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

Brunori, Paolo ORCID: 0000-0002-1624-905X, Hufe, Paul and Mahler, Daniel (2023) The roots of inequality: estimating inequality of opportunity from regression trees and forests. Scandinavian Journal of Economics, 125 (4). 900 - 932. ISSN 0347-0520

Text (The roots of inequality estimating) - Published Version Available under License Creative Commons Attribution. Download (619kB)

• Author
• Scopus publication

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.

Item Type:	Article
Official URL:	https://onlinelibrary.wiley.com/journal/14679442
Additional Information:	© 2023 The Authors
Divisions:	International Inequalities Institute
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences
JEL classification:	C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C30 - General D - Microeconomics > D3 - Distribution > D31 - Personal Income, Wealth, and Their Distributions D - Microeconomics > D6 - Welfare Economics > D63 - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
Date Deposited:	21 Feb 2023 12:15
Last Modified:	20 Dec 2024 00:47
URI:	http://eprints.lse.ac.uk/id/eprint/118220

Actions (login required)

View Item