This project will investigate whether large-anchor economic development projects in relatively isolated rural areas provide spillover economic effects into other industries. Specifically, the authors will provide community-level analysis of business expansion and contraction by community type and characteristics for American Indian Reservations and adjacent communities. Using data going back to the 2000s, the project will examine the impact of the gaming industry on noncasino business establishments and their expansion (prior to the Great Recession of 2007–2009) in order to assess whether the development of the gaming industry has had a spillover impact on employment in these regions. The data allows the researchers to follow whether ancillary businesses (in the transportation, food services, retail operations, and lodging services industries) start up or expand in order to service the gaming industry. It will build upon previous data work allowing for the identification of whether an establishment is located on or off of an American Indian Reservation.

This project entails the collection of empirical metrics of merger outcomes in order to analyze effects beyond prices, taking into consideration other factors such as employment, innovation, and efficiencies. Scott Morton will collect empirical metrics of antitrust enforcement outcomes from publicly available data in company reports, earnings calls, Securities and Exchange Commission filings, and from other sources such as industry analysts and consulting services in order to create a novel dataset. Information will be collected before and after a merger. Data will then be compared to the outcomes predicted by the merging firms. A second component of the research will examine the purposes and outcomes of acquisitions in the high-tech sector to determine whether acquisitions are motivated by increased efficiencies or by the elimination of competitors, a question that is largely unexplored. This line of inquiry seeks to test whether recent acquisitions have stifled innovation. This project is poised to make a considerable contribution to our understanding of the effects of mergers and acquisitions. Little evidence currently exists, resulting in a high burden on the agencies to justify challenges to proposed mergers and acquisitions.

The extent to which income inequality can be traced to shifts in the distribution of rents and/or to declines of workers’ share of those rents is an open and important question, one that researchers have had difficulty answering due to data limitations. This research will link multiple administrative datasets to assess how concentration in managerial power contributes to rising wage inequality. The research will make an important contribution to our understanding of the larger forces generating income inequality—specifically, how corporate decision-making that fuels market concentration may also fuel income inequality

This project will look at how the concentration of corporate ownership and mergers and acquisitions affects inequality and workers’ well-being by evaluating the relationship between growing market concentration and the declining labor share of income. The research proposes to distinguish two channels by which greater concentration could matter: reduced product market competition, which would directly increase the profit share of gross domestic product and thereby reduce the labor share, and reduced labor market competition—which would directly reduce the labor share.

This research asks whether vertical disintegration strategies, such as outsourcing and franchising, are merely efficiency-enhancing or if they are also strategies to manipulate the legal boundaries of the firm to gain greater revenues and shift risk onto less powerful suppliers, contractors and franchisees. The research focuses specifically on franchises and proposes to build a unique new dataset based on financial data from court cases. Particular areas of exploration include questions of bargaining power, risk, royalty rates, and contract terms.

A structural change in the United States economy—huge new flows of personal information stemming from technological innovation—has enabled companies to classify, sort and rank individuals in ways previously unimaginable. This research proposes to use big data from car insurance providers to predict market decisions by looking at how regulators, members of industry and other key actors together establish the market rules by which personal data determines economic opportunity. It asks on what grounds policy and market actors conclude that it is fair to treat people differently in the marketplace based on their personal data “traces,” and seeks to show how some, but not other, ideas get embedded in markets over time.