Prasanna (Sonny) Tambe

Associate Professor of Operations, Information and Decisions
Co-Director, AI at Wharton

Contact Information

Primary Email:
tambe@wharton.upenn.edu

office Address:
3730 Walnut Street
558 Jon M. Huntsman Hall
Philadelphia, PA 19104

Research Interests: Economics of IT Labor, Technological Change and Reskilling, Algorithms and AI in HR and Management

Links: CV, Personal Website, LinkedIn

Overview

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.

Research

Prasanna Tambe and Tiantian Yang (2024), The Hidden Cost of IT Innovation: Access to Emerging Technologies and the Gender Wage Gap, MIS Quarterly, (forthcoming).
David Hsu and Prasanna Tambe (2024), 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.

Teaching

Past Courses

ECON4999 - Independent Study
Individual study and research under the direction of a member of the Economics Department faculty. At a minimum, the student must write a major paper summarizing, unifying, and interpreting the results of the study. This is a one semester, one c.u. course.
OIDD2450 - Analytics & Digital Econ
Students who take this course will engage with the world of data science using tools such as Tableau and R that are becoming increasingly popular in industry. The first half of the course is designed for students with limited experience with data projects, and while familiarity with R, via courses such as STAT 4050 or STAT 4700, will be ideal preparation, students with other programming exposure can pick up the required skills via review sessions and self-instruction. The second half of the course extends students' experience to industry applications of text mining and machine learning and requires students to work with more unstructured data. Each week of the course will be devoted to analysis of a data set from a particular industry (e.g. HR, sports, fashion, real estate, music, education, politics, restaurants, non-profit work), which we will use to answer business questions by applying analytic techniques. The course is very hands-on, and students will be expected to become proficient at applying data to business decisions and at effectively analyzing large data sets to inform decisions about business problems.
OIDD2550 - A.I., Business, and Society
The progression of AI-based technologies promises to transform many aspects of business, labor, and even society. The goal of this course is to provide students with an understanding of the capabilities of modern AI technologies, with an emphasis on being able to critically assess where they can provide business and societal value, and where they may create new challenges. This course is intended to provide a framework for people who may have to confront the legal, ethical, and economic challenges that are likely to arise around AI. A goal of the course is to ensure that students who complete the course are comfortable enough in the inner-workings of these technologies to think critically across many AI contexts as well as different domains ranging from public policy, to criminal justice, to health inspections, HR, and marketing. The course is oriented around hands-on labs, exams, discussions, and presentations. Labs will reinforce your learning of how AI works, and how it is being used to solve business problems. A coding background is not required, but students should be willing to engage with code to a limited degree in order to complete the labs. During labs, students will combine data and algorithms to provide a foundation for understanding the deep challenges that AI brings to organizations. The class is particularly suitable for students who will be searching for jobs in the business of technology, such as product management and business analytics, as well as those interested in the larger social implications of AI technologies.
OIDD3990 - Supervised Study
This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.
WH2970 - Wh Industry Exploration
WIEP features short-term courses that focus on various industries and feature visits to businesses, lectures, extracurricular activities, and networking opportunities with alumni. Students must apply online: https://undergrad-inside.wharton.upenn.edu/wiep/

Activity

Latest Research

Prasanna Tambe and Tiantian Yang (2024), The Hidden Cost of IT Innovation: Access to Emerging Technologies and the Gender Wage Gap, MIS Quarterly, (forthcoming).

All Research