Many studies report on the association between 2D:4D, a putative marker for prenatal testosterone exposure, and economic preferences. However, most of these studies have limited sample sizes and test multiple hypotheses (without preregistration). In this study we mainly replicate the common specifications found in the literature for the association between the 2D:4D ratio and risk taking, the willingness to compete, and dictator game giving separately. In a sample of 330 women we find no robust associations between any of these economic preferences and 2D:4D. We find no evidence of a statistically significant relation for 16 of the 18 total regressions we run. The two regression specifications which are statistically significant have not previously been reported and the associations are not in the expected direction, and therefore they are unlikely to represent a real effect.

Neoclassical theory holds that different sources of income are fungible at the margin. In contrast, mental accounting holds that appropriate uses for income vary by source, making them infungible. This study investigates which theory better describes giving at the margin when income may have multiple sources. Dictators accrue differing amounts of (1) earned income from a real-effort task, (2) windfall income, or (3) both. I find that dictators treat marginal earned and windfall income as partially infungible, supporting mental accounting. Dictators who had a single income source gave 14% of a marginal windfall token and 5% of a marginal earned token. Strikingly, dictators who had income from both sources were sharply less generous with each, giving only 2% and 1%, respectively. Multiple accounts enabled greater selfishness at the margin. A follow-up experiment shows that two accounts must be qualitatively different, not just multiple in number, to produce this effect.

Expectations-based reference dependence has been shown to be important across a variety of contexts in Psychology and Economics. Do expectations play a role in moral judgment? The higher our beliefs are relative to an outcome, do we punish more harshly? This paper reports a series of experiments investigating the hypothesis that expectations as reference points per se affect punishment. The experimental design varies the expectation the Punisher holds just before she learns what actually occurred. In tandem with the manipulation, expectations are shown to vary significantly and substantially. However, punishment does not respond to these exogenous changes in expectations. After 17 sessions, 295 Punishers, and six experimental setups, expectations are shown not to affect punishment in any systematic way.

Experimental asset markets with a constant fundamental value ($\mathrm{F}\mathrm{V}$) have grown in importance in recent years. A methodological examination of the robustness of experimental results in such a setting which has been shown to produce bubbles, however, is lacking. In a laboratory experiment with 280 subjects, we investigate whether specific design features are sufficient to influence experimental results. In detail, we (1) vary the visual representation of the price chart, and (2) provide subjects with full information about the FV process. We find overvaluation and bubble formation to be reduced when trading prices are displayed at the upper end of the price chart. Surprisingly, we do not find any effects when subjects have full information about the FV process.

Loss framing and checklist formatting are two oft-cited tools for encouraging behavior change, but there is little causal evidence on their impact in field settings. We partnered with the City of Philadelphia to test the effectiveness of these tools to increase completion of the City’s wellness program. In our experiment, 5235 City employees and retirees were randomly assigned to receive one of four postcard versions (using a 2 × 2 design), whereby we varied both framing (gain or loss) and how instructions were provided (information only or information in checklist format). Our results suggest that neither loss framing nor the checklist formatting significantly influenced the likelihood that individuals would complete the wellness tasks, or how quickly they completed the tasks. We conclude that this specific form of employee behavior may be difficult to influence through the “passive” behavioral interventions we tested, and suggest that a more “active” approach may be required in such instances.

The evidence on the welfare effects of kinship is mixed, suggesting both positive and adverse effects of kinship. This study looks into the differential effects of kinship on trusting and trustworthy behaviour by investigating the subjects’ motives and drivers of differential behaviour towards kin and non-kin. We conducted an economic experiment with households of rural India. We found that kin are trusted more than non-kin and that differential trust towards kin and non-kin is mainly driven by higher other-regarding preferences towards kin rather than being due to differences in expected reciprocity between kin and non-kin. We observed a heterogeneous effect of kin on trustworthy behaviour: kin exhibit low trustworthiness when they are not close to other kin, while they exhibit higher trustworthiness when they have close kin in the network.

We propose a framework for identifying discrete behavioural types in experimental data. We re-analyse data from six previous studies of public goods voluntary contribution games. Using hierarchical clustering analysis, we construct a typology of behaviour based on a similarity measure between strategies. We identify four types with distinct stereotypical behaviours, which together account for about 90% of participants. Compared to the previous approaches, our method produces a classification in which different types are more clearly distinguished in terms of strategic behaviour and the resulting economic implications.

Garbarino et al. (J Econ Sci Assoc. https://doi.org/10.1007/s40881-018-0055-4, 2018) describe a new method to calculate the probability distribution of the proportion of lies told in “coin flip” style experiments. I show that their estimates and confidence intervals are flawed. I demonstrate two better ways to estimate the probability distribution of what we really care about—the proportion of liars—and I provide R software to do this.