Thomas J. May

About Me

I am an Economics Ph.D. student at University of Minnesota. In the Fall of 2023, I will be joining the economics department of California State University, Fullerton, as an assistant professor. My research interests are macroeconomics, firm dynamics, and inequality. When I am not wading through data, I enjoy cooking, gardening, and snowboarding.

Working Papers

• Innovation and Competition Policy
  Updated Draft Coming Soon
  Abstract: It has become increasingly apparent to policymakers that optimal antitrust policy re- quires looking beyond traditional static analyses and considering the dynamic effects of policy. Such analysis is challenging as limited studies exist concerning dynamic compe- tition policy. This paper attempts to bridge this knowledge gap by developing a novel structural growth model containing the major motivations of mergers and acquisitions (M&A) activity. To enable estimation of the model, frontier natural language processing (NLP) techniques are employed to classify whether parties to an M&A transaction are currently operating in similar markets or whether acquirers are using M&A as an entry mechanism into new markets. Examining the overall impact of M&A on growth reveals a double-edged sword: policies that either completely shut down M&A or allow unre- stricted M&A both result in significantly lower growth rates than the baseline estimate. This motivates an optimal antitrust policy that accounts for dynamic effects.

• On the Nature of Entrepreneurship
  with: Anmol Bhandari, Tobey Kass, Ellen McGrattan, and Evan Schulz
  Current Draft
  Abstract: This paper elucidates the nature of entrepreneurship by comparing life-cycle income profiles and outcomes of individuals who share similar characteristics but differ in their choice of self- or paid-employment. Results are based on U.S. administrative data from the Internal Revenue Service and Social Security Administration over the period 2000–2015 for subgroups of the population differing by gender, marital status, education, occupation, industry, cohort, and employment status. Contrary to top-coded survey evidence based on relatively small samples and short panels, we find that entrepreneurs with at least twelve years in self-employment during our sample have significantly higher average income and steeper, more persistent, income growth profiles than their paid-employed peers with similar characteristics. Contrary to survey evidence, we find that new entrants into self-employment have higher labor incomes and lower asset incomes prior to entry relative to similar peers that do not enter. A theory of entrepreneurial choice is developed and compared to the subsample of young entrepreneurs in our data. We find that including firm-specific investment and selection under incomplete information is necessary if the theory is to match the observed income growth profiles and switching behavior for these young entrepreneurs.

Work In Progress

• More Data, More Inequality
  Description: Firms are spending billions on collecting and processing data about their customers. An important question is how firms are utilizing this data and how it shapes their relationship with their consumers. This project studies how firms can employ data to effectively price discriminate. The initial results are striking. Even under conservative assumptions, an increase in data collection efficiency leads to a substantial increase in asset inequality and a decrease in household welfare.
  
• Knowledge Workers
  with: Ellen McGrattan
  Description: Utilizing IRS admistrative data and machine learning, this project measures the labor input of firms’ investment in intangible capital. Using machine learning to identify worker types, this project can identify investment for each major type of that invest such as research and development, marketing, software, etc. Between the scope of the data — every firm in the US — and this specificity, this will be the most comprehensive measurement of intangible investments in the US.

References

Letter Writers:

• Professor Ellen McGrattan, erm@umn.edu
• Professor Anmol Bhandari, bhandari@umn.edu
• Professor Kjetil Storesletten, kstoresl@umn.edu

Placement Directors:

• Professors Manuel Amador and Mariacristina De Nardi, mneconplacedir@umn.edu

Placement Coordinator:

• Catherine Bach, mneconjm@umn.edu