Fairness preferences revisited

This study revisits Engelmann and Strobel (2004), which tests theoretical models of fairness preferences with simple distribution games in a classroom environment with German students. We attempt to generalize the findings from ES (2004) to a digital context with US subjects by executing three experimental waves on Amazon Mechanical Turk. As in the original study, we find similar results for efficiency concerns and ERC. However, we find that selfishness motives have larger power for rationalizing allocation decisions while maximin preferences do not explain choices in our context. We draw on a plethora of replication criteria that lead up to 80% replication success with a large variation across criteria. We do not find meaningful preference changes during the pandemic using panel data from the first to the second wave. By leveraging a third wave, we show that lower maximin motives may be attributable to cultural differences in fairness considerations while larger selfishness preferences may be driven by the high anonymity in an online relative to a classroom environment.