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
Bank balance sheet lending is commonly viewed as the predominant form of lending. We document and study two margins of adjustment that are usually absent from this view using microdata in the $10 trillion U.S. residential mortgage market. We first document the limits of the shadow bank substitution margin: shadow banks substitute for traditional “deposit-taking” banks in loans which are easily sold, but are limited from activities requiring on-balance-sheet financing.
In a monetary union, the risk-free rate cannot respond to country-level fiscal positions, leaving only default spreads and convenience yields to respond. Empirically, we find that convenience yields explain a large share of the variation in Eurozone sovereign bond yields. Eurozone countries earn larger convenience yields when they experience larger surpluses, suggesting convenience yields are important fiscal shock absorbers.
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'.
