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Wealth and Vulnerability to Climate Change: An Experimental Study on Burden Sharing among Heterogeneous Agents

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Abstract

Agents in global climate negotiations differ with respect to their vulnerability to the negative consequences of climate change, but also their ability to contribute to its prevention. Due to this multidimensional heterogeneity, agents disagree about how the costs of emission reduction ought to be shared and, as a consequence, efficiency is low. This experiment varies the two dimensions separately in a controlled setting. The results show that in groups that succeed in reaching a predefined threshold, the rich and the more vulnerable, ceteris paribus, tend to carry a larger share of the burden. Surprisingly, groups are most likely to master the challenge when poverty coincides with high vulnerability. In this case the rich and less vulnerable abstain from interpreting fair burden sharing in a self-serving manner. Instead, they seem to acknowledge the double-disadvantaged position of the poor and more vulnerable and voluntarily carry a larger share of the burden.

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Notes

  1. The expression catastrophic climate change refers to the threat of “abrupt, unpredictable, and potentially irreversible changes that have massively disruptive and large-scale impacts” that are triggered if global temperatures pass a certain threshold (Molina et al. 2014, p 6).

  2. Note that Milinski et al.’s way to model climate change deviates from the conventional approach chosen by environmental economists to model climate change in the context of (self-enforcing) international environmental agreements, where parties first choose to be part of an agreement or not and then choose their actions as signatories or non-signatories, respectively (e.g. Barrett (1994), Carraro and Siniscalco (1998) or Finus (2008)). Consequently, Milinski et al. (2008) have been criticized for providing subjects with no possibility to negotiate a treaty or to communicate as well as for assuming a certain threshold in their climate change game (Barrett 2013). The present study responds to this critique by providing subjects with a possibility to communicate (pledge mechanism) and by commenting on the assumption of a certain threshold in Sects. 4.1 (Equilibria and Best Responses) and 6 (Discussion and Conclusion).

  3. In the reverse case of rich/more vulnerable and poor/less vulnerable normative conflict is less likely since both criteria, wealth and vulnerability, suggest higher contributions of the first.

  4. Efficiency in this experiment is defined as the amount lost or wasted due to missing the threshold (see Sect. 5.6). By modeling vulnerability as a fixed number of tokens to be lost, the potential loss for each subject remained constant across rounds irrespective of her contributions to the public account in previous rounds. In contrast, modeling vulnerability in relative terms (as it is done in previous studies, e.g. by Milinski et al. (2008), Tavoni et al. (2011) or Burton-Chellew et al. (2013)) would have implied that the potential loss for each subject would have decreased with previous contributions since contributions would have reduced the number of tokens left in her private account (i.e. the number of tokens that would have been lost with a predefined probability). That is, the motivation of subjects with a preference for efficiency to contribute to the public account might have decreased conditional on their previous contributions. By modeling vulnerability in absolute terms and hence decoupling the amount to be lost from previous contributions, I tried to reduce the effect of such efficiency concerns to a minimum. A similar way to model vulnerability has been chosen by Barrett and Dannenberg (2014a, b).

  5. The pledge mechanism reflects the mechanism agreed on in the Lima Call for Climate Action in 2014 (UNFCCC 2014), which asks its signatories to submit national emission-reduction pledges (INDC: Intended Nationally Determined Contributions) that should be sufficient to keep global warming below 1.5 °C.

  6. For studies on the impact of different levels of refund and rebate on success rates see the meta-study by Croson and Marks (2000) or the experimental papers by Rauchdobler et al. (2010) and Marks and Croson (1999).

  7. Beneficiaries are defined as agents who benefit from contributions to the public account, i.e. emission reduction efforts and the prevention of climate change. Note that the literature sometimes considers those to be beneficiaries who benefit(ed) from emissions, e.g. through economic development (Hayward 2012).

  8. The allocation decisions of most subjects classified as other did not show a clear pattern. Their decisions could be explained by tremble, difficulties of understanding the task or irrationality.

  9. The results presented in this section are robust to decreasing the range to 5 tokens. This reduces the number of equal sharing groups slightly in BASE. Increasing the range to 15 tokens, on the other hand, increases the number of groups that have to be classified as other across all treatments.

References

  • Bagnoli M, McKee M (1991) Voluntary contribution games: efficient private provision of public goods. Econ Inq 29:351–366

    Article  Google Scholar 

  • Barrett S (1994) Self-enforcing international environmental agreements. Oxf Econ Pap 46:878–894

    Article  Google Scholar 

  • Barrett S (2013) Climate treaties and approaching catastrophes. J Environ Econ Manage 66:235–250

    Article  Google Scholar 

  • Barrett S, Dannenberg A (2012) Climate negotiations under scientific uncertainty. Proc Natl Acad Sci 109:17372–17376

    Article  Google Scholar 

  • Barrett S, Dannenberg A (2014a) Sensitivity of collective action to uncertainty about climate tipping points. Nat Clim Chang 4:36–39

    Article  Google Scholar 

  • Barrett S, Dannenberg A (2014b) Negotiating to avoid “Gradual” versus “Dangerous” Climate change: an experimental test of two prisoners’ dilemmas. In: Cherry TL, Hovi J, McEvoy D (eds) Towards a new climate agreement: conflict, resolution, and governance. Routledge, London, pp 61–75

    Google Scholar 

  • Benedick RE (1998) Ozone diplomacy. Harvard University Press, Cambridge

    Book  Google Scholar 

  • Bernard M, Reuben E, Riedl A (2014) Fairness and coordination: the role of fairness principles in coordination failure and success

  • Bicchieri C (2005) The grammar of society: the nature and dynamics of social norms. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Bochet O, Page T, Putterman L (2006) Communication and punishment in voluntary contribution experiments. J Econ Behav Organ 60:11–26

    Article  Google Scholar 

  • Brick K, Visser M (2015) What is fair? An experimental guide to climate negotiations. Eur Econ Rev 74:79–95

    Article  Google Scholar 

  • Brown TC, Kroll S (2017) Avoiding an uncertain catastrophe: climate change mitigation under risk and wealth heterogeneity. Clim Change 141:155–166

    Article  Google Scholar 

  • Burton-Chellew MN, May RM, West SA (2013) Combined inequality in wealth and risk leads to disaster in the climate change game. Clim Change 120:815–830

    Article  Google Scholar 

  • Carlsson F, Kataria M, Krupnick A et al (2013) A fair share: burden-sharing preferences in the United States and China. Resour Energy Econ 35:1–17

    Article  Google Scholar 

  • Carraro C, Siniscalco D (1998) International Institutions and Environmental Policy: International Environmental Agreements: Incentives and Political Economy. Eur Econ Rev 42:561–572

    Article  Google Scholar 

  • Croson R, Marks MB (2000) Step returns in threshold public goods: a meta- and experimental analysis. Exp Econ 2:239–259

    Article  Google Scholar 

  • Dannenberg A, Löschel A, Paolacci G et al (2015) On the provision of public goods with probabilistic and ambiguous thresholds. Environ Resour Econ 61:365–383

    Article  Google Scholar 

  • DARA (2012) Climate Vulnerability Monitor 2nd Edition: A Guide to the Cold Analysis of a Hot Planet. DARA and the Climate Vulnerable Forum, Madrid

  • Dohmen T, Falk A, Huffman D, Sunde U (2009) Homo reciprocans: survey evidence on behavioural outcomes. Econ J 119:592–612

    Article  Google Scholar 

  • Falk A, Becker A, Dohmen T et al (2016) The preference survey module: a validated instrument for measuring risk, time, and social preferences. IZA Discuss Pap No. 9674:

  • Finus M (2008) Game theoretic research on the design of International Environmental Agreements: insights, critical remarks, and future challenges. Int Rev Environ Resour Econ 2:29–67

    Article  Google Scholar 

  • Fischbacher U (2007) z-Tree: Zurich Toolbox for ready-made economic experiments. Exp Econ 10:171–178

    Article  Google Scholar 

  • Fischbacher U, Gächter S, Fehr E (2001) Are people conditionally cooperative? Evidence from a public goods experiment. Econ Lett 71:397–404

    Article  Google Scholar 

  • Freytag A, Güth W, Koppel H, Wangler L (2014) Is regulation by milestones efficiency enhancing? An experimental study of environmental protection. Eur J Polit Econ 33:71–84

    Article  Google Scholar 

  • Fuessel HM (2010) How inequitable is the global distribution of responsibility, capability, and vulnerability to climate change: a comprehensive indicator-based assessment. Glob Environ Chang 20:597–611

    Article  Google Scholar 

  • Gächter S, Renner E (2010) The effects of (Incentivized) belief elicitation in public goods experiments. Exp Econ 13:364–377

    Article  Google Scholar 

  • Gächter S (2006) Conditional cooperation: behavioral regularities from the lab and the field and their policy implications. CeDEx Discuss Pap 2006-03:

  • Gampfer R (2014) Do individuals care about fairness in burden sharing for climate change mitigation? Evidence from a lab experiment. Clim Change 124:65–77

    Article  Google Scholar 

  • Greiner B (2015) Subject pool recruitment procedures: organizing experiments with ORSEE. J Econ Sci Assoc 1:114–125

    Article  Google Scholar 

  • Hayward T (2012) Climate change and ethics. Nat Clim Chang 2:843–848

    Article  Google Scholar 

  • Henrich J, Heine SJ, Norenzayan A (2010) The weirdest people in the world? Behav Brain Sci 33:61–135

    Article  Google Scholar 

  • IPCC (2014) Climate change 2014: Synthesis Report. Geneva

  • Konow J (2000) Fair shares: accountability and cognitive dissonance in allocation decisions. Am Econ Rev 90:1072–1091

  • Konow J (2003) Which is the fairest one of all? A positive analysis of justice theories. J Econ Lit 41:1188–1239

    Article  Google Scholar 

  • Konow J (2010) Mixed feelings: theories of and evidence on giving. J Public Econ 94:279–297

    Article  Google Scholar 

  • Lange A, Löschel A, Vogt C, Ziegler A (2010) On the self-interested use of equity in international climate negotiations. Eur Econ Rev 54:359–375

    Article  Google Scholar 

  • Marks MB, Croson RTA (1999) The effect of incomplete information in a threshold public goods experiment. Public Choice 99:103–118

    Article  Google Scholar 

  • Milinski M, Sommerfeld RD, Krambeck H-J et al (2008) The collective-risk social dilemma and the prevention of simulated dangerous climate change. Proc Natl Acad Sci 105:2291–2294

    Article  Google Scholar 

  • Molina M, McCarthy J, Wall D, et al (2014) What we know: the reality, risks, and response to climate change. Am Assoc Adv Sci

  • Nikiforakis N, Noussair CN, Wilkening T (2012) Normative conflict and feuds: the limits of self-enforcement. J Public Econ 96:797–807

    Article  Google Scholar 

  • Nordhaus WD (2018) Climate change: the ultimate challenge for economics. Nobel Prize Committee

  • Rapoport A, Suleiman R (1993) Incremental contribution in step-level public goods games with asymmetric players. Organ Behav Hum Decis Process 55:171–194

    Article  Google Scholar 

  • Rauchdobler J, Sausgruber R, Tyran J-R (2010) Voting on thresholds for public goods: experimental evidence. Finanz/public Financ Anal 66:34–64

    Article  Google Scholar 

  • Reuben E, Riedl A (2013) Enforcement on contribution norms in public good games with heterogeneous populations. Games Econ Behav 77:122–137

    Article  Google Scholar 

  • Ringius L, Torvanger A, Underdal A (2002) Burden sharing and fairness principles in international climate policy. Int Environ Agreements 2:1–22

    Article  Google Scholar 

  • Schelling TC (1960) The strategy of conflict. Harvard University Press, Cambridge

    Google Scholar 

  • Stern N (2007) The economics of climate change: the stern review. Cambridge University Press, Cambridge, UK

    Book  Google Scholar 

  • Stiglitz JE (2015) Overcoming the Copenhagen failure with flexible commitments. Econ Energy Environ Policy 4:29–36

    Article  Google Scholar 

  • Tavoni A, Dannenberg A, Kallis G, Löschel A (2011) Inequality, communication, and the avoidance of disastrous climate change in a public goods game. Proc Natl Acad Sci 108:11825–11829

    Article  Google Scholar 

  • Toplak ME, West RF, Stanovich KE (2014) Assessing miserly information processing: an expansion of the cognitive reflection test. Think Reason 20:147–168

    Article  Google Scholar 

  • UNFCCC (2014) Decision CP.20: Lima Call for Climate Action

  • UNFCCC (2021) Party Groupings. https://unfccc.int/party-groupings. Accessed 24 Sep 2021

  • United Nations (1992) United Nations Framework Convention on Climate Change

Download references

Acknowledgments

The author thanks Astrid Dannenberg, Roman Hoffmann, Georg Kanitsar, Bernhard Kittel, Peter Martinsson, Wieland Mueller, Sabine Neuhofer, Ernesto Reuben, Karl Schlag, Jean-Robert Tyran, seminar participants at the University of Vienna and two anonymous reviewers for helpful comments.

Funding

This paper was realized with financial support by the Hochschuljubiläumsstiftung of the City of Vienna (Grant Number H-282946/2015).

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Correspondence to Ilona Reindl.

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Appendices

Appendix 1: Pledge Mechanism

Subjects in the experiment were asked at the end of round 3 and round 7 to make non-binding pledges about their contribution intentions for the remaining rounds. Comparing subjects’ pledges with their actual contributions in the respective rounds, I find that subjects in general respected their pledges. The two upper panels in Fig. 5 show that observations scatter closely around the diagonal line in successful groups. Wilcoxon signed rank tests find no significant differences between pledges and contributions in round 3 or round 7 (p = 0.707 and p = 0.667, respectively). In failing groups, depicted in the two lower panels of Fig. 5, contributions tended to be significantly lower than pledges in round 3 (Wilcoxon signed rank test, p = 0.000). While pledges in round 3 were not significantly different between successful and failing groups (Wilcoxon rank-sum test, p = 0.380), pledges in round 7 were significantly lower in failing groups (Wilcoxon rank-sum test, p = 0.000). There are no significant treatment differences.

Fig. 5
figure 5

Contribution-pledge comparison for successful (top) and failing groups (bottom)

These findings suggest that pledges—if respected—can serve as a useful coordination mechanism allowing groups to reach the threshold. This is in line with Tavoni et al. (2011) who found that groups that are able to communicate through a simple pledge mechanism are significantly more successful in reaching the threshold than groups that have no means to communicate.

Appendix 2: Experimental Instructions

2.1 Sample Instructions for WLT-VUL [Translated from German]

Welcome to the experiment and thank you for your participation. Please do not talk to the other participants of the experiment.

During the experiment, you and the other participants will be asked to make decisions. Your payout depends on your own decisions and the decisions of the other participants. You are paid individually, in private and in cash after the experiment. During the experiment, you will not be using Euro, but tokens (game coins). They are converted after the experiment at the following exchange rate: 1 token = 0.25 Euro.

Please take sufficient time to read the explanations and to make your decisions. The experiment is completely anonymous. Neither you nor the other participants will be told the identity of the players, either during the experiment or after it.

If you have questions, please raise your hand. One of the lab assistants will come to you and answer your question. The experiment will take about 60 min.

2.2 Proceedings of the Experiment

At the beginning of the experiment, all participants are randomly divided into groups of 4 players. They stay in the same group throughout the experiment and play with the same people. All members of a group receive the same information and face the same decisions. All decisions in the experiment are anonymous. For this purpose, each player is randomly assigned a player ID (a letter between A and D). The player IDs remain the same throughout the experiment. Your own player ID will be displayed in the top right-hand corner of your screen throughout the experiment (see figure on page 2).

In each group there are two purple players and two orange players. At the beginning of the experiment you will learn your own type of player as well as the type of player of the other members in your group. You can recognize the player type by the color in which the player ID is written (see figure on page 2).

At the beginning of the experiment, all participants will receive an initial endowment of 40 tokens. Earnings during the experiment are added to this endowment while losses are deducted.

The experiment consists of 10 rounds. At the beginning of each round each player receives a budget (round budget). The exact amount of tokens a player receives as a round budget depends on the type of player. Purple players receive 5 tokens, orange players 3 tokens. From this round budget you can invest an amount of your choice in a joint project with the other group members. Purple players can invest between 0 and 5 tokens in the group project, orange players between 0 and 3 tokens. Any token that you do not invest in the group project will be added to your private wealth. In total, purple players possess round budgets of 50 tokens (10 round × 5 tokens per round = 50 tokens), while orange players possess a total of 30 tokens (10 round × 3 tokens per round = 30 tokens).

After all members of a group have made an investment decision, all members will receive the following information (see figure below):

  1. (1)

    Amount of tokens each group member has invested in the group project in the current round (member investment in round x)

  2. (2)

    Amount of tokens all group members together have invested in the group project in the current round (group investment in round x). This equals the sum of all investments in (1).

  3. (3)

    Amount of tokens that each group member has invested in the group project in all rounds played so far (total member investment in rounds 1—x).

  4. (4)

    Amount of tokens that all group members have jointly invested in the group project in all rounds played so far (total group investment so far). This equals the sum of all investments in (3).

figure a

All tokens from the round budget that you do not invest in the group project will be added to your initial endowment. During the experiment, you will be kept informed about the current amount of your private wealth.

  • Private wealth = initial endowment + tokens from the round budgets you did not invest in the group project

2.3 Payment

Your payoff at the end of the experiment will depend on how many tokens your group has invested in the group project in total in all 10 rounds. There is a target value of 80 tokens. If this target value is reached, all members of the group will be paid the private wealth they accumulated up to the 10th round.

If the total investment in the group project at the end of round 10 is less than the target value of 80 tokens, each group member loses a certain amount of tokens. The lost tokens are deducted from the private wealth and the remaining wealth is paid out. The exact amount of tokens a player loses depends on the player type: Purple players lose 20 tokens, orange players lose 40 tokens.

2.4 Examples

  1. Example 1:

    You are an orange type and you have invested a total of 23 tokens in the group project. Your group has invested in total more than 80 tokens in the group project, i.e. the target value has been reached. Your payout is therefore 40 tokens (initial endowment) + 7 tokens (uninvested round budget [30-23]) = 47 tokens.

  2. Example 2:

    You are an orange type and you have invested a total of 23 tokens in the group project. Your group has invested in total less than 80 tokens in the group project, i.e. the target value was not reached. Your payout is therefore 40 tokens (initial endowment) + 7 tokens (uninvested round budget [30-23]) − 40 tokens (loss for orange types) = 7 tokens.

  3. Example 3:

    You are a purple type and you have invested a total of 23 tokens in the group project. Your group has invested in total more than 80 tokens in the group project, i.e. the target value has been reached. Your payout is therefore 40 tokens (initial endowment) + 27 tokens (uninvested round budget [50-23]) = 67 tokens.

  4. Example 4:

    You are a purple type and you have invested a total of 23 tokens in the group project. Your group has invested in total less than 80 tokens in the group project, i.e. the target value was not reached. Your payout is therefore 40 tokens (initial endowment) + 27 tokens (uninvested round budget [50-23]) − 20 tokens (loss for purple types) = 47 tokens.

  • Payment (if target value reached) = private wealth

    Payment (if target value not reached) = private wealth – loss

2.5 Announcements

Twice during the experiment, at the end of round 3 and at the end of round 7, you will have the opportunity to tell the other group members how many tokens you are planning to invest in the remaining rounds in the group project. This announcement is not binding, i.e. you are not required to actually invest the announced amount. Your announcement and other members' announcements will be shown to all group members before the next round begins.

2.6 End of the Experiment

At the end of the experiment your payout will be converted at an exchange rate of 1 token = 0.25 Euro. Before you receive your payment, we will ask you to complete a questionnaire. You will receive an extra payment of 5 Euro for answering these questions. Please take your time to answer the questionnaire. The payment will be made only after all participants have completed the questionnaire.

After having read and understood these explanations and the summary on the next page, please press NEXT. Please raise your hand if you have questions.

2.7 Summary

The experiment is played in groups of 4 players (2 purple players, 2 orange players) and consists of 10 rounds.

 

Purple player

orange player

Endowment

40 tokens

40 tokens

Round budget per round

5 tokens

3 tokens

Total round budget

50 tokens

30 tokens

Loss if target value not reached

20 tokens

40 tokens

  • Investments: In each round you can invest tokens from the round budget in a group project. Tokens that you do not invest in the group project will be added to your private wealth.

  • Private wealth: initial endowment + tokens from the round budgets you did not invest in the group project

  • Target value:80 tokens

  • Payment: Payment (if target value reached within 10 rounds) = Private wealth.

  • Payment (if target value not reached within 10 rounds) = Private wealth–loss.

Appendix 3: Descriptive Statistics

Table 5 provides an overview of basic descriptive statistics for the variables sex, age, cognitive abilities, risk preferences, positive reciprocity, negative reciprocity and political attitude. Randomization across treatments with respect to these variables was successful (Wilcoxon rank sum tests, p > 0.1 for all treatments and variables).

Table 5 Descriptive statistics

Risk preferences were elicited by providing subjects with five interdependent hypothetical choices between a lottery and a safe option. I followed the more time-efficient “staircase” procedure suggested by Falk et al. (2016), which results in a measure for risk preferences on a scale from 0 to 31, with higher values indicating more risk-lovingness. Cognitive abilities were elicited using a test by Toplak et al. (2014) consisting of four questions, with more questions answered correctly indicating higher cognitive abilities. Inspired by Dohmen et al. (2009), the measures for positive and negative reciprocity are based on an individual’s agreement with a statement on a scale from 0 (‘does not apply at all’) to 10 (‘totally applies’). The statements for positive reciprocity cover willingness to return a favor, the one for negative reciprocity the willingness to take revenge for a serious wrong. To measure political attitude, subjects were asked to describe their political position on a scale from 1 (right) to 10 (left).

Appendix 4: Determinants of Individual Contribution Behavior

The OLS regression model depicted in Table 6 confirms the results presented in Sect. 5.4. In successful groups (model (1)) the coefficients for more vulnerable types in VUL, rich types in WLT and rich/less vulnerable types in WLT-VUL are positive and significant, indicating higher contributions of these types relative to subjects in BASE. The coefficients of the other types are negative and also significant. Subjects’ ex-ante preferences regarding their own type have a positive and significant effect on individual contributions.

Table 6 OLS regression—determinants of individual contribution behavior

In failing groups (model (3)) the contributions of all types deviate negatively from BASE (remember that in the case of failing groups contributions were the highest in BASE). Note also the sharp drop in the value of R2 from 0.552 in model (1) to 0.149 in model (3), which emphasizes the strong differences in the contribution behavior of types, and thus their explanatory power in successful groups. Ex-ante preferences have no significant effect on individual contributions in failing groups.

Models (2) and (4) include several variables from the post-experimental survey that might affect individual contribution behavior: risk preferences, cognitive abilities, preferences for positive and negative reciprocity, sex and political attitude. There is, however, no evidence that any of these variables affects individual contributions in successful groups. This is also true for failing groups with the exception of positive reciprocity. An additional regression using first round contributions as dependent variable also finds no significant effect of these variables.

Appendix 5: Beliefs

This section complements the analysis provided in Sect. 5.5 with additional results on the role of subjects’ beliefs about group contribution and threshold achievement.

5.1 Beliefs About Group Contribution

From the literature on conditional cooperation we know that a large fraction of people makes their contributions contingent on the contributions of others or their expectations regarding others’ contributions (Fischbacher et al. 2001; Gächter 2006). The results of the panel model presented in Sect. 5.5 show that in this experiment subjects also contribute conditionally. In addition, there is a strong, positive and significant correlation between subjects’ beliefs about group contribution and individual contributions in the respective round (Spearman’s r = 0.505, p = 0.000).

Subjects’ beliefs about group contribution tend to be quite accurate, although there is a significant difference between beliefs and actual contributions (Wilcoxon signed rank test, p = 0.000). More precisely, beliefs were on average equal to 7.05 tokens per round (sd = 3.29) compared to an average actual group contribution of 7.49 tokens per round (sd = 3.01). Figure 6 shows how group contributions and the respective beliefs evolved over time in successful and failing groups. Note that beliefs in the first round were not significantly higher in successful groups (8.28 tokens) than in failing groups (7.93 tokens; Wilcoxon rank-sum test, p = 0.121). However, group contributions in the first round were significantly larger in successful groups (8.91 tokens) than in failing groups (6.89 tokens) (Wilcoxon rank-sum test, p = 0.000). Remember that groups needed an average contribution of 8 tokens per round to reach the threshold by round 10. While only 4 out of 33 successful groups (12.1%) contributed less than the required amount in the first round, this share was significantly larger in failing groups, where 5 out of 9 groups (55.5%) failed to contribute the required sum in the first round. This can be considered evidence for a signaling function of the first round of contributions since they demonstrate subjects’ willingness to contribute. Figure 6 also shows that groups did a good job of updating their beliefs. The correlation between group contribution in \(t-1\) and subjects’ beliefs about group contributions in \(t\) is strong and highly significant (Spearman’s r = 0.493, p = 0.000).

Fig. 6
figure 6

Group contribution and beliefs about group contribution, by group success

5.2 Beliefs About Threshold Achievement

The results of the panel model presented in Sect. 5.5 show that subjects were hesitant in adjusting their beliefs about the likelihood of reaching the threshold unless it was definitely out of reach. In that case, subjects adjusted their beliefs and contributions immediately and drastically. The following complements this analysis.

Initially, subjects were in general very optimistic about the threshold being reached. On a scale from 1 (very unlikely) to 7 (very likely), subjects assessed the likelihood on average with 5.23 behind a veil of ignorance and with 5.19 in round 1 after being informed about their type. Notably, members of successful groups were significantly more optimistic in the first round about threshold achievement than members of failing groups (5.29 vs. 4.83; Wilcoxon rank-sum test, p = 0.018). Also, final treatment differences in success rates were already reflected in initial beliefs: Subjects were most optimistic about reaching the threshold in WLT-VUL (5.4 behind the veil of ignorance, 5.45 in round 1) and least optimistic in VUL (4.89 both behind the veil of ignorance and in round 1). The difference is weakly significant (Wilcoxon rank-sum tests, p = 0.016 behind the veil and p = 0.013 for round 1) for these two treatments and insignificant for all other treatment comparisons.

These results lend support to the hypothesis that threshold beliefs affect group success, even though subjects do not adjust their beliefs continually between rounds. Instead, initial beliefs seem to play a crucial role, in particular because they are correlated with first round contributions, which in turn have a signaling effect as has been shown in the first part of this section.

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Reindl, I. Wealth and Vulnerability to Climate Change: An Experimental Study on Burden Sharing among Heterogeneous Agents. Environ Resource Econ 82, 791–823 (2022). https://doi.org/10.1007/s10640-022-00672-3

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