The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees
Paul Hufe and
Gerszon Daniel Mahler
No 252, ifo Working Paper Series from ifo Institute - Leibniz Institute for Economic Research at the University of Munich
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 in equality 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.
Keywords: Equality of opportunity; machine learning; random forests. (search for similar items in EconPapers)
JEL-codes: D31 D63 C38 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big and nep-eur
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Working Paper: The roots of inequality: Estimating inequality of opportunity from regression trees (2018)
Working Paper: The roots of inequality: estimating inequality of opportunity from regression trees (2018)
Working Paper: The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees (2017)
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