Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/176872 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
ifo Working Paper No. 252
Verlag: 
ifo Institute - Leibniz Institute for Economic Research at the University of Munich, Munich
Zusammenfassung: 
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.
Schlagwörter: 
Equality of opportunity
machine learning
random forests.
JEL: 
D31
D63
C38
Dokumentart: 
Working Paper

Datei(en):
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