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，对于该群体的劳动力市场经历和轨迹的基本问题尚未达成共识。尽管他们的教育程度很高，但他们的劳动力市场结果的经验证据却分散且混杂。此外，这项研究按性别、出生地和国籍分类调查了他们的劳动力市场经历。该项目还对减少融入障碍产生了影响。本研究使用 NSF 科学家和工程师统计数据系统 (SESTAT) 和人口普查局纵向雇主-家庭动态 (LEHD) 数据和十年一次人口普查/美国社区调查 (ACS) 数据中的限制访问纵向关联调查数据。这使其能够克服以前的数据限制，这些限制使得准确研究该人口群体中工人的结果和轨迹变得困难，因为多样性是该人群的标志，无法做到这一点。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——来表示人口收入轨迹中的个体异质性。轨迹模型揭示了经济不平等，例如工人的国籍和世代群体之间以及该群体与其他人口群体之间的经济不平等。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响进行评估，被认为值得支持审查标准。