This project is a data collection effort that seeks to fill an important gap: the undercounting of strikes. The author will collect data on all strikes in the United States regardless of size, duration, or whether workers are unionized or not. There is a perceived rise in collective action, but current data sources are not collecting information on the full universe of strikes. This effort will define a strike as “a temporary stoppage of work by a group of workers in order to express a grievance or to enforce a demand. Such a grievance or demand may or may not be workplace related.” A rigorous search and verification protocol by the author will ensure that a strike occurred. Data will be collected on other variables related to the strike, and all data will be publicly accessible on an interactive map. This project will extend recent research that has found strikes decreased in both amount and scale, and that strike effectiveness declined. It seeks to provide information on the outcomes of strikes for workers and their organizations, and under what conditions strikes are most effective. A notable extension is an investigation of whether strikes can respond to identity-based inequality, such as racial discrimination or sexual harassment, by studying noneconomic demands.

This project asks how the racial composition of workers’ professional networks affect their wage growth and access to outside opportunities. Existing research has estimated that the inclusion of underrepresented workers in the U.S. economy since 1960 significantly contributed to U.S. GDP growth. Yet racial divides in earnings and opportunities persist. Understanding the sources of these gaps is critical for understanding growing income inequality and fostering broad-based growth. While many explanations have been proposed, relatively little is known about the extent of U.S. labor market segregation and its consequences today. This project will use restricted firm-level data to explore the role of workers’ professional networks in the persistence of race disparities in the U.S. labor market. Building on recent work showing that workers are able to renegotiate their wages when labor demand increases in their networks, and that referred workers are often similar to referrers in terms of age, gender, and race, the author proposes to estimate the impact of changes in the composition of the professional networks of workers to investigate whether the impact of an increase in local labor demand is sensitive to the size of the professional networks for underrepresented groups. This analysis will then be used to calibrate a search model that estimates the contribution of labor market segregation to the wage gap.

This project extends ongoing work by this research team on nursing home unionization and COVID-19 preparedness. The interdisciplinary team takes a mixed-methods approach to estimate the causal link between collective bargaining and workplace safety prior to and during the pandemic. The researchers have built a proprietary dataset of union status of all 15,000 nursing homes in the United States and merged it with publicly available Center for Medicare and Medicaid Services workplace-level data on COVID-19 outcomes in nursing homes. They will use this dataset to examine the impact of unionization on health and injury outcomes (and racial differences) before and during COVID-19 in the 2016–2021 period. Using an event-study difference-in-difference framework will allow them to capture the validity of parallel trends assumptions, and the proposed regression discontinuity design will help to establish the causal effect of unionization on worker COVID-19 infection outcomes. This is an important question that has significant resonance right now, as we are seeing a resurgence of union activity among major employers.

This project seeks to extend research on “deaths of despair” to look specifically at the causes of such deaths for Native Americans, and Native American women and girls in particular. Deaths of despair among Native Americans are proportionately higher than among any other group in the United States and have increased at almost twice the rate of non-Hispanic White Americans. Are the predictors of a death of despair for White constituents, especially men (joblessness, high rates of unemployment), different than those for Native American women and girls? The project will study this in the context of fracking. Preliminary analysis provides compelling, suggestive evidence that higher rates of employment and earnings among non-Hispanic White men due to extraction booms, measured by the fracking industry and building on existing literature, in proximity to Native lands and to Native American girls and women likely induces more human trafficking activity that disproportionately affects Native women and girls. In turn, this may also induce behaviors to “cope” with such contexts, such as increased alcohol and substance use and suicides among Native women and girls. The authors convincingly argue that what might reduce deaths of despair (jobs and higher wages) for one group (non-Hispanic White men) might result in higher rates of deaths of despair for another marginalized group, Native women and girls.

There is a large literature exploring the tension between increasing minimum wages in order to raise the hourly wages of workers and having these increases offset by reductions in overall employment or hours worked by low-wage employees. Understanding the distributional impacts of minimum wage increases is therefore essential. This project seeks to provide some of the first empirical evidence on how minimum wage reforms change firms’ occupational composition, distribution of hours, and scheduling practices. To do this, the authors will leverage shift-level microdata for the near-universe of employees and contract workers at U.S. nursing homes from the Payroll Based Journal program. The nursing home industry is an attractive setting for this research as it is a major employer of low-wage workers, especially certified nursing assistants, who provide the majority of patient care at nursing homes, are typically paid at or just above the minimum wage, and the majority of whom are immigrants and women of color. Moreover, many low-wage staff intend to work in the industry throughout their careers, in stark contrast to more heavily studied low-wage industries such as restaurants and retail, where many workers expect to leave the industry quickly. Accordingly, wage policies in the nursing home sector have the potential to not only affect the economic well-being of low-income workers but also shape gender and racial pay divides. In addition, the nursing home industry is of particular interest to regulators and policymakers since Medicaid and Medicare finance the vast majority of long-term care, and policies that affect the wages of employees in this sector will correspondingly affect state and federal budgets. Employment or composition changes may also have important consequences for quality of care.

This project investigates the relationship between two trends: the significant rise in regional inequality in the United States, with a small number of highly educated urban cities accounting for a growing share of national income, and the increasing dominance of large, multiregion services firms such as Amazon.com Inc., Walmart Inc., and Starbucks Corp., as reflected in measures of average firm size and market concentration. These “superstar” firms create high-paying, high-skill jobs in large cities and low-paying jobs in nonurban areas across the country, displacing local and regional competitors in the process and leading to increasingly unequal demand for skills across regions. Kleinman will utilize private data from Dun and Bradstreet and Burning Glass Technologies, as well as public data from the American Community Survey and the Panel Database on Incentives and Taxes. He will combine an empirical investigation of the relationship between firms’ spatial expansion and regional inequality with the development and quantification of a novel economic geography model that features expansion of multiregion firms to analyze the labor demand of firms across different markets and occupations.