Superstars at Work: Increasing Returns to Scale across Occupations
with Erik Brynjolfsson and Seth Benzell
Labor incomes in the U.S. are characterized by increasingly fat-tails. The “Superstar” theory of wage inequality (Rosen 1981) suggests that this is because ICTs have made certain labor markets more “winner-take-all”. This would be by enabling workers at the top of the income distribution to better leverage their expertise and market their services across larger markets with fewer frictions. This paper explores how the wages of different occupations vary with market size and ICT intensity, to test whether the superstar labor theory can explain patterns in US wage inequality. Using a large-scale, high-frequency administrative payroll dataset on the employment history of over 25 million U.S. workers from 2014 through 2022, we measure how wages scale with market size across occupations, industries, and time. We focus on two dimensions of market size: establishment size and commuting zone size. The average job in our data is 11.7% higher paid in an establishment which is twice as large, and 7.5% higher in a commuting zone which is twice as large. Consistent with previous research, CEO wages increase approximately 20% with a doubling of establishment size (Gabaix and Landier, 2008). We proceed beyond this literature by classifying all occupations by how they scale. In our preferred specification, we find CEOs, Athletes, Lawyers, Nuclear Technicians, and four other types of managers see their wages increase the quickest with establishment size. Classifying occupations by their characteristics, we find variation consistent with the hypothesis that wage scaling within firms is related to the level of decision making responsibility. Occupations with the maximum O*NET score for “Impact of Decisions on Co-workers or Company Results” see their wages scale 16% faster with establishment payroll than occupations with the minimum score. Finally, we find workers in AI and IT-focused industries and in establishments with more IT developers experience faster wage scaling with market size, consistent with digitization creating winner-take-all labor markets. In future work, we will extend Gabaix and Landier (2008)’s approach to decompose scaling by market size into estimates of the scaling effect, talent distribution, and direct contribution of each occupation to output.
IT and Innovation: How did the Internet affect firms’ reliance on science?
This paper examines how the Internet facilitates the utilization of science in industrial innovation. I find that the Internet enables firms to discover “hidden gems”- commercializable yet under-recognized scientific findings in less prestigious journals, from early-career scientists, with fewer academic citations, and with higher forward patent citations. I compiled a database that contains 541,568 patent citations to scientific papers from 3,651 public firm-locations between 1992 and 2000, and identified the staggered adoption of basic Internet at these firms. Using a difference-in-differences framework, I show that access to the Internet at firm-locations is associated with a 9.3% increase in the likelihood of citing scientific papers; and up to 13.2% increase in citing the "hidden gem" papers. These findings suggest that IT reshapes the process of firm sourcing knowledge in innovation. By reducing search cost, IT enables firms to have equal access to previously less noticeable scientific knowledge and thus discover their commercial value. The results shed light on how IT reinforces the link between science and innovation.
Cornell Innovation, Entrepreneurship, and Technology Brownbag Workshop (Ithaca, Sep 2021)
Cornell Strategy and Business Economics Workshop (Ithaca, Oct 2021)
Information Systems Student Presentations Over the Cloud Workshop (ISPOC) (Virtual, Nov 2021)
the 14th Workshop on the "Organisation, Economics and Policy of Scientific Research" WOEPSR22 (Leuven, Belgium, April 2022)
the 2nd annual International Conference on the Science of Science and Innovation (ICSSI) (Northwestern University, Evanston, IL, June 2023)
How does labor mobility affect business adoption of a GPT? The case of machine learning
with Natarajan Balasubramanian and Chris Forman
Minor revision at Strategic Management Journal
We investigate how worker mobility influences the adoption of a new general-purpose technology (GPT). Using data from over 153,000 establishments between 2010 and 2018, we observe establishment decisions to adopt machine learning. Taking advantage of state-level changes to the enforceability of noncompete agreements as an exogenous shock to worker mobility, we find that changes that facilitate worker movements are associated with a significant decline in the likelihood of adoption. Moreover, the magnitude of establishment response depends upon characteristics of the establishment and the location in which it resides, in particular, establishment size and number of large establishments in the same industry-location. These results are consistent with the view that increases in worker mobility lead to greater risks for establishments that are contemplating adoption of a new GPT that involves significant downstream innovation.
Workshop on Information Systems and Economics (WISE) (Virtual, Dec 2020)
Temple-CMU-NYU 2020 Conference on Artificial Intelligence, Machine Learning, and Business
Analytics (Virtual, Dec 2020)
ISB 2nd AI & Strategy Consortium (Virtual, Jan 2021)
Wharton Innovation Doctoral Symposium (WINDS) (Virtual, Feb 2021)
the 19th ZEW Conference on the Economics of Information and Communication Technologies (Virtual, June 2021)
AOM Symposium- Machine Learning, Artificial Intelligence, and Strategy: Emerging Research on
the Importance of Complements (Virtual, Aug 2021)
2021 NBER Economics of Artificial Intelligence Conference (Virtual, Sep 2021)
with Natarajan Balasubramanian, Christopher Forman, Aija Elina Leiponen, Prithwiraj Choudhury, Kristina Steffenson McElheran, Robert Channing Seamans, Ryan Allen, Stephen Michael Impink and Wang Jin. Proceedings of the Academy of Management. July, 2021.
Artificial intelligence (AI) and machine learning (ML) represent general purpose technologies that are rapidly diffusing among businesses. These technologies have the potential to transform industries and to impact the performance of firms. They also present important challenges for managers. Firms investing in general purpose technologies like AI require complements to realize value from them and to align with unique firm needs. In this symposium we bring together four papers that examine various aspects of the diffusion of impact of AI and ML in businesses and how these are affected by the presence of complements at the individual, organizational, and ecosystem level. Together these papers will shed new light on the implications of managerial decisions related to this important set of technologies.
The impact of high-speed railway (HSR) expansion on entrepreneurial firm dynamics: Evidence from China
This paper investigates the impact of China’s high-speed railway (HSR) expansion on its entrepreneurial activities using firm registration data between 2011 to 2015. I find that connecting to HSR benefits mega-cities, while has lead a reduction in new firm entry in smaller cities. To address the non-random railway station placement problem, I constructed an instrumental variable of a hypothetical HSR station network that subjects to global construction cost minimization. I also adopted a market access approach similar to Donaldson (2018), where I calculated the impact of HSR on each city by capturing the changes in all its market access using a reduced-form expression derived from general equilibrium trade theory. I demonstrate that non-connection-induced market access significantly increases firm entry in mega cities by 2%, while decreases firm entry in Tier-3 and Tier-4 cities by up to 5%.