

0.32 0.15 1.04 0.86 0.35** 0.09

0.16 10.02 10.88 0.47 10.07 10.04

0.24 0.11 0.77 0.64 0.26 0.06

10.66 10.35 11.27 0.04 10.23 0.03

0.20*** 0.10*** 0.66* 0.55 0.22 0.05

0.55 0.13 0.71 0.62 10.30 0.02

0.19*** 0.09 0.63 0.52 0.21 0.05

10.28 10.11 0.17 11.30 0.19 10.13

0.30 0.14 0.97 0.80 0.33 0.08

1980 group charactersitics % Persons 25` with no high school diploma % Persons 25` college graduates % Employed persons ages 16 to 64 % Persons in professional/specialty occupations % Self-employed persons % Persons in poverty % Female-headed familes with children Mean household income

10.23 10.06 0.37 10.03 0.30 0.14 10.12 10.02

0.10** 0.10 0.21* 0.10 0.19 0.20 0.13 0.46

10.05 10.21 10.48 0.01 10.43 10.10 10.22 0.33

0.10 0.10** 0.21** 0.10 0.20** 0.20 0.14 0.46

0.10 10.10 0.20 10.09 0.04 10.06 10.15 10.16

0.11 0.10 0.23 0.11 0.21 0.22 0.15 0.49

0.02 0.13 0.16 10.14 0.08 0.19 0.18 0.12

0.08 0.08* 0.17 0.08* 0.16 0.16 0.11 0.37

0.08 0.02 10.68 0.01 10.50 0.01 10.11 0.00

0.07 0.07 0.14*** 0.07 0.13*** 0.14 0.09 0.31

0.05 0.08 0.03 10.07 0.21 10.68 0.10 0.10

0.06 0.06 0.14 0.07 0.13 0.13*** 0.09 0.30

10.08 0.28 10.05 10.03 10.16 0.24 10.15 0.23

0.10 0.10*** 0.21 0.10 0.20 0.20 0.14 0.46

1980 metropolitan statistical area residence Los Angeles New York Philadelphia Washington, DC

1.43 2.76 11.93 3.55

3.31 4.42 5.42 4.33

1.46 4.87 11.28 0.95

3.34 4.46 5.47** 4.36

1.21 5.67 8.68 10.91

3.58 4.78 5.87 4.68

0.58 10.49 13.38 16.30

2.65 3.53 4.33 3.46*

15.46 12.68 11.78 15.46

2.27** 3.03 3.72 2.97*

12.25 11.60 13.37 11.40

2.17 2.89 3.55 2.83

8.70 15.14 14.97 4.28

3.33** 4.45*** 5.46*** 4.35

147.22 0.29 2.27

29.20

49.90 0.27 2.13

31.60

118.53 0.04 1.14

Explanatory Variables

Constant R square (adjusted) F(20,35)

*p , 0.1. **p , 0.01. ***p , 0.001.

88.48 29.46*** 0.27 2.10

23.33

100.30 20.04*** 132.58 19.09* 0.76 0.84 10.42 16.77

Standard Error

61.79 29.40** 0.76 10.65


n % No High School Diploma

121

122


Given the exploratory nature of this study, conservative, two-tailed tests of statistical significance were used to assess all individual coefficients. One or more neighborhood-related variables proved statistically significant (minimum 5 percent significance level) in four measures of progress investigated: college graduation rates, professional occupations, employment rates, and poverty rates. Together with the F-tests, these results support the position that at least some dimensions of neighborhood context as measured here provide independent power in explaining certain aspects of immigrants’ socioeconomic advancement during the 1980s.22 Unfortunately, multicollinearity undoubtedly was present, according to criteria established by Belsley, Kuh, and Welsch (1980), thereby obfuscating the interpretation of the coefficients and their individual statistical significance levels. In particular, immigrant exposures to whites, blacks, and Hispanics were highly collinear; only the first is included in the model reported in table 4, so it must be interpreted cautiously. Moreover, exposures to whites, households on public assistance, and households not employed were correlated. Finally, the interaction of exposure to own group and that group’s proportion of households with no high school diploma (our proxy for ethnic capital) proved too highly correlated with its two constituent variables and was dropped from the model reported in table 4. Despite these cautions following from the econometric problems encountered, several results are robust enough for discussion. Perhaps most startling was the strong, statistically significant correlation between exposure to one’s own immigrant group and subsequent decreases in employment rates and increases in poverty rates. In no other regressions did this variable prove statistically significant. To test the robustness of these results, we estimated an alternative version of equation 2 for all seven measures of PROGRESS wherein the GROUP SES variables were replaced by a series of dummies representing various immigrant groups. Insufficient degrees of freedom precluded using dummies for all groups, so only those that evinced large, statistically significant coefficients in a preliminary regression involving only metropolitan statistical areas (MSA) and immigrant group dummies were employed. This specification resulted in the owngroup exposure coefficient dropping to statistical insignificance in the employment rate equation, and dropping in magnitude but remaining highly statistically significant in the poverty rate equation.23 As the own-group exposure variable employed here successfully measures the extent to which immigrants are embedded in their own enclave economy, the results suggest that the previously hypothesized negative aspects of such enclaves (Massey and Denton 1987; Gilbertson 1995; Waldinger 1996, 1997) may outweigh any advantages. Immigrants in this sample apparently were harmed economically from greater residential exposure to other members of their group.24 22 Since the article’s focus is on neighborhood effects, we do not discuss results related to group characteristics or metropolitan area. Note that coefficients of the 1980 values of the same variable that appears in change form on the left-hand side of the equation are expected to be negative, tautologically. 23 In the employment rate equation, Vietnamese immigrants were the clear outlier; in the poverty rate equation, Vietnamese and, to a lesser extent, Soviet immigrants were outliers. 24

This is also consistent with the findings of Bates (1994) and Sanders and Nee (1996) that excessive reliance by immigrant businesses on ethnic networks produced lower profits and greater chances of failure.


123

Greater exposure to neighbors who were not employed was associated with decreasing rates of college graduation. Greater exposure to neighbors who had not completed high school was associated with a retardation of immigrants’ movement into professional occupations. A final complementary finding was that greater exposure to those on public assistance was associated with smaller gains in immigrant employment rates. These results were robust in the previous specification involving immigrant group fixed effects. Collectively, these findings support the notion that neighborhoods isolated from the world of work and characterized by minimal educational achievement impose multifaceted, deleterious economic impacts on immigrants, just as they do for native-born residents (Wilson 1987, 1996). Specifically, they suggest that the public educational quality, job accessibility, role models, information, and norms associated with a neighborhood where low-education, nonemployment, and welfare receipt are dominant retard the educational, occupational, and employment achievements of immigrants residing there. Exposure to whites also proved to have some explanatory power, but in a manner not expected from the theory of segmented assimilation. Immigrants’ greater residential exposure to whites was associated with lower gains in employment, a result that was robust to alternative specifications. We place little credence in this result, however, given the extreme degrees of multicollinearity among exposure rates to whites, blacks, and Hispanics.

Conclusions, Caveats, and Implications for Research and Policy In our sample of 14 pre-1980 immigrant groups across five metropolitan areas, we have found considerable variation in the degree of socioeconomic progress evinced during the 1980s. Moreover, the portrait of advancement requires different shading, depending on whether one examines educational attainments, occupational characteristics, employment rates, or income. What is certain about this picture of diversity is that there can be no generalizations about immigrant progress between the two most recent censuses. More importantly, we produced the first cross-sectional statistical evidence that various demographic and socioeconomic aspects of immigrants’ neighborhoods provide independent power in explaining variations in multiple dimensions of immigrant group advancement during the 1980s. First, we found strong support for the notion that a neighborhood consisting of poorly educated, nonworking, welfare-dependent people retards the educational, professional, and employment prospects of immigrants residing there. At the extreme, this situation could help spawn a new immigrant underclass (Zimmermann and Tobin 1995; Enchautegui and Sparrow 1997; Clark 1998). From a research perspective, this result is consistent with the predictions of the MOS conceptual model, as well as with growing empirical evidence about the effects of neighborhood on generic (not immigrant) residents (Briggs 1997; Brooks-Gunn, Duncan, and Aber 1997; Ellen and Turner 1997; Galster and Killen 1995). Second, we found (more surprisingly) evidence that higher residential exposure to other members of one’s immigrant group is associated with greater increases in poverty and, perhaps, smaller gains in employment for that group during the subsequent decade. While we have reasons to believe that own-group exposure rates should be correlated with the strength of ethnic enclaves, we recognize the many limitations of our proxy and urge additional experimentation. Were our findings to be replicated, however, they would pose profound chal-

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lenges for those alleging the economic benefits of ethnic enclaves. Clearly, additional work on the mechanisms through which neighborhood context might affect this and other socioeconomic outcomes of its immigrant residents is mandated (Briggs 1997; Ellen and Turner 1997). From a policy perspective, identifying relationships between neighborhood-opportunitystructure context and immigrant socioeconomic advancement contributes to the debate about personal responsibility versus spatial inequalities in opportunity. Much of the recent evolution in social policy, as epitomized in the welfare reforms, has been characterized by a belief that individuals will become successful if they merely take more responsibility for acquiring the means to economic independence and then use these means in the workforce. The contrary belief suggested by the findings of this research is that nostrums about individual responsibility are naive and unfair in light of spatially variant opportunities to acquire skills, role models, encouragement, and information about and access to quality education, jobs, and capital. As stressed at the outset, this study should be viewed as exploratory. Clearly, there are shortcomings because of data limitations yielding measurement problems and specification shortcomings, as well as ambiguities in interpreting causation. As such, caution must be exercised in interpreting results or drawing unwarranted conclusions. Nevertheless, we believe that these explorations have proven sufficiently provocative that additional research efforts should prove fruitful.

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