Abstract
A growing literature has documented the displacement effects of tougher interior immigration enforcement measures; yet, we still lack an understanding of where the displaced populations are choosing to relocate. In this paper, we address this question using Arizona as a case study. Specifically, we examine the destinations of Mexican non-citizens leaving Arizona for other states in the union following the adoption of tougher enforcement measures using two different groups of control states: one consisting of all states that had not adopted similar measures and another one derived using the synthetic control method. We find that Mexican non-citizens who migrated from Arizona to other US states went, primarily, to New Mexico and California. Other destination states differed with the control group being used, underscoring the sensitivity of this type of analysis to the choice of control group. Furthermore, the trajectories of Mexican non-citizens leaving Arizona overlapped with those of non-Hispanic natives, hinting on the role that socioeconomic and political factors, in addition to potential complementarities between immigrants and natives, might have played in explaining the destinations of Mexican non-citizens leaving Arizona after 2007.
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Notes
We stop at 2012 since this is the year when the Supreme Court ruled against sections of SB1070, declaring them unconstitutional.
Because the ACS only allows us to track flows across US states, we use removal data to assess if the outflows from AZ that do not seem to flow into other US states mirrored CBP removal figures.
In addition to following the convention of using ethnicity and citizenship traits as predictors of likely unauthorized immigration status, we use the figures published by the Center on Migration Studies regarding the undocumented population to check if using low-educated Mexican non-citizens (those with less than a high school degree) provided a reasonable approximation to the figures they published.
Maricopa County and its sheriff, Joe Arpaio, have been at the forefront of aggressively pursuing unauthorized immigrants through workplace raids and the profiling of Latinos (Santos 2014).
We construct this artificial panel by taking advantage of three sets of variables in the American Community Survey: (1) migration status during the last year, (2) state or country of residence 1 year ago, and (3) current state of residence. That way, we can observe where an observation lives in year t + 1 and where an observation lives in year t, allowing us to identify interstate movements in the ACS.
Our estimates of the Mexican non-citizen population in our sample are fairly close to the estimates for the unauthorized Mexican population in each state gathered by the Center for Migration Studies (CMS) of New York. See: http://data.cmsny.org/state.html. At any rate, we also experiment with alternative definitions of the likely unauthorized population that further restrict the sample to those less than 45 years of age and to those with no more than a high school diploma. Results prove robust to the use of these smaller samples.
Furthermore, this drop appears in contrast to what is observed for the share of non-Hispanic natives, which grew by 6% between 2007 and 2012 (figures, not shown, are available from the authors). Appendix Table 4 shows the raw numbers to construct this figure.
See Appendix Table 5 in the appendix for a list of states with universal E-Verify mandates or omnibus immigration bills alike LAWA and SB1070 in Arizona.
Appendix Table 6 shows the flows in and out of Arizona and different states during this time period.
Appendix Table 7 shows the same data for all five states bordering Arizona.
We group all other US states with smaller populations of Mexican non-citizens to minimize measurement error concerns (Aydemir and Borjas 2011).
The multinomial logit assumes that adding another destination (e.g., Mexico) does not affect the relative odds of choosing any of the US destinations (i.e., the independence of irrelevant alternatives or IIA). Note, however, that the IIA refers to choices that might be considered close substitutes—an unlikely assumption in the case of Mexico as opposed to other US states.
Individual level characteristics (such as age, gender, and dummies for whether the respondent is married, has less than a high school education, employed and, in the latter case, for whether s/he is a wage and salaried worker) and characteristics of the state s the ith respondent is leaving at time t, such as the shares of the state’s total population in industries with the highest concentration of immigrant labor (i.e., agriculture, construction, retail, food services, and administrative support) are included as controls. In that manner, we address uneven impacts of the past recession on Arizona’s labor market given the boom of the construction sector prior to the downturn in Arizona and the large share of Mexican non-citizens employed in that industry.
At this point, it is worth pointing out that this coefficient could be potentially capturing the effect of increased border enforcement or local immigration enforcement initiatives adopted in the state after 2007, as was the case with some 287(g) agreements and, more importantly, Secure Communities. Note, however, that border enforcement and local initiatives were not unique to Arizona. Border enforcement should have impacted other states in the synthetic control group, such as California or Texas. Similarly, 287(g) agreements and, especially, Secure Communities were adopted by many counties and states in our control groups. Therefore, their role in the interaction coefficient capturing differences in the behavior of low-skilled Mexican non-citizens leaving Arizona, as opposed to those leaving other states in the control group, should be of second order.
We do not have detailed enough information on the residential location of individuals in our sample to construct a meaningful distance variable. Hence, we experimented with including a dummy indicative of whether the state is one that border Arizona, potentially making it a more likely destination. However, the model fails to converge when including that regressor.
We repeat the analysis for large enough subsamples of low-skilled Mexican non-citizens, such as males and younger individuals under 40 years of age. The results from these heterogeneity analyses are displayed in Appendix Tables 8 and 9. As can be seen therein, we continue to see the same mobility patterns observed in Table 2 for the entire sample.
Mixed-status households are those with some undocumented members and some legal residents/US citizens.
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Amuedo-Dorantes, C., Lozano, F.A. Interstate Mobility Patterns of Likely Unauthorized Immigrants: Evidence from Arizona. J Econ Race Policy 2, 109–120 (2019). https://doi.org/10.1007/s41996-018-0023-7
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DOI: https://doi.org/10.1007/s41996-018-0023-7