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Demography
Published in: Demography 5/2016

13-09-2016

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

Authors: Isabelle Chort, Maëlys de la Rupelle

Published in: Demography | Issue 5/2016

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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.
previous article Changes in the Transnational Family Structures of Mexican Farm Workers in the Era of Border Militarization
next article Premigration School Quality, Time Spent in the United States, and the Math Achievement of Immigrant High School Students

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Appendix
Available only for authorised users
Footnotes
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).
 
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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Metadata
Title
Determinants of Mexico-U.S. Outward and Return Migration Flows: A State-Level Panel Data Analysis
Authors
Isabelle Chort
Maëlys de la Rupelle
Publication date
13-09-2016
Publisher
Springer US
Published in
Demography / Issue 5/2016
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-016-0503-9

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