Abstract
Much of the global evidence on intergenerational income mobility is based on sub-optimal data. In particular, two-stage techniques are widely used to impute parental incomes for analyses of lower-income countries and for estimating long-run trends across multiple generations and historical periods. We propose applying machine learning methods to improve the reliability and comparability of such estimates. Supervised learning algorithms minimize the out-of-sample prediction error in the parental income imputation and provide an objective criterion for choosing across different specifications of the first-stage equation. We use our approach on data from the United States and South Africa to show that under common conditions it can limit the bias generally associated to mobility estimates based on imputed parental income.
Highlights
A large empirical literature in economics focuses on the estimation of the degree of income persistence across generations
We show the usefulness of our methodological approach by testing its performance on the Panel Study of Income Dynamics (PSID), a longitudinal income survey data from the United States
We suggest the use of a machine learning approach to improve the standard two-sample two-stage method for estimating the intergenerational income elasticity in sub-optimal data conditions
Summary
A large empirical literature in economics focuses on the estimation of the degree of income persistence across generations. In high-income countries, and in an increasing number of low and middle-income countries, the new empirical analyses have allowed comparisons of the extent of social mobility across nations with different economic systems and values (Solon 2002; Björklund and Jäntti 2009) as well as over time and space for a subset of countries (Aaronson and Mazumder 2008; Olivetti and Paserman 2015) These comparisons have shown significant variation in the degree of intergenerational income inequality, thereby paving the way for the investigation of the institutional and policy features that can help explain the observed patterns (Blanden 2013; Chetty et al 2014)
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