Lei Xu

E-mail: [email protected]

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Professional Experience



Work In Progress

Technology Adoption in Dependency Networks: A Study of the Python Programming Language with Xintong Han (Concordia University)
R&R Management Science
This paper studies how network structure can affect the speed of adoption. In particular, we model the decision to adopt Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies adoption costs. Moreover, packages form dependency networks through dependency relationships with other packages, and they face an additional adoption cost if the dependency packages lack Python 3 support. We build a dynamic model of technology adoption that incorporates such a network and estimate it using a complete dataset of Python packages. Estimation results show that the average cost of one incompatible dependency is roughly three times the cost for updating one's own code. We conduct several counterfactual policies of targeted community-level promotion. The results show significant heterogeneous effects across communities and the role of the dependency network changes as packages become more interlinked. Moreover, we find packages have more incentive to free ride by delaying adoption if dependencies are more likely to adopt.

Platform Competition with Local Network Effects
This paper presents a dynamic model of price competition between two networks in which consumers value local network effects. Specifically, each consumer’s utility level depends on the number of her neighbors in the same network. Consumers in different neighborhoods choose their networks, and each network competes for new customers in different neighborhoods with a homogeneous entry price. I characterize equilibrium market structure with a combination of analytical and numerical solutions, and compare them to results from network effect models that are global, in which a consumer benefits from all other consumers in the same network. I provide sufficient conditions such that one firm dominants both local markets, as well as sufficient conditions that each firm is the dominant one in each local market.


Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market with Stephanie Assad (Queen's), Robert Clark (Queen's) and Daniel Ershov (TSE)
Journal of Political Economy 2023
We provide the first empirical analysis of the relationship between algorithmic pricing (AP) and competition by studying the impact of adoption in Germany's retail gasoline market, where software became widely available in 2017. Because adoption dates are unknown, we identify adopting stations by testing for structural breaks in AP markers, finding most breaks to be around the time of widespread AP introduction. Because station adoption is endogenous, we instrument using headquarters adoption. Adoption increases margins, but only for non-monopoly stations. In duopoly stations, margins increase only if both stations adopt, suggesting that AP has a significant effect on competition.

Seller Reputation and Price Gouging: Evidence from the COVID-19 Pandemic with Luis Cabral (NYU Stern)
Economic Inquiry 59 (2021)
We test the theory that seller reputation moderates the effect of demand shocks on a seller's propensity to price gouge. From mid January to mid March 2020, 3M masks were priced 2.72 times higher than Amazon sold them in 2019. However, the difference (in price ratios) between a post-COVID-19 entrant and an established seller is estimated to be about 1.6 at times of maximum scarcity, that is, post-COVID-19 entrants price at approximately twice the level of established sellers. Similar results are obtained for Purell hand sanitizer. We also consider cumulative reviews as a measure of what a seller has to lose from damaging its reputation and, again, obtain similar results. Finally, we explore policy implications of our results.

What Makes Geeks Tick? A Study of Stack Overflow Careers with Tingting Nian (UC Irvine) and Luis Cabral (NYU Stern)
Management Science 66 (2020)
Many online platforms rely on users to voluntarily provide content. What motivates users to contribute content for free, however, is not well understood. In this paper, we use a revealed-preference approach to show that career concerns play an important role in user contributions to Stack Overflow, the largest online Q&A community. We investigate how activities that can enhance a user's reputation vary before and after the user finds a new job. We contrast this reputation-generating activity with activities that do not improve a user's reputation. After finding a new job, users contribute 23.7% less in reputation-generating activity; by contrast, they reduce their non-reputation-generating activity by only 7.4%. These findings suggest that users contribute to Stack Overflow in part because they perceive such contributions as a way to improve future employment prospects. We provide direct evidence against alternative explanations such as integer constraints, skills mismatch, and dynamic selection effects.