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Determinants of Mexico-U.S. Outward and Return Migration Flows: A State-Level Panel Data Analysis

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Demography

Abstract

Using a unique panel data set of state-to-state outward and return migration flows between Mexico and the United States from 1995 to 2012, this study is the first to analyze Mexico-U.S. migration at the state level and explore simultaneously the effect of economic, environmental, and social factors in Mexico over two decades. Pairing origin and destination states and controlling for a rich structure of fixed effects, we find that income positively impacts migration outflows, especially for Mexican states of origin with a recent migration history and for low-educated migrant flows, suggesting the existence of credit constraints. We find evidence that drought causes more out-migration, while other climatic shocks have no effect. Violence is found to increase out-migration flows from border states and to decrease migration from other Mexican states, especially where violence is directed at migrants. Last, return flows are larger when income growth at destination is lower, consistent with the accumulation of savings as a primary motivation of migrants. Exploring the impact of the crisis, we find evidence of significant changes in the geography of migration flows. Traditional flows are drying up, and new migration corridors are rising, with implications on the composition of the Mexican population in the United States. Although the effect of income on flows in both directions is unchanged by the crisis, the negative effect of violence on out-migration tends to reverse at the end of the period. Overall, this study emphasizes the interest of analyzing disaggregated flows at the infra-country level.

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Notes

  1. We include an interaction term between our GDP variable and a dummy variable equal to 1 for years 2003 to 2012, accounting for the 2003 change of the definition of state-level GDP aggregates by the Mexican Instituto Nacional de Estadística y Geografía (INEGI).

  2. Following Durand et al. (2001), historical migrant-sending states are Aguascalientes, Colima, Durango, Guanajuato, Jalisco, Michoacán, Nayarit, San Luis Potosí, and Zacatecas.

  3. Bertoli and Moraga (2013) referred to this phenomenon as a “multilateral resistance factor,” in analogy with the trade literature (Rose and Van Wincoop 2001). This concept captures the fact that bilateral migration flows between j and k depend not only on the relative attractiveness of origin state j and destination state k but also on the attractiveness of all other destinations. For example, an improvement of employment prospects in Durango may create incentives for individuals from the neighboring state of Zacatecas to migrate to Durango rather than to any U.S. state.

  4. Formally,

    \( \ln \left(GDP\ ME{X}_{j,t-1}\right)= \ln \left({\displaystyle {\sum}_{l\ne j}\frac{w_{jl}}{wj}GD{P}_{l,t-1}}\right), \)

    where GDP l , t − 1 is the GDP per capita of Mexican state l at time t--1; \( {w}_{jl}=\frac{1}{\mathit{\ln}\left({d}_{jl}\right)} \), with d jl the great circle distance between the capital cities of Mexican states j and l; and \( {w}_j={\displaystyle {\sum}_{l\ne j}{w}_{jl}} \), following the trade literature (Xu and Wang 1999).

  5. Weights for distance are the same as for the mean GDP variable described in footnote 4.

  6. By choosing to focus on migration push factors, and including origin and destination × year fixed effects, we cannot capture all time-varying, origin-specific multilateral resistance factors. However, we account for the origin-specific, time-invariant attractiveness of all other destinations as well as the time-varying attractiveness of all destinations common to all origin states.

  7. Details on the survey can be found online (h ttp://www.colef.net/emif/eng/).

  8. The survey design is explained in detail in each yearly report provided by the EMIF team, available online (http://www.colef.mx/emif/publicacionesnte.php).

  9. http://www.colef.net/emif/diseniometodologico.php

  10. As Carriquiry and Majmundar (2013) noted, the fact that survey weights are not adjusted for nonresponse suggests that the EMIF data may underrepresent illegal migrants, who are less likely to respond than legal ones. The resulting bias, if any, is indeterminate. However, we check the robustness of our results by running regressions on undocumented male Mexico-U.S. flows only. The results for undocumented flows (available upon request) are very close to those presented in this article. We are thus quite confident that potential deviations from random sampling due to the original survey design do not significantly bias our results and, in the worst case, would lead us to underestimate the effects or our variables.

  11. The results presented here are robust to using total flow data.

  12. We drop year 2003 for lack of information on the U.S. state of destination.

  13. More information on the Historical Hurricane Tracks can be found online (http://www.csc.noaa.gov/hurricanes/).

  14. Population values are linearly extrapolated for missing years.

  15. Read more at the INEGI website (h ttp://www.inegi.org.mx/).

  16. According to Santos Silva and Tenreyro (2006), estimates obtained with the PPML method are rather insensitive to the restriction of the sample to nonzero flows.

  17. See the online version of this article for color depictions of Figs. 1 and 2.

  18. As noted earlier, we interact the GDP variable with a dummy variable for the post-2003 period in order to account for the change in 2003 in the definition of the GDP aggregate used by the INEGI.

  19. The coefficients on the GDP per capita for historical and new migration states are significantly different at the 1 % level.

  20. These states are Veracruz, Tabasco, San Luis Potosí, and Chiapas, which are regularly mentioned for their high number of kidnappings (Comisión Nacional de Los Derechos Humanos 2011). Tamaulipas is also known for a high level of violence, but its effect is captured by the interaction with the dummy variable for common border.

  21. Given the relatively low proportion of high-educated migrants, resulting in a large proportion of zero cells, estimation with Mexican state and U.S. state × year fixed effects did not converge. However, we are confident that results are not driven by the structure of fixed effects because for total and low-educated migration flows, results are very similar whatever the type of fixed effects included. For the same reason, we could not consider the very small flows of migrants with tertiary education.

  22. Return flows include here air travelers since 2009 and repatriated individuals. However, results are robust to the restriction of our sample to voluntary land returns, with only marginal differences in border and network effects.

  23. Durand et al. (1996) used the presence of a bank in the community as an infrastructure indicator; however, we do not observe enough yearly variation in the number of banks at the federated state level in Mexico.

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Acknowledgments

We thank the Editors and two anonymous referees for their valuable comments. We are also grateful to Simone Bertoli, Eve Caroli, Philippe De Vreyer, Nathalie Picard, and Michele Tuccio for helpful comments and suggestions, and François Libois for providing us with the TRMM satellite rainfall data. We would also like to thank our discussants and participants to the 2015 XV Conference on International Economics (San Sebastian, Spain); the 2015 64th AFSE Congress (Rennes, France); the 2015 32nd “Journées de Micróeconomie Appliquée” (Montpellier, France); the 2015 International Conference on Globalization and Development (Goettingen, Germany); the 2014 5th GRETHA International Conference on Economic Development (Bordeaux, France); the 2014 6th Edition of the Summer School in Development Economics, University of Salerno (Ascea, Italy); and audiences to seminar presentations at THEMA (University of Cergy-Pontoise) and CES (Paris 1 University). This research was conducted as part of the project Labex MME-DII (ANR11-LBX-0023-01). TRMM data were acquired as part of the Tropical Rainfall Measuring Mission. The algorithms were developed by the TRMM Science Team. The data were processed by the TRMM Science Data and Information System (TSDIS) and the TRMM office; they are archived and distributed by the Goddard Distributed Active Archive Center. TRMM is an international project jointly sponsored by the Japan National Space Development Agency (NASDA) and the U.S. National Aeronautics and Space Administration (NASA) Office of Earth Sciences.

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Authors and Affiliations

  1. Université Paris-Dauphine, PSL Research University, IRD, LEDa, DIAL, 75016, Paris, France

    Isabelle Chort

  2. THEMA, Université de Cergy-Pontoise, 33 bd du Port, 95011, Cergy-Pontoise, France

    Maëlys de la Rupelle

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  1. Isabelle Chort
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Correspondence to Isabelle Chort.

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Chort, I., de la Rupelle, M. Determinants of Mexico-U.S. Outward and Return Migration Flows: A State-Level Panel Data Analysis. Demography 53, 1453–1476 (2016). https://doi.org/10.1007/s13524-016-0503-9

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