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

IF 1.3 4区 经济学 Q3 ECONOMICS Scandinavian Journal of Economics Pub Date : 2023-08-14 DOI:10.1111/sjoe.12530
Paolo Brunori, Paul Hufe, Daniel Mahler
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引用次数: 1

Abstract

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.
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不平等的根源:从回归树和森林估计机会不平等*
我们提出使用机器学习方法来估计机会不平等,并说明回归树和森林代表了对现有方法的实质性改进:它们降低了临时模型选择的风险,并在机会不平等估计中权衡了向上和向下的偏差。通过对31个欧洲国家的横截面的实证应用,说明了回归树和森林的优点。我们表明,相对于我们首选的方法,任意模型选择可能导致机会估计不平等的显著偏差。这些偏差反映在点数估计和国家排名中。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
52
期刊介绍: The Scandinavian Journal of Economics is one of the oldest and most distinguished economics journals in the world. It publishes research of the highest scientific quality from an international array of contributors in all areas of economics and related fields. The journal features: - Articles and empirical studies on economic theory and policy - Book reviews - Comprehensive surveys of the contributions to economics of the recipients of the Alfred Nobel Memorial Prize in Economics - A special issue each year on key topics in economics
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