Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/176872
Authors: 
Brunori, Paolo
Hufe, Paul
Mahler, Gerszon Daniel
Year of Publication: 
2018
Series/Report no.: 
Ifo Working Paper 252
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 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.
Subjects: 
Equality of opportunity
machine learning
random forests.
JEL: 
D31
D63
C38
Document Type: 
Working Paper
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