Abstract
How does intergenerational educational mobility change under educational expansion? This paper examines this question in Mexico, which enacted two important school expansion plans between 1959 and 1992. Using the 2011 Mexican Social Mobility Survey, I analyze how intergenerational mobility changes under different phases of expansion reform, and how do these trends vary according to the particular stage of the schooling process. Main findings indicate that mobility patterns are not stalled across cohorts, as reproduction theories predict. However, they do not reflect equalization at all levels of education either, as modernization hypotheses anticipate. Expansion reforms, especially the “11-year plan,” are associated with positive trends in mobility in primary and lower-secondary schooling, but also with a decrease in intergenerational mobility at higher levels of education. Thus, these findings are consistent with the maximally maintained inequality hypothesis.
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Notes
The countries included in the study were the U.S, the Federal Republic of Germany, England and Wales, Italy, Switzerland, the Netherlands, Sweden, Japan, Taiwan, Poland, Hungary, and Czechoslovakia.
Subsequent theories have also incorporated the role of qualitative differences within each particular level of schooling as a mechanism through which upper-class families ensure their advantage in the educational attainment process (Lucas 2001). Yet this mechanism is beyond the scope of the present article.
Primary education comprises 6 years. Secondary education consists of other 6 years, which include three years of lower secondary and three years of upper secondary.
Specifically, those who answered “Pre-school or Pre-kinder” or “None/I did not go to school” were classified as having “Less than Primary.” Moreover, those who responded “General Secondary” and “Technical Secondary” were classified as having “Some Secondary,” as these grades correspond to Lower-secondary education according to the ISCED standards. Then, “General Preparatory” or “Technical Preparatory” was considered as “Complete Secondary.” Respondents who answered “Technical Education with some Primary” were classified as “Some Secondary” (If the respondent reported less grades than needed to complete this education level or did not have a certificate of completion, their level of education was considered as “Completed Primary”), and those who responded “Technical Education with some Secondary” (If the respondent reported less grades than needed to complete this education level or did not have a certificate of completion, their level of education was considered as “Some Secondary.”) were coded as “Complete Secondary.” Finally, those who responded “Normal” (This category refers to the “Normal School of Education” which trains individuals to become school teachers in Mexico), “Professional,” or “Postgraduate” were classified as having “Some Postsecondary.”
I did not include father’s ISEI in the creation of these profiles, given that this variable was not interacted by birth cohort in the preferred model.
In the case of multiple imputation using chained equations (MICE), Rubin’s recommendation is to include all potentially relevant variables for predicting X in the multiple imputation model (Rubin 1996). The key idea is to use all available information that enhances the prediction of the missing cases, usually this includes the dependent variable of the main analysis. Following these recommendations, the imputation model for these variables included all the predictors of my substantive models, all dependent variables, and all sample design variables (Van Buuren et al. 1999; Little and Rubin 2002). Also, I included a rich set of other measures that theoretically could predict the missingness of these variables, such as age, parental assets, and other socioeconomic characteristics of the parent’s household. This procedure, which included the creation of 10 new datasets, resulted in the imputation of 97% (2080 observations) of the missing cases corresponding to father’s ISEI, 94% (960 observations) of the missing cases in mother’s education, and 96% (1392 observations) of missing cases in father’s education.
Implemented in Stata using the package seqlogit (Buis 2011).
In addition, I also estimated a baseline model that included an interaction term between gender and parent’s education to check whether the role of parental education varies by gender of the offspring. As Table 8 in the Appendix shows, these interactions terms are very small in magnitude and nonsignificant across educational transitions. Indeed, t-tests do not reject the null hypothesis that these coefficients are equal to zero. Thus, I decided not to include an interaction between parental education and gender in the preferred model.
Two methodological remarks need to be made. First, I introduce each interaction term with birth cohorts one at a time. I start with (i) parent’s education, (ii) father’s ISEI, and then (ii) gender (Tables 10, 11 and 12 in the appendix). In the case of parent’s education, most interaction terms are insignificant with some notable exceptions. For primary completion, father’s education by cohort 1 has a positive and statistically significant effect compared to the base category (father’s education by cohort 5). Similarly, for achieving some postsecondary, the interaction between mother’s education by cohort 3 has a positive and statistically significant effect, while father’s education by cohort 3 has a negative and significant effect. In the case of father’s ISEI, interactions are small and insignificant. In contrast, gender by cohort 1 interactions has a sizable and negative effect compared to the base category for almost all transitions. Second, in order to have more parsimonious models, I decided not to include interaction terms between father’s ISEI and birth cohorts as neither of these terms significantly improved model fit (This according to a t test performed for each outcome). The results of the model that includes all interaction terms between family background predictors and gender with birth cohorts are presented in Table 13 in the appendix.
As seen in Fig. 8, point estimates for women born in cohort 1 have extremely high confidence intervals. This is partly because only 12 female respondents from this cohort attained some postsecondary education, which makes this outcome a rare event. Given that these predictions might be especially unstable, I decided not to consider them as the initial benchmark to test if they were statistically significant differences between cohorts.
Given that the seqlogit package in Stata does not allow for the inclusion of survey strata, estimates for the model with no unobserved heterogeneity contains small differences with the estimates of our preferred model in Table 4.
The exception being those in the secondary completion model, where cohort 1 loses significance in the last scenario.
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Acknowledgements
I specially want to thank Mike Hout, David Greenberg, Florencia Torche, and Lawrence Wu for their valuable feedback and guidance. I also thank Fundación Espinosa Rugarcía (ESRU) for providing me the 2011 Mexican Social Mobility Survey (MSMS). Research reported in this publication was supported by The Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number P2CHD047879. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Appendix
Appendix
See Tables 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 and 19.
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Urbina, D.R. Intergenerational Educational Mobility During Expansion Reform: Evidence from Mexico. Popul Res Policy Rev 37, 367–417 (2018). https://doi.org/10.1007/s11113-018-9466-4
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DOI: https://doi.org/10.1007/s11113-018-9466-4