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Prasanna (Sonny) Tambe is an Associate Professor of Operations, Information and Decisions at the Wharton School at the University of Pennsylvania. His research focuses on the economics of technology and labor. Recent research projects focus on 1) understanding how firms compete for software developers, 2) how software engineers choose technologies in which to specialize, and 3) how AI is transforming HR management.
Much of this research has uses Internet-scale data sources to measure labor market activity at novel levels of granularity. His published papers have analyzed data from online job sites and other labor market intermediaries that generate databases of fine-grained information on workers’ skills and career paths or on employers’ job requirements. He is a co-author of “The Talent Equation: Big Data Lessons for Navigating the Skills Gap and Building a Competitive Workforce,” published by McGraw Hill in 2013.
His research has been published or is forthcoming in a number of academic journals including Management Science, Information Systems Research, MIS Quarterly, The Review of Financial Studies, Industrial and Labor Relations Review, Communications of the ACM, and Information Economics and Policy and it has been supported by the Alfred P. Sloan Foundation. His research has also won a number of awards, including the Best Published Paper in Information Systems Research and two papers have been nominees for the Best Published IS Paper in Management Science. He currently serves on the editorial board of Management Science and in the past, has served on the editorial board of Information Systems Research.
Professor Tambe received his S.B. and M.Eng. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and his Ph.D. in Managerial Science and Applied Economics from the Wharton School of the University of Pennsylvania.
David Hsu and Prasanna Tambe (Working), Startup Labor Markets and Remote Work: Evidence from Job Applications.
Abstract: Does offering remote work allow startup firms to attract more experienced and more diverse (gender and race) talent? We examine job listings and job applicant behavior on a leading platform in this space, AngelList Talent, amid the COVID-19 pandemic-induced shutdowns. We first characterize the jobs and organizations offering remote work before the shutdowns. We then leverage the context to help address the empirical confound of job design (including offering remote jobs) as co-determined with unobserved job and firm characteristics. By doing so, we estimate the change in applicant characteristics to job postings which are (exogenously) shifted to being remote. This design is a window into evaluating a managerial choice (offering remote work) which will likely become more salient in post-pandemic job design. We find that offering remote-eligible work attracts more experienced and diverse job applicants.
Abstract: General purpose technologies like information technology typically require complementary firm-specific human and organizational investments to create value. These complementary investments produce a form of capital, which we call Information technology-related intangible capital ("ITIC''). An understanding of how the accumulation of ITIC contributes to economic growth and differences among firms has been hindered by the lack of measures of the stock of ITIC. We use a new, extended firm-level panel on IT labor investments along with Hall’s Quantity Revelation Theorem to construct measures of both the prices and quantities of ITIC over the last thirty years. We find that 1) prices vary significantly for ITIC, 2) significant quantities of ITIC have been accumulating since the 1990s, with ITIC accounting for at least 25% of firms’ assets by the end of our panel, 3) that it has disproportionately accumulated in small subset of high-value, superstar firms, and 4) that the accumulation of ITIC predicts future productivity.
Abstract: This paper uses deep learning and natural language processing (NLP) methods on the US patent corpus to evaluate their predictive power in estimating two measures of patent value: (i) investor reaction to patent announcements as measured in Kogan et al., 2017 and (ii) forward citations. While forward citations have traditionally been used as measures of economic value in the literature, their utility is mainly retrospective. Contemporaneous predictions of patent value, as embodied in investor reactions to patent grants, can be important for managers and policy-makers for prospective decision making. We compare the prediction performance of models using the structured features of the patent (number of citations, technology class, etc.) to deep learning and NLP methods. Relative to linear regression models using the same features, deep learning models reduce mean absolute error (MAE) by approximately 32%. Incorporating patent text further lowers the MAE by 13%.
New research from Wharton's Peter Cappelli and Prasanna Tambe points out the promise and pitfalls of using artificial intelligence in human resources, where algorithms can’t easily replace human decision-making.Knowledge @ Wharton - 8/30/2019