The Roots of Inequality
This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods minimize the risk of arbitrary and ad hoc model selection. Second, they provide a standardized way to trade 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.
| Main Authors: | , , |
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| Format: | Working Paper biblioteca |
| Language: | English |
| Published: |
World Bank, Washington, DC
2018-02
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| Subjects: | INEQUALITY, REGRESSION ANALYSIS, EQUALITY OF OPPORTUNITY, MACHINE LEARNING, LIVING CONDITIONS, POVERTY MEASUREMENT, |
| Online Access: | http://documents.worldbank.org/curated/en/502141519144475516/The-roots-of-inequality-estimating-inequality-of-opportunity-from-regression-trees https://hdl.handle.net/10986/29410 |
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