Over the last 25 years, more than a hundred dictator game experiments have been published. This meta study summarises the evidence. Exploiting the fact that most experiments had to fix parameters they did not intend to test, in multiple regression the meta study is able to assess the effect of single manipulations, controlling for a host of alternative explanatory factors. The resulting rich dataset also provides a testbed for comparing alternative specifications of the statistical model for analysing dictator game data. It shows how Tobit models (assuming that dictators would even want to take money) and hurdle models (assuming that the decision to give a positive amount is separate from the choice of amount, conditional on giving) provide additional insights.

The dhreg command implements maximum likelihood estimation of the double-hurdle model for continuously distributed outcomes. The command includes the option to fit a p-tobit model, that is, a model that estimates only an intercept for the hurdle equation. The bootdhreg command (the bootstrap version of dhreg) may be convenient if the data-generating process is more complicated or if heteroskedasticity is suspected. The xtdhreg command is a random-effects version of dhreg applicable to panel data. However, this estimator differs from standard random-effects estimators in the sense that the outcome of the first hurdle applies to the complete set of observations for a given subject instead of applying at the level of individual observations. Command options include estimation of a correlation parameter capturing dependence between the two hurdles.

Judges in multiple US states, such as New York, Pennsylvania, Wisconsin, California, and Florida, receive a prediction of defendants’ recidivism risk, generated by the COMPAS algorithm. If judges act on these predictions, they implicitly delegate normative decisions to proprietary software, even beyond the previously documented race and age biases. Using the ProPublica dataset, we demonstrate that COMPAS predictions favor jailing over release. COMPAS is biased against defendants. We show that this bias can largely be removed. Our proposed correction increases overall accuracy, and attenuates anti-black and anti-young bias. However, it also slightly increases the risk that defendants are released who commit a new crime before tried. We argue that this normative decision should not be buried in the code. The tradeoff between the interests of innocent defendants and of future victims should not only be made transparent. The algorithm should be changed such that the legislator and the courts do make this choice.

This experimental article helps to understand the motives behind cooperation in the prisoner’s dilemma. It manipulates the pay-off in case both players defect in a two-player, one-shot prisoner’s dilemma and explains the degree of cooperation by a combination of four motives: efficiency, conditional cooperation, fear and greed. All motives are significant but some become only significant if one controls for all remaining ones. This seems to be the reason why earlier attempts at explaining choices in the prisoner’s dilemma with personality have not been successful.

Causes and Management of Conflicts