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Reasons behind public goods provision

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Keywords:

Experimental Economics; Social Dilemmas; Collective Action; Public Goods.

D

EPARTMENT OF

E

CONOMICS

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NIVERSITY OF

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ARMA VIA KENNEDY 8 43100PARMA mraimondi@live.it M. Raimondi1

Reasons behind public goods provision (JEL Classification: C92 ; D64 ; D80 ; H41)

1I wish to thank Franco Donzelli and Alessandro Arrighetti for their unstinting help and supervision of my work.

I would particularly like to thank Antonio Filippin for his valuable collaboration in this work and for his help and creative support in experimental design. Finally I would also like to thank Salvatore Curatolo, Andrea Lasagni, Mario Menegatti, Antonio Affuso, Davide Tondani, and Vincenzo Dall’Aglio, for their advice and helpful observations. All errors are mine.

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Abstract

In order to examine the role of non-economically measurable factors into the models of voluntary provision of public goods, Experimental Economics showed two systematic findings: (a) in early rounds of game, individual contributions are surprisingly high and increasing, but they tend to decrease progressively for all the agents and to reach a level of cooperation close to zero and (b) complete free riding is never observed. In explaining these evidences, experimental economists have pointed out the importance of factors such as kindness, confusion and strategic cooperation and most laboratory experiments have used clever designs to control for particular factors in order to isolate the effects of others. However, attempts to disentangle and measure the relevant variables of voluntary cooperation are partially and mainly realised comparing pairs of variables, which leaves one of the three hypotheses unexplained. In particular, there are at least two aspects left to be explained: (1) which has more influence in determining the level of contributions: kindness or confusion? (2) What is the role of strategic cooperation? Earlier experiments appear not able to include kindness, confusion and strategic cooperation in the same model and it seems to be very important to separate strategic cooperation from other altruistic motivations.

In this work I aim at providing evidence on the way that kindness, confusion and strategic cooperation all affect contributions and to discuss new findings in the measurement and in the disentanglement of these variables made by using a new experimental design with the Voluntary Contribution Mechanism (VCM). I will adopt the same approach as Andreoni (1995), subtracting one component in each treatment and leaving other components as reasonable (and measurable) explanations for cooperation.

Introduction

In the real life we observe that a large proportion of people contribute to public goods, despite strong incentives to free ride. This observation has often persuaded researchers studying public goods to re-examine models of giving and the role of non-economically measurable factors.

Experimental Economics is very interesting in this respect given that the lack of free riding is a widespread result of public goods experiments. The laboratory shows two systematic findings: (1) subjects’ contributions are much greater than predicted by standard economic theories of free riding and (2) contributions decrease over the course of multiple-round games.

In explaining these evidences, experimental economists have pointed out the importance of factors such as kindness, confusion and strategic cooperation.

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The term “kindness” covers all altruistic behaviours and tendency for cooperation based on social values. Confusion means that subjects make errors and do not correctly understand the game’s payoffs. Strategic cooperation means behaviour aiming to obtain individual benefits through cooperation.

Economists have used different approaches to separate confusion, altruistic behaviour and strategic considerations in public goods games and in experimental works. But despite recent and creative experiments, our understanding of behaviour in simple public goods games remains incomplete and there has been no structured disentanglement of these factors.

In this PaperI aim to provide evidence on the way that kindness, confusion and strategic cooperation all affect contributions and to discuss new findings in the measurement and in the disentanglement of these variables made by using a new experimental design with the Voluntary Contribution Mechanism (VCM). In particular, I find evidence that in such an experimental design (1) all these variables appear important in explaining decisional processes of agents, (2) confusion and strategic cooperation are the two most important explanatory variables and (3) confusion is at work not only in the first rounds of the game, but during the whole experimental session, while (4) kindness appears less incisive than usual.

The Paperis organized as follows. Section 1 contains a brief introduction to Experimental Economics and its methods; Section 2 presents the literature on the voluntary provision of public goods in Experimental Economics; Section 3 reports my experimental design; Section 4 presents the empirical results and, finally, Section 5 offers conclusions and thoughts about further research.

1. A brief introduction to Experimental Economics

In this section I briefly present some aspects of experimental methodology and the most important procedural rules for experimental design2

.

In Economics literature, Experimental Economics has developed at a fast pace over the last decades, dealing with many aspects of microeconomics as well as of Public and Environmental Economics.

The term “experimental” refers to an empirical approach aimed at identifying causal relations by means of manipulation of a single variable in a controlled environment. For a long time Economics was considered a non-experimental science that could undergo verification and control only through the tools of Statistics and Econometrics. But, nowadays, experimental studies are acquiring ever growing consideration3

thanks to increasingly important contributions to Behavioural Economics and Cognitive Economics made by Games Theory in the work of Morgenstern and Von Neumann. Today, the consideration for Experimental

2 For an in-depth investigation on introductive aspects of experimental methodology and design and main types of experiments, procedures,

history and a review of the literature, see Friedman and Saunders (1994).

3 So much so that in 2002, after a long period when interest in the discipline increased, the Nobel Prize was awarded to Daniel Kahneman and

Vernon Smith for their wide-ranging heterodox work that examines human decision, institutions and economic behaviour through a laboratory controlled experimental method.

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Economics has greatly changed and the use of experimental methods in Economics appears suitable to answer not only theoretical but also epistemological and cognitive enquiries about the discipline.

The seminal contributions of Vernon Smith (1976) identify usefulness and the need for an experimental approach. Smith claims that first, Experimental Economics allows a theory to be tested and thus allows choice between alternative theories. Second, it permits an analysis of why empirical evidence may depart from theoretical predictions. Third, it allows identification of empirical regularities that might form a basis for new theories. Last but not least, it allows evaluation of economic policies and different institutional regulation models (Smith, 1994).

Experimental Economics started with the main goal of studying behavioural choices and decision processes made by individuals in different environments. Research then developed in four main directions: experiments on behavioural economics, experiments on markets and institutions, experiments on the main results of Game Theory, and, more recently, experiments on individual and organisation learning dynamics. The core of Experimental Economics is the method and the importance given to the conditions under which experiments are carried out. Experiments are “controlled”4

rather than “natural”5

and they are conducted alongside laboratory and field experiments.

Field experiments and laboratory experiments have fairly similar methods: the researcher randomizes a sample (subjects or other units) into treatments, controls some variables and compares the outcomes of the treatments. Of course, in the field experiments there is the advantage that outcomes are observed in a natural rather than in an artificial environment, although there may be more difficulties in the randomization and control processes of the independent variables6

.

Experimental Economics has a common terminology and a set of procedural rules summarized as follows: (a) an experimental framework consists of a structural environment within which a user can systematically vary one or more key structural features (treatment factors);

(b) given an experimental framework, an hypothesis consists of a conjecture of the following form: “If the treatment factor(s) specified by the user take form A, then the experimental outcomes the user observes will take form B”;

(c) an experimental design consists of a careful description of how a particular hypothesis can be experimentally tested;

(d) an experimental session is identified by a sequence of periods, games, or other decision tasks involving the same group of subjects on the same day;

(e) a treatment is something that researchers administer to experimental units. A treatment identifies a unique environment or configuration of treatment variables, i.e., of information, experience, incentives, and rules.

4 A controlled experiment generally compares the results obtained from an experimental sample against a control sample, which is practically

identical to the experimental sample except for the one aspect whose effect is being tested.

5 Natural experiments rely solely on observations of the variables of the system under study, rather than on manipulation of just one or a few

variables as occurs in controlled experiments.

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This means that each group of subject is “treated” with a different set of conditions or with a different set of rules to see the effects and to compare the results. Each treatment can be composed by several “trials”; (f) a cohort is a group of subjects that participated in a session;

(g) finally, the term “laboratory economics” identifies, rather than the physical place where the analyses take place, the conditions for which the researcher must be able to fully control all the economic variables involved in the experiment7

.

The experimental design follows a strict series of rules which act in his natural environment.

First, one important rule is procedural regularity: the experimenter must follow a routine that can be replicated, so must standardize procedures and environment, and report them in detail8

.

A second critical factor is the subjects’ motivation: participants must receive salient rewards that correspond to the incentives assumed in the relevant theory or application. In fact, the stakes affect the responses: subjects can take seriously their evaluations if the stakes are consistent in term of real money, while they evaluate indifferently when stakes are not salient. For these reasons experimental economists are generally suspicious of experiments without salient monetary rewards.

Salience is thus the idea that rewards should be linked to choices9

and that they should be high enough to outweigh any cost deriving from taking part in the experiment, and thus satisfy the “dominance” principle. Third, an experimental design must fulfil the saliency condition which defines that every changes in decisions have a prominent effect on rewards. It requires that: (1) subjects perceive the relationship between decisions made and payoff outcomes, and (2) induced rewards are high enough to matter in the sense that they dominate subjective costs of making decisions and trades.

Another important rule is unbiasedness: experiments should be conducted in a manner that does not lead participants to perceive any particular behavioural pattern as being correct or expected, unless explicit suggestion is a treatment variable.

The calibration rule stipulates that an experiment needs to be designed with an eye to the data generated. Calibration involves the establishment of a clear basis for comparison. Calibration also means that the experimental design accounts for theoretically irrelevant factors that quite regularly affect performance, such as experience with the experimental environment, group effects, and the order in which treatments are presented. It is necessary to check for all these factors.

The design parallelism rule imposes closeness to natural situations rather than closeness to the theories that economists have devised. In Experimental Economics theories are simple and stylized versions of reality. If

7 The importance given to the conditions where the experiment is conducted is testified by Alvin Roth’s words: “Incidentally, when I speak of

‘laboratory experiments’, I am not speaking of the location where experiments are conducted, (...). Rather I am speaking of experiments in which the economic environment is very fully under the control of the experimenter, who also has relatively unimpeded access to the experimental subjects. (…)” (Roth, 1988) and Vernon Smith, “Every laboratory experiment is defined by an environment, specifying the initial endowments, preferences and costs that motivate exchange. This environment is controlled using monetary rewards to induce the desired specific value/cost configuration. (…)” (Smith, 1991)

8 Standard practices involve, for example, design and report of instructions, illustrative examples and tests of understanding, criteria for answering

questions, presence of “trial” or “practice periods”, procedures for matching subjects and roles, the physical environment, the use of laboratory assistants, special devices, and computerization, pilot sessions (decision about a session being a pilot rather than a regular session is made in advance, and not changed after looking at the data).

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the goal is to test a theory, the laboratory environment ought to mimic the assumptions of the theory rather than of reality.

Furthermore, it is necessary to structure in detail the overall instructions for the experiment and provide the subjects with the rules they need to carry out their actions. Instructions are written and read aloud before the beginning of the experiment in order for there to be common knowledge of the rules of the game. This also allows other researchers to replicate the experiment.

“Matching protocol”, “reputation building” and learning are also relevant as long as the game is repeated with the risk that subjects will interact repeatedly.

These circumstances can affect the results given that decisions can be affected by learning, reputation, or reciprocal identification. It is necessary to evaluate “order effects” in handling the subjects and so it is necessary to check that a result is not due to the order in which choices are made. A further requirement is the monotony principle: subjects must never be completely satisfied with the incentive.

Finally, privacy must be taken into account: subjects should never be informed of other participants’ losses and earnings. The anonymity of all participants must be guaranteed and mutual knowledge must not interfere with choosing conduct. There is never however total control over individual behaviour. Total control is rather a distinctive characteristic of simulations where final behaviour is determined by the theoretical assumptions as reproduced in a laboratory.

Despite the advantages that experiments provide in terms of reiteration and control of variables, there are certain drawbacks. These can be seen in the selection of the subjects involved in the experiment (mainly students or professionals), in the comparative simplicity of the laboratory environment compared to the complex economic reality, in the objective difficulty of setting and controlling a laboratory environment and in the monetary costs of the experiments.

The problem of “parallelism” is crucial: the results obtained in the laboratory, under certain conditions, must be valid outside the laboratory, where these conditions do not hold.

Experimental economists often improve experimental methods by combining both laboratory and field techniques in order to better solve the problem of “external validity. This is useful for identifying causal relations in circumstances and extrapolating evidence of more general applicability from the experimental set-up (Guala, 1999).

2. Results of public goods experiments

In this Section I summarize the main aspects of public goods provision problem in the experimental approach. First of all, I present the two recurrent results obtained in the laboratory which shed light on both the results in line with the theory predictions (cooperation fails and public goods are not produced) and on

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results which conflict with theoretical predictions (contributions to public good are high and increasing in the first rounds and there is lack of complete free riding). Secondly, I discuss the main explanatory variables of cooperation (kindness, strategic cooperation and confusion) and, finally, I focus on literature that attempts to measure and separate these variables. I will show that despite a great number of experiments trying to identify and disentangle among the various explanations for voluntary cooperation, no consensus view has as yet emerged.

2.1 The public goods problem

The standard game utilised in experiments is the “linear public goods game” (LPGG), where both the production function of the public good and the payoffs are linear. We have a group of subjects endowed with amounts of “tokens” that can either be kept (private investment) or contributed (public good). Subjects play the same game for a finite number of periods. In each period, every subject is endowed with an endowment of wi. The subject must then divide this endowment between a contribution to a private account (yi ) that yields a constant return to the private investor only, and a contribution to a public account (ci ) where consumption benefits accrue to all group members.

In formal terms, we have two goods, a private good (Y )and a public good (X) and a set of N individuals: n = 1,….N.

Each individual receives the endowment wi at the beginning of each round and the public good is realised by a production function given by:

) C (g

X =

where C is the total amount of the contributions to the public good, that is ∑

= i=1,..,Nci

C ,

and ci is the individual contribution to the public good given by i

i

i w y

c = − .

For each subject, the individual earnings from the public good are C ) N / ( ) C (g xi = = α ,

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where α is the rate of return on the public good and α/N is the marginal per capita return of contribution (MPCR)10

.

Each agent has the following payoff function:

i i i i i( y ,x ) py x U = +

where p is the marginal return to a unit of private good. So, the marginal rate of substitution between the private and the public good is given by:

) y / U /( ) c / U ( M = ∂ i ∂ ∂ i ∂ i

In a LPGG, both the parameters α and p are constant and chosen to create a social dilemma: α/N < p < α

Typically, in experiments parameterized in this way, the agent maximizing the utility function, subject to a budget constraint ((wi = yi + ci ) and a non-negativity constraint (ci ≥ 0), has a dominant strategy that is to contribute nothing to the public good. The Nash Equilibrium is thus to invest in the private good (full free riding). In contrast, for the group as a whole the Pareto efficient outcome is to invest all endowments in the public account.

I now present a simple numerical example to explain the predictions of this game-theoretical model applied to voluntary provision of public goods. The standard LPPG game can be structured as a Prisoner’s Dilemma (PD) and described by means of a simple numerical example in the simplest case of two-person game.

Let us assume only two subjects endowed with 1 unit of resources and that they can choose to contribute (all or nothing) to provide a public good or to keep for themselves the endowment. The kept endowment (i.e. the private good) has an unitary rate of return (p = 1) while the rate of return on the public good is 50% (a = 1.5). If both players contribute, they will earn 1.5. If one contributes while the other does not contribute, the one who contribute earns 0.75 while the other earns 1.75. If neither contributes, they will earn their endowment, that is 1.

The set of strategies for each player is “contribute” and “do not contribute”: Si = (C; NC) and the public good game played has the following payoffs’ matrix:

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Table 3: Payoff matrix of the PG-PD game Player 2

Contribute to the public good (C)

Do not contribute o the public good (NC) Contribute to the public good

(C)

1.5 ; 1.5 0.75 ; 1.75 Player 1

Do not contribute to the public good (NC)

1.75 ; 0.75 1 ; 1 A table summarizing the properties of this game is the following:

Table 4 – The PD game

Players I = {1, 2}

Nature Not present

Action Ai = A = {C,NC} ={“Contribute”,“Do not contribute”} Order of play Players move simultaneously

Information set Nobody sees other’s choice when moving

Strategies

{ }

{ }

{ }

C s,

{ }

NC s : S NC s, C s : S 2 1 1 2 2 2 1 1 1 1 = = = =

Payoffs Outcome (C,C): 1 gets 1.5; 2 gets 1.5 Outcome (C,NC): 1 gets 0.75; 2 gets 1.75 Outcome (NC,C): 1 gets 1.75; 2 gets 0.75

Outcome (NC,NC): 1 gets 1; 2 gets 1

As well as in the standard PD game, in this PD-PG the best response of one player to the strategy chosen by the other player is the free riding strategy, that is “do not contribute”. The strategy “contribute” is strictly dominated by “do not contribute” because each player is better off, for every choice of strategy of the other player, from choosing “do not cooperate”: the payoffs obtained from the free riding strategy are strictly greater than the payoffs obtained from cooperation. Hence, the only Nash Equilibrium (NC ; NC) implies free riding.

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Experimental research has shown that human behaviour often departs from the predictions implied by the homo economicus. The voluntary provision of public goods, and collective action in general, is a topic in which Experimental Economics reveals some of these contrasts and provides interpretations that complete and extend the predictions of the Theory.

Experiments on public goods, in fact, show two fundamental results: (a) in early rounds of game, individual contributions are surprisingly high and increasing, but, in the second part of game, they tend to decrease progressively for all the agents and to reach a level of cooperation close to zero and (b) complete free riding is never observed. This pattern is a constant evidence of public goods experiments.

The final result in term of cooperation is very similar to the predictions of Game Theory models and it shows that spontaneous collective action is impossible and that private agents do not provide public goods at efficient levels (or do not provide any public good).

In general, in the absence of free rider sanctions and also when a “costly sanction” is not possible, a progressive reduction of individual contributions and the public goods sub-production trend seems to be a constant factor in the experiments, when the game horizon is finite and especially in the last rounds of the experiment. The individual contributions decrease half way through the rounds, when several agents opt for selfish behaviours towards the end of the game and collective action fails.

In order to explain the decline of contributions, experimenters invoke the MPCR (marginal per capita return), the subjects heterogeneity and, particularly, the learning and the strategy hypotheses. The MPCR tends to decrease at group size increases, so that changes in group size and MPCR can affect the substitution rate between private and public good and, as a consequence, the incidence of free riding (Isaac and Walker, 1988). The second variable that determines decreasing contributions is the heterogeneity of the subjects: when players are different in their individual characteristics or in their initial endowments, the individual contributions are smaller and the trend is toward free riding (Marwell and Ames, 1979, 1980).

In particular, contributions decrease if the initial endowments are asymmetric and if the marginal profit levels that individuals gain from the public good are different11

. But the most important explanatory variables in the decline of contributions are the learning and the strategy hypotheses (Andreoni (1988) and Croson (1996). The learning hypothesis suggests that subjects learn the incentives of the game throughout the experiment: the average contribution declines over time because subjects learn at different speeds. The strategy hypotheses, on the other hand, suggests that subjects play strategically to influence their partners' actions because the information is incomplete12

.

Free riding takes place if everybody thinks that everybody will behave rationally. But if a subject thinks that the others will behave irrationally, he will “educate” them by choosing free riding. So, initial cooperation will turn into defection. If some player also believe that the others think they have not understood free riding, they will demonstrate their rationality by free riding. This will lead again to defection.

11 See Isaac, McCue and Plott (1985) Isaac and Walker (1988). 12 Kreps et al., (1982) describe this process for the finitely repeated PD.

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The evidence of very high and increasing contribution levels in the initial rounds of the game represents the second fundamental feature of public goods experiments and conflicts, in part, with classical predictions. Experimental Economics presents important works on the voluntary production of public goods which show how cooperation can arise, at least in the initial stages of the game, even though theory predicts that individual incentives prevail. In fact, in the first rounds, subjects contribute at levels that vary from 30% to 70%, when evaluating the trade-off between individual benefit maximisation and altruistic targets pursuit (Ledyard, 1995).

This finding on initial contribution outcome has received strong empirical support, but there is as yet no agreement on the underlying reasons. One of the most innovative characteristics of the experimental approach is the opportunity to interpret this kind of phenomena through social and human factors and thus acknowledge the role of social evolution and group structure in determining behaviour.

2.3 Reasons behind voluntary contributions

The experimental suggestions to explain cooperation can be organized into three broad categories: (a) decision errors and confusion; (b) strategic cooperation arising from selfish motivation or from incomplete information and uncertainty about other players’ motivations, payoffs and rationality; (c) altruism or “kindness”.

(a) In the laboratory it is possible that experimenters fail to communicate adequately the incentives to the subjects, or that some of the players do not figure out that the dominant strategy is free-riding, or, maybe, they do so only late during the game. In other words, subjects who do not grasp the true incentives for one reason or another make mistakes and depart from the dominant strategy. These situations can lead to the lack of free-riding as a dominant strategy in the laboratory and are generically defined as “confusion”.

Andreoni (1995) states that if the goal of laboratory experiments is to control the incentives of subjects and to remove social and cultural influences to the greatest extent possible, then cooperation observed is probably due to subjects who misunderstand the rules or the incentives in the experiment.

Confusion is a potentially very important variable to the experimental analysis: with an equilibrium prediction of zero contribution to the public good, contributing too much is the only way a confused subject can err. Hence, there is the risk to mistakenly identifying contributions due to confusion as “cooperation”.

Confusion is also linked with the net cost of a contribution. Goere, Holt and Laury (2002) measure the importance of noise and altruism in public goods games: when the net cost of a contribution is small, the importance of errors and confusion in determining voluntary cooperation can be greater. The net monetary loss from making a contribution is the difference between the value of a token that is kept and a token that is contributed to the public good: because the net loss is positive, the Nash Equilibrium is to contribute

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nothing. If this net loss is relatively small, the effects of noise are probably greater and lead to more contributions.

(b) The second category of explanation is strategic cooperation and it is due to selfish motivations. By strategic behaviour I mean the willingness to signal a particular strategy profile to the other subjects in order to persuade them to behave in the same way.

On one hand, understanding that the social optimum is obtained when all subjects fully contribute to the provision of the public good, a subject could signal his/her willingness to achieve this goal by contributing to the public good. On the other hand, knowing that higher individual payoff is obtained by free riding, when all other subjects contribute to the public good, a subjects could contribute in the first rounds of the game to persuade the others to do the same: he will defects later. Such players are called “rational cooperators”13

. Strategic signalling leads to cooperative choices but the subject aim of the behaviour is fundamentally selfish in that it aims for individual benefits. This is essentially the characteristic that differentiates strategically cooperative behaviour from altruistic behaviour: both strategic cooperation and altruism lead to contributions to the public good but strategic players contribute to obtain advantages per se, while altruistic players contribute to realizing a public good that they do not necessarily consume. Strategic cooperation occurs for many reasons and has been investigated in different experiments. One important reason is reciprocity, as first hypothesised by Sugden (1984), who suggests that subjects decide on their contribution according to the contribution desired from other subjects. The reciprocity principle constitutes a rule which prevents subjects opting for free riding strategy in VCM, if other subjects contribute to public good.

A second way of defining strategic cooperation is “conditional cooperation”: subjects are willing to increase their contributions to the public good when other subjects’ contributions are increasing. Conditional cooperation has been tested in many experiments14

. Keser (1996), for example, demonstrates that the agents’ behaviour is conditioned by others’ cooperation and that in public goods games with VCM, conditional cooperators and free riders coexist.

Brandts and Schram (2001) apply a contribution function approach to study voluntary contributions: in this approach each subject in each period has a complete contribution function that determines various level of contribution for various marginal rates of transformation between a public and a private good

They show that a sizeable group of subjects deviates from the behaviour predicted by standard theory insofar as the subjects have strategic reason to cooperate. These subjects’ behaviours are interdependent over time and the importance of confusion and errors is considerably lowered.

Another interesting contribution to the explanation of strategic cooperation is the work of Ferraro, Rondeau and Poe (2003) who design a public goods experiment in which human subjects interact with virtual players.

13 Kreps, Milgrom, Robert, and Wilson (1982)

14 See Croson (1998), Sonnemans, van Dijk and van Winden (1999), Keser (2000), Keser and van Winden (2000), Brandts and Schram (2001)

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They hypothesize that humans playing a one-shot public good game with virtual players will contribute less than humans playing with other humans. The differences in contribution levels are attributed to strategic cooperation and altruism. They remove the incentives for altruistic behaviour by making subjects aware that virtual players do not obtain benefits from the public good and thus reveal the importance of strategic cooperation. Virtual players are programmed to behave as if they were humans, and humans are aware that virtual players do not receive any payoff. Ferraro, Rondeau and Poe reveal the importance of strategic cooperation with their finding that humans playing with virtual players behave no less strategically than humans playing with humans.

A further contribution on the importance of strategic cooperation is the work of Fishbacher, Gaechter and Fehr (2001). In this experiment subjects are grouped and told that the game do not have repetitions. They are then asked to make two decisions: (1) an unconditional contribution decision, that is the decision about how much they want to contribute before knowing others’ contributions; (2) a conditional contribution decision, that is the decision about how much they want to contribute after finding out the average contribution of the group.

A random mechanism chooses whose of the two decision will be considered to calculate payoffs and ensures that the subjects have incentives to take their decisions seriously. In this treatment the authors are exclusively interested in the individual preferences and in finding evidence of conditional cooperation. They observe that the average contribution is rather higher than free riding predictions, and that conditional cooperators who replay other players’ contributions constitute about 50% of total players.

(c) The third broad category of explanations for cooperation and the absence of free riding are altruistic reasons, which I refer to collectively as ‘kindness’. Kindness suggests that subjects have a non-monetary component in their utility function that is difficult to control and measure.

For a long time, models based on kindness seemed to provide a complete explanation for the lack of free riding: however, many experiments have shown that this approach cannot fully explain individual behaviour in the voluntary provision of public goods.

The utility function of an altruist subject may have three variables: the individual consumption of the private good, the individual contribution and the total level of public good provision. Furthermore, kindness may be observed from two perspectives: the pure altruism and the impure altruism, or warm-glow15

.

By pure altruism preferences I mean that a subject’s utility increases both in the total group payoff and in individual payoff: subjects contribute to public good because they recognize its social value, but they understand that the other players also need to contribute for the public good to be provided. So they only contribute altruistically if other players contribute. I remark that the difference between strategic cooperation

15 Definitions are counterintuitive because the altruism concerned with strategic considerations is termed “pure” and the altruism that one could

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and impure altruism is that while strategic cooperation leads subjects to contribute in order to directly obtain earnings, impure altruism does not pursue individual benefits.

The second component on kindness is so-called warm-glow, a kind of unconditioned altruism16

. Warm-glow preferences mean that the act of contributing increases a subject’s utility regardless of how much it increases group payoffs. Subjects participate in public good without considerations about what the other players do, because warm-glow motives imply benefits from cooperation per se, regardless of the effective provision of the good.

In other words, warm-glow induces the agent to cooperate without taking other agents cooperation into consideration and independently of the fact that other people benefit from the public good. These agents in fact have a purely egoistic reason for donating (Andreoni, 1990).

The two components have different effects on voluntary provision of public goods. When both the group size and the value of the public good increase, pure altruism determines very large effects on contribution rates, while warm-glow leaves them basically unchanged. Kindness is therefore greater when subjects assign positive value to their own contributions (Falkinger, 1995) or when they believe that contributions have a “social” value.

In fact an important branch of Psychology as well as some economic literature now focuses on studying imitation and social ties17

. This is because each subject tends to behave differently when interacting with different subjects and the fundamental components are the feelings that individuals develop.

A study by Hu and Liu finds evidence of different levels of kindness due to experimental environments and personal characteristics (Hu and Liu, 2002). They show that female and older students are more cooperative than other students. They define kindness as the willingness “to benefit another person” and reciprocity as the “reason that lead to reciprocal altruism and reasons for direct benefit that lead to the so-called calculating altruism”, and find a stable share of altruists.

Finally, kindness occurs in individual preferences of subjects. Russel, Bjorner and Clark (2003) assign considerable importance to altruistic preferences in determining voluntary cooperation and, in particular they show that the level of public goods provision increases with altruistic preferences. They support the existence of such preferences through changes in framing of a questionnaire in a two-countries empirical study. This study of altruistic behaviour is interesting because it removes or reduces the problem of the bias involved with people who respond by guessing “what they think they ought to want instead of what they do want for themselves”: In each frame, Russel, Bjorner and Clark supply only one set of motives so that each subject sees only one “trigger”.

16 Hu and Liu (2003), Crumpler and Grossman (2008).

17 For more important references see Stroebe and Frey (1982), Offerman, Sonnemans and Schram (1996), Van Lange, Otten, De Bruin and

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2.4 Attempts to disentangling among the explanatory hypotheses

Most subsequent laboratory experiments have used clever designs to eliminate or control for particular factors in order to isolate the effects of others. However, attempts to disentangle and measure the relevant variables of voluntary cooperation are partially and mainly realised comparing pairs of variables, which leaves one of the three hypotheses unexplained.

In this section I briefly discuss three important works aiming at separating and measuring these variables in order to provide a framework for my experiment. These three contributions are: (a) the experiment of Andreoni (1995a); (b) the experiment of Palfrey and Prisbrey (1997) and (c) the experiment of Houser and Kurzban (2002).

(a) Andreoni (1995a) proposes an experiment aimed at disentangling cooperation due to social and cultural factors from what can be explained by errors and confusion affecting subjects’ choices. He defines “kindness” as all possible types of tastes for cooperation connected with social sphere, such as altruism, warm-glow and, in general, other-regarding behaviour. The experiment aims to separate noise from kindness by comparing contributions across different treatments18

.

The first treatment is the standard VCM, called Regular Condition, where each player has a sum to divide into two accounts, a private account that yields a 100% return to the player, and a public account that guarantees a specific fraction of all deposits to all participants, independently of the individual contribution made. In this condition each player is paid directly the earnings he makes in the experiment.

The second treatment, called Rank Condition, is a peculiar VCM in which players are paid a fix amount according to their performance relative to the others: the subject with the highest experimental earnings gets the highest monetary payment, payments are established in advance and decrease with rank.. This payoff structure removes all the incentives to cooperate: in fact, a subject’s contribution to the public good increases his/her earnings as much as the earnings of the others. However, in contributing to the public good, the subject foregoes some private yield, therefore lowering his/her position in the rank and decreasing his/her monetary earnings.

Finally, in the third treatment, called Reg-Rank Condition, subjects are rewarded according to their rank position, but they are also paid experimental earnings as in the Regular Condition. In this way, Andreoni makes the three treatments directly comparable: in the Reg-Rank Condition, and obviously in the Regular Condition, voluntary contribution can be explained in terms of both confusion and kindness. In the Rank condition, where incentives to cooperation are removed, voluntary contribution can be explained only by confusion and errors that subjects make in considering the rules of the game and the payoff structure. So

18 Andreoni defines three experimental conditions involving 40 subjects divided in groups of five players. The subjects are divided into two rooms

and in each room a different condition of the experiment is conducted for four groups of five players. The subjects are assigned randomly to a new group each round in order to avoid the possibility of reputation-building.

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kindness is what accounts for the difference in the contribution levels in the Reg-Rank and in the Rank conditions.

Subjects in the Regular Condition are expected to be the most cooperative and subjects in the Rank Condition are expected to be the least cooperative.

Table 1 shows the percentage of endowments contributed to the public good in the three conditions pooling all trials and the percentage of subjects contributing zero (free riders) in each condition.

Table 1: Percentage of endowment contributed to the public good and percentage of subjects contributing zero.

Round

1 2 3 4 5 6 7 8 9 10 All

Percentage of endowment contributed to the public good Regular Condition 56.0 59.8 55.2 49.6 48.1 41 36.0 35.1 33.4 26.5 44.07 Reg-Rank Condition 45.8 45.4 32.6 25.0 23.1 17.8 11.3 9.5 8.3 9.0 22.79 Rank Condition 32.7 20.3 17.7 9.9 9.2 6.9 8.1 8.3 7.1 5.4 12.55 Percentage of subjects contributing zero (free riding)

Regular Condition 20.0 12.5 17.5 25.0 25.0 30.0 30.0 37.5 35.0 45.0 27.75 Reg-Rank Condition 10.0 22.5 27.5 40.0 35.0 45.0 50.0 67.5 70.0 65.0 43.25 Rank Condition 35.0 52.5 65.0 72.5 80.0 85.0 85.0 85.0 92.5 92.5 74.5

The results conform with the pattern of earlier experiments: the endowments contributed are 56% in the first round of Regular Condition and decay to 26% by round 10. As expected, Regular subjects are more cooperative than Reg-Rank and Rank subjects and Reg-Rank subjects are more cooperative than Rank subjects. The significance of these differences is confirmed by a Mann-Whitney test.

Table 2 shows the percentages of cooperation explained by different hypotheses. For the comparison, Andreoni uses the percentages of total free riders so that the Rank Condition is the condition where this percentage is expected to be higher and cooperation is intended to be the absence of free riding.

The difference of free riders between Rank and Reg-Rank condition is the cooperation explained by kindness, while the difference between a complete free riding (100) and the Rank condition free riding is cooperation explained by confusion. The relative weight of kindness and confusion in explaining cooperation is calculated as percentage of “100 – free riding rate” in Regular Condition.

If free riding (subjects contributing zero) in Regular Condition is 27,75%, cooperation will be 100 – 27, 75, that is 72,25% accounted for at 31,25% by kindness and for 25,5% by confusion. Kindness thus accounts for 43,41% , confusion accounts for 33,33% while the remaining 23,26% of cooperation is unexplained.

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Table 2. Percentages of cooperation explained by kindness and by confusion

Round

Variable 1 2 3 4 5 6 7 8 9 10 All

Percentage of cooperation explained Kindness (Rank – Reg-Rank) 25.0 30.0 37.5 32.5 45.0 40.0 35.0 17.5 22.5 27.5 31.25 (as percentage of 100 – Regular) 31.3 34.3 45.5 43.3 60.0 57.1 50.0 28.0 34.6 50.0 43.1 Confusion (100 - Rank ) 65.0 47.5 35.5 27.5 20.0 15.0 15.0 15.0 7.5 7.5 25.0 (as percentage of 100 – Regular) 81.3 54.3 42.4 36.7 26.7 21.4 21.4 24.0 11.5 13.6 33.33

Total contribution explained 74.43

Hence, the experimental evidence may explain about the 80% of cooperation and shows that both kindness and confusion are important explanatory factors.

The patterns of these two variables are quite different. Confusion is decreasing over time: it starts at very high level (81%) in the first round but falls rapidly after five rounds (26.7) reaching a low level (13.6%) in the last round; kindness increases in the first rounds and decreases half way through: it doubles in five rounds reaching a peak in the round 5 (60%), then slowly decreases to a lower value (28%) over last rounds19

.

The patterns of kindness and confusion has interesting implications for the pattern of cooperation: the total amount of cooperation is stable over rounds 1-6 but, in the same period, confusion falls sharply while kindness increases. After round 6, kindness falls but confusion is stable.

An explanation for this could be individuals who start confused, then learn the dominant strategy but try to cooperate before turning to free riding: in this case, kindness depends in reciprocity.

Finally, there are some subject who cannot be classified as cooperative for either kindness or confusion, so the percentage of explained cooperation is not 100%. Andreoni makes three suggestions on how to complete the classification: (a) all of these subjects are confused and the average cooperation could be 43% kindness and 57% confusion; (b) all of these subjects are “affected” by kindness and the average cooperation is 67% kindness and 33% confusion; (c) these subjects are equally altruistic and confused which means that cooperation is half kindness and half confusion.

The level of public good provision when kindness matters is about twice as high as the level reached in the Rank Condition where incentives to cooperate are removed and subjects choose the dominant strategy of contributing their whole endowment to the private account if confusion does not matter.

(18)

Lastly, Andreoni suggests that the decline of the cooperation rate may be due to frustrated attempts at kindness and not to learning20

.

(b) Palfrey and Prisbrey (1997) investigate the role played by errors and confusion in explaining the observed contribution pattern.

In their first experiment (Palfrey and Prisbrey, 1996) subjects21

face different marginal rates of transformation (MRT) between the private and the public good in every period.

The laboratory environment consists of N subjects endowed with discrete units of a private good. The MRT between the public good and the private good is one-for-one and the form of individual monetary payoff functions is linear, as usually in VCM experiments. They define r the marginal value of the private good and V the marginal value of the public good.

All the subjects in a group have the same marginal value of the public good but they are randomly assigned different marginal values of private good. The dominant strategy of subjects who value the public good more highly is to contribute all of their endowment, while the dominant strategy of subjects who value the private good more is to free ride.

Subjects repeat the game many times and at each repetition they are reassigned a new marginal value of the private good.

In comparison with other similar experiments, where the marginal value of the private good exceeds the marginal value of the public good leading to free riding, this experiment yields different results:

(a) contribution occurs and most players violate the dominant strategy and contribute even when the marginal value of the private good exceeds of three (or more) times that of the public good;

(b) when r gets closer to V, cooperation increases;

(c) subjects can be classified according their tendency to violate the dominant strategy; (d) learning and experience diminish cooperation;

(e) the dominant strategy to cooperate (when V exceeds r) is violated as well as the dominant strategy to free ride (when r exceeds V).

Interpreting the data generated by the variation of the MRT across periods, it is possible to observe that confusion and errors are strong determinants of positive contribution. In addition, observed “overcontribution” may be attributable to confusion and to a few altruistic subjects.

20 Further studies on this matter are provided by Anderson, Goere and Holt (1998); Bolton, Brandts and Katok (2001); Fischbacher, Gatchter and

Fehr (2001); Andreoni and Miller (2002); Andreoni, Brown and Vesterlund (2004); Croson, Fatas and Neugebauer (2005); Andreoni (2007) and Crumpler and Grossman (2008).

21 The experimental design had four experimental session and involved 48 subjects divided into groups. Subjects were asked to allocate their

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In a later work Palfrey and Prisbrey (1997) present a model that allows for confusion, kindness and “repeated game effect” and they separate kindness into two components: altruism and warm-glow effect. They then attempt to separate all these components.

In Palfrey and Prisbrey’s early experiments all subjects were faced with the same r and the dominant strategy was usually free ride. But here, the value of r change after two rounds and there are subjects with the dominant strategy of contributing. The subjects play four sequences of ten periods matched with three other subjects22

that are the same in all trials. The marginal evaluation of the private good changes after the first two sequences in order to control the effect of experience.

In the first two sequences it is assumed that the evaluation of the private good exceeds the evaluation of the public good, so free riding is the dominant strategy for all individuals. In other sequences, the marginal value of the public good increases encouraging contribution.

At the beginning of the experiment the distribution from which the random values of ri and the value of V are drawn are announced. In each subsequent period the subject decides how many tokens to keep and how many tokens to contribute.

The choices of each individual given different values of ri allow us to estimate the response functions. It is found that most players violate the dominant strategy of free riding and contribute about half of their endowment even when the marginal valuation of the private good is much higher than the marginal valuation of the public good.

But when the marginal valuations of the public good and of the private good are more similar, more violations of the dominant strategy occur. Finally, repetition and experience of the game diminish the violations of the free riding predictions.

The altruism effect measures the additional utility that an individual obtains when the monetary payoff of other individuals increases by one unit and the warm-glow effect measures the additional utility that an individual obtains simply from contributing a unit of his endowment. In Palfrey and Prisbrey’s model, altruism effect occurs when contributions increase with the value of the public good and warm-glow effect occurs when the difference between the public value and the token value is increasing (other factors held constant).

By varying both the token values and the public good values for individuals, the authors construct the contribution function and separate the effect of these variables on contribution rates. They find clear evidence that altruism plays little or no role in individual’s choices, while, warm-glow23

and confusion play significant and important roles.

(c) In more recent years, Houser and Kurzban (2002) have attempted to discriminate more clearly among the competing theories of cooperative behaviour . With the aim of providing additional evidence for the role of

22 The four sequences occur in a single session involving 16 subjects.

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confusion in explaining the usual contribution pattern, they define an alternative experiment to replicate Andreoni’s. It involves 52 subjects divided in two treatments. The first (Human Condition) replicates Andreoni’s Regular Condition and involves 20 students. The second (Computer Condition) involves 32 subjects and humans interact with virtual players24

.

The Computer Condition removes kindness from subjects contributions, and leaves confusion as the only variable influencing strategies. Subjects are assigned to groups in which the other players are computers, in such a way that they have no chance to benefit themselves or other human players by contributing. The authors claim that voluntary cooperation is thus completely due to confusion, although they are aware that subjects could play altruistically in order to benefit the experimenter.

Subjects playing the Computer Condition know that the three other group members are virtual players, whose contributions are predetermined. Before their contribution choice, human subjects are told the total number of tokens that the computers will contributing that round. The aim of this information is to make sure that players do not falsely interpret the non-constant pattern of computer contributions as a response to their own actions.

The authors are aware of the imperfect comparability of the two treatments: in particular, they refer that “since the game’s incentives might be relatively more transparent in the computer condition, confusion in the computer treatment might be systematically less than confusion in the human treatment. That is, our design might bias downwards our inferences about confusion’s importance in the human treatment” (Houser, and Kurzban, 2002, p. 4).

The authors predict the mean level of voluntary contributions in the Human Condition will be higher than that in the Computer Condition, and interpret the difference in mean contribution between the two conditions as kindness.

Results show that the mean cooperation in the Human Condition is significantly larger than in the Computer Condition.in every round. In addition, there is evidence that confusion accounts for more cooperation in earlier than later experimental rounds, and that the decrease in contribution may be due to lowering of confusion.

While Andreoni explains the decline of contributions as attempts at kindness frustrated by lack of reciprocity, Houser and Kurzban find little evidence of this effect, and stress the role of confusion and errors in explaining subjects behaviour. Their explanation of cooperation, as for many other Experimental Economics researchers, is the existence of altruistic players.

An important variable that makes a subject contribute is the effort to create and enlarge the social networks and groups. Repetitions of the game and reciprocity help to explain cooperation when the agents are interested in building reputation in the group. In fact, individuals’ behaviour depends on others’ intentions: people usually want to help those who want to help them, and hurt those who want to hurt them. So reciprocators are cooperative if other agents in the group are cooperative.

24 Both the conditions are the standard linear public good games played in the Andreoni’s experiment in which a subject in a four-player group has

to choice how divide his endowment between an Individual and a Group Exchange: as in the standard game, the MPCR on public contributions is 0.5.

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Croson (1998) and Sonnemans, Van Dijk and Van Winden (2002) show that iterations are important and that agents are more cooperative when the game is repeated.

3. Experimental design and procedures.

The experiments summarized above constitute attempts to disentangle among the explanatory variables of cooperation but there is at least two aspects left to be explained:

(1) which has more influence in determining the level of contributions: kindness or confusion? In fact, previous results disagree in to assign the relative importance to these variables.

(2) What is the role of strategic cooperation? Earlier experiments appear not able to include kindness, confusion and strategic cooperation in the same model and to clearly separate the effects of these three variables.

Thus, it seems to be very important to separate strategic cooperation from other altruistic motivations because earlier attempts are partial in this sense and tend to underestimate strategic cooperation.

This work examines the different motivations behind cooperation. It adopts the same approach as Andreoni, subtracting one component in each treatment, leaving other components as reasonable (and measurable) explanations for cooperation.

With this methodology, I can hopefully estimate what fraction of voluntary contributions is due to confusion, what fraction is due to the two components of kindness (pure altruism and warm – glow) and what fraction is due to strategic cooperation.

A first hypothesis I want to verify is the possibility that the role of kindness may be reduced once strategic behaviour is taken into account. According to this motivation, subjects contribute to the public good only to strategically signal this behaviour to other subjects in order to obtain individual benefits: group wellbeing is not a target per se, but only a spillover effect linked with pursuing its individual benefit.A second objective of my analysis is to verify whether confusion is a very important factor in determining contribution levels during the whole game or if its role is limited to the initial rounds of the game.The third aim of this analysis is to examine both components of kindness and, possibly, to separate and measure them independently.

The model for the public goods game in the experiment is the standard twenty-round LPGG with VCM illustrated above illustrated. 100 undergraduate economics students at the University of Parma participated: as first/third – year students they were not skilled in collective action. In each condition they were paid directly on the basis of their payoffs in all rounds and received a show up fee of 5 Euro. On average, subjects earned 15,75 Euros and were in the laboratory for about 40 minutes. They were given verbal and written instructions for the game25

and provided with some examples of hypothetical investment decisions.

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The experiment consists of five treatments26

. The first treatment replicates Andreoni’s Regular Condition (RC) and involves one group of twenty students endowed with 0.75 Euro in each of the twenty rounds. Subjects were asked to divide their endowments between private and public investment.

The instructions provide information and examples about private and public rate of return and they help the players in determining the payoffs: the part of the endowment invested in the private investment individual earning enters directly in a one-to-one relation, while the part assigned to the public investment is put together with all the other public contributions, multiplied by the rate of return of 50% and equally divided among the twenty members of the group27

.

At the end of each round subjects are shown a summary of the main results: the individual payoff of the last round, the individual payoff since the beginning of the game, the sum of all contributions to the public investment in the last round and its individual value taking account of the interest rate.

In this treatment cooperation can be supported by all the three variables: altruism (pure and impure), strategic behaviour and confusion.

The second treatment is termed the “Next Experiments Condition” (NEC) and involves twenty different students who are asked to divide their endowment between the usual private investment and the “next experiments” fund. In other words, subjects could refund part of the endowment to the experimenter in order to finance future experiments. All contributed sums are multiplied by the same rate of return as in the Regular Condition, that is, a rate of 50%. The subjects are informed that they will not participate in such future experiments themselves, so therefore their only earnings are the private investment payoff.

In this treatment we expect subjects to consider that the “next experiments” fund had some social interest and importance because it allows further research and allows other students to participate and be paid. In addition we expect subjects to consider that they are not going to directly benefit form this investment since other students will take part in the future experiments.

This condition aims to remove incentives for strategic cooperation because the subjects cannot obtain direct benefits from the public good (next experiments), without affecting altruistic motivations. So, contributions to the public can be explained as a consequence of only two variables, the confusion and the altruism (pure and impure):

The third and the fourth treatments are very similar to the second (NEC): they are designed in order to separate and measure the two components of altruistic behaviour, pure altruism and warm-glow. The third treatment is called “Next Experiments Without Information” (NEWI) and it differs from NEC only as far as the amount of information that experimenter communicates to subject is concerned. In this treatment, the subjects are not told the overall amount of contributions after each round.

By hiding information about other players’ contribution I remove the “strategic” component of altruism that make a subject cooperate only if the other subjects do the same. Because the players contribute to future

26 The experiment was programmed and performed with the software z-Tree (Fischbacher, 2007).

27 Each cent placed in the private investment returns one cent to the player, while each cent placed in the public investment generates a return of

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experiments independently of other players’ choices, total contribution in this treatment is a function of two variables: confusion and warm-glow.

With the fourth treatment, called “Next Experiments With Threshold” (NEWT) I try to identify the other altruistic component: pure altruism. In this condition subjects are told that they can choose between the usual two investments but they are also made aware that the sum of contributions has to achieve, on average, a minimum level in order to be used for future experiments. If such a level is not reached, all refunds are lost. The threshold is 25% of the overall amount of endowments; this level has been chosen to be sufficiently high to make warm-glow useless, and sufficiently low to be reachable by “strategic altruism” given that in Andreoni’s Reg-Rank Condition the average contribution was about 23%.

At the end of each round subjects can view on their monitor a summary with their last individual payoff, their individual cumulate payoffs, the sum of all refunds, the value of refunds multiplied by the rate of return and information about payment of the threshold.

In this treatment warm-glow incentives are removed because, under these conditions, players should recognize the social value of the public good, but they should be willing to contribute only if the other players are contributing, otherwise their refunds are useless.

The last treatment is designed to remove confusion, the third variable that affect cooperation, and it is termed “Regular Condition Without Confusion” (RCWC) As well established in the literature, confusion is mainly present in the first rounds of the game and tends to decrease with the learning processes of the subjects.

I replicate the RC treatment and attempt to remove the confusion using more detailed initial instructions containing more examples and hints about the best strategy to be played according to other players strategy. In particular additional information suggests that (1) the most efficient choice is to contribute; (2) the free riding strategy determines an individual benefit; (3) free riding undermine efficiency. If the design is successful in eliminating confusion, the variables that affect the voluntary cooperation are altruism and strategic behaviour.

Each treatment finish with a brief questionnaire28

that collects some information about gender, age, education and participation in social networks.

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4. Results.

In Figure 1 I show the graph of individual average contributions and in Table 3 I list the average percentage of endowments contributed to the public good in each round of my five treatments.

I find results conform to the usual patterns of contribution and, in particular, conform to those of Andreoni’s RC although they are lower in level (26% of endowment contributed versus 44,07%) Contribution is high in the first rounds of the game (43,07%), and rapidly decreases over rounds 1-10 (19,60% in round 11) reaching 16,48 in round 18. As in Andreoni’s experiment, in the last two round, the average contribution surprisingly increases (18,4%). Table 4 shows the percentages of cooperation attributed to explanatory variables per round.

Figure 1 – Patterns of average contribution to the public good per round

Key:

RC = Regular Condition

NEC = Next Experiments Condition

NEWI = Next Experiments without information Condition NEWT = Next Experiments with threshold Condition RCWC = Regular Condition without confusion

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Table 3 – Percentages of endowment contributed to the public good per round Treatments Rounds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 All RC (s; c; k) 43.07 42.27 39.60 39.87 33.40 33.00 26.67 29.33 28.27 24.93 19.60 17.20 18.93 18.93 18.53 16.68 16.44 16.48 18.48 18.37 26.00 NEC (c; k) 26.67 23.67 31.67 29.67 23.00 23.13 19.07 16.20 15.60 20.13 14.60 24.07 17.20 20.00 16.07 15.33 13.73 14.93 11.87 10.40 19.35 NEWI (c; aW) 22.13 21.60 24.27 23.13 26.00 15.60 19.33 19.80 16.07 20.87 14.47 19.73 19.67 19.80 19.60 13.80 12.80 15.47 16.44 16.68 18.86 NEWT (c; ai) 22.33 22.67 19.53 17.40 12.13 18.07 21.47 23.33 18.40 24.80 22.60 23.80 15.93 18.07 13.67 16.40 11.47 9.40 6.93 10.00 17.42 RCWC (s; k) 33.33 39.00 20.73 20.40 13.73 9.73 7.87 15.73 14.73 15.67 9.00 6.93 5.00 9.93 9.60 11.33 5.53 4.53 2.33 11.93 13.35

Table 4 – Percentages of cooperation attributed to explanatory variables per round

Variables Rounds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 All Confusion (RC-RCWC) 9.74 3.27 18.87 19.47 19.67 23.27 18.80 13.60 13.54 9.26 10.60 10.27 13.93 9.00 8.93 5.35 10.91 11.95 16.15 6.44 12.65 Strategic Behaviour (RC-NEC) 16.40 18.60 7.93 10.20 10.40 9.87 7.60 13.13 12.67 4.80 5.00 -6.87 1.73 -1.07 2.46 1.35 2.71 1.55 6.61 7.97 6.65 Altruism (RCWC)-(RC-NEC) 16.93 20.40 12.80 10.20 3.33 -0.14 0.27 2.60 2.06 10.87 4.00 13.80 3.27 11.00 7.14 9.98 2.82 2.98 -4.28 3.96 6.70 Key: RC = Regular Condition

NEC = Next Experiments Condition

NEWI = Next Experiments without information Condition NEWT = Next Experiments with threshold Condition RCWC = Regular Condition without confusion

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RC shows the higher contribution rates to public good over all the five treatments, which is to be expected as this is the only treatment where since all the motivations are at work. Average contribution in the RC (26%) is twice as much as of RCWC (13,35%) and is also higher than NEC, NEWI and NEWT contributions (19,35%, 18,36% and 17,42% respectively).

The significance of these differences has been tested with a Mann-Whitney rank sum U-test with a normal distribution29

which shows if differences in mean contribution across all five treatments are significant. The values of the test are reported in Table 5:

Table 5 – Two sample Wilcoxon rank sum (Mann-Whitney) test

treatments RC NEC NEWI NEWT RCWC

RC - - - z = 2.381 Prob > |z| = 0.0173 z = 2.097 Prob > |z| = 0.0360 z = 2.813 Prob > |z| = 0.0049 z = 4.031 Prob > |z| = 0.0001 NEC - - - - - - z = 0.014 Prob > |z| = 0.9892 z = 0.757 Prob > |z| = 0.4488 z = -2.989 Prob > |z| = 0.0028 NEWI - - - - - - - - - z = 0.703 Prob > |z| = 0.4818 z = -3.192 Prob > |z| = 0.0014 NEWT - - - - - - - - - - - - z = -2.556 Prob > |z| = 0.0106 RCWC - - - - - - - - - - - - - - - Key: RC = Regular Condition

NEC = Next Experiments Condition

NEWI = Next Experiments without information Condition NEWT = Next Experiments with threshold Condition RCWC = Regular Condition without confusion

Tests indicate that RC means are significantly higher than in other treatments. In other words whenever any of the candidate explanatory factors is removed cooperation becomes significantly lower.

The interpretation of these result is that kindness (pure and impure altruism), confusion and strategic cooperation are all important factors that affect subjects’ propensity to voluntarily participate in collective production of public goods.

Second, when confusion is removed, the rate of contribution decreases significantly more than when either strategic cooperation or altruism (pure and impure) are removed. Hence confusion appears to be the most important variable in influencing voluntary propensity to collective action.

Patterns of RC and RCWC contributions are fairly similar but, when subjects are more skilled about rules, payoff structure and possible scenarios of game, voluntary cooperation is strikingly lower not

29 The test organizes the data by subjects, it calculates the mean contribution for each player and rank the means for the joint sample. The

References

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