The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees
Publikation: Working paper › Forskning
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.
Originalsprog | Engelsk |
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Antal sider | 51 |
Status | Udgivet - 2017 |
Navn | DISEI - Università degli Studi di Firenze - Working Papers |
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Nummer | 18 |
Vol/bind | 2017 |
ID: 187243302