I am a Ph.D. candidate in Business Economics at Harvard University on the 2024-2025 academic job market.

My research focuses on industrial organization, with secondary interests in econometrics and finance.

You can download my CV here.

Feel free to reach me at jgortmaker@g.harvard.edu. For inquiries about software, you can contact me directly or use the issue tracker.

Job Market Paper

Open Source Software Policy in Industry Equilibrium

Open source software (OSS) is a form of public knowledge widely provided and relied on by the private sector. To study the effects of growing government involvement in this critical public good, I build a new empirical model where high-tech firms choose software inputs and developer labor in competitive equilibrium. For estimation, I create a new dataset of OSS and in-house investment for the global web development industry, where software choices are directly observable. I simulate counterfactuals to assess the global impact of China tightening its recent internet restrictions on cross-border OSS collaboration or increasing its financial support for domestic OSS. I find that stricter restrictions do little to boost domestic OSS investment. Instead, lost spillovers raise web development costs in China by $2 per dollar of disincentive and $7 globally. Heightened subsidies prove more effective at increasing domestic investment and cut global costs by $11 per dollar of subsidy—tripling if the US responds in kind.

Published and Accepted Papers

Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP
with Chris Conlon, 2024, Accepted at Journal of Econometrics

We delineate a general framework for incorporating many types of micro data from summary statistics to full surveys of selected consumers into Berry, Levinsohn, and Pakes (1995) style estimates of differentiated products demand systems. We extend recommended practices for BLP estimation in Conlon and Gortmaker (2020) to the case with micro data and implement them in our open-source package PyBLP. Monte Carlo experiments and empirical examples suggest that incorporating micro data can substantially improve the finite sample performance of the BLP estimator, particularly when using well-targeted summary statistics or “optimal micro moments” that we derive and show how to compute.

Best Practices for Differentiated Products Demand Estimation with PyBLP [Publication] [Appendix]
with Chris Conlon, 2020, RAND Journal of Economics, 51(4), 1108-1161

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. Berry, Levinsohn, and Pakes (1995) provide a flexible random coefficients logit model which accounts for the endogeneity of prices. This article reviews and combines several recent advances related to the estimation of BLP-type problems and implements an extensible generic interface via the PyBLP package. Monte Carlo experiments and replications suggest different conclusions than the prior literature: multiple local optima appear to be rare in well-identified problems; good performance is possible even in small samples, particularly when “optimal instruments” are employed along with supply-side restrictions.

Working Papers

Labor Reactions to Credit Deterioration: Evidence from LinkedIn Activity
with Jessica Jeffers and Michael Lee, Reject & Resubmit at Management Science

We provide the first analysis of workers’ on-the-job networking activity following their firm’s credit deterioration. Using high-frequency networking on LinkedIn, we show that workers initiate more connections immediately following adverse credit shocks. We propose a simple model in which workers are driven by concerns about both unemployment and reduced future prospects at their firm. Consistent with this model and distinct from prior work, we find that the stronger response of high-value workers is magnified when the firm is far from bankruptcy. We further show that elevated networking activity is associated with departures and diminished profitability in following years, consistent with on-the-job networking being a source of fragility for firms.

Software

PyBLP: BLP Demand Estimation [Documentation] [Issues]
with Chris Conlon

PyHDFE: High Dimensional Fixed Effect Absorption [Documentation] [Issues]
with Anya Tarascina

Other Writing

Demand Estimation Mixtape Session [Intro] [Day 1] [Exercise 1] [Day 2] [Exercise 2] [Day 3] [Exercise 3]
with Ariel Pakes