The slider task: An oTree implementation for studying incentive effects in online experiments

The slider task is one of the most utilized computerized real-effort tasks in experimental research. However, previous laboratory results suggest that it might lead to boundary solutions and therefore be prone to type-II errors when used to detect treatment effects on effort provision. We introduce a novel oTree implementation of the slider task and assess its output elasticity in online experiments, where its properties are likely to differ from laboratory environments. In particular, we advance that subjects exhibit a greater response to incentives when participating online, due to the higher opportunity costs associated with completing the task from home. This result would suggest that, in online environments, the slider task is well-suited to detecting treatment-induced changes in output. We conduct a between-subjects online experiment in which participants complete the slider task under varying monetary incentives. We compare the incentive effects observed in our study to those reported in similar laboratory experiments.