J. Aislinn Bohren

Designing Experiments to Measure Spillover Effects

This paper formalizes the design of experiments intended specifically to study spillover effects. By first randomizing the intensity of treatment within clusters and then randomly assigning individual treatment conditional on this cluster-level intensity, a novel set of treatment effects can be identified. The paper develops a formal framework for consistent estimation of these effects, provides explicit expressions for power calculations, and shows that the power to detect average treatment effects declines precisely with the quantity that identifies the novel treatment effects. A demonstration of the technique is provided using a cash transfer program in Malawi.

Informational herding with model misspecification

This paper demonstrates that a misspecified model of information processing interferes with long-run learning and offers an explanation for why individuals may continue to choose an inefficient action, despite sufficient public information to learn the true state. I consider a social learning environment where agents draw inference from private signals, public signals and the actions of their predecessors, and sufficient public information exists to achieve asymptotically efficient learning. Prior actions aggregate multiple sources of information; agents face an inferential challenge to distinguish new information from redundant information. I show that when individuals significantly overestimate the amount of new information contained in prior actions, beliefs about the unknown state become entrenched and incorrect learning may occur. On the other hand, when individuals sufficiently overestimate the amount of redundant information, beliefs are fragile and learning is incomplete. When agents have an approximately correct model of inference, learning is complete - the model with no information-processing bias is robust to perturbation.

Using Persistence to Generate Incentives in a Dynamic Moral Hazard Problem

I study how the persistence of past choices can be used to create incentives in a continuous time stochastic game in which a large player, such as a i¬ rm, interacts with a sequence of short-run players, such as customers. The long-run player faces moral hazard and her past actions are imperfectly observed – they are distorted by a Brownian motion. Persistence refers to the fact that actions impact a payoi¬€relevant state variable, e.g. the quality of a product depends on both current and past investment choices. I obtain a characterization of actions and payoi¬€s in Markov Perfect Equilibria (MPE), for a i¬ xed discount rate. I show that the perfect public equilibrium (PPE) payoi¬€ set is the convex hull of the MPE payoi¬€ set. Finally, I derive sui¬ƒcient conditions for a MPE to be the unique PPE. Persistence creates ei¬€ective intertemporal incentives to overcome moral hazard in settings where traditional channels fail. Several applications illustrate how the structure of persistence impacts the strength of these incentives.

The Dynamics of Discrimination: Theory and Evidence

We model the dynamics of discrimination and show how its evolution can identify the underlying cause. We test these theoretical predictions in a field experiment on a large online platform where users post content that is evaluated by other users on the platform. We assign posts to accounts that exogenously vary by gender and history of evaluations. With no prior evaluations, women face significant discrimination, while following a sequence of positive evaluations, the direction of discrimination reverses: posts by women are favored over those by men. According to our theoretical predictions, this dynamic reversal implies discrimination driven by biased beliefs.

Stochastic Games in Continuous Time: Persistent Actions in Long-Run Relationships, Second Version

This paper studies a class of continuous-time stochastic games in which the actions of a long-run player have a persistent eect

Optimal Contracting with Costly State Verification, with an Application to Crowdsourcing

A firm employs workers to obtain costly unverifiable information for example, categorizing the content of images. Workers are monitored by comparing their messages. The optimal contract under limited liability exhibits three key features: (i) the monitoring technology depends crucially on the commitment power of the firm virtual monitoring, or monitoring with arbitrarily small probability, is optimal when the firm can commit to truthfully reveal messages from other workers, while monitoring with strictly positive probability is optimal when the firm can hide messages (partial commitment), (ii) bundling multiple tasks reduces worker rents and monitoring inefficiencies; and (iii) the optimal contract is approximately efficient under full but not partial commitment. We conclude with an application to crowdsourcing platforms, and characterize the optimal contract for tasks found on these platforms.

Informational Herding with Model Misspecification, Second Version

This paper demonstrates that a misspecified model of information processing interferes with long-run learning and allows inefficient choices to persist in the face of contradictory public information. I consider an observational learning environment where agents observe a private signal about a hidden state, and some agents observe the actions of their predecessors. Prior actions aggregate multiple sources of correlated information about the state, and agents face an inferential challenge to distinguish between new and redundant information. When individuals significantly overestimate the amount of new information, beliefs about the state become entrenched and incorrect learning may occur. When individuals sufficiently overestimate the amount of redundant information, beliefs are fragile and learning is incomplete. Learning is complete when agents have an approximately correct model of inference, establishing that the correct model is robust to perturbation. These results have important implications for timing, frequency and strength of policy interventions to facilitate learning.