The Roots of Inequality : Estimating Inequality of Opportunity from Regression Trees
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 inequ...
| Main Authors: | , , |
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| Language: | English |
| Published: |
World Bank, Washington, DC
2018
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/502141519144475516/The-roots-of-inequality-estimating-inequality-of-opportunity-from-regression-trees http://hdl.handle.net/10986/29410 |
| Summary: | 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. |
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