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Risk preferences and contract choices

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Abstract

We conducted a series of field experiments to investigate the ability of experimentally measured risk preferences to predict the contractual choices of workers in the real labour market. In a first set of experiments we twice measured workers’ risk preferences using the lottery approach of Holt and Laury (Am Econ Rev 92(5):1644–165, 2002). These workers subsequently participated in a contract-choice experiment, making 12 decisions. For each decision, the worker chose between his/her regular piece-rate contract and a particular fixed wage contract, each distinguished by the level of the fixed wage. One of the twelve decisions was then chosen at random and the worker was paid according to his/her choice for that decision over a period of two working days. We estimate the effect of risk preferences on contractual choices, controlling for measurement error and worker ability. Risk preferences effectively predict contract choices—risk-averse workers are more likely to select fixed-wage contracts. High-ability workers prefer piece-rates.

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Notes

  1. Related papers exploit laboratory experiments to consider the risk-incentives trade-off from the point of view of the firm. Corgnet and Hernán-González (2020) and Chowdhury and Karakostas (2019) find that principals diminish incentives in risky settings.

  2. Other evidence supporting sorting on the basis of HL measured risk preferences is more indirect. For example, Bellemare and Shearer (2010) conducted HL lotteries on a sample of tree planters who were paid piece rates. They noted that tree planters had a higher risk tolerance, on average, than was reported by Dave et al. (2010) who used a more representative sample. The evidence that we present adds to these studies, allowing for a direct test of sorting since we measure the risk preferences and observe the contract choices for the same set of workers. By applying the HL lotteries and the contract-choice experiments to the same sample, we can link the contracts chosen to individual risk preferences.

  3. Dohmen and Falk (2011) state: “An interesting question is whether the sorting patterns that we observe in the lab generalize qualitatively to labor markets outside the lab.” (Cadsby, Song, and Tapon, 2007) also address the issue and finish their paper stating: “Future research should explore the effectiveness of such mechanisms and their interactions with various forms of pay for performance in different organizational settings.”

  4. Such heterogeneity has been noted in other contexts as well; see, for example, (Bombardini and Trebbi, 2012) and Petrolia et al. (2013).

  5. Occasionally workers will be paid a fixed wage, if, for example, conditions on a block are very poor or a block is unexpectedly added to a contract; see Paarsch and Shearer (2000) for a discussion.

  6. Firms are required by law to pay the British Columbia minimum wage. This involves topping up a worker’s earnings if the worker does not plant enough trees to earn the minimum wage. Workers who are consistently incapable of doing so are fired.

  7. The firm is subject to fines if government inspections uncover too many poorly planted trees.

  8. The firm periodically moves workers between crews if and when more workers are needed in a given area. These decisions were taken by upper management and were independent of worker performance during the experiment.

  9. A complete description of the experimental protocol is available in the Supplementary file "Appendix 2".

  10. All of the planters working in the firm at the visited locations were invited to participate. They were compensated $20 for their time, typically 20-25 minutes. All of the planters agreed to participate.

  11. Workers did not know that the lottery would be repeated at higher stakes when they were participating in the LS lottery.

  12. Participants in the lotteries were aware that there would be a second experiment in which they could influence the manner in which they were paid. However the exact nature of the contract-choice experiment (including the contracts that would be offered) was not known.

  13. The low number of inconsistent responses is possibly due to the highly educated workforce. As noted in Section 5.2, over 85% of the workers in our sample have some university education and over 65% have a university degree. This contrasts with studies such as Jacobson and Petrie (2009) where participants had an average of primary education or Dave et al. (2010) where only 11% of participants had a post-secondary education.

  14. The decision sheets, along with a complete presentation of the experimental protocol, are presented in Supplementary file "Appendix 2".

  15. The small number of participants in each session and the close proximity of the researcher during the experiment, minimised opportunities for discussion during the experiment and the possibility of peers affecting choices.

  16. A complete presentation of the experimental protocol is provided in Supplementary file "Appendix 1".

  17. The reader should be aware that this may introduce some ambiguity into the risky contract.

  18. A worker who did not want to be paid under fixed wages could simply choose his/her regular piece rate at each decision.

  19. The worker’s choices are said to be consistent if he/she switches only once from lottery A to lottery B.

  20. Discussions with this worker revealed that he/she would have chosen the fixed-wage contract only if the guaranteed wage was greater than $700.

  21. We only use observations for which earnings are greater than the minimum wage of $83.6 in British Columbia. This excludes planters who only worked part of a day.

  22. See Shearer (2004) for a discussion of this point.

  23. The test was conducted using the signrank command in Stata. The Kolmogorov-Smirnov test also failed to reject the null hypothesis that the distributions were identical.

  24. Restricting the sample to workers who made consistent choices in both lotteries had no effect on the results.

  25. The reader should be aware that other interpretations of the changing responses are possible. Changing risk preferences (Holt and Laury, 2002; Bombardini and Trebbi, 2012) is one possibility, although it is not clear why the risk preferences measured in the HS lottery would be more relevant for predicting contract choices than those from the LS lottery (see Section 7, below). Order effects (Harrison, Johnson, McInnes, and Rutström, 2006) could also be present. While we do not rule these out, we note that earnings from the LS lottery have no predictive power over HS lottery choices. The coefficient on earnings in the LS lottery is statistically insignificant in a regression explaining the number of safe choices in the HS lottery. The p-value is over 0.6. It is also possible that individual’s strategies evolved as they learned about how to play the lottery. Many experimental studies include preliminary practice rounds to allow participants to learn. (Lusk and Shogren (2007) provide a discussion of the benefits of this in the experimental auctions literature). Again, while we do not rule this out, we note that a natural measure of learning is comparing the number of mistakes (or inconsistent choices) that were made in filling out the lottery sheet. In our case, the number of inconsistent choices was negligible in both LS lottery and HS lottery, suggesting that learning was not a primary consideration. Another possiblity is that participants took the lottery more seriously at higher stakes. Levitt and List (2007b) argue that as stakes rise, monetary considerations take a more prominent place in individuals’ utility functions, affecting experimental responses.

  26. The results are qualitatively the same if we use average daily earnings to measure ability.

  27. Below, we exploit the ORIV methods, developed by Gillen et al. (2019), to control for measurement error. Following their suggestion, we standardarize the regression variables by subtracting off their mean and dividing by their standard deviation. For comparability, we use the same standardized variables in all regressions reported in Table 8.

  28. The results are qualitatively the same if we include this worker in our sample, although slightly less significant. For example, the coefficient on safe choices in the modified ORIV regression in Table 8 remains negative and statistically significant at the 10% level of significance with a p-value of 0.068.

  29. Including worker characteristics, such as age, gender, experience, tenure and education, had no significant effect on the regression results.

  30. Measurment error in explanatory variables causes OLS estimators to be biased towards zero; see, for example (Davidson and MacKinnon, 2004).

  31. This is similar to arguments over the relative importance of ability bias and measurement error in determining OLS estimates of the returns to schooling. Applying instrumental variables to those models often led to larger estimates of the return to schooling. This caused many researchers to doubt the relative importance ability bias; see Card (1999) for a detailed discussion.

  32. We received a number of comments from the participants during the HS lottery expressing how much more exited and nervous they were in drawing their chips. While anecdotal, this is consistent with participants taking this lottery more seriously.

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Authors and Affiliations

  1. Ministry of Revenue, Government of Québec and CRREP (Laval University), Québec City, Canada

    Jean-Louis Bago

  2. Economics Department, Laval University, CRREP, CIRANO and IZA, Québec City, Canada

    Bruce Shearer

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  1. Jean-Louis Bago
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  2. Bruce Shearer
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Correspondence to Bruce Shearer.

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We thank Roberto Weber, Marie Claire Villeval and two anonymous referees for their helpful comments and suggestions. We also thank Sylvain Dessy, Claude-Denis Fluet, Bernard Fortin, Sonia Laszlo, and Luca Tiberti, as well as participants at the the Societé canadienne des sciences économiques (Ottawa, 2017) and the Canadian Economic Association Meetings (McGill, 2018). Financial support from SSHRC (Shearer), FRQSC (Shearer) and CRREP (Bago) is gratefully acknowledged. The replication material for this study is available at https://osf.io/vewp9.

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Bago, JL., Shearer, B. Risk preferences and contract choices. Exp Econ 25, 1374–1398 (2022). https://doi.org/10.1007/s10683-022-09768-5

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