Vita

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

Click here for a PDF version

Jeff Gortmaker

Updated April 2019 • jeffgortmaker.com/cv/
(212) 720-7593 • jeff.gortmaker@ny.frb.org
(617) 895-8590 • jeff@jeffgortmaker.com

Education

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

Research Positions

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

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

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

Working Papers

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.

Work in Progress

“Employee Reactions to Financial Distress: Evidence from LinkedIn Activity”
with Jessica Jeffers and Michael Lee

We investigate the extent to which employees respond to their employer's financial distress. We use anonymized employee activity on LinkedIn, such as initiating connections, to infer employees' sensitivity to perceived jumps in the probability or cost of their employer's financial distress. We explore various dimensions of the cross-section of employees, including differences across seniority levels, and whether the employees leave or stay at the firm.

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

We uncover a new channel for the transmission of US monetary policy through the money market fund (MMF) industry: investor sophistication. We show that since 2015, sophisticated investors get an additional 12 basis points for a 100 basis point increase in the monetary policy stance. Comparing pre- and post-crisis monetary policy regimes, we also show that this sophistication channel has become stronger after the Fed's introduction of the reverse repo facility. We investigate the extent to which this differential pass-through to ultimate cash investors has implications for ultimate borrowers. Exploiting a regulatory dataset of rich security-level information, we relate changes in MMF portfolios to market power and lending relationships.

“Information Spillover and Disclosure Decisions”
with Anne Heinrichs

In this paper, we identify geographic clusters of competitive firms, and, using data on confidential treatment orders filed with the SEC, explore how a higher likelihood of information spillover affects firms' public disclosure decisions. Within clusters of firms, we evaluate various proxies for the likelihood of information spillover, including popularity of public transport and density of social organizations.

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.

Awards and Honors

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

Presentations

“Panel Discussion: Cloudera and Big Data Capabilities,” Research Town Hall, FRBNY, September 2018
“Money and Payments Data Warehouse,” Research and Statistics Developer Forum, FRBNY, April 2018
“Agents Who Know Their Principals,” New York State Economic Association Conference, October 2017

Teaching Positions

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

Other Positions

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