Generalized trust and prosocial behavior

Transcription

1 Generalized trust and prosocial behavior Regina Kühne June 11, 2012 This is a DRAFT. Please do not quote or cite. Comments are very welcome ( to regina.kuehne@wiwi.hu-berlin.de) Abstract There is enormous empirical evidence from lab and field experiments that people frequently engage in prosocial behavior. In a simple model, I analyze the evolution of prosocial behavior where heterogeneity in beliefs about the fellow men s willingness to help is one of the driving forces of the evolution. As I abstract from the possibility of reputation building and punishment between anonymous partners, I remove the main motives for prosocial behavior and reduce it to a simple non-strategic decision. The motivation to behave prosocially is then mainly intrinsic and based on preferences for behaving cooperatively and positive beliefs about the behavior of others. The model suggests that it exists a stable equilibrium with a positive number of cooperative people in a society. JEL: D03 D64 D83 Keywords: trust, beliefs, prosocial behavior, conditional cooperation, indirect evolutionary approach PhD Candidate, Institute of Public Economics at Humboldt University Berlin, regina.kuehne@wiwi.hu-berlin.de 1

2 1 Introduction There is overwhelming evidence from experiments and everyday experience that motivation for human behavior is more complex than maximizing material payoffs. The classical models which are based on this assumption often fail to predict behavior in social interactions. One important conduct that classical models often cannot explain is prosocial behavior: an action that mainly benefits others at a cost to oneself. This concerns such diverse areas like helping strangers, tax compliance, voting, donation of blood, giving to charities and the like. This paper relates prosocial behavior to generalized trust i.e. to positive expectations about the behavior of others. The concept of generalized trust is to be distinguished from particularized trust. The latter refers to trust between people who know each other and are hence able to base their expectation about the other s behavior on experience from face-to-face interactions or via third parties. Generalized trust, however, is the extension of interpersonal trust to people who the trusting individual does not know and hence has no direct information on. The importance of generalized trust for various economic phenomenons including growth (e.g. Knack and Keefer, 1997) and civic participation (e.g. La Porta, Silanes, Shleifer, and Vishny, 1997) has increasingly been acknowledged. Motivated by this evidence, economists have analyzed in lab experiments how prosocial behavior in encounters with strangers is related to generalized trust (Glaeser, Laibson, Scheinkman, and Soutter, 2000; Gächter, Herrmann, and Thöni, 2004; Anderson, Mellor, and Milyo, 2004; Thöni, Tyran, and Wengström, 2012). The authors mainly conclude that generalized trust is positively related to voluntary contributions. This is in line with the general finding of dictator and public good game experiments that voluntary contributions are positively correlated with beliefs about others contributions even in non-strategic situation where the dominant strategy is to contribute nothing. This fascinating empirical result is difficult to explain by conventional models. However, beliefs and trust is rarely accounted for in economic models. The novelty of this paper is to incorporate beliefs about the others behavior into a simple theoretical model of prosocial behavior in encounters between strangers. This kind of setting is especially interesting as interactions between anonymous partners are increasingly frequent and tend to replace face-to-face interactions in many areas of life. In this setting, I abstract completely from possibilities of punishment and reputation building which are seen as some of the main driving forces of cooperation. In this way, there is no incentive to behave prosocially due to strategic reasons. Instead motives are mainly intrinsic i.e. behavior is driven by preferences for cooperation and beliefs about the others behavior only. As the behavior of one agent is affecting the behavior of others, it is of special interst to look at the dynamics of preferences and beliefs. I therefore analyze the 2

3 coevolution of prosocial behavior and generalized trust using an indirect evolutionary approach. Depending on a few parameters, a stable equilibrium can emerge with a positive probability of cooperative behavior. 2 Literature on prosocial behavior in non-strategic situations Prosocial behavior of individuals has been studied extensively in lab experiments. Public good games, dictator games and helping games are good instruments to analyze prosocial behavior as own contributions mainly increase the payoff of others. In all games, the dominant strategy is to give nothing in order to maximize economic payoffs. However, it has been shown that there is much more cooperation than predicted by standard theory even in anonymous situations 1. In dictator games, individuals tend to give on average between 20 % and 30 % of their endowment to the other player. However, contributions tend to decay over time with repeated games. In addition, there is a solid body of empirical evidence that individuals do care about how their behavior compares to the actions of others. Informing individuals of others decision tends to change the individual s behavior as they adjust to the amount given by others. This has been shown in lab experiments with dictator games (e.g. Krupka and Weber, 2009; Duffy and Kornienko, 2010) and public good games (e.g. Kurzban, McCabe, Smith, and Wilson, 2001; Fischbacher, Gachter, and Fehr, 2001; Zafar, 2011) as well as in field experiments on charitable giving (e.g. Chen, Harper, Konstan, and Li, 2010; Shang and Croson, 2009; Frey and Meier, 2004; Andreoni and Scholz, 1998) and contributions to an online community (Chen, Harper, Konstan, and Li, 2010) where information on the amount given by others or the share of people giving changed contribution behavior in the way that individuals tend to adjust to the mean contribution. If there is no information available on the others behavior, individuals also tend to give more when they belief that others are giving more (e.g. Iriberri and Rey-Biel, 2008; Zafar, 2011; Thöni, Tyran, and Wengström, 2012). However, this kind of correlation of believes and contributions does not establish causation. The correlation might as well be due to a false consensus effect which causes individuals to believe that others tend to contribute the same as them (Ross, Greene, and House, 1977). To overcome this problem, Fischbacher, Gachter, and Fehr, 2001 developed an experiment where people indicate how much they would contribute to a public good given all possible average contributions of the other group members. They find that approximately 50 % of the people were conditional cooperators who increase their contributions when others contribute more, whereas about 25 % where always free-riding and the rest showed more complicated patterns. 1 See Camerer, 2003 and Engel, 2011 for an overview of evidence on dictator games 3

4 Similar observations have been made when people play public good games repeatedly and update their beliefs. Fischbacher and Gachter, 2010 find that when people learn about the behavior of others they update their beliefs about how much others are contributing in the next round and will adjust their behavior. A similar tendency has also been observed in laboratory experiments when beliefs about what others are doing are not explicitly elicited but rather a wider measure of beliefs is applied, namely a measure of generalized trust. Gächter, Herrmann, and Thöni, 2004; Anderson, Mellor, and Milyo, 2004 and Thöni, Tyran, and Wengström, 2012 use survey questions 2 to elicit individual level of trust in order to investigate the link between trust and voluntary contributions in public good games. They mainly find a positive correlation between generalized trust and voluntary contributions. However, the causal relationship is ambiguous. There is no study which investigates how a change in individual trust level changes prosocial behavior. Though experiments vary in their settings, there are two stylized facts that can be drawn from these studies: (i) individuals differ with respect to their preferences for cooperation, and (ii) beliefs about other people s willingness to contribute matter. There are two major competing explanations for prosocial behavior which is conditional of the behavior of others. It may be triggered by notions of fairness such as reciprocity. Or it might be a result of the wish to behave appropriately by conforming to a norm 3. In both cases, beliefs about the behavior of others are taken as a signal either for an appropriate or a fair behavior. A few studies try to discriminate whether individuals engage in social comparison due to reciprocity or conformity (Bardsley and Sausgruber, 2005; Falk, 2004; Bohnet and Zeckhauser, 2004) but results are unclear and both seems to be a driving motivation. There are a few theoretical models which explain conditional cooperation in non-strategic settings including model of altruism, self-signaling, reciprocity, conformity, inequality aversion and norm compliance. The most closely related to the model developed in this paper is Traxler and Spichtig, The authors assume that there is a norm to contribute to a public good whose strength is perceived differently by individuals. In their model, an individual voluntarily contributes to a public good if internal or external sanction from norm violation outweighs the cost of norm compliance. As the strength of the norm is determined by the share of people complying with the norm, they are able to show the evolution of the norm. In the model developed here, individuals have a preference for cooperation and these preferences 2 There are several survey questions that try to capture different aspects of trust. However, the standard trust question of the General Social Survey ( Generally speaking, would you say that most people can be trusted or that you can t be too careful in dealing with people ) is primarily taken to investigate the link between trust and different economic phenomena. 3 Other explanations include competitive motives and inequality aversion. See Fehr and Schmidt, 2006 and Meier, 2007 for a survey of theories. 4

5 are endogenous which means that they may change through experience in encounters. Instead of heterogeneous norm sensitivities, subjects have subjective beliefs about how others behave incorporating the notion of generalized trust. Besides their preferences for cooperation, they condition their behavior on what they belief others are doing. 3 A simple evolutionary model of prosocial behavior Consider the following situation. There are randomly chosen pairwise encounters between members of a large population. When they meet, one subject is in the need of help and can only be helped by the other subject. The latter can either cooperate and thus help the one in need or shirk. His move is denoted by x i {0, 1} where x i = 1 when he cooperates and x i = 0 when he shirks. Shirking is costless but adds nothing to the payoff of the other agent. Cooperation costs c but results in a payoff b for the other individual where b > c > 0. The payoff Π i (x i ) for move x i is Π i (x i ) = x i c for agent i and Π j (x i ) = x i b for agent j. Cooperating in this manner is socially efficient. Preferences The two subjects are strangers who cannot rely on past of future behavior to establish mutual benefit from cooperation. In addition, those encounters are not directly observable by other members of the population. In this way punishment or reputation building are not possible and the decision is therefore a non-strategic one with a strong incentive to free-ride. One of the motives that drives prosocial behavior in this kind of situations is altruism: the tendency to behave prosocially towards unrelated others. If a population member derives utility from the benefit of others, the individual would decide to cooperate. On the contrary, a selfish individual would decide to shirk. However, as seen above, individuals do not decide independently of what they expect others would do in their situation. If a altruistic individual assumes that a considerable part of her fellow men would not cooperate, he would suffer from not behaving appropriately. The same holds true for selfish individuals. If they assume that a considerable share people would behave cooperatively, they would gain utility from doing the same. They would comply to what they perceive to be the norm. The Utility that person i derives from Π i (x i ) and Π j (x i ) draws heavily on a preference-model developed by Levine, 1998: U i (x i ) = Π i (x i ) + β ij Π j (x i ) for all i j 5

6 β ij = a i + λa s i 1 + λ with λ > 0 The coefficient of the own monetary payoff is normalized to one for all individuals. However, the weight on person j s payoff varies over individuals and depends on the parameters a i and a s i. a i {0, 1} is person i s unconditional goodwill towards j. If a i = 1, j s payoff enters i s utility function directly; i is altruistic. If a i = 0, he is unaffected by j s payoff. The parameter a s i indicates i s subjective belief of how many of his fellow men would help in this situation, reflecting the individual level of generalized trust. The beliefs are often flawed since the encounters are not directly observable by others. However, the expected value corresponds to the true share of altruistic people and people helping in the population i.e. a s i N(α, σ). As subjects talk about their experience in the encounters, they distribute limited information. This process is not explicitly modeled here. λ incorporates an element of fairness or norm compliance. If is λ 0, this causes subject i to consider what others are doing in his decision. Here, I assume that λ > 0 which is in line with finding in experiments (see section 2). Person i will decide to cooperate if U i (1, α) > U i (0, α). This is true iff c + a i + λa s i 1 + λ b > 0 a s i > c(1 + λ) bλ a s i > Z(a i ) Z(1) < Z(0) a i λ In order to allow individuals to be conditional cooperators, an assumption need to be made regarding the values of b, c, and λ: Assumption A1: The values of b, c, and λ need to allow that 1 < Z(0) < Z(1) < 0 i.e. λ 1+λ > c b > 1 1+λ. The probability that person i helps person j is then: p t = α t f + (1 α t )g where f = F (Z(1)) = P (Z(1) < a s i ), as i N(α t, σ) and g = F (Z(0)) = P (Z(0) < a s i ), as i N(α t, σ) 6

7 pt p t Α t Αt Figure 1: Probability that individual j receives help with varying α and 45 -line (dashed). This means that f is the probability that person i behaves cooperatively given that he is altruistic; g is the probability that i is cooperative, given he is not altruistic. Since Z(1) < Z(0), 1 > f > g > 0 needs to be true too. Both g and f increase with α as individuals condition their behavior to some extend on what they believe others would do in this situation. As a result of conditional cooperation, the probability that someone helps is higher (lower) than the share of altruistic individuals in the population for high (low) levels of α (see figure 1 for an illustration). Equilibrium I do not think altruistic preferences are genetically given, but rather think it is a trait acquired through socialization or experience. In that sense, subject j - the subject in the need of help - is able to develop or lose altruistic preferences through experience made in the encounter. I assume a subject to change his preferences if the behavior of the other deviates strongly from his expectation i.e. if he did not anticipate to be helped yet received help or, vice versa, he was expecting to be helped but did not receive any help. When they adopt a trait, they change their preferences in the direction of the experience they had. Who received help is then more likely to return help given his beliefs. Assume that subject j has altruistic preferences (i.e. a i = 1) and he expected a relatively high share of his fellow men to behave cooperatively (i.e. a s i > y, with y denoting the threshold for learning; 0.5 < y < 1) then he would be disappointed and change his preferences. Vice versa for a subject j with non-altruistic preferences. He would not change his preferences through his experience if he expected to be helped. Only if his expectations where sufficiently low (i.e. a s i < (1 y), with (1 y) denoting the threshold for learning) he would change his preferences. Note that the preference of his opponent does not have any influence but only his behavior. 7

8 Ω(Α) Η(Α) Αt Figure 2: Learning probabilities η and ω with varying α. Let η denote the probability that a subject with altruistic preferences changes his preferences and ω denote the probability that a selfish subject would change his preferences, then we can write them as: η = P (a s i > y) mit a s i N(α t, σ) ω = P (a s i < 1 y) mit a s i N(α t, σ) While η increases with increasing α, ω decreases with growing α. Figure 2 depicts both learning probabilities with varying α. As the behavior of one subject influences the behavior and preferences of others, it is interesting to look at the dynamic development of altruistic preferences. The share of the population with altruistic preferences is determined by the share in the previous period plus the share of people that changed their behavior. α t = α t 1 + ω(1 α t 1 )p t 1 ηα t 1 (1 p t 1 ) (1) α = ω(1 α t 1 )p t 1 ηα t 1 (1 p t 1 ) (2) 8

9 Figure 3: Evolution of α The share of altruistic people in the population will then increase if: ω(1 α t 1 )p t 1 > ηα t 1 (1 p t 1 ) ω η > α t 1(1 p t 1 ) (1 α t 1 )p t 1 This holds true for low level of α. However, for high level of α, we will find that ω η < α t 1(1 p t 1 ) (1 α t 1 )p t 1 Figure 3 shows the evolution of people with altruistic preferences. It emerges an inner stable equilibrium given parameters do fulfill assumption A1. Comparative statics The equilibrium share of altruistic people in a population depends on several factors: - An decreases of the cost to benefit ratio will increase the equilibrium value of α. The higher individuals perceive the benefit of helping to be relative to the cost which helping creates to them, the more they are likely to help. This holds true for both individuals with altruistic and non-altruistic preferences. - Biased beliefs about the behavior of others also changes the equilibrium level of α. A high (low) level of generalized trust in a society results in a relatively high (low) level of cooperative behavior. - The effect of a change of the threshold parameter for learning (y) and the parameter incor- 9

10 porating fairness or norm compliance (λ) is ambiguous. It can both increase and decreases the equilibrium share depending on a certain threshold level of α, shifting the equilibrium towards the extreme points of α. 4 Concluding remarks Encounters with strangers constitute an increasingly large proportion of our everyday social interactions. We are interacting with people who we do not know, to whom we are not liked by any obligation or affection and thus to whom we owe nothing. Still, we are frequently engaging in behavior that is costly and mainly benefits a stranger. The model shown in this paper explains variation in behavior in this kind of situations. As shown in many lab experiments, individuals are more likely to behave cooperatively when they think others do so as well. The model suggests that a high level of generalized trust in a society promotes a relatively high level of cooperative behavior even among strangers. This is in line with findings of empirical studies that relate generalized trust to different economic phenomenons (e.g. Knack and Keefer, 1997; La Porta, Silanes, Shleifer, and Vishny, 1997). Another factor that facilitates cooperative behavior is the perception of the benefit of behaving cooperatively. The higher a person perceives the benefit of his action the more likely he is to behave cooperatively. This has certain policy implications. Especially in situations where formal contracts are limited or too onerous and personal relations are not possible, information on positive behavior of others may help to improve cooperation by correcting exceedingly pessimistic beliefs. Additionally, making people more aware of the benefit of their behavior may promote cooperative behavior. References Anderson, Lisa R., Jennifer M. Mellor, and Jeffrey Milyo (2004). Social Capital and Contributions in a Public-Goods Experiment. In: The American Economic Review 94.2, pp Andreoni, James, and John Karl Scholz (1998). An Econometric Analysis of Charitable Giving with Interdependent Preferences. In: Economic Inquiry 36.3, pp Bardsley, Nicholas, and Rupert Sausgruber (2005). Conformity and reciprocity in public good provision. In: Journal of Economic Psychology 26.5, pp

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