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Employer Verification Mandates and Infant Health

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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.

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  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.


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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.

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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

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


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


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

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

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