Bernard Black
Bernard Black

Nicholas J. Chabraja Professor, Northwestern University Law School
Professor of Finance (Courtesy)

Print Overview

Bernard S. Black is Professor of Finance at Kellogg School of Management, and will be Nicholas D. Chabraja Professor at Northwestern University School of Law and Kellogg School of Management (beginning Sept. 1, 2010). He is also managing director of the Social Science Research Network, and founding chairman of the annual Conference on Empirical Legal Studies. Professor Black received a B.A. from Princeton University, an M.A. in physics from University of California at Berkeley and a J.D. from Stanford Law School. He was Professor of Law at Stanford Law School from 1998-2004 and at Columbia Law School from 1988-1998. His principal research areas are law and finance, international corporate governance, health care and medical malpractice, and corporate and securities law. His books include To Sue is Human: A Profile of Medical Malpractice Litigation (forthcoming 2010, with David Hyman, William Sage, Charles Silver, and Kathryn Zeiler), The Law and Finance of Corporate Acquisitions (2nd ed., with Ronald Gilson, 1995 and supplement 2003) and Guide to the Russian Law on Joint Stock Companies (with Reinier Kraakman and Anna Tarassova (1998).

Print Vita
J.D., 1982, Law, Stanford Law School, Stanford University
M.A. (A.B.D. in physics), 1977, Physcis, University of California, Berkeley
A.B., 1975, Physics, Princeton University, magna cum laude

Academic Positions
Nicholas D. Chabraja Professor, School of Law and Kellogg School of Management, Northwestern University, 2010-present
Professor of Finance, McCombs School of Business, University of Texas, 2004-2010
Hayden W. Head Regents Chair for Faculty Excellence, School of Law, University of Texas, 2004-2010
Professor of Law, (George E. Osborne Professor 2003-2004), Stanford Law School, Stanford University, 1998-2004
Professor of Law, Columbia Law School, Columbia Univerisity, 1992-1998
Associate Professor, Columbia Law School, Columbia University, 1988-1991

Print Research

Print Teaching
Full-Time / Evening & Weekend MBA
Summer Research Internship (LAWSTUDY-699)
The Summer Research Internship will offer students an opportunity to do substantive work with a professor during the summer months, learn about the process of legal scholarship, maintain resume continuity, and earn two credits toward graduation. It also can fill the gap in research opportunities for students who do not yet qualify for the Senior Research program. The internship would be two credits, Pass/ No Credit and limited to the summer session - Interns would be required to meet with the faculty member once per week for one hour. - Meetings can be held on a distance basis. - The interns and faculty member would agree on the form of a "deliverable" work product due at the end of the summer session. - Regardless of the form of the work product, participation in the internship would not count toward the graduation writing requirement, nor will it be a vehicle for commencing work on an anticipated journal note or comment. - Paid research assistants do not qualify for this program. Students must elect paid RA status or this for-credit program. To enroll, complete a counter registration form, signed by the supervising professor to reflect approval. Enrollment will be manually accomplished in the Registration and Records Office.

Introduction to Applied Econometrics III: Research Design for Causal Inference (MECS-478-0)
Much empirical research stands or falls on whether it provides a credible basis for causal inference: Does a change in the predictor variable x cause a change in an outcome variable y. This course introduces methods for credible causal inference, emphasizing intuition and hands-on work with real data. It follows Applied Econometrics I and II, but should be accessible to students with other reasonable prior methods training (e.g., non-Kellogg graduate students). Topics include: Rubin causal model (causal inference as missing data problem); inference in randomized experiments; difference-in-differences (including triple differences; event studies; synthetic controls); regression discontinuity; the logic behind instrumental variables (you will already know the math); matching, propensity score weighting, and subclassification methods for observational studies; assessing and achieving covariate balance; continuous treatments; causal inference with panel data; treatment effect heterogeneity and local treatment effects; when to use (and not use) regression methods; Rosenbaum and Manski bounds.