Daniel Martin
Daniel Martin

Visiting Assistant Professor of Managerial Economics & Decision Sciences

Print Overview

Daniel Martin is a Visiting Assistant Professor at Kellogg and an Assistant Professor at the Paris School of Economics.  He studies information economics, including topics such as why consumers only pay partial attention to information about product quality and why firms do not voluntarily disclose information about product quality.  His research has appeared in the top journals of the American Economic Association and the Royal Economic Society.  Before receiving a PhD in Economics from New York University, Daniel was the co-founder of a small business, which is now one of the leading providers of IT services in the Carolinas.

Print Vita
PhD, 2013, Economics, New York University
MBA, 2002, University of North Carolina
MA, 2007, Economics, New York University
BA, 1998, Economics, Vanderbilt University

Academic Positions
Assistant Professor, Paris School of Economics, 2013-present
Visiting Assistant Professor, Kellogg School of Management, Northwestern University, 2014-2015

Other Professional Experience
Co-Founder, WorkSmart Inc., 2001-2009

Print Research
Caplin, Andrew and Daniel Martin. Forthcoming. A Testable Theory of Imperfect Perception. Economic Journal.
Caplin, Andrew, Mark Dean and Daniel Martin. 2011. Search and Satisficing. American Economic Review. 101: 2899-2922.
Working Papers
Martin, Daniel, Ginger Jin and Michael Luca. Failures of Unraveling in Disclosure Experiments.
Martin, Daniel. Bayesian Revealed Preferences.
Martin, Daniel and Mark Dean. Measuring Rationality with the Minimum Cost of Revealed Preference Violations.
Martin, Daniel. Strategic Pricing with Rational Inattention to Quality.

Print Teaching
Full-Time / Part-Time MBA
Business Analytics II (DECS-431-0)
This sequel to DECS-430 extends the statistical techniques learned in that course to allow for the exploration of relationships between variables, primarily through multivariate regression. In addition to learning basic regression skills, including modeling and estimation, students will deepen their understanding of hypothesis testing and how to make inferences and predictions from data. Students will also learn new principles such as identification and robustness. The course has an intense focus on managerially relevant applications, cases and interpretations.