Labor Allocation in Paid Crowdsourcing: Experimental Evidence on Positioning, Nudges and Prices

This paper reports the results of a natural field experiment where workers from a paid crowdsourcing environment selfselect into tasks and are presumed to have limited attention. In our experiment, workers labeled any of six pictures from a 2 x 3 grid of thumbnail images. In the absence of any incentives, workers exhibit a strong default bias and tend to select images from the top-left (“focal”) position; the bottomright (“non-focal”) position, was the least preferred. We attempted to overcome this bias and increase the rate at which workers selected the least preferred task, by using a combination of monetary and non-monetary incentives. We also varied the saliency of these incentives by placing them in either the focal or non-focal position. Although both incentive types caused workers to re-allocate their labor, monetary incentives were more effective. Most interestingly, both incentive types worked better when they were placed in the focal position and made more salient. In fact, salient non-monetary incentives worked about as well as non-salient monetary ones. Our evidence suggests that user interface and cognitive biases play an important role in online labor markets and that salience can be used by employers as a kind of “incentive multiplier”.