We study optimal service allocation through queues when agents have homogeneous preferences and must be incentivized to enter and remain in the queue. The designer controls entry, exit, service priority, and information about queue priorities. Under a mild regularity condition, the optimal mechanism admits agents only up to a cutoff queue length, never removes admitted agents, serves them according to a first-come, first-served (FCFS) rule, and provides no information beyond recommendations. FCFS is also necessary for optimality in a rich domain. The result identifies a new role for queue disciplines in regulating agents’ beliefs and dynamic incentives, and uncovers a hitherto unrecognized virtue of FCFS in this regard.
We develop a generalized theory of rationed fertility to analyze treatment effect heterogeneity in the child quantity-quality trade-off. An exogenous increase in fertility can be either desired or undesired. We derive a positive rationing income effect on child quality for desired fertility increases but a negative rationing income effect for undesired fertility increases. We propose an econometric framework to identify treatment effects of desired and undesired fertility increases and estimate a structural model to gauge the quantitative importance of the novel rationing income effect. Our study highlights the importance of distinguishing between desired and undesired changes when evaluating social programs.
We explore a model of non-Bayesian information aggregation in networks. Agents noncooperatively choose among Friedkin-Johnsen-type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission, leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule amplifies the disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, exacerbating the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups.
We study how signaling affects equilibrium outcomes and welfare in an online credit market, using detailed data on loan characteristics and borrower repayment. We build and estimate an equilibrium model in which a borrower may signal her default risk through the reserve interest rate. Comparing markets with and without signaling relative to the benchmark with no asymmetric information, we find that adverse selection destroys as much as 34% of total surplus, up to 78% of which can be restored with signaling. We also estimate backward-bending supply curves for some markets, consistent with the prediction made by Stiglitz and Weiss in 1981.
When choice is stochastic, revealed preference analysis often relies on random utility models. However, it is impossible to infer preferences without assumptions on the distribution of utility noise. We show that this difficulty can be overcome by using response time data. A simple condition on response time distributions ensures that choices reveal preferences without distributional assumptions. Standard models from economics and psychology generate data fulfilling this condition. Sharper results are obtained under symmetric or Fechnerian noise, where response times allow uncovering preferences or predicting choice probabilities out of sample. Application of our tools is simple and generates remarkable prediction accuracy.