Conditional cooperation extends to the field

At least in the laboratory, conditional cooperation seems to be a qualitatively robust phenomenon. But does this extend to the field, in particular with different subjects pools than students? To start with, the phenomenon is replicable and appears to be even stronger in the field. A number of experiments retain the standard laboratory design but recruit non-standard subjects, such as soldiers (Fehr et al., 1998a), sports-card traders (List, 2006), MBA’s (Hannan et al., 2002), or CEOs (Fehr & List, 2004).

They confirm the results obtained with student subjects, with non-students tending to behave even more pro-socially and conditionally so. The same results have been obtained by Falk et al. (2010) who did find no evidence that a representative sample from the Zurich citizen behave different in the investment game than a student sam-ple from the University of Zurich (rather the opposite). They also find no evidence for a selection bias (more pro-social students are not overrepresented due to self-selection into the experiments) in student samples. Further one-shot trust game exper-iments performed in various locations worldwide (e.g. Willinger et al., 2003; Ashraf et al., 2006; Greig & Bohnet, 2008; Bohnet et al., 2008) produced results which at times differ quantitatively but not qualitatively.

Here I focus on particularly strong evidence that is provided by implementations of the experimental games used in the laboratory in large representative surveys. This approach allows to replicate the lab experiments with a large, heterogeneous sample of people and for linking large amounts of socio-demographic data to experimentally elicited behavior. Fehr et al. (2002c, 2003) integrated the investment game into the German Socio-Economic Panel (SOEP).²⁸ They had a sub-sample of 429 subjects that did not differ significantly in observables from the general 2003 wave of the SOEP comprising about 25 thousand individuals. The second movers’ responses were elicited with the strategy method and first movers’ beliefs were elicited by non-incentivized direct question. On average, the results replicate familiar patterns from the laboratory: Out the first movers, only 17 percent transferred nothing, 60 percent transferred between 2 and 5 Euros, and 11 percent transferred the full endowment of 10 Euros. Furthermore, first-movers’ transfers were strongly positively correlated with elicited beliefs about return transfers.

But again, our primary focus is on second mover behavior. Most importantly, the transfer of the average second mover is increasing in the amount they received, that is, conditioned on information about their coplayer’s behavior. Relating behavior to socio-demographics, young second movers (below the age of 35) give significantly

that had to make a contribution decision first. The other players were informed about this decision and then made their decisions simultaneously. The researchers also elicited the followers’ beliefs about the other followers’ contributions, both before and after being informed about the leader’s action, which allowed them to determine how the leader’s contribution influences the beliefs about other followers’

contributions. They find that the leader’s contribution positively influences the followers’ beliefs about other followers’ contributions significantly. Furthermore, the followers’ actual contributions match their beliefs quite closely.

28The SOEP is a large representative household panel. The experiment had the same protocol as the investment game in the laboratory and subjects were paid by cheque sent a few days later.

less whereas old second over (above 65) give significantly more than the intermedi-ate age group (35 to 55). Second movers without German citizenship, who are un-employed or recently separated from their (marriage) partner return less than the re-spective reference group, those with good health tend to return more. Such variables as gender, income situation, expressed worries about the own economic situation, or education status were unrelated to behavior in both first and second movers.

The study was significantly extended to a much larger sample and various accom-panying experiments aimed at investigating the sensitivity of the design (the strategy method vs. direct response method, removal of the equal split option, high stakes), and is reported in Naef & Schupp (2009a). Most interestingly, Naef & Schupp (2009a) used the full sample in order to compare behavior of students and non-students. They found no significant difference between them as second movers.²⁹ Bellemare & Kröger (2007) conducted a similar trust game experiment with a sam-ple that was representative for the Netherlands, and also found that students trans-ferred less than a representative population sample.³⁰ In a trust game experiment conducted with 18 to 84 year old participants recruited from an online panel, Gar-barino & Slonim (2009) find socio-economic and demographic information to explain little of first and second mover behavior. Gächter et al. (2004) also found no relation-ship between socio-economic background and behavior in a one-shot public good experiment. Thöni et al. (2012) find the usual patterns in a public good experiment with a sample representative for the Danish population. More research is certainly warranted, but the current evidence points towards the conclusion that cooperative behavior and its conditioning on coplayers’ actions does not differ overwhelmingly along socio-demographic differences.

Still, the above studies keep the highly structured, critics might say «artificial», experimental games from the laboratory and people generally know that they partic-ipate in an experiment. Can conditional cooperation also be demonstrated in natural environments, and even if subjects are not aware of their participation in an exper-iment? Existing evidence points towards the answer «yes, but...». Gneezy & List (2006) conducted a field experiment in a labor relation setting involving two distinct tasks: data entry for a university library and door-to-door fund-raising for a research

29As a first mover, students transfer on average significantly more (61 percent of the endowment) than non-students (50 percent of the endowment). This difference upholds even when controlling (statisti-cally) for age, income, and the level of education, which are key characteristics along which students and non-students typically differ. Furthermore, since Naef & Schupp (2009a) found students to be less risk averse than non-students, risk preferences may be conjectured to be the primary cause of the differ-ences in transfers, but the differdiffer-ences remain highly significant after controlling for a measure of risk tolerance.

30In related large-scale experiments, Carpenter et al. (2008) found with a sample representative for a community in Vermont (USA) that students transferred significantly less than non-students in a dictator game. In a representative ultimatum game experiment in Taiwan, no difference was found between students and non-students (Fu et al., 2007). Harrison et al. (2002) found that in Denmark, students have a six percentage point higher discount rate than non-students. In a similar study in Denmark, students were found to be more risk-averse than non-students (Harrison et al., 2007). Concerning the latter result, note the conflict to Naef & Schupp (2009a).

center. Consistent with laboratory gift-exchange games worker performance in the first few hours on the job is considerably higher in a «gift treatment» than in a «non-gift treatment». After the initial few hours, however, no difference in outcomes is observed, and overall the gift treatment yielded inferior aggregate outcomes for the employer: with the same budget the employer would have logged more data for the library and raised more money for the research center by using the market-clearing wage rather than by trying to induce greater performance with a gift of higher wages.

Kube et al. (2006, 2011b) extended the design to allow also for negative reciprocity, and found that the gift (wage increase) does not work well in the long run (if at all) as well, whereas a wage reduction (the negative reciprocity treatment) had a signifi-cant and lasting negative impact on performance. Thus, there appears to be a marked asymmetry of «positive» (reciprocating cooperative behavior in-kind) and «negative»

reciprocity (reciprocating non-cooperative behavior in-kind).

In a similar field experiment designed to measure worker responses in a tree-planting firm to a monetary gift from their employer (Bellemare & Shearer, 2009), firm managers told a crew of tree planters they would receive a pay raise for one day as a result of a surplus not attributable to past planting productivity. A comparison of planter productivity on the day the gift was handed out with productivity on previous and subsequent days of planting on the same block showed that the gift had a sig-nificant and positive effect on daily planter productivity, controlling for planter-fixed effects, weather conditions and other random daily shocks. Kube et al. (2011a) finds in a similar labor context that non-monetary gifts have a much stronger impact than monetary gifts of equivalent value. They also observe that when workers are offered the choice, they prefer receiving money but reciprocate as if they received a non-monetary gift. Furthermore, non-monetary gifts can effectively trigger reciprocity if the employer invests more time and effort into the gift’s presentation. This suggests that the employer’s intention and not only the consequences are important in triggering reciprocity (see also section 2.3.6).

Falk (2007) reports evidence from a field experiment in collaboration with a char-itable organization, that sent roughly 10,000 solicitation letters to potential donors.

One-third of the letters contained no gift, third contained a small gift, and one-third contained a large gift. In support of the hypothesis, the relative frequency of donations increased by 17 percent if a small gift was included and by 75 percent for a large gift compared to the no gift condition. Also in a charity context, Frey & Meier (2004) exploited the fact at the University of Zurich each students has, upon regis-tering for the new semester, the opportunity to donate, in addition to the tuition fee, to one of two charity funds, one helping needy students with subsidized loans (CHF 7 donation), and the other generally supporting foreign students (CHF 5 donation).

Students in the treatment conditions received information about about the donation behavior of the other students, in one condition that 64 percent of the other students donated, in the other condition that 46 percent made a donation in the past (those fre-quencies were actual frefre-quencies from past but different semesters). Students in the control condition received no information. Frey & Meier (2004) found (i) positive

and significant correlation between students’ expectations (which were also elicited) and own donations, and (ii) that students who were informed that 64 percent donated in the past are more likely to donate than those who received the information that only 46 percent donated.³¹

In a similar natural field experiment, Heldt (2008) asked tourists using a cross-country skiing slope for a donation to help for keeping the slope well-prepared. He also manipulated the information people got, and found that those who were told that 70 percent of the other tourists donated contributed significantly more than those who received no further information. In a natural field experiment by Martin & Randal (2008), visitors to an art gallery in New Zealand were given the opportunity to leave a donation to the museum in a transparent box. In one condition there was already some money in the box, in another condition the box was empty. They found that people donate significantly more in the former than in the latter condition.

Shang & Croson (2009) conducted a natural field experiment around a fund-raising tour for a public radio station. In the three treatment conditions subjects were told that they had just another member who contributed either USD 75, 180, or 300. In the control condition no such information was given. The researchers found that callers who were confronted with a previous pledge of USD 300 donated signifi-cantly more than people in the control condition. Callers in the other two information conditions also contributed more than the control group, but those differences were not statistically significant.

Alpizar et al. (2008) conducted a natural field experiment at a national park in Costa Rica in which visitors were asked for donations for the park’s maintenance.

They found that (i) contributions made in public in front of the solicitor were a quar-ter higher than contributions made in private, (ii) giving subjects a small gift before requesting a contribution or (iii) telling subjects that the typical contribution of oth-ers is USD 2 (a small contribution, instead of telling nothing) both increases the likelihoodof a positive contribution but decreases the size of the average contribution compared with providing no reference information, whereas (iv) providing a high reference level (USD 10) increases both the likelihood and the average size of the donation.

There are also studies that connect laboratory and field experiments. Benz &

Meier (2008) replicated the natural field experiment described above (Frey & Meier, 2004) in a laboratory, which involved exactly the same donation decision to the same funds as in the field experiment. As a control condition, they also conducted a sec-ond experiment, which involved the same donation decision but to another charity unrelated to the university. They find that behavior in the two lab experiments are significantly correlated with behavior in the field experiment, approximately to the

31Within the same experiment, Meier (2005) explores the role of framing effects and found that their influence is limited. People behave in a conditionally cooperative way if informed either about the number of contributors or about the equivalent number of non-contributors. The positive correlation between group behavior and individual behavior is, however, weaker when the focus is on the defectors.

He also finds gender differences in social comparison.

same degree. This holds up in more refined statistical analysis. Along the same lines, Carpenter & Seki (2011) conducted a finitely repeated public goods experiment (with and without opportunities to express disapproval) with Japanese fishermen in the lab-oratory, and related their behavior in the experiment to collected data on their daily fishing activities. The researchers used the data from the lab to statistically derive five measures of social preferences for each fisherman: his level of unconditional cooperativeness; his conditional cooperativeness; the propensity to disapprove; the fisherman’s response to received social disapproval; and finally, the level of the un-conditional response to disapproval. The results show that fishing productivity is significantly related to the experimentally derived measures of social preferences.

Finally, there are also some informative studies that use survey data or other ex-isting data sets. Using survey data from 30 West and East European countries, Frey

& Torgler (2007) found marked negative correlation between perceived tax evasion and tax morale. Using survey data from the European Values Survey (EVS), Tor-gler et al. (2009) found a similar (small but significant) positive relation between perceived environmental cooperation (reduced public littering) and voluntary envi-ronmental morale. Using retrospective survey data on charitable giving (the Giving the Netherlands Panel Study 2005), Wiepking & Heijnen (2011) find that in the case of door-to-door donations, social information affects perceived social norms for giv-ing and, through this perception, influences the level of actual donations. They also found that people in different income categories donate roughly the same amounts in separate instances (they use the same social information), and as a result people in lower income households donate a higher percentage of their income to charitable or-ganizations. Ferrary (2003) finds gift exchange being the principle rule of exchange in the social networks that underlie the industrial networks of Silicon Valley.

In sum, the evidence from field experiments support the evidence from the labo-ratory qualitatively in that conditioning on other’s cooperation is common, while the order of magnitude may differ.

2.2.6 Conclusion

In this section I considered one fundamental question with which I concluded chapter 1 in greater detail: Do real people only cooperate for tactical reasons? A large body of evidence shows clearly «no». But in this section I have put special emphasis on evidence that is informative with respect to our guiding question: What does the propensity of individuals in a given group to cooperate with one another has to do with the availability of information they have about each others’ actions?

The received body of evidence shows that part of observed non-tactical coopera-tive behavior is not, but a significant part is conditioned on the coplayer’s behavior in settings in which this information is readily available. This holds even for one-shot interactions for which the traditional behavioral model based on rational selfishness predicts no cooperation and therefore no conditioning at all (see section 1.1). How-ever, the evidence is not yet conclusive on the quantitative importance. While mea-surement of the degree of conditioning is in principle not subject to the same

prob-lems as measurement of absolute degrees of non-tactical cooperation (see e.g. Zizzo, 2004; Bardsley, 2008), because it is conceptually a difference between the level of cooperation between contingencies, there is still much unexplained variation in the degree of measured non-tactical conditional cooperation. Nonetheless, around half of the subjects typically exhibits a tendency to return cooperation with cooperation even if a material return from doing so is ruled out.

But if some subjects are willing to incur a cost to confer a benefit to someone else, are there also subjects who are willing to incur a cost to impose a cost on someone else (such behavior was defined as spite in section 1.1.1)? I turn to this question in the next section, again with a focus on the degree of conditioning on coplayers’ past behavior.