Abstract
Firms face an optimization problem that requires a maximal quantity output given a quality constraint. But how do firms incentivize quantity and quality to meet these dual goals, and what role do behavioral factors, such as loss aversion, play in the tradeoffs workers face? We address these questions with a theoretical model and an experiment in which participants are paid for both quantity and quality of a real effort task. Consistent with basic economic theory, higher quality incentives encourage participants to shift their attention from quantity to quality. However, we also find that loss averse participants shift their attention from quality to quantity to a greater degree when quality is weakly incentivized. These results can inform managers of appropriate ways to structure contracts, and suggest benefits to personalizing contracts based on individual behavioral characteristics.
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Notes
For instance, economists have used behavioral economics theories of gift exchange and framing to induce greater productivity of workers in a field setting—see Gneezy and List (2006) for gift exchange and Hossain and List (2012) on framing. Other notable papers include the merits of competitive or piece rate incentive schemes, including the gender gap in competitiveness (Gneezy et al. 2003), and various profit-sharing compensation schemes (Nalbantian and Schotter 1997). While many of these papers have incorporated quality considerations into their work, none of them have evaluated quality of output directly.
In the spirit of forthrightness, we note that we designed the experiment to test whether any of the following behavioral characteristics affect the quantity-quality tradeoff: loss aversion, risk aversion, ambiguity aversion, and overconfidence. We were expecting that loss-aversion would play the major role, given the previous findings of Shupp et al. (2013). But it was not until we confirmed that loss aversion indeed affects the tradeoff more than other characteristics, that we formally incorporated loss aversion into our model. Therefore, our model is merely meant to guide the reader’s intuition around loss aversion rather than the other behavioral characteristics we tested.
Additional related work includes Eriksson et al. (2009) who use a real-effort experiment to examine how feedback about performance of others impacts quantity and quality under pay-for-performance and tournament payment schemes, and Bracha and Fershtman (2013) who study how competitive incentive schemes affect the combination of cognitive and labor efforts provided by workers.
Helper et al. (2010) suggest that a piece rate may actually have a negative impact on quantity when the production process is complex and quality is unobservable. Similarly, Rubin and Sheremeta (2016) show that even in the gift-exchange context uncertainty about quality can significantly decrease quantity.
As we noted in the introduction, we focus on loss aversion, rather than the other behavioral characteristics we test, because we found ex post that loss aversion affects subjects’ quantity-quality tradeoff. The model is meant merely to guide intuition, rather than provide a strict set of predictions tested in the experiment.
Including a positive cost of \(e_{1}\) in the agent’s utility function would change none of the comparative statics results.
In a previous version of the paper, we modeled the reference point as the “sure thing” wage of \(w_{1}\). That is, agents could invest all of their effort in \(e_{1}\) and receive wage \(w_{1}\) with certainty. The main results from this model are broadly similar to the one proposed here. Most importantly, the amount agents spend on \(e_{2}\) is decreasing in \(\theta\) in both models.
Kőszegi and Rabin (2006) model “gain/loss” utility, where agents gain if their payoff is higher than their reference point. For simplicity, we focus only on “loss” utility.
Formally, \(\frac{{\partial^{2} E\left[ U \right]}}{{\partial e_{2} \partial w_{2} }} = \left[ {\theta 1_{A} u^{\prime}\left( X \right) + 1} \right]\left[ {\left( {1 - e_{2} } \right)p'\left( {e_{2} } \right) - p\left( {e_{2} } \right)} \right] - \theta 1_{A} \left( {w_{1} + w_{2} + c_{1} \left( {e_{2} ,a} \right)} \right)\left( {1 - e_{2} } \right)p\left( {e_{2} } \right)u''\left( X \right)\), where \(X = \left( {w_{1} + w_{2} } \right)\left( {1 - e_{2} } \right) - c\left( {e_{2} ,a} \right)\).
To see this, note that \(E\left[ {q^{L} } \right] = (1 - e_{2} )\left( {1 - p\left( {e_{2} } \right)} \right)\), which is clearly decreasing in \(e_{2}\).
We find no statistically significant differences for any of the outcomes reported in this section when comparing T-0.25 and T-0.50 or when comparing T-1.00 and T-3.00.
These p values are for comparison between pooled treatments. Similar results hold for comparisons for unpooled treatments. This is true of all comparisons presented in this section. Unpooled results are available upon request.
In Appendix C, we fine-tune our measure of quality to include answers that are close to the correct answer but not correct (i.e., “guesstimates”). We find that higher quality incentives decrease the number of participants “guesstimating” the correct answer.
A correlation analysis shown in Table B4 in Appendix B indicates that there is a strong correlation between ambiguity-aversion and risk-aversion (ρ = 0.67), and somewhat weaker correlation between loss-aversion and ambiguity-aversion (ρ = 0.30) and loss-aversion and risk-aversion (ρ = 0.35).
Including interaction terms with overconfidence and ambiguity does not yield any statistically significant results, and we therefore do not report these results for the sake of brevity. The interaction terms with risk aversion do yield statistically significant results when the number of answers submitted is the dependent variable, but not in regressions with the other dependent variables reported in this section. These results are available upon request.
We calculated numerous metrics of choosing “quality” or “quantity” (also see Table 8). For instance, another metric we considered was that a participant chose quality if they spent as much time submitting an answer as the minimum time it took them to submit an answer in part 6 (where quantity was not incentivized and payouts were the same across treatments). Results are similar in all specifications, and the statistics associated with other metrics are available upon request. Moreover, in all of the definitions we do not count decisions made in the last 30 s or decisions made in the participant’s last answer because the decision-making calculus at the end of the five minute period may be different than in the first 4 min. For instance, one who can correctly answer a problem in 10 s (meaning that she should focus on quality in most of the treatments) has incentive to input a quick answer if there are only 6 s remaining.
It is possible that some participants may choose to focus on quality simply because they enjoy adding numbers. Holmstrom and Milgrom (1991) note that “we shall not suppose that all work is unpleasant. A worker on the job may take pleasure in working up to some limit”.
The same conclusion stands when we drop the first 34 s of experiment, which is one standard deviation above the mean time taken to answer a question in part 6 (Kruskal–Wallis test, p value <0.01).
See the following article in the New York Times: http://www.nytimes.com/2013/01/12/nyregion/new-york-city-hospitals-to-tie-doctors-performance-pay-to-quality-measures.html?pagewanted=2&_r=1&hp.
References
Al-Ubaydli, O., Andersen, S., Gneezy, U., & List, J. A. (2015). Carrots that look like sticks: Toward an understanding of multitasking incentive schemes. Southern Economic Journal, 81, 538–561.
Baker, G. P. (1992). Incentive contracts and performance measurement. Journal of Political Economy, 100, 598–614.
Bowles, S. (2009). When economic incentives backfire. Harvard Business Review, 87, 22–23.
Bowles, S., & Polania-Reyes, S. (2012). Economic incentives and social preferences: Substitutes or complements? Journal of Economic Literature, 50, 368–425.
Bracha, A., & Fershtman, C. (2013). Competitive incentives: Working harder or working smarter? Management Science, 59, 771–781.
Camerer, C. F., Loewenstein, G., & Rabin, M. (2003). Advances in behavioral economics. Princeton: Princeton University Press.
Cason, T. N., Masters, W. A., & Sheremeta, R. M. (2010). Entry into winner-take-all and proportional-prize contests: An experimental study. Journal of Public Economics, 94, 604–611.
Charness, G., & Grieco, D. (2014). Creativity and financial incentives. Working Paper.
Copeland, A., & Monnet, C. (2009). The welfare effects of incentive schemes. Review of Economic Studies, 76, 93–113.
DellaVigna, S., Lindner, A., Reizer, B., & Schmieder, J. F. (2017). Reference-dependent job search: Evidence from Hungary. Quarterly Journal of Economics, forthcoming.
Ederer, F. P., & Manso, G. (2013). Is pay-for-performance detrimental to innovation? Management Science, 59, 1496–1513.
Erat, S., & Gneezy, U. (2016). Incentives for creativity. Experimental Economics, 19, 269–280.
Eriksson, T., Poulsen, A., & Villeval, M. C. (2009). Feedback and incentives: Experimental evidence. Labour Economics, 16, 679–688.
Falk, A., & Kosfeld, M. (2006). The hidden costs of control. American Economic Review, 96, 1611–1630.
Fehr, E., & Rockenbach, B. (2003). Detrimental effects of sanctions on human altruism. Nature, 422, 137–140.
Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10, 171–178.
Frey, B. S. (1993). Does monitoring increase work effort? The rivalry with trust and loyalty. Economic Inquiry, 31, 663–670.
Fryer, R. G., Levitt, S. D., List, J. A., & Sadoff, S. (2012). Enhancing the efficacy of teacher incentives through loss aversion: A field experiment. Working paper.
Gneezy, U., & List, J. A. (2006). Putting behavioral economics to work: Testing for gift exchange in labor markets using field experiments. Econometrica, 74, 1365–1384.
Gneezy, U., Meier, S., & Rey-Biel, P. (2011). When and why incentives (don’t) work to modify behavior. Journal of Economic Perspectives, 25, 191–209.
Gneezy, U., Niederle, M., & Rustichini, A. (2003). Performance in competitive environments: Gender differences. Quarterly Journal of Economics, 118, 1049–1074.
Gneezy, U., & Rustichini, A. (2000a). A fine is a price. Journal of Legal Studies, 29, 1–17.
Gneezy, U., & Rustichini, A. (2000b). Pay enough or don’t pay at all. Quarterly Journal of Economics, 115, 791–810.
Godager, G., Henning-Schmidt, H., & Iversen, T. (2016). Does performance disclosure influence physicians’ medical decisions? An experimental analysis. Journal of Economic Behavior & Organization, 131, 36–46.
Griffith, T. L. (1993). Monitoring and performance: A comparison of computer and supervisor monitoring. Journal of Applied Social Psychology, 23, 549–572.
Haigh, M. S., & List, J. A. (2005). Do professional traders exhibit myopic loss aversion? An experimental analysis. Journal of Finance, 60, 523–534.
Helper, S., Kleiner, M. M., & Wang, Y. (2010). Analyzing compensation methods in manufacturing: piece rates, time rates, or gain-sharing? Working Paper.
Hennig-Schmidt, H., Selten, R., & Wiesen, D. (2011). How payment systems affect physicians’ provision behavior—An experimental investigation. Journal of Health Economics, 30, 637–646.
Ho, T. H., Lim, N., & Camerer, C. F. (2006). Modeling the psychology of consumer and firm behavior with behavioral economics. Journal of Marketing Research, 43, 307–331.
Holmstrom, B., & Milgrom, P. (1991). Multitask principal-agent analyses: Incentive contracts, asset ownership, and job design. Journal of Law Economics and Organization, 7, 24–52.
Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92, 1644–1655.
Hossain, T., & List, J. A. (2012). The behavioralist visits the factory: Increasing productivity using simple framing manipulations. Management Science, 58, 2151–2167.
Imas, A., Sadoff, S., & Samek, A. (2017). Do people anticipate loss aversion? Management Science, 63, 1271–1284.
Johnson, R. M., Reiley, D. H., & Muñoz, J. C. (2015). “The war for the fare”: How driver compensation affects bus system performance. Economic Inquiry, 53, 1401–1419.
Kachelmeier, S. J., Reichert, B. E., & Williamson, M. G. (2008). Measuring and motivating quantity, creativity, or both. Journal of Accounting Research, 46, 341–373.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–292.
Kőszegi, B., & Rabin, M. (2006). A model of reference-dependent preferences. Quarterly Journal of Economics, 121, 1133–1165.
Laffont, J. J., & Martimort, D. (2009). The theory of incentives: The principal-agent model. Princeton: Princeton University Press.
Laske, K., & Schroeder, M. (2015). Quantity, quality, and novelty: Direct and indirect effects of incentives on creativity. Nwe York: Mimeo.
Lazear, E. P. (2000). Performance pay and productivity. American Economic Review, 90, 1346–1361.
Madrian, B. C. (2014). Applying insights from behavioral economics to policy design. Annual Review of Economics, 6, 663–688.
Mauboussin, M. J. (2012). The success equation: Untangling skill and luck in business, sports, and investing. Brighton: Harvard Business Press.
Nalbantian, H. R., & Schotter, A. (1997). Productivity under group incentives: An experimental study. American Economic Review, 87, 314–341.
Niederle, M., & Vesterlund, L. (2007). Do women shy away from competition? Do men compete too much? Quarterly Journal of Economics, 122, 1067–1101.
Paarsch, H. J., & Shearer, B. (2000). Piece rates, fixed wages, and incentive effects: Statistical evidence from payroll records. International Economic Review, 41, 59–92.
Rietz, T., Schniter, E., Sheremeta, R. M., & Shields, T. W. (2017). Trust, reciprocity and rules. Economic Inquiry, forthcoming.
Rubin, J., & Sheremeta, R. (2016). Principal-agent settings with random shocks. Management Science, 62, 985–999.
Shearer, B. (2004). Piece rates, fixed wages and incentives: Evidence from a field experiment. Review of Economic Studies, 71, 513–534.
Shupp, R., Sheremeta, R. M., Schmidt, D., & Walker, J. (2013). Resource allocation contests: Experimental evidence. Journal of Economic Psychology, 39, 257–267.
Syverson, C. (2011). What determines productivity? Journal of Economic Literature, 49, 326–365.
Wilcox, N. (2008). Stochastic models for binary discrete choice under risk: A critical stochastic modeling primer and econometric comparison. In J. C. Cox & G. W. Harrison (Eds.), Research in experimental economics (Vol. 12, pp. 197–292)., Risk aversion in experiments Bingley: Emerald.
Acknowledgements
We thank Marie Claire Villeval, the editor of this journal, for her guidance and two anonymous reviewers for their suggestions and comments. We thank David Clingingsmith, Catherine Eckel, Sue Helper, Jonathan Meer, Matthew Sobel, Scott Shane, Jingjing Zhang and seminar participants at Case Western Reserve University, Texas A&M, University of Southern California, and the University of Technology Sydney for helpful comments. We also thank Kevin Guo, Christa Gibbs, Kathryn Carroll and students at the Behavioral and Experimental Economics Research Group for excellent research assistance. Any remaining errors are ours.
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Rubin, J., Samek, A. & Sheremeta, R.M. Loss aversion and the quantity–quality tradeoff. Exp Econ 21, 292–315 (2018). https://doi.org/10.1007/s10683-017-9544-1
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DOI: https://doi.org/10.1007/s10683-017-9544-1