Doing good or doing harm Khadjavi & Lange - KYSQkysq.org/docs/Khadjavi_Lange.pdf · 1 Doing Good or Doing Harm – Experimental Evidence on Giving and Taking in Public Good Games

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    Doing Good or Doing Harm Experimental Evidence on Giving and

    Taking in Public Good Games

    Menusch Khadjavi and Andreas Lange*

    University of Hamburg

    August, 2011

    Abstract.

    This paper explores motives and institutional factors that impact the voluntary provision of

    public goods. We compare settings where players can only contribute with those where their

    actions may also reduce the public good provision. While the giving decision has received

    substantial interest in the literature, e.g. on charitable giving, many real world applications

    involve actions that may diminish public goods. Our results demonstrate that the option to

    take significantly changes contribution decisions. Through a series of treatments that vary in

    the action set and the initial stock of the public good, we show that fewer agents contribute to

    the public good when their action set allows for taking. As a result, the provision level of the

    public good is reduced. Extending the action set to the take domain thereby allows us to

    provide a new interpretation of giving in (linear) public good games: giving positive amounts

    in a standard public good game may just reflect a desire to avoid the most selfish option,

    rather than a warm glow from giving. As such, doing good may just mean not doing (too

    much) harm.

    Keywords: public good, voluntary provision, experiments

    JEL: H41, C91

    * Correspondence: Department of Economics, University of Hamburg, Von Melle Park 5,

    20146 Hamburg, Germany ([email protected],

    [email protected]).

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    1 Introduction

    A functioning society relies on a sufficient provision of public goods. These can be financed

    through taxes or through voluntary individual contributions. Both provision channels have

    received substantial attention in the literature. However, it is equally important that existing

    public goods are not exploited by individuals to their own personal advantage.

    Examples are widespread where individuals may both contribute to a public good or

    reduce its provision level. They range from environmental pollution where agents can emit or

    try to reduce overall pollution by investing in offsets (Kotchen 2009), legal vs. illegal

    employment, illegal claim of social services and tax evasion, to managers whose actions may

    enhance the performance of a firm or just exploit the work contribution of others.

    While doing good has been explored in numerous laboratory and field experiments on the

    economics of charities, the option of individuals doing harm to the public for their own

    private benefit has received much less attention in the literature.1 In this paper we investigate

    how individuals behave when their action space allows for giving and taking, i.e. contributing

    to a public good or exploiting it. Our study lends new insights into the motives of individuals

    in private-public interactions. To our knowledge, simultaneous giving and taking decisions

    have not been addressed in the literature.

    Andreoni (1995) examines the extreme settings where agents can either only take or only

    give. Comparing giving (positive frame) and taking (negative frame) decisions, he finds that

    public good provision levels are significantly lower in the negative frame compared to the

    positive frame. As a possible explanation, Andreoni suggests that agents may receive a

    relatively large warm glow from giving, while only getting a relatively small cold prickle

    from taking. These findings have been confirmed by Park (2000) for a linear public good

    1 For an overview of earlier studies of public good games, see Ledyard (1995). Social dilemma games and public

    good games often find that behavior of individuals differs from the standard game theoretic prediction. While the

    prediction in standard linear public good games is that participants give nothing to the public good, studies

    present robust evidence that group contributions to the public good are significantly greater than zero (often

    around 50 to 60 percent of total possible contributions) in the first period, and even though with a declining

    trend mostly remain significantly greater than zero in subsequent periods (see e.g. Isaac and Walker 1988,

    Isaac et al. 1994, Gchter et al. 2008).

    Positive giving decisions in public good games, dictator and ultimatum, and other games are in conflict with

    the Nash prediction of payoff-maximization and have led to a series of theoretical explanations based on other-

    regarding, social preferences. See Meier (2007) for a recent survey.

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    game and Sonnemans et al. (1998) for a comparison of step-level public goods versus step-

    level public bads. 2

    In this paper, we explore the more general and realistic setting where the action space

    allows agents to give and take. We perform a series of experimental treatments that differ

    with respect to the initial allocation given to the private and the public accounts. Besides the

    extreme cases of (i) a voluntary contribution mechanism (VCM) with no initial resources in

    the public account where agents can solely give and (ii) an inverse public good treatment

    where all resources are in the public account and subjects can only take,3 we consider two

    additional treatments that start with a positive allocation in both the private and the public

    account. In the first, the action space allows both giving and taking decisions. In the second,

    the action set is again limited to the giving domain. Our experimental design allows us to

    investigate how an extension of the action space changes the behavior in public good settings.

    The importance of the taking domain has been demonstrated by List (2007) and Bardsley

    (2008) who investigate the impact of the action set manipulation on subjects willingness to

    transfer money in dictator games. They find that providing dictators with the opportunity to

    take resources from recipients leads fewer dictators to give positive amounts, while mean

    offers decrease significantly and even turn to be negative. We use our combination of

    treatments to show new insights into the motives of giving to and taking from public goods.

    Our results demonstrate that the option to take significantly changes contribution

    decisions. Fewer subjects contribute to the public good when their action set allows for

    taking. The provision level is least in the inverse public good setting where subjects can only

    take from an initially existing public account. In line with Andreoni (1995), more subjects

    choose the most selfish action, i.e. transfer the maximal allowed amount to their private

    account when taking is possible.

    Importantly, the percentage of subjects who give positive amounts to the public good also

    declines when taking options are introduced for identical initial allocations to public and

    private accounts. That is, we find that not only those subjects who contribute zero when

    limited to the giving domain will exploit a chance to take from the public good when this is

    2 Cubitt et al. (2010) follow a similar approach in a one-shot setting with second-stage punishment option and

    ex-post elicitation of emotions, but largely find insignificant results. For a broader comparison of framing effects

    in public good experiments, see Cookson (2000). 3 This treatment is comparable to the take frame of Andreoni (1995) and Cubitt et al. (2010). Note that an inverse

    framing of the public good game resembles, but does not correspond to a typical common pool resource game.

    The inherent and the functional structures differ significantly. While some use of the common pool resource is

    socially optimal, every unit of depletion of the public good lets the outcome diverge from the social optimum.

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    possible. These results extend findings by List (2007) and Bardsley (2008) to the domain of

    public goods.

    Our findings suggest new insights into the motives of giving. They are not consistent with

    standard impure altruism models (Andreoni 1990) which assume warm glow utility to stem

    from the number of tokens a subject allocates to the public good. Rather, they suggest that

    doing good (and potentially some feeling of warm glow) depends on the chosen action

    relative to the set of available actions.4 In other words, if a subject could potentially diminish

    or destroy the public good, doing good may just mean not doing (too much) harm. Our

    results also relate to the literature on crowding out of voluntary contributions through a tax-

    financed provision of public goods (e.g., Andreoni 1993, Chan et al. 2002, Bergstrom et al.

    1986). We confirm earlier results that crowding out of private contributions is incomplete

    when agents are limited to positive contributions to the public good. However, for the case

    where agents can also diminish the provision level of the public good, our findings suggest

    that a tax-financed provision may backfire such that the final public good provision is

    reduced.

    The remainder of this paper is organized as follows. Section 2 presents the experimental

    design of the study. Results are presented and discussed in section 3. Section 4 provides a

    concluding discussion.

    2 Experimental Design

    Our experimental design consists of four treatments. They include the standard linear public

    good game in which participants receive their endowments in a private account and are able to

    contribute to the public good (VCM), i.e. to transfer part of their endowment from their

    private to public account. The other treatments differ in the initial allocation to the public

    good and in the action set that is available to agents. Players always interact in groups of four.

    The payoff to an agent i in the respective treatments is given by

    = + + where denotes the per capita return to the public good with 0 < < 1 < , represents the initial endowment of i in treatment t (and is the same for all n group members), denotes 4 A similar idea was introduced by Rabin (1993) when modeling a theory of reciprocity: the kindness of an

    action is defined relative to the range of payoffs that the player could allocate to other persons.

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    is transfer to the public good account, and is the initial allocation to the public good account. In the experiment, we chose = 4 and = 0.4.

    The first three treatments vary the initial allocation to agents and the public good as well

    as in the action space. Parameters in treatment t are given by: = 20 = [, 20 ] First, the baseline (VCM) treatment has ! = 0, i.e. no initial allocation to the public good exists, the agents can contribute any amount between 0 and 20 units to the public good

    account.

    Second, we consider an inverse of the standard game (INV) in which agents do not have

    any initial private endowment ("# = 20), but instead all budget is allocated to the public good ("# = 4 20 = 80) and players are able to take their share from the public good, i.e. transfer any amount between 0 and 20 units to their private account.

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    Third, we study an intermediate situation in which 40 percent of the budget is initially

    allocated to the public good while players receive 60 percent of the budget in their private

    account and may either contribute to or take from the public good. We denote this treatment

    by VGT Voluntary Give or Take. That is, &' = 8 such that the initial endowment of each player is 12, while the public good level initially is given by &' = 4 8 = 32. Players are able to take up to 8 units out of the public good (by choosing = 8) or to give up to 12 units to the public good ( = 12).

    Fourth, we limited the VGT treatment to the giving domain, so that we get a second

    VCM, called VCM*. Its parameters are calibrated so that ! = &' and ! = !. Like in the VGT treatment, 32 units are initially allocated to the public account, while 12 units are allocated to each of the four private accounts. The action set is however limited to [0, 20 &']. That is, players may now contribute between 0 and 12 units to the public account.

    The standard game theoretic prediction for all treatments is that agents will contribute no

    units of their endowments to the public good and if taking is possible transfer the maximal

    allowable amount to their private account. We therefore would predict ! = 0, "# =20, &' = 8, and ! = 0. In order to compare decisions across treatments, we discuss the persons effective contribution level + in the results section. 5 In our experiment, this treatment asked agents to decide on the transfer to their public account, i.e. their

    decision was on [0,20].

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    Contrary to the equilibrium prediction of zero contributions, many experiments have

    demonstrated positive contributions by (at least some) players in the first period as well as

    subsequent periods of a session (Ledyard 1995). Social preferences, bounded-rationality and

    strategic considerations might explain such departures from the standard game theoretic

    prediction (Meier 2007) or warm-glow (Andreoni 1990).

    With our experimental design, we contribute to the understanding of motives of giving.

    Our combination of treatments that vary both the action set and the initial endowment of the

    public good is novel in several respects. While VCM, VCM*, and INV either allow for taking

    or for giving, our VGT treatment provides players with both the option to give and to take.

    Thereby, we are able to directly test for impure altruism. Based on the warm glow theory of

    giving, the share of players deciding to give positive amounts from their endowment in the

    VGT treatment should not be significantly different from the share in VCM* as the initial

    income of players is identical. Comparing VGT and VCM* therefore directly allows to study

    the effect of extending the action set to the taking domain.

    The treatments can further provide useful information on crowding out behavior. Relative

    to VCM, VCM* resembles a situation where agents income is reduced in order to provide the

    public good. Agents are then able to further add to the public good. This comparison

    corresponds to the literature on crowding out of voluntary contributions through public

    provision of public goods (e.g. Bergstrom et al. 1986). Here, we are able to measure the

    magnitude of crowding out in our public good game where taxation is inconspicuous rather

    than explicit. This situation resembles what Eckel et al. (2005) analyze in their experiments

    on the crowding out hypothesis with respect to charitable giving and refer to as fiscal illusion.

    Note again that in case players take from the public good in the VGT, this is not motivated by

    explicit taxation. By comparing VCM and VGT, we can further study how this crowding

    effect changes when agents can not only add to a publicly provided public good, but also

    diminish it by selfish actions.

    All experimental sessions were conducted in the computer laboratory of the Faculty of

    Economic and Social Sciences, University of Hamburg, Germany in January and April 2011.

    Each session lasted approximately one hour. We used z-Tree (Fischbacher 2007) to program

    and ORSEE (Greiner 2004) for recruiting. In total, 160 subjects participated in the

    experiment. All were students with different academic backgrounds, including economics.

    Each of our 8 sessions consisted of 10 periods. Once the participants were seated and

    logged into the terminals, a set of instructions was handed out and read out loud by the

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    experimenter.6 In order to ensure that subjects understood the respective game, experimental

    instructions included several numerical examples and participants had to answer nontrivial

    control questions via their computer terminals.7 At the beginning of the experiment subjects

    were randomly assigned to groups of four. The subjects were not aware of whom they were

    grouped with, but they did know that they remained within the same group of players for all

    periods.

    At the end of each period, participants received information about their earnings, the

    cumulative group contribution to or extraction from of the group account and the final amount

    of units in the group account. Subjects were never able to identify individual behavior of

    group members. At the end of the experiment, one of the periods was randomly selected as

    the period that determined earnings with an exchange rate between Euro and token of 3 EUR

    = 10 tokens. Including a show-up fee of 4 EUR, the average payment over all treatments was

    11.70 EUR. Table 1 summarizes the information for all 8 sessions.

    3 Results

    We craft the results summary by both pooling the data across all periods and reporting

    treatment differences for the first period. We later explore the effects of time on contribution

    schedules in more detail.

    Since the action space and therefore the decisions differed across the treatments, Table 2

    reports the decisions along with the corresponding public good contribution level per player

    relative to the Nash equilibrium prediction in INV, VCM, VCM* and VGT. As stated in the

    section 2, this is given by + . At the group level, this normalized contribution coincides with the provision level of the public good.

    Table 2 provides summary statistics for decisions in all treatments and the corresponding

    contribution levels in VCM, VCM*, VGT, and INV. Across all periods, in VCM, each player

    contributed 7.71 tokens on average, resulting in a public good provision level of

    4*7.71=30.84 tokens. In VCM* the average contribution was 12.84 tokens.8 In VGT, the

    average contribution was 7.23, while it is substantially smaller in INV with 4.44. For the first

    6 We mainly followed the instructions of Fehr and Gchter (2000), but slightly changed the wording. For

    instance, instead of contributions to a project, instructions asked participants to divide tokens between a private

    and a group account. Instructions can be found in Appendix B. 7 In case a participant did not answer the questions correctly, she was given a help screen that explained the

    correct sample answers in detail. We believe this might further reduce experimenter demand effects compared to

    individual talks with subjects. See Zizzo (2010) for more information on experimenter demand effects. 8 Note that the minimum contribution in the VCM* was 8 tokens, because the public account contained 8 tokens

    per person already and taking was no option.

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    period, the mean contributions are 11.75 tokens for the VCM, 14.25 tokens for the VCM*,

    8.61 tokens for the INV, and 11.53 tokens for the VGT treatment. Figure 1 depicts

    contribution levels by period.

    The differences between the treatments are confirmed by a series of tests. Based on

    Mann-Whitney tests with the average contribution by one group across all periods as the unit

    of observation, INV results in smaller contributions than VGT (5% level) and VCM* (1%

    level). While Mann-Whitney does not show a significant difference between INV and VCM,

    this difference is confirmed by a Welchs t-test (10% level).9 Contributions are greater in

    VCM* compared to all other treatments. Of course, this is also due to the minimum

    contribution of 8 tokens. There is no significant difference in contributions between VCM and

    VGT. Table 3 summarizes these test results.

    Using the individual contributions in the first period as the unit of observation, Mann

    Whitney tests again confirm the difference between INV and VGT, VCM (both differences

    significant at 10%), and VCM* (1% level).

    We can therefore formulate the following results:

    Result 1. Average provision of the public good in the inverse public good game

    (INV) is less than in VCM, VCM* and VGT.

    Result 2. Average provision of the public good in the VCM* is greater than in

    VCM, INV and VGT.

    Result 3. There is no significant difference in the provision of the public good

    between VCM and VGT.

    Further evidence for these results can be found through a series of linear regression models as

    illustrated in Table 4. The regressions predict the contribution to the public good (in tokens)

    as a result of the different treatments. We test the INV against the VCM, VCM* and VGT.

    Averaged across all periods, the INV treatment leads to less contributions than VCM (3.3

    tokens, statistically significant at the 10% level), VCM* (8.4 tokens 1% level), and VGT (2.8

    9 The reason for using the Welch t-test is as follows: In order to be allowed to use a Mann-Whitney test, the

    variances of the samples need to be equal (see, e.g., Zimmerman 1992, Fay and Proschan 2010 and Ruxton

    2006). A Levenes test leads to no significant difference in variances for the VCM and VGT such that a Mann-

    Whitney test is valid. In contrast, a Levenes test shows that the variances of the samples of VCM and INV are

    not equal (p = 0.014) such that a Welchs t test for unequal variances can be used (a Shapiro-Wilk W test cannot

    reject the hypothesis of normality for both distributions).

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    tokens, 5% level). Table 4 also separates the effects between the first five and last five

    periods. It should be noted that our regressions indicate a declining trend in contributions over

    the periods that also reduces the difference between the treatments.

    Result 1 suggests an interesting effect of allowing agents to take from the public good.

    Contrary to ideas of status quo bias, a larger initial endowment of the public good account

    does not lead to an increased provision. Rather, reversing the public good to a taking game in

    INV diminishes the contribution levels. We thereby confirm findings by Andreoni (1995) and

    Park (2000). Importantly, this effect is large enough for INV to be inferior to VGT where

    agents can give and take.

    However, the effect appears to be primarily driven by the fact that INV does not allow for

    giving. Comparing VCM and VGT, we do not find any significant differences. In fact, Figure

    1 shows almost identical contribution rates. While this suggests that without changing the

    range of possible contributions introducing a taking domain has no significant on average

    contributions impact as long as giving remains possible, we will later see important

    differences between the underlying distributions. When taking options are introduced that

    extend the range of possible contribution levels (VGT vs. VCM*), contributions not

    surprisingly go down. This is particularly driven by the fact that the most selfish option in

    VCM* still corresponds to a positive contribution level.

    Our results also shed some new light on the extent to which private contributions to a

    public good are crowded out by government contributions that are financed through taxes

    (e.g., Andreoni 1993, Chan et al. 2002, Bergstrom et al. 1986). The payoff structure of VCM*

    relative to VCM can be reinterpreted as having private income of agents reduced (taxed)

    while simultaneously providing a public good at the corresponding level (tax-based financing

    of public good). This public finance literature suggests that crowding out is incomplete, i.e.

    tax-financed provision may increase the total provision level of a public good. We find

    evidence for this incomplete crowding out even in our linear public good setting by

    comparing contributions in VCM vs. VCM*. However, Result 3 indicates that this finding is

    driven by the assumption of non-negative contributions. When extending the action set to the

    taking domain, i.e. when agents have options to diminish the public good to their own private

    advantage, we find complete crowding out (VGT vs. VCM). Note that we find this result even

    though the reduced allocation to the private account in VGT and VCM* was not framed as ex

    ante taxation. The setting thereby resembles fiscal illusion (Eckel et al. 2005). It might not be

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    unreasonable to speculate that crowding out will even be larger when taking options exist in

    settings without fiscal illusion.

    After this discussion of average contribution levels, we now have a closer look at the

    individual decisions and how they are affected by the option to take. In Table 5, we report the

    percentage of the respective most selfish action, i.e. where players maximize the units in their

    private account (MaxSelf), that is, give 0 in VCM and VCM*, take 8 in VGT or take 20 in

    INV. We further report the percentage of actions that correspond to contributing the

    maximum to the public good (MaxPubl), i.e. give 20 in VCM, give 12 in VCM* and VGT, or

    take 0 in INV. Finally, we give the percentage of choices in VCM, VCM* and VGT that

    transfer a positive amount to the public good.

    Across all periods, 16.6% of the decisions in VCM involve a full allocation to the public

    good. This statistic is 18.3% in VCM*. In VGT 11.3% of all individuals decide to contribute

    all resources to the public good, while only 4.8% of choices are not withdrawing any tokens

    from the public good in INV. Corresponding, while 52.7% of actions involve the maximal

    transfer to the private account (MaxSelf) in INV, 45.3% in VGT, and 33.9% in VCM, only

    25.3% of decisions involve zero giving in VCM*. In all treatments, there is a declining trend

    in cooperation and an increase in fully selfish behavior.

    In order to identify how taking options change the behavior of agents due to changed

    intentions rather than due to reactions to behavioral changes of others, we now concentrate on

    period 1 decisions. We use a series of Fishers exact tests to compare the distributions for the

    different treatments. In INV, 36.4% of players take out the maximum amount. Less players

    choose the corresponding selfish action in VGT (20.0%, difference significant at the 10%

    level), VCM* (12.5%, at 5% level) and in VCM (11.1%, at 1% level). We can therefore

    conclude that the action space in a public good setting matters for the display of selfish

    behavior that maximizes the players own payoff while minimizing the contributions to the

    public good.

    Result 4. More players choose the most selfish action in INV than in VCM,

    VCM* and VGT.

    Result 4 indicates that extending the action set to the taking domain can lead to a substantial

    change in behavior. Interestingly, the standard public good setting appears to reduce the

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    number of agents who act fully selfishly compared to settings where agents take from an

    initially existing public good account.

    We now have a closer look at giving in VCM, VCM* and VGT. In all three treatments,

    agents could add to the public good account. Considering first period decisions only, 89% of

    players give a positive amount in VCM and 87.5% in VCM*, while only 65% give in VGT.

    The differences between VCM and VGT as well as VCM* and VGT are both significant at

    the 5% level. They remain stable over the periods.

    Result 5. Fewer players give a positive amount if the action space allows for

    giving and taking (VCM vs. VGT and VCM* vs. VGT).

    A random effects probit regression for the probability of a positive giving in Table 6 confirms

    this finding. The estimated coefficients for VCM and VCM* are positive and statistically

    significant.

    Taking jointly, Results 4 and 5 allow us to gain further insights into the motives of giving.

    Giving in public good games is often interpreted as a sense for efficiency, conditional

    cooperation or agents gaining a warm glow from giving (Andreoni 1990). Our results show

    that introducing the option to take from the public good does not merely induce a smaller total

    provision level because some agents will transfer tokens to their private accounts. Rather, the

    percentage of players who contribute positive amounts to the public good declines. 10

    Our findings are therefore neither consistent with some status quo bias, nor with a strict

    version of warm glow. If an agents warm glow utility from contributing was driven by the

    number of tokens she allocates to the public good, we would not expect to see the differences

    between VGT and VCM* and VCM. Our results are, however, consistent with a modified

    version in which an agents utility depends on the chosen action relative to the available set of

    actions. In order to capture this idea, we posit a kindness measure for voluntary actions.

    Inspired by Rabin (1993) who applied this idea in his theory of intention-based social

    preferences, we define

    Kindness of contribution = )*+,-./01+234,+301563137-.809934.:*01+234,+3016-;37-.809934.:*01+234,+301563137-.809934.:*01+234,+301 10

    Note that this comparison could not have been made based on VCM and INV and therefore goes beyond the

    existing literature.

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    In terms of our notation, this measure is defined by ( min)/(max min). The distribution of this kindness measure for first period actions (i.e. when decisions are not yet

    blended with information about previous periods and group member behavior) are depicted in

    Figure 2. While we demonstrated differences between INV and other treatments for the most

    selfish and the most publicly oriented action, pairwise (nonparametric) two-sample exact

    Kolmogorov-Smirnov tests of the distributions of the kindness measures cannot not reject the

    null hypothesis of equality of the respective distributions.

    The idea that actions has to be seen relative to the available action set is also consistent

    with List (2007) and Bardsley (2008) who observe that giving in dictator games is not the

    same as doing good. Correspondingly, taking (from a public good or directly from other

    subjects) may not be readily interpreted as doing harm.11

    Rather, when subjects compare

    their actions to all feasible actions in situations that allow for taking, doing good may simply

    mean not doing (too much) harm.

    4 Conclusion

    The last decades have seen an enormous interest of economists in providing insights why

    people give to public goods. A diverse and insightful public good game literature has emerged

    that studies voluntary contributions to public goods. By mainly focusing on the giving

    decision, the public good game literature has largely ignored a simple and obvious twist to

    how individual actions may affect the provision of public goods: agents may not only engage

    in giving, but may also choose actions that diminish the public good. Environmental amenities

    serve as a prominent, yet not exclusive example.

    In this paper we report findings from experiments that allow a direct comparison of the

    impact of allowing takings to the provision of public goods. We study modifications of a

    standard linear public good game that vary the initial provision level of the public good and

    the degree to which agents may contribute to or degrade the public good.

    We provide a number of interesting and important insights. First, if the action set only

    allows for taking from an initially provided public good, the resulting provision level of the

    public good is smaller than in any situation where agents can (also) contribute positive

    amounts. Additionally, the share of agents who engage in the most selfish action is larger.

    Secondly, fewer agents give positive amounts to the public good if they also hold the

    11

    This is at least true if there are no implicitly or explicitly defined formal or informal norms and rules.

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    opportunity to take from it. Extending the action space to taking from the public good

    therefore has an important impact on its provision level.

    Our findings add insights to the motives of agents to give. Our findings suggest that

    explanations of giving based on warm glow theories may have to take the action space into

    account as well: doing good (and potentially generating a feeling of warm glow) depends on

    the chosen action relative to the set of available actions. When agents can diminish or destroy

    the public good, doing good may just mean not doing too much harm.

    Naturally, this paper can only provide initial insights into how and why individuals

    contribute to or diminish the provision of public goods. It provides an interesting avenue for

    future research. For a better understanding how to overcome social dilemmas, it is necessary

    to both explore which institutions induce agents to provide public goods and which ones

    discipline agents to refrain from exploiting them.

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  • 16

    Appendix A Figures and Tables

    Figure 1. Average contributions in INV, VCM, VCM* and VGT for all groups over all

    periods.

    Figure 2. Contributions relative to maxima in INV, VCM, VCM* and VGT, period 1 only.

    0

    2

    4

    6

    8

    10

    12

    14

    16

    18

    20

    1 2 3 4 5 6 7 8 9 10

    Co

    ntr

    ibu

    tio

    n

    Period

    INV VGT VCM VCM*

    010

    20

    30

    40

    010

    20

    30

    40

    0 .5 1 0 .5 1

    INV VCM

    VCM* VGT

    Perc

    ent

    contribution_ratioGraphs by treatment

  • 17

    Table 1. Summary of experimental sessions.

    Session Number of groups Number of participants Treatment

    1 5 20 INV

    2 6 24 VGT

    3 5 20 VCM

    4 6 24 INV

    5 4 16 VGT

    6 4 16 VCM

    7 5 20 VCM*

    8 5 20 VCM*

    Note: Numbers of groups across treatments are not perfectly equal due to some registered subjects not showing

    up.

    Table 2. Summary statistics of VCM, VCM*, INV and VGT.

    Statistic

    First period All 10 periods (means)

    VCM VCM* INV VGT VCM VCM* INV VGT

    Mean

    decision

    11.75

    (7.52)

    6.25

    (4.18)

    -11.39

    (7.80)

    3.53

    (7.55)

    7.71

    (7.69)

    4.84

    (4.42)

    -15.56

    (6.19)

    -0.77

    (7.87)

    Mean

    contribution

    11.75

    (7.52)

    14.25

    (4.18)

    8.61

    (7.80)

    11.53

    (7.55)

    7.71

    (7.69)

    12.84

    (4.42)

    4.44

    (6.19)

    7.23

    (7.87)

    % of positive

    contribution

    88.89

    100.00

    63.64

    80.00

    66.11

    100.00

    47.27

    54.75

    Mean

    contribution

    conditional

    on > 0

    13.22

    (6.62)

    14.25

    (4.18)

    13.54

    (5.28)

    14.41

    (5.36)

    11.66

    (6.59)

    12.84

    (4.42)

    9.39

    (5.89)

    13.21

    (5.83)

    Note: Standard deviations for individual level data in parentheses.

  • 18

    Table 3. Results of test statistics for comparison of group contributions, all 10 periods.

    (row vs. column comparison) Treatment

    VCM VCM* INV

    Treatment

    VCM* >

    (p = 0.0412)

    INV <

    (p = 0.0557)

    <

    (p = 0.0001)

    VGT = <

    (p = 0.0012)

    >

    (p = 0.0411)

    Note: All test statistics are (nonparametric) Mann Whitney tests except for INV vs. VCM. The table is to be read

    row vs. column. For instance, group contributions are significantly greater in the VCM* compared to the VCM.

    The comparison of INV vs. VCM is done using a Welch t test because of unequal variances.

  • 19

    Table 4. Linear Regression of Contribution Levels of Treatments VCM, VCM*, INV and

    VGT.

    Dependent Variable: Contribution Level

    Independent

    Variables

    (I)

    All 10

    periods

    (II)

    All 10

    periods

    (III)

    All 10

    periods

    (IV)

    Periods 1 to 5

    (V)

    Periods 6 to 10

    VCM 3.270*

    (1.813)

    3.270*

    (1.814)

    3.745**

    (1.842)

    3.745**

    (1.841)

    2.795

    (1.894)

    VCM* 8.394***

    (1.071)

    8.394***

    (1.072)

    7.473***

    (1.201)

    7.473***

    (1.200)

    9.315***

    (1.087)

    VGT 2.789**

    (1.236)

    2.789**

    (1.236)

    3.193**

    (1.513)

    3.193**

    (1.513)

    2.385**

    (1.145)

    Period 6-10 -3.528***

    (0.394)

    -3.573***

    (0.483)

    Period 6-10_VCM -0.949

    (0.884)

    Period 6-10_VCM* 1.843**

    (0.803)

    Period 6-10_VGT -0.807

    (1.039)

    Constant 4.441***

    (0.695)

    6.205***

    (0.745)

    6.227***

    (0.819)

    6.227***

    (0.819)

    2.655***

    (0.637)

    Observations 1600 1600 1600 800 800

    Individuals 160 160 160 160 160

    Groups 40 40 40 40 40

    Note: Random effects estimation clustered at group level; INV is the baseline. Standard errors in parentheses,

    significance: *p < 0.10, **p < 0.05, ***p < 0.01.

  • 20

    Table 5. Percentage of decisions with (i) highest possible contribution (MaxPubl), (ii) the

    least possible contribution (MaxSelf), (iii) transferring a positive amount to the public good in

    VCM, VCM* and VGT, and percentage of groups with zero provision of the public good.

    Statistic

    First period All 10 periods (means)

    VCM VCM* INV VGT VCM VCM* INV VGT

    % of decisions

    MaxPubl 33.33 25.00 20.45 25.00 16.66 18.25 4.77 11.25

    % of decisions

    MaxSelf 11.11 12.50 36.36 20.00 33.89 25.25 52.73 45.25

    % of decisions with

    positive giving 88.89 87.50 - 65.00 66.11 74.75 - 41.50

    % of groups with

    group account = 0 0.00 0.00 0.00 0.00 10.00 0.00 14.55 15.00

    Table 6. Probit Regression of Giving a Positive Amount for Treatments VCM, VCM* and

    VGT.

    Dependent Variable:

    Binary variable on whether a positive amount was given (yes = 1)

    Independent Variables

    (VI)

    All 10 periods

    (VII)

    All 10 periods

    VCM 1.005***

    (0.305)

    1.141***

    (0.349)

    VCM* 1.353***

    (0.301)

    1.537***

    (0.345)

    Period 6-10 -1.043***

    (0.106)

    Constant -0.319

    (0.204)

    0.162

    (0.238)

    Observations 1160 1160

    Individuals 116 116

    Groups 29 29

    Note: Random effects estimation; VGT is the baseline. Standard errors in parentheses, significance: *p < 0.10,

    **p < 0.05, ***p < 0.01.

  • 21

    Appendix B Experimental Instructions

    General Instructions for Participants

    Welcome to the Experiment Laboratory!

    You are now taking part in an economic experiment. You will be able to earn a considerable

    amount of money, depending on your decisions and the decisions of others. It is therefore

    important that you read these instructions carefully.

    The instructions which we have distributed to you are solely for your private information. It is

    prohibited to communicate with other participants during the experiment. Should you

    have any questions please raise your hand and an experimenter will come to answer them. If

    you violate this rule, we will have to exclude you from the experiment and from all payments.

    During the experiment you will make decisions anonymously. Only the experimenter knows

    your identity while your personal information is confidential and your decisions will not be

    traceable to your identity.

    In any case you will earn 4 Euros for participation in this experiment. The additional earnings

    depend on your decisions. During the experiment your earnings will be calculated in tokens.

    At the end of the experiment your earned tokens will be converted into Euros at the following

    exchange rate:

    1 Token = 0,30 ,

    and they will be paid to you in cash.

    The experiment consists of 10 periods in which you always play the same game. The

    participants are divided into groups of 4. Hence, you will interact with 3 other participants.

    The composition of the groups will remain the same for all 10 periods. Please mind that you

    and all other participants decide anonymously. Therefore group members will not be

    identifiable over the periods.

    At the end of the experiments you will receive your earning from one out of the ten periods

    converted in Euros (according to the exchange rate above) in addition to the 4 Euros for your

    participation. The payout period will be determined randomly. You should therefore take the

    decision in each period seriously, as it may be determined as the payout period.

    The following pages describe the course of the experiment in detail.

  • 22

    Rules of the Game

    Each player faces the same assignment. Your task (as well as the task of all others) is to

    allocate tokens between your private account and a group account.

    At the beginning of each period each participant receives 20 tokens in a private account. You

    have to decide how many of these 20 tokens you transfer to a group account, and how many

    you keep in your private account. Your transfer can be between 0 and 20 tokens (only whole

    numbers).

    [INV: At the beginning of each period there are 80 tokens in the group account and no

    tokens in your private account. You have to decide how many of the 80 tokens you leave in

    the group account and how many tokens you transfer to your private account. Your transfer

    can be between 0 and 20 tokens (only whole numbers).]

    [VGT: At the beginning of each period each participant receives 12 tokens in a private

    account. There are 32 tokens in a group account. You have to decide how many of these 32

    tokens you leave in the group account and how many of the 12 tokens you transfer from your

    private account to the group account respectively. Your transfer input is related to the group

    account, so that a negative input means a transfer from the group account to your private

    account and positive inputs mean transfers from your private account to the group account.

    Your transfer can be between -8 and 12 tokens (only whole numbers).]

    [VCM*: At the beginning of each period each participant receives 12 tokens in a private

    account. There are 32 tokens in a group account. You have to decide how many tokens you

    transfer to the group account. Your transfer can be between 0 and 12 tokens (only whole

    numbers).]

    Your total income consists of two parts:

    (1) the tokens which you have kept in your private account,

    (2) the income from the group account. This income is calculated as follows:

    [INV: (1) the tokens which you have transferred to your private account]

    Your income from the group account =

    0,4 times the total amount of tokens in the group account

    Your income in tokens in a period hence amounts to

    (20 - your transfer) + 0.4 *(total amount of tokens in the group account).

  • 23

    [INV: (transfer to the private account) + 0.4*(total amount of tokens in the group account)]

    [VGT, VCM*: (12 your transfer) + 0.4*(total amount of tokens in the group account)]

    The income of each group member from the group account is calculated in the same way, this

    means that each group member receives the same income from the group account. Suppose

    the sum of transfers to the group account of all group members is 60 tokens. In this case each

    member of the group receives an income from the group account of 0.4*60 = 24 tokens. If

    you and your group members transfer a total amount of 9 tokens to the group account, then

    you and all other group members receive an income of 0.4*9 = 3.6 tokens from the group

    account. Every token that you keep in your private account yields 1 token of income to you.

    [INV, VGT, VCM*: similar or same examples.]

  • 24

    Information on the Course of the Experiment

    At the beginning of each period the following input screen is displayed:

    The Input Screen:

    The period number is displayed on the top left. The top right shows the time in seconds.

    This is how much time is left to make a decision.

    At the beginning of each period your endowment contains 20 tokens (as described above).

    You decide about your transfer to the group account by typing a whole number between 0 and

    20 into the input window. You can click on it by using the mouse.

    [INV: At the beginning of each period the group account contains 80 tokens. You decide

    about your transfer to your private account by typing a whole number between 0 and 20 into

    the input window. You can click on it by using the mouse.]

    [VGT: At the beginning of each period the group account contains 32 tokens. You decide

    about your transfer to your private account or your transfer to the group account by typing a

    whole number between -8 and 12 into the input window. You can click on it by using the

    mouse.]

  • 25

    [VCM*: At the beginning of each period the group account contains 32 tokens. You decide

    about your transfer to the group account by typing a whole number between 0 and 12 into the

    input window. You can click on it by using the mouse.]

    When you have decided about your transfer to the group account, you have also chosen how

    many tokens you keep to yourself, that is (20 - your transfer) tokens [differs by treatment].

    When you have typed in your decision, you need to press the Enter Button (by use of the

    mouse). By pressing the Enter Button your decision for the period is final and you cannot go

    back.

    After all group members have made their decisions, your income from the period will be

    displayed on the following income screen. You will see the sum of transfers to the group

    account and your income from your private account. You will also see your total income in

    that period.

    The Income Screen:

    As described above, your income is

    (20 your transfer) + 0,4*(total amount of tokens in the group account).

    [INV: (transfer to the private account) + 0.4*(total amount of tokens in the group account)]

    [VGT, VCM*: (12 your transfer) + 0.4*(total amount of tokens in the group account)]

    Good luck in the experiment!