Job Market Paper
Complex Innovation and the “Visible Hand”: the Role of Knowledge Interdependence in Employee Entrepreneurship.
[Ed Snider Center Working Paper No.5, 2022] [Link]
[Best Paper Proceedings of the 83rd Annual Meeting of the Academy of Management]
[Nomination for the William H. Newman Award of the 83rd Annual Meeting of the Academy of Management]
[Finalist of the 42nd SMS Annual Conference Ph.D. Paper Prize, 2022]
Abstract: How does growing knowledge interdependence in firm innovation activities affect potential entrepreneurs' decision to start their own business ventures? To answer this question, I adopt an abductive approach and leverage matched employee-employer data from the U.S. Census Bureau between 2000-2014. Results show that higher knowledge interdependence is negatively associated with employee entrepreneurship, and the negative effect is even stronger, not weaker, among the highest-performing individuals. This indicates that knowledge interdependence does not merely raise the bar of entry. Instead, firms implement better pay-for-performance compensation schemes to retain valuable human assets, which also leads to greater compensation dispersion within firms with higher knowledge interdependence. Together, these create a strong selection on the quality of spin-outs being formed especially by individuals ranked highest on the human capital distribution. I use an economic model to summarize these empirical insights and show that knowledge interdependence could also raise between-firm income inequality when it generates large enough profits on the product market.
Declining Science-Based Startups: Strategic Human Capital and the Value of Working in Startups vs. Established Firms (with Thomas Åstebro and Serguey Braguinsky)
Revise & Resubmit at Strategic Entrepreneurship Journal
Ed Snider Center Working Paper No.5, 2022 [Link]
NBER Working Paper No. 27787 [NBER Paper]
Abstract: How does strategic human capital relate to startup formation? We document that since 1997, the rate of startup formation has precipitously declined for firms operated by U.S. Ph.D. recipients in science and engineering. To explain this decline, we build on the strategic human capital literature showing that increasing knowledge complexity is associated with less entrepreneurship and examine the associated time trends. We find that the decline in startup formation is accompanied by earnings decline, increasing work complexity in R&D, and more administrative work for founders. Thus, founding a startup is becoming harder, while established firms are becoming increasingly more attractive workplaces for PhDs. There is a silver lining, however, as more recent startups appear to be of higher quality and grow faster, conditional on survival.
The Burdens of Technology Adoption on Entrepreneurship – How does the Adoption of EHRs Impact Physician Self-employment in the U.S. Healthcare Sector?
Preparing for disclosure clearance by the U.S. Census Bureau
Abstract: In the past two decades, there has been a trend of rapid adoption and implementation of health information technology (HIT), namely the Electronic Health Records (EHRs) in the U.S. healthcare sector. Meanwhile, the share of self-employed physicians dropped drastically with increasing market concentration for hospitals. I argue that efficient knowledge hierarchies and complementary capabilities of HIT in hospitals help mitigate burdens following the adoption and implementation of EHRs better than independent physician-owned practices. Preliminary results using publicly available data indicate that a higher level of adoption of EHRs at nearby hospitals is associated with a lower level of self-employment as well as lower returns to self-employment in the same county. To further investigate the causal relationship, I implement Diff-in-diffs design and an instrument variable approach by linking the HIMSS analytics survey to the employer-employee matched dataset of the U.S. Census Bureau and the American Community Survey (ACS) to trace EHRs adoption, job mobility, and entrepreneurial activities of physicians employed at a near census of non-federal hospitals in 29 U.S. states between 2005 and 2014. Results are being prepared for disclosure clearance by the U.S. Census Bureau.
Mega Firms and Recent Trends in the U.S. Innovation: Empirical Evidence from the U.S. Patent Data (with Serguey Braguinsky, Joonkyu Choi, Karam Jo, and Seula Kim)
Presented at: FSRDC Annual Research Conference (Kansas City Fed, 2022); NBER Megafirms and the Economy Conference (2023)
Abstract: We use the U.S. patent data merged with firm-level datasets and establish new facts about recent trends in “novel patents” – inventions that introduce new technology combinations. We find that such innovations were declining from 1980 to 2008, and a strong rebound has followed since then. Our evidence suggests that mega firms play an important role in driving these trends as compared to their smaller competitors and VC-backed startups. In particular, the share of novel patents by mega firms has been increasing since the early 2000s, and they are increasingly creating new combinations integrating Information and Communications Technology (ICT) components with non-ICT components. Furthermore, we find that those new technology combinations created by mega firms are becoming more influential and increasingly built on by other firms than those created by their smaller competitors or VC-backed startups by having more follow-on patents. Overall, our findings suggest that mega firms are increasingly engaging in experiments that create room for other entities to conduct follow-up innovations.
Abstract: This paper constructs a patent assignee-firm longitudinal bridge between U.S. patent assignees and firms using firm-level administrative data from the U.S. Census Bureau. We match granted patents applied between 1976 and 2016 to the U.S. firms recorded in the Longitudinal Business Database (LBD) in the Census Bureau. Building on existing algorithms in the literature, we first use the assignee name, address (state and city), and year information to link the two datasets. We then introduce a novel search-aided algorithm that significantly improves the matching results by 7% and 2.9% at the patent and the assignee level, respectively. Overall, we are able to match 88.2% and 80.1% of all U.S. patents and assignees respectively. We contribute to the existing literature by 1) improving the match rates and quality with the web search-aided algorithm, and 2) providing the longest and longitudinally consistent crosswalk between patent assignees and LBD firms.
Works in Progress
”Management and Organizational Practices, Business Dynamism, Employee Sorting, and Entrepreneurship” with Rajshree Agarwal, Serguey Braguinsky, Seth Carnahan, Joonkyu Choi, Martin Ganco, John Haltiwanger, Florence Honore, Seula Kim, and Evan Starr, (Data analysis stage with U.S. Census data)
"How costly is green product innovation? Evidence from a structural estimation" with Zhimin Chen (Swiss Finance Institute), and Shirley Tang (Washington University in St. Louis)