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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: ifo Working Paper ; No. 252

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Personal Income, Wealth, and Their Distributions ; Equity, Justice, Inequality, and Other Normative Criteria and Measurement ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
• Thema: Equality of opportunity ; machine learning ; random forests.

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Brunori, Paolo ; Hufe, Paul ; Mahler, Gerszon Daniel
• Ereignis: Veröffentlichung
• (wer): ifo Institute - Leibniz Institute for Economic Research at the University of Munich
• (wo): Munich
• (wann): 2018

Weitere Informationen

• Handle: http://hdl.handle.net/10419/176872
• Letzte Aktualisierung: 20.09.2024, 08:21 MESZ