The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees
Research output: Working paper › Research
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 inequality 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.
Original language | English |
---|---|
Number of pages | 51 |
Publication status | Published - 2017 |
Series | DISEI - Università degli Studi di Firenze - Working Papers |
---|---|
Number | 18 |
Volume | 2017 |
ID: 187243302