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The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees and Forests

Paolo Brunori (), Paul Hufe () and Daniel Gerszon Mahler ()
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Paolo Brunori: University of Florence
Paul Hufe: University of Bristol
Daniel Gerszon Mahler: World Bank

No 14689, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In this paper we propose the use of machine learning methods to estimate inequality of opportunity. We illustrate how our proposed methods—conditional inference regression trees and forests—represent a substantial improvement over existing estimation approaches. First, they reduce the risk of ad-hoc model selection. Second, they establish estimation models by trading 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 may 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. Our results illustrate the practical importance of leveraging machine learning algorithms to avoid giving misleading information about the level of inequality of opportunity in different societies to policymakers and the general public.

Keywords: equality of opportunity; machine learning; random forests (search for similar items in EconPapers)
JEL-codes: C38 D31 D63 (search for similar items in EconPapers)
Pages: 84 pages
Date: 2021-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf and nep-ore
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Citations: View citations in EconPapers (2)

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