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Legalization and human capital accumulation

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Abstract

This paper presents new evidence regarding the effects of legalization on the training of immigrants who were granted legal status through the US Immigration Reform and Control Act (IRCA) of 1986. Our findings point to a large increase in the immigrants’ incidence of training relative to comparable groups of natives following legalization. While training gains are higher for males, wage gains are higher for females. We also show that an important part of these changes in labor market outcomes occurs through occupation changes by newly legalized immigrants.

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Notes

  1. Other related studies of the USA and Europe include Reyneri (1998), Barcellos (2010), and Fasani (2014). Sampaio et al. (2013) argues the evidence on wage gains following legalization is not robust to a modest amount of correlation between unobserved immigrant characteristics and unobserved wage determinants.

  2. See Gonzales 1997 for a review of the political evolution and the socioeconomic position of immigrants that followed IRCA.

  3. Wages in the NLSY79 were censored above 1000 and below 0.5, which affected five observations in the NLSY sample, despite which a few very high values remain.

  4. In the Appendix, we report the exact questions from the LPS and NLSY surveys that were used to construct the training indicator variables. In the Supplemental Material, available upon request, we show descriptive statistics for relevant dates, by sub-sample.

  5. In the Supplemental Material, we show that our sample size is comparable with that reported in other papers that used the LPS and NLSY surveys.

  6. The results of unconditional (without covariates) BA and DD estimates for all individuals, and by gender and skill level, are reported in Table 10 in the Appendix.

  7. The results of unconditional (without covariates) BA and DD estimates for all individuals, and by gender and skill level, are reported in Tables 11 and 12 in the Appendix.

  8. The decision to estimate the effect of interest by gender and skill level is formally validated performing Chow tests. The results of these tests are reported in the Supplemental Material.

  9. 9 Note that skill biased technical change, which here could be modeled as a higher α for skilled individuals, while not necessary for skilled workers to train more than unskilled workers, would make this result more likely.

  10. 10 These results could also reflect the outcome of a household decision process where primary earners men—choose careers with high skill acquisition content, while secondary earners women—choose careers with higher starting salaries. A phenomenon of this type was already identified by Cobb-Clark et al. (2005) in Australia.

  11. The differences in observables in the pre-treatment period remain after we split the data by gender and skill level, as it can be seen in the Supplemental Material.

  12. In the Appendix, we report the complete results of the DD estimates, including estimates for all covariates. Results for the other estimators (BA, CC, and MDD) are available upon request.

  13. In the Appendix, we report the complete results of the DD estimates, including estimates for all covariates. Results for the other estimators (BA, CC, and MDD) are available upon request.

  14. Similar results for BA and MDD nonlinear models are available upon request.

  15. Results for estimates of covariates in nonlinear models are available upon request.

  16. Similar results for BA and MDD nonlinear models are available upon request.

  17. Results of the sensitivity analysis for wage and occupational quality by skill level are available upon request. In all regressions, differences in the estimated effects between unskilled and skilled workers are not statistically significant.

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Acknowledgments

We wish to thank Carolina Concha for expert research assistance. We also wish to thank three anonymous referees for their helpful suggestions. Facundo Sepúlveda acknowledges financial support from FONDECYT (Fondo Nacional de Desarrollo Científico y Tecnológico) project No 1151116. Nieves Valdés acknowledges financial support from FONDECYT (Fondo Nacional de Desarrollo Científico y Tecnológico) project No 11130058. Fabio Méndez acknowledges financial support in the form a Summer Research Grant from the Sellinger School of Business and Management at Loyola University Maryland. All errors are ours.

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Authors and Affiliations

  1. Loyola University Maryland, 4501 N. Charles St., Baltimore, MD, 21210-2699, USA

    Fabio Méndez

  2. Universidad de Santiago de Chile, Av. Lib. B O’Higgins 3363, Estación Central, Santiago, Chile

    Facundo Sepúlveda

  3. Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Oficina 255-A, Peñalolén, Santiago, Chile

    Nieves Valdés

Authors
  1. Fabio Méndez
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  2. Facundo Sepúlveda
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  3. Nieves Valdés
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Corresponding author

Correspondence to Fabio Méndez.

Additional information

Responsible editor: Klaus F. Zimmermann

Electronic supplementary material

Below is the link to the electronic supplementary material.

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Appendix

Appendix

1.1 Questions on training in LPS and NLSY79

LPS respondents are asked:

“Before you applied for temporary residence, did you ever attend a formal job training program in the United States? Please do not include any informal job training you received while you were working.”

“(Since you applied for temporary residence) Have you attended any formal job training program?”

NLSY79 respondents are asked:

“(Besides the training we’ve already talked about) Since (date of last interview), have you received training from any (other) source, such as the kinds of places listed on this card? For example, training in a business college, nurses program, an apprenticeship program, a vocational-technical institute, or any of these other kinds of sources?”

1.2 Tables

Table 10 Training: before–after estimates, pre-treatment differences, and (unconditional) difference-in-difference estimates, by gender
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Table 11 Wage: before–after estimates, pre-treatment differences, and (unconditional) difference-in-difference estimates, by gender
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Table 12 Wage by occupation: before–after estimates, pre-treatment differences, and (unconditional) difference-in-difference estimates, by gender
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Table 13 Training: DD estimates in OLS regressions
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Table 14 Wage growth: DD estimates in OLS regressions
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Table 15 Occupational quality: DD estimates in OLS regressions
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Table 16 Robustness: test of balance in pre-treatment observables between treatment and controls after propensity score matching, by gender and skill level
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Méndez, F., Sepúlveda, F. & Valdés, N. Legalization and human capital accumulation. J Popul Econ 29, 721–756 (2016). https://doi.org/10.1007/s00148-016-0585-0

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  • DOI: https://doi.org/10.1007/s00148-016-0585-0

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