Abstract
While academic and policy circles have given much attention to the assimilatory experiences of Mexican immigrants in the United States, less is known about those who stay behind—an especially unfortunate oversight given the increasing number of Mexican youth with migrant family members. Of the studies on this topic, most have sought to identify the effect that migration has on youths’ migratory and educational aspirations, often using qualitative methods in individual sending communities. The present article supplements this research in two ways: (1) in addition to assessing educational outcomes, the scope of the analysis is expanded to include nonmigrants’ interaction with another homeland institution of upward mobility: the labor market; and (2) using a large demographic data set, statistical techniques are employed to adjust for unobserved selectivity into the migrant family-member population, thus accounting for a potentially serious source of bias. The results suggest that youth in migrant-sending families are less likely to complete the educational transitions leading up to postsecondary school and have a lower probability of participating in the local economy. The results also indicate that unobserved factors play a “nonignorable” role in sorting youth into migrant and nonmigrant families.
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Notes
Hanson and Woodruff (2003), for example, found that family member migration is influenced by some of the same hard-to-observe factors that contribute to the educational attainments of nonmigrant children. It is unclear whether the same is true for youths’ decisions concerning labor force participation.
In theory, for a youth to count as a migrant and enter the analysis sample, he or she would have to (1) temporarily out-migrate; (2) return to Mexico prior to enumeration; and (3) do so before turning age 19. Although the Mexican census data offer no way to formally assess how often this situation occurs, other analyses using different data sources suggest that it is infrequent. Durand et al. (2001), for example, found that more than 90% of temporary Mexican migrants are older than age 18 upon their initial departure. Thus, if 10% of temporary Mexican migrants are 18 and younger (thereby meeting the first criterion), one can presume that an even smaller fraction of temporary Mexican migrants satisfy the first and second criteria.
Auxiliary analyses indicated that only a small proportion of Mexican youth start their own household by age 18.
Again, because individuals are increasingly likely to themselves engage in migratory behavior as they approach adulthood, these analyses are not able to assess the final transition in Mexico’s education system (e.g., the move from secondary to university-level studies). This is a regrettable but necessary restriction in scope.
The four levels of total family income—a variable that does not include earnings that children themselves contribute, nor income derived from foreign remittances—correspond to quartiles in the income distribution.
The elements in x and w need not be entirely disjoint. Thus, in addition to specifying exclusion restrictions, the right-hand side of the selection equation also contains indicators of parental education, age structure and number of siblings, urban-rural status, dwelling characteristics and amenities, and indigenous group membership. See supplementary appendix table for more details.
In addition to examining empirical associations between the outcomes of interest and the pretreatment variables at the bivariate level, I regressed each of my dependent variables onto the socioeconomic, demographic, geographic, and family compositional variables included in x, as well as the additional predictors in w. In all three models, the resulting parameter estimates for the pretreatment variables were substantively trivial in magnitude. These results are available upon request.
By construction, z and λ in Eq. 7 are inversely related, such that a larger estimated value for z implies a smaller λ.
A Huber-White sandwich estimator was used in the second stage to correct for heteroskedasticity (White 1980).
The standard errors generated by the switching regressions may be deflated because of the presence of an estimated quantity (the inverse Mills’ ratio) in the second-stage probit models. Because of the unusually large sample size, however, it is unlikely that any such bias would meaningfully alter my substantive conclusions.
I evaluated Eq. 10 by using the mean values of the k characteristics in x and w.
In supplementary analyses, I estimated average treatment effects on the treated separately for boys and girls. For both educational transitions, the resulting estimates did not differ significantly by gender. Although not completely unsurprising, this result is consistent with at least some previous work (Kandel 2003; Kandel and Kao 2001), and may reflect, at least in part, Mexico’s closing gender gap in compulsory education.
Given what is known about the gender composition of the Mexican labor force, one might reasonably surmise that this pattern reflects heterogeneity in the relationship between family member migration and youths’ tendency to engage in paid labor, most notably between nonmigrant boys and girls. That is, females would seem more likely to be situated near the origin of the horizontal axis, and thus be relatively less susceptible to the influence of family member migration. An auxiliary by-gender analysis (not shown) bore out this speculation. Although the family member migration effect was negative for both boys and girls, the index sufficient estimate obtained for the female subsample was smaller in magnitude, resulting in a flatter and less pronounced curve. As mentioned, this finding is not entirely unexpected, particularly given the low rates at which females participate in the Mexican economy and the large differences between boys and girls in terms of their domestic roles and responsibilities (Levison et al. 2001).
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Acknowledgement
Earlier versions of this paper were presented at the 2006 meetings of the American Sociological Association and the 2007 meetings of the Research Committee on Social Stratification and Mobility (RC28). I am grateful to John Robert Warren, Scott Eliason, Jennifer C. Lee, Elaine M. Hernandez, Chris Uggen, and Teresa Swartz for their helpful comments and suggestions; and to the Minnesota Population Center for its invaluable research support. All errors and omissions, however, are solely my responsibility.
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Parameter estimates for the selection equation predicting family-member migration (DOC 24.5 kb)
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Halpern-Manners, A. The Effect of Family Member Migration on Education and Work Among Nonmigrant Youth in Mexico. Demography 48, 73–99 (2011). https://doi.org/10.1007/s13524-010-0010-3
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DOI: https://doi.org/10.1007/s13524-010-0010-3