Lei Xu

Toulouse School of Economics

Contact:
Room MF008
21 Allée de Brienne
31015 Toulouse, France
E-mail: lei.xu@tse-fr.eu

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Professional Experience

Education

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Research

Technology Adoption in a Hierarchical Network [Job Market Paper] with Xintong Han (Concordia University)
This paper studies the effect of network structure on technology adoption, in the setting of the Python programming language. A major release of Python, Python 3, provides more advanced but backward-incompatible features to Python 2. We model the dynamics of Python 3 adoption by package developers. Python packages form a hierarchical network through dependency requirements. The adoption decision involves not only updating one's own code base, but also dealing with dependencies lacking Python 3 support. We build a dynamic model of technology adoption where each package makes an irreversible decision to provide support for Python 3. The optimal timing of adoption depends on the prediction of future states, for the package itself as well as each of its dependencies. With a complete dataset of package characteristics for all historical releases, we are able to draw the complete hierarchical structure of the network, and simplify the estimation by grouping packages into different layers based on the dependency relationship. We study how individuals' adoption decisions can propagate through the links. We also test various counterfactual policies that can promote a faster Python 3 adoption process.

What Makes Geeks Tick? A Study of Stack Overflow Careers with Tingting Nian (NYU Stern) and Luis Cabral (NYU Stern)
2nd Round R&R Minor Revision at Management Science 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.

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.