Understanding the Sources of Inequality Throughout the Earnings Distribution

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The proposed project comprises three interconnected subprojects. Project 1 conducts an international comparison of educational and income inequality. It analyzes education inequality across 40 countries using the Survey of Adult Skills data, focusing on factors that impact educational and income disparities. Standard measures like the Gini coefficient and percentile ratios from OECD data are used for income inequality. For education inequality, it defines two measures: (i) "stuck at the bottom," indicating the fraction of children who stay low-educated like their parents, and (ii) "rise to the top," representing those who rise from a low-educated family to become highly-educated. Regressions at the country level explore key factors of educational systems that explain these inequalities.Project 2 uses individual-level longitudinal data from Norway and the UK to understand the influence of geography on inequality. While these countries have differing inequality levels, the trend in inequality is similar, making them comparable. The analysis explores how birthplace and geographic mobility impact overall and geographic inequality, categorizing inequality into explainable and unexplainable factors. It examines factors like area-level, school-level, student-level, and family-level influences on inequality across regions with varying levels of deprivation. Longitudinal data allows for an examination of how these factors change over time.Project 3 focuses on causal mechanisms behind inequality. It employs a "natural experiment" to estimate the relationship between education and inequality. By comparing students exposed to different economic conditions, it assesses the impact of education on inequality. For instance, students' willingness to invest in education is influenced by the state of the economy. Using birth cohorts in the same geographic area, the project compares "lucky" and "unlucky" students to determine how education affects inequality upon entering the labor market. Data on student achievement is used to control for differences in ability levels. This project can be conducted in Norway, the UK, or both, depending on the findings from the earlier projects.