Upward and Downward Bias When Measuring Inequality of Opportunity - Paolo Brunori; Vito Peragine; Laura Serlenga (Institute of Labor Economics (IZA), 2018)
Estimates of the level of inequality of opportunity have traditionally been interpreted as lower bounds due to the downward bias resulting from the partial observability of circumstances that affect individual outcome. We show that such estimates may also suffer from upward bias as a consequence of sampling variance. The magnitude of the latter distortion depends on both the empirical strategy used and the observed sample. We suggest that, although neglected in empirical contributions, the upward bias may be significant and challenge the interpretation of inequality of opportunity estimates as lower bounds. We propose a simple criterion to select the best specification that balances the two sources of bias. Our method is based on cross-validation and can easily be implemented with survey data.To show how this method can improve the reliability of inequality of opportunity measurement, we provide an empirical illustration based on income data from 31 European countries. Our evidence shows that estimates of inequality of opportunity are sensitive to model selection. Alternative specifications lead to significant differences in the absolute level of inequality of opportunity and to the re-ranking of a number of countries. This confirms the need for an objective criterion to select the best econometric model when measuring inequality of opportunity.
Keywords: C52; D3; D63; 330; inequality of opportunity; model selection; cross-validation; variance-bias trade-off