This project asks whether standard merger simulation techniques in industrial organization effectively predict price changes in observed mergers, and if not, if predictions depart from reality systematically and in a way consistent with efficiencies or coordinated effects. Using scanner data, the authors will run a standard merger simulation on a large set of completed mergers and compare predictions to outcomes, creating a comprehensive retrospective of the effects of mergers on prices, which will inform us of whether typical approved mergers in the United States tend to increase prices. They will also study the sources of the prediction error.

Regulation of merger policy is a primary tool of competition policy in the United States. Merger simulations are used to decide whether mergers are anti-competitive or whether they should be permitted. This ambitious project could provide a wealth of information about consummated mergers and the predictive power of merger simulation techniques, contributing to the infrastructure used to regulate competition.

With the Federal Reserve’s focus on promoting maximum employment, it is important for researchers to understand how U.S. monetary policy affects firms’ employment differently. This research project will analyze how the age and size of a firm impacts the firm’s responsiveness to monetary policy. Using the Business Dynamics Statistics and Quarterly Workforce Indicators datasets from the U.S. Census Bureau, the researcher will attempt to provide new evidence on the distributional effect of monetary policy on the employment of heterogenous firms in the United States from 1977 to 2007.

Despite the existence of a vast literature on U.S. labor market discrimination, there is still a lack of empirical evidence on the degree to which the private enforcement of anti-discrimination legislation through the federal courts has influenced racial divides in earnings and other socioeconomic outcomes. Using the Federal Court Cases: Integrated Data Base on Civil Terminations, the authors will create a set of comprehensive measures of civil rights enforcement at the court level, providing the opportunity to track changes in enforcement across 90 U.S. District Courts between 1970 and 2019. These measures of enforcement will be linked to socioeconomic outcomes using data at the individual and household levels in order to shed light on how enforcement of civil rights legislation via the courts influence labor market outcomes and intergenerational mobility of minority groups. In addition, the authors will create a comprehensive dataset on the political party composition of judges across courts and over time to examine how presidential appointees have influenced the evolution of civil rights enforcement and their implications for racial inequities in U.S. labor market outcomes and intergenerational income mobility.

This project is part of a broader agenda to develop quantitative macroeconomic models that can be used to study the distributional implications of macroeconomic shocks and policies. Existing macroeconomic models are limited in their ability to create realistic dynamics in the distribution of labor income in response to macroeconomic shocks. Since labor income is one of the most important dimensions of economic inequality and the most important determinant of economic welfare for the majority of U.S. households, this is a significant limitation. This paper will begin by developing a theory to demonstrate the new heterogeneity that is going to be a key force in the model. It will be followed by an empirical section to show the fact that the model is seeking to address and to highlight the important empirical facts that other models are missing, such as that not all labor income is similarly cyclical and that expansionary and productive occupations have systematically different experiences. The final section addresses the general equilibrium consequences of this heterogeneity, a step that is critically important for doing policy counterfactuals.

Rising income inequality and declining labor market prospects are among the most salient features of the U.S. labor market today, particularly for non-college-educated men who are more likely to work in jobs which involve exposure to the elements, including in construction, agriculture, or warehousing. Growing evidence indicates that temperature stress may have important impacts on cognitive performance, labor capacity, and workplace safety, suggesting that added extreme heat due to climate change may significantly reduce earnings and job quality for many low-skilled workers. This project asks whether climate change could exacerbate recent trends in economic inequality and, if so, the possible scope for workplace adaptation and policy reforms. This project will examine the impact of a 2006 California policy to mandate a heat-illness-prevention standard on worker injuries, employment, wages, and profitability. It will provide additional evidence on the impact of temperature on workplace safety and contribute descriptive analysis on how various demographic groups are differentially exposed to occupational extreme temperature risk. The research uses administrative data on 11 million workplace injuries in California, injury and fatality data from the federal Office of Safety and Health Administration and the U.S. Bureau of Labor Statistics, and quasi-experimental variation in daily temperature at the U.S. ZIP code level from the National Climatic Data Center. This analysis will allow for an occupation by community zone level breakdown of workplace climate exposure by race, formal education, and immigrant status. The novel coronavirus and COVID-19, the disease caused by the virus, has placed a spotlight on the health and economic inequalities among workers in the United States and so, too, will climate change. By exploring the overall and disparate impacts on workers, this research will provide concrete evidence on policies to mitigate such impacts.

The U.S. labor market literature relies, for the most part, on government survey data, which is invaluable for identifying economywide trends and differentials but lacks the detail needed about tasks and occupations to detect changes in how work is organized. This project will use two datasets assembled by Burning Glass Technologies containing millions of job postings and millions of resumes. The data offer the scope and scale to make fine distinctions and explore the array of skills embodied in workers and the tasks called out by employers. This project has three aims. First, it will examine variation within occupational categories and connections across categories. Given the growth of within-occupational inequality, understanding within-occupation heterogeneity in skills and other attributes is extremely important. Second, it will use resume data to examine worker mobility. This is a novel approach and is likely to offer important insights about labor market mobility. Third, it will investigate job quality, including nonwage benefits and employment relations, and the incidence of nonstandard work (temporary, on call, and contract based). Nonstandard work is not regularly measured in government surveys, so exploring whether job posting data can offer insight into the prevalence of nonstandard work arrangements and whether such arrangements are associated with job mobility and access to nonwage benefits, as well as differences in wage levels between similar occupations with different employment relations, could lay the groundwork for future research.