The analysis of data from experiments in economics routinely involves testing multiple null hypotheses simultaneously. These different null hypotheses arise naturally in this setting for at least three different reasons: when there are multiple outcomes of interest and it is desired to determine on which of these outcomes a treatment has an effect; when the effect of a treatment may be heterogeneous in that it varies across subgroups defined by observed characteristics and it is desired to determine for which of these subgroups a treatment has an effect; and finally when there are multiple treatments of interest and it is desired to determine which treatments have an effect relative to either the control or relative to each of the other treatments. In this paper, we provide a bootstrap-based procedure for testing these null hypotheses simultaneously using experimental data in which simple random sampling is used to assign treatment status to units. Using the general results in Romano and Wolf (Ann Stat 38:598–633, 2010), we show under weak assumptions that our procedure (1) asymptotically controls the familywise error rate—the probability of one or more false rejections—and (2) is asymptotically balanced in that the marginal probability of rejecting any true null hypothesis is approximately equal in large samples. Importantly, by incorporating information about dependence ignored in classical multiple testing procedures, such as the Bonferroni and Holm corrections, our procedure has much greater ability to detect truly false null hypotheses. In the presence of multiple treatments, we additionally show how to exploit logical restrictions across null hypotheses to further improve power. We illustrate our methodology by revisiting the study by Karlan and List (Am Econ Rev 97(5):1774–1793, 2007) of why people give to charitable causes.

We use a within-subjects design to study how responsibility for the payoffs of different number of others influences the choices under risk, and how choosing together with another person changes these decisions. After controlling for the regression to the mean, we find a weak effect of responsibility for one other person on risk taking as compared to choosing just for oneself. We, however, do find that the number of others influenced by the choice matters: when it increases from one to three, risk averse subjects choose riskier options and risk loving subjects choose more cautiously, which pushes the choices towards the modal risk preferences in the population. Mutual responsibility makes choices for others shift even more in the same direction. The observed behavior is in accordance with the blame avoidance hypothesis: decision makers with responsibility try to reduce the amount of blame for their choices, which is minimal when the choices for others are consistent with what they would have chosen for themselves.

We conduct a series of experiments to examine the effects of the make and take fee structure currently used by equity exchanges in the U.S. We examine the effects of these fees on measures of market quality (efficiency, book depth, and the bid-ask spread). We find spreads to be smaller in the presence of make and take fees, and we note that this fee structure seems to induce buyers (moreso than sellers) to compete for rebates from the exchange leading to higher prices and lower profits. To test whether our results are due to the make and take fee structure or are artefacts of trading fees in general, we performed a second set of experiments in which traders on both sides of a transaction were assessed an identical fee. These identical trading fees do not appear to significantly affect our market quality measures.

This paper studies whether and how strategy revision opportunities affect levels of collusion in indefinitely repeated two-player games. Consistent with standard theory, we find that such opportunities do not affect strategy choices, or collusion levels, if the game is of strategic substitutes. In games of strategic complements, by contrast, revision opportunities lead to more collusion. We discuss alternative explanations for this result.

We explore the efficiency and distributive implications of a multilateral bargaining model with endogenous production of the surplus under two different timings: ex ante and ex post bargaining. Both timings are commonly observed in business partnerships and alliance formations. The theoretical predictions confirm an intuitive economic tenet: in ex post bargaining, effort is considered sunk and opportunistic bargaining behavior will dissuade players from producing. On the other hand, ex ante bargaining entails an allocation of ownership shares that induces at least certain members to invest in the common fund because their return is guaranteed. Experiments show opposite results: ex post bargaining yields almost fully efficient outcomes while the reverse timing entails near zero efficiency. The psychological theory of inequity is useful in reconciling these divergent results.

Temporary changes in biological state, such as hunger, can impact decision making differently for men and women. Food scarcity is correlated with a host of negative economic outcomes. Two explanations for this correlation are that hunger affects economic preferences directly or that hunger creates a mindset that focuses on scarcity management to the detriment of other decisions. To test these predictions, we conduct a lab-in-the-field experiment in a health screening clinic in Shanghai, recruiting participants who finish their annual physical exam either before or after they have eaten breakfast. We compare the hungry and sated groups on their risk, time and generosity preferences as well as their cognitive performance. Our results show that men and women respond to hunger in opposite directions, thus hunger reduces the gender gap in decision quality, risk aversion and cognitive performance, but creates one in generosity. Finally, we examine several biomarkers and find that higher blood lipid levels are correlated with greater choice inconsistency, risk aversion and generosity. We contribute to emerging insights on the biological foundations for economic preferences and outcomes.

This paper introduces a new matching mechanism that is a hybrid of the two most common mechanisms in school choice, the Boston Mechanism (BM) and the Deferred Acceptance algorithm (DA). BM is the most commonly used mechanism in the field, but it is neither strategyproof nor fair. DA is the mechanism that is typically favored by economists, but it is not Pareto efficient. The new mechanism, the Secure Boston Mechanism (sBM), is an intuitive modification of BM that secures any school a student was initially guaranteed but otherwise prioritizes a student at a school based upon how she ranks it. Relative to BM, theoretical results suggest that sBM is an improvement in terms of strategyproofness and fairness. We present experimental evidence using a novel experimental design that confirms that sBM significantly increases truth-telling and fairness. Relative to DA, theoretical results suggest that sBM can be a Pareto improvement in equilibrium but the efficiency comparison of sBM and DA is theoretically ambiguous. We present simulation evidence that suggests that sBM often does Pareto dominate DA when DA is inefficient, while sBM and DA very often overlap when DA is efficient. Overall, our results strongly support the use of sBM over BM and suggest that sBM should be considered as a viable alternative to DA.

We use experimental methods to evaluate a simplified interbank market. The design is a laboratory adaptation of the analysis of interbank market fragility by Allen and Gale (J Eur Econ Assoc 2:1015–1048), and features symmetric banks who allocate deposit endowments between cash and illiquid assets prior to the incidence of a shock. Following the shock liquidity-deficient banks trade assets for cash. Treatments include variations in the shock type, as well as alterations in the range of permissible asset prices. Consistent with Allen and Gale, we find that while interbank trading substantially increases investment activity, the markets are frequently characterized by price variability and a stochastic distribution of investment outcomes.

We experimentally investigate the relationship between (un)kind actions and subsequent deception in a two-player, two-stage game. The first stage involves a dictator game. In the second-stage, the recipient in the dictator game has the opportunity to lie to her counterpart. We study how the fairness of dictator-game outcomes affects subsequent lying decisions where lying hurts one’s counterpart. In doing so, we examine whether the moral cost of lying varies when retaliating against unkind actions is financially beneficial for the self (selfish lies), as opposed to being costly (spiteful lies). We find evidence that individuals engage in deception to reciprocate unkind behavior: The smaller the payoff received in the first stage, the higher the lying rate. Intention-based reciprocity largely drives behavior, as individuals use deception to punish unkind behavior and truth-telling to reward kind behavior. For selfish lies, individuals have a moral cost of lying. However, for spiteful lies, we find no evidence for such costs. Taken together, our data show a moral cost of lying that is not fixed but instead context-dependent.

The present study explores the value of machine learning techniques in the classification of communication content in experiments. Previously human-coded datasets are used to both train and test algorithm-generated models that relate word counts to categories. For various games, the computer models of the classification are able to match out-of-sample the human classification to a considerable extent. The analysis raises hope that the substantial effort going into such studies can be reduced by using computer algorithms for classification. This would enable a quick and replicable analysis of large-scale datasets at reasonable costs and widen the applicability of such approaches. The paper gives an easily accessible technical introduction into the computational method.