Embedded Files
1. Digital Economics
Personalized content, engagement, and monetization in a mobile puzzle game, with Christian Helmers, Alessandro Iaria, Julian Runge, and Stefan Wagner (Volume 98, January 2025, International Journal of Industrial Organization)
Abstract: Digital technologies have reduced the cost of collecting detailed information on consumer characteristics and behavior. Despite the large literature on the consequences of using these data to personalize prices, little is known about content personalization. Using detailed player-level data from a mobile puzzle game and a novel structural model of player behavior, we investigate the effects on revenue of personalizing game difficulty using observable player characteristics. Our results show that, while average difficulty across players is successfully set by the game developer to maximize revenue, personalization can further increase revenue by 71%. Personalized difficulty leads to an overall increase in player engagement and, consequently, revenue generation in the form of in-app purchases. Although the largest relative increase in revenue comes from the smallest spenders, most of the absolute increase in revenue comes from a further increase in spending by the largest spenders.
CESifo Working Paper No. 11226
Abtract: This paper examines the impact of the Digital Markets Act (DMA) on consumer behavior, focusing on changes in Google’s search result presentation in the European Union (EU). Specifically, it investigates the effects of Google’s removal of clickable maps in search results, a modification implemented in January 2024. This change forces users to perform additional searches to access Google Maps or alternative mapping services, thus increasing search costs. Using a difference-in-differences approach, we compare Google search volumes from EU to non-EU countries before and after the implementation of the DMA. By eliminating Google Maps’ advantage of being only one click away from Google Search users, we find that EU consumers search significantly more for online mapping services. We measure a 25% and 18% increase in Google’s search volume for the query terms maps and google maps, resulting in an excess of 34,407,000 and 8,901,000 searches over six months, respectively. This search increase suggests potential exposure to alternative mapping services. However, searches for services like apple maps and bing maps also rose, but not as significantly. Moreover, traffic data shows a non-significant decrease in visits to Google Maps, suggesting minimal migration to alternative services. These findings indicate that removing Google’s one-click advantage can lead to higher search costs for users without significantly boosting the discovery or adoption of alternative mapping services in the short run.
Platform Information Provision : Evidence from an Online Auction Platform, with Pierre-Francois Darlas
Abtract: Digital platforms have reduced search costs, fostering niche product markets. However, these markets often suffer from limited and asymmetric information between buyers and sellers due to a lack of consumer feedback. This paper examines Catawiki's solution, where over 240 experts provide value estimates for rare collectibles being auctioned on a digital platform. Using data from 57,000 listings, we analyze the impact of these estimates on final prices and seller behavior. By leveraging both minimum and maximum expert estimates, we isolate the effect of increasing the maximum estimate while holding the minimum estimate fixed. Our findings indicate that higher expert estimates increase final bidden prices, suggesting buyer trust. Sellers also adjust their behavior by setting fewer reserve prices for items with high estimates, leading to more bids. Despite potential conflicts of interest stemming from the platform's dual role as matchmaker and advisor, our results show that expert estimates are influential even when potentially overinflated. This study underscores the critical role of platform-provided information in enhancing market efficiency.
How to improve both the pay and quality of microtasking platforms ? (Working Paper Arriving Soon with Chiara Belletti)
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. As firms do not always monitor the quality of the work done with sufficient frequency, a moral hazard problem may arise. 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 underestimate quality. We discuss several mitigation strategies. We suggest a more accurate back-of-the-envelope correction based on a firm’s own monitoring rate to increase the employers awareness about the potential data quality. Finally, we discuss incentive schemes to induce higher quality work. Using 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.
Study of Deemed Suppliers on VAT Tax Fraud on French Digital Platforms (Paquet TVA, DAC 7) (joint work with French Ministry of Public Finances (DGFIP, Matthieu Chtioui).
2. Antitrust Policy and Labor Markets
Wages, Hires, and Labor Market Concentration, with Ioana Marinescu and Ivan Ouss (2021, Journal of Economic Behavior and Organization)
Abstract: How does employer market power affect workers? We compute the concentration of new hires by occupation and commuting zone in France using linked employer-employee data. Using instrumental variables, we find that a 10% increase in labor market concentration decreases hires by 3.2% and their hourly wage by nearly 0.5%, as hypothesized by monopsony theory. Based on a simple merger simulation, we find that a merger between the top two employers in the retail industry would be most damaging, with about 30 million euros in annual loss to the wage bill of new hires, and a 3000 decrease in annual hires.
Media : Libération , Concurrences , Resolution Foundation, Groupe d'Experts sur le SMIC
Non-Compete Clauses and Salary Arbitration : Quasi-experimental Evidence from Major League Baseball (submitted)
Abstract: Workers subject to a non-compete clause can suffer wage loss, raising the question of the adequate policy response. This article shows that eligibility to salary arbitration, by which a third party can be called upon to resolve a wage dispute, can be an effective way to raise incomes. Exploiting a quasi-random discontinuity in the rule determining eligibility for players in Major League Baseball subject to non-compete clauses, this article leverages a Regression Discontinuity Design to identify the effects of salary arbitration eligibility. Workers right above the eligibility threshold have wages over 50% higher than those right below. No severe side effects were detected, including compression of the wage distribution, lower productivity, or higher unemployment.
How to Detect and Measure Labor Market Collusion? (Job Market Paper)
Abstract: With the aim of expanding the set of tools available to antitrust practitioners, this paper develops two new econometric methods to detect and measure the effects of labor market cartels. The first method is reduced form and aims to estimate wage loss. It exploits the inter-percentile difference between high earners and low earners within a difference-in-differences framework. This approach is simple to implement, can easily be explained to non-economists, measures heterogeneous effects, and requires no additional data compared to that necessary for a before-after analysis. The method is illustrated by revisiting the 1986-8 case of collusion in Major League Baseball, measuring an average yearly income loss of 26%. Second, this paper develops a structural model of labor market competition for the purpose of detecting collusive behavior. Applied to the data, it reveals that at the beginning of the cartel, there were heightened barriers to mobility across firms, rising profits, and a decreasing labor share of income. Surprisingly, these patterns sustain past the end date of the cartel, suggesting important and underestimated long-run effects. Finally, the structural model is used to simulate counter-factual wages, revealing that the yearly average wage should have been at least 30% higher.
3. Applied Econometrics
Abstract: Log-linear models are prevalent in empirical research. Yet, how to handle zeros in the dependent variable remains an unsettled issue. This article clarifies it and addresses the "log of zero'' by developing a new family of estimators called iterated Ordinary Least Squares (iOLS). This family nests standard approaches such as log-linear and Poisson regressions, offers several computational advantages, and corresponds to the correct way to perform the popular log(Y+1) transformation. We extend it to the endogenous regressor setting (i2SLS) and overcome other common issues with Poisson models, such as controlling for many fixed-effects. We also develop specification tests to help researchers select between alternative estimators. Finally, our methods are illustrated through numerical simulations and replications of landmark publications.
Among the Top 525 most downloaded papers of SSRN.
Stata Software available from : https://github.com/ldpape/iOLS-i2SLS
Media : Empirical Legal Studies, Eviews, David Giles' Blog.
Abstract: In his first term in office, President Donald Trump appointed 174 judges to U.S. District Courts. Weanalyze the impact of these appointments on the ideology of the judges that helm the trial courts ofthe U.S. federal judiciary, as well as the ability of ideologically-motivated litigants to engage in “judgeshopping.” Using judge-level information on case assignment rates and ideology in an instrumenteddifference-in-differences approach, we show that Trump’s appointments both shifted the ideology ofthe U.S. district court bench significantly to the “right” and increased litigants’ ability to select a veryconservative judge with high probability. The latter effect is driven by Trump’s appointment of veryconservative judges to seats in rural court divisions where all or substantially all cases have traditionallybeen assigned to a single judge. We present evidence that greater ability to engage in ideologically-motivated judge shopping led to a large increase in civil rights case filings in impacted divisions, particularlycases concerning subjects with a recognized ideological valence, such as reproductive rights, religiousliberty, and immigration. A simple counterfactual exercise reveals that randomizing case assignment atthe judicial district level would reduce the number of politically motivated case filings by approximately 62% over a four-year period.
Stata Software for Instrumented Differences-in-Differences (IVDID) based on Miyaji (2025) available at : https://github.com/ldpape/ivdid
Google Sites
Report abuse