Combining “real effort” with induced effort costs: the ball-catching task

Abstract

We introduce the “ball-catching task”, a novel computerized task, which combines a tangible action (“catching balls”) 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 tasks with the use of induced values, which is useful for theory-testing purposes as well as for applications.Electronic supplementary materialThe online version of this article (doi:10.1007/s10683-015-9465-9) contains supplementary material, which is available to authorized users.

Team formation with complementary skills

“…All parts of the experiment used the same real‐effort task, which is a modified version of the ball‐catching game developed by Gächter, Huang, and Sefton (). In the task, computerized balls move from the top of the computer screen downward and the participant must “catch” them by aligning a computerized tray under the balls as they reach the bottom of the screen.…”

“…The timing and placement of the balls is such that any participant who is comfortable using a computer is expected to be able to collect every single ball, with the occasional error . Finally, we eliminate the cost per click that was used by Gächter et al () and instead use a time cost, consistent with the parameter described in Section . The cost of collecting balls for 60 s was tokens, or 0.167 tokens/s.…”

“…The likelihood of winning the contest was determined by the team's points and its competitor's points . The winner of the contest was determined probabilistically according to a piecewise linear contest success function (Büyükboyaci & Robbett, ; Gächter et al, ; Gill and Prowse, ). Specifically, team producing units of output competing against team producing units of output wins the contest with probability (bounded by 0 and 1) .…”

“…Participants were paid their earnings from the piece rate stage and two randomly selected contests from the second and third parts, to avoid potential wealth effects. 5 All parts of the experiment used the same real-effort task, which is a modified version of the ball-catching game developed by Gächter, Huang, and Sefton (2016). In the task, computerized balls move from the top of the computer screen downward and the participant must "catch" them by aligning a computerized tray under the balls as they reach the bottom of the screen.…”

LIONESS Lab – A Free Web-Based Platform for Conducting Interactive Experiments Online

“…LIONESS Lab was specifically designed for conducting interactive experiments online with participants recruited from crowd-sourcing platforms or from the participant pool of research institutes. However, it can also be used in a laboratory (Arechar et al 2018;Molleman and Gächter 2018), for lab-in-the-field experiments (e.g., Glowacki and Molleman 2017), or to conduct experiments involving non-interactive tasks such as surveys or questionnaires (e.g., de Quidt et al 2017;Gächter et al 2016). An important advantage of this portability is that the screens of experimental participants are exactly the same in the physical lab and online, facilitating comparisons between them.…”