Collaborative Excellence in Research: Skill Acquisition, Technical Change and Differential Employment and Income Trajectories

卓越的研究协作：技能获取、技术变革以及差异化就业和收入轨迹

• 批准号: 2101244
• 负责人: Andria Smythe
• 金额: $ 48.47万
• 依托单位: Howard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2101244&HistoricalAwards=false
• 关键词: Collaborative; Excellence; Research; Skill; Acquisition

This research project will construct a unique historical data set to test how technological change interacts with group identity to affect employment and earning trajectories of groups differently. Testing economic models of differential labor market effects is difficult because of lack of appropriate data; a situation compounded by technological innovations. The researchers have access to historical data on post-military careers and wage trajectories between 1910 and 1940 of two groups of men who were trained and worked in the same unit performing the same military tasks during World War I. This data will be merged with individual level data from decennial census to create a unique historical data set. The researchers will use this data set to identify whether the observed differential employment and wage trajectories of these groups is due to technical change or discrimination. At the heart of this research is the careful merging of historical data from personal records, census records, and military records to answer this important question. Apart from the policy implication of this research, the project will involve undergraduate researchers from Historically Black Colleges and Universities, hence contribute to broadening participation in STEM education. The results of this research will shed light on differential labor market outcomes as well as provide inputs into policies to reduce earnings inequality.This proposed research project will merge personal, employment, earnings trajectories, and census data to test how technological innovation interacts with discrimination to determine differential labor outcomes of different groups. The PIs will merge the post-military employment, earnings, and census records of two groups of people who received the same training and did the same jobs in signals during World War I. The data are for the period 1910 to 1940 and the project will use difference-in-difference estimation strategy to establish causal identification of how the interaction of technical change and group differences affect the dynamics and trajectories of employment and incomes of otherwise comparable workers. The major focus and importance of this project is the unique historical data set the PIs will create for this project that will also allow other researchers to study differential labor market dynamics in the presence of technical change. Besides policy implication of this research, the project will involve undergraduate researchers from Historically Black Colleges and Universities, hence contribute to broadening participation in STEM education.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.

本研究将建立一个独特的历史数据集，以测试技术变革如何与群体身份相互作用，以不同方式影响群体的就业和收入轨迹。由于缺乏适当的数据，测试不同劳动力市场效应的经济模型是困难的;技术创新加剧了这种情况。研究人员获得了1910年至1940年期间两组男性的军事生涯和工资轨迹的历史数据，这些男性在第一次世界大战期间在同一单位接受训练和工作，执行相同的军事任务。这些数据将与十年一次的人口普查的个人数据合并，以创建一个独特的历史数据集。研究人员将利用这些数据集来确定这些群体观察到的就业和工资轨迹差异是由于技术变革还是歧视。这项研究的核心是仔细合并来自个人记录、人口普查记录和军事记录的历史数据，以回答这个重要问题。除了这项研究的政策影响外，该项目还将涉及历史上黑人学院和大学的本科研究人员，从而有助于扩大STEM教育的参与。本研究的结果将有助于揭示劳动力市场的结果差异，并为减少收入不平等的政策提供参考。本研究计划将结合个人、就业、收入轨迹和人口普查数据，检验技术创新与歧视之间的相互作用，以确定不同群体的劳动结果差异。 PI将合并两组人的退伍后就业，收入和人口普查记录，这两组人在第一次世界大战期间接受了相同的培训并在信号中做了相同的工作。这些数据是1910年至1940年期间的数据，该项目将使用差异中的差异估计策略来确定技术变革和群体差异的相互作用如何影响就业和收入的动态和轨迹的因果关系。该项目的主要重点和重要性是PI将为该项目创建的独特历史数据集，这也将使其他研究人员能够在技术变革的情况下研究差异劳动力市场动态。除了这项研究的政策意义，该项目将涉及本科研究人员从历史上的黑人学院和大学，从而有助于扩大参与STEM教育。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。