Manav Raj is an Assistant Professor of Management at the Wharton School of the University of Pennsylvania. His research studies are: a) how firms respond to innovation and technological change, with a focus on digital platforms and technologies; and b) how institutional features and non-market forces affect innovation and entrepreneurship. Manav graduated from Dartmouth College in 2015, with a major in Economics and a minor in Public Policy. Prior to entering the Ph.D. program at NYU, he worked as a consultant with Cornerstone Research in Boston.
Deepak Hegde, Alexander Ljungqvist, Manav Raj (2021), Quick or Broad Patents? Evidence from U.S. Startups, Review of Financial Studies.
Abstract: We study the effects of patent scope and review times on startups and externalities on their rivals. We leverage the quasi-random assignment of U.S. patent applications to examiners and find that grant delays reduce a startup’s employment and sales growth, chances of survival, access to external capital, and future innovation. Delays also harm the growth, access to external capital, and follow-on innovation of the patentee’s rivals, suggesting that quick patents enhance both inventor rewards and generate positive externalities. Broader scope increases a startup’s future growth (conditional on survival) and innovation but imposes negative externalities on its rivals’ growth and innovation.
Edward Felten, Manav Raj, Robert Seamans (2021), Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses, Strategic Management Journal.
Abstract: We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars.
Manav Raj (2021), A House Divided: Legislative Competition and Young Firm Survival in the United States, Strategic Management Journal.
Abstract: Features of the institutional environment influence the performance of firms. In this research, I examine how one aspect of the institutional environment, competition between parties within legislatures, relates to young firm mortality. I argue that higher legislative competition provides legislators with more power to reward favored interests and thus contributes to a competitive environment that benefits well-connected incumbents and imposes negative consequences on young firms. Using data on state legislature composition in the United States and both an ordinary least squares and instrumental variables empirical strategy, I find that legislative competition has a positive relationship with young firm mortality and this relationship is partially mediated by incentives that favor incumbents. In doing so, I highlight that political competition can have negative consequences for some firms.
Edward Felten, Manav Raj, Robert Seamans (2019), The Effect of Artificial Intelligence on Human Labor: An Ability-Based Approach, Academy of Management Proceedings.
Abstract: While artificial intelligence (AI) promises to spur economic growth, there is concern that this may come at the expense of human labor. We utilize data on advances in AI together with occupational definitions to construct an occupation-level measure of the impact of AI, and use this measure to investigate whether and under what circumstances AI may act as a substitute or a complement to labor. We provide broad evidence that occupations impacted by AI may see a decline in wages, but growth in employment, and that this is particularly the case for occupations with complementary skills & technologies. In addition, high-income occupations experience a growth in employment, suggesting that AI may exacerbate inequality.
Abstract: Since the first decades of the 20th century, there has been concern that automation, including mechanization, computing, and more recently robotics and artificial intelligence (AI), will take away jobs and damage the labor market. There has also been concern that large, dominant firms will capture whatever value is created by automating technologies. In an effort to understand these issues, a wide variety of scholars have studied automation. Automation has been studied at a number of levels, including country, industry, firm, occupation, and even the occupational-task level, and by a range of disciplines, including economics, innovation, management, organizational theory, sociology, and strategy. This annotated bibliography attempts to include a range of literature that speaks to these different levels and different disciplines. It includes articles that are older, foundational pieces so readers can familiarize themselves with the major work in the area, as well as more recent articles so readers can get a sense of current research interests and opportunities. Notably, much of the recent research is focused on the effects of AI and robotics on workers, firms, and the economy. It is likely that there will be a large increase in research in this space in the coming years, especially as more data on the adoption of these technologies becomes available, and that this research will tell us much more about how these technologies are affecting our economy in the 21st century as well as inform our understanding of automation more generally.
Abstract: This article provides an introduction to artificial intelligence, robotics, and research streams that examine the economic and organizational consequences of these and related technologies. We describe the nascent research on artificial intelligence and robotics in the economics and management literature and summarize the dominant approaches taken by scholars in this area. We discuss the implications of artificial intelligence, robotics, and automation for organizational design and firm strategy, argue for greater engagement with these topics by organizational and strategy researchers, and outline directions for future research.
Manav Raj and Robert Seamans, “AI, Labor, Productivity, and the Need for Firm-Level Data”. In The Economics of Artificial Intelligence: An Agenda, edited by Ajay Agrawal, Joshua S. Gans and Avi Goldfarb, (Chicago: University of Chicago Press, 2019)
Edward Felten, Manav Raj, Robert Seamans (2018), A Method to Link Advances in Artificial Intelligence to Occupational Abilities, American Economic Association Papers & Proceedings, 108, pp. 54-57.
Abstract: Prior episodes of automation have led to economic growth and also to many changes in the workplace. We expect the same from artificial intelligence (AI). The link between AI and labor is complex, however. To assist researchers and policymakers, we provide a method that links advances in AI to occupational abilities, and then aggregates from these abilities to the occupation level. We demonstrate the method by estimating which occupational descriptions have changed the most due to advances in AI between 2010 and 2015, and check our estimates using the Bureau of Labor Statistics scheduled update to occupational descriptions in 2016.
The course is designed to meet the needs of future managers, entrepreneurs, consultants and investors who must analyze and develop business strategies in technology-based industries. The emphasis is on learning conceptual models and frameworks to help navigate the complexity and dynamism in such industries. This is not a course in new product development or in using information technology to improve business processes and offerings. We will take a perspective of both established and emerging firms competing through technological innovations, and study the key strategic drivers of value creation and appropriation in the context of business ecosystems. The course uses a combination of cases, simulation and readings. The cases are drawn primarily from technology-based industries. Note, however, that the case discussions are mainly based on strategic (not technical) issues. Hence, a technical background is not required for fruitful participation.
MGMT7310001 ( Syllabus )
MGMT7310002 ( Syllabus )