GDIM, we do not include in our specification any control variable other than a dummy indicator for the survey in which the data were collected (to control for differences in the data collection process).<sup>6</sup> An additional measure of relative mobility is the intergenerational correlation (IC) in education— the correlation between and . IC is defined similarly to IP but in terms of the standard cc pp deviation of schooling. It measures the increase in standard deviations of a child’s education ii ii ss ss associated with a one standard deviation increase in his or her parents’ education. A higher correlation indicates a lower degree of mobility. **4. Educational mobility and economic development: Empirical framework** To analyze the relationship between educational mobility and economic development, we rely on a framework similar to that of Neidhofer and others (2023). Our main empirical specification is the following: c c c c c c , (7) cc cc xx hh ii where _Yctct_ is the level of income in country YY = ωω + ττ + ββMM _c_ in year + δδ _t_ ; ω are country fixed effects; τ are year fixed XX + δδ HH + δδ II + εε where _Yctct c t_ effects; _Mct_ is the aggregate level of mobility in country _c_ in year _t_ , calculated using maternal education;<sup>7</sup> **_X_** _ct_ , **_H_** _ct_ , and **_I_** _ct_ are vectors of control variables; ε is a residual; and and are parameters to be estimated. As control variables, we include contemporary country-level variables ββ δδ ( **_X_** _ct_ ), such as income inequality (measured by the Gini index) and the log of population size (which we include with a quadratic polynomial). We also include a set of cohort-level variables ( **_H_** _ct_ ), such as the average years of education of the workforce and its standard deviation. These two variables aim to control for different levels and allocations of human capital, respectively. We also include

> 6 Including any control variable that relates to individual or household characteristics would then result in estimates of IP conditional on those characteristics. This could be desirable depending on the objective of the study. In our current setting, where we are interested in comparing the correlation between differences in mobility across countries and differences in country-level economic outcomes, we explicitly do not want to condition the estimates of mobility to individual or household level characteristics.

> 7 Several studies note that mothers’ years of schooling have a stronger effect than father’s years of schooling on the intergenerational educational mobility of both daughters and sons (see Ranasinghe (2015) for Australia; Bjorklund <u>Lindahl, and Plug  (2006)</u> for Sweden; <u>Tansel (2015)</u> for Türkiye; and <u>Azomahou and Yitbarek (2016)</u> for eight countries in Sub-Saharan Africa). However, Lam (1999) and Girdwood and Leibbrandt (2009) demonstrate that, in South Africa, the link between children and the father’s education is stronger than that of the mother. Also, while maternal education is a stronger determinant of children’s achievements in urban China, paternal education plays a more important role in rural China (Wang et al. 2024). The Life in Transition Survey collects information on the education of both parents. Our study is based on mothers’ years of schooling; we use specifications with the education of the father and the education of both parents for sensitivity analysis.

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