We conduct the first natural field experiment to explore the relationship between the "meaningfulness" of a task and worker effort. We employed about 2,500 workers from Amazon's Mechanical Turk (MTurk), an online labor market, to label medical images. Although given an identical task, we experimentally manipulated how the task was framed. Subjects in the meaningful treatment were told that they were labeling tumor cells in order to assist medical researchers, subjects in the zero-context condition (the control group) were not told the purpose of the task, and, in stark contrast, subjects in the shredded treatment were not given context and were additionally told that their work would be discarded. We found that when a task was framed more meaningfully, workers were more likely to participate. We also found that the meaningful treatment increased the quantity of output (with an insignificant change in quality) while the shredded treatment decreased the quality of output (with no change in quantity). We believe these results will generalize to other short-term labor markets. Our study also discusses MTurk as an exciting platform for running natural field experiments in economics.

Each year, more than 250 students in the Chicago Public Schools (CPS) are shot. The authors of this paper worked with the leadership of CPS to build a predictive model of shootings that helped determine which students would be included in a highly targeted and resource intensive mentorship program. This paper describes our predictive model and offers a preliminary evaluation of the mentoring intervention performed by Youth Advocate Programs, Inc. (YAP). We find little evidence that the intervention reduces school misconducts or improves educational outcomes. The scale of intervention was too small to generate meaningful findings on shootings.