Understanding the Sources of Inequality Throughout the Earnings Distribution

拟议项目包括三个相互关联的分项目。项目1对教育和收入不平等进行了国际比较。它使用成人技能调查数据分析了40个国家的教育不平等，重点关注影响教育和收入差距的因素。基尼系数和经合组织数据中的百分位数比率等标准措施用于衡量收入不平等。对于教育不平等，它定义了两个衡量标准：（i）"卡在底部"，表示像父母一样受教育程度低的儿童的比例，和（ii）"上升到顶部"，表示那些从受教育程度低的家庭上升到受教育程度高的人。在国家一级的回归探讨教育系统的关键因素，解释这些不平等。项目2使用个人层面的纵向数据从挪威和英国了解地理对不平等的影响。虽然这些国家的不平等程度不同，但不平等趋势相似，因此具有可比性。分析探讨了出生地和地理流动性如何影响总体和地理不平等，将不平等分为可解释和不可解释的因素。它考察了地区一级、学校一级、学生一级和家庭一级等因素对不同贫困程度地区不平等的影响。纵向数据有助于研究这些因素如何随时间变化，项目3侧重于不平等背后的因果机制。它采用了一种"自然实验"来评估教育与不平等之间的关系。通过比较处于不同经济条件下的学生，它评估了教育对不平等的影响。例如，学生对教育投资的意愿受到经济状况的影响。该项目利用同一地理区域的出生队列，比较"幸运"和"不幸"的学生，以确定教育如何影响进入劳动力市场后的不平等。学生成绩的数据用于控制能力水平的差异。这个项目可以在挪威、联合王国或两者都进行，这取决于早期项目的结果。

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.