Economic Decisions for Others Are More Favorable for Close Than Distant Clients

Abstract

This research investigates the role of social distance between decision makers and their clients. In 11 experiments (total N = 1,653), participants decided about unfair and hyper-fair offers in an advisor game for themselves or for a client who varied in social distance (e.g., for a close friend vs. a stranger). Participants were strongly influenced by client identity. They systematically accepted more hyper-fair offers for themselves and close clients than for distant clients, while client identity played no role in unfair offers. We show that the driving mechanism of this client privileging effect is joy (happy-for-ness) participants experience particularly for close clients, while envy did not explain this effect. Across all types of clients and experiments, hyper-fair offers were accepted at only 86% which can only be explained by participants being not exclusively motivated by absolute monetary payoffs but also, to some extent, factoring in nonmonetary concerns.

Keywords

self-other decision making social distance joy for others social comparison

In matters of great importance that bear legal, medical, or financial consequences, outsourcing decisions to others is a common and highly adaptive course of action. Individuals consult lawyers, appoint a financial advisor, or even allow a surrogate to manage end-of-life medical care. These instances vary in terms of the degree of responsibility that is put on the other person, ranging from full (the other person de facto makes the decision) to partial (the other person gives advice on a decision). In vocational settings, the latter is more common than the former. In fact, as of 2010, more than half of all American households sought advice from a financial professional (Mark, Matvos, & Seru, 2016). Whenever we put decisions into the hands of someone else, we do so trusting that they will decide in our best interests. But is this trust warranted? In a 2016 poll conducted by the American Association of Individual Investors (AAII), 65% of Americans do not trust their financial advisors to act in their best interests, and that is a considerably high number, which should cast some doubt. Fortunately, as of June 2017, there are novel protective measures in place to ensure Americans’ financial well-being. Due to the implementation of the so-called Fiduciary Rule (Honest Brokers, 2017), financial advisors are required to act in their clients’ best interests and to put their clients’ interests above their own in all circumstances.

A growing body of research in psychology and related fields has explored the differences between making decisions for the self versus for others (see, for instance, Garcia-Retamero & Galesic, 2012; Kray & Gonzales, 1999; Petrova, Garcia-Retamero, & van der Pligt, 2016; Pronin, Olivola, & Kennedy, 2008; Zikmund-Fisher, Sarr, Fagerlin, & Ubel, 2006). For one, there is some evidence that decision making for others can be a very thorough process. In particular, individuals deciding for others (vs. themselves) examine and search for more information (Jonas & Frey, 2003; Kray, 2000), prefer more choice options (Polman, 2012a), render both a more exhaustive attribute- and alternative-search (Y. Liu, Polman, Liu, & Jiao, 2018; Polman, 2010), and are even more creative (Polman & Emich, 2011). Moreover, when deciding for others (vs. themselves), individuals are more concerned with justifying their decisions to others (Lu, Liang, & Duan, 2017) and also tend to focus more on desirability than on feasibility (Lu, Xie, & Xu, 2012). In the domain of risky decision making, decisions on behalf of others have been found to be both more and less risky than decisions made for the self or equal to decisions for the self (for a recent meta-analysis, see Polman & Wu, submitted). For instance, in risky decision-making scenarios with monetary payoffs (both hypothetical and actual), no differences between deciding for a friend versus for the self were found (e.g., Stone, Yates, & Caruthers, 2002). In contrast, in physical safety scenarios, individuals tend to make different, that is more risk-averse, decisions for friends than for themselves (Stone, Choi, De Bruin, & Mandel, 2013). Social values theory (Dore, Stone, & Buchanan, 2014; Stone & Allgaier, 2008) explains these differences by stating that risky decision making for others is predominantly shaped by norms prescribing which decision alternative is the most socially sanctioned one. Thus, in scenarios in which risk is considered as a value, individuals tend to make more risk-seeking decisions for others than for themselves (Stone & Allgaier, 2008).

Other findings offer insight into the emotional involvement of individuals deciding for others and show, for instance, that individuals deciding for others experience lower loss aversion (Andersson, Holm, Tyran, & Wengström, 2014; H. Liu, Wang, Yao, Yang, & Wang, 2017; Polman, 2012b) and feel less fatigue after decision making (Polman & Vohs, 2016). In line with this, research has shown that the endowment effect, that is, sellers—as compared to buyers—ascribing more value to things merely because they own them, disappears when individuals made decisions for others in the role of brokers (Zhang, Zhang, & Li, 2016). Furthermore, individuals deciding for others (vs. themselves) are more willing to make changes from their current states of affair, which implies that they are less susceptible to the status quo effect (Lu & Xie, 2014). In addition to differences in emotional involvement, research also showed differences in goal focus between decision making for others versus for the self by suggesting that individuals’ choices for others are more indulgent and pleasure seeking (Laran, 2010) and also more variety seeking (Choi, Kim, Choi, & Yi, 2006). Despite the numerous differences between decision making for others versus the self, some findings suggest that there are also crucial similarities. Within the scope of distributive justice, for instance, both decisions for the self and others are concerned with the “maximin principle,” that is, trying to maximize the minimum possible payoff for both themselves and others (Kameda et al., 2016).

The Identity of the Client: Social Distance and In-Group Favoritism

Strikingly, however, researchers only recently began to investigate how decisions for others are influenced by the social relation, which can be located on a continuum of social distance (for a recent review on psychological distance, see Trope & Liberman, 2010) between a decision recipient and a decision maker (cf. Greenstein & Xu, 2015; Montinari & Rancan, 2013). Based on classical evidence on in-group favoritism, that is, individuals habitually acting more favorably toward those they share any form of group membership with (Tajfel, 2010; Tajfel, Billig, Bundy, & Flament, 1971), a possible prediction could be that individuals deciding for others should also act more favorably on behalf of those others they have a close, rather than a distant, social relationship with. In-group favoritism can be expressed in many ways (cf. Brewer, 1979, 1999; for a recent review, see also Dunham, 2018), among them, and most importantly for the present agenda, in allocation of resources. Individuals, on the average, allocate more resources to in-group than out-group members, even if the distinction between groups is completely arbitrary (Tajfel, 1970). A defining feature of in-group favoritism as conceptualized in Tajfel’s work is that it necessarily entails favoring one group at the expense of another group. In line with this conceptualization, research showed that best friends are treated preferentially when individuals allocate money to either themselves, a friend, an acquaintance, or a stranger, with the latter bearing the expense of the former (Aron, Aron, Tudor, & Nelson, 1991) and are even willing to forgo a hypothetical amount of money for themselves to give $75 to a close person, which means that individuals bear the expense themselves (Jones & Rachlin, 2006).

Going beyond these contributions, we want to tackle favoritism from a different angle, that is, we want to explore whether favoritism persists under circumstances that are meant to enforce equality and fairness. Under such circumstances, resources are not scarce, and individuals are not asked to allocate resources between themselves and/or other individuals but to make decisions on behalf of these individuals’ interest with only those other individuals’ interest in mind and without another individual or group bearing the expense of a favorable decision. We hypothesize that even in such situations void of favoritism as an objective or rational reason, individuals cannot help but make more generous decisions on behalf of those they have a close as compared to a more distant social relationship with. We propose to term this type of behavior as in-group privileging, to clearly demarcate it from in-group favoritism: whereas, in-group favoritism is limited to decision making that takes place between at least two other individuals because it necessarily means favoring one individual over the other; in-group privileging can also emerge in decision making on behalf of a single other individual.

However, one could also hypothesize that a smaller social distance between decision maker and decision recipient might be rather harmful than beneficial for the decision recipient and that privileging is more likely to occur on behalf of an out-group as compared to an in-group member. In fact, in his self-evaluation maintenance model, Tesser (1988) argues that in certain contexts individuals tend to even favor distant over close others because close others are more relevant social comparison standards than distant others and thus may evoke more envy (Tesser, 1988). We envy our neighbor next door more than an anonymous resident in another part of the town. This is in line with Kindleberger’s (1978) observation stating that “there is nothing so disturbing to one’s well-being and judgment as to see a friend get rich” (p. 25) and leads to contradicting predictions compared to in-group favoritism theory (Tajfel et al., 1971). This is ultimately an empirical question; and we will discuss this in the General Discussion in the light of our findings.

Aim of the Present Work

We explore the impact of social distance on decision making for others and cross this with the value of the outcome to be decided about. In a nutshell, we let participants decide on disadvantageous and advantageous distributions proposed to themselves, friends, and more distant clients (such as strangers). We predicted that participants would treat themselves and close clients more favorably than distant clients, accepting more advantageous offers for the former than the latter.

To test this, we used a modified version of the ultimatum game (UG; Güth, Schmittberger, & Schwarze, 1982), hereafter advisor game. In the standard UG, a proposer proposes a distribution of a fixed amount of money between herself and the responder, and the responder decides whether to accept or reject the offer. If the responder accepts an offer, both the responder and the proposer receive their corresponding shares; but if the responder rejects an offer, neither of the two receives any gain. The classical finding here is that responders generally reject offers that are inequitable (cf., Friedman & Savage, 1948; Thaler, 1988). The UG allows straightforward manipulations of both advisor-client distance and outcome value.

We manipulated client identity by letting participants play the role of the responder and instructing them that they would decide on offers either for themselves or on behalf of a client of varying social distance (e.g., a close friend vs. a stranger). If participants accepted a given offer for themselves, they and the proposer would receive their corresponding shares. If, however, participants accepted a given offer for their clients, only their clients and the proposer would receive their corresponding shares, whereas the participants would not receive anything at all. This basic fact rules out that participants making decisions for their clients could be driven by the motivation to gain monetary profit.

We manipulated outcome value (good vs. bad deals) by exploring the decisions for unfair and hyper-fair offers. In unfair offers, the responder is offered less than 50% of the total amount of money—a disadvantageous distribution. In hyper-fair offers (Falk, Fehr, & Fischbacher, 2003; Hennig-Schmidt, Li, & Yang, 2004; Henrich et al., 2001), the responder is offered more than 50% of the total amount of money—an advantageous distribution. Exploring decision making for others within the domain of hyper-fair offers provides an especially important contribution of this work for two reasons. First, rejecting hyper-fair offers is a relevant yet neglected phenomenon (for a review, see Hennig-Schmidt et al., 2004). Second, and more crucially, hyper-fair offers are detrimental to the proposer and thus constitute a conflict between monetary and motivational (i.e., treating the proposer fairly) incentives. We predict that this conflict is solved differently depending on for whom a decision is made. Previous research has already investigated the effects of playing the UG on behalf of a third party within the domain of unfair offers and demonstrated that participants reject unfair offers equally often regardless of whether they decide for themselves or on behalf of a third party (Civai, Corradi-Dell’Acqua, Gamer, & Rumiati, 2010; Corradi-Dell’Acqua, Civai, Rumiati, & Fink, 2013). It is noteworthy that third parties in these previous studies were anonymous participants acting as responders in the next experimental sessions. Moreover, it is noteworthy that in these studies, UG trials were intermingled by a so-called Free-Win task, in which participants were offered the same amount of money as in the UG trial without the proposer being involved and that, due to fMRI and skin conductance measurements, sample sizes were rather small (N = 34, and N = 23; Civai et al., 2010; Corradi-Dell’ Acqua et al., 2013).

The present implementation of decision making for others in the advisor game goes beyond these prior studies in three ways. First, we do not only explore decisions on unfair but also on hyper-fair offers, which in general has received insufficient attention in the literature until now. Second, we do not only let participants decide for anonymous other persons but manipulate social distance between participant and client incrementally. Third, we explicitly instruct participants to take full responsibility for optimal decisions for all of the clients—regardless of the identities of these clients.

Experiments 1a and 1b: Decisions for Self, Friend, or Stranger

From a hypothetical proposer endowed with 100 €, participants received offers that varied between 0 and 100 € in 10 € increments. They were instructed to decide about the offers either for themselves, a close friend, or a stranger. Experiment 1a was conducted as the first study in this project to gauge the effect size of the impact of social distance between decision maker and client on acceptance rates. Then, Experiment 1b was conducted as a direct highly powered preregistered replication of Experiment 1a (see power analyses above).

Method

Data treatment, a priori power analyses, and research transparency for all experiments

Since no previous evidence on the impact of responder identity of acceptance rates in the UG is available, we conducted Experiment 1a as a pilot to gauge the basic effect size of the responder identity on acceptance rates. The crucial systematic effect that we found across all the present experiment is the higher acceptance rates of hyper-fair offers for close than distant clients, which reached an average effect size of d_z = 0.72 in that pilot (d_z = 0.71 for self vs. stranger, and d_z = 0.73 for friend vs. stranger). This effect requires N = 18 to be replicated with a power of 0.80 according to G*Power (Faul, Erdfelder, Lang, & Buchner, 2007), which informed the sample choices of the later experiments (the sample sizes we chose exceeded this required sample size by a wide margin). In the later meta-analysis (see below) we found an average effect size of d_z = 0.41 (d_z = 0.38 for self vs. stranger, d_z = 0.44 for friend vs. stranger), which requires a sample size of N = 49 to replicate with a power of 0.80. Thus, all the present experiments are well powered. For the experiments reported in this article, we report all exclusion of data (if any), all manipulations, and all measures. Experiments 1b, 4, 5b, and 6a were preregistered. The materials and data are archived under https://osf.io/g6hmu and will be made public upon publication.

Participants

A total of 68 students of a German university (n = 51 female; M_age = 23, SD = 5) were approached on the campus and invited to take part in the 5 minutes experimental task on laptops for a candy reward. In Experiment 1b, N = 167 students of a German university (n = 128 female; M_age = 24, SD = 7) were approached on the campus and invited to take part in an experimental session in the laboratory on the campus for a reward of 2 €.

Materials and procedure

In a computer-directed task and as part of a larger battery of unrelated tasks, participants were instructed to engage in hypothetical negotiation scenarios, in which they could neither win nor lose any real money (the advisor game). Participants were further informed that a hypothetical anonymous person (the proposer) would propose different offers of how to split an amount of 100 € between herself and the participant. These offers could range from 0 € to 100 € in 10 € steps, that is, 0 €, 10 €, 20 € and so on to 100 €. Crucially, participants were instructed that they would either have to decide on these offers on behalf of themselves or on behalf of a given client. This client could either be a close friend of the participant or a complete stranger. Both factors, the offered amount and decision role were manipulated within-subjects, with the sequence of all 33 resulting trials re-randomized anew for each participant. In each trial, both the amount of money offered by the proposer and the individual on which behalf participants would have to decide (self vs. friend vs. stranger) were presented on the screen until participants indicated whether they wanted to accept or reject the offer. Acceptance versus rejection of an offer was indicated by pressing the left and the right control key, respectively. After participants had indicated their decision, the next trial appeared. The task took 5 minutes.

Results

Experiment 1a

First, we split the 11 levels of offer into two distinct categories: unfair (including offers from 0 € to 40 €, that is, five trials) and hyper-fair offers (including offers from 60 € to 100 €, that is, five trials). Then, the proportion of accepted offers averaged over the five respective trials per condition was analyzed in a 3 (responder identity: self, friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) analysis of variance (ANOVA). We found a main effect of responder identity, F(2, 66) = 10.30, p < .001, η_p² = .24, as well as for the offered amount, F(1, 67) = 20.75, p < .001, η_p² = .43. The condition means aggregated over Experiment 1a and 1b are depicted in Figure 1.

Figure 1.

Probability of accepting an offer as a function of responder identity.

In addition, an interaction between responder identity and the offered amount, F(2, 66) = 16.93, p < .001, η_p² = .34, surfaced. Planned comparisons revealed that within the category of unfair offers, participants accepted less offers for themselves (M = .87, SE = .04) than for a stranger (M = .49, SE = .04), t(67) = 2.15, p = .036, 95% CI_difference [0.01, 0.14], d_z = 0.26, which is a sideline finding that did not reliably replicate later. There were no further reliable differences between accepting unfair offers for oneself vs. for a friend vs. for a stranger (all ts < 1.7). More importantly, within the category of hyper-fair offers, participants accepted more offers for themselves (M = .87, SE = .03) than for a stranger (M = .58, SE = .05), t(67) = 5.86, p < .001, 95% CI_difference [0.19, 0.39], d_z = 0.71, and also more offers for a friend (M = .84, SE = .04) than for a stranger, t(67) = 5.99, p < .001, 95% CI_difference [0.17, 0.34], d_z = 0.73. No difference was found between participants’ acceptance rate of hyper-fair offers for themselves versus a friend (t < 1.4). The condition means aggregated over Experiment 1a and 1b are depicted in Figure 2.

Figure 2.

Acceptance rates of hyper-fair offers as a function of responder identity.

Experiment 1b was a generously powered preregistered replication to further examine the effect size and replicability of our effects (https://osf.io/ucrdn).

Experiment 1b

The 3 (responder identity: self, friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA found again a main effect of responder identity, F(2, 165) = 3.92, p = .022, η_p² = .05, as well as for the offered amount, F(1, 166) = 235.52, p < .001, η_p² = .59 (see Figure 1). In addition, again, an interaction between responder identity and the offered amount, F(2, 165) = 12.78, p < .001, η_p² = .13, surfaced. Planned comparisons revealed that within the category of unfair offers, there were no reliable differences between accepting offers for oneself versus for a friend versus for a stranger (all ts < 1.6).

In contrast, within the category of hyper-fair offers, participants accepted more offers for themselves (M = .94, SE = .01) than for a stranger (M = .86, SE = .02), t(166) = 3.54, p = .001, 95% CI_difference [0.03, 0.12], d_z = 0.27 and also more offers for a friend (M = .96, SE = .01) than for a stranger, t(166) = 5.32, p < .001, 95% CI_difference [0.06, 0.14], d_z = 0.41. There was no difference between participants’ acceptance rate of hyper-fair offers for themselves and for a friend; t < 1.6 (see Figure 2).

Discussion

Manipulating social distance between decision maker and client in the advisor game affected participants’ acceptance rates for hyper-fair but not for unfair offers. Regardless of whether participants played the advisor game for themselves, a friend, or a stranger, the rejection rate of unfair offers is about 46%. The high rejection rate of unfair offers is largely in line with previous findings from numerous UG experiments (cf., Camerer, 2003). Moreover, the missing difference in rejection rate of unfair offers between self and client provides further evidence for the findings by Civai et al. (2010), suggesting that responders even reject unfair offers when their own payoff is not affected. This result is also supportive of the literature on third-party punishment (cf., Fehr & Fischbacher, 2004) because it suggests that decision makers acts as third parties who are motivated to punish unfair behavior directed at their clients by rejecting unfair offers, although they have no own payoffs at stake, and the violation of the norm of fairness only affects their clients.

Much more striking are our findings in the domain of hyper-fair offers. Although participants are willing to accept those advantageous distributions for themselves and a friend by 92%, they do significantly less so for strangers (86%). At first glance, this behavior seems puzzling. Moreover, it contrasts with participants’ behavior observed in the domain of unfair offers: whereas participants seem to be motivated to protect their clients regardless of identity from unfair treatments by the proposer, they grant hyper-fair offers more likely to themselves and friends. On top of that, it is also worth noting that even though a 92% acceptance rate for hyper-fair offers is significantly higher than 86%, it is still significantly lower than 100%. Rejecting hyper-fair offers can only be explained by participants being not exclusively motivated by self-interest and maximal payoffs for their clients but also take into account non-monetary motives—and differently so depending on client identity. Previous research has already found rejecting hyper-fair offers to be a non-negligible phenomenon, and identified social concern (i.e., injuring bilateral fairness) to be the main motive for it (cf. Hennig-Schmidt et al., 2004; Sadrieh & Osterholt, 1998). We will test in Experiments 5a/b whether this explanation holds for the present experimental setup. In the next experiments, the basic findings within hyper-fair offers should be replicated with more levels of social distance.

Experiments 2 and 3: Replications With Additional Layers of Client Identity

We replicated the set-up of Experiment 1 in two further Experiments to generalize the effect. In Experiment 2 (N = 108), participants were asked to decide either for themselves, a friend, a casual acquaintance, or a stranger. In Experiment 3 (N = 90), participants were asked to decide either for themselves, a family member, a friend, a casual acquaintance, or a stranger. In both experiments, we replicated the present basic effect: the likelihood of accepting hyper-fair offers decreased with increasing social distance, while client identity did modulate the acceptance rates for unfair offers. Due to space constraints, we report these experiments in the Supplemental Materials. After establishing and replicating the effect, in the next experiment, we sought to rule out various alternative explanations.

Experiment 4: Ruling Out Alternative Explanations

We designed a stronger test of our prediction by emphasizing several facts in the task instructions that should make it less probable that participants would deprive distant clients of advantageous distributions. Specifically, participants thus far might have misinterpreted the process of offer generation and allocation in the way that they thought that the advisor game is a zero-sum game and that if they accept hyper-fair offers for distant clients, this would lead to fewer hyper-fair offers for themselves and friends. Thus, we more explicitly informed participants of the following rules. First, the total shares allocated to either themselves, friends, acquaintances, and strangers are independent from one another. Second, rejection of offers cannot affect the generation of future offers. Third, and to highlight their personal responsibility, we more explicitly emphasized that participants were in charge of deciding for all clients in the best interests of these clients, irrespective of the clients’ social statuses. Fourth, we emphasized that the decisions are completely anonymous and that their friends and acquaintances would not learn about their decisions, to avoid social demand effects. Fifth, we emphasized that in this hypothetical game their friends and acquaintances would not be able to share their profit with them, to discourage vested interests. We preregistered Experiment 4 through OSF (https://osf.io/pwvey).

Method

Participants

A total of 156 students at a German university (n = 121 female; M_age = 23, SD = 3) were approached on campus and invited to take part in a 7-minute experimental session in the laboratory on the campus for a reward of 3 € or course credit.

Materials and procedure

The method was similar to Experiment 2, except that we added a more explicit instruction about the four game features listed in the introduction of this experiment.

Results

The 4 (responder identity: self, friend, acquaintance, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA found a main effect of responder identity, F(3, 153) = 10.40, p < .001, η_p² = .17, as well as for the offered amount, F(1, 155) = 193.62, p < .001, η_p² = .56. The condition means are depicted in Figure 3.

Figure 3.

Probability of accepting an offer as a function of responder identity in Experiment 4 (error bars are SEMs).

In addition, an interaction between responder identity and the offered amount, F(3, 153) = 7.94, p < .001, η_p² = .14, surfaced. No differences in acceptance rates for unfair offers occurred as a function of responder identity (all ts < 1.8).

For hyper-fair offers, participants accepted more offers for themselves (M = .95, SE = .01) than for a stranger (M = .79, SE = .03), t(155) = 5.62, p < .001, 95% CI_difference [0.10, 0.21], d_z = 0.45, and for an acquaintance (M = .83, SE = .03), t(155) = 5.11, p < .001, 95% CI_difference [0.07, 0.17], d_z = 0.41. In addition, they accepted more offers for a friend (M = .94, SE = .01) than for a stranger, t(155) = 5.81, p < .001, 95% CI_difference [0.10, 0.20], d_z = 0.47 and more offers for a friend than for an acquaintance, t(155) = 5.58, p < .001, 95% CI_difference [0.07, 0.16], d_z = 0.45. There was no difference between accepting offers for oneself versus for a friend (t < 0.3). The condition means are depicted in Figure 4.

Figure 4.

Acceptance rates of hyper-fair offers as a function of responder identity in Experiment 4 (error bars are SEMs).

Discussion

Even when emphasizing that all offers were independent from each other, that participants had the responsibility to act in the best interest of each of the clients, that their close ones would not learn about their decisions and would not be able to share their profit with them, we again found the same deprivation of clients of greater social distance compared to clients being close ones or participants themselves. Furthermore, and across clients, hyper-fair offers were again accepted at only 88%. The next experiments should gauge the driving mechanisms of this client privileging effect.

Experiments 5a und 5b: Computer Proposer

In accordance with our predictions, we found strong and reliable client privileging in Experiments 1 to 4, particularly for advantageous distributions. Our basic effect along with the finding that acceptance rates never increase to 100%, however, could be explained in line with Hennig-Schmidt and colleagues’ (2004) reasoning, that is, by the assumption that participants’ decisions are motivated by social concern for the proposer, particularly in trials with distant clients. To explore this, we replicated the set-up with the proposer not being a person, but a random computer algorithm making varying offers (cf., Van’t Wout, Kahn, Sanfey, & Aleman, 2006). Experiment 5a was a first test employing an online survey finding only a marginal client privileging effect. Experiment 5b was a preregistered laboratory replication, finding a strong client privileging effect.

Method

Participants

In Experiment 5a, N = 169 volunteers (n = 118 female; M_age = 35, SD = 14) were recruited for an online survey by posting recruiting ads in various social media. In Experiment 5b, N = 99 students of a German university (n = 77 female; M_age = 23, SD = 1) were approached on the campus and invited to take part in the experimental task on laptops for a candy reward. Note that although these sample sizes vary due to logistic and timing reasons, both experiments are still highly powered (see the meta-analysis below).

Materials and procedure

The method was similar to the previous experiments except that we informed participants that the offers in the advisor game would be randomly generated by a computer algorithm.

Results

Experiment 5a

The proportion of accepted offers was analyzed in a 3 (responder identity: self, friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA. In this analysis, there was only a significant main effect for the offered amount, F(1, 168) = 271.28, p < .001, η_p² = .62. As can be seen in Figure 5, the usual main effect of accepting more hyper-fair offers for oneself and for a friend than for a stranger is descriptively present but reaches only marginal significance level, F(2, 167) = 2.60, p = .078.

Figure 5.

Probability of accepting an offer proposed by a computer as a function of responder identity in Experiment 5a (error bars are SEMs).

Experiment 5b was a preregistered replication to further examine the effect size and replicability of our effects (https://osf.io/w534d).

Experiment 5b

The 3 (responder identity: self, friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA found a main effect of responder identity, F(2, 95) = 13.76, p < .001, η_p² = .23, as well as for the offered amount, F(1, 96) = 57.31, p < .001, η_p² = .37 (see Figure 6).

Figure 6.

Probability of accepting an offer proposed by a computer as a function of responder identity in Experiment 5b (error bars are SEMs).

Crucially, an interaction between responder identity and the offered amount, F(2, 95) = 18.27, p < .001, η_p² = .28, surfaced. Planned comparisons revealed that within the category of unfair offers, participants accepted less offers for themselves (M = .53, SE = .04) than for a stranger (M = .59, SE = .03), t(96) = 2.33, p = .022, 95% CI_difference [0.01, 0.12], d_z = 0.24. No further reliable differences emerged (all ts < 1.7).

In contrast, within the category of hyper-fair offers, participants once again accepted more offers for themselves (M = .89, SE = .02) than for a stranger (M = .65, SE = .04), t(96) = 5.76, p < .001, 95% CI_difference [0.16, 0.32], d_z = 0.59 and also more offers for a friend (M = .90, SE = .02) than for a stranger, t(96) = 6.68, p < .001, 95% CI_difference [0.18, 0.33], d_z = 0.68. There was no difference between participants’ acceptance rate of hyper-fair offers for themselves and for a friend, t < 0.8 (see Figure 7).

Figure 7.

Acceptance rates of hyper-fair offers as a function of responder identity in Experiment 5b (error bars are SEMs).

Discussion

Replacing a human proposer by a random offer generator still led to client privileging effects. While Experiment 5a yielded only a marginal effect, the preregistered laboratory Experiment 5b documented a client-privileging effect of an effect size similar to the earlier studies (average d_z = .64). Moreover, acceptance rates still did not increase to 100% but flatlined at 87% for self and friends and at 73% for strangers. This is striking because replacing the former human proposer by a random offer generation clearly rules out the earlier speculation of social concern being the main motive for rejecting hyper-fair offers (cf. Hennig-Schmidt et al., 2004; Sadrieh & Osterholt, 1998). However, Hennig-Schmidt and colleagues (2004) suggest further, although less common, motives for rejecting hyper-fair offers, among them emotional reasons. Since exploring the dynamics behind this (surely fascinating) sideline finding is beyond the scope of the current project, we strongly encourage future research on this matter. The remaining experiments should thus test the affective mechanisms driving client privileging, namely envy and joy for others.

Experiments 6a and 6b: The Role of Envy

As a first possible affective mechanism driving our client privileging effect, we gauged the causal role of envy. Envy is defined as the painful emotion that arises when someone lacks another person’s superior quality, achievement, or possession (Lange & Crusius, 2015; Parrott & Smith, 1993; R. H. Smith & Kim, 2007; Van de Ven, Zeelenberg, & Pieters, 2009), with money being a strong elicitor of envy (H. J. Smith & Leach, 2004). Accordingly, participants experience strong envy in the laboratory when another ostensible participant wins more money in a game of chance than themselves (Dvash, Gilam, Ben-Ze’ev, Hendler, & Shamay-Tsoory, 2010; Shamay-Tsoory et al., 2009).

In our set-up, envy should be stronger for hyper-fair than for unfair offers because one only little envies a client a bad deal. More importantly, however, envy should be enhanced for hyper-fair offers for strangers compared to friends, following the same pattern as the acceptance rates in the previous experiments. Crucially, envy should mediate the impact of client identity on acceptance behavior. We tested this by asking for envy ratings. As participants can hardly report envy regarding offers directed to themselves, we only realized the conceptually relevant conditions of offers for a friend versus a stranger. Experiment 6a was preregistered (https://osf.io/fzynh) and only employed envy ratings, while Experiment 6b employed both envy ratings and decisions on offers.

Method

Participants

A total of 137 students at a German university (n = 106 female; M_age = 24, SD = 7) were approached on the campus and invited to take part in the 5-minute experimental task on laptops for a candy reward. In Experiment 6b, N = 108 students of a German university (n = 84 female; M_age = 23, SD = 5) were approached on the campus and invited to take part in an experimental session in the laboratory on the campus for a reward of 2 €.

Materials and procedure

The methods were closely modeled after Experiments 1 to 4 with two important modifications. First, in Experiment 6a, participants were simply asked to indicate how envious of a given offer they felt on a scale from 0 (not at all) to 10 (very much). In Experiment 6b, participants were instructed to indicate both, how envious of a given offer they felt, and whether they wanted to accept or reject the offer. Importantly, participants had to indicate self-reported envy of an offer first, and only afterwards decide on the offer. Second, participants were only presented with offers addressed to either their close friends or strangers as their clients. The sequence of all 22 resulting trials was re-randomized anew for each participant.

Results

Experiment 6a

The amount of self-reported envy was subjected to a 2 (responder identity: friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA. Again, we found a main effect of responder identity, F(1, 136) = 14.53, p < .001, η_p² = .10, as well as for the offered amount, F(1, 136) = 185.43, p < .001, η_p² = .58, as well as an interaction, F(1, 136) = 13.99, p < .001, η_p² = .09. The condition means are depicted in Figure 8.

Figure 8.

Self-reported envy of an offer in Experiment 6a as a function of responder identity (error bars are SEMs).

Planned comparisons revealed that for unfair offers, participants reported marginally more envy of offers proposed to a stranger (M = 1.24, SE = .11) than to a friend, (M = 1.09, SE = .11), t(136) = 1.96, p = .052, 95% CI_difference [0.00, 0.29], d_z = 0.17. For hyper-fair offers, participants reported much more envy of offers proposed to a stranger (M = 4.12, SE = .25) than to a friend (M = 3.39, SE = .23), t(136) = 4.01, p < .001, 95% CI_difference [0.37, 1.10], d_z = 0.34.

Experiment 6b

When analyzing the proportion of accepted offers in a 2 (responder identity: friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA, we again found a main effect of responder identity, F(1, 107) = 12.49, p < .001, η_p² = .11, as well as for the offered amount, F(1, 107) = 165.41, p < .001, η_p² = .61, as well as a marginally significant interaction, F(1, 107) = 3.88, p = .051, η_p² = .04. The condition means are depicted in Figure 9.

Figure 9.

Probability of accepting an offer as a function of responder identity in Experiment 6b (error bars are SEMs).

The amount of self-reported envy was subjected to a 2 (responder identity: friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA, we found the same effects as in the previous experiments: a main effect of responder identity, F(1, 107) = 11.11, p = .001, η_p² = .11, as well as for the offered amount, F(1, 107) = 174.16, p < .001, η_p² = .62, and an interaction between responder identity and the offered amount, F(1, 107) = 6.88, p = .010, η_p² = .06. The condition means are depicted in Figure 10.

Figure 10.

Self-reported envy of an offer in Experiment 6b as a function of responder identity (error bars are SEMs).

Envy as a mediator

We hypothesized that the effect of responder identity on acceptance rates was mediated by self-reported envy and that both the direct and indirect effect in this framework were moderated by the offered amount. To test this, a moderated mediation framework was planned (Model 8 in the PROCESS macro (Hayes, 2015) for SPSS). Responder identity (coded as 0 = stranger vs. 1 = friend) served as the independent variable, acceptance rates as dependent variable, envy as the mediator, and the offered amount (codes as 0 = unfair offer vs. 1 = hyper-fair offer) was entered as a moderator of the links between responder identity and acceptance, and envy, respectively (see Figure 11). We report regression weights and 95% confidence intervals (CIs) based on 10,000 bootstrap samples.

Figure 11.

Conceptual diagram of the moderated mediation model computed in Experiment 6b.

The model explained a significant proportion of variance, F(4, 427) = 81.79, p < .001 R² = .43. Contrary to our hypothesis, there was no evidence for moderated mediation, b = 0.01 [−0.02, 0.00]. Furthermore, envy did not directly, b = 0.01 [−0.00, 0.02], affect acceptance rates, and responder identity did not directly, b = −0.14 [−0.76, 0.47], affect envy. The relation between responder identity and acceptance rates was only moderated by the offered amount for hyper-fair, b = 0.08 [0.01, 0.17], but not for unfair offers, b = 0.03 [−0.05, 0.11].

Discussion

Exploring the possible underlying mechanism envy, we did indeed find that envy elicited by the offers followed the same pattern as the acceptance rates: envy increased with increasing amount of offer, and it did more so for strangers than friends. However, the mediation analyses showed that self-reported envy did not mediate the relation between the identity of the client and acceptance rates. Thus, the final experiment gauged the role of another possible affective mechanism, namely joy participants experience for their clients.

Experiments 7: The Role of Joy

Another possible affective mechanism driving the present client privileging effect might be a positive feeling for close clients rather than a negative feeling (envy) for distant clients.¹ Thus, we explored the role of joy one feels for the client when learning about a certain offer. This positive feeling when learning about a good outcome for another person is called symhedonia by Royzman and Rozin (2006). Different words have been used by researchers to describe the joy in response to another person’s fortune, as for instance sympathetic enjoyment (Heider, 1958), empathic joy (Batson et al., 1991), joy (Pietraszkiewicz & Wojciszke, 2014), vicarious joy (Kawamichi, Tanabe, Takahashi, & Sadato, 2013), or happy-for (Heider, 1958). However, these terms are not used in everyday life but rather expressions like “I am happy for you.” As there is no consensus on the technical term, we will use the term happy-for-ness for the sake of clarity and grammatical simplicity (Boecker & Topolinski, 2018). The preposition for is crucial to differentiate this emotion from the basic emotion happiness, which is not a social comparison-based emotion.

Happy-for-ness should follow the opposite pattern of envy. It should increase with increasing amounts offered, and it should be higher for close than distant others, which was already shown in another domain by Royzman and Rozin (2006). We tested this pattern along with its mediational role by assessing both happy-for-ness and envy. We assure high statistical power for the mediational analysis by collecting a large participant sample.

Method

Participants

A total of 401 participants (n = 204 female; M_age = 37, SD = 11) were recruited through MTurk.

Materials and procedure

The methods were identical to those implemented in Experiment 6b except that participants were additionally asked how happy for their client they felt in regard to a given offer on a scale from 0 (not at all) to 10 (very much).

Results

The 2 (responder identity: friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA again revealed a significant main effect of responder identity, F(1, 400) = 5.25, p = .023, η_p² = .01, for the offered amount, F(1, 400) = 1533.49, p < .001, η_p² = .79, and a significant interaction, F(1, 400) = 13.91, p < .001, η_p² = .03. The condition means are depicted in Figure 12.

Figure 12.

Probability of accepting an offer as a function of responder identity in Experiment 7 (error bars are SEMs).

Planned comparisons revealed that participants once again accepted more hyper-fair offers for a friend (M = 0.96, SE = .01) than for a stranger (M = 0.92, SE = .01), t(400) = 4.25, p < .001, 95% CI_difference [0.02, 0.06], d_z = 0.21. No difference in acceptance rates as a function of responder identity occurred for unfair offers (t < 1.1). This time, however, the 2 (responder identity: friend, stranger; within-subjects) × 2 (offer: unfair offers, hyper-fair offers; within-subjects) ANOVA on self-reported envy only found a significant main effect for the offered amount, F(1, 400) = 440.66, p < .001, η_p² = .52, and no further effects (all Fs < 2.5). The same ANOVA on self-reported joy for others, on the other hand, found a significant main effect of responder identity, F(1, 400) = 30.36, p < .001, η_p² = .07, for the offered amount, F(1, 400) = 1543.22, p < .001, η_p² = .79, and a significant interaction, F(1, 400) = 41.03, p < .001, η_p² = .09. The condition means are depicted in Figure 13.

Figure 13.

Self-reported happy-for-ness regarding an offer in Experiment 7 as a function of responder identity (error bars are SEMs).

Planned comparisons showed that, for hyper-fair offers, participants experienced more happy-for-ness for a friend (M = 7.95, SE = .08) than for a stranger (M = 7.42, SE = .01), t(400) = 7.39, p < .001, 95% CI_difference [0.39, 0.67], d_z = 0.37. No difference in happy-for-ness as a function of responder identity occurred for unfair offers (t < 0.9).

Happy-for-ness for others as a mediator

Next, we tested whether the effect of responder identity on acceptance rates was mediated by self-reported happy-for-ness and/or envy, and whether direct and indirect effects in this framework were moderated by the offered amount. To this end, we computed a moderated mediation framework using the PROCESS macro for SPSS (Hayes, 2015) similar to Experiment 6b. Responder identity served as the independent variable, acceptance rates as dependent variable, happy-for-ness and envy were entered as parallel predictors, and the offered amount (unfair vs. hyper-fair offer) was entered as a moderator of the links between responder identity and acceptance, happy-for-ness, and envy, respectively (model 8, see Figure 14). We report regression weights and 95% CIs based on 10,000 bootstrap samples.

Figure 14.

Moderated mediation model with self-reported happy-for-ness and envy as mediators of the relation between responder identity and acceptance, moderated by offer.

The model explained a significant proportion of variance, F(5,1589) = 1051.25, p < .001 R² = .77. There was a direct effect of responder identity on acceptance, happy-for-ness, and envy. Furthermore, happy-for-ness predicted acceptance, and there was an indirect effect of responder identity on acceptance via happy-for-ness. Envy did not predict acceptance, and there was no indirect effect via envy. Finally, only the mediation via happy-for-ness was moderated by the offered amount. Although happy-for-ness mediated the effect of responder identity on acceptance both for unfair, b = 0.38 [0.33, 0.42], and hyper-fair offers, this effect was stronger for hyper-fair offers, b = 0.42 [0.37, 0.47].

Taken together, this means that the effect of responder identity on acceptance rates was partially mediated via happy-for-ness, while envy did not play a role.

Discussion

Further exploring the underlying affective mediator of our client privileging effect, we assessed both envy and happy-for-ness along with acceptance rates. While the impact of client identity and offer on envy found in Experiments 6a/b could not be replicated, happy-for-ness was affected in a psychologically plausible manner (cf., Royzman & Rozin, 2006): it increased with increasing amount of offer, and it did so stronger for close than for distant others. Crucially, happy-for-ness partially explained the client privileging effect.

Meta-Analysis

We conducted a meta-analysis of Experiments 1 to 5, where participants were only asked to accept or reject the offers without additional measures (such as envy) that might distort the effect size of the behavioral effect. This meta-analysis indicated a reliable relationship between acceptance of hyper-fair offers and responder identity. Participants consistently accepted more hyper-fair offers for themselves than for strangers (d_z = 0.41) and for friends than for strangers (d_z = 0.47).

General Discussion

Across the present experiments, we find the replicable pattern that participants accepted more hyper-fair offers for themselves and close clients than for distant clients, although participants were explicitly instructed to discount the identity of the client; and the prime importance of equity, fairness, and neutrality were strongly emphasized. Apparently, not the same norms of equality apply at all times and regardless of the target of a norm enforcement. This client privileging cannot be explained by vested interests because participants were informed that sharing the profit with their clients after the game would be impossible. In addition, the effect cannot be explained by participants’ desire to maintain close relationships or a positive social reputation because it also occurred when it was stressed that clients would never learn about participants’ decisions. Moreover, participants experiencing different levels of social concern as a function of client identity is an unlikely explanation because client privileging also occurred when the proposer was only a nonsentient computer algorithm (Experiments 5a/b). The lacking effect of social distance for unfair offers, on the contrary, is completely in line with previous evidence that showed that unfair offers are even rejected when individuals play the UG on behalf of a third party and their own payoffs are therefore unaffected (Civai et al., 2010; Corradi-Dell’ Acqua et al., 2013).

Exploring the driving mechanism of this effect, we assessed both a negative emotion (envy) and a positive emotion (joy, or happy-for-ness). Envy was indeed affected in a similar vein as acceptance rates: it increased with increasing amounts offered, and did so more strongly for distant than for close clients. However, envy did not statistically mediate the impact of client identity on acceptance rates. Happy-for-ness, however, that is, the empathic joy one experiences regarding an offer made to the client, turned out to be an important mediator of our effect. Not only did happy-for-ness increase with increasing amounts offered and did so more strongly for close (vs. distant) clients, it also statistically explained the impact of the offered amount and client identity on acceptance rates. Thus, the psychology behind the client privileging effect is a rather positive, compassionate one. Participants feel more joy for their client for high offers when that client is a socially close person compared to when that client is socially distant, which drives people to accept a given offer.

This has interesting conceptual implications for the psychology of social comparison-based emotions. While social psychology has identified several important moderators of social emotions (e.g., Salovey & Rodin, 1984), the social relation to the person has not received much attention. Here, we show that joy for the other (and to a certain extent envy, although not replicated in Experiment 7), are moderated by social distance. Our findings regarding the social emotions envy and empathic joy involved in decision making for others can also be seen as a test of the self-evaluation maintenance model by Tesser (1988), which argues that individuals might tend to favor distant over close others, since close others are more relevant social comparison standards than distant others and thus may evoke more envy (Tesser, 1988). We find that strangers’ fortunes evoke less joy and more envy than friends’ fortunes, which is at odds with the self-evaluation maintenance model.

Our experiments employed the factors amount of offer and client identity as within-subjects factors. Thus, the question is whether client privileging would also occur in between-subjects designs. We argue, however, that in the real-world, client privileging plays a role exactly in such “within-subjects” settings, when a certain advice giver or decision maker is exposed to a range of different clients varying in social distance along the daily working routine. The rare case in which an individual is put to a decision for others once, in a one-off setting, is thus of a lesser ecological and psychological interest. To at least take a first look into the dynamics of a between-subjects setting, we ran Experiment 8, reported in the Supplemental Materials, in which we show that presenting a friend or a stranger as a possible client evokes social closeness versus distance and thus accompanying representations of in-group versus out-group both when implemented in a within- and between-subjects design.

Furthermore, another striking finding in the present experiments is that, across all types of clients, acceptance rates for hyper-fair offers did not reach 100% but flatlined at 86%. The main motive for rejecting hyper-fair offers as suggested in the literature (cf. Hennig-Schmidt et al., 2004) is social concern, which we rule out by replacing the former human proposer by a random computer algorithm (Experiment 5/b). However, there are further motives that have been found to unleash this phenomenon, namely emotional, ethical, or moral reasons, and nonexpectancy/unlikeliness of hyper-fair offers. Since the present data allow only for indirectly inferring motives from observed decisions, the phenomenon of rejecting hyper-fair offers needs to be investigated more systematically in future research.

Future Research Avenues

Besides its more general theoretical implications, the present client-privileging effect is psychologically interesting in its own, and its moderating factors and boundary conditions should be explored in future research. For instance, realizing a more fine-grained resolution of social distance, what happens if actual kinship is pitted against social distance (the brother we have not seen in years vs. an acquaintance we see regularly but do not have a strong bond with). In addition, is it only social distance or any kind of psychological distance, such as spatial distance (Trope & Liberman, 2010), that affects decision making for others? Would a participant in an experiment in New York treat clients from New York differently than clients from Los Angeles or from Paris? And how would that interact with social distance (a friend abroad versus a stranger in the same city)? Are participants aware of their bias, and if yes, do they feel guilty? If being made aware of this bias, are they able and willing to correct for it? In addition, future research shall explore the processing systems involved in this bias: will client privileging even increase under cognitive load since participants can less efficiently control for happy-for-ness, or will it decrease because participants do not have the mental capacity to represent the relative differences in offers and clients? These and other questions should be addressed in future research.

Supplemental Material

Ruessmann_OnlineAppendix – Supplemental material for Economic Decisions for Others Are More Favorable for Close Than Distant Clients

Supplemental material, Ruessmann_OnlineAppendix for Economic Decisions for Others Are More Favorable for Close Than Distant Clients by Janna Katrin Ruessmann and Sascha Topolinski in Personality and Social Psychology Bulletin

Supplemental Material

Supplemental_Materials – Supplemental material for Economic Decisions for Others Are More Favorable for Close Than Distant Clients

Supplemental material, Supplemental_Materials for Economic Decisions for Others Are More Favorable for Close Than Distant Clients by Janna Katrin Ruessmann and Sascha Topolinski in Personality and Social Psychology Bulletin

Footnotes

Authors’ Note

The materials and data are archived under and will be made public upon publication.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We thank Lea Boecker from Leuphana University for her valuable input regarding the emotional state of happy-for-ness. This research was funded by the Center for Social and Economic Behavior, University of Cologne.

Notes

Supplemental Material

Supplemental material is available online with this article.

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