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The paper surveys the experimental literature on centralized matching markets, covering school choice and college admissions models. In the school choice model, one side of the market (schools) is not strategic, and rules (priorities) guide the acceptance decisions. The model covers applications such as school choice programs, centralized university admissions in many countries, and the centralized assignment of teachers to schools. In the college admissions model, both sides of the market are strategic. It applies to college and university admissions in countries where universities can select students, and centralized labor markets such as the assignment of doctors to hospitals. The survey discusses, among other things, the comparison of various centralized mechanisms, the optimality of participants’ strategies, learning by applicants and their behavioral biases, as well as the role of communication, information, and advice. The main experimental findings considered in the survey concern truth-telling and strategic manipulations by the agents, as well as the stability and efficiency of the matching outcome.

Attitudes towards risk and uncertainty have been indicated to be highly context-dependent, and to be sensitive to the measurement technique employed. We present data collected in controlled experiments with 2,939 subjects in 30 countries measuring risk and uncertaintly attitudes through incentivized measures as well as survey questions. Our data show clearly that measures correlate not only within decision contexts or measurement methods, but also across contexts and methods. This points to the existence of one underlying \"risk preference\", which influences attitudes independently of the measurement method or choice domain. We furthermore find that answers to a general and a financial survey question correlate with incentivized lottery choices in most countries. Incentivized and survey measures also correlate significantly between countries. This opens the possibility to conduct cultural comparisons on risk attitudes using survey instruments.

Allocating appointment slots is presented as a new application for market design. Online booking systems are commonly used by public authorities to allocate appointments for visa interviews, driver's licenses, passport renewals, etc. We document that black markets for appointments have developed in many parts of the world. Scalpers book the appointments that are offered for free and sell the slots to appointment seekers. We model the existing first-come-first-served booking system and propose an alternative batch system. The batch system collects applications for slots over a certain time period and then randomly allocates slots to applicants. The theory predicts and lab experiments confirm that scalpers profitably book and sell slots under the current system with sufficiently high demand, but that they are not active in the proposed batch system. We discuss practical issues for the implementation of the batch system and its applicability to other markets with scalping.

A particular adaptation of Gale's top trading cycles (TTC) procedure applied to school choice, the so-called TTC mechanism, has attracted much attention both in theory and practice due to its superior efficiency and incentive features. We discuss and introduce alternative adaptations of Gale's original procedure that can offer improvements over TTC in terms of equity, along with various other distributional considerations. Instead of giving all the trading power to those students with the highest priority for a school, we argue for the distribution of the trading rights of all slots of each school: This allows them to trade in a thick market where additional constraints can be accommodated by choosing an appropriate pointing rule. We propose a particular mechanism of this kind, the equitable top trading cycle (ETTC) mechanism, which is also Pareto efficient and group strategyproof just like TTC and eliminates avoidable justified envy situations. ETTC generates significantly fewer justified envy situations than TTC both in simulations and the lab.

We test experimentally the Gale–Shapley Deferred Acceptance mechanism versus two versions of the Iterative Deferred Acceptance Mechanism, in which students make applications one at a time. A significantly higher proportion of stable outcomes is reached under Iterative Deferred Acceptance Mechanism than under Deferred Acceptance. The difference can be explained by a higher proportion of subjects following an equilibrium truthful strategy under iterative mechanisms than the dominant strategy of truthful reporting under Deferred Acceptance. We associate the benefits of iterative mechanisms with the feedback on the outcome of the previous application that they provide to students between steps.

We document experimentally how biased self-assessments affect the outcome of labor markets. In the experiments, we exogenously manipulate the self-confidence of participants in the role of workers regarding their relative performance by employing hard and easy real-effort tasks. Participants in the role of firms can make offers before information about the workers’ performance has been revealed. Such early offers by firms are more often accepted by workers when the real-effort task is hard than when it is easy. We show that the treatment effect works through a shift in beliefs; that is, under-confident agents are more likely to accept early offers than overconfident agents. The experiment identifies a behavioral determinant of unraveling, namely biased self-assessments. The treatment with the hard task entails more unraveling and thereby leads to lower efficiency and less stability, and it shifts payoffs from high- to low-quality firms.This paper was accepted by Uri Gneezy, behavioral economics.

When applying to a university, students and their parents devote considerable time acquiring information about university programs in order to form preferences. We explore ways to reduce wasteful information acquisition, that is, to help students avoid acquiring information about out-of-reach schools or universities, using a market design approach. Focusing on markets where students are ranked by universities based on exam scores, we find that, both theoretically and experimentally, a sequential serial dictatorship mechanism leads to higher student welfare than a direct serial dictatorship mechanism. This is because the sequential mechanism informs students about which universities are willing to admit them, thereby directing their search. Our experiments also show that the sequential mechanism has behavioral advantages because subjects deviate from the optimal search strategy less frequently than under the direct mechanism. Furthermore, providing historical cutoff scores under the direct mechanism can increase student welfare, especially when the information costs are high, although the observed effect is weaker than that of a sequential mechanism.

Following the advice of economists, school choice programs around the world have lately been adopting strategy-proof mechanisms. However, experimental evidence presents a high variation of truth-telling rates for strategy-proof mechanisms. We crash test the connection between the strategy-proofness of the mechanism and truth-telling. We employ a within-subjects design by making subjects take two simultaneous decisions: one with no strategic uncertainty and one with some uncertainty and partial information about the strategies of other players. We find that providing information about the out-of-equilibrium strategies played by others has a negative and significant effect on truth-telling rates. That is, most participants in our within-subjects design try and fail to best-respond to changes in the environment. We also find that more sophisticated subjects are more likely to play the dominant strategy (truth-telling) across all the treatments. These results have potentially important implications for the design of markets based on strategy-proof matching mechanisms.

We document experimentally how biased self-assessments affect the outcome of labor markets. In the experiments, we exogenously manipulate the self-confidence of participants in the role of workers regarding their relative performance by employing hard and easy real-effort tasks. Participants in the role of firms can make offers before information about the workers' performance has been revealed. Such early offers by firms are more often accepted by workers when the real-effort task is hard than when it is easy. We show that the treatment effect works through a shift in beliefs; that is, under-confident agents are more likely to accept early offers than overconfident agents. The experiment identifies a behavioral determinant of unraveling, namely biased self-assessments. The treatment with the hard task entails more unraveling and thereby leads to lower efficiency and less stability, and it shifts payoffs from high- to low-quality firms.