This page describes my academic background. For paper abstracts, project descriptions, and other information, see research, software, or the PDF version of this document.

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Jeff Gortmaker

Updated July 2019 •
(617) 895-8590 •


Harvard University
PhD in Business Economics, 2019 - Present

Columbia University
BA in Economics, Mathematics, and Statistics (minor), 2013 - 2017

Research Positions

Federal Reserve Bank of New York
Research Analyst, Research Group, Money and Payment Studies, 2017 - 2019

Columbia University Economics Department
RA for Andres Drenik, 2017
RA for Chris Conlon, 2016

Columbia Business School
RA for Anne Heinrichs, Research Internship Program, 2016

Working Papers

Labor Reactions to Financial Distress: Evidence from LinkedIn Activity
with Jessica Jeffers and Michael Lee

We investigate workers' reactions to signals of their firms' financial condition using anonymized networking activity on LinkedIn. We show significant increases in weekly connection formation following the announcement of possible impending downgrades to a firm's credit rating. More senior, more skilled, and less mobile workers have the strongest reactions, but increased connection activity appears in both workers who leave and workers who stay at the firm. Reactions to firms' financial conditions are asymmetric: we do not find evidence of a change in networking activity for positive credit rating news. We also do not find similar reactions following economic signals such as missed earnings. These results point to possible unique labor implications of debt financing.

Best Practices for Differentiated Products Demand Estimation with pyblp
with Chris Conlon

Differentiated products demand systems are a workhorse for understanding the price effects of mergers, the value of new goods, and the contribution of products to seller networks. BLP (1995) provide a flexible workhorse model which accounts for the endogeneity of prices and is based on the random coefficients logit. While popular, there exists no standardized generic implementation for the BLP estimator. This paper reviews and combines several recent advances related to the estimation of BLP-type problems and implements an extensible generic interface via the "pyblp" package. We conduct a number of Monte Carlo experiments and replications which suggest different conclusions than the prior literature: local optima appear to be rare in well-identified problems; it is possible to obtain good performance even in small samples and without exogenous cost-shifters, particularly when "optimal instruments" are employed along with supply-side restrictions.

Agents Who Know Their Principals: Social Connections, Institutional Investment, and Executive Compensation

I study how institutional investment and executive compensation are related to social connections between executives at public U.S. firms and employees of large institutional investors. I first develop and solve a principal-agent problem in which social connections reduce the marginal cost to investors of monitoring executives. Comparative statics and intuition indicate that from an optimal contracting perspective, executive-investor connections should be associated with greater investment, greater executive compensation, and lower pay-performance sensitivity. I validate each prediction in regression analysis of panel data spanning 1999 to 2015. Since similar predictions are provided by an alternate perspective that emphasizes executive influence over connected investors, I exploit an additional optimal contracting prediction about firm risk as well as the richness of my dataset to differentiate between the two perspectives. Overall, my analysis tentatively points towards optimal contracting more than executive influence as the dominant channel underlying the observed associations.

Authored Software

pyblp: BLP Demand Estimation with Python 3
with Chris Conlon

We develop an open-source Python 3 package for estimating demand with BLP-type random coefficients logit models. Features include supply-side moments, demographics, nesting parameters, optimal instruments, partial ownership matrices, analytic gradients, fixed effect absorption, fixed point acceleration, sparse grid integration, synthetic data construction, merger simulation, and parametric bootstrapping of post-estimation outputs.

pyhdfe: High Dimensional Fixed Effect Absorption with Python 3
with Anya Tarascina

We develop an open-source Python 3 package for absorbing high dimensional fixed effects. Features include matrix residualization, degrees of freedom computation, and common convergence criteria to facilitate algorithm comparisons.

Other Writing

The Transmission of Monetary Policy and the Sophistication of Money Market Fund Investors
with Marco Cipriani and Gabriele La Spada

In this Liberty Street Economics blog post, we describe the impact of recent rate increases on the yield paid by money market funds (MMFs) to their investors and show that the impact varies depending on investors’ sophistication.

Awards and Honors

First Place, Undergraduate Paper Competition, New York State Economic Association, 2017
Romine Prize, Best Honors Thesis in Economics, Columbia University, 2017
Junior Phi Beta Kappa, Columbia University, 2016
Parker Prize for Summer Research, Columbia University, 2016

Teaching Positions

TA, Columbia Business School, Corporate Transactions and Financial Modelling (MBA), 2017 - 2018
Tutor, Columbia University, Statistics Help Room, 2015 - 2017
TA, Columbia University, Calculus I, 2015

Other Positions

Fed Challenge Judge, FRBNY, 2017
Software Development RA, Law and Public Policy Lab, Columbia University, 2015 - 2016
Software Development Intern, Quovo, 2015