Preferences for charitable giving in auctions can be modeled by assuming that bidders receive additional utility proportional to the revenue raised by an auctioneer. The theory of bidding in the presence of such preferences results in a very counterintuitive prediction which is that, in many cases, bidders having preferences for charitable giving does not lead to a substantial revenue advantage for an auctioneer. We test this theory and this prediction with a series of experiments. In one experiment we induce charitable preferences exactly as specified in the model to see if bidders respond to them as predicted. We find that they do. We then conduct a second experiment in which the revenue from the auctions is donated to actual charities to verify the robustness of the prediction when charitable preferences are generated by a more natural source and find again that the theoretical prediction holds: even strong charitable preferences do not result in substantial revenue increases to the auctioneer.

We experimentally analyze the role of speculators, who have no use value for the objects on sale, in auctions. The environment is a uniform-price sealed-bid auction for 2 identical objects, followed by a free-form bargaining resale market, with one positive-value bidder, and either one or two speculators who may choose simultaneously whether to enter the auction. We show that the bidder accommodates speculators by reducing demand in the auction and subsequently purchasing in the resale market, which encourages entry by speculators. The presence of multiple speculators induces each speculator to enter less often, but increases competition in the auction and the auction price. Speculators earn positive profits on average, except when multiple speculators enter the auction.

A marketable permit system (MPS) has been suggested as a solution to environmental problems. Although the properties of MPSs under non-trader settings, in which each player is exclusively either a seller or a buyer, are well documented, little research has explored how MPSs perform under trader settings, in which each player can be both a seller and a buyer. We institute two auctions of trader settings in MPS experiments: a double auction (DA) and a uniform price auction (UPA). We then evaluate and compare their performances both with each other and with those under non-trader settings. The main results are as follows: DAs under trader settings perform much worse than do DAs under non-trader settings, whereas UPAs perform well, regardless of the trader and non-trader settings. UPAs are more efficient and generate more stable prices than do DAs under trader settings, and a considerable proportion of trades in DAs under trader settings consist of “flips” that could be considered speculation or errors. Thus, UPAs are likely to work better than DAs under trader settings.

We consider payment schemes in experiments that model infinite-horizon games by using random termination. We compare paying subjects cumulatively for all periods of the game; with paying subjects for the last period only; with paying for one of the periods, chosen randomly. Theoretically, assuming expected utility maximization and risk neutrality, both the cumulative and the last period payment schemes induce preferences that are equivalent to maximizing the discounted sum of utilities. The last period payment is also robust under different attitudes toward risk. In comparison, paying subjects for one of the periods chosen randomly creates a present-period bias. We further provide experimental evidence from infinitely repeated prisoners’ dilemma games that supports the above theoretical predictions.

Bayes’ statistical rule remains the status quo for modeling belief updating in both normative and descriptive models of behavior under uncertainty. Some recent research has questioned the use of Bayes’ rule in descriptive models of behavior, presenting evidence that people overweight ‘good news’ relative to ‘bad news’ when updating ego-relevant beliefs. In this paper, we present experimental evidence testing whether this ‘good-news, bad-news’ effect is present in a financial decision making context (i.e. a domain that is important for understanding much economic decision making). We find no evidence of asymmetric updating in this domain. In contrast, in our experiment, belief updating is close to the Bayesian benchmark on average. However, we show that this average behavior masks substantial heterogeneity in individual updating behavior. We find no evidence in support of a sizeable subgroup of asymmetric updators.

Experimental double-auction commodity markets are known to exhibit robust convergence to competitive equilibria under stable or cyclical supply and demand conditions, but little is known about their performance in truly random environments. We provide a comprehensive study of double auctions in a stochastic setting where the equilibrium prices, trading volumes and gains from trade are highly variable across periods, and with commodity traders who may buy or sell their goods depending on market conditions and their individual outcomes. We find that performance in this stochastic environment is sensitive to underlying market conditions. Efficiency is higher and convergence to the competitive equilibrium stronger when the potential gains from trade are high and when the equilibrium spans a wide range of quantities, implying a large number of marginal trades. Speculative re-trading is prevalent, especially among those who have little to gain under equilibrium pricing. Those with the largest expected gains typically earn far less than predicted, while those with little or no predicted earnings gain modestly from speculation, leading to some redistribution of gains from high to low expected earners. Excessive trading volumes are associated with negative efficiencies in markets with low gains from trade, but not in the high-gains markets, where zero-sum trading and re-trading appear to enforce efficiency and near-equilibrium pricing. Buyers earn more relative to their competitive equilibrium benchmark than sellers do. Introducing trader specialization leads to fewer trading errors and higher market efficiency, but it does not eliminate zero-sum trading and re-trading.

This paper empirically compares the use of straightforward verses more complex methods to estimate public goods game data. Five different estimation methods were compared holding the dependent and explanatory variables constant. The models were evaluated using a large out-of-sample cross-country public goods game data set. The ordered probit and tobit random-effects models yielded lower p values compared to more straightforward models: ordinary least squares, fixed and random effects. However, the more complex models also had a greater predictive bias. The straightforward models performed better than expected. Despite their limitations, they produced unbiased predictions for both the in-sample and out-of-sample data.

We report on the results of an experiment with a statistical choice task involving the toss of a fair coin. In our experiment, participants had to decide whether they were willing to pay a price to switch from betting on the future performance of one player to betting on that of another player. The switch was from a player who had been previously less successful in betting on five coin flips to another one who had been more successful in the same task. We conducted a treatment with the Becker–DeGroot–Marschak mechanism and one in which participants were faced with a fixed price. In both cases, participants exhibit a strong bias towards placing their bets on players with a good guessing history in the coin toss task. Participants’ behaviour is compatible with prescriptive luck beliefs, that is, the idea that luck is a somehow deterministic and personal attribute.

This technical note briefly explains the structure of the z-Tree dataset and presents ztree2stata, a data converting tool for researchers who conduct economic experiments using z-Tree and analyze the data with Stata.

In this article, we highlight how simulation methods can be used to analyze power of economic experiments. We provide the powerBBK package programmed for experimental economists, that can be used to perform simulations in STATA. Power can be simulated using a single command line for various statistical tests (nonparametric and parametric), estimation methods (linear, binary, and censored regression models), treatment variables (binary, continuous, time-invariant or time varying), sample sizes, experimental periods, and other design features (within or between-subjects design). The package can be used to predict minimum sample sizes required to reach a user-specific level of power, to maximize power of a design given the researcher supplied a budget constraint, or to compute power to detect a user-specified treatment order effect in within-subjects designs. The package can also be used to compute the probability of sign errors—the probability of rejecting the null hypothesis in the wrong direction as well as the share of rejections pointing in the wrong direction. The powerBBK package is provided as an .ado file along with a help file, both of which can be downloaded here (http://www.bbktools.org).

There is growing concern about the extent to which economic games played in the laboratory generalize to social behaviors outside the lab. Here, we show that it is possible to make a game much more predictive of field behavior by bringing contextual elements from the field to the lab. We report three experiments where we present the same participants with different versions of the dictator game and with two different field situations. The games are designed to include elements that make them progressively more similar to the field. We find a dramatic increase in lab–field correlations as contextual elements are incorporated, which has wide-ranging implications for experiments on economic decision making.