THE ECONOMETRICS OF INTERGENERATIONAL MOBILITY

SUMMARY

This project studies the intergenerational mobility of well-being, which is arguably the most important dimension of inequality with huge policy implications.  This type of inequality stems from how the socio-economic position of the children as they grow up to become adults relates to that of their parents. Put differently, it measures the degree of fluidity between the parental socio-economic status and offspring’s socio-economics status as adults.  Broadly defined, the overall objectives of the project focus on the development and application using real data of a novel class of intergenerational econometric models of poverty traps to consider how the joint evolution of income, capabilities, and social influences can generate poverty traps. The challenge of separately identifying the effects of family and social influences will be mediated by considering the effects of shocks such as family shocks. The objectives of this project revolve around three interrelated ideas. First, when one examines the intergenerational mobility of well being it is not sufficient to examine how income is transmitted from parents to offsprings, but also consider how the skills of parents are transmitted to the skills of the children, which in turn, these capabilities/skills joined with incentives and social environment determine child outcomes, which ultimately factor into income. Second, since the timing of parental investments and shocks matters for the long-run outcomes of the children, the analysis must examine how the trajectory of parent’s outcomes is transmitted to the trajectory of a child’s outcomes. Third, linear models of the standard empirical approach are too restrictive because they ignore nonlinearities suggested by theoretical models of credit constraints or neighborhood effects that can generate poverty traps or persistent poverty under certain conditions. The main reason why we are interested in intergenerational mobility is that it provides insights into the equality opportunity among individuals, which means that outcome inequalities are not defensible when a person is not responsible for them and hence individuals should be compensated to “level the playing field”.  Understanding the role of social influences on poverty traps is also important because it provides a deeper understanding of the intergenerational transmission mechanism. 

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The following six tasks toward the objectives of the project have been performed. First, a new class of econometric models was developed that allows for threshold effects in both private and social incentives of the intergenerational transmission mechanism of socioeconomic status. Threshold like relationships between parent and offspring income can be produced by the theoretical intergenerational mobility models of credit constraints or neighborhood effects.  In particular, I propose a threshold spatial regression autoregressive model and develop estimation and inference.   Second, we proposed an econometric strategy to identify the genetic and environmental effects in twin studies via the prism of an economic environment of volitional decision making of the parents to invest in their children that describe the various technologies and preferences of the parents.  Third, the datasets for empirical work were constructed based on the National Longitudinal Survey of the Youth (NLSY), Panel Study of Income Dynamics (PSID), and US Census data. Three different but complementary datasets are constructed that include measures of the parent's and child's family income and their characteristics such as age, gender, education, family type. Fourth, using NLSY data we documented that the observed patterns of economic mobility exhibit heterogeneity across socioeconomic groups and whether the nature of the heterogeneity can be explained by different levels of persistence in the intergenerational transmission of cognitive abilities and non-cognitive skills across socioeconomic groups. In doing the varying coefficient model was employed to estimate nonparametric (local) measures of intergenerational mobility (as measured by income and schooling attainment), cognitive abilities, and noncognitive skills as smooth functions of log parental permanent income.  Fifth, using the dataset the interplay between social and family influences is investigated by estimating a large number of models including the threshold spatial regression autoregression and the smooth varying coefficient spatial autoregression. The findings documented the presence of nonlinearities and spatial externalities in the intergenerational mobility which are consistent with social influences and credit market constraints as mechanisms of transmission of the status of parents to children. Sixth, using the third dataset I investigated the role of credit constraints and family influences by focusing on the role of trajectories of exposures during childhood and young adulthood on intergenerational transmission of well-being. In doing so, a functional data analysis approach was employed that examines the role of the timing of parental income during their childhood or young adulthood years in the US. The findings showed that parental investments are more productive in the early and late childhood or young adulthood, highlighting the importance of the timing of human capital investments. Furthermore, we uncovered evidence of heterogeneity due to socioeconomic status and family structure that suggests that the timing of the shocks for the disadvantaged children is an important factor for their upward mobility. Finally, we look into the cross-country experiences of mobility by studying the relationship between mobility and inequality, the so-called, the Great Gatsby Curve. 

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This project has made progress beyond the state of the art by doing several things. First, it has contributed to the literature of social interactions by developing estimation and inference for an econometric model of intergenerational mobility that allows for threshold type nonlinearities as well as for both family and social influences.   Second, it has provided evidence consistent with both social influences and credit constraints as mechanisms of intergenerational transmission of socioeconomic status. Furthermore, it provided evidence that the intergenerational persistence in cognitive abilities explains the heterogeneity in intergenerational mobility. Finally, using functional regressions it provided evidence that the timing of the shocks related to socioeconomic status and family structure can have a key role in the upward mobility of individuals, especially for disadvantaged children. The findings suggest that the standard linear IGE model is misspecified and can lead to a misleading inference. This may help explain the finding that the statistical evidence of social influences based on linear models is generally weak. Hence, policymakers should be cautious and instead, consider models that allow for the nonlinear effects of family and social influences. What this means is that one cannot evaluate a large policy intervention by a proportional scaling up of the effects found from small policy intervention. More generally, the methods and the techniques developed to identify poverty traps and other mechanisms that determine social mobility will be of independent interest and can be applied to any problems in economics, epidemiology, sociology, and finance.  The results will spark further interest in the academic community to study intergenerational mobility in view of the availability of new datasets. The research results will have an impact on European society and economy.