Published and Accepted Papers
Delegated Recruitment and Statistical Discrimination (w/ Jacob Kohlhepp) - NEW VERSION
Journal of Economic Theory 222 (December 2024): 105936 [SSRN]
We study how delegated recruitment shapes talent selection. Firms often pay recruiters via refund contracts, which specify a payment upon the hire of a suggested candidate and a refund if a candidate is hired but terminated during an initial period of employment. We develop a model of delegated recruitment and show that refund contracts with strong screening incentives lead to statistical discrimination in favor of candidates with more precise productivity information. This contrasts with a first-best direct-hiring benchmark, where the firm has option value from uncertain candidates. Under tractable parametric assumptions, we provide a closed-form expression for the unique equilibrium contract and show that it features strong screening incentive. As a result, candidates with lower expected productivity but more informative signals ("safe bets") are hired over candidates with higher expected productivity but less informative signals ("diamonds in the rough").
Journal of Economic Theory 222 (December 2024): 105936 [SSRN]
We study how delegated recruitment shapes talent selection. Firms often pay recruiters via refund contracts, which specify a payment upon the hire of a suggested candidate and a refund if a candidate is hired but terminated during an initial period of employment. We develop a model of delegated recruitment and show that refund contracts with strong screening incentives lead to statistical discrimination in favor of candidates with more precise productivity information. This contrasts with a first-best direct-hiring benchmark, where the firm has option value from uncertain candidates. Under tractable parametric assumptions, we provide a closed-form expression for the unique equilibrium contract and show that it features strong screening incentive. As a result, candidates with lower expected productivity but more informative signals ("safe bets") are hired over candidates with higher expected productivity but less informative signals ("diamonds in the rough").
Working Papers
Consumer Reviews and Dynamic Price Signaling - JMP (w/ Jacob Kohlhepp)
[SSRN] [PDF] [Slides]
Pricing decisions are crucial for managing a firm's reputation and maximizing profits. Consumer reviews reflect both the product quality and its price, with more favorable reviews being left when a product is priced lower. We study whether such review behavior can induce a firm to manipulate the review process by underpricing its product, or pricing it below current consumers' willingness to pay. We introduce an equilibrium model with a privately informed firm repeatedly selling its product to uninformed but rational consumers who learn about the quality of the product from past reviews and current prices. We show that underpricing can arise only when the firm reputation is low and then only under a specific condition on consumers' taste shock distribution, which we fully characterize. Rating manipulation unambiguously benefits consumers, because it operates via underpricing.
[SSRN] [PDF] [Slides]
Pricing decisions are crucial for managing a firm's reputation and maximizing profits. Consumer reviews reflect both the product quality and its price, with more favorable reviews being left when a product is priced lower. We study whether such review behavior can induce a firm to manipulate the review process by underpricing its product, or pricing it below current consumers' willingness to pay. We introduce an equilibrium model with a privately informed firm repeatedly selling its product to uninformed but rational consumers who learn about the quality of the product from past reviews and current prices. We show that underpricing can arise only when the firm reputation is low and then only under a specific condition on consumers' taste shock distribution, which we fully characterize. Rating manipulation unambiguously benefits consumers, because it operates via underpricing.
Efficient Information Aggregation in DeGroot Model
[paper]
We introduce a social planner in DeGroot model who aims to improve the time asymptotic information aggregation in finite observational networks. We show that in any connected network it is possible to achieve the best information aggregation by reassigning the attention individuals pay to each others’ opinions. We provide an algorithm that constructs a solution to this problem. We also identify the necessary and sufficient condition on the network for achieving the best information aggregation in average-based updating learning model for homogeneous private signals. Finally, we demonstrate an approach of increasing the speed of learning.
[paper]
We introduce a social planner in DeGroot model who aims to improve the time asymptotic information aggregation in finite observational networks. We show that in any connected network it is possible to achieve the best information aggregation by reassigning the attention individuals pay to each others’ opinions. We provide an algorithm that constructs a solution to this problem. We also identify the necessary and sufficient condition on the network for achieving the best information aggregation in average-based updating learning model for homogeneous private signals. Finally, we demonstrate an approach of increasing the speed of learning.
Work in Progress
Bayesian Echo Chambers
[slides]
I study the impact of echo chambers on social learning outcomes in a setting featuring agents who are aware of the presence of echo chambers and are fully Bayesian. I introduce a novel sequential learning model, where the agent’s private signal is correlated with her neighbors’ signals conditional on the state of the world (echo chambers), and agents sequentially learn about the changing state of the world by observing their neighbors’ actions and their own private signals. I introduce a measure of echo chambers in this social learning environment, that depends both on the network homophily and the signal correlation, and analyze how the presence of echo chambers affects opinion polarization and asymptotic learning accuracy.
[slides]
I study the impact of echo chambers on social learning outcomes in a setting featuring agents who are aware of the presence of echo chambers and are fully Bayesian. I introduce a novel sequential learning model, where the agent’s private signal is correlated with her neighbors’ signals conditional on the state of the world (echo chambers), and agents sequentially learn about the changing state of the world by observing their neighbors’ actions and their own private signals. I introduce a measure of echo chambers in this social learning environment, that depends both on the network homophily and the signal correlation, and analyze how the presence of echo chambers affects opinion polarization and asymptotic learning accuracy.