Research
Research interests
Applied Economics, Business Ecoconomics, Digital Economics, Digital Marketing, Economics of Information Systems, Industrial Organization, Platform Strategy, Quantitative Marketing.
Job Market Paper
- “Rating Systems and the End-Game Effect: When Reputation Works and When it Doesn’t”, with Elizaveta Pronkina (Amazon) and Michelangelo Rossi (Télécom Paris), 2024 (Current Draft).
- Reject & Resubmit, Marketing Science.
- Draft: Latest Version
- Abstract: Do rating systems provide incentives when sellers are about to exit a market? Using data from Airbnb, this paper examines how end-game considerations influence hosts’ effort decisions and how these effects depend on accumulated reputation capital. We exploit the implementation of the Home-Sharing Ordinance in Los Angeles, which forced ineligible hosts to leave the platform, to identify sellers who could anticipate their imminent exit. Effort is measured through guest ratings in categories directly tied to host behavior (communication, check-in, and cleanliness) and compared to ratings on location, which are unaffected by effort. In a Difference-in-Differences framework, we find that effort-related ratings decline significantly in the final transactions of exiting hosts, with larger reductions among listings holding stronger reputational capital or longer review histories. At the same time, competitive pressure from neighboring listings with strong reputation mitigates these declines: when nearby competitors have high ratings, hosts face higher reputational costs from underperforming even as they approach exit. These findings reveal that the effectiveness of reputation systems erodes near market exit, yet the competitive pressure from highly rated nearby listings can partially sustain service quality and trust on digital platforms.
Work in Progress
- “How to Assess and Improve the Quality of Crowd-Sourced Data Work”, with Louis Daniel Pape (Télécom Paris).
- Draft: available upon request.
- Abstract: Micro-tasking platforms enable the collection of data used to train machine learning algorithms and artificial intelligence. However, a classical Principal-Agent problem may limit the quality of the data produced by micro-taskers as firms do not always monitor the quality of the work done with sufficient frequency. We develop a structural model of equilibrium demand and supply of effort to measure quality and monitoring behavior. We estimate the parameters of this model using proprietary data from a leading micro-tasking platform. We find that metrics relying on observed task rejection severely underestimate the quality/effort with which data annotation tasks are performed. This suggest AI is being built with mis-annotated data. We discuss several mitigation strategies. We find that increasing the pay of micro-taskers along with more frequent monitoring could help improve the quality of the data. Finally, we discuss incentive schemes to induce higher quality work by relying on counter-factual simulations. We show that charging penalties for workers with a rejected task could induce higher effort and require less monitoring from the firms.
- “Can Platform Integration Mitigate Discovery Loss in the Digital Consumption Funnel?”, with Denzel Glandel (LMU Munich) and Tobias Kretschmer (LMU Munich).
- Draft available soon.
- “Replaceables? Profile Restarting on Digital Platforms. Evidence from the Short-Term Rental Market”, with Joerg Claussen (LMU Munich) and Michail Batikas (NOVA Lisbon).
- Draft available soon.
- “Picky Drivers: Reputation and Selectivity on a Car-Pooling Platform”, with Dianzhuo Zhu (University of Lille).
- Draft available soon.
Policy Reports and Other Publications
- “Crowdworking in France and Germany”, with Ulrich Laitenberger, Daniel Erdsiek, and Paola Tubaro, 2021.
- Country chapter Italy, with Riccardo Norbiato (PSE) in Social Protection of Non-Standard Workers and the Self-Employed During the Pandemic, Spasova S., Ghailani D., Sabato S. and Vanhercke B.