Collaborative Research: The Effect of H-1B Workers on the Innovation and Productivity in US Firms

合作研究：H-1B 员工对美国企业创新和生产力的影响

• 批准号: 1535723
• 负责人: Giovanni Peri
• 金额: $ 17.03万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535723&HistoricalAwards=false
• 关键词: Collaborative; Research; Effect; 1B; Workers

Science, Technology, Engineering, and Math (STEM) workers are critical for the innovative activities and technological growth of US firms and local economies. Foreign-born workers are a potential source of STEM labor that might therefore be capable of generating positive dynamic effects on output, wage, and employment growth that could benefit US firms, workers, and investors alike. On the other hand, foreign STEM workers could compete with native-born workers, depress wages, and possibly discourage Americans from pursuing STEM occupations. The net effects of foreign-born STEM labor will depend upon the strength of these two countervailing effects. This project will examine these issues using data variation driven by numerical quotas on H-1B work permits -- the main channel of entry to US labor markets for skilled foreign-born workers -- that affect the distribution of foreign-born STEM workers across firms. This project produces reliable estimates of the impacts of an increase in the number of H-1B workers on US firms' productivity, employment, wages, and growth. This provides a very important policy evaluation tool to assess costs and benefits of changing the H-1B quota as discussed in recent immigration reforms. Using these estimates, we will be able to simulate how changes in the number of H-1B workers can affect firm growth and outcomes for US workers under different policy scenarios.The first phase of the project will merge various datasets. Individual H-1B application records for 2000-2012 -- including information about the employer, location, type of work permit, and year -- were obtained from the US Department of Labor and US Citizenship and Immigration Services. They will be merged with detailed firm-level information -- derived from confidential data in the US Census -- containing all relevant firm outcomes (such as revenues, wages paid, employment, profits and expenses). The resulting database will be used to produce a complete picture of the link between H-1B workers and the performance of firms in the US between 2000 and 2012. This will provide very important information on the nexus between the H-1B program and firm-level innovation and productivity. The second phase of the project will exploit the fact that lotteries were used to allocate H-1B work permits in 2007 and 2008. This random distribution allows credible estimation of the impact of foreign skilled workers on outcomes of US firms. The analysis will quantify the effect of one additional H-1B worker on firm productivity, investment, innovative activity, and profits. It will then estimate the effects of these workers on the employment and wages of native-born STEM workers. Finally, it will assess how long it takes for these effects to appear so that we can characterize the time evolution of these outcomes.

科学，技术，工程和数学（STEM）工作者对美国公司和当地经济的创新活动和技术增长至关重要。外国出生的工人是STEM劳动力的潜在来源，因此可能能够对产出、工资和就业增长产生积极的动态影响，从而使美国公司、工人和投资者受益。另一方面，外国STEM工人可能会与本土出生的工人竞争，压低工资，并可能阻止美国人从事STEM职业。外国出生的STEM劳动力的净效应将取决于这两种抵消效应的强度。该项目将使用H-1B工作许可证数量配额驱动的数据变化来研究这些问题-这是外国出生的熟练工人进入美国劳动力市场的主要渠道-影响了外国出生的STEM工人在公司中的分布。该项目对H-1B工人数量的增加对美国公司的生产率，就业，工资和增长的影响进行了可靠的估计。这提供了一个非常重要的政策评估工具，以评估在最近的移民改革中讨论的改变H-1B配额的成本和收益。使用这些估计，我们将能够模拟在不同政策情景下H-1B工人数量的变化如何影响公司增长和美国工人的结果。该项目的第一阶段将合并各种数据集。2000年至2012年的个人H-1B申请记录-包括有关雇主，地点，工作许可类型和年份的信息-从美国劳工部和美国公民及移民服务局获得。它们将与公司一级的详细信息合并-这些信息来自美国人口普查的机密数据-其中包含所有相关的公司结果（如收入、支付的工资、就业、利润和费用）。由此产生的数据库将被用来产生一个完整的图片之间的联系H-1B工人和公司的业绩在美国2000年和2012年之间。这将为H-1B计划与公司层面的创新和生产力之间的关系提供非常重要的信息。该项目的第二阶段将利用2007年和2008年使用彩票分配H-1B工作许可证的事实。这种随机分布允许可信地估计外国技术工人对美国公司业绩的影响。该分析将量化一名额外的H-1B工人对公司生产力，投资，创新活动和利润的影响。然后，它将估计这些工人对本土出生的STEM工人的就业和工资的影响。最后，它将评估这些影响出现需要多长时间，以便我们能够描述这些结果的时间演变。