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Abstract
We test experimentally the Gale-Shapley Deferred Acceptance (DA) mechanism versus two versions of the Iterative Deferred Acceptance Mechanism (IDAM), in which students make applications one at a time. A significantly higher proportion of stable outcomes is reached under IDAM than under DA. 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 DA. We associate the benefits of iterative mechanisms with the feedback on the outcome of the previous application 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.

We conduct a field experiment with 302 workers of the microcredit company in Russia to study the effects of the different designs of a contest for monetary prizes at the workplace. We consider a standard all-pay auction design with two and four prizes of different size and compare it to "parallel" contests with the same prizes, but where participants have to choose the prize prior to the start of the competition and then the winner is selected only among the players who chose the same prize. Despite the theoretical predictions, the parallel contests lead to higher efforts for all players, but mainly by lower-ability players. Division of prizes leads to the predicted effects. In parallel contests, too many players choose the higher prize than equilibrium suggests. Overall, the parallel version of contests appeared to be more profitable for the firm.

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. (JEL C92, D47)

We study a college admissions problem in which colleges accept students by ranking students' efforts in entrance exams. Students' ability levels affect the cost of their efforts. We solve and compare equilibria of "centralized college admissions" (CCA) where students apply to all colleges and "decentralized college admissions" (DCA) where students only apply to one college. We show that lower ability students prefer DCA whereas higher ability students prefer CCA. Many predictions of the theory are supported by a lab experiment designed to test the theory, yet we find a number of differences that render DCA less attractive than CCA compared to the equilibrium benchmark.

We run laboratory experiments where subjects are matched to colleges, and colleges are not strategic agents. We test the Gale-Shapley Deferred Acceptance (DA) mechanism versus the Iterative Deferred Acceptance Mechanism (IDAM), a matching mechanism based on a new family of procedures being used in the field, in which information about tentative allocations is provided while students make choices. We consider two variations of IDAM: one in which they are only informed about whether they are tentatively accepted or not (IDAM-NC) and one in which students are additionally informed at each step of the tentative cutoff values for acceptance at each school (IDAM). A significantly higher proportion of stable outcomes is reached both under IDAM and IDAM-NC than under DA. The difference can be explained by a higher proportion of subjects following an equilibrium strategy akin to truthful behavior under IDAM and IDAM-NC than truthful behavior itself under DA. Moreover, the provision of intermediate cutoff values in IDAM leads to higher rates of equilibrium behavior than in IDAM-NC and a higher robustness of stability between the rounds of experiments. Our findings provide substantial support for the rising practice of using iterative mechanisms in centralized college admissions in practice.

We run a field experiment to test the truth-telling rates of the theoretically strategy-proof Top Trading Cycles mechanism (TTC) under different information conditions. First, we asked first-year economics students enrolled in an introductory microeconomics unit about which topic, among three, they would most like to write an essay on. Most students chose the same favorite topic. Then we used TTC to distribute students equally across the three options. We ran three treatments varying the information the students received about the mechanism. In the first treatment students were given a description of the matching mechanism. In the second they received a description of the strategy-proofness of the mechanism without details of the mechanism. Finally, in the third they were given both pieces of information. We find a significant and positive effect of describing the strategy-proofness on truth-telling rates. On the other hand, describing the matching mechanism has a significant and negative effect on truth-telling rates. (author's abstract)

We test the effect of the amount of information on the strategies played by others in the theoretically strategy-proof Top Trading Cycles (TTC) mechanism. We find that providing limited information on the strategies played by others has a negative and significant effect in truth-telling rates relative to full or no information about others' strategies. Subjects report truthfully more often when either full information or no information on the strategies played by others is available. Our results have potentially important implications for the design of markets based on strategy-proof matching algorithms. (author's abstract)

A particular adaptation of Gale's top trading cycles procedure 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 among those who are entitled to a slot at that school, allowing them to trade in a thick market where additional constraints can be accommodated. We propose a particular mechanism of this kind, the Equitable Top Trading Cycles (ETTC) mechanism, which is also Pareto efficient and strategy-proof just like TTC and eliminates justified envy due to pairwise exchanges. Both in simulations and in the lab, ETTC generates significantly fewer number of justified envy situations than TTC. (author's abstract)

We introduce a new mechanism for matching students to schools or universities, denoted Iterative Deferred Acceptance Mechanism (IDAM), inspired by procedures currently being used to match millions of students to public universities in Brazil and China. Unlike most options available in the literature, IDAM is not a direct mechanism. Instead of requesting from each student a full preference over all colleges, the student is instead repeatedly asked to choose one college among those which would accept her given the current set of students choosing that college. Although the induced sequential game has no dominant strategy, when students simply choose the most preferred college in each period (denoted the straightforward strategy), the matching that is produced is the Student Optimal Stable Matching. Moreover, under imperfect information, students following the straightforward strategy is an Ordinal Perfect Bayesian Equilibrium. Based on data from 2016, we also provide evidence that, due to shortcomings which are absent in the modified version that we propose, the currently used mechanism in Brazil fails to assist the students with reliable information about the universities that they are able to attend, and are subject to manipulation via cutoffs, a new type of strategic behavior that is introduced by this family of iterative mechanisms and observed in the field.

We report on experiments to replicate Plott and Zeiler's (2005) findings that the WTP-WTA gap disappears when using procedures that are aimed at reducing misunderstandings, such as training rounds for the BDM mechanism. Following the design by Plott and Zeiler (2005) and Isoni, Loomes, and Sugden (2011) who re-ran the Plott/Zeiler experiments to study the paid practice rounds with lotteries, we replicate the findings from the lottery tasks where a WTP-WTA gap is present in all studies. However, unlike in the two previous studies the WTP-WTA gap does not disappear in the main task where subjects state their WTA or WTP for a mug. We introduce two additional lottery tasks to classify subjects and find that even for the most rational group of subjects who never make dominated choices in the paid practice rounds, the WTP-WTA gap in the mug task exists. The findings are replicated in a similar experiment with USB sticks instead of mugs. (author's abstract)

We document experimentally how biased self-assessments affect the outcome of matching markets. In the experiments, we exogenously manipulate the self-confidence of participants regarding their relative performance by employing hard and easy real-effort tasks. We give participants the option to accept early offers when information about their performance has not been revealed, or to wait for the assortative matching based on their actual relative performance. Early offers are accepted more often when the task is hard than when it is easy. We show that the treatment effect works through a shift in beliefs, i.e., underconfident agents are more likely to accept early offers than overconfident agents. The experiment identifies a behavioral determinant of unraveling, namely biased self-assessments, which can lead to penalties for underconfident individuals as well as efficiency losses.