A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
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Results 115 resources
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In randomized controlled trials, treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes that treat all units with probability one half, a certain matched-pair design achieves the maximum statistical precision for estimating the average treatment effect. In an important special case, the optimal design pairs units according to the baseline outcome. In a simulation study based on datasets from ten randomized controlled trials, this design lowers the standard error for the estimator of the average treatment effect by 10 percent on average, and by up to 34 percent, relative to the original designs.
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We provide evidence that industries' supply curves are convex. To guide our empirical analysis, we develop a model in which capacity constraints at the firm level generate supply curves that are convex in logs at the industry level. The industry's capacity utilization rate is a sufficient statistic for the supply elasticity. Using data on capacity utilization and three different instruments, we estimate the supply curve and find robust evidence for an economically sizable degree of convexity. The nonlinearity we identify has several macroeconomic implications, including that responses to shocks are state dependent and that the Phillips curve is convex.
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We exploit a policy designed to randomly allocate roommates in a large South African university to investigate whether interracial interaction affects stereotypes, attitudes and performance. Using implicit association tests, we find that living with a roommate of a different race reduces White students' negative stereotypes towards Black students and increases interracial friendships. Interaction also affects academic outcomes: Black students improve their GPA, pass more exams and have lower dropout rates. This effect is not driven by roommate's ability.
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We study a dynamic stopping game between a principal and an agent. The principal gradually learns about the agent's private type from a noisy performance measure that can be manipulated by the agent via a costly and hidden action. We fully characterize the unique Markov equilibrium of this game. We find that terminations/market crashes are often preceded by a spike in manipulation intensity and (expected) performance. Moreover, due to endogenous signal manipulation, too much transparency can inhibit learning and harm the principal. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.
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Increases in the minimum wage can substantially reduce earnings inequality. To demonstrate this, we combine administrative and survey data with an equilibrium model of the Brazilian labor market. We find that a 128 percent increase in the real minimum wage in Brazil between 1996 and 2018 had far-reaching spillover effects on wages higher up in the distribution. The increased minimum wage accounts for 45 percent of a large fall in earnings inequality over this period. At the same time, the effects of the minimum wage on employment and output are muted by reallocation of workers toward more productive firms.
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In a model with multiple Pareto-ranked equilibria, we show that the set of equilibria shrinks if we allow trade in assets that pay based on the realization of a sunspot acting as an equilibrium-selection device. When the probability of a low-output outcome is high, the desire to insure against it leads the poor to promise large transfers to the rich in the high-output state. The rich then lose the incentive to exert the effort needed to sustain the high output. Thus the opening of financial markets may destroy the high equilibrium.
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A regulator faces a stream of agents engaged in crimes with stochastic returns. The regulator designs an amnesty program, committing to a time path of punishments for criminals who report their crimes. In an optimal program, time variation in the returns from crime can generate time variation in the generosity of amnesty. I construct an optimal time path and show that it exhibits amnesty cycles. Amnesty becomes increasingly generous over time until it hits a bound, after which the cycle resets. Agents engaged in high return crime report at the end of each cycle, while agents engaged in low return crime report always.
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This paper introduces a stylized model to capture distinctive features of waiting list allocation mechanisms. First, agents choose among items with associated expected wait times. Waiting times serve a similar role to that of monetary prices in directing agents' choices and rationing items. Second, the expected wait for an item is endogenously determined and randomly fluctuates over time. We evaluate welfare under these endogenously determined waiting times and find that waiting time fluctuations lead to misallocation and welfare loss. A simple randomized assignment policy can reduce misallocation and increase welfare.
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We document that international migrants concentrate more in expensive cities—the more so, the lower the prices in their origin countries are—and consume less locally than comparable natives. We rationalize this empirical evidence by introducing a quantitative spatial equilibrium model, in which a part of immigrants' income goes toward consumption in their origin countries. Using counterfactual simulations, we show that, due to this novel consumption channel, immigrants move economic activity toward expensive, high-productivity locations. This leads to a more efficient spatial allocation of labor and, as a result, increases the aggregate output and welfare of natives.
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We examine methods for evaluating interventions designed to improve decision-making quality when people misunderstand the consequences of their choices. In an experiment involving financial education, conventional outcome metrics (financial literacy and directional behavioral responses) imply that two interventions are equally beneficial even though only one reduces the average severity of errors. We trace these failures to violations of the assumptions embedded in the conventional metrics. We propose a simple, intuitive, and broadly applicable outcome metric that properly differentiates between the interventions, and is robustly interpretable as a measure of welfare loss from misunderstanding consequences even when additional biases distort choices.