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Immigration enforcement and labor supply: Hispanic youth in mixed-status families

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Abstract

This study evaluates the labor supply behavior of US-born Hispanic youth in response to immigration enforcement. We draw on the added-worker effect and underscore immigration enforcement actions as a factor influencing labor supply decisions within immigrant families. We argue that while immigration enforcement reduces labor supply among non-citizens, the labor supply among US-born Hispanic youth in mixed-status families increases. Using the Current Population Survey and data on immigration-related arrests, we find that an unexpected surge in arrests increases labor force participation of US-born Hispanic youth by 6 percentage points and weekly hours worked by up to 20%.

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Data availability

The data analyzed in this study was gathered from the Integrated Public Use Microdata Series, Current Population Survey: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2022. and is available at https://doi.org/10.18128/D030.V10.0.

Notes

  1. Data obtained from ICE biannual reports to Congress on deported migrants claiming US-born children. See, for example, U.S. Immigration and Customs Enforcement (2018a).

  2. Studies also find labor market impacts associated with employment-based immigration policies, such as E-Verify (Amuedo-Dorantes and Bansak 2014; Bohn et al. 2015), as well as impacts on US citizens access of public services (Watson 2014) and overall political engagement (Amuedo-Dorantes and Lopez 2017a; Amuedo-Dorantes and Bucheli 2023).

  3. Following Bucheli et al. (2021) and Xu et al. (2016), we designate mixed-status families status when at least one US-born child in a family unit resides with at least one non-US-citizen parent.

  4. Immigration enforcement actions primarily target Hispanic immigrants, with individuals born in Latin American countries accounting for approximately 97% of all deportations in recent years (U.S. Immigration and Customs Enforcement 2018b).

  5. A related strand in the literature studies the impact of employment-based immigration policies such as E-Verify. Amuedo-Dorantes and Bansak (2014) document an increase in employment among non-Hispanic native workers following the adoption of employment verification mandates. Likewise, Orrenius and Zavodny (2015) report E-Verify led to higher employment of naturalized male Mexican immigrants and raised earnings of US-born Hispanic men.

  6. Prior studies on the added-worker effect in different settings have found limited evidence of intra-household labor supply responses to a member’s displacement or wage loss (e.g., Ayhan 2018; Hardoy and Schøne 2014; Halla et al. 2020).

  7. Stephens Jr (2002) even defines the added-worker effect as the “labor supply response of wives to their husbands’ job losses.”

  8. Data available at https://trac.syr.edu/phptools/immigration/arrest/. Last accessed March 2023.

  9. The dataset is compiled from 480,000 apprehensions registered during the 44 months and does not include border apprehensions conducted by US Customs and Border Protection.

  10. See: https://www.ice.gov/secure-communities. Accessed March 2023.

  11. The U.S. Office of Management and Budget defines metropolitan statistical areas based on entire counties or county-equivalents (U.S. Office of Management and Budget 2010). Thus, we link the monthly county number of arrests to the MSA level by aggregating counties contained within each MSA. This process was conducted using the 2014 county-to-MSA jurisdictions crosswalk data provided by the National Bureau of Economic Research, accessible at https://data.nber.org/cbsa-msa-fips-ssa-county-crosswalk/.

  12. See, for example, Smith and Whitely (2018), Fox 5 Atlanta (2019), and PBS News Hour (2019) for journalistic accounts of unexpected raids at places of work. In another operation, even local law enforcement accused ICE of “misleading” them (Todd 2017).

  13. The simple moving average is calculated as: \(\mu _{m,k}=\frac{1}{k}\sum ^{t}_{i=t-k+1} A_{m,i}\). The moving standard deviation is calculated as: \(\sigma _{m,k}=\sqrt{ \frac{\sum ^{t}_{i=t-k+1} (A_{m,i}-\mu _{m,k})}{k-1}}\).

  14. We characterize expectations about immigration enforcement using this approach, given that it relies squarely on past experiences with enforcement actions in an environment where information about enforcement strategies and priorities are asymmetric.

  15. To calculate the rate of arrests, we used the period and MSA-specific levels of arrests while maintaining the populations of foreign-born individuals constant at its 2014 level.

  16. To further illustrate the nature of the arrest shock variable, Figure B.1 shows the trend in ICE arrests for four representative MSAs. Panel A corresponds to the top two MSAs with the highest number of arrests. Panel B corresponds to the top two MSAs with the highest number of shocks. Each illustrated data point reflects the number of arrests in the respective MSA and period (month and year). The red crosses indicate when the monthly number of arrests exceeded the six-month moving average by 1 s.d. (\(S_{m,t}=1\)). Also, for context, we distinguish between the Obama and Trump administrations by the faint gray shading.

  17. The CPS data was obtained from Flood et al. (2021)

  18. The main results are robust to including summer months. See Panel A in Table B.4.

  19. We avoid using country of birth as a marker for US citizenship as it would also include naturalized citizens. This definition excludes cases in which US-born youth have suffered the deportation of their non-citizen parent but stayed in the United States with a citizen parent or relative.

  20. Our study aims to identify a unique sample—mixed-status households with US-born children between 16 and 18 years of age. By design, ours is an unrepresentative subset of the broader population of unauthorized immigrants. Therefore, our approach to identifying likely mixed-status households is a modified version of the commonly applied “demographic approach” where we avoid using educational attainment as a characteristic to refine the sample of interest.

  21. Hispanic ethnicity has also been used to capture co-ethnic spillover effects of immigration policies, such as Secure Communities (Alsan and Yang 2022).

  22. Using ethnicity to sharpen the identification of our targeted treatment group is widely applied throughout the literature to study “likely unauthorized” immigrant residents (e.g., Orrenius and Zavodny 2009; Amuedo-Dorantes and Bansak 2014; Amuedo-Dorantes et al. 2018). We interrogate the underlying presumptions behind this approach in the following subsections and the Supplementary Appendix (Table B.2).

  23. We also experiment with an inverse hyperbolic sine transformation of the number of work hours and verify the consistency of the results.

  24. We also run alternative specifications where we control for MSA-specific linear time trends. See Supplementary Appendix Table B.4 for results following these specifications.

  25. Following Kuka et al. (2020) and Alsan and Yang (2022) we experiment with alternative model specifications that include race-by-year and race-by-state fixed effects. Results are available upon request. We also estimate models that separately control for: (1) MSA-specific seasonal shocks, (2) MSA-specific seasonality interacted with an indicator for households with low educational attainment, and (3) Hispanic-specific month and year seasonality at the MSA level. Our findings are robust to these modeling choices and results are available upon request.

  26. Note that immigration enforcement actions conducted by ICE are often facilitated by local law enforcement agencies. See Table A.1 for a disaggregation of the apprehension methods and programs.

  27. Following the Frisch-Waugh-Lowell theorem, we individually residualized the number of annual ICE arrest shocks and MSA characteristics by partialling out year and MSA fixed effects to remove potential trends. Then we standardized the residualized variables to facilitate the interpretation of regression coefficients.

  28. The ACS data was obtained from Ruggles et al. (2022).

  29. We also experiment with correlating the MSA characteristics with the arrests rate per 1000 foreign-born individuals to verify that our use of a shock is a more exogenous measure of variation in immigration arrests. As seen in column 2 of Table 3 the residualized rate of arrests is correlated with several characteristics, including the unemployment rate and labor concentration in the natural resources and mining, and leisure and hospitality industries.

  30. In separate regressions by age cohort, we find larger effects among 16-year-olds and positive, although imprecise, estimates for 17- and 18-year-olds. Results from these regressions are available upon request.

  31. We also estimate the effect of immigration arrest shocks on labor hours, conditional on employment. The point estimates are positive and of comparable magnitude to the ones presented in Table 6.

  32. While the CPS does not distinguish authorized and unauthorized immigration status, segments of the population estimated by Taylor et al. (2011) and Capps et al. (2016) are encompassed in our data. The methods used to estimate these populations come from the same data sets used in our analysis. See the methodological description in Taylor et al. (2011) and Capps et al. (2016).

  33. In equation 2 the three-way interaction with the shock placebo is expressed as \(\beta ^{placebo}_{7}(S^{Placebo}_{mt}\times H_{i}\times M_{i})\).

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Acknowledgements

The authors are grateful to Francisca Antman, Aimee Chin, Kalena Cortes, Monica Garcia-Perez, Steve Hemelt, Brady Horn, Tom Mroz, Sandra Orozco-Aleman, Kira Villa, Madeline Zavodny, Josh Smith, and participants at the Spring 2021 Immigrants and the US Economy NBER Meeting, the 2021 American Society for Hispanic Economists (ASHE) Virtual Seminar Series, and the 2021 AEA Annual Conference. We also thank editor Terra McKinnish and two anonymous reviewers for helpful comments. Finally, we are thankful to the Center for Growth and Opportunity at Utah State University for support of an earlier version of this manuscript.

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  1. University of North Carolina at Chapel Hill, Chapel Hill, USA

    Joaquin Alfredo-Angel Rubalcaba

  2. The University of Texas at El Paso, El Paso, USA

    José R. Bucheli

  3. The University of Texas at Dallas, Dallas, USA

    Camila Morales

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  1. Joaquin Alfredo-Angel Rubalcaba
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Correspondence to Joaquin Alfredo-Angel Rubalcaba.

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Rubalcaba, J.AA., Bucheli, J.R. & Morales, C. Immigration enforcement and labor supply: Hispanic youth in mixed-status families. J Popul Econ 37, 43 (2024). https://doi.org/10.1007/s00148-024-01022-x

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