Prasanna (Sonny) Tambe

David Hsu and Prasanna Tambe (Forthcoming), Remote Work and Job Applicant Diversity: Evidence from Technology Startups, Management Science.

Abstract: A significant element of managerial post-COVID job design regards remote work. In an era of renewed recognition of diversity, equity and inclusion, employers may wonder how diverse (gender and race) and experienced job applicants respond to remote job listings, especially for high-skilled technical and managerial positions. Prior work has shown that while remote work allows employee flexibility, it may limit career promotion prospects, so the net effect of designating a job as remote-eligible is not clear from an applicant interest standpoint, particularly when recruiting females and underrepresented minorities (URM). We analyze job applicant data from a leading startup job platform that spans long windows before and after the COVID-19 pandemic-induced shutdowns of March 2020. To address the empirical challenge that remote job designation may be co-determined with unobserved job and employer characteristics, we leverage a matching approach (and an alternative method which leverages the sudden shutdowns) to estimate how applicant characteristics differ for otherwise similar remote and onsite job postings. We find that offering remote work attracts more experienced and diverse (especially URM) job applicants, with larger effects in less diverse geographic areas. A discrete change in job posting to remote status (holding all else constant) is associated with an approximately 15% increase in applicants who are female, 33% increase in applicants with URM status, and 17% increase in applicant experience. Using the application data, we estimate an average estimated compensating wage differential for remote work that is about 7% of posted salaries in this labor market.

John Horton and Prasanna Tambe (Draft), The Death of a Technical Skill.

Po-Hsuan Hsu, Dokyun Lee, Prasanna Tambe, David Hsu (Working), Deep Learning, Text, and Patent Valuation.

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%.

Prasanna Tambe and Tiantian Yang (Under Review), Gender, Tech Bubbles, and the IT Earnings Gap.

Description: Under Review at MIS Quarterly

Prasanna Tambe, Lorin M. Hitt, Daniel Rock, Erik Brynjolfsson (Working), Digital Capital and Superstar Firms.

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.