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Workplace Concentration of Immigrants

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Demography

Abstract

Casual observation suggests that in most U.S. urban labor markets, immigrants have more immigrant coworkers than native-born workers do. While seeming obvious, this excess tendency to work together has not been precisely measured, nor have its sources been quantified. Using matched employer–employee data from the U.S. Census Bureau Longitudinal Employer-Household Dynamics (LEHD) database on a set of metropolitan statistical areas (MSAs) with substantial immigrant populations, we find that, on average, 37 % of an immigrant’s coworkers are themselves immigrants; in contrast, only 14 % of a native-born worker’s coworkers are immigrants. We decompose this difference into the probability of working with compatriots versus with immigrants from other source countries. Using human capital, employer, and location characteristics, we narrow the mechanisms that might explain immigrant concentration. We find that industry, language, and residential segregation collectively explain almost all the excess tendency to work with immigrants from other source countries, but they have limited power to explain work with compatriots. This large unexplained compatriot component suggests an important role for unmeasured country-specific factors, such as social networks.

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Notes

  1. Abowd et al. (1999) presented evidence on this for France. Abowd et al. (2005) summarized related and largely consistent evidence for the United States. Understanding why firms matter so much for key economic outcomes for workers remains an open and active research question.

  2. We do not have space here to fully review the broad range of social science research that has considered the clustering of firms and workers across geographic locations. For example, in the geography literature, Allen Scott has written extensively in this area; see Scott (1988, 2006) and references therein. We also note the contributions of Massey (1984) and Nee et al. (1994).

  3. Cabrales et al. (2008) emphasized a different skill-based sorting mechanism: if a worker’s utility depends on both absolute and relative wages and movement of workers is costless, complete segregation by skill is optimal.

  4. Our neighborhood network index described later yields patterns similar to that found by Bayer et al. (2008). We find that the mean fraction of an employer’s workforce that lives in the same tract is about 1.9 % for both natives and immigrants. The roughly equivalent statistic from Bayer et al. (2008) is 0.94 %.

  5. Even at establishments with at least two matched long-form workers, HNM have data on only a subset of workers, which leads to variation in estimates of worker characteristics. With a 1-in-20 sampling rate, only 43 % of 50-employee establishments would have five or more matches. To see the effects of this sampling variation, consider an establishment with 50 workers, 25 of them immigrants; if only four workers are matched, the probability of observing the actual immigrant share (=0.5) is only 37.5 %; with 12.5 % probability, the establishment would have a measured immigrant share of either 0 or 1.

  6. Lengermann et al. (2004) also took advantage of the LEHD database to explore variation in immigrant concentration across employers, but with a focus on explaining immigrant–native earnings differences.

  7. See Abowd et al. (2006) for a full description of the database.

  8. See Abowd et al. (2006) for details. We use all 10 implicates in our analysis for the establishment characteristics and assign each a 1/10 weight.

  9. The model uses the following variables: worker age and sex; 11 country-of-origin groups; log earnings; whether the worker was employed for each of quarters 1, 2, and 3 of 2000; three-digit industry; MSA; working population density; establishment age and size; and the number of establishments owned by the firm. With this set of controls, immigrant status has a relatively modest negative association with the probability of matching. Our objective is for weighted estimates to match statistics that we compute from the full set of LEHD workers. Tables S1 and S2 in Online Resource show that even on an unweighted basis, we do well. Comparing the first column of Tables S1 and S2 illustrates the close correspondence between means for the full sample and the weighted matched sample. We match about 70 % of long-form respondents who live in our sample of MSAs and report working for a nonagricultural private sector employer or a state or local government, but we also match many long-form respondents who do not report an in-scope job.

    Recent work by Abraham et al. (2013) used a match of the CPS data with the LEHD data to address coverage issues. They found that substantial numbers of in-scope CPS workers are not present in the LEHD data and vice versa. CPS workers who do not show up in LEHD are disproportionately likely to have low earnings, to have short job durations, and to be elderly. Controlling for such factors, immigrant status has only a modest negative effect on matching.

  10. More precisely, we started from the list of MSAs used in Singer (2004). We drop 14 of Singer’s 45 MSAs because we do not have the data we need for those areas.

  11. In computing the coworker share, we equally weight all coworkers regardless of whether they hold other jobs. However, the set of observations used in our regressions includes only the job where an individual received their highest earnings in that quarter (primary job).

  12. Among natives who speak English poorly or not at all, roughly 90 % speak Spanish at home. Natives include those born in Puerto Rico (but working in our set of MSAs). Natives from Puerto Rico account for roughly 70 % of natives who report not speaking English at all and 20 % of those speaking English poorly.

  13. Census tracts are small geographic areas with a population between 1,500 and 8,000 individuals. They are designed to be relatively homogeneous with respect to socioeconomic characteristics. The limited distance between residents of a census tract—both in terms of geography and socioeconomic factors—suggests that the likelihood of interactions among residents of the same tract is high relative to the likelihood of interactions between residents of different tracts.

  14. In our sample, there are on average 49 employers per tract (excluding tracts that are strictly residential). Seven percent of tracts with employment have only one employer, and for those tracts, the variable is 0. Only 9 % of workers in our sample work in single-employer tracts.

  15. This decomposition nests the well-known Oaxaca-Blinder decomposition.

  16. Online Resource 1 contains additional analysis of differences in concentration by firm size. We find greater concentration at small firms, but this does not account for much of the overall concentration because immigrants and natives do not differ greatly in terms of the propensity to work at small businesses. Online Resource 1 also includes an analysis regarding the statistical relationship between concentration and business size.

  17. Our list differs from the top 18 based on overall U.S. employment in that it includes Taiwan but excludes Colombia. Our ordering of countries by share of employment also differs somewhat.

  18. Notes to Table 4 give more details. The empirical specification is in Online Resource 1.

  19. The final row of Table 4 gives the average across our 18 groups for each column, which is closely related to the top rows of Table 2. Here, the sum of own- and other-country concentration is 18.3 with only MSA and country-of-origin controls, and 8.8 with the full set of controls. Both figures are slightly larger than the corresponding estimates from Table 2 (17.1 and 3.3, respectively). One reason for these differences is that Table 2 estimates include immigrants from countries with smaller populations in the United States, which tend to have lower levels of concentration.

  20. See Online Resource 1, section A.1, for the estimating equations.

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Acknowledgments

We thank the NIH for financial support and participants at the SOLE and WEAI meetings and workshops at the University of Chicago, the University of Kentucky, and the Center for Economic Studies for helpful comments. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau or the Comptroller of the Currency. All results have been reviewed to ensure that no confidential information is disclosed.

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Correspondence to Seth Sanders.

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Andersson, F., García-Pérez, M., Haltiwanger, J. et al. Workplace Concentration of Immigrants. Demography 51, 2281–2306 (2014). https://doi.org/10.1007/s13524-014-0352-3

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