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Replication in experimental economics' highlights the importance of replicating previous economic experiments for understanding the robustness and generalizability of behavior. Replication enables experimental findings to be subjected to rigorous scrutiny. Despite this obvious advantage, direct replication remains relatively scant in economics. One possible explanation for this situation is that publication outlets favor novel work over tests of robustness. This volume of Research in experimental economics raises awareness of the need for replication by being the first collection of papers specifically dedicated to the replication of previously published work. The chapters, by leading researchers in the field, explore the robustness of topics from the effects of subsidizing charitable giving to people's ability to backwards induct and from the impact of social history on trust to the role of isolation on valuation. Readers will gain a better understanding of the role that replication plays in scientific discovery as well as valuable insights into the robustness of previously reported findings.

Experimental economics. --- Replication (Experimental design) --- Experimental design --- Economics --- Methodology --- Experimental economics --- E-books --- Business & Economics --- Econometrics.

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Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but has also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the complete relevant belief distribution incentive-compatibly. We find that subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to negative, and (3) conservative, updating too little in response to both positive and negative signals. These biases are substantially less pronounced in a placebo experiment where ego is not at stake. We also find that (4) a substantial portion of subjects are averse to receiving information about their ability, and that (5) less confident subjects are causally more likely to be averse. We unify these phenomena by showing that they all arise naturally in a simple model of optimally biased Bayesian information processing.

Zonder onderwerpscode: economie

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We conduct field experiments in a large real-world social network to examine why decision makers treat friends more generously than strangers. Subjects are asked to divide surplus between themselves and named partners at various social distances, where only one of the decisions is implemented. In order to separate altruistic and future interaction motives, we implement an anonymous treatment where neither player is told at the end of the experiment which decision was selected for payment and a non-anonymous treatment where both players are told. Moreover, we include both games where transfers increase and decrease social surplus to distinguish between different future interaction channels including signaling one's generosity and enforced reciprocity, where the decision maker treats the partner to a favor because she can expect it to be repaid in the future. We can decompose altruistic preferences into baseline altruism towards any partner and directed altruism towards friends. Decision makers vary widely in their baseline altruism, but pass at least 50 percent more surplus to friends compared to strangers when decision making is anonymous. Under non-anonymity, transfers to friends increase by an extra 24 percent relative to strangers, but only in games where transfers increase social surplus. This effect increases with density of the network structure between both players, but does not depend on the average amount of time spent together each week. Our findings are well explained by enforced reciprocity, but not by signaling or preference-based reciprocity. We also find that partners' expectations are well calibrated to directed altruism, but that they ignore decision makers' baseline altruism. Partners with high baseline altruism have friends with higher baseline altruism and are therefore treated better.

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Can informing people of high community support for social distancing encourage them to do more of it? In theory, the impact of such an intervention on social distancing is ambiguous, and depends on the relative magnitudes of free-riding and perceived-infectiousness effects. We randomly assigned a treatment providing information on true high rates of community social distancing support. We estimate impacts on social distancing, measured using a combination of self-reports and reports of others. While experts surveyed in advance expected the treatment to increase social distancing, we find that its average effect is close to zero and significantly lower than expert predictions. The treatment's effect is heterogeneous, as predicted by theory: it decreases social distancing where current COVID-19 cases are low (where free-riding dominates), but increases it where cases are high (where the perceived-infectiousness effect dominates).