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 assess the quantitative relevance of expectations-driven sovereign debt crises, focusing on the southern European crisis of the early 2010s and the Argentine default of 2001. The source of multiplicity is the one proposed by Calvo. Crucial for multiplicity is an output process characterized by long periods of either high growth or stagnation, which we estimate using data for these countries. Our analysis suggests that expectations-driven debt crises are quantitatively relevant but state dependent, as they occur only during periods of stagnation. Expectations and how they respond to policy are the major factors explaining default rates and credit spread differences between Spain and Argentina.
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.
In this paper we construct a stochastic overlapping-generations general equilibrium model in which households are subject to aggregate shocks that affect both wages and asset prices.We use a calibrated version of the model to quantify how the welfare costs of severe recessions are distributed across different household age groups.The model predicts that younger cohorts fare better than older cohorts when the equilibrium decline in asset prices is large relative to the decline in wages, as observed in the data.Asset price declines hurt the old, who rely on asset sales to finance consumption, but benefit the young, who purchase assets at depressed prices.In our preferred calibration, asset prices decline more than twice as much as wages, consistent with the experience of the US economy in the Great Recession.A model recession is approximately welfare-neutral for households in the 20-29 age group, but translates into a large welfare loss of around 10% of lifetime consumption for households aged 70 and over.
We develop a model of political cycles driven by time-varying risk aversion. Agents choose to work in the public or private sector and to vote Democratic or Republican. In equilibrium, when risk aversion is high, agents elect Democrats—the party promising more redistribution. The model predicts higher average stock market returns under Democratic presidencies, explaining the well-known “presidential puzzle.” The model can also explain why economic growth has been faster under Democratic presidencies.
In the data, Democratic voters are more risk averse, and risk aversion declines during Democratic presidencies. Public workers vote Democratic, while entrepreneurs vote Republican, as the model predicts.