Toulouse School of Economics
21 Allée de Brienne
31015 Toulouse, France
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- Postdoctoral Researcher Toulouse School of Economics, 2016 - 2019
- Visiting Scholar Rotman School of Management, University of Toronto, Business Economics, 2014 - 2015
- Visiting Scholar NYU Stern School of Business, Business Economics, 2012 - 2014
- Ph.D. McGill University, Economics, 2010 - 2016
- M.A. McGill University, Economics, 2009 - 2010
- B.A. McGill University, Joint Honours in Economics and Finance, 2005 - 2009
- Empirical IO
- Applied Micro
Technology Adoption in Input-Output Networks
[Job Market Paper]
with Xintong Han (Concordia University)
This paper investigates the role of network structure on technology adoption. In particular, we study how the network of individual agents can slow down the speed of adoption. We study this issue in the context of the Python programming language by modeling the decisions to adopt Python version 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies adoption costs. Moreover, packages form an input-output network through dependency on other packages in order to avoid writing duplicate code, and they face additional adoption costs from dependencies without Python 3 support. We build a dynamic model of technology adoption that incorporates the input-output network. With a complete dataset of package characteristics for historical releases and user downloads, we draw a complete input-output network and develop a new estimation method based on the dependency relationship. Estimation results show the average cost of one incompatible dependency is one-third the cost of updating a package's code. Simulations show the input-output network contributes to 1.5 years of adoption inertia. We conduct counterfactual policies of promotion in subcommunities and find significant heterogeneous effects on the adoption rates due to differences in network structure.
What Makes Geeks Tick? A Study of Stack Overflow Careers
with Tingting Nian (UC Irvine) and Luis Cabral (NYU Stern) Accepted 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.