Thomas J. May

About Me

I am an assistant professor at California State University, Fullerton and a Project PIPE-LINE data science faculty fellow. My research interests are macroeconomics, firm dynamics, and regulatory policy. Additionally, I am actively developing techinuqes to apply machine learning and big data to macroeconomic questions. When I am not wading through data, I enjoy cooking, gardening, and snowboarding.

Working Papers

• On the Nature of Entrepreneurship
  Revise and Resubmit, Journal of Political Economics
  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.

Working Projects

• Antitrust Policy and Economic Growth
  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.

• Using Machine Learning to Classify M&A Transaction
  Abstract: This paper develops a novel methodology for classifying relationships between parties in a merger and acquisition transaction. Understanding the motives of an M&A transaction is essential in researching the economic impact of M&A. Given the high number of transactions per year, manually classifying every transaction is unfeasible. This paper proposes a novel methodology using a large-language model to determine if a transaction has possible horizontal or vertical linkages. With sufficient information, this model is highly accurate. Focusing on transactions involving U.S. firms, 38% of transactions were only horizontally linked, 38% were only vertically linked, 12% were both horizontally and vertically linked, and 10% had no linkages. This pattern is robust across time and presidential administrations. The resulting data can be used by any researcher studying M&A transactions.

• Using Operator Learning To Solve Hetergenous Agent Macroeconomic Models with Aggregate Uncertainity