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Measuring Geographic Migration Patterns Using Matrículas Consulares

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Demography

Abstract

In this article, we show how to use administrative data from the Matrícula Consular de Alta Seguridad (MCAS) identification card program to measure the joint distribution of sending and receiving locations for migrants from Mexico to the United States. Whereas other data sources cover only a small fraction of source or destination locations or include only very coarse geographic information, the MCAS data provide complete geographic coverage of both countries, detailed information on migrants’ sources and destinations, and a very large sample size. We first confirm the quality and representativeness of the MCAS data by comparing them with well-known household surveys in Mexico and the United States, finding strong agreement on the migrant location distributions available across data sets. We then document substantial differences in the mix of destinations for migrants from different places within the same source state, demonstrating the importance of detailed substate geographical information. We conclude with an example of how these detailed data can be used to study the effects of destination-specific conditions on migration patterns. We find that an Arizona law reducing employment opportunities for unauthorized migrants decreased emigration from and increased return migration to Mexican source regions with strong initial ties to Arizona.

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Notes

  1. In the case of EMIF, the data record the planned destinations of those intending to migrate.

  2. As of October 2015, the MMP had surveyed 154 communities whose combined populations accounted for 1.03 % of the Mexican population in 2010.

  3. See, for example, Garip and Asad (2016), Massey (1986), Massey and Espinosa (1997), Palloni et al. (2001), and Winters et al. (2001). DiMaggio and Garip (2012) provided a survey of the sociology literature on networks.

  4. McKenzie and Rapoport (2010) showed that the presence of previous migrants disproportionately increases migration probability for less educated individuals. Dolfin and Genicot (2010) examined the effects of family and community contacts on migration with and without the assistance of smugglers. Woodruff and Zenteno (2007) showed that migration increases microenterprise development in source locations.

  5. See Bartel (1989), Bauer et al. (2002), McConnell (2008), Jaeger (2000), Lafortune and Tessada (2014), and Patel and Vella (2013) on destination choices; and Mundra and Rios-Avila (2016) and Munshi (2003) on labor market success.

  6. These 13 jurisdictions are CA, CO, CT, DC, DE, HI, IL, MD, NM, NV, UT, VT, and WA. The take-up rate is likely somewhat higher in these destinations compared with the rest of the country. Our analysis focuses on differences in destinations selected by migrants from different sources. As long as any higher take-up in these destinations occurs for individuals from all sources, our central conclusions will be unaffected.

  7. As of October 2017, the tabulations are available online at http://www.ime.gob.mx/gob/estadisticas/2016/usa/estadisticas_usa.html.

  8. The tabulations are similar in structure to those provided by the U.S. Internal Revenue Service, reporting counts of migrants for each source-destination pair in the United States. See Kaplan and Schulhofer-Wohl (2012) for an example.

  9. Table S3 (Online Resource 1) provides an annual breakdown of the number of card issuances. In addition, officials at IME were kind enough to provide annual summary statistics on the share of card issuances reflecting new cards versus renewals. In every year, less than 3 % are renewals.

  10. The survey covered consulates in Los Angeles, New York, Chicago, Atlanta, Dallas, Raleigh, and Fresno.

  11. We do not compare MCAS and the Mexican Migration Project (MMP) data because MMP communities are generally far smaller than and not necessarily representative of their municipios, so there is no comparable geographic unit across the two data sets.

  12. Throughout this analysis, we use the U.S. state as the destination-level geography. Much of the analysis relies on the public-use microdata version of the ACS (Ruggles et al. 2010), and the substate geographical definitions in that data source (PUMAs) do not align well with the consular areas. For consistency, therefore, we adopt the U.S. state as the definition of a destination. The consular areas, however, are composed of U.S. counties, and future work can match U.S. data sets with county-level coverage to the destination geography shown in Fig. S1 (Online Resource 1).

  13. We report the R2 for both the logged and nonlogged version of these comparisons in each figure.

  14. Because the shares sum to one across states in each data set, states whose shares are larger in MCAS than in the ACS are offset by states whose MCAS shares are smaller than in the ACS. Because the log function is concave, states with larger shares in the MCAS appear closer to the 45-degree line than states with smaller shares in the MCAS.

  15. The different number of covered migrants is not surprising because the sample size for the ENADID is calibrated to ensure accurate reporting of domestic fertility rates rather than migration rates. We thank Fernando Riosmena for helpful discussion on the design of the ENADID.

  16. This discrepancy likely occurs because the EMIF asks about a migrant’s intended destination, which is subject to change.

  17. For more detail on the data points underlying this histogram, see Online Resource 1, section 6, which includes example municipio distributions and the relevant comparisons with those municipios’ state.

  18. Random variation could account for some observed differences between municipio-level and state-level destination distributions, particularly because some municipios are quite small. In Online Resource 1, section 7, we implement a permutation test to demonstrate that the observed differences are far larger than those that could plausibly be explained by small municipio samples and random variation.

  19. For the full distribution of destinations chosen by migrants from these two sources, see Figs. S8 and S9 (Online Resource 1).

  20. In Online Resource 1, section 8, we corroborate the regression analysis following Eq. (2) with summary statistics on the time-series evolution of return migration and emigration rates for municipios with initially higher and lower rates of connection to Arizona.

  21. Return migration flows are identified in the 2005 Conteo and in the 2010 Mexican Census through a question that records country of residency five years prior to the date when the survey was administered. The count of return migrants does not include any individuals who were living in Mexico five years previously but who moved to the United States and back within the five-year window.

  22. The percentage difference in the growth of return migration rates would be 100 × e0.5 × 0.558 – 1 = 32.1 %. Compared with growth rates of roughly 300 % in untreated states, this treated state would see a growth rate of roughly 400 %.

  23. This weighting addresses the fact that population growth rates are heteroskedastic, with smaller populations experiencing more variable percentage growth in migration rates.

  24. Specifically, we use the rreg command in Stata, which implements the robust regression procedure described by Li (1985).

  25. A small number of emigrants who left in 2005 are not observable because the survey asks only about the start date of the most recent trip, so individuals who emigrated first in 2005, returned to Mexico, and then emigrated again are treated as having emigrated in the year of their most recent trip to the United States.

  26. Hoekstra and Orozco-Aleman (2017) examined a related question using a later Arizona law, SB 1070, which imposed unprecedented immigration enforcement measures. They used EMIF data to document decreased intended migration to Arizona among unauthorized Mexican migrants after the law’s passage. Our novel contribution is to document changes in realized return migration and emigration at the Mexican source level. Given the shortcomings of EMIF described in the section MCAS Data Quality, such an analysis would not be feasible using that data source.

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Acknowledgments

This project was supported by a grant from the Berkman Faculty Development Fund at Carnegie Mellon University. This research has benefited from research, administrative, and computing support provided by the University of Colorado Population Center (CUPC; Project 2P2CHD066613-06), funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The content is solely the responsibility of the authors and does not necessarily represent the official views of CUPC or NICHD. The authors would like to thank Alexandra Chouldechova, Terra McKinnish, Fernando Riosmena, and Lowell Taylor, and participants at the 2017 Population Association of America Annual Meeting and the University of Oxford Workshop on Immigration, Health, and Well-Being for helpful comments. Benjamin Mayer provided excellent research assistance. Special thanks to Edith Soto Ramírez at the Instituto de los Mexicanos en el Exterior for extensive discussions regarding the Matrícula Consular de Alta Seguridad data. Remaining errors are our own.

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Authors and Affiliations

  1. Carnegie Mellon, Heinz College, 4800 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Maria Esther Caballero & Brian K. Kovak

  2. Department of Economics, University of Colorado, 256 UCB, Boulder, CO, 80309, USA

    Brian C. Cadena

  3. Institute of Labor Economics (IZA), Bonn, Germany

    Brian C. Cadena & Brian K. Kovak

  4. National Bureau of Economic Research, Cambridge, MA, USA

    Brian K. Kovak

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  1. Maria Esther Caballero
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Correspondence to Brian K. Kovak.

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Caballero, M.E., Cadena, B.C. & Kovak, B.K. Measuring Geographic Migration Patterns Using Matrículas Consulares. Demography 55, 1119–1145 (2018). https://doi.org/10.1007/s13524-018-0675-6

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