Calendrier du 15 mai 2023
Roy Seminar (ADRES)
Du 15/05/2023 de 17:00 à 18:15
Salle R1-09, Campus Jourdan, 75014 Paris
MARIOTTI Thomas (TSE)
*Keeping the Agents in the Dark: Private Disclosures in Competing Mechanisms
écrit avec Andrea Attar, Eloisa Campioni, and Alessandro Pavan
We study the design of market information in games in which several principals contract with several privately informed agents. We investigate a new dimension of these games, namely, the possibility for the principals to asymmetrically inform the agents about how their mechanisms respond to their messages. We document two effects of such private disclosures. First, they raise the principals' individual payoff guarantees, protecting them against their competitors' threats. Second, by enlarging the set of incentive-compatible correlation patterns between the principals' decisions and the agents' types, they can be used to support equilibrium outcomes and payoffs that cannot be supported in their absence, no matter how rich the message spaces are allowed to be. These results challenge the folk theorems à la Yamashita (2010) and the canonicity of the universal mechanisms of Epstein and Peters (1999), calling for a novel approach to the analysis of these games. The one proposed here retains various elements of standard mechanism design theory while accommodating for competition in mechanisms and private disclosures.
Econometrics Seminar
Du 15/05/2023 de 16:00 à 17:15
CREST, room 3001
NOACK Claudia (Oxford)
Flexible Covariate Adjustments in Regression Discontinuity Designs
Empirical regression discontinuity (RD) studies often use covariates to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than the linear adjustment estimators that are currently used widely in practice. Our approach can accommodate a possibly large number of either discrete or continuous covariates. It involves running a standard RD analysis with an appropriately modified outcome variable, which takes the form of the difference between the original outcome and a function of the covariates. We characterize the function that leads to the estimator with the smallest asymptotic variance, and show how it can be estimated via modern machine learning, nonparametric regression, or classical parametric methods. The resulting estimator is easy to implement, as tuning parameters can be chosen as in a conventional RD analysis. An extensive simulation study illustrates the performance of our approach.
Texte intégral
Du 15/05/2023 de 13:00 à 14:00
Maison des Sciences Economiques, Salle 116
Paris Migration Economics Seminar
Du 15/05/2023 de 12:30 à 13:30
Salle R1.14, Campus Jourdan
FELFE Christina (U. Würzburg)
On the formation of ingroup bias: The role of ethnic diversity and cultural distance
Régulation et Environnement
Du 15/05/2023 de 12:00 à 13:15
Salle R1-09, Campus Jourdan, 75014 Paris
ELLIOTT Robert (Burmingham)
*“Centralising the enforcement of environmental regulations: Using machine learning to aid policy evaluation in China”
écrit avec Matt Cole, Bowen Lu, TuanVan Vu and Zongbo Shi
To overcome key challenges in environmental policy evaluation we use machine
learning based weather normalisation techniques to strip out the effect of weather
on air pollution estimates. Combined with Augmented Synthetic Control Methods
(ASCM) we provide a causal estimate of the impact of China’s decision to centralise
environmental policy enforcement. Focusing on Hebei province we find that the
recently introduced Central Environmental Inspection Policy led to a short term
reduction in PM2.5 and SO2 immediately after the inspection. However, within 3
months of the inspection team leaving, pollution levels had returned to previous levels.
Comparisons with Difference-in-Difference estimations show the importance of both
weather normalising and using an ASCM approach, particularly in the absence of
parallel pre-trends.