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.

该项目调查了美国增长最快的人口群体的经济成果和轨迹，特别是其科学和工程领域的劳动力。迄今为止，关于这一群体的劳动力市场经历和发展轨迹的基本问题还没有达成共识。尽管他们的教育程度很高，但关于他们在劳动力市场上的表现的经验证据是分散和混合的。此外，本研究还调查了他们的劳动力市场经历在性别、出生和国籍上的分解。该项目还对减少合并和代际流动的障碍产生影响。本研究使用了美国国家科学基金会科学家和工程师统计数据系统（SESTAT）中的限制访问链接纵向调查数据、人口普查局的纵向雇主-家庭动态（LEHD）数据和十年一次的人口普查/美国社区调查（ACS）数据。这使得它能够克服以前的数据限制，这使得很难准确地研究这一人口群体中的工人的结果和轨迹。因为多样性是这一群体的标志，未能对其进行分类将导致有偏见或不完整的叙述。该项目采用了两种广泛使用的统计方法——多层次增长曲线模型和基于群体的轨迹模型——来表示人口中收入轨迹的个体异质性。这些生命轨迹模型的发现揭示了经济上的不平等，例如在工人的国籍和世代群体之间，以及在这个和其他人口群体之间。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。