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

Paolo Brunori; Paul Hufe; Daniel Gerszon Mahler

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

Paolo Brunori, Paul Hufe, Daniel Gerszon Mahler

Abstract

We propose a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. In particular, we illustrate how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, they minimize the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.

Original language	English
Number of pages	51
Publication status	Published - 2017

Series	DISEI - Università degli Studi di Firenze - Working Papers
Number	18
Volume	2017

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

Abstract

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