The roots of inequality: estimating inequality of opportunity from regression trees and forests

Paolo Brunori, Paul Hufe and Daniel Mahler

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We propose the use of machine learning methods to estimate inequality of opportunity and to illustrate that regression trees and forests represent a substantial improvement over existing approaches: they reduce the risk of ad hoc model selection and trade off upward and downward bias in inequality of opportunity estimates. The advantages of regression trees and forests are illustrated by an empirical application for a cross-section of 31 European countries. We show that arbitrary model selection might lead to significant biases in inequality of opportunity estimates relative to our preferred method. These biases are reflected in both point estimates and country rankings.

Keywords: equality of opportunity; machine learning; random forests (search for similar items in EconPapers)
JEL-codes: C30 D31 D63 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2023-10-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Citations: View citations in EconPapers (17)

Published in Scandinavian Journal of Economics, 1, October, 2023, 125(4), pp. 900 - 932. ISSN: 0347-0520

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:118220

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