首页 | 本学科首页   官方微博 | 高级检索  
     


Upper and Lower Bound Estimates of Inequality of Opportunity: A Cross-National Comparison for Europe
Authors:Rafael Carranza
Affiliation:Institute for New Economic Thinking at the Oxford Martin School, Department of Social Policy and Intervention, and Nuffield College, University of Oxford
Abstract:I provide lower and upper bound estimates of inequality of opportunity (IOp) for 32 European countries, between 2005 and 2019. Lower bound estimates use machine learning methods to address sampling variability. Upper bound estimates use longitudinal data to capture all-time invariant factors. Across all years and countries, lower bound estimates of IOp account from 6 percent to 60 percent of total income inequality, while upper bound estimates account from 20 percent to almost all income inequality. On average, upper bound IOp saw a slight decrease in the aftermath of the Great Recession, recovering and stabilizing at around 80 percent of total inequality in the second half of the 2010s. Lower bound estimates for 2005, 2011, and 2019 show a similar pattern. My findings suggest that lower and upper bound estimates complement each other, corroborating information and compensating each other's weaknesses, highlighting the relevance of a bounded estimate of IOp.
Keywords:equality of opportunity  inequality  longitudinal data  machine learning
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号