Skip to main content


Log in

Employer Verification Mandates and Infant Health

  • Original Research
  • Published:
Population Research and Policy Review Aims and scope Submit manuscript


In recent decades, several states have enacted their own immigration enforcement policies. This reflects substantial variation in the social environments faced by immigrants and native-born citizens, and has raised concerns about unintended consequences. E-Verify mandates, which require employers to use an electronic system to ascertain legal status as a pre-requisite for employment, are a common example of this trend. Drawing on birth certificate data from 2007 to 2014, during which 21 states enacted E-Verify mandates, we find that these mandates are associated with a decline in birthweight and gestational age for infants born to immigrant mothers with demographic profiles matching the undocumented population in their state as well as for infants of native-born mothers. In observing negative trends for both immigrants and natives, our findings do not support the hypothesis that E-Verify has a distinct impact on immigrant health; however, the broader economic, political, and demographic contexts that coincide with these policies, which likely impact the broader community of both immigrants and natives, may pose risks to infant health.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  1. Other factors that may be associated with local immigration policies include the size and political power of those advocating for the policies, the media attention given to immigrants, the racial and religious composition of lawmakers, as well as the demand for unskilled, low-wage labor (for related discussion see Commins and Wills 2017; Pearson‐Merkowitz et al. 2016; Steil and Vasi 2014). We focus specifically on the size of the foreign-born population and the political leanings of the state government as evidence linking these conditions to the passage of restrictive immigration laws is particularly consistent.

  2. It is important to note that in many cases an increase in the size of the foreign-born population can contribute to economic revitalization in declining rural communities and in some instances serve to promote immigrant integration policies (Filindra 2018). Many residents and communities have also responded positively to new immigrant neighbors and taken steps to make the community more welcoming (e.g., providing bilingual services). As Massey and Capoferro (2008) documents, responses to immigrants in new destination locations can be best described as mixed or ambivalent rather than uniformly negative. But, since we are focused here on communities that pass restrictive legislation, their contexts and responses tend to be more negative.

  3. These states include Maine, Montana, New Hampshire, North Dakota, South Dakota, Vermont, West Virginia, and Alaska.

  4. Beginning in 2011, the Division of Vital Statistics stopped making available information on mother’s education from states that had not adopt the 2003 revision.

  5. CMS used American Community Survey to establish a “pool of potential undocumented immigrants” using information in the survey on nativity and citizenship. This pool was adjusted using information on other survey responses that suggested the sample member was/was not undocumented, information about the foreign-born population from the 2010 U.S. Census, and information on legalization application and visa overstays from the Department of Homeland Security. More detailed information on how CMS derived these estimates is available in Warren (2014). It is important to note that the state-level distributions provided by CMS are estimates. The data cannot identify undocumented immigrants with certainty. Additionally, the information contained in CMS’ revised ACS data is in no way used to isolate individual sample members in any way that would compromise their confidentiality.

  6. The countries are Mexico, Canada, El Salvador, Guatemala, Honduras, Dominican Republic, Haiti, Jamaica, Nicaragua, Argentina, Brazil, Colombia, Ecuador, Peru, Venezuela, China, India, Pakistan, South Korea, Philippines, Vietnam, Ethiopia, Ghana, Nigeria, and Poland.

  7. The specific boundaries of 18–44 were necessary because of the categorical specification of age in the CMS data.

  8. Conditional independence is a necessary simplifying assumption, but unfortunately untestable when documentation status is unknown. Roughly, it corresponds to excluding interaction terms in a regression model. For example, if—among immigrants from one country—single women were more likely to be documented than married women but among immigrants from another country married women are, we would be unable to capture this using our data since we do not have information that permits us to determine the joint distribution of country of origin and marital status.

  9. We tested a number of different thresholds in creating a binary version of the proxy measure and we also tried using the fully continuous version when interacting the proxy measure with the E-Verify indicator; all these alternative specifications yielded consistent results.

  10. We also ran modified poisson models with robust standard errors to generate relative risk ratios (Zou 2004). These produced similar point estimates as the odds ratios reported here. With rounding, the odds ratios and risk ratios were often identical and at most showed a difference of a few percentage points, which were not substantively important given the magnitudes of the ratio. However, given the extremely high computational demands of producing robust standard errors for risk ratios that are adjusted for state-level cluster in a large dataset like natality records, we opt to report odds ratios.

  11. This model is equivalent to a difference-in-difference analysis. However, we avoid that terminology since it requires the assumption that the control group (in this case native whites) are not experiencing notable changes in health and/or environment. As discussed above, this is a problematic assumption given the conditions that surround E-Verify passage. We therefore interpret these results as a test for statistical significance of differences in trends.

  12. We code conception dates and E-Verify dates at the level of weeks (e.g., the 5th week in 2008). It should be noted that gestational age is reported in weeks, but it is only possible to know the month and year of a birth. Therefore, to estimate conception dates, we assume all births took place in the second week of the month.

  13. For both immigrant groups in panels 1 and 2, odds ratios for universal and public E-Verify are statistically significantly different at the .05 level when predicting low birth weight. However, the universal and public E-Verify odds ratios are statistically equivalent for the native white group in panel 3.

  14. Odds ratios for universal and public E-Verify cannot be distinguished for any of the three groups when predicting pre-term delivery.

  15. We tested a number of different demarcations in the proxy measure and tried interacting it with the E-Verify indicators; all these alternative specifications yielded results consistent with what we report here, but for other research questions or data these alternative uses of the proxy could provide additional insights.


  • Acevedo-Garcia, D., Soobader, M.-J., & Berkman, L. F. (2005). The differential effect of foreign-born status on low birth weight by race/ethnicity and education. Pediatrics, 115(1), e20–e30.

    Google Scholar 

  • Almeida, J., Biello, K. B., Pedraza, F., Wintner, S., & Viruell-Fuentes, E. (2016). The association between anti-immigrant policies and perceived discrimination among Latinos in the US: A multilevel analysis. Social Science and Medicine-Population Health, 2, 897–903.

    Google Scholar 

  • Amuedo-Dorantes, C., & Arenas-Arroy, E. (2017). Immigrant fertility in the midst of intensified enforcement. GLO Discussion Paper. Retrieved from on 7/10/2019.

  • Amuedo-Dorantes, C., & Bansak, C. (2012). The labor market impact of mandated employment verification systems. American Economic Review, 102(3), 543–548.

    Google Scholar 

  • Amuedo-Dorantes, C., & Bansak, C. (2014). Employment verification mandates and the labor market outcomes of likely unauthorized and native workers. Contemporary Economic Policy, 32(3), 671–680.

    Google Scholar 

  • Amuedo-Dorantes, C., Puttitanun, T., & Martinez-Donate, A. P. (2013). How do tougher immigration measures affect unauthorized immigrants? Demography, 50(3), 1067–1091.

    Google Scholar 

  • Anastasopoulos, L. J., (2014). (When) Race matters: the effect of immigrant race and place on support for anti-immigration laws. Available at SSRN: Accessed 25 Aug 2019.

  • Austin, John C. (2017). Segregation and changing populations shape rust belt’s politics. Washington, DC: Brookings Institution.

    Google Scholar 

  • Behrman, J. R., & Rosenzweig, M. R. (2004). Returns to birthweight. Review of Economics and Statistics, 86(2), 586–601.

    Google Scholar 

  • Beniflah, J. D., Little, W. K., Simon, H. K., & Sturm, J. (2013). Effects of immigration enforcement legislation on hispanic pediatric patient visits to the pediatric emergency department. Clinical Pediatrics, 52(12), 1122–1126.

    Google Scholar 

  • Bharadwaj, P., Lundborg, P., & Rooth, D.-O. (2017). Birth weight in the long run. Journal of Human Resources, 53(1), 189–231.

    Google Scholar 

  • Blumer, H. (1958). Race prejudice as a sense of group position. Pacific Sociological Review, 1, 3–7.

    Google Scholar 

  • Bohn, S., & Lofstrom, M. (2012). Employment effects of state legislation against the hiring of unauthorized immigrant workers. Discussion Paper series. Forschungsinstitut zur Zukunft der Arbeit, No. 6598, Institute for the Study of Labor (IZA), Bonn, Accessed 25 Aug 2019.

  • Bohn, S., Lofstrom, M., & Raphael, S. (2014). Did the 2007 Legal Arizona Workers Act reduce the state’s unauthorized immigrant population? Review of Economics and Statistics, 96(2), 258–269.

    Google Scholar 

  • Brown, J. David, Misty L. Heggeness, Suzanne M. Dorinski, Lawrence W. & Moises Y. (2019). Estimating the potential effects of adding a citizenship question to the 2020 Census. IZA Institute of Labor Economics Discussion Paper Series (IZA DP No. 12087).

  • Bruce, D., & Schuetze, H. J. (2004). The labor market consequences of experience in self-employment. Labour Economics, 11(5), 575–598.

    Google Scholar 

  • Burgard, S. A., Brand, J. E., & House, J. S. (2009). Perceived job insecurity and worker health in the United States. Social Science and Medicine, 69(5), 777–785.

    Google Scholar 

  • Catalano, R. (1981). “Contenting with rival hypotheses in correlation of aggregate time-series (CATS): An overview for community psychologsits. American Journal of Community Psychology, 9(6), 667–679.

    Google Scholar 

  • Collins, W. J., David, R. J., Handler, A., Wall, S., & Andes, S. (2004). Very low birthweight in African American infants: The role of maternal exposure to interpersonal racial discrimination. American Journal of Public Health, 94(12), 2132–2138.

    Google Scholar 

  • Commins, M. M., & Wills, J. B. (2017). Reappraising and extending the predictors of states’ immigrant policies: Industry influences and the moderating effect of political ideology. Social Science Quarterly, 98(1), 212–229.

    Google Scholar 

  • David, R. J., & Collins, J. W. (1997). Differing birth weight among infants of U.S.-Born Blacks, African-Born Blacks, and U.S.-Born Whites. New England Journal of Medicine, 337(17), 1209–1214.

    Google Scholar 

  • Dehejia, R., & Lleras-Muney, A. (2004). Booms, busts, and babies’ health. The Quarterly Journal of Economics, 119(3), 1091–1130.

    Google Scholar 

  • Dreby, J. (2015). Everyday illegal: When policies undermine immigrant families. Berkeley, CA: University of California Press.

    Google Scholar 

  • Figlio, D. N., Guryan, J., Karbownik, K., & Roth, J. (2016). Long-term cognitive and health outcomes of school-aged children who were born late-term vs full-term. JAMA pediatrics, 170(8), 758–764.

    Google Scholar 

  • Filindra, A. (2018). “Is “Threat” in the eye of the researcher? Theory and measurement in the study of state-level immigration policymaking. Policy Studies Journal..

    Article  Google Scholar 

  • Fountain, C., & Bearman, P. (2011). Risk as social context: Immigration policy and autism in California. Sociological Forum, 26(2), 215–240.

    Google Scholar 

  • Fuentes-Afflick, E., & Lurie, P. (1997). Low birth weight and Latino ethnicity: Examining the epidemiologic paradox. Archives of Pediatrics and Adolescent Medicine, 151(7), 665–674.

    Google Scholar 

  • Hacker, K., Chu, J., Arsenault, L., & Marlin, R. P. (2012). Provider’s perspectives on the impact of immigration and customs enforcement (ICE) activity on immigrant health. Journal of Health Care for the Poor and Underserved, 23(2), 651–655.

    Google Scholar 

  • Hawes, D. P., & McCrea, A. M. (2018). Give us your tired, your poor and we might buy them dinner: Social capital, immigration, and welfare generosity in the American States. Political Research Quarterly, 71(2), 347–360.

    Google Scholar 

  • Kandel, W., & Parrado, E. (2005). Restructuring of the US meat processesing industry and new Hispanic migrant destinations. Population and Deveopment Review., 31(3), 447–471.

    Google Scholar 

  • Kreif, N., Grieve, R., Hangartner, D., et al. (2015). Examination of the synthetic control method for evaluating health policies with multiple treated units. Health Economics, 25(12), 1514–1528.

    Google Scholar 

  • Lauderdale, D. S. (2006). Birth outcomes for Arabic-named women in California before and after September 11. Demography, 43(1), 185–201.

    Google Scholar 

  • Leerkes, A., Bachmeier, J. D., & Leach, M. A. (2013). When the border is “Everywhere”: State-level variation in migration control and changing settlement patterns of the unauthorized immigrant population in the United States. International Migration Review, 47(4), 910–943.

    Google Scholar 

  • Leerkes, A., Leach, M., & Bachmeier, J. (2012). Borders behind the border: An exploration of state-level differences in migration control and their effects on US migration patterns. Journal of Ethnic and Migration Studies, 38(1), 111–129.

    Google Scholar 

  • Lichter, D. T. (2012). Immigration and the new racial diversity in rural America. Rural Sociology, 77(1), 3–35.

    Google Scholar 

  • Massey, D. S. (2013). Comment: Building a better underclass. Demography, 50(3), 1093–1095.

    Google Scholar 

  • Massey, D. S., & Capoferro, C. (2008). The geographic diversification of Amerian immigration. In D. S. Massey (Ed.), New faces in new places: The changing geography of American immigration. New York: Russell Sage Foundation.

    Google Scholar 

  • National Conference of State Legislatures. (2017). Immigration laws and current state immigration legislation. Washington, DC. Retrieved April 1, 2018, from

  • Newman, B. J., Johnston, C. D., Strickland, A. A., & Citrin, J. (2012). Immigration crackdown in the American workplace. State Politics & Policy Quarterly, 12(2), 160–182.

    Google Scholar 

  • Novak, N. L., Geronimus, A. T., & Martinez-Cardoso, A. M. (2017). Change in birth outcomes among infants born to Latina mothers after a major immigration raid. International Journal of Epidemiology, 46(3), 839–849.

    Google Scholar 

  • Nowrasteh, A. (2018). E-Verify could have increased crime in Arizona. Washington, DC: Cato Institute.

    Google Scholar 

  • O’Campo, P., Eaton, W. W., & Muntaner, C. (2004). Labor market experience, work organization, gender inequalities and health status: Results from a prospective analysis of US employed women. Social Science and Medicine, 58(3), 585–594.

    Google Scholar 

  • Oken, E., Kleinman, K. P., Rich-Edward, J., & Gillman, M. (2003). A nearly continuous measure of birth weight for gestational age using a United States national reference. BMC Pediatrics, 3, 6.

    Google Scholar 

  • Orrenius, P. M., & Zavodny, M. (2016). Do state work eligibility verification laws reduce unauthorized immigration? IZA Journal of Migration, 5(1), 5.

    Google Scholar 

  • Paral, Rob. (2017). Looking back to look forward: Lessons from the immigration histories of Midwestern Cities. Chicago, IL: The Chicago Council on Global Affairs.

    Google Scholar 

  • Passel, J., & Cohn, D. (2017). Size of US unauthorized immigrant workforce stable after the Great Recession. Washington D.C.: Pew Research Center. Retrieved from on 7/10/2019.

  • Pearson-Merkowitz, Shanna, Filindra, Alexandra, & Dyck, Joshua J. (2016). When partisans and minorities interact: Interpersonal contact, partisanship, and public opinion preferences on immigration policy. Social Science Quarterly, 97(2), 311–324.

    Google Scholar 

  • Philbin, M. M., Flake, M., Hatzenbuehler, M. L., & Hirsch, J. S. (2017). State-level immigration and immigrant-focused policies as drivers of Latino health disparities in the United States. Social Science & Medicine, 199, 29–38.

    Google Scholar 

  • Quillian, L. (1995). Prejudice as a response to perceived group threat: Population composition and anti-immigrant and racial prejudice in Europe. American Sociological Review, 60(4), 586–611.

    Google Scholar 

  • Rosenblum, M. R. & Hoyt L. (2011). “The basics of E-Verify, the US employer verification system.” Migration Information Source. Migration Policy Institute, Washington D.C. Retrieved from (December 1, 2016).

  • Steil, J. P., & Vasi, I. B. (2014). The new immigration contestation: Social movements and local immigration policy making in the United States, 2000–2011 1. American Journal of Sociology, 119(4), 1104–1155.

    Google Scholar 

  • Toomey, R. B., Umaña-Taylor, A. J., Williams, D. R., Harvey-Mendoza, E., Jahromi, L. B., & Updegraff, K. A. (2014). Impact of Arizona’s SB 1070 immigration law on utilization of health care and public assistance among Mexican-origin adolescent mothers and their mother figures. American Journal of Public Health, 104(S1), S28–S34.

    Google Scholar 

  • Torche, F., & Sirois, C. (2018). Restrictive immigration law and birth outcomes of immigrant women. American Journal of Epidemiology.

    Article  Google Scholar 

  • Van Hook, J., Bean, F. D., Bachmeier, J. D., & Tucker, C. (2014). Recent trends in coverage of the Mexican-born population of the United States: Results from applying multiple methods across time. Demography, 51(2), 699–726.

    Google Scholar 

  • Vargas, E. D. (2015). Immigration enforcement and mixed-status families: The effects of risk of deportation on Medicaid use. Children and Youth Services review, 57, 83–89.

    Google Scholar 

  • Walker, K. E., & Leitner, H. (2011). The variegated landscape of local immigration policies in the United States. Urban geography, 32(2), 156–178.

    Google Scholar 

  • Warren, R. (2014). Democratizing data about unauthorized residents in the United States: Estimates and public-use data, 2010 to 2013. Journal on Migration and Human Security, 2(4), 305–328.

    Google Scholar 

  • Watson, T. (2014). Inside the refrigerator: Immigration enforcement and chilling effects in Medicaid participation. American Economic Journal: Economic Policy, 6(3), 313–338.

    Google Scholar 

  • White, K., Yeager, V. A., Menachemi, N., & Scarinci, I. C. (2014). Impact of Alabama’s immigration law on access to health care among Latina immigrants and children: Implications for national reform. American Journal of Public Health, 104(3), 397–405.

    Google Scholar 

  • Ybarra, V. D., Sanchez, L. M., & Sanchez, G. R. (2016). Anti-immigrant anxieties in state policy: The great recession and punitive immigration policy in the American states, 2005–2012. State Politics & Policy Quarterly, 16(3), 313–339.

    Google Scholar 

  • Zingher, J. N. (2014). The ideological and electoral determinants of laws targeting undocumented migrants in the US states. State Politics & Policy Quarterly, 14(1), 90–117.

    Google Scholar 

  • Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702–706.

    Google Scholar 

Download references


This research was generously supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development at the National Institutes of Health (R21 HD082557-02). This project benefited greatly from data developed and made available to us by the Center for Migration Studies of New York. Previous versions of this research were presented at the 2014 Annual Meetings of the Population Association of America in Boston, MA and at the Population, Education, and Health Seminar Series, University of Missouri, Columbia, MO.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Kate W. Strully.

Additional information

Publisher's Note

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


Appendix 1

See Tables 6, 7, 8, 9.

Table 6 Odds ratios from individual-level logistic regression models predicting infant health for mothers from top 25 sending countries of undocumented immigrants in the US
Table 7 Odds ratios from individual-level logistic regression models predicting infant health for native-born White mothers
Table 8 Odds ratios from individual-level logistic regression models including interactions between E-Verify policy and nativity group
Table 9 Coefficients from aggregated state-level OLS regressions predicting the percentage of births to married mothers and more educated mothers

Appendix 2

Derivation of Probabilities

In this paper, we are interested in understanding how the probability of being undocumented according to a previously estimated proxy measure that relates to key outcomes. Let D denote the proxy-based measure of documentation status (either documented or undocumented). We have information on four characteristics which we denote X1, X2, X3, and X4. We are interested in calculating p(D|X1, X2, X3, X4). By Bayes rule we have

$$ p(D|X_{1} ,X_{2} ,X_{3} ,X_{4} ) = \frac{{p(X_{1} ,X_{2} ,X_{3} ,X_{4} |D)p\left( D \right)}}{{p\left( {X_{1} ,X_{2} ,X_{3} ,X_{4} } \right)}} $$

If we assume that the characteristics are conditionally independent, we can write

$$ p(D|X_{1} ,X_{2} ,X_{3} ,X_{4} ) = \frac{{p(X_{1} |D)p(X_{2} |D)p(X_{3} |D)p(X_{4} |D)p\left( D \right)}}{{p\left( {X_{1} ,X_{2} ,X_{3} ,X_{4} } \right)}}. $$

Using the definition of conditional probability this can be rewritten as

$$ p(D|X_{1} ,X_{2} ,X_{3} ,X_{4} ) = \frac{{p(D|X_{1} )p\left( {X_{1} } \right)}}{p\left( C \right)} \cdot \frac{{p(D|X_{2} )p\left( {X_{2} } \right)}}{p\left( D \right)} \cdot \frac{{p(D|X_{3} )p\left( {X_{3} } \right)}}{p\left( D \right)} \cdot \frac{{p(D|X_{4} )p\left( {X_{4} } \right)}}{p\left( D \right)} \cdot \frac{p\left( D \right)}{{p\left( {X_{1} ,X_{2} ,X_{3} ,X_{4} } \right)}} $$

Finally, we can re-write this as

$$ p(D|X_{1} ,X_{2} ,X_{3} ,X_{4} ) \propto \frac{{p(D|X_{1} )p(D|X_{2} )p(D|X_{3} )p(D|X_{4} )}}{{[p\left( D \right)]^{{}} }} $$

From this we can estimate the probability of being undocumented (Undoc) versus documented (Doc) according to the previously estimated proxy measure as

$$ p({\text{Undoc}}|X_{1} ,X_{2} ,X_{3} ,X_{4} ) = \frac{{{ \Pr }({\text{Undoc}}|X_{1} )Pr({\text{Undoc}}|X_{2} ){ \Pr }({\text{Undoc}}|X_{3} ){ \Pr }({\text{Undoc}}|X_{4} )}}{{\left( {C_{1} + C_{2} } \right) \cdot [{ \Pr }\left( {\text{Undoc}} \right)]^{3} }} $$


$$ C_{1} = \frac{{{ \Pr }({\text{Undoc}}|X_{1} ){ \Pr }({\text{Undoc}}|X_{2} ){ \Pr }({\text{Undoc}}|X_{3} ){ \Pr }({\text{Undoc}}|X_{4} )}}{{[{ \Pr }\left( {\text{Undoc}} \right)]^{2} }} $$


$$ C_{2} = \frac{{{ \Pr }({\text{Doc}}|X_{1} ){ \Pr }({\text{Doc}}|X_{2} ){ \Pr }({\text{Doc}}|X_{3} ){ \Pr }({\text{Doc}}|X_{4} )}}{{[{ \Pr }\left( {\text{Doc}} \right)]^{2} }}. $$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Strully, K.W., Bozick, R., Huang, Y. et al. Employer Verification Mandates and Infant Health. Popul Res Policy Rev 39, 1143–1184 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: