Welcome to the Management Division of Columbia Business School! Our website offers a window into the teaching and research activities of the division.
We explore the forces that affect the performance of organizations by studying individual and interpersonal behavior, group interactions, organizational structure and strategic interactions. The insights are relevant for established and large firms to small and growing entrepreneurial ventures. The members of our division are scholars and practitioners that shed light on management questions from different disciplines that include psychology, strategy, sociology, political science, and economics.
The Management Division prepares leaders for the future of business based on our theoretical and empirical research at the scientific frontier. We publish cutting edge research and translate it into insights that are practical and tangible for business leaders of today and tomorrow.
S.T. Lee Professor of Business; Chair of Management Division
Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and
We assess how changes in the scientific consensus around equilibrium climate sensitivity (ECS), as captured by the IPCC’s Fifth (AR5) and Sixth (AR6) Assessment Reports, impact policymakers’ willingness to take climate action. Taking the IPCC’s reports at face value, the ECS estimates in AR6 would have lowered a policymaker’s willingness to act on climate relative to AR5 due to a narrower "likely" range. However, Bayesian updating may reverse this conclusion.
Screening human capital based on signals such as job applications or entrepreneurial pitches is crucial for organizations. Signals are often informative insofar as they require differential knowledge and effort to produce. Generative AI (GAI) complicates screening by lowering the cost of producing impressive signals. We model the informational effects of GAI, showing that applicants' access to GAI can increase—but also decrease—an evaluator's screening mistakes. This result depends on how GAI affects experts' signals compared to non-experts'.
We explore how traders' equity capitalization influences asset prices in a framework that accounts for market power. In our model traders with capital constraints engage in transactions in an imperfectly competitive market. We demonstrate that looser capital constraints elevate both asset prices and price impact, which diminishes market liquidity. Using Canadian Treasury auction data, we illustrate how to apply our model to quantify these effects. We estimate the shadow costs of capital constraints by exploiting a temporary policy exemption during 2020-2021.
In evaluative contexts, evaluatees typically seek to present themselves in a favorable light, while evaluators ask penetrating questions to assess these claims. Here we develop a framework to identify curveball questions: ones that are on-topic yet perplexing (i.e., difficult to predict) relative to past discourse. We develop a language-based measure of curveball questions and apply it to a corpus of quarterly earnings calls.
