Person:

Yin, Ming

Online labor markets such as Amazon Mechanical Turk (MTurk) have emerged as platforms that facilitate the allocation of productive effort across global economies. Many of these markets compensate workers with monetary payments. We study the effects of performance-contingent financial rewards on work quality and worker effort in MTurk via two experiments. We find that the magnitude of performance contingent financial rewards alone affects neither quality nor effort. However, when workers working on two tasks of the same type in a sequence, the change in the magnitude of the reward over the two tasks affects both. In particular, both work quality and worker effort increase (alternatively decrease) as the reward increases (alternatively decreases) for the second task. This suggests the existence of the anchoring effect on workers’ perception of incentives in MTurk and that this effect can be leveraged in workflow design to increasethe effectiveness of financial incentives.

With a large amount of tasks of various types, requesters in crowdsourcing platforms often bundle tasks of different types into a single working session. This creates a task switching setting, where workers need to shift between different cognitive tasks. We design and conduct an experiment on Amazon Mechanical Turk to study how occasionally presented performancecontingent monetary rewards, referred as monetary interventions, affect worker performance in the task switching setting. We use two competing metrics to evaluate worker performance. When monetary interventions are placed on some tasks in a working session, our results show that worker performance on these tasks can be improved in both metrics. Moreover, worker performance on other tasks where monetary interventions are not placed is also affected: workers perform better according to one metric, but worse according to the other metric. This suggests that in addition to providing extrinsic monetary incentives for some tasks, monetary interventions implicitly set performance goals for all tasks. Furthermore, monetary interventions are most effective in improving worker performance when used at switch tasks, tasks that follow a task of a different type, in working sessions with a low task switching frequency.