We study the robustness of Krupka and Weber's method (2013) for eliciting social norms. In two online experiments with more than 1200 participants on Amazon Mechanical Turk, we find that participants’ response patterns are invariant to differences in the salience of the monetarily incentivized coordination aspect. We further demonstrate that asking participants for their personal first- and second-order beliefs without monetary incentives results in qualitatively identical responses in the case that beliefs and social norms are well aligned. Overall, Krupka and Weber's method produces remarkably robust response patterns.

The Inclusion of Other in the Self (IOS) scale is a popular tool to measure interpersonal closeness that is increasingly being used in economics. We develop and validate a continuous version of the IOS scale. This Continuous IOS scale gives a finer measure and addresses the reluctance of subjects to report low scores on the standard IOS scale. We also propose a version of the standard IOS scale that meets its original design features. Our IOS scales are easy-to-use, well-documented, standardised, and available at https://github.com/geoffreycastillo/ios-js.

We ask whether social preferences measured in subjects who come to the laboratory when invited are systematically different from those of subjects who only respond when an online option is available. Subjects participated in two types of third-party (other–other) dictator games and a trust game, either in the lab or on-line. In the third party dictator games, the dictator divides $20 between two other individuals, one of whom is a member of their in-group. (We also varied types of in-group between a real group and an artificial group.) In the trust game, the first-mover decides how much of the endowment to send to the second-mover. The second-mover receives the amount sent tripled by the experimenter and decides how much to send back to the trustee. Across all the games, we find no statistically significant differences in social preferences measured in-lab and on-line.

We explore gender differences in individuals’ motivations. We focus on guilt aversion and moral commitment. Our experiment supports the idea that men are more guilt-averse than women, while moral motivations drive more women's actions in a random dictator game with pre-play communication.

We introduce a novel way to elicit individuals’ strength of altruistic motivation in the context of charitable donations, ranging from pure warm glow to pure altruism. Using the giving-type elicitation task of Gangadharan et al. (2018) and assuming that individuals maximise a Cobb–Douglas impure altruism utility function, as is used in Ottoni-Wilhelm et al. (2017), we can uniquely identify the strength of altruistic motivation for impure altruists, which is typically found to be the largest category of donors. We compare the introduced measure to an alternative survey-based elicitation from Carpenter (2021).

While people are surprisingly cooperative in social dilemmas, cooperation is fragile to the emergence of defection. Punishment is a key mechanism through which people sustain cooperation, but when are people willing to pay the costs to punish? Using data from existing work on punishment in public goods games conducted in industrialized countries throughout the world (Herrmann et al. in Science, 319(5868):1362–1367, 2008. https://doi.org/10.1126/science.1144237), I find first that those who contribute more are consistently punished less. Second, in many study locations, there are insignificant differences in the propensity of those who contribute and defect to punish. Finally, those who contribute and defect both carry out punishment against defectors. Some defectors do punish cooperators, but less often than they punish other defectors. The determinants of punishment are largely consistent across cities.

MTVE is an open-source software tool (citeware) that can be applied in laboratory and online experiments to implement video communication. The tool enables researchers to gather video data from these experiments in a way that these videos can be later used for automatic analysis through machine learning techniques. The browser-based tool comes with an easy user interface and can be easily integrated into z-Tree, oTree (and other experimental or survey tools). It provides the experimenters control over several communication parameters (e.g., number of participants, resolution), produces high-quality video data, and circumvents the Cocktail Party Problem (i.e., the problem of separating speakers solely based on audio input) by producing separate files. Using some of the recommended Voice-to-Text AI, the experimenters can transcribe individual files. MTVE can merge these individual transcriptions into one conversation.