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Occupational Dualism and Intergenerational Educational Mobility in the Rural Economy: Evidence from China and India
... For example, households living in remote areas could experience intergenerational poverty because schooling opportunities might not exist for generations. Public policy to scale up education could then potentially reduce inequality by reducing returns to schooling as well as improving earnings for those in the bottom of the income distribution (see, for example, Brunori et al., 2013;Emran et al., 2020). Table 2 reports results from pulled regression of asset-based Gini coefficient on share of employment and value-added in the three broad sectors. ...
This paper examines how inequality could be tackled through structural transformation using unit record data from the Demographic and Health Surveys (DHS) for Africa. Results suggest inequality between countries tends to be higher when the share of labour employed or value-added in the agriculture sector is higher, while no association is observed for industry and services sectors contributions to GDP or employment. Within-country inequality however tends to be strongly affected by structural change. A 1 standard deviation growth in the movement of labour from low- to high-productivity sectors could decrease overall inequality by 0.5% and inequality of opportunity by 1.1%. Results from other data sources strongly support these findings suggesting that positive structural transformation could lead to sustained reduction in inequality in Africa. Other factors correlated strongly with inequality reduction include human capital, which tend to have large and significant income or asset reducing effect in Africa, particularly at higher level of education, while the pace of urbanisation exacerbates it incidence.
... Since ∂θ 1 ∂β 1 > 0, the eects of economic factors on IGC bear the same sign as IGRC. For example, Becker-Tomes model implies that ∂β 1 ∂R p > 0 where R p denotes returns to education for parents (see, for example, Emran et al. (2020b)). We can nd out the eects of higher parental returns to education on IGC as follows (assume σ 2 > 0): ...
A large empirical literature on intergenerational educational mobility measures relative mobility by the slope of a conditional expectation function (CEF) relating children's education to parental education. Three measures are widely used: intergenerational regression coefficient (IGRC) with years of schooling as the indicator of educational attainment, intergenerational correlation (IGC) when years of schooling is normalized by its standard deviation, and intergenerational rank-rank slope (IRRS) when schooling ranks in a generation is adopted. The existing evidence suggests that conclusions from IGRC vs. IGC vary substantially, but there is no systematic evidence on whether the IRRS estimates also lead to conflicting conclusions. Using data free of coresidency bias from three developing countries with 42 percent of world population in 2000 (China, India, Indonesia), we provide evidence that the IRRS estimates may lead to dramatically different conclusions about spatial heterogeneity (rural/urban) and evolution across cohorts, especially when the mobility CEF is concave or convex. The rank-rank CEF is consistently more convex (or less concave) compared to the other two CEFs. When different measures lead to conflicting conclusions it is not clear how to interpret the evidence and advise the policymakers. We develop a simple approach to interpret the IGC estimate in terms of the Becker-Tomes model that provides a foundation for a comparative study of IGC vs. IGRC. We find that the idiosyncratic component of children's schooling variance unrelated to the family background plays an important role in IGC. The elasticity of IGC w.r.t IGRC is less than 1 implying that the IGC estimates are less responsive to changes in economic forces (such as credit constraint and returns to education) raising questions about the suitability of IGC for understanding the role of changing economic conditions in intergenerational mobility. This also provides an explanation for the puzzle in the literature that IGRC estimates across cohorts show substantial improvements, but the IGC estimates suggest no significant changes. When the mobility CEF is quadratic, by construction, the quadratic coefficient of the CEF for IGC is much larger than that of the CEF for IGRC. This implies that IGC estimates mechanically generate much stronger persistence at the top (for convex) or bottom (for concave) of the distribution. We report evidence that, unlike income, calculating schooling ranks by mid-rank method may fail to neutralize the effects of changing inequality across generations, making IGC a preferable measure for tackling changes in cross-sectional inequality. It is difficult to interpret IRRS in terms of the Becker-Tomes model. The inequality of opportunity approach (Roemer (1998)) suggests that policy advice should focus on the causal effects of policies on the influence of inherited circumstances on children's education which is captured by IGRC. From this perspective, a policy such as school construction or trade liberalization should be considered effective in improving relative educational mobility if the causal effect on IGRC is negative even when a policy fails to affect IRRS significantly.
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