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

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 ine...

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Bibliographic Details
Published inThe Scandinavian journal of economics Vol. 125; no. 4; pp. 900 - 932
Main Authors Brunori, Paolo, Hufe, Paul, Mahler, Daniel
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.10.2023
Subjects
Bias
Forests
Inequality
Risk reduction
Trees
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Summary: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.
ISSN:0347-0520
1467-9442
DOI:10.1111/sjoe.12530