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 inequality of opportunity. First, the new methods minimiz...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C
The World Bank
2018
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| Schriftenreihe: | World Bank E-Library Archive
|
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (35 Seiten) |
| DOI: | 10.1596/1813-9450-8349 |
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| 520 | |a 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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| discipline | Wirtschaftswissenschaften |
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| doi_str_mv | 10.1596/1813-9450-8349 |
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| publisher | The World Bank |
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| spellingShingle | Brunori, Paolo The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees |
| title | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees |
| title_auth | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees |
| title_exact_search | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees |
| title_exact_search_txtP | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees |
| title_full | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees Brunori, Paolo |
| title_fullStr | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees Brunori, Paolo |
| title_full_unstemmed | The Roots of Inequality Estimating Inequality of Opportunity from Regression Trees Brunori, Paolo |
| title_short | The Roots of Inequality |
| title_sort | the roots of inequality estimating inequality of opportunity from regression trees |
| title_sub | Estimating Inequality of Opportunity from Regression Trees |
| url | https://doi.org/10.1596/1813-9450-8349 |
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