Abstract

We introduce the a novel computerized real effort task, which combines “real” efforts with induced material cost of effort. The central feature of the ball-catching task is that it allows researchers to manipulate the cost of effort function as well as the production function, which permits quantitative predictions on effort provision. In an experiment with piece-rate incentives we find that the comparative static and the point predictions on effort provision are remarkably accurate. We also present experimental findings from three classic experiments, namely, team production, gift exchange and tournament, using the task. All of the results are closely in line with the stylized facts from experiments using purely induced values. We conclude that the ball-catching task combines the advantages of real effort with induced values, which is useful for theory-testing purposes as well as for applications.

Highlights

  • We introduce the ‘‘ball-catching task’’, a novel computerized task, which combines a tangible action (‘‘catching balls’’) with induced material cost of effort

  • The ball-catching task shares an advantage of real effort tasks in that subjects are required to do something tangible in order to achieve a level of performance, as opposed to choosing a number

  • Evidence reported by Lazear (2000) and lab evidence provided by Dickinson (1999) support comparative static predictions of basic incentive theory, whereas we show that in the ball-catching task the theory predicts activity levels accurately

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Summary

The ball-catching task

The lab version of the ball-catching task is a computerized task programmed in z-Tree (Fischbacher 2007), and requires subjects to catch falling balls by moving a tray on their computer screens. As will be discussed later, flexibility in all these parameters will allow tight control over the production function in this task, that is, the relationship between the number of balls caught and the number of clicks made. To catch the falling balls, the subject can move the tray by mouse clicking the ‘‘LEFT’’ or ‘‘RIGHT’’ buttons below the task box. At the top of the screen, the number of balls caught (CATCHES) and the number of clicks made (CLICKS) are updated in real time.

Experimental design and comparative static predictions
Comparative statics results
Result
The production function
Comparing the predicted and actual number of clicks
Team production
Gift exchange
Tournament
The ball-catching task on Amazon Mechanical Turk
Findings
Discussion
Conclusion
Full Text
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