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

这一研究项目将构建一个独特的历史数据集，以检验技术变化如何与群体认同相互作用，以不同的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很困难；这种情况因技术创新而变得更加复杂。研究人员可以获得1910年至1940年两组男性的退伍后职业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部队接受训练和工作，执行相同的军事任务。这些数据将与十年一次的人口普查中的个人水平数据合并，以创建一个独特的历史数据集。研究人员将使用这一数据集来确定观察到的这些群体的就业和工资轨迹差异是由于技术变化还是歧视。这项研究的核心是仔细地合并来自个人记录、人口普查记录和军事记录的历史数据，以回答这个重要的问题。除了这项研究的政策含义外，该项目还将包括来自历史上黑人学院和大学的本科生研究人员，从而有助于扩大对STEM教育的参与。这项研究的结果将揭示劳动力市场的差异结果，并为减少收入不平等的政策提供投入。这项拟议的研究项目将合并个人、就业、收入轨迹和人口普查数据，以测试技术创新如何与歧视相互作用，以确定不同群体的差异劳动结果。PIS将合并两组人的军后就业、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过相同的培训并从事相同的信号工作。数据是1910至1940年间的数据，该项目将使用差异估计策略来建立因果识别，以确定技术变化和群体差异的交互作用如何影响其他可比较的工人的就业和收入的动态和轨迹。这个项目的主要关注点和重要性是PI将为这个项目创建的独特的历史数据集，这也将允许其他研究人员在技术变化存在的情况下研究不同的劳动力市场动态。除了这项研究的政策含义外，该项目还将邀请来自历史上的黑人学院和大学的本科生研究人员参与，从而有助于扩大对STEM教育的参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.