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，尤其是其在科学和工程领域的劳动力。迄今为止，关于该组的劳动力市场经验和轨迹的基本问题尚无共识。他们的劳动力市场成果的经验证据分散并混合了他们的高教育程度。此外，这项研究调查了他们的劳动力市场经历，这些经历在性别，耶稣降生和国籍之间分裂。该项目还对减少基础设施和代际移动性的障碍产生了影响。本研究使用了NSF的科学家和工程师统计数据系统（SESTAT）和人口普查局纵向雇主 - 雇主居住动态（LEHD）数据和美国年大社区（ACSENAIL CENSUS/AMERIANGE）（ACS CENSUS CENSUS/AMEMERAS数据）中的受限制访问联系在一起的纵向调查数据。这使其能够克服以前的数据限制，这使得难以准确研究该人口组中工人的结果和轨迹。由于多样性是该人群的标志，因此无法分解人口会导致叙事偏见或不完整。该项目适应了两种广泛使用的统计方法：多级生长曲线模型和基于群体的轨迹模型 - 代表人群的收入轨迹中的个体异质性。这些生命课程轨迹模型的发现揭示了经济不平等，例如跨工人的国籍和发电群体，以及该人口统计组之间。该奖项反映了NSF的法定使命，并被认为是通过基金会的智力优点和更广泛的影响审查标准来通过评估来获得支持的。