Hi, I am a post-doctoral researcher at Toulouse School of Economics.
My main interests are in microeconomic theory, particularly rankings of statistical experiments and information aggregation. I apply my findings to political economy and finance.
You can find my cv here.
Email: kailin.chen@tse-fr.eu
Working Papers
Ranking Statistical Experiments via the Linear Convex Order and the Lorenz Zonoid: Economic Applications. (supplementary material)
Revise & Resubmit @ Econometrica
Previously circulated under the title ``Experiments in the Linear Convex Order".
Extended abstract in EC’25.
This paper introduces a novel ranking of statistical experiments, the linear-Blackwell (LB) order, which can equivalently be characterized by (i) the dispersion of the induced posterior and likelihood ratios in the sense of the linear convex order, (ii) the size of the Lorenz zonoid (the set of statewise expectation profiles), or (iii) the variability of the posterior mean. We apply the LB order to compare experiments in binary-action decision problems and in decision problems with quasi-concave payoffs, as analyzed by Kolotilin, Corrao, and Wolitzky (2025). We also use it to compare experiments in moral hazard problems, building on Holmström (1979) and Kim (1995), and in screening problems with ex post signals.
Fishing for Approval. (with Stephan Lauermann and Mehmet Ekmekci)
Revise & Resubmit @ Review of Economic Studies
This paper analyzes situations in which a candidate requires a single endorsement from one of a group of approval agencies and undertakes a costly search to obtain it. The candidate's ability to approach multiple agencies in succession and ``fish'' for approval exempts high-quality candidates from accidental rejections but enables low-quality candidates to obtain erroneous approvals. While the possibility of fishing hurts the agencies in some particular cases, it will be generally beneficial for them. In particular, when the number of agencies becomes large, the outcome approaches the agencies' first-best outcome.
Revise & Resubmit @ Theoretical Economics
This paper studies an exponential bandit model in which a group of agents collectively decide whether to undertake a risky action. This action is implemented if the fraction of agents voting for it exceeds a predetermined threshold. Building on Strulovici (2010), which assumes the agents' payoffs are independent, we explore the case in which the agents' payoffs are correlated. During experimentation, each agent learns individually whether she benefits from the risky action; in this way, she also gains information about its overall desirability. Furthermore, each agent is able to learn indirectly from the others, because in making her decisions, she conditions on being pivotal (i.e., she assumes her vote will determine the collective outcome). We show that, when the number of agents is large, increasing the threshold for implementing the risky action leads to increased experimentation. However, information regarding the overall desirability of the risky action is effectively aggregated only if the threshold is sufficiently low.
Previously circulated under the title ``Learning from Strategic Sources".
Extended abstract in EC’24.
This paper studies information aggregation in informal elections by examining a cheap-talk model with multiple senders and one receiver. Each sender observes a noisy private signal about an unknown state and sends a message; the receiver observes the message tally and chooses the final policy choice. Unlike in formal elections, the tally does not mechanically determine the policy. We establish an equilibrium no-conflict property according to which communication can reveal only information that induces the same preferred policy for both the receiver and the senders. Building on this property, we show that whether information aggregation and complete learning occur depends not only on the magnitude of the conflict of interest between the senders and the receiver but more fundamentally on the nature of that conflict. In particular, the presence of a disagreement state, in which the senders and the receiver have opposing policy preferences, precludes full information aggregation and complete learning even as the number of senders grows without bound. We also identify a discontinuity in information transmission relative to the implications of the existing literature. Finally, introducing a mediator can improve information transmission and restore efficiency.
Short Notes