# What you don't know, can't hurt you: Avoiding donation requests for environmental causes

Valeria Fanghella, Lisette Ibanez, John Thøgersen

► To cite this version:

Valeria Fanghella, Lisette Ibanez, John Thøgersen. What you don't know, can't hurt you: Avoiding donation requests for environmental causes. Ecological Economics, 2025, 233, 10.1016/j.ecolecon.2025.108578. hal-04982503

HAL Id: hal-04982503

https://hal.inrae.fr/hal-04982503v1

Submitted on 7 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# What you don't know, can't hurt you: Avoiding donation requests for environmental causes

Valeria Fanghella<sup>a, D</sup>, Lisette Ibanez<sup>b, D</sup>, John Thøgersen<sup>c, *, D</sup>

## ARTICLE INFO

JEL classification:

C90

D91

Keywords:

Dictator game

Opt-out option

ENGO

Social information

Temporal spillover

## ABSTRACT

Recent research suggests that people are willing to pay to avoid requests for prosocial behavior. However, it is unknown whether this applies to private pro-environmental requests. To study this, we conducted a pre-registered, incentivized online experiment where participants played two consecutive dictator games with an environmental charity of their choice. In stage 1, we varied the type of dictator game and the information provided in a 2 × 2 factorial between-subject design: (i) a standard dictator game versus one with a costly opt-out option; (ii) with or without social information about the average donation made by participants in a previous session. All participants played a standard dictator game in stage 2, the primary aim of which was to capture temporal spillovers from stage 1. Overall, 9 % of participants opted out, leading to lower donations in the dictator game with the costly opt-out option. Providing social information decreases donations in the standard dictator game and appears to increase opt-outs when the costly opt-out option is available, but not statistically significant. Distinct spillover effects emerged depending on the options available and decisions made in stage 1, indicating that the context and motivation of the initial behavior affect the direction of the temporal spillover.

## 1. Introduction

Climate change poses significant threats to human health, livelihoods, and economic stability, with significant risks already emerging with global warming of 1.5 °C (IPCC, 2021). Achieving the less than 1.5 °C target demands rapid and extensive changes across all sectors of human activity. Voluntary mitigation behaviors, such as living car-free, adopting a vegan diet, and shifting to renewable electricity, are also called for (Ivanova et al., 2020). However, these voluntary mitigation behaviors often entail personal costs for individuals. Therefore, understanding the motivations behind them is crucial for promoting them effectively (van Valkengoed et al., 2022). Yet, as this paper explores, the same motivations driving pro-environmental behavior may paradoxically cause individuals to avoid opportunities for further reducing their environmental impact.

Avoidance of pro-social behavior is common in various real-world contexts. Examples include walking away from solicitors (Andreoni et al., 2017; Trachtman et al., 2015), refusing to open the door to them

(DellaVigna et al., 2012), and avoiding private charitable requests (Adena and Huck, 2020; Knutsson et al., 2013; Tonin and Vlassopoulos, 2013). The dictator game (DG), which elicits altruistic preferences by making “dictators” split an endowment between themselves and a recipient, has been adapted to study this phenomenon. In a seminal paper, Dana et al. (2006) show that many dictators are willing to pay a dollar to exit the DG when given the option. This exit prevents the recipient from knowing that a DG was played and how much (or little) the dictator had transferred to them. Dictators willing to pay to quietly exit the DG—sacrificing part of their endowment to preserve their social image—have been consistently observed in laboratory studies (e.g., Broberg et al., 2007; Klinowski, 2020; Lazear et al., 2012).

However, whether individuals would pay to avoid requests for private pro-environmental behaviors remains unclear. An important distinction exists between requests involving social interactions and those involving private requests. Research on “ask avoidance” has typically focused on requests that involve social interactions or that benefit other people.<fnref n="1" /> Avoiding requests involving social interactions

<footnote><sup>a</sup> Grenoble Ecole de Management, 38000 Grenoble, France</footnote>

<footnote><sup>b</sup> CEE-M, Université de Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>c</sup> Aarhus University, Department of Management, Aarhus, Denmark</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: valeria.fanghella@grenoble-em.com (V. Fanghella), lisette.ibanez@inrae.fr (L. Ibanez), jbt@mgmt.au.dk (J. Thøgersen).</footnote>

<footnote n="1">¹ While Knutsson et al. (2013) studied the avoidance of private donation requests from recycling machines in supermarkets, where individuals could donate their returned deposit to charity, the beneficiaries of the charity were families in poor countries.</footnote>
can be a strategy to protect one's social image while pursuing one's self-interest (Dana et al., 2006). Avoiding private requests can be a strategy to protect one's self-image while acting selfishly (Adena and Huck, 2020; Tonin and Vlassopoulos, 2013). Social image motivation is a known driver of pro-social (e.g., Ariely et al., 2009; Lacetera and Macis, 2010) and pro-environmental (e.g., Delmas and Lessem, 2014; Yoelia et al., 2013) behaviors. However, while self-image motivation is also a known driver of pro-social behavior (e.g., Dana et al., 2007; Grossman and van der Weele, 2017), its impact on pro-environmental behavior is less clear.

Selfish behavior appears to affect self-image less when the behavior's direct consequences affect the environment rather than other people (Fanghella and Thøgersen, 2022). Some mechanisms used to cope with the disutility of selfish behavior that affects others, such as moral cleansing (e.g. Brañas-Garza et al., 2013; Sachdeva et al., 2009) or strategic information avoidance (e.g. Dana et al., 2007; Espinosa and Stoop, 2021; Spiekermann and Weiss, 2016), are less prevalent when the selfish behavior impacts the environment (d'Adda et al., 2024; Fanghella and Thøgersen, 2022; Gholamzadehmir et al., 2019; Ho et al., 2016; Lind et al., 2019; Meijers et al., 2018; Momsen and Ohndorf, 2020, 2022; Stikvoort et al., 2016). Thus, the psychological costs may be lower when declining a pro-environmental request than when declining a pro-social request.

Investigating whether and to what extent prior research on self-image motivation and avoidance of private requests applies to pro-environmental behaviors has both theoretical and practical implications. For theory, it adds a boundary condition to research on self-image motivation and request avoidance. For praxis, it guides policymakers in designing effective mitigation policies.

In this study, we conducted a pre-registered, incentivized online experiment (N = 1400), where participants played two consecutive DGs with an environmental non-governmental organization (ENGO) as the recipient. In stage 1, we varied the type of DG and the information provided to participants with a 2 × 2 factorial between-subjects design: (i) standard DG or DG with a costly opt-out; (ii) with or without providing social information about the average donation by participants in a previous session (including non-donors). The first factor allows us to estimate participants' tendency to avoid a pro-environmental request via the DG with the costly opt-out (from here on, just “opt-out”) that allowed participants to renounce a fixed part of their endowment to avoid playing the DG (Dana et al., 2006). The second factor allows us to study the interplay between request avoidance and social information, by investigating whether information about others' pro-environmental behavior increases the probability of participants avoiding the DG. In stage 2, all participants played a standard DG. Here, the primary goal was to measure spillover effects from the treatments and the decisions made in stage 1.

Our study contributes to the literature in at least three ways. First, we investigate the tendency to avoid pro-environmental requests in the absence of social interactions. We look for “reluctant sharers”, who donate to preserve their self-image but would rather keep their money if they could do so without compromising their self-image (Klinowski, 2020). In our experiment, individual participants have no audience for their decisions. The recipient, an ENGO, is unaware of the existence of the experiment and of the origin or lack thereof of any donation and therefore cannot be disappointed by a low donation or interpret it as a signal of a participant's low level of pro-sociality. Thus, opting out represents a strategy to overlook the pro-environmental request, while maintaining a positive self-image, even without donating (Adena and Huck, 2020).

To assess the strength of self-image motivation in the context of pro-environmental behavior, we made the opt-out option costly. When the opt-out option is free, participants face no cost, while potentially gaining benefits from opting out, making this an appealing choice. In a setting similar to ours with a costly opt-out option and no social interactions, Dana et al. (2006) found that barely any dictators exited a DG with a participant recipient. Their results suggest that self-image motivation

alone is insufficient to cause participants to pay to avoid a DG with a participant recipient; instead, avoidance is primarily driven by social image motivation. By contrast, the two studies we are aware of that found that people opted out from a private donation request included a free opt-out option (Adena and Huck, 2020; Tonin and Vlassopoulos, 2013). Hence, we add to this literature by assessing whether self-image motivation is strong enough for reluctant sharers to pay to avoid a private pro-environmental request.

Second, we explore the extent to which social information affects request avoidance. While social information is often used to encourage pro-social (Agerström et al., 2016; Frey and Meier, 2004; Shang and Croson, 2009) and pro-environmental (Andor and Fels, 2018; Farrow et al., 2017) behaviors, it remains uncertain whether it increases or decreases the tendency of individuals to avoid these requests. Klinowski (2020) finds that information about high previous donations increases the share of people freely opting out of a donation request. In contrast, d'Adda et al. (2024) find no significant effect of social pressure (in the form of a picture of an environmental activist) on information avoidance about the emissions caused by air conditioning. Our study directly tests this issue by examining how descriptive norms, one of the most commonly used frames of social information (Helferich et al., 2023), influence the likelihood of a costly exit from a pro-environmental request. A better understanding of the psychological costs of social information is needed to measure the welfare effects of interventions that rely on it (Allcott and Kessler, 2019).

Our third contribution relates to the impact of an opt-out option on temporal spillovers.<fnref n="2" /> The literature has largely studied spillovers between pro-environmental behaviors (e.g., Aasen et al., 2024; Clot et al., 2016; Lanzini and Thøgersen, 2014; see Maki et al. (2019) and Blanken et al. (2015) for reviews) and suggests that spillover effects depend on people's motivation to behave pro-environmentally (Thøgersen and Ölander, 2003). The context in which decisions are made is also essential in the way spillovers will lead either to consistency or to moral compensation (Maki and Rothman, 2017). For instance, Ghesla et al. (2019) show that default options lead individuals to subsequently adopt similar behaviors. Alt and Gallier (2022) find that spillover effects depend on how the initial pro-social behavior has been incentivized, with social information mitigating negative temporal spillover. Our study examines how temporal spillover depends on the context of the initial behavior (i.e., whether or not the participant is forced to make a decision about how much to donate to the ENGO) and on the motivation underlying the decision. We also provide new evidence on the effect of social information on temporal spillovers, which to date has received little attention (see Alt and Gallier (2022) for an exception).

The rest of the paper proceeds as follows. In Sections 2 and 3, we describe the pre-registered experimental design and behavioral predictions, respectively. Section 4 presents the results, followed by Section 5, which discusses the main findings and concludes.

## 2. Materials and methods

### 2.1. Design

The experimental flow of our study is depicted in Fig. 1. First, participants were asked to choose one ENGO from a set of three: WWF, Greenpeace, and Friends of the Earth.<fnref n="3" /> Providing some alternatives is common (e.g., Schmitz, 2019), because it reduces the probability that

<footnote n="2">² Following Nilsson et al. (2017), we define temporal spillover as the impact that performing behavior A at one point in time has on repeating behavior A at a later time. It differs from behavioral spillover, which refers to the effect that engaging in behavior A has on performing behavior B.</footnote>

<footnote n="3">³ These organizations are quite popular in the UK and are seen as efficient in their resource use (https://www.theguardian.com/environment/2013/nov/27/1/in-10-uk-adults-environmental-group, last accessed on 10/01/2022).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Experimental flow.</figcaption></figure>

Note: * participants allocate £10 between themselves and an ENGO; ** participants decide to either: (i) keep £9 (pay £1) to avoid the DG or (ii) enter the DG and allocate £10 between themselves and the ENGO.

participants will not find an organization that they like. We provided a short description of each ENGO based on information publicly available on the ENGO's website and randomized the order of presentation of the ENGOs. We explained that participants would be associated with their chosen ENGO for the entire study but did not reveal what subsequent decisions they would be making.

### 2.1.1. Stage 1

Next, participants entered stage 1, which consisted of a DG with their chosen ENGO. The type of DG and the social information provided varied across participants, with a 2 × 2 factorial between-subjects design. The two DG types were (1) a *Standard* DG, where participants decided how to split an endowment of £10 between themselves and their chosen ENGO, versus (2) an *Optout* DG, similar to the standard DG, but with a costly opt-out option. In this second treatment, participants were offered the option to skip the screen with the DG at a cost of £1 of their £10 endowment. The cost to opt out is lost and paid neither to the participant nor to the ENGO. When opting out, the resulting allocation was thus (£9, £0), where £9 is the participant's payoff and £0 is the ENGO's payoff. Participants who opted in would next decide how to split the £10 endowment between themselves and their chosen ENGO, as in the *Standard* treatment. The decision to opt out is therefore inefficient and dominated by the decision to opt in, as the DG includes two allocations, (£9, £1) and (£10, £0), which are both superior to the opt-out allocation (£9, £0). For clarity, we refer to opting out or in only in the context of the DG with the opt-out option; these terms do not refer to whether participants donate once they see the DG. In that case, we simply refer to their behavior as making a donation (or not).

The DG in the *Optout* treatment followed a similar procedure to that of Dana et al. (2006) (and replicated by, e.g., Broberg et al., 2007; Klinowski, 2020), with two main differences. First, in these previous studies, another participant served as the recipient but remained uninformed about the DG if the dictator opted out. In our study, we used an ENGO as the recipient, which remained ignorant of participants' decisions in any case, making social image concerns less important and self-image concerns more important for participants' decisions.

Second, in previous studies, dictators first decided how much to allocate to the recipient and were only afterward offered the option to exit the DG. In our experiment, participants in the *Optout* treatment were first presented with the option to opt out of or in to the DG and only saw the DG if they opted in. Hence, participants who opted out did not see the screen displaying the DG, but instead proceeded to the next stage of the experiment. We believe that this approach more precisely targets self-image motivation: By opting out, the pro-environmental request stays “hidden” from participants, allowing them to overlook it and avoid

the psychological costs of selfish behavior.

The second treatment variation involved two levels of social information: (1) social information versus (2) no social information. In the treatments with social information, participants were informed of the average donation (including those allocating zero) made by participants who played the standard DG in the pilot study.<fnref n="4" /> This approach is based on studies by Frey and Meier, 2004 and Krupka and Weber (2009), who found that providing social information from random subsamples of the population—even if it portrays somewhat selfish behavior—can increase generosity. We used the following text: “For your information, we previously conducted a similar survey on Prolific: participants gave on average £5.5 to the environmental organization”. We used the expression “a similar survey” to minimize deception. This information was provided to participants before the sharing decision in the *Standard-SN* treatment (i.e., the standard DG with social information), and before the decision to opt out in the *Optout-SN* treatment (i.e., the DG with opt-out option and social information). Information about the average behavior of similar peers is an indicator of the descriptive norm for this behavior (e.g., Agerström et al., 2016; Schultz et al., 2007).

We provided the same social information to participants playing the two types of DG to avoid different social information affecting pro-environmental behavior differently. We did not manipulate the reference sample used to create the norm or the norm itself to avoid deception.

The primary aims of stage 1 were to investigate (a) how many would pay to avoid playing the DG and (b) to what extent doing so was influenced by the social information provided.

### 2.1.2. Stage 2

Stage 2 examined the effect of the options seen and the decisions made in stage 1 on subsequent pro-environmental behavior. Participants were not informed about the content of stage 2 until after completing stage 1. This was to prevent participants from engaging in strategic compensation behavior by reducing their donation in the first stage, knowing that they could compensate for it in the second stage (Gneezy et al., 2014). In stage 2, all participants played a standard DG with the same ENGO recipient and £10 endowment as in stage 1.

In both stages, participants chose how to split the endowment between themselves and the ENGO by selecting from a list of possible allocations. As the equal split (£5, £5) can represent an easy default option

<footnote n="4">⁴ The pilot study was conducted on Prolific with 45 participants (not included in the main study). It included a standard DG and a DG with opt-out option and an ENGO recipient selected by the participant from the same set as in the main study.</footnote>
(as found by e.g., Fanghella et al., 2023), we removed it from the set of available allocations (as in Bellemare et al., 2008). Participants were thus forced to lean toward either a selfish or an altruistic decision. Available allocations started from (£0, £10) and incremented in steps of £1 until they reached the allocation of (£4, £6). The next available allocations were (£4.5, £5.5) and (£5.5, £4.5). Allocations then continued to increment by £1, from (£6, £4) to (£10, £0). We randomized the order of presentation of the allocations in the list (ascending versus descending) and kept it consistent across the two stages for all participants.

In both stages, we included comprehension checks to ensure that participants understood the instructions. Participants could not proceed to the next section unless they had answered the questions correctly.

### 2.1.3. Final survey

After the experiment, participants completed a survey about their environmental preferences, donating and volunteering habits, and stated preferences regarding the proposed ENGOs. This section also included an attention check to identify any participants not paying attention. Those who failed the attention check were screened out. We retrieved socio-demographic characteristics from Prolific database. More details about the participants' characteristics and answers to the survey are reported in Table 1.

Pre-registration of the experimental design and hypotheses, along with the full experimental instructions, are available at this link (https://osf.io/r4gnm/?view_only=9a764481e27e42f0acdc6757ea28f1d6).<fnref n="5" /> Screenshots of the original experimental instructions are reported in the Supplementary Material, Section 1.

### 2.2. Implementation

We recruited participants from the online platform Prolific in January 2022. To be eligible for the study, participants had to be 18 years old or older, live in the UK, and speak English as their first language. The average completion time was 8.5 min. Participants received a fixed participation payment of £1, corresponding to an average hourly wage of about £7.

The final sample consisted of 1400 participants,<fnref n="6" /> randomly assigned to the four treatments described earlier. The sample size was calculated based on a trade-off between statistical power and available budget. The study aimed to detect various effects: the impact of the opt-out option on donations, the impact of social information on donations and opt-outs, and the impact of the spillover on differences in donations between the two stages. Our pilot study with the Standard and Optout treatments indicated that the opt-out option has a medium-effect size, though this estimate is rather imprecise (d = 0.62, 95% CI = 0.01, 1.28). Based on a recent meta-analysis by Melnyk et al. (2022), we expected that descriptive norms would have a medium effect size when they encourage socially approved behavior (d = 0.58), as in our study. Finally, spillover effects in between-subject designs tend to have a small effect size (d = 0.3) (Blanken et al., 2015). As we focus on within-subject analysis, we use d = 0.3 as a conservative estimate for the effect size of

<table><caption>**Table 1**
Summary statistics.</caption><thead><tr><th></th><th>Standard</th><th>Standard-SN</th><th>Optout</th><th>Optout-SN</th><th>P-value<sup>1</sup></th></tr></thead><tbody><tr><td>N</td><td>291</td><td>280</td><td>388</td><td>441</td><td></td></tr><tr><td colspan="6">Panel A: Participant characteristics</td></tr><tr><td>Female</td><td>0.62</td><td>0.55</td><td>0.57</td><td>0.65</td><td>0.03</td></tr><tr><td>Age (years)</td><td>38.8<br>(13)</td><td>39.6<br>(12.8)</td><td>39.8<br>(13)</td><td>39.8<br>(13)</td><td>0.73</td></tr><tr><td>Graduate</td><td>0.57</td><td>0.58</td><td>0.57</td><td>0.57</td><td>0.98</td></tr><tr><td>Student</td><td>0.13</td><td>0.1</td><td>0.14</td><td>0.13</td><td>0.52</td></tr><tr><td>Income (k)</td><td>22.7<br>(16.4)</td><td>23.8<br>(15.4)</td><td>23<br>(14.8)</td><td>23<br>(15.7)</td><td>0.86</td></tr><tr><td>EnvID</td><td>3.66<br>(0.89)</td><td>3.64<br>(0.87)</td><td>3.67<br>(0.81)</td><td>3.65<br>(0.80)</td><td>0.95</td></tr><tr><td>Donating</td><td>2.51<br>(1.19)</td><td>2.6<br>(1.19)</td><td>2.6<br>(1.18)</td><td>2.51<br>(1.16)</td><td>0.58</td></tr><tr><td>Volunteering</td><td>2.32<br>(1.39)</td><td>2.31<br>(1.39)</td><td>2.17<br>(1.36)</td><td>2.28<br>(1.36)</td><td>0.45</td></tr><tr><td>Likes WWF</td><td>4.18<br>(0.73)</td><td>4.23<br>(0.69)</td><td>4.21<br>(0.69)</td><td>4.18<br>(0.68)</td><td>0.71</td></tr><tr><td>Likes Friends of the Earth</td><td>3.28</td><td>3.37</td><td>3.40</td><td>3.38</td><td>0.44</td></tr><tr><td>Likes Greenpeace</td><td>(0.94)</td><td>(0.93)</td><td>(0.96)</td><td>(0.90)</td><td></td></tr><tr><td>Likes Greenpeace</td><td>3.54<br>(0.81)</td><td>3.57<br>(0.84)</td><td>3.64<br>(0.80)</td><td>3.59<br>(0.81)</td><td>0.41</td></tr><tr><td colspan="6">Panel B: Decisions</td></tr><tr><td colspan="6">Selected ENGO</td></tr><tr><td>WWF</td><td>0.63</td><td>0.65</td><td>0.63</td><td>0.65</td><td>0.87</td></tr><tr><td>Friends of the Earth</td><td>0.21</td><td>0.20</td><td>0.20</td><td>0.22</td><td>0.88</td></tr><tr><td>Greenpeace</td><td>0.16</td><td>0.15</td><td>0.17</td><td>0.13</td><td>0.35</td></tr><tr><td colspan="6">Stage 1</td></tr><tr><td>Opt-out</td><td>—</td><td>—</td><td>0.08</td><td>0.10</td><td>0.39</td></tr><tr><td>Average donation</td><td>5.48</td><td>4.89</td><td>4.55</td><td>4.22</td><td>&lt;<br>0.001</td></tr><tr><td>Extensive margin</td><td>(2.82)</td><td>(2.81)</td><td>(3.09)</td><td>(2.85)</td><td></td></tr><tr><td>Intensive margin</td><td>0.92<br>5.93</td><td>0.89<br>5.48</td><td>0.87<br>5.25</td><td>0.85<br>4.98</td><td>0.02<br>&lt;<br>0.001</td></tr><tr><td>Extensive margin</td><td>(2.44)</td><td>(2.37)</td><td>(2.71)</td><td>(2.40)</td><td></td></tr><tr><td>Intensive margin</td><td>(2.44)</td><td>(2.37)</td><td>(2.71)</td><td>(2.40)</td><td></td></tr><tr><td colspan="6">Stage 2</td></tr><tr><td>Average donation</td><td>5.28</td><td>4.83</td><td>4.66</td><td>4.37</td><td>&lt;<br>0.001</td></tr><tr><td>Extensive margin</td><td>(2.89)</td><td>(2.92)</td><td>(2.96)</td><td>(2.74)</td><td></td></tr><tr><td>Intensive margin</td><td>0.91<br>5.78<br>(2.50)</td><td>0.89<br>5.45<br>(2.50)</td><td>0.92<br>5.07<br>(2.73)</td><td>0.90<br>4.84<br>(2.45)</td><td>0.47<br>0.001</td></tr><tr><td>Total donation (Stage 1 + 2)</td><td>10.64</td><td>9.65</td><td>9.21</td><td>8.59</td><td>&lt;<br>0.001</td></tr><tr><td>Extensive margin</td><td>(5.55)</td><td>(5.56)</td><td>(5.91)</td><td>(5.42)</td><td></td></tr></tbody></table>

Note: envID is measured as the unweighted average response to the following items (Panzone et al., 2018): “Being environmentally responsible is an important part of who I am”; “I view myself as an environmentally responsible person”; “Being environmentally responsible is not really important to me” (R); “I strongly aspire to be environmentally responsible”. Donating and Volunteering represent how often participants donate money and volunteer for ENGOs, respectively. *Likes WWF*, *Likes Friends of the Earth* and *Likes Greenpeace* represent stated support for each of the proposed ENGOs and are measured as the un-weighted average response to the following items: “I am familiar with the association”; “I trust the association”; “I support their actions.” All responses are given on a 5-point Likert scale. *Extensive margin* represents the share of participants donating a positive amount (for stage 1 this variable is 0 for opt-outs). *Intensive margin* represents the amount donated conditional on donating a positive amount. Standard deviations in parentheses. ¹ From ANOVA.

spillovers in our study.

Based on our pilot study and previous literature (Broberg et al., 2007; Dana et al., 2006; Klinowski, 2020; Lazear et al., 2012; Tonin and Vlassopoulos, 2013), we expected a low opt-out rate, between 10 and 20 %. We therefore assigned more participants to the treatments with the DG with the opt-out option to ensure sufficient power to detect the effect

<footnote n="5">⁵ We made some minor changes to the hypotheses formulation after receiving peer feedback. The new hypotheses better fit the collected data and our research questions. We report these changes in the Supplementary Material, Section 2. In the same section, we also report testing of the original hypotheses and show that they lead to the same conclusions as the updated formulations.</footnote>

<footnote n="6">⁶ Of the 1442 participants who began the main study, 1404 completed it. We rejected 4 participants, two of them because they took less than 5 min to complete the experiment and failed the attention check; one completed the survey in less than one minute with the wrong completion code, and one used a Prolific ID that was not associated with any identifier in the Prolific database. Fourteen participants saw a table in which the the ENGO and participant payoffs were interchanged. We reached out to these participants and asked them to verify their allocations. Our results remain unchanged whether we include or exclude them (available upon request).</footnote>
of social information on opting out. We assigned 20 % of participants to each of the *Standard* and *Standard-SN* treatments (with N≈285 for each treatment) and 30 % to each of the *Optout* and *Optout-SN* treatments (with N≈420 for each treatment).<fnref n="7" /> Considering a power of 0.80 and a significance threshold of 0.05, our design allows us to detect small effect sizes of social information and spillovers in the treatments with the standard DG and when comparing donations in the *Standard* versus *Optout* treatments (*t*-tests can detect an effect size of at least d = 0.24). Moreover, it allows us to detect medium effect sizes for the impact of social information on opt-outs when comparing *Optout* and *Optout-SN* (assuming that between 42 and 84 participants opt out per treatment, the detectable effect size by *t*-tests ranges between 0.43 and 0.62). Finally, we can detect small effect sizes of temporal spillovers in the DG treatments with the opt-out option (d≈0.20, depending on the share of opt-outs).

At the end of the data collection, 10 % of participants were randomly selected to receive a bonus, based on their decisions in either stage 1 or stage 2, not both, to avoid an effect of accumulation of earnings and cross-task contamination (Charness et al., 2016). Indeed, since we expected a treatment effect in stage 1, we would be unable to disentangle income effects from moral compensation effects in stage 2 had we compensated for both stages. Moreover, random lottery payments (both within and between subjects) tend to yield results that are consistent with paying for all decisions (Baltussen et al., 2012; Clot et al., 2018; Umer, 2023). Overall, 146 participants received a bonus,<fnref n="8" /> for a total of £798.50, and we donated £460, £78 and £123.50, to WWF, Greenpeace, and Friends of the Earth, respectively.

### 3. Behavioral predictions

In the *Optout* treatment, participants decide whether to opt in or out of facing the pro-environmental request. The decision to opt out is inefficient and dominated by the decision to opt in to the DG and select allocations more favorable to either the participant or the ENGO. Therefore, participants should not pay to opt out if they behave rationally or consistent with standard models of social preferences where utility derives from the distribution of payoffs (e.g., Charness and Rabin, 2002; Fehr and Schmidt, 1999).<fnref n="9" />

However, we hypothesize that some individuals donate reluctantly. These reluctant sharers are motivated by self-image concerns: They want to see themselves as generous or environmentally conscious. Hence, if they face a pro-environmental request and choose to donate nothing, they may experience a conflict between their behavior and their desired self-image (Bénabou and Tirole, 2006), leading to cognitive dissonance (Festinger, 1957) and/or feelings of guilt, shame, or discomfort (Keltner and Gross, 1999; Shipley and van Riper, 2022). The opt-out option allows them to act as if the pro-environmental request did not exist (Adena and Huck, 2020), liberating selfish behavior at the relatively low cost of the opt-out option. Deciding to ignore the consequences of one's behavior is perceived as less immoral than deciding to behave in that way while being aware of the associated consequences (Krupka and

Weber, 2013).

Overall, we expect that reluctant sharers, who feel compelled to donate a relatively high amount (higher than the opt-out cost<fnref n="10" />) to maintain a positive self-image, choose to opt out. This should reduce the average donations to the ENGOs compared to when opting out is not possible, as reluctant sharers donate a positive amount when they cannot avoid the pro-environmental request. This forms the basis of our first hypothesis.

**Hypothesis 1.** In stage 1, average donation is higher in the *Standard* treatment than in the *Optout* treatment.

Next, we hypothesize that the impact of social information depends on the presence of the opt-out option. Individuals tend to comply with descriptive norms<fnref n="11" /> for various reasons: they may want to conform to (Bernheim, 1994) or reciprocate (Rabin, 1993) others' donations or view others' donations as a signal of ENGO quality (Vesterlund, 2003). A channel that is central to our study but that has received less attention in the literature (see Klinowski (2020) for an exception), is the interplay between social information and self-image. Peer behavior sets a moral standard that individuals feel compelled to meet to maintain their self-image. This phenomenon should increase donations when there is no opt-out option, because participants who would otherwise donate less comply with the descriptive norm. However, when the opt-out option is available and the average peer donation is salient, a larger percentage of reluctant sharers may choose to opt out. Donating less than the descriptive norm could damage their self-image, and they may prefer to avoid the request altogether rather than risk donating less than what is socially acceptable. Thus, social information regarding previous peer donations should increase the number of opt-outs. These mechanisms lead to the following hypotheses.

**Hypothesis 2.** In stage 1, in the standard DG, average donation is higher when social information is displayed (*Standard-SN*) than when it is not displayed (*Standard*).

**Hypothesis 3.** In the DG with opt-out, more participants opt out when social information is displayed (*Optout-SN*) than when it is not displayed (*Optout*).

Experimental research often finds evidence that individuals' pro-social and pro-environmental behaviors tend to decline over time (e. g., Blanken et al., 2015; Maki et al., 2019). We expect a similar pattern in our experiment. Donations in stage 1 provide private benefits in the form of warm glow feelings (Andreoni, 1989, 1990), which derive from the act of donating itself, rather than from the impact of the donation on the ENGO. This warm glow results, for example, from an improved self-image, positive emotions, and/or satisfaction from altruistic behavior (Ottoni-Wilhelm and Vesterlund, 2023). Because the marginal utility of warm glow diminishes with each subsequent donation, especially when made close in time (Schmitz, 2019) as in our experiment, we expect donations to decrease in stage 2. This reduction should occur regardless of whether participants have the option to opt out, but only among those who donate in stage 1. Specifically, we predict a reduction in average donations in the *Standard* treatment and among those who opt in to the DG in the *Optout* treatment. Hence, we hypothesize the following:

<footnote n="7">⁷ The slight imbalance between the number of participants in *Optout* and *Optout-SN* is due to variations in the Qualtrics allocation process to treatments.</footnote>

<footnote n="8">⁸ The same incentive mechanism was also used for the pilot study. Of the 146 participants receiving a bonus, four were drawn from the pilot study sample and 142 from the main study.</footnote>

<footnote n="9">⁹ If opt-out was free, participants who did not donate anyway may have been indifferent between opting out of and opting in to the DG.</footnote>

<footnote n="10">¹⁰ Note that by fixing the cost to opt out at £1, we are not able to detect participants donating for self-image motivation with willingness to pay lower than £1. We decided to fix the price in this way, first, to be able to compare our results with those of Dana et al. (2006) and replications, and second, to ensure comprehension, as a multiple price list mechanism like that used by Broberg et al. (2007) may be more difficult to implement in the online environment than in the laboratory.</footnote>

<footnote n="11">¹¹ Except for pure altruists, who see others' and their own donations as substitutes. Thus, their marginal utility from donating declines with the information that others donate more. In our experiment, we cannot identify this pattern because we informed participants of only one descriptive norm.</footnote>
**Hypothesis 4.** In the *Standard* treatment, average donation is lower in stage 2 than in stage 1.

**Hypothesis 5.** In the *Optout* treatment, average donation among participants opting in is lower in stage 2 than in stage 1.

Finally, we predict that reluctant sharers who opted out in stage 1 donate a positive amount in stage 2. These participants avoid the pro-environmental request in stage 1 but cannot do so in stage 2. As previously discussed, when unable to avoid the request, reluctant sharers donate to avoid cognitive dissonance and negative emotions caused by the discrepancy between their behavior and their desired self-image. This leads to our last hypothesis.

**Hypothesis 6.** Those who opted out in stage 1 donate a positive amount in stage 2.

## 4. Empirical specification

To test the causal effects of the type of DG and the presence of social information on donations in stage 1, we relate our outcome variables ($y_i$) *donation* and *opt-out* with a vector of individual characteristics ($X_i$), including treatment assignment, and an error term ($\epsilon_i$):

$$y_i = f(X_i, \epsilon_i; \beta) \quad (1)$$

Depending on the nature of $y_i$, we employ different functional forms for $f(.)$ to estimate the parameter vector $\beta$. For all our estimates, we first present the results where $X_i$ only includes treatment variables. Each treatment is coded as a dummy variable: *Standard-SN* represents the standard DG with social information, *Optout* represents the DG with the opt-out option, and *Optout-SN* represents the DG with the opt-out option with social information. The *Standard* treatment serves as the baseline category.

We then add to $X_i$ participants' characteristics that may relate to donation decisions, to control if estimated treatment effects are sensitive to the inclusion of these variables. The characteristics included are coded as follows: gender (dummy variable *female* = 1 if participant is female); age (continuous variable *age*, in years); education (dummy variable *graduate* = 1 if participant has a graduate degree); personal income per year after tax (continuous variable *income*, in thousands of €); student status (dummy variable *student* = 1 if participant is student); environmental preferences (continuous variable *envID*, measured as unweighted response to a 4 items scale adapted from Panzone et al. (2018), as detailed in Table 1); and stated ENGO preferences (a continuous variable for each ENGO, measured as unweighted response to 3 items about perception of the ENGO, as detailed in Table 1). The summary statistics of these variables are reported in Table 1.

To test *Hypotheses 1 and 3* on treatment effects on donations in stage 1, the outcome variable $y_i$ captures donations in stage 1, and $f(.)$ is a double-hurdle model (Cragg, 1971). The double-hurdle model is particularly suitable for analyzing data from DGs (Engel, 2011), as it explicitly models the decision of whether to donate any amount (first hurdle) separately from the decision of how much to donate, conditional on donating (second hurdle). The first hurdle is estimated with a probit model, and the second hurdle with a Tobit model. This allows us to estimate treatment effects on three measures of donation: the extensive margin, i.e., the share of participants donating a positive amount; and the intensive margin, i.e., the average donation conditional on donating a positive amount; the average donation, which is estimated combining both hurdles.

To test *Hypothesis 2*, on the effect of social information on the probability to opt out, the outcome variable $y_i$ is coded as dummy variable which takes on the value of 1 if participant opts out and 0 otherwise. In this case, $f(.)$ reflects a standard probit model. We estimate this model only on the participants assigned to the DG with the opt-out option, as opt-out decisions are not observed for the other participants.

Both the double-hurdle and probit models are estimated via

maximum likelihood methods. We present the results in Section 5.2.1, where we report the estimated marginal effects for continuous variables and the estimated discrete probability effects for dummy variables.

The remaining hypotheses focus on the within-treatment difference between decisions at stages 1 and 2 (*Hypotheses 4 and 5*) and on the behavior at stage 2 among participants who opted out in stage 1 (*Hypothesis 6*). To test whether average donations differ across stages 1 and 2, we use paired sample t-tests, which account for the repeated nature of the data. In line with our predictions in Section 3, this analysis is restricted to participants who played the DG in stage 1, either because they had to (i.e., they were assigned to one of the treatments with the standard DG) or because they opted in (into the treatments with the DG with the opt-out option). For *Hypothesis 6*, we test whether the average donation in stage 2 by participants who opted out in stage 1 differs from zero, using a one-sample t-test. All alternative hypotheses are two-tailed. The results of these tests are reported in Section 5.2.2.

In Supplementary Material, Section 4, we provide a series of robustness checks that involve varying model specifications and sample composition. All the results reported in Section 5 are robust to these checks, indicating that our results are not dependent on the models used.

## 5. Results and discussion

### 5.1. Summary statistics

Table 2 reports participants' socio-demographic characteristics in Panel A and decisions in Panel B, by treatment. Panel A shows that the randomization was generally effective. Sixty percent of the sample is female; this share is unbalanced between treatments. Since gender generally correlates with pro-environmental behavior (e.g., May et al., 2021), we conduct a robustness check to assess whether this imbalance affects the results reported in Section 5.2. In the Supplementary Material, Section 4, we rule out this possibility. The average participant age is 39.6 years; 57 % of participants have earned an undergraduate degree, and 13 % are students. Environmental preferences (denoted as *EnvID*), as well as donation and volunteering frequency (denoted as *Donating* and *Volunteering*, respectively), are balanced across treatments. Both stated (captured by the variables *Likes WWF*, *Likes Friends of the Earth* and *Like Greenpeace*) and revealed (captured by the distribution of the selected ENGO) preferences over ENGOs are equally distributed across experimental conditions.<fnref n="12" />

Panel B reports the summary statistics of the decisions per treatment and stage. Average donation in stage 1 among participants in the *Standard* treatment is £5.48 out of the £10 endowment. This result is consistent with results from laboratory studies with DGs with unearned endowment and a charity recipient (Umer et al., 2022), but slightly higher than in previous studies on Prolific with unearned endowment, random lottery payment, and an ENGO as recipient (e.g., Fanghella et al., 2019; Fanghella and Thøgersen, 2022). One reason for the higher donations may be that participants in our study could choose among different ENGOs, which has been shown to lead to high donations (e.g., Carpenter et al., 2008).

Average donation in the *Standard* treatment is statistically significantly higher than in all other treatments: in the *Standard-SN* treatment average donation is £4.83, and in the *Optout* and *Optout-SN* is £4.55 and £4.22, respectively. The lower average donation in the opt-out treatments can be explained by 9.2 % of participants opting out (8 % in *Optout* and 10 % in *Optout-SN*). Therefore, in these treatments, the extensive margin is lower than in the *Standard* treatment (i.e., fewer participants donate a positive amount). Interestingly, also the intensive margin differ across treatments, and in particular it appears lower when

<footnote n="12">¹² Supplementary Table S3 shows a significant positive relationship between stated support for a specific ENGO and its selection in the experiment, indicating the validity of the decisions made in the experiment.</footnote>
<caption>**Table 2**</caption>

Double-hurdle model results of treatment effect on average donation (columns (1) and (2)) and probit model results of treatment effect on opt-out (columns (3) and (4)) in stage 1, marginal effects.

<table><thead><tr><th rowspan="2"></th><th colspan="3">(1) Donation stage 1 w/out covariates</th><th colspan="3">(2) Donation stage 1 with covariates</th><th rowspan="2">(3) Opt out w/out covariates</th><th rowspan="2">(4) Opt out with covariates</th></tr><tr><th>Avg</th><th>E</th><th>I</th><th>Avg</th><th>E</th><th>I</th></tr></thead><tbody><tr><td>Standard-SN</td><td>-0.587*<br>(0.235)</td><td>-0.032<br>(0.024)</td><td>-0.448*<br>(0.211)</td><td>-0.546*<br>(0.220)</td><td>-0.028<br>(0.022)</td><td>-0.434*<br>(0.203)</td><td></td><td></td></tr><tr><td>Optout</td><td>-0.933***<br>(0.228)</td><td>-0.058*<br>(0.023)</td><td>-0.677**<br>(0.209)</td><td>-0.937***<br>(0.215)</td><td>-0.064**<br>(0.022)</td><td>-0.668***<br>(0.202)</td><td></td><td></td></tr><tr><td>Optout-SN</td><td>-1.254***<br>(0.214)</td><td>-0.076***<br>(0.023)</td><td>-0.946***<br>(0.194)</td><td>-1.311***<br>(0.201)</td><td>-0.091***<br>(0.023)</td><td>-0.953***<br>(0.185)</td><td>0.017<br>(0.020)</td><td>0.022<br>(0.020)</td></tr><tr><td>Female</td><td></td><td></td><td></td><td>0.927***<br>(0.151)</td><td>0.069***<br>(0.017)</td><td>0.665***<br>(0.144)</td><td></td><td>-0.028<br>(0.021)</td></tr><tr><td>Age</td><td></td><td></td><td></td><td>0.022***<br>(0.006)</td><td>0.001<sup>†</sup><br>(0.001)</td><td>0.017**<br>(0.006)</td><td></td><td>-0.000<br>(0.001)</td></tr><tr><td>Income</td><td></td><td></td><td></td><td>0.008<br>(0.006)</td><td>0.000<br>(0.001)</td><td>0.007<br>(0.005)</td><td></td><td>-0.000<br>(0.001)</td></tr><tr><td>Graduate</td><td></td><td></td><td></td><td>-0.246<br>(0.156)</td><td>0.008<br>(0.018)</td><td>-0.323*<br>(0.144)</td><td></td><td>-0.034<br>(0.022)</td></tr><tr><td>Student</td><td></td><td></td><td></td><td>0.152<br>(0.241)</td><td>0.014<br>(0.028)</td><td>0.094<br>(0.221)</td><td></td><td>-0.029<br>(0.033)</td></tr><tr><td>EnvID</td><td></td><td></td><td></td><td>0.749***<br>(0.104)</td><td>0.033**<br>(0.011)</td><td>0.662***<br>(0.100)</td><td></td><td>-0.022<sup>†</sup><br>(0.013)</td></tr><tr><td>Likes WWF</td><td></td><td></td><td></td><td>0.515***<br>(0.119)</td><td>0.049***<br>(0.013)</td><td>0.307**<br>(0.117)</td><td></td><td>-0.020<br>(0.016)</td></tr><tr><td>Likes Greenpeace</td><td></td><td></td><td></td><td>-0.031<br>(0.119)</td><td>-0.004<br>(0.014)</td><td>-0.014<br>(0.111)</td><td></td><td>0.016<br>(0.017)</td></tr><tr><td>Likes Friends of the Earth</td><td></td><td></td><td></td><td>0.046<br>(0.103)</td><td>0.015<br>(0.011)</td><td>-0.032<br>(0.097)</td><td></td><td>-0.021<br>(0.013)</td></tr><tr><td>N</td><td>1400</td><td>1400</td><td>1400</td><td>1382</td><td>1382</td><td>1382</td><td>829</td><td>814</td></tr></tbody></table>

Note: Columns (1) and (2): result from estimating a double-hurdle model, marginal effects; Avg represents both steps of the hurdle model, E represents the extensive margin, I represents the intensive margin. Baseline treatment: Standard. Columns (3) and (4): result from estimating a Probit model. Baseline treatment: Optout. Marginal effects and discrete probability effects. Robust standard errors in parentheses. <sup>†</sup> p < 0.10, <sup>∗</sup> p < 0.05, <sup>**</sup> p < 0.01, <sup>***</sup> p < 0.001.

social information is reported for the same version of the DG (£5.95 in Standard vs £4.83 in Standard-SN; £5.25 in Optout vs £4.98 in Optout-SN). The descriptive results from stage 1 provide preliminary evidence against Hypothesis 2, which predicted a positive effect of social information on average donation, but are in line with Hypotheses 1 and 3, which anticipated a negative effect of the opt-out option.

We note that the share of opt outs in our experiment is lower than in previous studies with a DG with opt-out option and a participant recipient. With a payment scheme similar to ours, about 28 and 43 % of the sample opted out in studies 1 and 2 (replication condition) by Dana et al. (2006). In a setting with a free opt-out, Lazear et al. (2012) found that 72 % of participants in Barcelona and 50 % in Berkeley opted out (Study 1). In an adapted version of the DG in which participants chose the probability that the costly opt-out would be implemented, Klinowski (2020) found a preference for opting out in 41 % of the sample. The lower opt-out rate in our study may be due to lower psychological costs of selfish behavior when it affects the environment rather than other people (Fanghella and Thøgersen, 2022). Alternatively, individuals may be more generous toward an ENGO of their choice than toward a person recipient (Engel, 2011; Umer et al., 2022), making them less willing to opt out from the DG. This possibility is partly supported by the fact that, as discussed above, donations in our study are at the high end of what has been found in previous DGs. More research is needed to disentangle these two effects.

Finally, Panel B shows that decisions significantly differ also in stage 2, leading to differences in total donations too (i.e., combined donations from stages 1 and 2). In stage 2, the average donation remains highest in the Standard treatment (£5.28) but decreases compared to stage 1, consistent with the expected negative temporal spillover in the standard DG with no social information (Hypothesis 4). The difference between stages 1 and 2 is less pronounced in the other treatments. Finally, it is interesting to note that the total donation is highest in the Standard treatment—suggesting that forcing individuals to make a donation decision, without any additional intervention, can lead to high level of pro-environmental behavior.

Further details about the distributions of donations per treatment and stage are shown in Figs. 1A and 2A in the Appendix. In the next section, we formally test for differences across treatments and stages based on our behavioral predictions.

## 5.2. Hypothesis testing

### 5.2.1. Behavior in stage 1

Table 2 reports the results from estimating Eq. (1). Columns (1) and (2) report the double-hurdle model results for treatment effect in stage 1 on average donation (“Avg”), extensive margin (“E”) and intensive margin (“I”), without and with covariates, respectively; columns (3) and (4) present the Probit model results on the probability of opting-out without and with covariates, respectively.

In line with Hypothesis 1, including an opt-out option reduces average donations to the ENGOs. The difference in average donations between the Standard and Optout treatments is £0.93 (“Avg” in columns (1) and (2)), which is almost 10 % of the initial endowment. This reduction in the average donation is due to both lower extensive and intensive margins in the Optout treatment compared to the Standard treatment. The reduction in the extensive margin aligns with our prediction that the opt-out option decreases the proportion of respondents donating to the ENGOs. Specifically, the extensive margin drops by 6 percentage points (as shown by the variable Optout in “E” in columns (1) and (2)), corresponding closely with the 8 % of participants who chose to opt out. This suggests that the opt-out option directly accounts for the decrease in donations. The reduction in the intensive margin in the Optout treatment (“I” in columns (1) and (2)), suggests that opting in to the DG may affect utility, potentially through warm glow feelings that lead to lower donations when facing the donation request. Alternatively, this could mean that reluctant sharers donate high amounts when they cannot avoid the request, as suggested by Lazear et al. (2012).

In contrast to Hypothesis 2, we do not find that social information increases donations in the standard DG. Rather, social information significantly reduces average donation by about £0.5–0.6 (i.e., 5–6 % of
the initial endowment), as shown by the coefficient of the variable *Standard-SN* (“Avg” in columns (1) and (2)). This is due to a lower intensive margin in the *Standard-SN* treatment than in the *Standard* treatment (“I” in columns (1) and (2)), while there is no significant effect on the extensive margin (“E” in columns (1) and (2)). Note that the descriptive norm (£5.5) we communicated to participants was very close to the average donation in the *Standard* treatment. Hence, rather than (just) setting a moral standard that individuals feel compelled to meet to maintain their self-image, the communicated amount appears to have anchored the donations at £5.5 in the *Standard-SN* treatment, also among those who would otherwise have donated more (note that the intensive margin in *Standard-SN* is £5.48, which matches the descriptive norm, see Table 1).<fnref n="13" />

As outlined in Section 2.1, our choice of social information was based on the studies by Frey and Meier (2004) and Krupka and Weber (2009), which suggest that providing social information based on random sub-samples of the population—even if it portrays somewhat selfish behavior—increases generosity. This led to the expectation that informing participants about the average behavior among similar peers, which was rather pro-social (above the equal split), would increase donations. In contrast, we found an apparent “backfiring” effect of social information, which is similar to what Schultz et al. (2007) found in the context of energy conservation. Specifically, they found that communicating the average behavior of similar peers had a negative effect on the behavior of those performing better than average. Consistent with their findings, additional analyses reported in the Supplementary Table S5 show that the backfiring effect reported in Table 2 mainly arises among participants who would have donated more than the descriptive norm. Donations above the descriptive norm decrease significantly in the *Standard-SN* treatment compared to the *Standard* treatment; donations below the descriptive norm are not affected. This pattern is consistent with self-serving conditional cooperation (Fischbacher et al., 2001), that is, the tendency to reciprocate others' contributions, but at lower levels. According to this interpretation, when informed about the descriptive norm, high contributors decrease their donations to align with their peers' donations, while low contributors do not increase their donations, as this would not serve their self-interest.

Next, we find directional evidence that social information tends to increase opt-outs, as predicted by Hypothesis 3, but the effect is not statistically significant (columns (3) and (4)). The increase is equivalent to 2 percentage points, which may be too small an effect to be detected as statistically significant with our sample size. However, despite the modest absolute difference in the number of opt-outs between the *Optout* and *Optout-SN* treatments, the 20.7 % increase in opting out is noteworthy. Hence, if this effect held with a larger sample, it would lead to a significant impact of social information on avoiding a pro-environmental request.

Finally, it is worth noting that environmental identity positively correlates with the three measures of donation (column (2)), while it weakly decreases the likelihood of opting out (column (4)).<fnref n="14" /> This suggests that, in line with our predictions, participants who are primarily driven by pro-environmental preferences and care about the distribution of payoffs are more likely to donate, and donate a higher amount, while those primarily moved by motivation to preserve their self-image are more likely to avoid the pro-environmental request, if possible.

### 5.2.2. Differences between stages 1 and 2

Table 3 presents the results of testing our hypotheses about temporal spillovers. Panel A shows that we can reject the null hypothesis that

<caption>**Table 3**</caption>

Test on equality of average donations between stages 1 and 2, by treatment.

<table><thead><tr><th>Treatment</th><th>N</th><th>Stage 1</th><th>Stage 2</th><th>P-value<sup>1</sup></th></tr></thead><tbody><tr><td colspan="5">Panel A: Standard DG</td></tr><tr><td>Standard</td><td>291</td><td>5.48</td><td>5.29</td><td>0.021</td></tr><tr><td>Standard-SN</td><td>280</td><td>4.89</td><td>4.83</td><td>0.435</td></tr><tr><td colspan="5">Panel B: DG with opt-out, excluding opt-outs</td></tr><tr><td>Optout</td><td>356</td><td>4.95</td><td>4.87</td><td>0.080</td></tr><tr><td>Optout-SN</td><td>397</td><td>4.69</td><td>4.64</td><td>0.279</td></tr><tr><td colspan="5">Panel C: DG with opt-out, only opt-outs</td></tr><tr><td>Optout</td><td>32</td><td>0</td><td>2.34</td><td>&lt;0.001</td></tr><tr><td>Optout-SN</td><td>44</td><td>0</td><td>1.92</td><td>&lt;0.001</td></tr><tr><td colspan="5">Panel D: DG with opt-out, all</td></tr><tr><td>Optout</td><td>388</td><td>4.55</td><td>4.66</td><td>0.080</td></tr><tr><td>Optout-SN</td><td>441</td><td>4.22</td><td>4.37</td><td>0.022</td></tr></tbody></table>

donations are equal across the two stages in the *Standard* treatment, and it confirms Hypothesis 4, which predicts a reduction in donations to the ENGO in stage 2 among participants initially exposed to the standard DG. In addition, Panel B shows statistically weak ($p < 0.10$) support for Hypothesis 5, which predicts a reduction in donations among participants in the *Optout* treatment who chose not to opt out.

Panel C supports Hypothesis 6, predicting that participants who opt out in stage 1 donate a positive amount in stage 2. The average donation by those who opt out in stage 1 is significantly higher than 0 in stage 2 for both the *Optout* and *Optout-SN* treatments. This suggests that reluctant sharers donate when faced with an unavoidable pro-environmental request. In comparison, participants who opt in to the DG but donate nothing in stage 1 also tend to donate nothing in stage 2.<fnref n="15" /> These participants appear to behave like standard economic agents.

Together, our results echo previous studies (e.g., Ghesla et al., 2019; Thogersen and Õlander, 2003), finding that the context of the initial behavior and the motivations behind it determine the type of spillover. Participants who responded positively to the initial donation request tended to lower (by 3.5 %) their subsequent donations, as also found in previous research (e.g., Maki et al., 2019). This decrease is smaller (only 1.6 %) when participation in the DG is voluntary. Spillover effects also vary among those who refrain from donating in stage 1, depending on their motivation to do so. Those who opted out from the DG, i.e., reluctant sharers, tended to compensate for their initial selfish behavior by donating in stage 2, whereas those who opted in but donated nothing in stage 1, i.e., standard economic agents, consistently refrained from donating.

Some additional results worth noting emerge from Table 3. First, social information appears to mitigate the negative temporal spillover. When receiving social information about peers' average donations, donations did not decline among participants in the *Standard* treatment or among those in the *Optout* treatment who did not opt out. This suggests that the anchoring effect of social information neutralizes the negative temporal spillover effect, as found by Alt and Gallier (2022). Second, Panel D shows positive temporal spillover effects when opt-out is possible in stage 1, with donations increasing both without (weakly statistically significant) and with social information (statistically significant). This suggests that, whereas the freedom to avoid a pro-environmental request decreases initial donations, this negative effect may at least be partly compensated by an increase in subsequent pro-

<footnote n="1">¹ From two-tailed paired t-test for Panels A, B and D; from two-tailed one-sample t-test for average donation equal to 0 for Panel C.</footnote>

<footnote n="13">¹³ Note that we do not find any social information effect on the standard deviations of donations.</footnote>

<footnote n="14">¹⁴ Further details on the relationship between decisions in stage 1 and participant characteristics are presented in the Supplementary Material, Table S4.</footnote>

<footnote n="15">¹⁵ Forty-three participants in the *Optout* and *Optout-SN* treatments opted in and gave nothing in stage 1. Their average donation in stage 2 was £0.25 (standard deviation of 0.23), which is statistically indistinguishable from 0 (two-tailed one-sample t-test for average donation equal to 0, $p = 0.28$).</footnote>
environmental donations.

## 6. Conclusion

This study investigates whether self-image motivation is enough to make reluctant sharers pay to avoid a private donation request in a setting with no social interactions and where the recipient is not another person but an ENGO. We identified reluctant sharers by offering participants the possibility to opt out from the DG at a fixed price of £1. As such, they could ignore the pro-environmental request without suffering a blow to their positive self-image. Nine percent of participants who received this offer took it, preferring to incur a monetary cost to avoid the request to share their endowment with an ENGO that they like (i.e., their favorite among three options). However, they still donated a positive amount in a second DG, where they could not avoid the request.

We find a lower share of opt-outs than in previous studies where the DG has a participant recipient (e.g., Broberg et al., 2007; Dana et al., 2006; Klinowski, 2020). With a participant recipient (i.e., a person), donations may be motivated by a desire to protect one's social image (Dana et al., 2006). When the request is for an anonymous donation to a worthy cause, such as a charity or NGO, as in our study, it is the donor's self-image, rather than their social image, that is at stake (Adena and Huck, 2020; Tonin and Vlassopoulos, 2013). Reluctant sharers are clearly not motivated by the cause, which leaves self-image concerns as the likely motive for their positive donations in contexts where they are forced to decide whether and how much to donate. It is well documented that self-image motivation is a driver of pro-social behavior (e.g., Dana et al., 2007; Grossman and van der Weele, 2017), but to the best of our knowledge, this is among the first studies to document its impact on pro-environmental behavior.

Besides its theoretical relevance, how and why people respond to requests for donations from charities, NGOs, and not least ENGOs, is arguably of higher practical relevance than how they respond to requests for donations to another person. In particular, the finding that many donate to preserve a positive self-image rather than because of a deep-felt concern for or commitment to the cause is of high practical relevance for ENGO fundraising. Hence, ENGOs (and probably other NGOs and charities as well) should consider ways to support the positive self-image of potential donors. For example, they might use thank-you mailings and provide personalized feedback on the impact of individual donor gifts, as currently practiced by some NGOs.<fnref n="16" />

In addition, we contribute new insight on how social information affects pro-environmental donations and request avoidance. In contrast to some prior research (e.g., Agerström et al., 2016; Frey and Meier, 2004; Shang and Croson, 2009), we find that providing social information about the average donation among similar peers negatively affects donations. (We also find directional evidence that this kind of social information increases donation request avoidance, but further research is needed to corroborate this finding.) It appears that the average donation among similar peers acts as an anchor for participants' assessment of the appropriate donation, which reduces larger-than-average donations more than it increases lower-than-average donations. One possible solution might be praising donors for larger-than-average donations (Schultz et al., 2007). Another could be to communicate other or supplementary types of social information, such as the behavior of the top 20th percentile (Allcott, 2011).

Our study also contributes to understanding how temporal spillover effects depend on the context of the participant's initial behavior (i.e., whether an option to opt out from the DG is offered) and on the motivation underlying the behavior. First, we find that those who donate initially in a standard DG setting tend to reduce their subsequent donations. Second, we find that the reduction is more than halved when

potential donors have opted in to the initial donation decision. Third, we find that this negative temporal spillover effect disappears completely when social information is provided. This final result is similar to the mitigation of negative spillover effects by social information reported by Alt and Gallier (2022). Thus, it appears that, while information about similar participants' average donations may not increase immediate contributions, it can mitigate negative temporal spillover effects. The practical implication of this finding is that incorporating social information can be a means to maintaining engagement when targeting repeated pro-environmental donations and behaviors, such as using public transport or selecting vegan meals.

As a final caveat, it is important to mention that, like all others, this study has limitations. As discussed above, social information is not limited to average behavior among similar peers, which might backfire for individuals whose behavior is already above average. Future studies should complement ours by systematically investigating how different frames of social information affect pro-environmental behavior and request avoidance. For example, they might communicate the contribution of high-performing individuals (e.g., Klinowski, 2020), or the extensive margin (as in Frey and Meier, 2004 and Krupka and Weber, 2009), rather than the intensive margin (as we did).

Finally, we included two stages in our study to assess the effect of the opt-out option on temporal spillovers. Following the literature (Charness et al., 2016), we used a random lottery payment to reduce cross-task contamination and isolate the effects of warm glow and self-image motivation on spillovers. However, we cannot completely rule out that the payment scheme influenced our results, especially because participants were informed at the beginning of the experiment that it would involve multiple stages. We reduced this risk by not informing participants from the outset that they would be making two donations to the ENGO, but we cannot rule out that some of them might have anticipated a second request upon seeing the first one, potentially influencing their initial behavior (Gneezy et al., 2014). Hence, future research might complement our findings by varying payment schemes or by coupling different pro-environmental behaviors rather than soliciting the same behavior twice in consecutive DGs. It would be ideal if future research also included field experiments using a setup similar to Andreoni et al. (2017).

### CRediT authorship contribution statement

**Valeria Fanghella:** Conceptualization, Formal analysis, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing. **Lisette Ibanez:** Conceptualization, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing. **John Thøgersen:** Conceptualization, Funding acquisition, Methodology, Writing – review & editing.

### Declaration of competing interest

The authors declare no competing interests.

### Acknowledgements

This research benefitted from funding by Grenoble Ecole de Management, Centre for Environmental Economics-Montpellier and Aarhus University. We are grateful for feedback received by participants of the joint GEM-GAEL seminar series at Grenoble Ecole de Management, France, 2023; of the 2023 ESA World Meeting, Lyon, France; of the 2023 annual meeting of the French Association of Experimental Economics, Montpellier, Lyon.

<footnote n="16">¹⁶ E.g., https://unwrapped.oxfam.org.au/gift/christmas-goat.</footnote>
## Appendix A

<figure><img src="image_5.png" /><figcaption>**Fig. 1A.** Distribution of donations in stage 1 per treatment.</figcaption></figure>

Note: Vertical bars represent the percentage of participants donating the corresponding amount to the ENGO of their choice. In Panels C and D, the vertical bar corresponding to the zero amount represents the sum of the percentages of the participants opting in to the DG and donating 0 (dark gray) and of the participants opting out the DG (light gray).

<figure><img src="image_6.png" /><figcaption>**Fig. 2A.** Distribution of donations in stage 2 per treatment.</figcaption></figure>

Note: Vertical bars represent the percentage of participants donating the corresponding amount to the ENGO of their choice.

## Appendix B. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolecon.2025.108578.

### Data availability

The data are made available in the supplementary material.

### References

Aasen, M., Thøgersen, J., Vatn, A., Stern, P.C., 2024. The role of norm dynamics for climate relevant behavior: a 2019–2021 panel study of red meat consumption. *Ecol. Econ.* 218, 108091. https://doi.org/10.1016/j.ecolecon.2023.108091.

Adena, M., Huck, S., 2020. Online fundraising, self-image, and the long-term impact of ask avoidance. *Manag. Sci.* 66 (2), 722–743. https://doi.org/10.1287/mnsc.2018.3232.

Agerström, J., Carlsson, R., Nicklasson, L., Guntell, L., 2016. Using descriptive social norms to increase charitable giving: the power of local norms. *J. Econ. Psychol.* 52, 147–153. https://doi.org/10.1016/j.joep.2015.12.007.

Allcott, H., 2011. Social norms and energy conservation. *J. Public Econ.* 95 (9–10), 1082–1095. https://doi.org/10.1016/j.jpubeco.2011.03.003.

Allcott, H., Kessler, J.B., 2019. The welfare effects of nudges: a case study of energy use social comparisons. *Am. Econ. J. Appl. Econ.* 11 (1), 236–276. https://doi.org/10.1257/app.20170328.
Alt, M., Gallier, C., 2022. Incentives and intertemporal behavioral spillovers: a two-period experiment on charitable giving. J. Econ. Behav. Organ. 200, 959–972. https://doi.org/10.1016/j.jebo.2022.05.028.

Andor, M.A., Fels, K.M., 2018. Behavioral economics and energy conservation – a systematic review of non-price interventions and their causal effects. Ecol. Econ. 148, 178–210. https://doi.org/10.1016/j.ecolecon.2018.01.018.

Andreoni, J., 1989. Giving with impure altruism: applications to charity and Ricardian equivalence. J. Polit. Econ. 97 (6), 1447–1458. https://doi.org/10.1086/261662.

Andreoni, J., 1990. Impure altruism and donations to public goods: a theory of warm-glow giving. Econ. J. 100 (401), 464. https://doi.org/10.2307/2234133.

Andreoni, J., Rao, J.M., Trachtman, H., 2017. Avoiding the ask: a field experiment on altruism, empathy, and charitable giving. J. Polit. Econ. https://doi.org/10.1086/691703.

Ariely, D., Bracha, A., Meier, S., 2009. Doing good or doing well? Image motivation and monetary incentives in behaving prosocially. Am. Econ. Rev. 99 (1), 544–555. https://doi.org/10.1257/aer.99.1544.

Baltussen, G., Post, G.T., van den Assem, M.J., Wakker, P.P., 2012. Random incentive systems in a dynamic choice experiment. Exp. Econ. 15 (3), 418–443. https://doi.org/10.1007/s10683-011-9306-4.

Bellemare, C., Kröger, S., Van Soest, A., 2008. Measuring inequity aversion in a heterogeneous population using experimental decisions and subjective probabilities. Econometrica 76 (4), 815–839. https://doi.org/10.1111/j.1468-0262.2008.00860.x.

Bénabou, R., Tirole, J., 2006. Incentives and prosocial behavior. Am. Econ. Rev. 96 (5), 1652–1678. https://doi.org/10.1017/CBO9781107415324.004.

Bernheim, B.D., 1994. A theory of conformity. J. Polit. Econ. 102 (5), 841–877.

Blanken, I., Van De Ven, N., Zeelenberg, M., 2015. A meta-analytic review of moral licensing. Personal. Soc. Psychol. Bull. 41 (4), 540–558. https://doi.org/10.1177/0146167215572134.

Brañas-Garza, P., Bucheli, M., Paz Espinosa, M., García-Muñoz, T., 2013. Moral cleansing and moral licenses: experimental evidence. Econ. Philos. 29 (2), 199–212. https://doi.org/10.1017/S0266267113000199.

Broberg, T., Ellingsen, T., Johannesson, M., 2007. Is generosity involuntary? Econ. Lett. 94 (1), 32–37. https://doi.org/10.1016/j.econlet.2006.07.006.

Carpenter, J., Connolly, C., Myers, C.K., 2008. Altruistic behavior in a representative dictator experiment. Exp. Econ. 11 (3), 282–298. https://doi.org/10.1007/s10683-009193-x.

Charness, G., Rabin, M., 2002. Understanding social preferences with simple tests. Q. J. Econ. 117 (3), 817–869.

Charness, G., Gneezy, U., Halladay, B., 2016. Experimental methods: pay one or pay all. J. Econ. Behav. Organ. 131, 141–150. https://doi.org/10.1016/j.jebo.2016.08.010.

Clot, S., Grolleau, G., Ibanez, L., 2016. Do good deeds make bad people? Eur. J. Law Econ. 42 (3), 491–513. https://doi.org/10.1007/s10657-014-9441-4.

Clot, S., Grolleau, G., Ibanez, L., 2018. Shall we pay all? An experimental test of random incentivized systems. J. Behav. Exp. Econ. 73, 93–98. https://doi.org/10.1016/j.socec.2018.01.004.

Cragg, J.G., 1971. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 39 (5), 829. https://doi.org/10.2307/1909582.

d'Adda, G., Gao, Y., Golman, R., Tavoni, M., 2024. Strategic information avoidance, belief manipulation and the effectiveness of green nudges. Ecol. Econ. 222 (February), 108191. https://doi.org/10.1016/j.ecolecon.2014.01981.

Dana, J., Cain, D.M., Dawes, R.M., 2006. What you don't know won't hurt me: costly (but quiet) exit in dictator games. Organ. Behav. Hum. Decis. Process. 100, 193–201. https://doi.org/10.1016/j.ohbdp.2005.10.001.

Dana, J., Weber, R.A., Kuang, J.X., 2007. Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness. Economic Theory 33 (1), 67–80. https://doi.org/10.1007/s00199-006-0153-z.

DellaVigna, S., List, J.A., Malmendier, U., 2012. Testing for altruism and social pressure in charitable giving. Q. J. Econ. 127 (1), 1–56. https://doi.org/10.1093/qje/qirj050.

Delmas, M.A., Lesmer, N., 2014. Saving power to conserve your reputation? The effectiveness of private versus public information. J. Environ. Econ. Manag. 67 (3), 353–370. https://doi.org/10.1016/j.jeem.2013.12.009.

Engel, C., 2011. Dictator games: a meta study. Exp. Econ. 14 (4), 583–610. https://doi.org/10.1007/s10683-011-9283-7.

Espinosa, R., Stoop, J., 2021. Do people really want to be informed? Ex-ante evaluations of information-campaign effectiveness. Exp. Econ. https://doi.org/10.1007/s10683-020-09692-6, 0123456789.

Fanghella, V., Thogersen, J., 2022. Experimental evidence of moral cleansing in the interpersonal and environmental domains. J. Behav. Exp. Econ. 97, 101838. https://doi.org/10.1016/j.socec.2022.101838.

Fanghella, V., D'Adda, G., Tavoni, M., 2019. On the use of nudges to affect spillovers in environmental behaviors. Front. Psychol. 10, 1–18. https://doi.org/10.3389/fpsyg.2019.00061.

Fanghella, V., Faure, C., Guetlein, M.-C., Schleich, J., 2023. Locus of control and other-regarding behavior: experimental evidence from a large heterogeneous sample. J. Econ. Psychol. 95 (January), 102605. https://doi.org/10.1016/j.joep.2023.102605.

Farrow, K., Grolleau, G., Ibanez, L., 2017. Social norms and pro-environmental behavior: a review of the evidence. Ecol. Econ. 140, 1–13. https://doi.org/10.1016/j.ecolecon.2017.04.017.

Fehr, E., Schmidt, K.L.M., 1999. A theory of fairness, competition, and cooperation. Q. J. Econ. https://doi.org/10.2307/j.ctvcm4j8j14.

Festinger, L., 1957. A Theory of Cognitive Dissonance. Stanford University Press.

Fischbacher, U., Gächter, S., Fehr, E., 2001. Are people conditionally cooperative? Evidence from a public goods experiment. Econ. Lett. 71 (3), 397–404. https://doi.org/10.1016/S0165-1765(01)00394-9.

Frey, B.S., Meier, S., 2004. Social comparisons and pro-social behavior: testing "conditional cooperation" in a field experiment. Am. Econ. Rev. 94 (5), 1717–1722. https://doi.org/10.1257/0002828043052187.

Ghesla, C., Grieder, M., Schmitz, J., 2019. Nudge for good? Choice defaults and spillover effects. Front. Psychol. 10 (FEB). https://doi.org/10.3389/fpsyg.2019.00178.

Gholamzadehmir, M., Sparks, P., Farsides, T., 2019. Moral licensing, moral cleansing and pro-environmental behaviour: the moderating role of pro-environmental attitudes. J. Environ. Psychol. 65, 101334. https://doi.org/10.1016/j.jenvp.2019.101334.

Gneezy, U., Imas, A., Madarász, K., 2014. Conscience accounting: emotion dynamics and social behavior. Manag. Sci. 60 (11), 2645–2658. https://doi.org/10.1287/mmsc.2014.1942.

Grossman, Z., van der Weele, J.J., 2017. Self-image and willful ignorance in social decisions. J. Eur. Econ. Assoc. 15 (1), 173–217. https://doi.org/10.1093/jeea/jyw001.

Helferich, M., Thøgersen, J., Bergquist, M., 2023. Direct and mediated impacts of social norms on pro-environmental behavior. Glob. Environ. Chang. 80 (June 2022). https://doi.org/10.1016/j.gloenvcha.2023.102680.

Ho, B., Taber, J., Poe, G., Bento, A., 2016. The effects of moral licensing and moral cleansing in contingent valuation and laboratory experiments on the demand to reduce externalities. Environ. Resour. Econ. 64 (2), 317–340. https://doi.org/10.1007/s10640-014-9872-y.

IPCC, 2021. In: M.-D. V. Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B.R., Maycock, T.K., Waterfield, T., Yelekçi, O., Yu, R., Zhou, B. (Eds.), Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/9781009157896.

Ivanova, D., Barrett, J., Wiedenhofer, D., Macura, B., Callaghan, M., Creutzig, F., 2020. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 15 (9). https://doi.org/10.1088/1748-9326/ab8589.

Keltner, D., Gross, J.J., 1999. Functional accounts of emotions. Cognit. Emot. 13 (5), 467–480. https://doi.org/10.1080/026999399371940.

Klinowski, D., 2020. Reluctant donors and their reactions to social information. Exp. Econ. 24 (2), 515–535. https://doi.org/10.1007/s10683-020-09670-y.

Knutsson, M., Martinsson, P., Wollbrant, C., 2013. Do people avoid opportunities to donate? A natural field experiment on recycling and, charitable giving. J. Econ. Behav. Organ. 93, 71–77. https://doi.org/10.1016/j.jebo.2013.07.015.

Krupka, E.L., Weber, R.A., 2009. The focusing and informational effects of norms on pro-social behavior. J. Econ. Psychol. 30 (3), 307–320. https://doi.org/10.1016/j.joepr.2008.11.005.

Krupka, E.L., Weber, R.A., 2013. Identifying social norms using coordination games: why does dictator game sharing vary? J. Eur. Econ. Assoc. 11 (3), 495–524. https://doi.org/10.1111/jeea.12006.

Lacetera, N., Macis, M., 2010. Social image concerns and prosocial behavior: field evidence from a nonlinear incentive scheme. J. Econ. Behav. Organ. 76 (2), 225–237. https://doi.org/10.1016/j.jebo.2010.08.007.

Lanzini, P., Thøgersen, J., 2014. Behavioural spillover in the environmental domain: an intervention study. J. Environ. Psychol. 40, 381–390. https://doi.org/10.1016/j.jenvp.2014.09.006.

Lazear, E.P., Malmendier, U., Weber, R.A., 2012. Sorting in experiments with application to social preferences. Am. Econ. J. Appl. Econ. 4 (1), 136–163.

Lind, J.T., Nyborg, K., Pauls, A., 2019. Save the planet or close your eyes? Testing strategic ignorance in a charity context. Econ. Econ. 161, 9–19. https://doi.org/10.1016/j.ecolecon.2019.02.010.

Maki, A., Rothman, A.J., 2017. Understanding pro-environmental intentions and behaviors: the importance of considering both the behavior setting and the type of behavior. J. Soc. Psychol. 157 (5), 517–531. https://doi.org/10.1080/00224545.2016.1215968.

Maki, A., Carrico, A.R., Raimi, K.T., Truelove, H.B., Araujo, B., Yeung, K.L., 2019. Meta-analysis of pro-environmental behaviour spillover. Nat. Sustain. 2 (4), 307–315. https://doi.org/10.1038/s41893-019-0263-9.

May, A.M., McGarvey, M.G., Gustafson, C.R., Mieno, T., 2021. Gender, environmental issues and policy: an examination of the views of male and female economists. Ecol. Econ. 182, 106877. https://doi.org/10.1016/j.ecolecon.2020.106877.

Mijers, M.H.C., Noordewier, M.K., Verlegh, P.W.J., Zebregts, S., Smit, E.G., 2018. Taking close others' environmental behavior into account when striking the moral balance? Evidence for vicarious licensing, not for vicarious cleansing. Environ. Behav. 51 (9–10), 1027–1054. https://doi.org/10.1177/0013916518773148.

Melnyk, V., Carrillat, F.A., Melnyk, V., 2022. The influence of social norms on consumer behavior: a meta-analysis. J. Mark. 86 (3), 98–120. https://doi.org/10.1177/0022429211029199.

Momsen, K., Ohndorf, M., 2020. When do people exploit moral wiggle room? An experimental analysis of information avoidance in a market setup. Ecol. Econ. 169, 106479. https://doi.org/10.1016/j.ecolecon.2019.106479.

Momsen, K., Ohndorf, M., 2022. Information avoidance, selective exposure, and fake (?) news: theory and experimental evidence on green consumption. J. Econ. Psychol. 88 (October 2021), 102457. https://doi.org/10.1016/j.joep.2021.102457.

Nilsson, A., Bergquist, M., Schultz, W.P., 2017. Spillover effects in environmental behaviors, across time and context: a review and research agenda. Environ. Educ. Res. 23 (4), 573–589. https://doi.org/10.1080/13504622.2016.1250148.
Ottoni-Wilhelm, M., Vesterlund, L., 2023. Motives to give in economics and psychology: a step toward unification. J. Econ. Behav. Organ. 216 (October), 354–365. https://doi.org/10.1016/j.jebo.2023.09.018.

Panzone, L.A., Ulph, A., Zizzo, D.J., Hilton, D., Clear, A., 2018. The impact of environmental recall and carbon taxation on the carbon footprint of supermarket shopping. J. Environ. Econ. Manag., 102137 https://doi.org/10.1016/j.jeem.2018.06.002.

Rabin, M., 1993. Incorporating fairness into game theory and economics. Am. Econ. Rev. 83 (5), 1281–1302.

Sachdeva, S., Iliev, R., Medin, D.L., 2009. Sinning saints and saintly sinners: the paradox of moral self-regulation. Psychol. Sci. 20 (4), 523–528. https://doi.org/10.1111/j.1467-9280.2009.02326.x.

Schmitz, J., 2019. Temporal dynamics of pro-social behavior: an experimental analysis. Exp. Econ. 22 (1), 1–23. https://doi.org/10.1007/s10683-018-9583-2.

Schultz, P.W., Nolan, J.M., Cialdini, R.B., Goldstein, N.J., Griskevicius, V., 2007. The constructive, destructive, and reconstructive power of social norms. Psychol. Sci. 18 (5), 429–434. https://doi.org/10.1111/j.1467-9280.2007.01917.x.

Shang, J., Croson, R., 2009. A field experiment in charitable contribution: the impact of social information on the voluntary provision of public goods. Econ. J. 119 (540), 1422–1439. https://doi.org/10.1111/j.1468-0297.2009.02267.x.

Shipley, N.J., van Riper, C.J., 2022. Pride and guilt predict pro-environmental behavior: a meta-analysis of correlational and experimental evidence. J. Environ. Psychol. 79, 101753. https://doi.org/10.1016/j.jenvp.2021.101753.

Spiekermann, K., Weiss, A., 2016. Objective and subjective compliance: a norm-based explanation of “moral wiggle room”. Games Econ. Behav. 96, 170–183. https://doi.org/10.1016/j.geb.2015.11.007.

Stikvoort, B., Lindahl, T., Daw, T.M., 2016. Thou shall not sell nature: how taboo trade-offs can make us act pro-environmentally, to clear our conscience. Ecol. Econ. 129, 252–259. https://doi.org/10.1016/j.ecolecon.2016.05.012.

Thøgersen, J., Ølander, F., 2003. Spillover of environment-friendly consumer behaviour. J. Environ. Psychol. 23 (3), 225–236. https://doi.org/10.1016/S0272-4944(03)00018-5.

Tonin, M., Vlassopoulos, M., 2013. Experimental evidence of self-image concerns as motivation for giving. J. Econ. Behav. Organ. 90, 19–27. https://doi.org/10.1016/j.jebo.2013.03.011.

Trachtman, H., Steinkruger, A., Wood, M., Wooster, A., Andreoni, J., Murphy, J.J., Rao, J.M., 2015. Fair weather avoidance: unpacking the costs and benefits of “avoiding the ask”. J. Econ. Sci. Assoc. 1 (1), 8–14. https://doi.org/10.1007/s40881-015-0006-2.

Umer, H., 2023. Effectiveness of random payment in experiments: a meta-analysis of dictator games. J. Econ. Psychol. 96 (February), 102608. https://doi.org/10.1016/j.joep.2023.102608.

Umer, H., Kurosaki, T., Iwasaki, I., 2022. Unearned endowment and charity recipient lead to higher donations: a meta-analysis of the dictator game lab experiments. J. Behav. Exp. Econ. 97, 101827. https://doi.org/10.1016/j.socec.2022.101827.

van Valkengoed, A.M., Abrahamse, W., Steg, L., 2022. To select effective interventions for pro-environmental behaviour change, we need to consider determinants of behaviour. Nat. Hum. Behav. 6 (11), 1482–1492. https://doi.org/10.1038/s41562-022-01473-w.

Vesterlund, L., 2003. The informational value of sequential fundraising. J. Public Econ. 87, 627–657. https://doi.org/10.1016/j.lithos.2004.08.003.

Yoelia, E., Hoffmanbc, M., Randcd, D.G., Nowak, M.A., 2013. Powering up with indirect reciprocity in a large-scale field experiment. Proc. Natl. Acad. Sci. USA 110 (SUPPL2), 10424–10429. https://doi.org/10.1073/pnas.1301210110.