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27-12-2022 | Original Paper

# Information disclosure under liability: an experiment on public bads

Authors: Julien Jacob, Eve-Angéline Lambert, Mathieu Lefebvre, Sarah Van Driessche

Published in: Social Choice and Welfare

## Abstract

We experimentally investigate the impact of information disclosure on managing common harms that are caused jointly by a group of liable agents. Subjects interact in a public bad setting and must choose ex ante how much to contribute in order to reduce the probability of causing a common damage. If a damage occurs, subjects bear a part of the loss according to the liability-sharing rule in force. We consider two existing rules: a per capita rule and a proportional rule. Our aim is to analyze the relative impact of information disclosure under each rule. We show that information disclosure increases contributions only under a per capita rule. This result challenges the classical results regarding the positive effects of information disclosure, since we show that this impact may depend upon the legal context. We also show that while a proportional rule leads to higher contributions than a per capita one, the positive effect of disclosure on a per capita rule makes it as efficient as a proportional rule without information disclosure.

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Appendix
Available only for authorised users
Footnotes
1
The 2004/35/CE Directive on environmental liability stipulates that “Member States may establish national rules covering cost allocation in cases of multiple party causation. Member States may take into account, in particular, the specific situation of users of products who might not be held responsible for environmental damage in the same conditions as those producing such products. In this case, apportionment of liability should be determined in accordance with national law.” Hence, Germany and France have both chosen to enforce a per capita sharing when several agents are responsible for a common harm (see German Federal Ministry of Justice (2022) paragraph 426 for the case of Germany, and Hocquet-Berg (2017) for the case of France).

2
The Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA), in its Section 113(f), allows for proportional liability in case of indivisible harm (see Kornhauser and Revesz 1989; Pinkowski 1996; Ferrey 2009). An example is the Colorado vs. ASARCO, Inc case in 1985.

3
Note here that if each tortfeasor invests the same amount, then the two rules are obviously equivalent.

4
Note that the terms “recognition mechanism” and “naming mechanism” could also have been used instead of “identification mechanism”.

5
Another example of such investments is provided by Foulon et al. (2002) as regards the pulp and paper industry in British Columbia (Canada): since 1990, plants are required to use a secondary wastewater treatment process if they are be allowed to operate.

6
As to the identification of the lowest contributors, in practice information is gathered about all contributions, but only the lowest contributors are made publicly identified. Considering again the example of water pollution in the UK, the Environment Agency yearly conducts a global assessment of pollution from water companies and publishes a ranking of them as regards their environmental performance. See Environment Agency of United Kingdom (2022) for an example.

7
Note also that some papers have investigated the channels through which information disclosure leads firms to reduce emissions. Konar and Cohen (1997) notably identified that a stock price decline due to information disclosure would lead firms to subsequently change their environmental behavior. The impact of such disclosure on firms’ financial performance has been analyzed in other papers (see e.g., Capelle-Blancard and Laguna 2010; Gonenc and Scholtens 2017).

8
Each subject can benefit from a reduced risk even if they did not contribute to it (non-excludable), and the fact that a subject enjoys that reduced risk (of having to pay for liability in case of harm) does not prevent another subject enjoying it as well (non-rivalrous). Thus, the two properties of a public good are satisfied in the case we consider.

9
Under severe law, subjects always have to compensate perfectly for any harm caused to others, i.e., the probability of detection is assumed to be 100%; under mild law, the probability of detection is 50% only.

10
In Andreoni and Petrie (2004), the composition of the groups changes every 8 periods, for a total of 40 periods.

11
We decided to give subjects 19 ECUs rather than 20 ECUs in order to avoid decisions based on heuristics such as focal points where subjects choose a given proportion (one quarter, one half, and so on) of their endowment (on this point, see notably Cohen et al. 1996).

12
The value of $$\alpha$$ is not directly related to the value of the endowment (of 19 ECUS) but results from the following trade-off: a low value of $$\alpha$$ leads to a high marginal benefit from investing, which may lead to a corner solution ($$x_{i}=19$$). Conversely, a high value of $$\alpha$$ leads to a low marginal benefit from investing, which reduces both the levels of the equilibrium values of x (in each of the four treatments we consider, see later) but also the difference between these equilibrium values, which could cause difficulties for the comparison between treatments in the empirical analysis. Overall, the value of 0.19 is, in our setup, the value which makes the best trade-off between the necessity to have interior solutions, and sufficiently different equilibrium values.

13
Note that the possibility of reaching a zero probability when all the four agents make a maximum contribution of 19 has no impact on the equilibrium values of contribution (see later). Moreover, in the data, we can see that the perspective of escaping risk has not been seen as an attractive one (see Table 3 below).

14
To avoid the subjects needing mathematical skills in order to understand the function of probability, they were not given the functional form of that function but a table of all probabilities instead. See the instructions in Appendix A.1.

15
A stands for anonymous.

16
ID stands for information disclosure.

17
As pointed out by Samek and Sheremeta (2014), photos capture and preserve the appearance of the person but do not allow for communication, which may confound the effects of identification alone. In addition to the photo, we therefore included first names.

18
We follow Gächter and Renner (2010) for belief elicitation. Subjects earned 6 ECUs if they correctly (± 0.5 ECUs) predicted the average contribution of the three other members and 3 ECUs divided by the (absolute) estimation error otherwise.

19
The model could fit with monetary gains/losses, due to a variation in the firm’s brand image or reputation. However, our experimental design does not include the possibility for other pecuniary losses other than those due to liability in case of a loss occurring.

20
Many SMEs are managed by owner-managers, who have at least a personal responsibility for the decisions they make for their company. They are personally identified, and are personally impacted by what happens to their company. Decisions are thus closed to those which are made for an individual purpose. As an illustration, Afsah et al. 1996 show that firms are sensitive to the opinion of local communities. We can note that SMEs, like larger firms, may also be subject to reputational monetary gains or losses, which are not captured by our experiment. As a consequence, the impact of this additional benefit/cost on SMEs could be higher than the one we measure in this experiment.

21
Deffains and Fluet (2013) use the word (dis)esteem which holds through the others’ view of the agent’s ability to care about, and to contribute in reducing, the expected common loss. This agent’s concern about the loss could be included in the model, through an additional cost in (3) such as: $$-p(x_{i}) \theta H$$, with $$\theta$$ the degree of the agent’s concern for the loss (with $$\theta \ge 0$$). (Dis)esteem is based on the underlying rationale that others cannot observe $$\theta$$, but they try to infer its value, especially through the information disclosed by the identification mechanism. So, esteem (resp. disesteem) plays a role when others think that we have a high (resp. low) value of $$\theta$$. Hence, $$\theta$$ is only an instrumental variable, and to ease the exposition we choose not to introduce it.

22
For simplicity, we only consider the case where no identification mechanism holds. However, we have to note that $$\beta _{i}=0$$ also captures the case where an identification mechanism holds but the agent is not sensitive to their social image. But it is not of particular relevance to distinguish these two cases for our analysis.

23
From a formal point of view, this Prediction is obtained from the comparison between Eqs. (4) and (8) on the one hand, and Eqs. (6) and (9) on the other hand. From the former comparison, we can deduce the impact of social image on the incentives to contribute in case of per capita sharing. From the later comparison, we can deduce the impact in case of proportional sharing. In both cases, the additional incentives (provided by the identification mechanism) depend on $$\beta _i$$, $$q^{r}(.)$$, and $$\Delta ^{r}$$. As a result, for $$\beta _i$$ and $$q^{r}(.)$$ given, $$\Delta ^{PC}>\Delta ^{PR}$$ leads to Prediction 3. But this can only be a conjecture, which we justify by the above argumentation.

24
Unless specifically noted, we report the significance levels of a two-sided Mann-Whitney rank-sum test taking individual averages as the unit of observation. Given the sample size of 60 subjects per treatment and the averages and standard deviations presented in Table 3, we must acknowledge that the statistical power of two samples comparisons can be low and is around 57% depending on the test.

25
See Appendix A.3 for the distributions of contributions per treatment.

26
The p value is based on the difference between the average contribution of the first four periods in each group and the average contribution of the last four periods in each group by individuals.

27
All results are robust to the use of other specifications such as OLS or individual clustered standard errors and whether or not socio-demographic variables are included.

28
We used this lagged variable rather than the subject’s belief about the average contribution of the other group members since we obtain the same results no matter which variable is employed, but the effects are more statistically significant with the former.

29
See column (3) of Table 8 in Appendix A.4.

30
Note that we used two different variables to account for subjects’ preferences toward risk (i.e., Risk-seeking and Gamble). There is no problem of correlation between these two variables as is often the case when risk attitudes are measured using different methods (incentivized and non-incentivized elicitations) (Attanasi et al. 2018; Crosetto and Filippin 2016).

31
See the example of Indonesia in Afsah et al. (1996).

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Title
Information disclosure under liability: an experiment on public bads
Authors
Julien Jacob
Eve-Angéline Lambert
Mathieu Lefebvre
Sarah Van Driessche
Publication date
27-12-2022
Publisher
Springer Berlin Heidelberg
Published in
Social Choice and Welfare
Print ISSN: 0176-1714
Electronic ISSN: 1432-217X
DOI
https://doi.org/10.1007/s00355-022-01444-0