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What Motivates Effort? Evidence and Expert Forecasts

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  • Stefano DellaVigna
  • Devin Pope

Abstract

How much do different monetary and non-monetary motivators induce costly effort? Does the effectiveness line up with the expectations of researchers? We present the results of a large-scale real-effort experiment with 18 treatment arms. We compare the effect of three motivators: (i) standard incentives; (ii) behavioral factors like present-bias, reference dependence, and social preferences; and (iii) non-monetary inducements from psychology. In addition, we elicit forecasts by behavioral experts regarding the effectiveness of the treatments, allowing us to compare results to expectations. We find that (i) monetary incentives work largely as expected, including a very low piece rate treatment which does not crowd out incentives; (ii) the evidence is partly consistent with standard behavioral models, including warm glow, though we do not find evidence of probability weighting; (iii) the psychological motivators are effective, but less so than incentives. We then compare the results to forecasts by 208 experts. On average, the experts anticipate several key features, like the effectiveness of psychological motivators. A sizeable share of experts, however, expects crowd-out, probability weighting, and pure altruism, counterfactually. This heterogeneity does not reflect field of training, as behavioral economists, standard economists, and psychologists make similar forecasts. Using a simple model, we back out key parameters for social preferences, time preferences, and reference dependence, comparing expert beliefs and experimental results.

Suggested Citation

  • Stefano DellaVigna & Devin Pope, 2016. "What Motivates Effort? Evidence and Expert Forecasts," NBER Working Papers 22193, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22193
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    Cited by:

    1. Barron, Kai & Gravert, Christina, 2018. "Confidence and Career Choices: An Experiment," Working Papers in Economics 715, University of Gothenburg, Department of Economics.
    2. Stefano DellaVigna & Devin Pope, 2016. "Predicting Experimental Results: Who Knows What?," NBER Working Papers 22566, National Bureau of Economic Research, Inc.
    3. Isabel Thielmann & Daniel W. Heck & Benjamin E. Hilbig, 2016. "Anonymity and incentives: An investigation of techniques to reduce socially desirable responding in the Trust Game," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 11(5), pages 527-536, September.
    4. Marco Faillo & Luigi Mittone & Costanza Piovanelli, 2018. "Cash posters in the lab," CEEL Working Papers 1801, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
    5. Jonathan Quidt & Francesco Fallucchi & Felix Kölle & Daniele Nosenzo & Simone Quercia, 2017. "Bonus versus penalty: How robust are the effects of contract framing?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 3(2), pages 174-182, December.
    6. Xavier Gabaix, 2017. "Behavioral Inattention," NBER Working Papers 24096, National Bureau of Economic Research, Inc.
    7. Alan Benson & Danielle Li & Kelly Shue, 2018. "Promotions and the Peter Principle," NBER Working Papers 24343, National Bureau of Economic Research, Inc.
    8. Goerg, Sebastian J. & Kube, Sebastian & Radbruch, Jonas, 2017. "The Effectiveness of Incentive Schemes in the Presence of Implicit Effort Costs," IZA Discussion Papers 10546, Institute for the Study of Labor (IZA).

    More about this item

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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