Bloise, Francesco, Brunori, Paolo and Piraino, Patrizio (2021) Estimating intergenerational income mobility on sub-optimal data: a machine learning approach. Journal of Economic Inequality, 19 (4). pp. 643-665. ISSN 1569-1721
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Official URL: | https://www.springer.com/journal/10888 |
| Additional Information: | © 2021, The Author(s) |
| Divisions: | International Inequalities Institute |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science H Social Sciences > HG Finance H Social Sciences > HC Economic History and Conditions |
| Date Deposited: | 30 Nov 2021 00:08 |
| Last Modified: | 17 Apr 2024 17:30 |
| URI: | http://eprints.lse.ac.uk/id/eprint/112762 |
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