To evaluate the distributional impact of remittances in origin communities, prior research studied how migrants’ selectivity by wealth varies with migration prevalence in the community or prior migration experience of the individual. This study considers both patterns; it examines selectivity separately in low- and high-prevalence communities and for first-time and repeat migrants. Based on data from 18,042 household heads in 119 Mexican communities from the Mexican Migration Project, the analyses show that (1) first-time migrants in low-prevalence communities come from poor households, whereas repeat migrants in high-prevalence communities belong to wealthy households; and (2) higher amounts of remittances reach wealthy households. These results suggest that repeat migration and remittances may be mechanisms for wealth accumulation in the study communities. Descriptive analyses associate these mechanisms with increasing wealth disparities between households with and without migrants, especially in high-prevalence communities. The study, similar to prior findings, shows the importance of repeat migration trips, which, given sustained remittances, may amplify the wealth gap between migrants and nonmigrants in migrant-sending communities. The study also qualifies prior findings by differentiating between low- and high-prevalence communities and observing a growing wealth gap only in the latter.
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Detailed information about the MMP is available online (http://mmp.opr.princeton.edu). The five communities surveyed as part of the pilot study in 1982 are excluded, as are the data collected nonrandomly from a small number of migrants in the United States.
Municipality or state average prices are used for communities with missing values.
The Ejido indicator is missing for five communities. We set the indicator to 0 in these communities to conserve sample size; however, the results remain identical if these communities are excluded from analysis.
We tested the robustness of our findings to two alternative measures of community distance to the United States: (1) distance to the closest international airport in Mexico, and (2) distance to the closest popular border crossing city, which includes Tijuana or El Paso, TX, prior to 1993; and Laredo, TX, El Centro, CA, and Nogales, AZ, thereafter, according to Orrenius (2006) and Singer and Massey (1998). The former measure takes into account the transportation networks in Mexico, and the latter considers the shifting enforcement zones in the United States. Both measures led to similar results in all models (available upon request).
Because wage in destination is a critical determinant of remittance behavior, migrants with missing wage information (about one-third of all migrants) are not used in the analysis. Alternative analysis with all the migrants, and without the wage variable, leads to similar wealth coefficient estimates (available upon request).
The results suggest the potential endogeneity of wealth indicators to migration or remittance outcomes, which may bias the empirical conclusions. To address this issue, we lag the household wealth indicators by one year. This approach does not solve the endogeneity problem if current migration decisions are correlated with past migration, which affects household wealth in the past, or if there are omitted variables related to both wealth and migration. We test for this possibility with a procedure suggested by Spencer and Berk (1981). We estimate two wealth equations (for land and property indicators, separately) with exogenous regressors (past rainfall and real interest rates, which are likely to affect wealth). We then add the residuals from these equations to the migration and remittance models as extra regressors. The coefficients for the regressors are jointly insignificant in both the migration (F statistic = 1.89, p = .39) and remittance (F statistic = 0.16, p = .85) models, and the null hypothesis that the wealth indicators are orthogonal to the errors cannot be rejected. These results suggest that the lagged wealth indicators can be treated as exogenous to current migration and remittance decisions. Crucially, this treatment does not preclude an association between wealth and past migration and remittances, but such an association does not seem to bias our estimates.
Communities were surveyed in different years by the MMP; therefore, our sample contains a different number of communities in each year. The number of communities is 119 in 1975, drops to 85 in 1995, and then drops further to 48 by 2000. Therefore, we restrict this analysis to the 1975–1995 period, during which the majority of the communities are observed consistently.
The estimates in Table 5 show that remittances decrease by 6 % per year that a migrant spends in destination. These estimates control for earnings differences among migrants, and therefore are not inconsistent with the raw comparisons presented here.
Because of the retrospective nature of the data, older—and consequently wealthier—individuals are observed in later years. To assure that the same age group is compared across time, we restrict the analysis to 25- to 45-year-olds in each year.
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This research was funded by the Clark and Milton Funds at Harvard University, and a Junior Faculty Synergy Semester Grant from the Weatherhead Center for International Affairs. I thank Fatih Unlu for suggesting some of the methodology of the paper, and Jeff Blossom at the Center for Geographic Analysis at Harvard for compiling the geographic data. I am grateful to Peter Azoulay, Cedric Deleon, Frank Dobbin, Roberto Fernandez, Andrew Foster, Stine Grodal, Alya Guseva, Emily Heaphy, William Kandel, Yaojun Li, Nancy Luke, Peter Marsden, Sigrun Olafsdottir, Robert Putnam, Kenneth Wachter, Arnout van de Rijt, Mary Waters, and Bruce Western for helpful advice.
Appendix A: Testing the Validity of the Distance-Prevalence Interaction as an Instrument for Migration
The first column of Table 6 presents the marginal effects of distance by community migration prevalence estimated in a probit model of U.S. migration. Migration prevalence is defined as the proportion of individuals who ever migrated in a community. (The Pearson’s correlation between distance and prevalence is only –.02.) Showing a nonlinear pattern, distance to border increases the odds of migrating, while its squared term decreases it. The effect of distance also depends on the migration prevalence in the community. For individuals in zero-migration-prevalence communities, for example, increasing the distance to border from 0 to 1,000 km decreases the probability of migrating approximately sevenfold. For individuals in medium-prevalence communities, where about 13 % of individuals have migrated, a similar increase in distance decreases the probability of migrating only threefold. As expected, the negative effect of distance is concentrated among individuals living in communities with low migration prevalence and suggests the validity of the interaction term for explaining variation in migration. As an alternative check for instrument validity, we tested for weak instruments by excluding the distance-prevalence interaction from the migration model. The resulting F statistic was 115.6 (df = 487,225), more than 10-fold the lower bound of 10 required to reject the hypothesis of weak instruments (Staiger and Stock 1997).
To provide evidence for instrument exogeneity, which is not directly testable, we examine the partial correlations between the instrument and migrants’ U.S. wages, which are strongly correlated with remittances. If the instrument is associated with the unobserved determinants of remittances, we would expect it to be correlated with the observed measures, such as U.S. wages, as well. The regression results in the second model of Table 6 show that distance to border and migration prevalence in community have statistically insignificant associations with migrants’ U.S. wages. Overall the evidence in Table 6 suggests the distance-prevalence interaction as a valid source of identification in the Heckman model.
Appendix B: Distribution of Household Wealth and Remittances in Mexico
Figure 2 shows histograms for logarithms of land, properties, remittances, and savings (nonzero values only), which are approximately normal in distribution.
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Garip, F. Repeat Migration and Remittances as Mechanisms for Wealth Inequality in 119 Communities From the Mexican Migration Project Data. Demography 49, 1335–1360 (2012). https://doi.org/10.1007/s13524-012-0128-6
- Wealth inequality