Skip to main content
SpringerLink
Log in

The effect of mass legalization on US state-level institutions: Evidence from the immigration reform and control act

  • Published:
Public Choice Aims and scope Submit manuscript

Abstract

A new case for immigration restrictions argues that migrants may transmit low productivity to their destination countries by importing low-quality economic institutions. Using the 1986 Immigration Reform and Control Act (IRCA) as a natural experiment, we test whether the legalization of undocumented immigrants affects the quality of state-level economic institutions in the United States. Using synthetic control models, we find that, in the short run, legalization may increase the burden of government spending. However, in the long run, we find statistically insignificant effects of legalization on economic institutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Borjas (2017) finds that undocumented immigrants have stronger work ethics than other groups in the population, so many immigrants likely were participating in labor markets prior to IRCA. However, as noted by Baker (2015), IRCA expanded educational opportunities and job advancement among beneficiaries, increasing competition with natives for more desirable jobs.

  2. Our study focuses on the effects of immigration on formal economic institutions and complements other studies, such as Pavlik et al. (2019), which examines the effects of immigration on informal institutions.

  3. In addition to the studies listed here, Forrester et al. (2019) examine the relation between immigration and terrorism, finding no relation as measured by the number of attacks or victims in destination countries.

  4. In a working paper, Padilla et al. (2020) also note a negative short-run relation between the quality of the institutions in immigrants’ home countries and destination countries that dissipates in the long run.

  5. Based on p-values presented in Tables 5 and 7.

  6. For further discussion of amnesty prior to IRCA, see Nowrasteh (2014) at https://www.cato.org/blog/legalization-or-amnesty-unlawful-immigrants-american-tradition.

  7. See Kossoudji et al. (2002), Rivera-Batiz(1999), Lozano et al. (2011), Pan (2012) and Amuedo-Dorantes et al. (2007).

  8. Hu (1998) finds that immigrant age at arrival is a significant determinant of welfare participation.

  9. See Donato et al. (1992), Donato et al. (1993), Sorensen et al. (1994), Bansak et al. (2001) and Bach et al. (1991).

  10. Stansel et al. (2018) summarize studies using the EFNA index, totaling 235 published papers and book chapters.

  11. Technically, the synthetic tracks economic freedom as if IRCA legalized very few individuals, rather than none at all. The synthetic closely approximates a situation in which IRCA never occurred because the states included in the synthetic control group experienced very small changes in their legalized populations.

  12. Possibly explained by disproportionality between the education predictor variable and outcomes owing to economic growth.

  13. New Jersey weighs heavily in the synthetic control estimations for the top six IRCA states, California plus Texas and the four IRCA states because of similar pre-treatment values for economic freedom and log GDP per capita. For example, New Jersey’s economic freedom score in 1981 is 4.53 and the top IRCA states’ economic freedom score is 4.77. Economic freedom in 1981 is 4.75 for California plus Texas and 4.82 for the four IRCA states. Income per capita (logged) for the top IRCA states (4.40), California plus Texas (4.40) and the four IRCA states (4.35) also is similar to New Jersey’s (4.36) in 1981. That evidence is suggestive that New Jersey is a major donor state because of similar pre-treatment values.

  14. The p-values capture the proportions of gaps from the in-place placebo tests that are larger than the gap between real and synthetic California. See Fig. 2 for the graphical representation of the in-place placebo tests.

  15. Because some debate exists in the literature regarding controlling for both predictors and outcomes lags in synthetic control models, we also estimate the effects of IRCA in SCMs matched both on no outcome lags and with outcome lags only. No statistically significant differences are found from either of those approaches. The results are reported in Table 10. In addition, we enter urbanization shares in place of naturalized population shares in our models. The results are almost identical, with all four figures following similar paths. We also enter an alternate top-six IRCA state sample based on the national share of a state’s population (California, Texas, Illinois, Arizona, New Mexico and Nevada). Again, the results are similar, providing support for concluding that the findings are not sensitive to the construction of the IRCA sample. We do not report the additional findings, but they are available upon request.

  16. We also estimate an alternative synthetic control method for multiple treated units following Cavallo et al. (2013) and Absher, Grier and Grier (2020). The results are consistent with our initial SCM findings as well as Xu (2017). Because of the similarities, we do not tabulate these results to save space, but they are available upon request.

  17. We generate results of the interactive fixed effects estimation (Xu 2017) without any control variables. The model passes the equivalence test, Wald test and the placebo test associated with goodness-of-fit. The results support our findings including control variables; thus, we do not tabulate them, but they are available upon request.

  18. The p-values correspond to the in-place placebo tests not reported but available upon request.

  19. We estimate the effects of IRCA on government spending for the other aggregated IRCA states. The short-run statistically significant increase in government spending holds for the top IRCA states and California plus Texas but is not present in the four IRCA states. We believe that the differences between the estimates when California is included (top six IRCA states, California plus Texas and California) and the four IRCA states suggest that California is the main driver of the government spending mechanism.

  20. https://files.medi-cal.ca.gov/pubsdoco/publications/masters-mtp/part1/obra_z01.doc

  21. These synthetic control results are not tabulated to save space but are available upon request.

  22. Clemens and Pritchett (2019) discuss how the transmission of institutions through migration depends on variation in the characteristics embodied in migrants.

References

  • Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review, 93(1), 113–132.

    Article  Google Scholar 

  • Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505.

    Article  Google Scholar 

  • Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495–510.

    Article  Google Scholar 

  • Absher, S., Grier, K., & Grier, R. (2020). The economic consequences of durable left-populist regimes in Latin America. Journal of Economic Behavior & Organization, 177, 787–817.

    Article  Google Scholar 

  • Al Haj, M (2004) Immigration and ethnic formation in a deeply divided society: The case of the 1990s immigrants from the former Soviet Union in Israel. (Vol. 91). Brill.

  • Amuedo-Dorantes, C., & De la Rica, S. (2007). Labour market assimilation of recent immigrants in Spain. British Journal of Industrial Relations, 45(2), 257–284.

    Article  Google Scholar 

  • Acemoglu, D., & Robinson, J. A. (2012). Why nations fail: The origins of power, prosperity and poverty. Crown Business.

    Google Scholar 

  • Bach, R. L., & Brill, H. (1991). Impact of IRCA on the US Labor Market and Economy. Institute for Research on Multiculturalism and International Labor.

    Google Scholar 

  • Baker, S. R. (2015). Effects of immigrant legalization on crime. American Economic Review, 105(5), 210–213.

    Article  Google Scholar 

  • Bansak, C., & Raphael, S. (2001). Immigration reform and the earnings of Latino workers: Do employer sanctions cause discrimination? ILR Review, 54(2), 275–295.

    Article  Google Scholar 

  • Borjas, G. J. (2015). Immigration and globalization: A review essay. Journal of Economic Literature, 53(4), 961–974.

    Article  Google Scholar 

  • Borjas, G. J. (2017). The labor supply of undocumented immigrants. Labour Economics, 46, 1–13.

    Article  Google Scholar 

  • Briggs, V. M. (1984). Immigration policy and the American labor force. Johns Hopkins University Press.

    Google Scholar 

  • Calavita, K (2010) Inside the state: The Bracero Program, immigration and the INS. Quid Pro Books.

  • Cavallo, E., Galiani, S., Noy, I., & Pantano, J. (2013). Catastrophic natural disasters and economic growth. Review of Economics and Statistics, 95(5), 1549–1561.

    Article  Google Scholar 

  • Clark, J. R., Lawson, R., Nowrasteh, A., Powell, B., & Murphy, R. (2015). Does immigration impact institutions? Public Choice, 163(3–4), 321–335.

    Article  Google Scholar 

  • Clemens, M. A. (2011). Economics and emigration: Trillion-dollar bills on the sidewalk? Journal of Economic Perspectives, 25(3), 83–106.

    Article  Google Scholar 

  • Clemens, M. A., & Pritchett, L. (2019). The new economic case for migration restrictions: an assessment. Journal of Development Economics, 138, 153–164.

    Article  Google Scholar 

  • De Chaisemartin, C., & d’Haultfoeuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996.

    Article  Google Scholar 

  • Donato, K. M., & Massey, D. S. (1993). Effect of the Immigration Reform and Control Act on the wages of Mexican migrants. Social Science Quarterly, 74(3), 523–541.

    Google Scholar 

  • Donato, K. M., Durand, J., & Massey, D. S. (1992). Stemming the tide? Assessing the deterrent effects of the Immigration Reform and Control Act. Demography, 29(2), 139–157.

    Article  Google Scholar 

  • Freedman, M., Owens, E., & Bohn, S. (2018). Immigration, employment opportunities and criminal behavior. American Economic Journal: Economic Policy, 10(2), 117–151.

    Google Scholar 

  • Freeman, R. B. (2006). People flows in globalization. Journal of Economic Perspectives, 20(2), 145–170.

    Article  Google Scholar 

  • Forrester, A. C., Powell, B., Nowrasteh, A., & Landgrave, M. (2019). Do immigrants import terrorism? Journal of Economic Behavior & Organization, 166, 529–543.

    Article  Google Scholar 

  • Gwartney, J., Lawson, R., Hall, J., & Murphy, R (2018) Economic freedom of the world: 2018 annual report. The Fraser Institute.

  • Goodman-Bacon, A (2018) Difference-in-differences with variation in treatment timing (No. w25018). National Bureau of Economic Research.

  • Grier, K., & Maynard, N. (2016). The economic consequences of Hugo Chavez: A synthetic control analysis. Journal of Economic Behavior & Organization, 125, 1–21.

    Article  Google Scholar 

  • Hu, W.Y (1998) Elderly immigrants on welfare. Journal of Human Resources. 711–741.

  • Kossoudji, S. A., & Cobb-Clark, D. A. (2002). Coming out of the shadows: Learning about legal status and wages from the legalized population. Journal of Labor Economics, 20(3), 598–628.

    Article  Google Scholar 

  • Lozano, F. A., & Sorensen, T (2011) The labor market value to legal status. IZA Discussion Paper No. 5492. Institute for the Study of Labor (IZA).

  • McClelland, R., & Gault, S. (2017). The synthetic control method as a tool to understand state policy. The Urban Institute.

    Google Scholar 

  • Nowrasteh, A., Forrester, A. C., & Blondin, C. (2020). How mass immigration affects countries with weak economic institutions: A natural experiment in Jordan. The World Bank Economic Review, 34(2), 533–549.

    Article  Google Scholar 

  • Nowrasteh, A (2014) Legalization or Amnesty for Unlawful Immigrants – An American Tradition. Cato At Liberty. https://www.cato.org/blog/legalization-or-amnesty-unlawful-immigrants-american-tradition

  • Orrenius, P. M., & Zavodny, M. (2003). Do amnesty programs reduce undocumented immigration? Evidence from IRCA. Demography, 40(3), 437–450.

    Article  Google Scholar 

  • Padilla, A., & Cachanosky, N. (2018). The Grecian horse: does immigration lead to the deterioration of American institutions? Public Choice, 174(3–4), 351–405.

    Article  Google Scholar 

  • Padilla, A., & Cachanosky, N (2020) Immigration and Economic Freedom of the US States: Does the institutional quality of immigrants’ origin countries matter?. Available at SSRN 3316415.

  • Pew Charitable Trusts. (2014). Immigration and Legalization Roles and Responsibilities of States and Localities. Pew Charitable Trusts.

    Google Scholar 

  • https://www.pewtrusts.org/~/media/legacy/uploadedfiles/pcs_assets/2014/ImmigrationandLegalizationReport2014pdf.pdf

  • Pan, Y. (2012). The impact of legal status on immigrants’ earnings and human capital: Evidence from the IRCA 1986. Journal of Labor Research, 33(2), 119–142.

    Article  Google Scholar 

  • Bologna Pavlik, J., Lujan Padilla, E., & Powell, B. (2019). Cultural Baggage: Do Immigrants Import Corruption? Southern Economic Journal, 85(4), 1243–1261.

    Article  Google Scholar 

  • Peri, G., & Yasenov, V. (2019). The labor market effects of a refugee wave synthetic control method meets the mariel boatlift. Journal of Human Resources, 54(2), 267–309.

    Article  Google Scholar 

  • Powell, B., Clark, J. R., & Nowrasteh, A. (2017). Does mass immigration destroy institutions? 1990s Israel as a natural experiment. Journal of Economic Behavior & Organization, 141, 83–95.

    Article  Google Scholar 

  • Rivera-Batiz, F. L. (1999). Undocumented workers in the labor market: An analysis of the earnings of legal and illegal Mexican immigrants in the United States. Journal of Population Economics, 12(1), 91–116.

    Article  Google Scholar 

  • Sorensen, E., & Bean, F. D. (1994). The Immigration Reform and Control Act and the wages of Mexican origin workers: evidence from Current Population Surveys. Social Science Quarterly, 75(1), 1–17.

    Google Scholar 

  • Stansel, D., Torra, J., & McMahon, F. (2018). Economic Freedom of North America 2018. Fraser Institute.

    Google Scholar 

  • Stansel, D., & Tuszynski, M. (2018). Sub-national Economic Freedom: A Review and Analysis of the Literature. Journal of Regional Analysis and Policy, 48(1), 61–71.

    Google Scholar 

  • Tuszynski, M., & Stansel, D (2020) Immigration and State Institutions: Does region of origin matter? Cato Journal, forthcoming.

  • Xu, Y. (2017). Generalized synthetic control method: Causal inference with interactive fixed effects models. Political Analysis, 25(1), 57–76.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mississippi State University, Mississippi State, P.O. Box 9580, Clinton, MS, 39762, USA

    Lili Yao

  2. Mississippi College, Box 4014, Clinton, MS, 39058, USA

    J. Brandon Bolen

  3. University of Tennessee At Chattanooga, 615 McCallie Ave, Chattanooga, TN, 37403, USA

    Claudia R. Williamson

Authors
  1. Lili Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Brandon Bolen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudia R. Williamson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudia R. Williamson.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

see Tables 9, 10 and 11

see Figs. 8 and 9

Table 9 Economic freedom and Texas
Full size table
Fig. 8
figure 8

Economic freedom and Texas, SCM with naturalization. Notes. We synthesize with five predictor variables: log GDP per capita (BEA), share of native population with at least a high school diploma in 1980 (IPUMS), share of naturalization in 1980 (IPUMS), economic freedom in 1981 and economic freedom in 1985

Full size image
Table 10 Synthetic control results, robustness tests
Full size table
Table 11 Impact of IRCA on economic freedom, dynamic event study regression estimates
Full size table
Fig. 9
figure 9

Economic freedom and California, robustness check. Notes. Data are collected from 1981–2016 with 1996 as shock year. We synthesize with six predictor variables: log GDP per capita (BEA), share of native population with at least a high school diploma in 1980 (IPUMS), share of naturalization in 1980 (IPUMS), economic freedom in 1985, economic freedom in 1990 and economic freedom in 1995. Donor states and weights include: New York (0.35), Arizona (0.33), Alaska (0.16), Massachusetts (0.15), Montana (0.02). RMSPE = 0.08

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, L., Bolen, J.B. & Williamson, C.R. The effect of mass legalization on US state-level institutions: Evidence from the immigration reform and control act. Public Choice 189, 427–463 (2021). https://doi.org/10.1007/s11127-021-00894-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11127-021-00894-x

Keywords

JEL Codes

Search

Navigation