Disaggregated Earnings Trajectories in the U.S. Labor Market: New Evidence from Linked Longitudinal Data

美国劳动力市场的分类收入轨迹：关联纵向数据的新证据

• 批准号: 2241738
• 负责人: Xi Song
• 金额: $ 31.57万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241738&HistoricalAwards=false
• 关键词: Disaggregated; Earnings; Trajectories; U.S.; Labor

This project investigates the economic outcomes and trajectories of the fastest growing demographic group in the United States and, in particular, its workforce in science and engineering. To date, there is no consensus on fundamental questions about the labor market experiences and trajectories for this group. Empirical evidence of their labor market outcomes is scattered and mixed despite their high educational attainment. Moreover, this study investigates their labor market experiences disaggregating across gender, nativity, and national origin. The project also yields implications for reductions to barriers to incorporation and intergenerational mobility.This study uses the restricted-access linked longitudinal survey data in NSF's Scientists and Engineers Statistical Data System (SESTAT) and Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) data and Decennial Census/American Community Survey (ACS) data. This enables it to overcome previous data limitations, which have made it difficult to accurately study the outcomes and trajectories of workers in this demographic group. Because diversity is a hallmark of this population, failing to disaggregate the population results in biased or incomplete narratives. The project adapts two widely used statistical methods—multi-level growth-curve models and group-based trajectory models—to represent individual heterogeneity in earnings trajectories in a population. Findings from these life-course trajectory models reveal economic inequality, such as across workers’ national origin and generation groups, as well as between this and other demographic groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目研究了美国发展中增长最快的人口组的成果和轨迹他们的高教育程度。 - 房屋动态（LEHD）数据和十年中心的人口普查/美国社区调查（ACS）数据。人口导致偏见或不完整的叙事。正如这个人口组和其他人口群体之间。该奖项反映了NSF的法定使命，并通过基金会的知识分子优点和更广泛的影响评论标准而获得了值得的评估。