Estimating intergenerational income mobility on sub-optimal data: a machine learning approach

Francesco Bloise, Paolo Brunori () and Patrizio Piraino
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Paolo Brunori: London School of Economics

The Journal of Economic Inequality, 2021, vol. 19, issue 4, No 2, 643-665

Abstract: 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.

Keywords: Intergenerational income mobility; Machine learning; Two-sample two-stage least squares (search for similar items in EconPapers)
JEL-codes: D31 D63 J62 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (13)

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Working Paper: Estimating intergenerational income mobility on sub-optimal data: a machine learning approach (2020)
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DOI: 10.1007/s10888-021-09495-6

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