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.

Estimating intergenerational income mobility on sub-optimal data: a machine learning approach / Bloise F.; Brunori P.; Piraino P.. - In: THE JOURNAL OF ECONOMIC INEQUALITY. - ISSN 1569-1721. - STAMPA. - 19:(2021), pp. 643-665. [10.1007/s10888-021-09495-6]

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

Brunori P.;
2021

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.
2021
19
643
665
Bloise F.; Brunori P.; Piraino P.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1284109
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