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Interpersonal discounting

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Abstract

Many discounting choices affect both the decision maker and at least one other person. The interpersonal nature of these choices is not well explored because the current empirical literature primarily focuses on estimating individual discount rates. We design a laboratory experiment to elicit interpersonal discount rates where individuals consider present versus future consumption tradeoffs for cases that involve both the self and others. By allowing for possible presence of others’ welfare in one’s utility function, our estimation results show that interpersonal discount rates are significantly different from traditional individual discount rates, particularly in situations when an individual may trade off his/her own future payment with the current payoff for others. We find support that the distinct interpersonal discount rate reflects a temporal form of other-regarding preferences.

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Notes

  1. Notable exceptions include Jacquet et al. (2013), Meyer (2013) and Jackson and Yariv (2014).

  2. Conversely, in a loss domain, de Oliveira and Jacobson (2017) found that when allocations involve losses, people are less patient in allocating costs to others than to themselves.

  3. We thank an anonymous referee for his or her suggestion of this scenario as a realistic example of an RBDR.

  4. Despite their importance, we do not provide detailed reviews on these studies as our focus is on time preference elicitation and the fact that we only adopt a very basic form of the dictator game in our design. We refer interested readers to Engel (2011) for a comprehensive review and meta-analysis including 129 dictator game studies.

  5. Other-regarding preferences may emerge due to many reasons. Studies have shown that motives such as the warm glow (Andreoni 1990) and reciprocity (Charness and Rabin 2002) are among many other reasons why someone may include another party’s payoff in his/her own utility function. In this study, we do not intend to dig deeply into the underlying motives, and we use the term “other-regarding preference” and “altruism” in a general sense to refer to the utility structure that includes another’s payoff.

  6. As examples, Caplin and Leahy (2004) argue that using a market rate of interest to represent social rates of discounting is only justifiable when preferences over all choices (including past choices) are time invariant. They show that “under reasonable conditions, policy makers should be more patient than private citizens” (Caplin and Leahy 2004, p. 1257–1258). Weitzman (1998) shows that the lowest possible discount rate should be used to discount the distant future, which is characterized by great uncertainty, to make the best possible investment decisions now. Similarly, Weitzman (2001) finds support for declining discount rates from survey responses collected among economists, starting at around 4% and declining to zero in the far future.

  7. Grijalva et al. (2017) review much of the current discussion about these two main competing approaches to eliciting IDRs in the laboratory setting. There are strengths and weaknesses of each approach and we do not weigh in here on whether one is preferred to another, as there is an ample supply of comments on the CTB (Andreoni and Sprenger 2015; Calford et al. 2014; Cheung 2015; Epper and Fehr-Duda 2015; Miao and Zhong 2015; Chakraborty et al. 2017) and DMPL (see Harrison et al. 2013 and references within.)

  8. Views about the CTB versus other elicitation approaches are mixed [e.g., comment papers in the American Economic Review and defensive responses articulated by Andreoni et al. (2015)]. The focus of this paper is not about the benefits and costs of each approach. Rather, we focus on trying to integrate preferences that one person has for another person into the elicitation of a discount rate.

  9. The authors claim to offer the first study to consider whether a temporal decision environment affects group contribution to public goods. They build on previous work on collective risk in Millinski et al. (2008).

  10. Charness et al. (2016) examined payment strategies used in 30 experimental studies from the literature, comparing pay-all strategies (pay subjects based on responses to all experimental exercises) versus pay-one (random selection of one exercise) and paying only a subset of participants, and comparing the benefits and costs of the various strategies. Pay-all may suffer from portfolio effects while pay-one may suffer from incentive effects. Based on their review, they find that pay-one or paying only a subset of individuals is at least as effective as pay-all.

  11. We implement the assignment of the counterpart using a set of ID cards. We allow each player in the room to pick an ID card at random. The random draw process with no replacement guarantees that each player is matched with another in the room and the resulting match may not be bilateral.

  12. Upon completion of the questions, a subject would view their results. If the subject answered a question correctly, green text, “Correct,” appeared on the screen, indicating to the researchers it was okay to move forward. If the subject answered incorrectly, red text indicated the subject had answered incorrectly, followed by an explanation. The researchers would then assist the subjects who missed a question(s) to ensure comprehension before continuing.

  13. Most laboratory experiments are conducted for short-term tradeoffs, i.e., typically weeks or months. Eckel et al. (2005) consider tradeoffs of 1 year or more. Grijalva et al. (2014) and Grijalva et al. (2017) examine tradeoffs of up to 20 years. Giglio et al. (2015) estimate discount rates using observable purchase decisions in the very long term housing rental market. All these studies with longer time horizon found relatively lower discount rates, ranging from 2–10%. Since delays over 5 years may potentially raise issues of trust in receiving the future payments, but delays less than 1 year may lose external validity in respect to social policies, we set 1 and 5 years as the future horizons in this study.

  14. In the dictator game, the average allocation to the self is slightly more than half, where 53% of the subjects split the money evenly with their counterpart. Only about 17% kept all $10 for themselves, and another 12% kept $8 of the $10 for themselves. Thus, there is some evidence of social preferences and caring for the welfare of others.

  15. Exercise 9 includes a token exchange ratio of 1:2, the same as one of the earlier exercises. This is to control for an income effect (Andreoni and Sprenger 2012a).

  16. Since 95.27% of the subjects’ allocation choices are in multiples of five tokens, rounding like this should minimally affect the precision of the estimation results. Alternative rounding of the data into 11 categories (in multiples of 10 tokens) produces similar results, which can be requested from the corresponding author.

  17. This rate is comparable to the rate of 5.5% obtained in a related paper, where the authors use the CTB approach solely to obtain an IDR (Grijalva et al. 2017).

  18. Harvey’s (1986) functional form for the discount factor is D(t) = (1/t)r, and Mazur’s (1984) functional form for the discount factor is D(t) = 1/(1 + rt), with corresponding discount rates of d(t) = (1/t)(−r/t) − 1, and d(t) = (1 + rt)1/t − 1, respectively. We also tried estimating the Loewenstein and Prelec (1992) model, but given our design with only two future delays (1 year and 5 years), the estimation does not converge.

  19. An exception is for BDR based on the Mazur model. The rate shown in Fig. 2 is negative. This is based on a negative parameter estimate that is not statistically significant.

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Acknowledgments

We thank the Hemingway family for financial support offered through an internal university grant, Hemingway Faculty Excellence award. We also acknowledge support from a U.S.D.A. Hatch grant. For helpful comments we thank Charles Sprenger, Marco Palma, Yvette Zhang, and seminar participants at 2015 annual meetings of the Economic Science Association, Dallas, Texas, the 23rd Annual Conference of the European Association of Environmental and Resource Economists, and at Colorado State University. All remaining errors herein are our own responsibility.

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Rong, R., Grijalva, T.C., Lusk, J. et al. Interpersonal discounting. J Risk Uncertain 58, 17–42 (2019). https://doi.org/10.1007/s11166-019-09297-2

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