within each discipline was not as great as it is; age and gender have no effect on portfolio choices.

This observation provides the last piece of evidence in favor of a professional-student discrepancy. Students' portfolio choices clearly reflect pro-social and pro-environmental preferences, but they are mostly dissociated from the discipline of study. In contrast, finance professionals' portfolio choices crucially depend on their positions and practices, and those choices are dissociated from their revealed pro-social preferences and their stated pro-environmental sensitivity.

# 6 Discussion and conclusion

We investigated the interplay between return, risk, and environmental impact of assets in a virtual portfolio composition experiment. By asking for 35 such compositions from 190 finance professionals and 279 students of many disciplines and by collecting behavioral and demographic data from additional tasks, we can determine common patterns and strong disparities between finance professionals and students.

Before commenting on the results we detailed in the previous sections, it is necessary to argue about our scientific approach. The question of replicability of social sciences and economics experiments is widely discussed (Camerer et al., 2018, 2016). Recently Strømland (2019) showed that preregistration improves the effect size estimation and reproducibility in experimental economics. Nosek et al. (2018) clearly pointed how important it is to distinguish postdiction<fnref n="31" /> and prediction<fnref n="32" /> to maintain scientific credibility. Preregistration precisely improves this credibility by achieving that objective. At the time we designed the experiment, preregistration was uncommon among economists. However, we would like to emphasize that the 3 main hypotheses of the paper detailed in the Predictions Subsection 2.2, as well as the main analysis and robustness tests (Sections 3 and 4), were constructed before the experiment was carried out, and are part of hypothesis testing (prediction). Besides, some of these hypotheses have not been confirmed (risk asymmetry, no “green generation” effect, ranking treatment effect). Finally, Section 5 is an exploratory approach and highlights some interesting hypotheses (regarding trading strategies and professionals' jobs) that would be interesting to better understand and test in future research.

<footnote n="31">³¹i.e., hypothesis-generation and the “use of data to generate hypotheses about why something occurred”.</footnote>

<footnote n="32">³²i.e., hypothesis-testing and the “acquisition of data to test ideas about what will occur”.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 4: CHOICES 6 TO 10 TO ELICIT PRUDENCE IN THE RPT TASK.</figcaption></figure>

<figure><img src="image_6.png" /></figure>
Portfolio patterns exhibited in the experiment mix conclusions from standard portfolio theory (attraction to return, aversion to risk) with overall pro-environmental preferences. While pro-environmental performance of portfolios involves the acceptance of lower expected returns, our main innovative and robust result is that risk, however, tends to temper pro-environmental investment intentions. In risky environments, participants have a clear rising interest in brown assets and those that have higher expected returns. The fact that risk may negatively affect positive-impact investment seems to us as a relevant track for future research.

We also assess similarities and significant asymmetries between green and brown assets, and show that finance professionals are more reluctant to take on the browner asset than students, in particular in green contexts. While being cautious about comparing professionals and students, we observe that the former show better environmental performance in portfolio composition than the latter. Professionals are less influenced by ranking information or tax penalties on asset returns. Social behavior and environmental opinion assessments are not significant predictors of the finance professionals' investment decisions. As a very exciting exploratory result, the category of financial job and the investment strategy followed in that job are strong predictors of average portfolio compositions.

Our two samples were selected from different cultures (France and Morocco), which account for some of the observed behavioral differences in our experiments. However, this difference must be put into perspective. First, links between culture and social preferences are not obvious. Looking at geographic features, Falk et al. (2018) do not find a statistical link between absolute latitude, geographic conditions, and level of altruism. Altruism is also not correlated with cultural items, with the exception of family ties. Second, the experiment has been carried out in French for both samples to prevent any differences in linguistic interpretation and favor convergence of preferences (Ogunnaike et al., 2010). Many finance professionals in Morocco have studied in France, and the French language is widely used by the Moroccan finance community.<fnref n="33" /> Third, the French and Moroccan banking cultures are very similar. Indeed, the PRO sample is largely composed of participants working in Moroccan financial institutions that have strong ties with French companies (cross-shareholdings or subsidiaries between the two countries' institutions).<fnref n="34" /> Finally, the academic literature that seeks to understand the differences

<footnote n="33">³³With 43,500 Moroccan students in France, Morocco is the leading country of origin of foreign students in France, accounting for 12% of all international students; see https://ressources.campusfrance.org/publications/chiffres_cles/fr/chiffres_cles_2021_fr.pdf)</footnote>

<footnote n="34">³⁴The interplay between personal traits and business culture is not well understood and is a fertile</footnote>
<figure><img src="image_7.png" /><figcaption>Figure 5: CHOICES 11 TO 15 TO ELICIT TEMPERANCE IN THE RPT TASK.</figcaption></figure>

### A.5.2 Prosociability

The Social Value Orientation (SVO) task was introduced by Murphy et al. (2011) to measure pro- and anti-social preferences. The task consists in choosing a payoff distribution between self and an anonymous counterpart from a set of nine possible distributions. We rely on the six-item version of the SVO to provide a specific non-negative aggregate score, which allows us to determine the SVO score. The SVO score is computed as a function of the average self-allocation $\bar{A}_s$ and the average transfer to the counterpart $\bar{A}_o$:

$$SVO = \arctan \left( \frac{\bar{A}_o - 50}{\bar{A}_s - 50} \right).$$

The result is an angle between -20° and 60°. Following Murphy et al. (2011), respondents can be categorized as competitive, individualist, pro-social, or altruist, depending on the interval in which the SVO score is located. We use the direct (continuous) score as an explanatory variable, and we check for robustness with the categorization proposed by Murphy et al. (2011). The instructions displayed on the participants' screen are given below, as well as a screenshot of the decision screen (Figure 6).
between finance professionals, students and the general population frequently compares samples of professionals and students of different nationalities<fnref n="35" /> (Kirchler et al., 2018; Schwaiger et al., 2020; Huber et al., 2021). Our study is not at odds with the latter. That said, potential differences in culture and stake size between professionals and students, combined with unobserved variables that could potentially drive these behaviors, suggest that extreme prudence should be used in comparing these two samples. Further research is needed to validate or refute those first results.

Although our experiment was mainly designed for finance professionals, it raises the issue of the generalizability of our findings. We provided some first answers in the comparison with the students portfolio choices, that showed marked contrasts. But heterogeneity exists even in the PRO sample itself, as we showed. This indicates that an experimenter might know much more about environment-related financial decisions by looking at finance professional participants' positions and practice than by testing the participants with associated tasks or surveys. The large difference between market traders and portfolio managers on one side and analysts on the other side, or between trend followers and traders who follow other strategies, could indicate that jobs and strategies including long-term and multidimensional information might enhance green preferences or attract individuals with specific green leanings. Finally, we stress the importance of looking at such details, as the literature is particularly interested in the singularities of finance professionals compared to laypeople (Haigh and List, 2005; Frechette, 2015; Weitzel et al., 2020; Holmen et al., 2021; Bottasso et al., 2022).

A key novelty of our experimental design is to model assets with negative environmental externalities, in contrast to most studies that focus on SRI. The latter exclusively consider the positive impact of assets and products. Several recent papers proposed mechanisms to implement negative externalities in the lab, such as killing mice Falk and Szech (2013) or avoiding a donation to fund a surgery of a leprosy patient in India (Bartling et al., 2015, 2019). We contribute to this strand of literature by proposing a mechanism that draws a parallel with delegated philanthropy. As a new protocol for negative ex-

<footnote>ground for future research (Guiso et al., 2015a). Modern corporations, such as firms or banks, can shape the corporation's culture through selection and recruitment. Therefore, it is likely that modern corporations have a strong impact on culture, as assessed by an experiment in the field, perhaps more than local culture has on the corporation. It is also very likely that the most successful corporate culture models are imitated by competitors and spread in the population. In any case, corporate culture is likely to be a key determinant of a firm's performance (Guiso et al., 2015b), and that being a finance professional is a more relevant trait than being an employee of a Moroccan or a French bank.</footnote>

<footnote n="35">³⁵Bchir (2014) compares French students with Tunisian farmers in a public good game. He controls for the cultural difference by adding a Tunisian students subject pool. French and Tunisian students are not different, but are equally less cooperative than Tunisian farmers.</footnote>
This third part consists of 6 periods. In each period, you have to decide how to divide a sum of money between you and a person participating in this experiment. You can't identify the other person and she can't identify you. There is no right or wrong response, and the data will be treated anonymously. For each of the 6 periods, indicate the allocation you would like to prefer by clicking on the corresponding button. Within each pair, randomly formed by the computer, there will be two successive draws. A first draw will determine the period to be used for remuneration. Then, a second draw will select one of the two in the pair so that her decision will apply to the pair, so either your decision will apply or the other's decision will apply. The amounts on the screens are expressed in ECU.

<table><caption>Figure 6: SCREENSHOT OF THE SVO TASK (FIRST ALLOCATION)</caption><tr><td>You receive</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td></tr><tr><td></td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>The other receives</td><td>85</td><td>76</td><td>68</td><td>59</td><td>50</td><td>41</td><td>33</td><td>24</td><td>15</td></tr></table>

<figure><img src="image_8.png" /></figure>

### A.5.3 Strategic thinking

In a third additional task, we measured the depth of reasoning using the 11-20 game from Alaoui and Penta (2016).

The game involves two players, assigned randomly to each other. Each one independently picks an integer number between 11 and 20. Three rules define the nominal reward of the player:

(a) he/she receives the chosen number,

(b) if his/her number is exactly one unit less the opponent's number, he/she receives an extra 20,
ternalities, our mechanism of delegated misanthropy raises some challenges: our findings may be caused by fundamental differences in how participants perceive the externality and how they trade off their (unobservable) moral cost for return. Eventually, the donation mechanism is associated with losses in green contexts and surpluses in brown ones, and this by itself generates asymmetry because of loss aversion (Kahneman and Tversky, 1979; Tversky and Kahneman, 1991).

We set the donation parameter at 50% to avoid extreme values, e.g. 10% or 80%. This calibration was reached after conducting several discussions with financial professionals and researchers. The aim was to propose a salient externality, involving significant transfers to third parties, in order to induce participants to perform meaningful and costly trade-offs between return/risk and environmental impact. This is in line with the concept of arbitrage by finance professionals, between ESG ratings (or carbon footprint, level of energy consumption, level of pollution emitted) of the company, together with its market risk and return. Setting the externality at a too low rate would have made the arbitrages insignificant, while setting it at a too high rate would have forced them by making the arbitrage too favorable. Regarding the robustness section, it is of interest to note that the externality, risk and return parameters are not modified in the baseline and ranking treatments, so that the differences in behavior are not directly related to the level of the externality. On the other hand, the tax treatment leads to a modification of the net return, which directly changes the relationship among the 3 parameters and might explain why participants invest more in green. It would be thus very valuable in future research to vary the externality parameter as widely as possible to identify the “dose-response function” of the externality tradeoffs.

Our findings suggest several tracks for future research. We already pointed out the importance of the reluctance to trade off risk for positive-impact investments. A deeper understanding of the behavioral mechanisms involved in those trade-offs would be valuable. With respect to methodology, there exists a huge variety of negative externalities, as well as a variety of types of rankings that can be implemented. Different combinations of them offer potential avenues for future research. An important challenge will be to understand how different types of externalities asymmetrically affect the love for green and the hate for brown.
(c) if his/her number is exactly his/her opponent's number, he/she receives an extra 10.

The simple game grounded on (a) and (b) has been introduced by Arad and Rubinstein (2012), as a one-shot game designed to assess the depth of strategic thinking. Level-0 (non strategic) optimal choice is given by considering only condition (a), and leads to choice 20. According to condition (b), Level-$k$ reasoning, for $k \ge 1$, follows from the best response to Level-$(k-1)$, and leads to choose the number $20-k$, or number 21 for Level-10. The cycle of best-responses is broken by condition (c). The condition (c) has been introduced by Alaoui and Penta (2016) to change the unique mixed Nash equilibrium of the original game of Arad and Rubinstein (2012) in a pure Nash equilibrium at 11$^{15}$. The instructions of the game, displayed on participants' screen, are given below.

There is only one decision in this part. You must choose an integer between 11 and 20. You will receive the amount in euros that you choose. In addition, at the end of the experiment, pairs of participants will be randomly formed, and the selected numbers will be compared.

If you choose the same number as the other member of your pair, you will receive an additional 10€. If you choose a number that is exactly one less than the other member of your pair, you receive an additional 20€.

Examples:

* If you choose 17 and the other member of your pair has chosen 19, then you receive 17€ and he receives 19€.

* If you choose 12 and the other member of your pair has chosen 13, then you will receive 32€ and he will receive 13€.

* If you choose 16 and the other member of your pair has chosen 16, then you will receive 26€ and he will receive 26€.

### A.5.4 Cognitive and Reflection Task

We relied on a standard seven questions cognitive reflection test (Frederick, 2005; Korniotis and Kumar, 2010; Toplak et al., 2011). The answer to each question is either right of wrong. This task provides therefore a score between 0 to 7, which serves as a

<footnote>15In order to study the individual strategic level, a pure Nash equilibrium, that is, an explicit choice of a number, is obviously preferable to a mixed Nash Equilibrium.</footnote>
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## A.6 Exploratory results: SVO and NEP

We re-estimate the portfolio model of Section 3 while adding the individual covariates from the control tasks. Average marginal effects (AMEs) are reported in Table 5.<fnref n="16" />

**Professionals (PRO).** For finance professionals, neither SVO nor NEP is reliably associated with $\lambda$ across contexts. Point estimates are small and statistically indistinguishable from zero at conventional levels. Hence, for PRO, revealed portfolio choices in the experiment appear largely orthogonal to stated pro-social and pro-environmental measures.

**Students (STUD).** By contrast, the student sample displays two context-specific associations. First, in *green* contexts (delegated philanthropy), the SVO score is positively related to $\lambda$: more pro-social students allocate a larger share to the greener asset. Second, in *brown* contexts (delegated misanthropy), the NEP score is positively related to $\lambda$: stu-

<footnote n="16">¹⁶As noted in the main text, excluding these controls leaves the core asset-level AMEs essentially unchanged.</footnote>
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**Takeaway.** These exploratory results help rationalize part of the green–brown asymmetry among students without altering the main conclusions. For professionals, neither SVO nor NEP explains portfolio choices in our setting; for students, SVO (in green) and NEP (in brown) provide complementary, context-dependent signals. Overall, the core inference of the paper—no risk–impact trade-off and stronger responsiveness to greener returns—remains unchanged once these individual covariates are included.

## A.7 Financial market professional questionnaire

A dropdown menu was attached to each question, the list of possible answers are given into the brackets.
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1. What is your current job? [1: Prop Trader; 2: Sales Trader; 3: Sales; 4: Market Maker; 5: Asset/Portfolio Manager; 6: Strategist/Economist; 7: Financial Analyst (buy side, sell side); 8: Risk analyst; 9: Private equity Manager; 10: Other]

2. What is your employer? [1: Bank; 2: Asset management company; 3: Trading company; 4: Broker; 5: Hedge fund; 6: Work for your own; 7: Private equity company; 8: Other]

3. In which jobs in finance do you have your main experiences? (You can choose various jobs) ? [1: Prop Trader; 2: Sales Trader; 3: Sales; 4: Market Maker; 5: Asset/Portfolio Manager; 6: Strategist/Economist; 7: Financial Analyst (buy side, sell side); 8: Risk analyst; 9: Private equity Manager; 10: Other]

4. What is the main strategy you employ to trade assets? [1: Technical Analysis; 2: Value investing; 3: Trend following; 4: Event Driven; 5: Global Macro; 6: Arbitrageur; 7: Market Making; 8: Scalping; 9: Merger Arbitrage; 10: Other; 11: None]

5. On which asset class are your working on? [1: Money Market; 2: Forex Market; 3: Commodities; 4: Equity; 5: Bonds (Fixed Income); 6: Private Equity; 7: Real estate; 8: Hedge Fund; 9: Other]

6. Do you work on: [1: Spot Market; 2: Market Derivatives; 3: Both; 4: Other]

7. What is the nationality of your company? [answer: ]

# B Data collection

## B.1 Samples

**The PRO sample** We collected the data of the PRO sample in major investment banks and asset management companies, in October 2019, using a mobile laboratory with tablets. We conducted experiments in compliance with the ethical rules of the experimental economics laboratory of Montpellier (LEEM) and in accordance with the deontological and compliance rules of the banks.<fnref n="17" /> This study has also been approved by the Institutional Review Board (IRB) of the Center of Environmental Economics of Montpellier (University of Montpelier). We used oTree software (Chen et al., 2016) to run the experiment. Figure 7 illustrates the typical environment in which the PRO data were collected. A total of 190 financial market professionals participated in the experiment: 66 of them were assigned to the baseline treatment, 63 to the ranking treatment and 61

<footnote n="17">¹⁷For reasons of confidentiality, we can not provide any further information publicly, however, we are at the disposition of the reviewers to give them of any additional information they may require.</footnote>
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to the tax treatment. Most of the participants worked in trading activities (proprietary traders, sales traders, asset managers, sales, trading room managers, quantitative engineers, structurers, financial analysts), while others were employed in support functions such as financial management or audit. Figure 8 shows the distribution of the jobs for the entire PRO sample. Finally, many of the members of the bank management board also took part in the experiment.<fnref n="18" />

<figure><img src="image_9.png" /><figcaption>Figure 8: FINANCIAL CATEGORIES OF THE PROFESSIONAL SAMPLE</figcaption></figure>

<figure><img src="image_10.png" /></figure>

**The STUD sample** The STUD sample consists of students from University of Montpellier. The data were collected during 2020, at the Laboratory for Experimental Economics of Montpellier.<fnref n="19" /> 279 student participants (45.16% of women, with an average age of 23.86 years) were involved in the experiment: 103 in the baseline treatment, 84 in

<footnote n="18">¹⁸74.2% of the participants work for a bank and 25.8% for "other" (mainly asset management companies or bank subsidiaries dedicated to asset management and portfolio management)</footnote>

<footnote n="19">¹⁹LEEM, http://leem.umontpellier.fr. The participant pool is managed by the ORSEE platform (Greiner, 2015).</footnote>
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the ranking treatment and 92 in the tax treatment. Figure 9 shows the distribution of
academic degrees and studied disciplines in the STUD sample.

<figure><img src="image_11.png" /><figcaption>Figure 9: ACADEMIC DEGREES AND STUDIED DISCIPLINES IN THE STUDENT SAMPLE</figcaption></figure>

## B.2 Practical procedures

The practical procedures for the PRO and the STUD samples differ in two respects.
First, they differ with respect to the final questionnaire that followed the control tasks.
For the PRO sample, the questions were related to current employment and professional
experience in the field of finance, while for the STUD sample, the questions were related
to their studies and socio-demographic information.

Second, the two samples differ with respect to how participants were matched and
compensated for the experiment. In the STUD sample, we organized standard sessions
with an even number of participants so that we could form pairs of players for the SVO
task and the 11-20 task.

In addition, all participants were paid for one (randomly chosen) portfolio choice
task and one (randomly selected) control task. For the PRO sample, we could neither
anticipate the number of participants in a session nor at what time they would attend the
session. We, therefore, needed to rely on the strategy method for the SVO and the 11-20
tasks. Pairs were formed at the end of each session. In addition, only one participant
out of 10 was randomly selected to be paid out for real. Selected participants were paid
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for one of the portfolio choice task and one control task, both randomly selected. We proceeded that way to provide strong incentives to the finance professional participants. For both samples, we relied on a random selection of the tasks to be paid. This procedure is common among experimentalists as it was demonstrated to be incentive-compatible (Starmer and Sugden, 1991; Charness et al., 2016; Clot et al., 2018). On average, a student earned 8.10 euros, and a (selected to be paid) finance professional 216.81 euros.

# C Predictions: Standard financial preferences

**Hypothesis 3 (Markowitz preferences).**

In composing their portfolios, participants seek return and avoid risk. This is revealed through:

1. an increase of the share $\lambda$ if the expected return of the greener asset, $\mu_g$, increases, and a decrease of $\lambda$ if the return of the browner asset, $\mu_b$, increases;

2. a decrease of $\lambda$ if the risk of the greener asset, $\sigma_g$, increases, and an increase of $\lambda$ if the risk of the browner asset, $\sigma_b$, increases.

These predictions follow directly from standard portfolio theory (Kroll et al., 1984; Markowitz, 2010) and are consistent with results typically obtained in experimental studies of risk preferences (Holt and Laury, 2002).

# D Additional details on procedures and sampling

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Donations were proportional to the participant's share invested in the colored asset and were transferred after the experiment. For example, investing 60% of the budget in the green asset generated a donation of 30 ECUs to the pro-environmental association. The conversion rate was the same for donations and for participant payments. Importantly, the donation amount was fixed and independent of the stochastic realization of the colored asset, unlike in Guenster et al. (2022), where the donation depended on outcomes.

**Professional incentives.** In the PRO sample, only one out of ten participants was randomly selected for payment, which generated high incentives for finance professionals. Overall, 20 professionals were selected, with an average realized payment of €216.81. This scheme was managed by the Moroccan Association of Trading Rooms (AMSM), which minimized the logistical burden. We chose this procedure for two reasons. First, numerous studies have shown that “paying one,” i.e., remunerating a subset of participants, is as effective as “paying all” (Harrison et al., 2007; Andersen et al., 2014; Beaud and Willinger, 2015; Charness et al., 2016). Second, this procedure allowed us to offer very high monetary incentives.

To benchmark the level of incentives, we compared our payments with the remuneration of Moroccan trading floor professionals. Using the 2019 Michael Page Morocco Salary Survey, the net average monthly salary of an experienced trading floor professional (with more than 10 years of experience) is estimated at 36,812 dirhams. On the basis of the legal annual working hours in Morocco (2,288 hours, excluding overtime), this corresponds to a net hourly wage of €18.23. By comparison, the expected earnings for our 45-minute experiment amounted to €21.68 per participant, and the average realized payment for the selected participants was €216.81. This substantial level of remuneration ensured that professional participants were strongly motivated. As an additional benchmark, in Kirchler et al. (2018), European professionals were paid one out of five, with an average of €52, corresponding to approximately €260 for the selected participants for a 45-minute session, very close to our incentive scheme.
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**Student incentives.** In the STUD sample, all participants were paid, with an average earning of €13.45 including the show-up fee. We could have applied the “pay one” rule, as in the PRO sample. However, since the STUD participants were recruited from the volunteer pool of the XXX Experimental Economics Laboratory, the rule of the laboratory is to pay all participants unless the design requires otherwise. Not paying a portion of participants could undermine future participation rates and bias the pool towards intrinsically motivated individuals. Even if “pay one” does not affect behavior in isolated experiments, it may reduce willingness to participate in the long run. This is why paying all was the most appropriate strategy.

Moreover, many studies (Charness et al., 2016) show no behavioral differences between “paying one” and “paying all,” which guarantees comparability between our PRO and STUD samples. Finally, remuneration was attractive: €13.45 corresponds to about 70% above the French minimum net wage in 2020 (€8.03), ensuring adequate incentives for student participants.

# E Analysis

## E.1 Portfolio task

The following table is used in Section 3.
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<table><caption>Table 8: MEAN (std) INVESTMENT IN THE GREENEST ASSET, BY TREATMENT AND SAMPLE</caption><thead><tr><th rowspan="2">Screen</th><th rowspan="2">Assets</th><th rowspan="2">ρ</th><th colspan="2">Baseline</th><th colspan="2">Ranking</th><th colspan="2">Tax</th></tr><tr><th>Professionals</th><th>Students</th><th>Professionals</th><th>Students</th><th>Professionals</th><th>Students</th></tr><tr><th></th><th></th><th></th><th>mean</th><th>std</th><th>mean</th><th>std</th><th>mean</th><th>std</th><th>mean</th><th>std</th></tr></thead><tbody><tr><td>1</td><td>N (15, 0) vs G (10, 0)</td><td></td><td>45.30</td><td>24.91</td><td>30.60</td><td>31.89</td><td>48.24</td><td>27.60</td><td>41.31</td><td>31.96</td><td>46.34</td><td>24.78</td><td>50.23</td><td>27.25</td></tr><tr><td>2</td><td>N (15, 0) vs B (20, 0)</td><td></td><td>61.29</td><td>28.67</td><td>45.85</td><td>41.42</td><td>65.70</td><td>28.48</td><td>64.46</td><td>33.85</td><td>61.46</td><td>27.28</td><td>69.77</td><td>25.55</td></tr><tr><td rowspan="3">4</td><td>N (15, 0) vs G (10, 25)</td><td></td><td>43.70</td><td>26.40</td><td>35.99</td><td>31.42</td><td>44.08</td><td>24.21</td><td>40.31</td><td>30.33</td><td>44.20</td><td>23.15</td><td>39.42</td><td>29.96</td></tr><tr><td>N (15, 0) vs G (20, 25)</td><td></td><td>50.33</td><td>21.85</td><td>43.06</td><td>33.80</td><td>55.81</td><td>22.48</td><td>52.75</td><td>29.45</td><td>52.70</td><td>25.23</td><td>58.11</td><td>29.04</td></tr><tr><td>N (15, 0) vs G (20, 50)</td><td></td><td>37.73</td><td>25.69</td><td>37.91</td><td>36.23</td><td>41.78</td><td>25.74</td><td>50.10</td><td>35.08</td><td>44.11</td><td>31.89</td><td>50.22</td><td>35.71</td></tr><tr><td rowspan="3">5</td><td>N (15, 0) vs B (30, 25)</td><td></td><td>64.55</td><td>25.29</td><td>57.79</td><td>36.68</td><td>67.51</td><td>24.74</td><td>58.06</td><td>36.29</td><td>57.31</td><td>26.82</td><td>69.37</td><td>29.03</td></tr><tr><td>N (15, 0) vs B (40, 25)</td><td></td><td>61.29</td><td>29.05</td><td>42.95</td><td>40.53</td><td>60.13</td><td>25.82</td><td>48.45</td><td>38.90</td><td>60.67</td><td>24.31</td><td>69.66</td><td>27.43</td></tr><tr><td>N (15, 0) vs B (40, 50)</td><td></td><td>70.68</td><td>25.30</td><td>58.38</td><td>35.56</td><td>71.38</td><td>27.13</td><td>58.89</td><td>33.88</td><td>68.49</td><td>23.97</td><td>72.48</td><td>26.74</td></tr><tr><td rowspan="3">6</td><td>N (20, 25) vs G (10, 0)</td><td></td><td>61.79</td><td>21.76</td><td>48.71</td><td>33.38</td><td>61.89</td><td>25.06</td><td>64.62</td><td>29.80</td><td>63.43</td><td>22.85</td><td>61.14</td><td>27.22</td></tr><tr><td>N (30, 25) vs G (10, 0)</td><td></td><td>57.03</td><td>24.22</td><td>36.78</td><td>34.57</td><td>54.84</td><td>23.99</td><td>53.13</td><td>33.90</td><td>61.10</td><td>23.87</td><td>64.78</td><td>24.17</td></tr><tr><td>N (30, 50) vs G (10, 0)</td><td></td><td>68.09</td><td>22.82</td><td>55.07</td><td>35.20</td><td>70.35</td><td>22.56</td><td>69.20</td><td>30.52</td><td>70.75</td><td>22.95</td><td>70.61</td><td>27.39</td></tr><tr><td rowspan="3">7</td><td>N (20, 25) vs B (20, 0)</td><td></td><td>48.65</td><td>31.42</td><td>55.54</td><td>32.72</td><td>52.75</td><td>28.75</td><td>48.74</td><td>34.53</td><td>49.43</td><td>29.29</td><td>53.34</td><td>35.58</td></tr><tr><td>N (30, 25) vs B (20, 0)</td><td></td><td>54.35</td><td>28.20</td><td>67.32</td><td>33.42</td><td>62.94</td><td>25.92</td><td>62.87</td><td>32.42</td><td>62.20</td><td>29.49</td><td>70.18</td><td>30.72</td></tr><tr><td>N (30, 50) vs B (20, 0)</td><td></td><td>45.89</td><td>28.12</td><td>49.09</td><td>34.60</td><td>51.38</td><td>26.54</td><td>47.14</td><td>32.95</td><td>48.03</td><td>32.80</td><td>57.35</td><td>33.62</td></tr><tr><td rowspan="3">8</td><td>N (20, 25) vs G (10, 25)</td><td>1</td><td>40.76</td><td>29.67</td><td>18.51</td><td>29.74</td><td>49.70</td><td>30.87</td><td>37.00</td><td>34.08</td><td>53.56</td><td>24.17</td><td>51.88</td><td>29.37</td></tr><tr><td>N (30, 25) vs G (20, 25)</td><td></td><td>44.33</td><td>28.34</td><td>23.32</td><td>30.96</td><td>46.30</td><td>29.17</td><td>41.39</td><td>34.91</td><td>49.10</td><td>24.39</td><td>52.45</td><td>27.96</td></tr><tr><td>N (30, 50) vs G (20, 50)</td><td></td><td>41.89</td><td>30.83</td><td>21.28</td><td>31.95</td><td>49.38</td><td>30.51</td><td>36.27</td><td>36.11</td><td>51.84</td><td>25.10</td><td>53.96</td><td>28.80</td></tr><tr><td rowspan="3">9</td><td>N (20, 25) vs G (10, 25)</td><td>0</td><td>44.27</td><td>27.59</td><td>24.17</td><td>30.63</td><td>45.52</td><td>27.60</td><td>32.67</td><td>29.48</td><td>50.69</td><td>22.60</td><td>50.52</td><td>28.12</td></tr><tr><td>N (30, 25) vs G (20, 25)</td><td></td><td>43.41</td><td>24.07</td><td>26.57</td><td>31.25</td><td>46.97</td><td>27.33</td><td>35.23</td><td>30.85</td><td>46.48</td><td>24.36</td><td>50.35</td><td>30.39</td></tr><tr><td>N (30, 50) vs G (20, 50)</td><td></td><td>43.45</td><td>24.16</td><td>29.53</td><td>33.26</td><td>46.73</td><td>27.04</td><td>37.23</td><td>32.34</td><td>51.59</td><td>23.80</td><td>53.21</td><td>30.15</td></tr><tr><td rowspan="3">10</td><td>N (20, 25) vs G (10, 25)</td><td>-1</td><td>47.38</td><td>25.44</td><td>34.82</td><td>26.17</td><td>48.59</td><td>25.30</td><td>45.60</td><td>27.94</td><td>48.02</td><td>21.51</td><td>51.72</td><td>26.81</td></tr><tr><td>N (30, 25) vs G (20, 25)</td><td></td><td>47.95</td><td>23.11</td><td>33.31</td><td>27.04</td><td>50.51</td><td>26.57</td><td>41.60</td><td>31.01</td><td>52.39</td><td>23.92</td><td>51.97</td><td>27.37</td></tr><tr><td>N (30, 50) vs G (20, 50)</td><td></td><td>47.48</td><td>22.61</td><td>36.20</td><td>25.18</td><td>53.37</td><td>26.74</td><td>48.77</td><td>27.57</td><td>52.70</td><td>21.61</td><td>56.39</td><td>25.58</td></tr><tr><td rowspan="3">11</td><td>N (20, 25) vs B (30, 25)</td><td>1</td><td>56.03</td><td>29.75</td><td>42.37</td><td>36.15</td><td>59.25</td><td>24.41</td><td>48.63</td><td>36.98</td><td>53.64</td><td>27.29</td><td>62.88</td><td>29.94</td></tr><tr><td>N (30, 25) vs B (40, 25)</td><td></td><td>54.95</td><td>30.50</td><td>47.56</td><td>38.52</td><td>62.22</td><td>27.56</td><td>51.70</td><td>38.93</td><td>59.77</td><td>27.40</td><td>60.70</td><td>32.29</td></tr><tr><td>N (30, 50) vs B (40, 50)</td><td></td><td>53.58</td><td>32.15</td><td>37.67</td><td>40.33</td><td>57.38</td><td>28.66</td><td>47.14</td><td>38.24</td><td>50.95</td><td>29.01</td><td>62.74</td><td>29.42</td></tr><tr><td rowspan="3">12</td><td>N (20, 25) vs B (30, 25)</td><td>0</td><td>56.15</td><td>32.05</td><td>41.68</td><td>34.69</td><td>62.17</td><td>27.30</td><td>49.17</td><td>36.53</td><td>58.61</td><td>27.86</td><td>65.85</td><td>29.08</td></tr><tr><td>N (30, 25) vs B (40, 25)</td><td></td><td>59.55</td><td>30.90</td><td>45.11</td><td>37.14</td><td>62.67</td><td>27.52</td><td>53.35</td><td>37.48</td><td>59.54</td><td>26.52</td><td>63.60</td><td>30.85</td></tr><tr><td>N (30, 50) vs B (40, 50)</td><td></td><td>53.67</td><td>33.54</td><td>43.29</td><td>36.13</td><td>58.17</td><td>30.15</td><td>49.26</td><td>37.39</td><td>58.38</td><td>26.05</td><td>60.23</td><td>31.78</td></tr><tr><td rowspan="3">13</td><td>N (20, 25) vs B (30, 25)</td><td>-1</td><td>53.88</td><td>29.50</td><td>43.21</td><td>35.04</td><td>62.57</td><td>26.99</td><td>52.76</td><td>34.31</td><td>57.97</td><td>23.69</td><td>60.76</td><td>28.95</td></tr><tr><td>N (30, 25) vs B (40, 25)</td><td></td><td>60.08</td><td>28.23</td><td>45.61</td><td>35.74</td><td>61.75</td><td>26.86</td><td>58.39</td><td>34.20</td><td>63.49</td><td>23.79</td><td>62.96</td><td>30.55</td></tr><tr><td>N (30, 50) vs B (40, 50)</td><td></td><td>61.83</td><td>26.56</td><td>46.49</td><td>32.80</td><td>63.84</td><td>25.04</td><td>57.99</td><td>31.00</td><td>59.21</td><td>22.37</td><td>59.86</td><td>29.72</td></tr><tr><td colspan="2">Overall</td><td></td><td>52.54</td><td>28.52</td><td>40.80</td><td>35.76</td><td>56.12</td><td>27.75</td><td>49.51</td><td>34.71</td><td>55.25</td><td>26.27</td><td>59.02</td><td>30.23</td></tr></tbody></table>
# Internet Appendix

## A Experimental tasks 51

A.1 Portfolio task 51

A.2 Screenshot of a screen from the portfolio task 51

A.3 Internet links to the instructions for all treatments 52

A.4 Control tasks 53

A.5 Detailed description of the control tasks 54

A.6 Financial market professional questionnaire 62

## B Data collection 63

B.1 Samples 63

B.2 Practical procedures 64

## C Analysis 65

C.1 Portfolio task 65

C.2 Control tasks 67

C.3 Econometric analysis 68
E.2 Students vs. finance professionals

This appendix provides complementary evidence to Section 4.1.

Table 4 shows that the Student dummy is negative and significant in green contexts,
indicating that students allocate less to greener assets than finance professionals. In
brown contexts, the difference is not significant.

Turning to responsiveness, the results suggest that students display stronger asymmetries than professionals. In particular, students display stronger responsiveness than professionals to both greener and browner asset returns in brown contexts, while these differences are weaker and often insignificant in green contexts.

Finally, equality tests reported in Tables 12 and 13 confirm that these differences
are statistically significant in several specifications. The overall pattern is that both
groups exhibit pro-environmental preferences, but students invest less in greener assets
and respond more strongly to return incentives in the presence of negative externalities.
# A Experimental tasks

## A.1 Portfolio task

### A.1.1 Translated instructions from French to English (professional version)

This first part is composed of 13 independent rounds. There is no link between the rounds.

**Portfolio composition**

At the beginning of each round you will be endowed with €100.00, which you will have to entirely invest in two assets, asset 1 and asset 2, in order to compose a portfolio. To compose your portfolio, enter the amount of your investment in Asset 1. This amount must be an integer between 0 and 100. The amount of your investment in Asset 2 will be calculated automatically (100 - the amount invested in Asset 1). During the first two rounds, you will only have to compose one portfolio. From round 3 onward, you will have three portfolios to compose in each round. This is specified in the instructions that follow.

**Types of assets**

There are three types of assets: neutral, green and brown. Each of these assets can be either riskless or risky. The expected return and standard deviation of each asset will be given in the asset description.

* A green asset A green asset is an environmentally responsible asset. For any amount you invest in a green asset we commit to pay half of the amount to [Green company](*). Example: you invest €50.00 in a green asset, we will pay €25.00 to [Green company]. (*) [Green company] enables individuals and companies to take concrete action in favour of reforestation throughout the world.

* A brown asset A brown asset is an environmentally irresponsible asset. For any amount you invest in a brown asset we commit to pay half of the amount to [Brown association](*). Example: you invest €50.00 in a brown asset, we will pay €25.00 to [Brown association]. (*) [Brown association] is an international organisation of oil and gas producers, one of their technical areas of expertise is shale gas.

* A neutral asset does not give rise to any action on our part.

**Realized return**

A riskless asset will have a realized return equal to the expected return. A risky asset will have a realized return that will be equal, with a 50/50 chance, to either the expected return plus its standard deviation or the expected return minus its standard deviation. If we note R the expected return of the asset and E its standard deviation, the realized return is equal to R + E with 50/50 chance or R - E with 50/50 chance. Example: for an asset with an expected return of 40% and a standard deviation of 50%, its realized return will be equal, with 50/50 chance, to 40% + 50% = 90% or 40% - 50% = -10%. Value of the portfolio The initial value of the portfolio is equal to €100.00. The final value of the portfolio is equal to €100.00 plus the proportion of each asset multiplied by its realized return. Example: your portfolio is composed of 35% Asset 1 and 65% Asset 2. The value of the portfolio is equal to €100.00 + 35% of the realized return of Asset 1 + 65% of the realized return of Asset 2. Your gain for this part At the end of the experiment, if you are selected to be paid (1 chance out of 10), one of the rounds of this part will be drawn at random. If it is round 1 or 2, the value of your portfolio for this round will be your payoff for this part. If it is a round between round 3 and round 13, a second draw will determine the portfolio (1, 2, or 3) that will be used to determine your payoff for the round and therefore your payoff for this part.

**Portfolio value**

The initial value of the portfolio is 100.00 ECU. The final value of the portfolio is equal to 100.00 ECU plus the proportion of each asset multiplied by its realized return.

Example: Your portfolio is composed of 35% of asset 1 and 65% of asset 2. The portfolio's value is equal to 100.00 ECU + 35% of the realized return of asset 1 + 65% of the realized return of asset 2.

**Gain in the game**

At the end of the experiment, if you are selected to be paid (1 chance out of 10), one of the rounds of this part will be drawn at random. If it is round 1 or 2, the value of your portfolio for this round will be your payoff for this part. If it is a round between round 3 and round 13, a second draw will determine the portfolio (1, 2, or 3) that will be used to determine your payoff for the round and therefore your payoff for this part.

## A.2 Screenshot of a screen from the portfolio task

Figure 2 displays one screen of the portfolio task as an example. The detailed instructions of the 3 treatments are provided in the next subsection. All characteristics of the 13 screens are provided in Table 1.
# E.3 Treatments: detailed procedures and instructions

## E.3.1 Experimental design and randomization

Participants were randomly assigned to one of three treatments: baseline, ranking, or tax. In the PRO pool, 66 participants faced the baseline treatment, 63 the ranking treatment, and 61 the tax treatment. In the STUD pool, 103 faced baseline, 84 ranking, and 92 tax. Treatments were randomly ordered and orthogonal to contexts (green vs. brown), asset characteristics (returns and risks), and the donation mechanism. These factors were under the exclusive control of the experimenter and entered the regressions only as categorical variables.

## E.3.2 Description of the ranking treatment

The ranking treatment was designed to provide real-time feedback on the relative "environmental performance" of portfolio choices. Participants were informed that the experiment had previously been conducted with a sample of 50 individuals from the same pool (PRO or STUD), under identical frames (green vs. neutral or neutral vs. brown). Their own allocation parameter $\lambda$ was compared with these 50 pre-recorded values, and a private ranking was displayed at the end of each decision. The benchmark participants did not know that their choices would later be used for comparison and had not themselves been exposed to a ranking mechanism. Two examples were provided in the instructions to illustrate how the ranking was computed from the allocation between the two available assets. The benchmark sample of 50 was drawn once per pool and remained fixed across all sessions of the ranking treatment. Therefore, all participants in the ranking treatment were compared against the same benchmark distribution. Full instructions and screenshots of the ranking interface are reproduced in Internet Appendix A.3.
<figure><img src="image_5.png" /><figcaption>Figure 2: SCREENSHOT OF A FRAME IN THE PORTFOLIO TASK (FRAME 4)</figcaption></figure>

## A.3 Internet links to the instructions for all treatments

### A.3.1 Baseline treatment

http://leem.umontpellier.fr/files/appendix/stranded_asset/screenshots_PRO_baseline.pdf

### A.3.2 Ranking treatment

http://leem.umontpellier.fr/files/appendix/stranded_asset/screenshots_PRO_ranking.pdf

### A.3.3 Tax treatment

http://leem.umontpellier.fr/files/appendix/stranded_asset/screenshots_PRO_tax.pdf
### E.3.3 Description of the tax treatment

The tax treatment introduced a convex penalty on the expected return of the browner asset. Formally, if $\Delta\mu = \mu_b - \mu_g$, the net expected return of the portfolio is:

$$ \mu_p = \lambda\mu_g + (1 - \lambda)(\mu_b - (1 - \lambda)\Delta\mu) = \mu_g + (1 - \lambda)\Delta\mu - (1 - \lambda)^2\Delta\mu. $$

This functional form ensures that the marginal penalty increases as $\lambda$ decreases: participants allocating little to the greener asset face stronger incentives to adjust upward. The resulting expected return function is concave in $\lambda$, increasing on the interval $[0, 1/2]$ and decreasing afterwards.

### Theoretical background and motivation

The ranking treatment builds on the literature on social comparison (Festinger, 1954) and on recent evidence that rankings shape financial risk-taking and sustainable investment decisions (Kirchler et al., 2018; Bault et al., 2008; Liu and Ma, 2020). Rankings are widely used in professional finance (e.g., performance tables) and in SRI contexts (e.g., ESG ratings, fund classifications).

The tax treatment draws on a large literature in environmental and financial economics. Convex taxation has been studied as a corrective mechanism for externalities (Nordhaus, 1992; Bovenberg and De Mooij, 1994; Goulder, 2013), as a proxy for corporate tax effects in capital markets (Sarkar, 2008; Graham, 2003), and for products associated with behavioral biases (Gerster and Kramm, 2019).

### Interpretation of treatment effects

The baseline average allocation to the greener asset was $\lambda = 52.5\%$ for professionals and $\lambda = 40.8\%$ for students. The convex nature of the tax implies stronger marginal incentives when baseline allocations are lower, which explains the stronger treatment effect among students. In the ranking treatment, baseline differences between professionals and students also matter: because students allocated less to green and neutral assets in the
## A.4 Control tasks

Task 1 was intended to measure risk preferences, following the higher-order risk-preferences methodology of Noussair et al. (2014) (relevant details are provided in Internet Appendix A.5.1). Respondents were given 15 binary choice questions involving compound lotteries to measure their risk aversion, prudence, and temperance. Risk aversion is related to the moment of the second order of the underlying risk distribution, while prudence and temperance relate to the third (skewness) and fourth moment (kurtosis) of the distribution. Skewness and kurtosis are dimensions absent from the portfolio task. But as shown by Bottasso et al. (2022), risk, prudence, and temperance are linked in the choices of finance professionals. Respective measures might then provide significant information regarding the participants’ relation to risk in portfolio decisions. The task provides three round scores between 0 and 5 for the three dimensions.

The second task is the Social Value Orientation introduced by Murphy et al. (2011) and detailed in Internet Appendix A.5.2. It provides a real number between -20 and 60 which relates to behaviors between being competitive and altruistic. We develop this task below.

The third task is the version of the 11–20 game, originally designed by Arad and Rubinstein (2012) and modified by Alaoui and Penta (2016). It is detailed in Internet Appendix A.5.3. It is a one-shot, two-player game to assess each participant’s level-k strategic depth of reasoning (Arad and Rubinstein, 2012), in a fashion similar to the beauty contest game (Brañas-Garza et al., 2012).

The fourth task is the cognitive reflection test, hereafter CRT (Frederick, 2005; Toplak et al., 2011; Korniotis and Kumar, 2010). It has seven numerical questions to measure cognitive and concentration skills. It provides a round score between 0 and 7. Corgnet et al. (2018) showed that such skills are predictors of investors’ performance. The third and fourth tasks are related to strategic skills and cognition. They are thus valid control variables for participants’ heterogeneous choices.

The last task is a survey of 15 questions of 5 modalities between disagreement and agreement (the NEP scale of Dunlap et al. (2000)). They relate to environmental opinion and provide a round number between 15 and 75 that reflects pro-environmental preferences.

In the previous section, these variables were added as control variables. Regressions that omit those variables provide coefficients that are almost identical to that of Table 4, so that any disparities are not caused by those variables. Our results are therefore robust
baseline, they could achieve relatively favorable ranks without dramatically increasing $\lambda$, which helps explain why the effect is concentrated in STUD green contexts.

These treatment-specific mechanisms complement the main results presented in Section 4.2, which report only the core estimates.
to individual heterogeneity induced by the control tasks. Still, it can be expected in the
same fashion as Holmen et al. (2021), that finance professionals obtain different results,
at least partly, for these tasks than other populations (in our case, students).

Two of the tasks are of particular interest in relation to our hypothesis of green preference. Following Riedl and Smeets (2017), we focus on the influence of revealed pro-sociability and expressed pro-environmental preferences on $\lambda$.

## A.5 Detailed description of the control tasks

### A.5.1 Higher order risk preferences

The risk, prudence, and temperance task (RPT task) developed by Noussair et al. (2014), and also used by Bottasso et al. (2022) with financial market professionals, has 15 binary choices to elicit levels of risk aversion, prudence and temperance. Five questions are dedicated to each of the three dimensions.

Table 7 displays the 15 questions. Notation [a_b] indicates an equiprobable lottery in which either a or b is added to the previous total, each with a probability of 0.5 (Bottasso et al., 2022).

1. For the elicitation of risk aversion, the choice is between a sure amount and a lottery (Figure 3).

2. The elicitation of prudence relies on two-stage lotteries. Each choice involves the same two sure outcomes (low and high). For one option, a second-stage lottery is attached to the low outcome, and for the other option, the same lottery is attached to the high outcome. A prudent participant chooses the option for which the lottery is attached to the high outcome (Figure 4).

3. Finally, to elicit temperance, two second-stage lotteries are attached to the outcome of the first-stage lottery: they are either attached to the same outcome or spread over the two outcomes. A temperate individual chooses to spread the additional risk according to the second-stage lottery (Figure 5).

The most straightforward treatment of this task is to count the number of choices revealing respectively more risk averse, more prudent, and more tempered preferences. Each of the three scores is thus a round number between 0 and 5.
## E.4 Control tasks

Table 9 reports the mean of the variable of interest for each of the control tasks, for both samples, as well as a column that displays the difference between the two samples and a column that reports the p-value of a two-sided t-test (H0 is the two samples have an equal mean).

<table><caption>Table 9: SUMMARY STATISTICS FOR THE CONTROL TASKS</caption><thead><tr><th></th><th>Professionals</th><th>Students</th><th>Diff.</th><th>p-value</th></tr></thead><tbody><tr><td>Risk aversion</td><td>2.537</td><td>2.803</td><td>-0.266</td><td>.065</td></tr><tr><td>Prudence</td><td>3.079</td><td>3.480</td><td>-0.401</td><td>.009**</td></tr><tr><td>Temperance</td><td>2.405</td><td>2.642</td><td>-0.237</td><td>.145</td></tr><tr><td>SVO</td><td>20.517</td><td>18.083</td><td>2.434</td><td>.080</td></tr><tr><td>CRT</td><td>3.516</td><td>3.319</td><td>0.197</td><td>.306</td></tr><tr><td>Eleven-twenty (choice)</td><td>15.821</td><td>16.509</td><td>-0.688</td><td>.007**</td></tr><tr><td>Eleven-twenty (expected payoff)</td><td>18.994</td><td>19.869</td><td>-0.875</td><td>.005**</td></tr><tr><td>NEP</td><td>56.826</td><td>56.283</td><td>0.543</td><td>.417</td></tr></tbody></table>

** 1%, * 5%

Students seem to be more risk averse and prudent than professionals but there is no difference between the two samples in terms of temperance. A Pearson correlation test shows there is no correlation between risk aversion and prudence ($r = 0.048, p = .512$ for professionals and $r = -0.009, p = .877$ for students) but a positive correlation between prudence and temperance ($r = 0.280, p < .001$ and $r = 0.262, p < .001$, respectively).

The average SVO score of professionals is slightly higher compared to the students, the difference is significant at the 10% level. But this score is also used to determine the class of behaviour to which the participant belongs, ranging from competitive, individualistic, pro-social or altruistic (see Murphy et al., 2011, for a detailed explanation). Figure 10 reports the distribution of the scores for the two samples. None of the participants in either sample revealed to be altruistic. On the other end of the spectrum, financial professionals do not appear to be more competitive than students (4.21% of the professionals and 2.15% of the students). Then, professionals are equally divided between individualists (47.37%) and pro-social (48.42%), while 57.35% of students are individualists and
<table><caption>Table 7: LIST OF CHOICE TASKS IN THE RPT TASK</caption><thead><tr><th>Name of task</th><th>Left lottery</th><th>Right lottery</th></tr></thead><tbody><tr><td>Riskav 1</td><td>20</td><td>[65-5]</td></tr><tr><td>Riskav 2</td><td>25</td><td>[65-5]</td></tr><tr><td>Riskav 3</td><td>30</td><td>[65-5]</td></tr><tr><td>Riskav 4</td><td>35</td><td>[65-5]</td></tr><tr><td>Riskav 5</td><td>40</td><td>[65-5]</td></tr><tr><td>Prud 1</td><td>[(90 + [20-20])-60]</td><td>[90-(60 + [20-20])]</td></tr><tr><td>Prud 2</td><td>[(90 + [10-10])-60]</td><td>[90-(60 + [10-10])]</td></tr><tr><td>Prud 3</td><td>[(90 + [40-40])-60]</td><td>[90-(60 + [40-40])]</td></tr><tr><td>Prud 4</td><td>[(135 + [30-30])-90]</td><td>[135-(90 + [30-30])]</td></tr><tr><td>Prud 5</td><td>[(65 + [20-20])-35]</td><td>[65-(35 + [20-20])]</td></tr><tr><td>Temp 1</td><td>[(90 + [30-30])-90 + [30-30]]</td><td>[90-(90 + [30-30] + [30-30])]</td></tr><tr><td>Temp 2</td><td>[(90 + [30-30])-90 + [10-10]]</td><td>[90-(90 + [30-30] + [10-10])]</td></tr><tr><td>Temp 3</td><td>[(90 + [30-30])-90 + [50-50]]</td><td>[90-(90 + [30-30] + [50-50])]</td></tr><tr><td>Temp 4</td><td>[(30 + [10-10])-30 + [10-10]]</td><td>[30-(30 + [10-10] + [10-10])]</td></tr><tr><td>Temp 5</td><td>[(70 + [30-30])-70 + [30-30]]</td><td>[70-(70 + [30-30] + [30-30])]</td></tr></tbody></table>

**Description:** As in Bottasso et al. (2022), [a_b] indicates an equiprobable lottery in which either a or b is received. Choice of the left lottery indicates the more risk averse, prudent and temperate, respectively.
40.50% are pro-social. Overall, based on a Kolmogorov-Smirnov test, we can reject the hypothesis that the two distributions are identical ($p = .042$).

<figure><img src="image_12.png" /><figcaption>Figure 10: DISTRIBUTION OF THE SVO SCORE IN BOTH SAMPLES</figcaption></figure>

The average CRT score of professionals is slightly higher than that of students, but is more homogeneous in the latter sample, especially for the highest scores. A Kolmogorov-Smirnov test does not reveal any significant difference, however ($p = .112$).

Finally, Figure 11 shows that both populations exhibit a similar distribution for the NEP score, meaning that they have similar concerns about environmental issues (Kolmogorov-Smirnov test, $p = .431$).

To summarize the results from the control tasks, professionals are less risk-averse and less prudent than students. They are more pro-social, and they are less successful at guessing the depth of reasoning of their cohort members. On the other hand, the two samples appear to have a similar profile in terms of temperance, cognitive abilities and environmental concern.
<figure><img src="image_6.png" /><figcaption>Figure 4: CHOICES 6 TO 10 TO ELICIT PRUDENCE IN THE RPT TASK.</figcaption></figure>

<figure><img src="image_7.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Figure 11: DISTRIBUTION OF THE NEP SCALE SCORE, BY SAMPLE</figcaption></figure>

E.5 Econometric analysis

E.5.1 Regression analysis

As noted in Table 4, the regression method is as follows. We compute estimated coefficients in a random-effect (RE) Tobit panel regression with robust clustered standard errors at the participant level. This accounts for potential heteroskedasticity and serial correlation of errors (in parenthesis). Estimations were done with StataCorp software (2019).<fnref n="20" />

As usual, *, **, *** account for 10%, 5% and 1% significance level respectively. The last lines, χ² and p-value, stand for the model non-null significance test.

In the analysis, the dependent variable is the proportion invested in the greener as-
set (λ). Context denotes screens used for estimation: *Green* context relates to screens
1,4,6,8,9, and 10; *Brown* context relates to screens 2,5,7,11,12, and 13. *All* combines
*Green* and *Brown* (screen 3 is excluded from the analysis). *N* is the number of related
observations (samples and selected screens).

<footnote n="20"><sup>20</sup>Stata: Release 16. Statistical Software. College Station, TX: StataCorp LLC. Further information on this class of models is available at https://www.stata.com/manuals/memetobit.pdf.</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 5: CHOICES 11 TO 15 TO ELICIT TEMPERANCE IN THE RPT TASK.</figcaption></figure>
Table 10 refers to the estimation in Section 4.1, while Table 11 refers to the estimation
in Section 5.

The *Green* dummy variable refers to the *Green* context (*Green* = 1 for Green context and 0 for Brown context). The *Student* dummy variable controls for the POOL type (*Student* = 1 if the participant belongs to the STUD sample, and 1 if he belongs to the PRO sample). *Correlation* is a categorical variable which equals 0 for screens 1 to 7, 1 for screens 8 and 11, 2 for screens 9 and 12, and 3 for screens 10 and 13.

Variables specific to the PRO sample, such as finance jobs and investment strategies
are presented in Table 6, while variables specific to the STUD sample, such as disciplines
are presented in Subsubsection 5.1.2.

In Table 11, all the variables present in Table 10 are taken as control variables, except
SVO and NEP which are shown (cf. Appendix E.4 for descriptive statistics). In addition,
Employer type (=0 if “bank”, =1 otherwise), Asset Class (categories: “Money market”,
“Forex market”, “Equity”, “Bonds and FI”, “Others”), Market type (categories: “spot
market”, “derivatives”, “both”, “others”)<fnref n="21" />, Age (in years), Gender (=0 for female, =1 for
male) and Student level (in years)<fnref n="22" /> are hidden control variables in Table 11.

<footnote n="21">²¹All of these variables are for professionals.</footnote>

<footnote n="22">²²These last 3 variables are for students.</footnote>
### A.5.2 Prosociality

The Social Value Orientation (SVO) task was introduced by Murphy et al. (2011) to measure pro- and anti-social preferences. The task consists in choosing a payoff distribution between self and an anonymous counterpart from a set of nine possible distributions. We rely on the six-item version of the SVO to provide a specific non-negative aggregate score, which allows us to determine the SVO score. The SVO score is computed as a function of the average self-allocation $\bar{A}_s$ and the average transfer to the counterpart $\bar{A}_o$:

$$SVO = \arctan \left( \frac{\bar{A}_o - 50}{\bar{A}_s - 50} \right).$$

The result is an angle between -20° and 60°. Following Murphy et al. (2011), respondents can be categorized as competitive, individualist, pro-social, or altruist, depending on the interval in which the SVO score is located. We use the direct (continuous) score as an explanatory variable, and we check for robustness with the categorization proposed by Murphy et al. (2011). The instructions displayed on the participants' screen are given below, as well as a screenshot of the decision screen (Figure 6).

This third part consists of 6 periods. In each period, you have to decide how to divide a sum of money between you and a person participating in this experiment. You can't identify the other person and she can't identify you. There is no right or wrong response, and the data will be treated anonymously. For each of the 6 periods, indicate the allocation you would like to prefer by clicking on the corresponding button. Within each pair, randomly formed by the computer, there will be two successive draws. A first draw will determine the period to be used for remuneration. Then, a second draw will select one of the two in the pair so that her decision will apply to the pair, so either your decision will apply or the other's decision will apply. The amounts on the screens are expressed in ECU.

### A.5.3 Strategic thinking

In a third additional task, we measured the depth of reasoning using the 11-20 game from Alaoui and Penta (2016).

The game involves two players, assigned randomly to each other. Each one independently picks an integer number between 11 and 20. Three rules define the nominal reward of the player:

(a) he/she receives the chosen number,
<table><caption>Table 10: ESTIMATED COEFFICIENTS OF TABLE 4</caption><thead><tr><th>Sample<br>Screens<br>N</th><th>(1)<br>ALL<br>All<br>15008</th><th>(2)<br>ALL<br>Green<br>7504</th><th>(3)<br>ALL<br>Brown<br>7504</th><th>(4)<br>PRO<br>Green<br>3040</th><th>(5)<br>PRO<br>Brown<br>3040</th><th>(6)<br>STUD<br>Green<br>4464</th><th>(7)<br>STUD<br>Brown<br>4464</th></tr></thead><tbody><tr><td colspan="8"><em>Asset characteristics</em></td></tr><tr><td>Expected return<br>of greener asset</td><td>1.19**<br>(0.09)</td><td>0.92**<br>(0.11)</td><td>1.43**<br>(0.13)</td><td>0.66**<br>(0.15)</td><td>0.93**<br>(0.14)</td><td>1.13**<br>(0.15)</td><td>1.85**<br>(0.20)</td></tr><tr><td>Expected return<br>of browner asset</td><td>-0.81**<br>(0.09)</td><td>-0.70**<br>(0.11)</td><td>-1.01**<br>(0.11)</td><td>-0.56**<br>(0.13)</td><td>-0.57**<br>(0.12)</td><td>-0.82**<br>(0.16)</td><td>-1.38**<br>(0.19)</td></tr><tr><td>Standard deviation<br>of greener asset</td><td>-0.51**<br>(0.04)</td><td>-0.36**<br>(0.04)</td><td>-0.69**<br>(0.05)</td><td>-0.39**<br>(0.06)</td><td>-0.59**<br>(0.06)</td><td>-0.34**<br>(0.06)</td><td>-0.78**<br>(0.08)</td></tr><tr><td>Standard deviation<br>of browner asset</td><td>0.50**<br>(0.04)</td><td>0.54**<br>(0.04)</td><td>0.52**<br>(0.05)</td><td>0.50**<br>(0.06)</td><td>0.42**<br>(0.05)</td><td>0.57**<br>(0.07)</td><td>0.61**<br>(0.08)</td></tr><tr><td>Correlation 1</td><td>-10.01**<br>(1.28)</td><td>-15.44**<br>(1.64)</td><td>-3.94*<br>(1.64)</td><td>-7.79**<br>(2.08)</td><td>-0.78<br>(1.84)</td><td>-21.82**<br>(2.47)</td><td>-6.80*<br>(2.65)</td></tr><tr><td>Correlation 0</td><td>-8.66**<br>(1.26)</td><td>-15.02**<br>(1.56)</td><td>-1.67<br>(1.62)</td><td>-8.60**<br>(1.83)</td><td>-2.33<br>(1.76)</td><td>-20.36**<br>(2.44)</td><td>-5.28*<br>(2.63)</td></tr><tr><td>Correlation -1</td><td>-3.97**<br>(1.13)</td><td>-7.89**<br>(1.42)</td><td>0.32<br>(1.60)</td><td>-5.07**<br>(1.90)</td><td>3.87<br>(1.87)</td><td>-10.05**<br>(2.07)</td><td>-2.74<br>(2.51)</td></tr><tr><td colspan="8"><em>Sample characteristics</em></td></tr><tr><td>Ranking treatment</td><td>7.41**<br>(2.41)</td><td>10.01**<br>(2.57)</td><td>3.99<br>(3.87)</td><td>4.56<br>(3.26)</td><td>2.53<br>(4.78)</td><td>14.09**<br>(3.91)</td><td>5.39<br>(5.87)</td></tr><tr><td>Tax treatment</td><td>16.04**<br>(2.12)</td><td>18.81**<br>(2.50)</td><td>12.69**<br>(3.74)</td><td>6.62*<br>(2.92)</td><td>-1.13<br>(4.35)</td><td>27.44**<br>(3.78)</td><td>24.46**<br>(5.85)</td></tr><tr><td>Student</td><td>-5.92**<br>(2.00)</td><td>-7.89**<br>(2.06)</td><td>-2.67<br>(2.95)</td><td></td><td></td><td></td><td></td></tr><tr><td>Green</td><td>-10.47**<br>(1.59)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="8"><em>Control variables</em></td></tr><tr><td>Intercept</td><td>37.38**<br>(9.45)</td><td>38.24**<br>(9.20)</td><td>25.44<br>(13.56)</td><td>55.08**<br>(12.53)</td><td>45.59**<br>(16.97)</td><td>16.89<br>(12.63)</td><td>1.93<br>(18.57)</td></tr><tr><td>Risk aversion</td><td>-1.16<br>(0.64)</td><td>-0.15<br>(0.69)</td><td>-2.32**<br>(1.04)</td><td>-0.29<br>(0.88)</td><td>-0.94<br>(1.32)</td><td>0.02<br>(1.01)</td><td>-3.64**<br>(1.51)</td></tr><tr><td>Prudence</td><td>0.44<br>(0.64)</td><td>-0.01<br>(0.62)</td><td>0.69<br>(1.01)</td><td>0.65<br>(0.87)</td><td>-0.49<br>(1.29)</td><td>-0.79<br>(0.86)</td><td>1.06<br>(1.49)</td></tr><tr><td>Temperance</td><td>-0.91<br>(0.58)</td><td>-1.10<br>(0.62)</td><td>-0.49<br>(0.94)</td><td>-0.81<br>(0.80)</td><td>-1.03<br>(1.23)</td><td>-1.40<br>(0.90)</td><td>0.11<br>(1.38)</td></tr><tr><td>SVO</td><td>0.24**<br>(0.06)</td><td>0.31**<br>(0.06)</td><td>0.17<br>(0.10)</td><td>0.11<br>(0.08)</td><td>0.24*<br>(0.11)</td><td>0.43**<br>(0.10)</td><td>0.01<br>(0.16)</td></tr><tr><td>NEP</td><td>0.37*<br>(0.15)</td><td>0.17<br>(0.15)</td><td>0.62**<br>(0.22)</td><td>-0.02<br>(0.20)</td><td>0.27<br>(0.28)</td><td>0.33<br>(0.21)</td><td>1.06**<br>(0.30)</td></tr><tr><td>CRT</td><td>-0.25<br>(0.48)</td><td>-1.67**<br>(0.52)</td><td>1.51<br>(0.81)</td><td>-1.53*<br>(0.62)</td><td>1.88*<br>(0.95)</td><td>1.60*<br>(0.77)</td><td>1.61<br>(1.23)</td></tr><tr><td>k-level</td><td>-0.26<br>(0.33)</td><td>-0.34<br>(0.42)</td><td>-0.99<br>(0.53)</td><td>0.23<br>(0.50)</td><td>-1.41*<br>(0.69)</td><td>-0.61<br>(0.61)</td><td>-0.93<br>(0.77)</td></tr><tr><td>σ² (Intercept)</td><td>369.73**<br>(38.76)</td><td>431.32**<br>(52.34)</td><td>1004.35**<br>(104.97)</td><td>257.15**<br>(33.26)</td><td>534.59**<br>(69.68)</td><td>560.91**<br>(97.65)</td><td>1411.10**<br>(200.70)</td></tr><tr><td>σ² (residual)</td><td>1162.16**<br>(63.55)</td><td>843.55**<br>(45.70)</td><td>902.87**<br>(53.69)</td><td>523.00**<br>(38.29)</td><td>509.43**<br>(40.72)</td><td>1125.58**<br>(79.38)</td><td>1266.65**<br>(97.26)</td></tr><tr><td>χ²</td><td>367.37</td><td>326.30</td><td>231.49</td><td>114.90</td><td>136.44</td><td>242.66</td><td>149.80</td></tr><tr><td>p-value</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr></tbody></table>
<caption>Figure 6: SCREENSHOT OF THE SVO TASK (FIRST ALLOCATION)</caption>

Select the distribution you prefer by clicking on the corresponding button.

<table><tr><td>You receive</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td><td>85</td></tr><tr><td></td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>The other receives</td><td>85</td><td>76</td><td>68</td><td>59</td><td>50</td><td>41</td><td>33</td><td>24</td><td>15</td></tr></table>

<figure><img src="image_9.png" /></figure>

(b) if his/her number is exactly one unit less the opponent's number, he/she receives an extra 20,

(c) if his/her number is exactly his/her opponent's number, he/she receives an extra 10.

The simple game grounded on (a) and (b) has been introduced by Arad and Rubinstein (2012), as a one-shot game designed to assess the depth of strategic thinking. Level-0 (non strategic) optimal choice is given by considering only condition (a), and leads to choice 20. According to condition (b), Level-$k$ reasoning, for $k \ge 1$, follows from the best response to Level-$(k-1)$, and leads to choose the number $20-k$, or number 21 for Level-10. The cycle of best-responses is broken by condition (c). The condition (c) has been introduced by Alaoui and Penta (2016) to change the unique mixed Nash equilibrium of the original game of Arad and Rubinstein (2012) in a pure Nash equilibrium at $11^{36}$. The instructions of the game, displayed on participants' screen, are given below.

<footnote n="36">³⁶In order to study the individual strategic level, a pure Nash equilibrium, that is, an explicit choice of a number, is obviously preferable to a mixed Nash Equilibrium.</footnote>
<table><caption>Table 11: ESTIMATED COEFFICIENTS OF TABLE 5</caption><thead><tr><th>Sample<br>Screens<br>N</th><th>(1)<br>PRO<br>All<br>6048</th><th>(2)<br>PRO<br>Green<br>3024</th><th>(3)<br>PRO<br>Brown<br>3024</th><th>(4)<br>STUD<br>All<br>8928</th><th>(5)<br>STUD<br>Green<br>4464</th><th>(6)<br>STUD<br>Brown<br>4464</th></tr></thead><tbody><tr><td>SVO</td><td>0.12<br>(0.08)</td><td>0.09<br>(0.08)</td><td>0.17<br>(0.11)</td><td>0.23**<br>(0.09)</td><td>0.43**<br>(0.10)</td><td>-0.01<br>(0.15)</td></tr><tr><td>NEP</td><td>0.05<br>(0.18)</td><td>-0.11<br>(0.18)</td><td>0.26<br>(0.25)</td><td>0.53*<br>(0.21)</td><td>0.29<br>(0.25)</td><td>0.79*<br>(0.35)</td></tr><tr><td><em>Finance jobs</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sales trading</td><td>-0.36<br>(3.38)</td><td>2.43<br>(3.32)</td><td>-3.41<br>(4.75)</td><td></td><td></td><td></td></tr><tr><td>Portfolio Manager</td><td>-7.21<br>(5.29)</td><td>-1.53<br>(5.46)</td><td>-13.19*<br>(6.44)</td><td></td><td></td><td></td></tr><tr><td>Analyst</td><td>15.75**<br>(4.47)</td><td>15.58**<br>(4.89)</td><td>16.49*<br>(6.54)</td><td></td><td></td><td></td></tr><tr><td>Support functions &amp; others</td><td>3.77<br>(4.48)</td><td>7.38<br>(4.82)</td><td>0.15<br>(6.78)</td><td></td><td></td><td></td></tr><tr><td><em>Investment strategies</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Value Investing</td><td>11.37*<br>(4.66)</td><td>6.99<br>(4.34)</td><td>16.30*<br>(6.43)</td><td></td><td></td><td></td></tr><tr><td>Mispricing</td><td>18.77**<br>(4.92)</td><td>13.32*<br>(6.02)</td><td>24.20**<br>(6.21)</td><td></td><td></td><td></td></tr><tr><td>Global Macro</td><td>10.87*<br>(4.33)</td><td>4.72<br>(3.89)</td><td>17.89**<br>(6.15)</td><td></td><td></td><td></td></tr><tr><td>None</td><td>1.20<br>(4.34)</td><td>-3.79<br>(4.33)</td><td>6.44<br>(5.39)</td><td></td><td></td><td></td></tr><tr><td>Market Making</td><td>11.63**<br>(3.70)</td><td>5.57<br>(3.53)</td><td>17.70**<br>(5.70)</td><td></td><td></td><td></td></tr><tr><td>Others</td><td>10.61*<br>(5.39)</td><td>5.68<br>(5.05)</td><td>17.74*<br>(8.12)</td><td></td><td></td><td></td></tr><tr><td><em>Disciplines</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Humanities</td><td></td><td></td><td></td><td>12.78<br>(9.18)</td><td>-2.43<br>(10.70)</td><td>24.89<br>(16.40)</td></tr><tr><td>Formal Sciences</td><td></td><td></td><td></td><td>7.51<br>(5.50)</td><td>1.83<br>(5.98)</td><td>13.92<br>(8.66)</td></tr><tr><td>Economics</td><td></td><td></td><td></td><td>6.70<br>(5.04)</td><td>1.38<br>(5.49)</td><td>12.07<br>(6.86)</td></tr><tr><td>Health</td><td></td><td></td><td></td><td>15.06**<br>(5.72)</td><td>7.37<br>(6.10)</td><td>21.62*<br>(9.39)</td></tr><tr><td>Natural Sciences</td><td></td><td></td><td></td><td>10.18<br>(6.71)</td><td>4.29<br>(6.64)</td><td>14.95<br>(9.01)</td></tr><tr><td>Social Sciences</td><td></td><td></td><td></td><td>9.53<br>(6.55)</td><td>5.67<br>(5.68)</td><td>15.38<br>(11.02)</td></tr><tr><td>Others</td><td></td><td></td><td></td><td>17.31*<br>(7.42)</td><td>13.64<br>(9.04)</td><td>21.64<br>(13.99)</td></tr><tr><td>Intercept</td><td>42.46**<br>(12.64)</td><td>48.51**<br>(12.12)</td><td>27.49<br>(17.13)</td><td>22.52<br>(20.62)</td><td>10.67<br>(23.27)</td><td>25.36<br>(33.79)</td></tr><tr><td>σ<sup>2</sup>(Intercept)</td><td>211.83**<br>(25.67)</td><td>200.12**<br>(24.97)</td><td>424.26**<br>(52.79)</td><td>406.77**<br>(60.42)</td><td>546.93**<br>(96.64)</td><td>1373.83**<br>(199.74)</td></tr><tr><td>σ<sup>2</sup>(residual)</td><td>636.82**<br>(40.74)</td><td>525.22**<br>(38.55)</td><td>512.31**<br>(40.95)</td><td>1649.37**<br>(119.87)</td><td>1125.48**<br>(79.37)</td><td>1266.40**<br>(97.23)</td></tr><tr><td>χ<sup>2</sup></td><td>213.28</td><td>176.14</td><td>174.06</td><td>276.77</td><td>268.98</td><td>160.56</td></tr><tr><td>p-value</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr></tbody></table>

E.5.2 Coefficient analysis

Table 12 describes the tests that are performed on the estimates to test the equality of two parameters in the regression displayed in Table 10. Applied tests are Wald tests with $H_0$ assuming equality of two parameters in the same random-effect Tobit panel regression. The table displays p-values, with bold font when value is below 5%, indicating that the two parameters are significantly different at this level.
There is only one decision in this part. You must choose an integer between 11 and 20. You will receive the amount in euros that you choose. In addition, at the end of the experiment, pairs of participants will be randomly formed, and the selected numbers will be compared.
If you choose the same number as the other member of your pair, you will receive an additional 10€. If you choose a number that is exactly one less than the other member of your pair, you receive an additional 20€.

*Examples:*

* If you choose 17 and the other member of your pair has chosen 19, then you receive 17€ and he receives 19€.

* If you choose 12 and the other member of your pair has chosen 13, then you will receive 32€ and he will receive 13€.

* If you choose 16 and the other member of your pair has chosen 16, then you will receive 26€ and he will receive 26€.

### A.5.4 Cognitive and Reflection Task

We relied on a standard seven questions cognitive reflection test (Frederick, 2005; Korniotis and Kumar, 2010; Toplak et al., 2011). The answer to each question is either right of wrong. This task provides therefore a score between 0 to 7, which serves as a proxy for abstract reasoning, problem-solving and mathematical formalization. The seven questions are given below. They were displayed on the same screen, one below the other.
<table><caption>Table 12: TESTS OF PARAMETERS OF GREENER VS BROWNER ASSETS IN THE SAME REGRESSION</caption><thead><tr><th rowspan="2">Line and column</th><th colspan="2">P-values</th></tr><tr><th>Expected Return</th><th>Standard Deviation</th></tr></thead><tbody><tr><td>L1C1 vs - L2C1</td><td><strong>0.0000</strong></td><td></td></tr><tr><td>L1C2 vs - L2C2</td><td><strong>0.0010</strong></td><td></td></tr><tr><td>L1C3 vs - L2C3</td><td><strong>0.0000</strong></td><td></td></tr><tr><td>L1C4 vs - L2C4</td><td>0.2373</td><td></td></tr><tr><td>L1C5 vs - L2C5</td><td><strong>0.0003</strong></td><td></td></tr><tr><td>L1C6 vs - L2C6</td><td><strong>0.0014</strong></td><td></td></tr><tr><td>L1C7 vs - L2C7</td><td><strong>0.0000</strong></td><td></td></tr><tr><td>L3C1 vs - L4C1</td><td></td><td>0.8419</td></tr><tr><td>L3C2 vs - L4C2</td><td></td><td><strong>0.0000</strong></td></tr><tr><td>L3C3 vs - L4C3</td><td></td><td><strong>0.0000</strong></td></tr><tr><td>L3C4 vs - L4C4</td><td></td><td><strong>0.0119</strong></td></tr><tr><td>L3C5 vs - L4C5</td><td></td><td><strong>0.0001</strong></td></tr><tr><td>L3C6 vs - L4C6</td><td></td><td><strong>0.0000</strong></td></tr><tr><td>L3C7 vs - L4C7</td><td></td><td><strong>0.0036</strong></td></tr></tbody></table>

**Description:** LxCy stands for the line x and the column y in Table 4. Lines 1 and 2 refer to expected return of the greener asset and browner asset respectively, lines 3 and 4 to the standard deviation of the greener and browner asset respectively. Columns 1 to 7 are described at the beginning of section 3.

In Table 13, we study differences in specifications between groups. To achieve it, we test if the coefficients (slopes) of the variable under study (e.g., expected return of the greener asset, standard deviation of the browner asset) are significantly different between groups (green versus brown contexts, professional versus student population).
1. If it takes 2 nurses 2 minutes to measure the blood pressure of 2 patients, how long would it take 200 nurses to measure the blood pressure of 200 patients? ____ minutes. [Correct answer: 2 minutes; intuitive answer: 200 minutes]

2. Soup and salad cost €5.50 in total. The soup costs €5 more than the salad. How much does the salad cost? ____ (in cents of €). [Correct answer: €0.25; intuitive answer: €0.5]

3. Sally is making sun tea. Every hour, the concentration of the tea doubles. If it takes 6 hours for the tea to be ready, how long would it take for the tea to reach half of the final concentration? ____ hours. [Correct answer: 5 hours; intuitive answer: 3 hours]

4. If John can drink one barrel of water in 6 days, and Mary can drink one barrel of water in 12 days, how long would it take them to drink one barrel of water together? ____ days. [correct answer: 4 days; intuitive answer: 9]

5. Jerry received both the 15th highest and the 15th lowest mark in the class. How many students are in the class? ____ students. [correct answer: 29 students; intuitive answer: 30]

6. A man buys a pig for €60, sells it for €70, buys it back for €80, and sells it finally for €90. How much has he made? ____ €. [correct answer: €20; intuitive answer: €10]

7. Simon decided to invest €8,000 in the stock market one day early in 2008. Six months after he invested, on July 17, the stocks he had purchased were down 50%. Fortunately for Simon, from July 17 to October 17, the stocks he had purchased went up 75%. At this point, Simon: (a) has broken even in the stock market, (b) is ahead of where he began, (c) has lost money. [correct answer: c, because the value at this point is €7,000; intuitive response b].

### A.5.5 Pro-environmental opinion

We relied on the New Environmenal Paradigm (NEP) to assess participants' environ-
mental friendliness. The NEP scale (Dunlap et al., 2000) is a very popular 15-question
survey to evaluate opinion about environmental issues. Answers to each question vary
from “fully disagree” to “fully agree” on a 5-levels Likert scale. The simplest and usual
treatment of the data is to quantify answers from 1 to 5, in order to provide a total score
on the range from 15 to 75.
We follow the methodology proposed by Wooldridge (2016), a regression model with qualitative information, by investigating the interaction coefficient between (1) the variable under study and (2) the dummy variable which enables to distinguish the two groups under consideration (Dummy Group). By default, we let all the variables and constant differ between groups, by including all the interaction coefficients between the studied groups and the explanatory variables. The general equation is the following:

$$ 
\begin{aligned}
\lambda = & \beta_0 + \beta_1 \textit{ReturnGreener} + \beta_2 \textit{ReturnBrowner} + \beta_3 \textit{StandardDevGreener} \\
& + \beta_4 \textit{StandardDevBrowner} + \beta_5 \textit{Correlation0} + \beta_6 \textit{Correlation1} \\
& + \beta_7 \textit{Correlationminus1} + \beta_8 \textit{Ranking} + \beta_9 \textit{Tax} + \beta_{10} \textit{Risk} \\
& + \beta_{11} \textit{Prudence} + \beta_{12} \textit{Temperance} + \beta_{13} \textit{SVO} + \beta_{14} \textit{NEP} \\
& + \beta_{15} \textit{CRT} + \beta_{16} \textit{LevelK} + \delta_0 \textit{Group} + \delta_1 \textit{Group.ReturnGreener} \\
& + \delta_2 \textit{Group.ReturnBrowner} + \delta_3 \textit{Group.StandardDevGreener} \\
& + \delta_4 \textit{Group.StandardDevBrowner} + \delta_5 \textit{Group.Correlation0} \\
& + \delta_6 \textit{Group.Correlation1} + \delta_7 \textit{Group.Correlationminus1} + \delta_8 \textit{Group.Ranking} \\
& + \delta_9 \textit{Group.Tax} + \delta_{10} \textit{Group.Risk} + \delta_{11} \textit{Group.Prudence} + \delta_{12} \textit{Group.Temperance} \\
& + \delta_{13} \textit{Group.SVO} + \delta_{14} \textit{Group.NEP} + \delta_{15} \textit{Group.CRT} + \delta_{16} \textit{Group LevelK}
\end{aligned}
\quad (1) $$

Estimates are computed based on a random effects Tobit regression in a panel data framework, with robust cluster standard errors at the participant level, to account for the within-cluster error correlations and the heteroskedasticity, such as displayed in Section E.5.

The null hypothesis (H0) is that the two groups have coefficients (slopes) that do not significantly differ on the variable of interest. The alternative hypothesis (H1) is that the coefficients significantly differ between the two groups on the variable of interest. To perform this test, we observe the interaction coefficients between the dummy variable Group and the selected variable under study (for example if ReturnGreener, δ₁ in Equation 1). If the interaction coefficient is significantly different from 0, then we can conclude that the two coefficients (slopes) differ between groups.
1. We are approaching the limit of the number of people the earth can support.

2. Humans have the right to modify the natural environment to suit their needs.

3. When humans interfere with nature it often produces disastrous consequences.

4. Human ingenuity will insure that we do NOT make the earth unlivable.

5. Humans are severely abusing the environment

6. The earth has plenty of natural resources if we just learn how to develop them.

7. Plants and animals have as much right as humans to exist.

8. The balance of nature is strong enough to cope with impacts of modern industrial nations.

9. Despite our special abilities humans are still participant to the laws of nature.

10. The so-called "ecological crisis" facing humankind has been greatly exaggerated.

11. The earth is like a spaceship with very limited room and resources.

12. Humans were meant to rule over the rest of nature.

13. The balance of nature is very delicate and easily upset.

14. Humans will eventually learn enough about how nature works to be able to control it.

15. If things continue on their present course, we will soon experience a major ecological catastrophe.

## A.6 Financial market professional questionnaire

A dropdown menu was attached to each question, the list of possible answers are given into the brackets.

1. What is your current job? [1: Prop Trader; 2: Sales Trader; 3: Sales; 4: Market Maker; 5: Asset/Portfolio Manager; 6: Strategist/Economist; 7: Financial Analyst (buy side, sell side); 8: Risk analyst; 9: Private equity Manager; 10: Other]

2. What is your employer? [1: Bank; 2: Asset management company; 3: Trading company; 4: Broker; 5: Hedge fund; 6: Work for your own; 7: Private equity company; 8: Other]

3. In which jobs in finance do you have your main experiences? (You can choose various jobs) ? [1: Prop Trader; 2: Sales Trader; 3: Sales; 4: Market Maker; 5: Asset/Portfolio Manager; 6: Strategist/Economist; 7: Financial Analyst (buy side, sell side); 8: Risk analyst; 9: Private equity Manager; 10: Other]

4. What is the main strategy you employ to trade assets? [1: Technical Analysis; 2: Value investing; 3: Trend following; 4: Event Driven; 5: Global Macro; 6: Arbitrageur; 7: Market Making; 8: Scalping; 9: Merger Arbitrage; 10: Other; 11: None]

5. On which asset class are your working on? [1: Money Market; 2: Forex Market; 3: Commodities; 4: Equity; 5: Bonds (Fixed Income); 6: Private Equity; 7: Real estate; 8: Hedge Fund; 9: Other]

6. Do you work on: [1: Spot Market; 2: Market Derivatives; 3: Both; 4: Other]

7. What is the nationality of your company? [answer: ]
<table><caption>Table 13: TESTS OF PARAMETERS IN DIFFERENT REGRESSIONS</caption><thead><tr><th rowspan="2">Line and column</th><th colspan="6">Interaction coefficients and p-values (in parentheses)</th></tr><tr><th>EX RET<br>GR vs BR</th><th>SD DEV<br>GR vs BR</th><th>Asymmetry<br>neutral</th><th>EX RET<br>PRO vs STUD</th><th>SD DEV<br>PRO vs STUD</th><th>STUD<br>GR vs BR</th></tr></thead><tbody><tr><td>L1C2 vs L1C3</td><td>-0.45<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C4 vs L1C5</td><td>-0.25<br>(0.134)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C6 vs L1C7</td><td>-0.63<br>(0.003)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C2 vs L2C3</td><td>0.27<br>(0.037)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C4 vs L2C5</td><td>0.01<br>(0.953)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C6 vs L2C7</td><td>0.49<br>(0.019)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C2 vs L3C3</td><td>0.31<br>(0.000)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C4 vs L3C5</td><td>0.19<br>(0.006)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C6 vs L3C7</td><td>0.41<br>(0.000)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C2 vs L4C3</td><td>0.04<br>(0.481)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C4 vs L4C5</td><td>0.09<br>(0.105)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C6 vs L4C7</td><td>-0.01<br>(0.879)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C2 vs -L3C3</td><td>-0.14<br>(0.005)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C4 vs -L3C5</td><td>-0.09<br>(0.145)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C6 vs -L3C7</td><td>-0.18<br>(0.017)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C4 vs L1C6 (green)</td><td></td><td>0.39<br>(0.062)</td><td></td><td></td><td></td><td></td></tr><tr><td>L1C5 vs L1C7 (brown)</td><td></td><td>0.75<br>(0.001)</td><td></td><td></td><td></td><td></td></tr><tr><td>L2C4 vs L2C6 (green)</td><td></td><td>-0.21<br>(0.312)</td><td></td><td></td><td></td><td></td></tr><tr><td>L2C5 vs L2C7 (brown)</td><td></td><td>-0.71<br>(0.001)</td><td></td><td></td><td></td><td></td></tr><tr><td>L3C4 vs L3C6 (green)</td><td></td><td>0.07<br>(0.408)</td><td></td><td></td><td></td><td></td></tr><tr><td>L3C5 vs L3C7 (brown)</td><td></td><td>-0.12<br>(0.227)</td><td></td><td></td><td></td><td></td></tr><tr><td>L4C4 vs L4C6 (green)</td><td></td><td>0.04<br>(0.627)</td><td></td><td></td><td></td><td></td></tr><tr><td>L4C5 vs L4C7 (brown)</td><td></td><td>0.14<br>(0.126)</td><td></td><td></td><td></td><td></td></tr><tr><td>L8C2 vs L8C3</td><td></td><td>-4.40<br>(0.118)</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Interaction coefficients are displayed, and p-values are shown in parentheses.
# B Data collection

## B.1 Samples

**The PRO sample** We collected the data of the PRO sample in major investment banks and asset management companies, in October 2019, using a mobile laboratory with tablets. We conducted experiments in compliance with the ethical rules of the experimental economics laboratory of Montpellier (LEEM) and in accordance with the deontological and compliance rules of the banks.37 This study has also been approved by the Institutional Review Board (IRB) of the Center of Environmental Economics of Montpellier (University of Montpelier). We used oTree software (Chen et al., 2016) to run the experiment. Figure 7 illustrates the typical environment in which the PRO data were collected. A total of 190 financial market professionals participated in the experiment: 66 of them were assigned to the baseline treatment, 63 to the ranking treatment and 61 to the tax treatment. Most of the participants worked in trading activities (proprietary traders, sales traders, asset managers, sales, trading room managers, quantitative engineers, structurers, financial analysts), while others were employed in support functions such as financial management or audit. Figure 8 shows the distribution of the jobs for the entire PRO sample. Finally, many of the members of the bank management board also took part in the experiment.38

<figure><img src="image_10.png" /><figcaption>Figure 7: MEETING ROOMS USED AS A LABORATORY</figcaption></figure>

**The STUD sample** The STUD sample consists of students from University of Montpellier. The data were collected during 2020, at the Laboratory for Experimental Eco-

<footnote n="37">³⁷For reasons of confidentiality, we can not provide any further information publicly, however, we are at the disposition of the reviewers to give them of any additional information they may require.</footnote>

<footnote n="38">³⁸74.2% of the participants work for a bank and 25.8% for "other" (mainly asset management companies or bank subsidiaries dedicated to asset management and portfolio management)</footnote>
<figure><img src="image_11.png" /><figcaption>Figure 8: FINANCIAL CATEGORIES OF THE PROFESSIONAL SAMPLE</figcaption></figure>

nomics of Montpellier.39 279 student participants (45.16% of women, with an average age of 23.86 years) were involved in the experiment: 103 in the baseline treatment, 84 in the ranking treatment and 92 in the tax treatment. Figure 9 shows the distribution of academic degrees and studied disciplines in the STUD sample.

<figure><img src="image_12.png" /><figcaption>Figure 9: ACADEMIC DEGREES AND STUDIED DISCIPLINES IN THE STUDENT SAMPLE</figcaption></figure>

## B.2 Practical procedures

The practical procedures for the PRO and the STUD samples differ in two respects. First, they differ with respect to the final questionnaire that followed the control tasks. For the PRO sample, the questions were related to current employment and professional experience in the field of finance, while for the STUD sample, the questions were related to their studies and socio-demographic information.

<footnote n="39">39 LEEM, http://leem.umontpellier.fr. The participant pool is managed by the ORSEE platform (Greiner, 2015).</footnote>
Second, the two samples differ with respect to how participants were matched and compensated for the experiment. In the STUD sample, we organized standard sessions with an even number of participants so that we could form pairs of players for the SVO task and the 11-20 task.

In addition, all participants were paid for one (randomly chosen) portfolio choice task and one (randomly selected) control task. For the PRO sample, we could neither anticipate the number of participants in a session nor at what time they would attend the session. We, therefore, needed to rely on the strategy method for the SVO and the 11-20 tasks. Pairs were formed at the end of each session. In addition, only one participant out of 10 was randomly selected to be paid out for real. Selected participants were paid for one of the portfolio choice task and one control task, both randomly selected. We proceeded that way to provide strong incentives to the finance professional participants. For both samples, we relied on a random selection of the tasks to be paid. This procedure is common among experimentalists as it was demonstrated to be incentive-compatible (Starmer and Sugden, 1991; Charness et al., 2016; Clot et al., 2018). On average, a student earned 8.10 euros, and a (selected to be paid) finance professional 216.81 euros.

# C Analysis

## C.1 Portfolio task

The following table is used in Section 3.
<table><caption>Table 8: MEAN (std) INVESTMENT IN THE GREENEST ASSET, BY TREATMENT AND SAMPLE</caption><thead><tr><th rowspan="2">Screen</th><th rowspan="2">Assets</th><th rowspan="2">ρ</th><th colspan="2">Baseline</th><th colspan="2">Ranking</th><th colspan="2">Tax</th></tr><tr><th>Professionals</th><th>Students</th><th>Professionals</th><th>Students</th><th>Professionals</th><th>Students</th></tr><tr><th></th><th></th><th></th><th>mean</th><th>std</th><th>mean</th><th>std</th><th>mean</th><th>std</th><th>mean</th><th>std</th></tr></thead><tbody><tr><td>1</td><td>N (15, 0) vs G (10, 0)</td><td></td><td>45.30</td><td>24.91</td><td>30.60</td><td>31.89</td><td>48.24</td><td>27.60</td><td>41.31</td><td>31.96</td><td>46.34</td><td>24.78</td><td>50.23</td><td>27.25</td></tr><tr><td>2</td><td>N (15, 0) vs B (20, 0)</td><td></td><td>61.29</td><td>28.67</td><td>45.85</td><td>41.42</td><td>65.70</td><td>28.48</td><td>64.46</td><td>33.85</td><td>61.46</td><td>27.28</td><td>69.77</td><td>25.55</td></tr><tr><td rowspan="3">4</td><td>N (15, 0) vs G (10, 25)</td><td></td><td>43.70</td><td>26.40</td><td>35.99</td><td>31.42</td><td>44.08</td><td>24.21</td><td>40.31</td><td>30.33</td><td>44.20</td><td>23.15</td><td>39.42</td><td>29.96</td></tr><tr><td>N (15, 0) vs G (20, 25)</td><td></td><td>50.33</td><td>21.85</td><td>43.06</td><td>33.80</td><td>55.81</td><td>22.48</td><td>52.75</td><td>29.45</td><td>52.70</td><td>25.23</td><td>58.11</td><td>29.04</td></tr><tr><td>N (15, 0) vs G (20, 50)</td><td></td><td>37.73</td><td>25.69</td><td>37.91</td><td>36.23</td><td>41.78</td><td>25.74</td><td>50.10</td><td>35.08</td><td>44.11</td><td>31.89</td><td>50.22</td><td>35.71</td></tr><tr><td rowspan="3">5</td><td>N (15, 0) vs B (30, 25)</td><td></td><td>64.55</td><td>25.29</td><td>57.79</td><td>36.68</td><td>67.51</td><td>24.74</td><td>58.06</td><td>36.29</td><td>57.31</td><td>26.82</td><td>69.37</td><td>29.03</td></tr><tr><td>N (15, 0) vs B (40, 25)</td><td></td><td>61.29</td><td>29.05</td><td>42.95</td><td>40.53</td><td>60.13</td><td>25.82</td><td>48.45</td><td>38.90</td><td>60.67</td><td>24.31</td><td>69.66</td><td>27.43</td></tr><tr><td>N (15, 0) vs B (40, 50)</td><td></td><td>70.68</td><td>25.30</td><td>58.38</td><td>35.56</td><td>71.38</td><td>27.13</td><td>58.89</td><td>33.88</td><td>68.49</td><td>23.97</td><td>72.48</td><td>26.74</td></tr><tr><td rowspan="3">6</td><td>N (20, 25) vs G (10, 0)</td><td></td><td>61.79</td><td>21.76</td><td>48.71</td><td>33.38</td><td>61.89</td><td>25.06</td><td>64.62</td><td>29.80</td><td>63.43</td><td>22.85</td><td>61.14</td><td>27.22</td></tr><tr><td>N (30, 25) vs G (10, 0)</td><td></td><td>57.03</td><td>24.22</td><td>36.78</td><td>34.57</td><td>54.84</td><td>23.99</td><td>53.13</td><td>33.90</td><td>61.10</td><td>23.87</td><td>64.78</td><td>24.17</td></tr><tr><td>N (30, 50) vs G (10, 0)</td><td></td><td>68.09</td><td>22.82</td><td>55.07</td><td>35.20</td><td>70.35</td><td>22.56</td><td>69.20</td><td>30.52</td><td>70.75</td><td>22.95</td><td>70.61</td><td>27.39</td></tr><tr><td rowspan="3">7</td><td>N (20, 25) vs B (20, 0)</td><td></td><td>48.65</td><td>31.42</td><td>55.54</td><td>32.72</td><td>52.75</td><td>28.75</td><td>48.74</td><td>34.53</td><td>49.43</td><td>29.29</td><td>53.34</td><td>35.58</td></tr><tr><td>N (30, 25) vs B (20, 0)</td><td></td><td>54.35</td><td>28.20</td><td>67.32</td><td>33.42</td><td>62.94</td><td>25.92</td><td>62.87</td><td>32.42</td><td>62.20</td><td>29.49</td><td>70.18</td><td>30.72</td></tr><tr><td>N (30, 50) vs B (20, 0)</td><td></td><td>45.89</td><td>28.12</td><td>49.09</td><td>34.60</td><td>51.38</td><td>26.54</td><td>47.14</td><td>32.95</td><td>48.03</td><td>32.80</td><td>57.35</td><td>33.62</td></tr><tr><td rowspan="3">8</td><td>N (20, 25) vs G (10, 25)</td><td></td><td>40.76</td><td>29.67</td><td>18.51</td><td>29.74</td><td>49.70</td><td>30.87</td><td>37.00</td><td>34.08</td><td>53.56</td><td>24.17</td><td>51.88</td><td>29.37</td></tr><tr><td>N (30, 25) vs G (20, 25)</td><td>1</td><td>44.33</td><td>28.34</td><td>23.32</td><td>30.96</td><td>46.30</td><td>29.17</td><td>41.39</td><td>34.91</td><td>49.10</td><td>24.39</td><td>52.45</td><td>27.96</td></tr><tr><td>N (30, 50) vs G (20, 50)</td><td></td><td>41.89</td><td>30.83</td><td>21.28</td><td>31.95</td><td>49.38</td><td>30.51</td><td>36.27</td><td>36.11</td><td>51.84</td><td>25.10</td><td>53.96</td><td>28.80</td></tr><tr><td rowspan="3">9</td><td>N (20, 25) vs G (10, 25)</td><td></td><td>44.27</td><td>27.59</td><td>24.17</td><td>30.63</td><td>45.52</td><td>27.60</td><td>32.67</td><td>29.48</td><td>50.69</td><td>22.60</td><td>50.52</td><td>28.12</td></tr><tr><td>N (30, 25) vs G (20, 25)</td><td>0</td><td>43.41</td><td>24.07</td><td>26.57</td><td>31.25</td><td>46.97</td><td>27.33</td><td>35.23</td><td>30.85</td><td>46.48</td><td>24.36</td><td>50.35</td><td>30.39</td></tr><tr><td>N (30, 50) vs G (20, 50)</td><td></td><td>43.45</td><td>24.16</td><td>29.53</td><td>33.26</td><td>46.73</td><td>27.04</td><td>37.23</td><td>32.34</td><td>51.59</td><td>23.80</td><td>53.21</td><td>30.15</td></tr><tr><td rowspan="3">10</td><td>N (20, 25) vs G (10, 25)</td><td></td><td>47.38</td><td>25.44</td><td>34.82</td><td>26.17</td><td>48.59</td><td>25.30</td><td>45.60</td><td>27.94</td><td>48.02</td><td>21.51</td><td>51.72</td><td>26.81</td></tr><tr><td>N (30, 25) vs G (20, 25)</td><td>-1</td><td>47.95</td><td>23.11</td><td>33.31</td><td>27.04</td><td>50.51</td><td>26.57</td><td>41.60</td><td>31.01</td><td>52.39</td><td>23.92</td><td>51.97</td><td>27.37</td></tr><tr><td>N (30, 50) vs G (20, 50)</td><td></td><td>47.48</td><td>22.61</td><td>36.20</td><td>25.18</td><td>53.37</td><td>26.74</td><td>48.77</td><td>27.57</td><td>52.70</td><td>21.61</td><td>56.39</td><td>25.58</td></tr><tr><td rowspan="3">11</td><td>N (20, 25) vs B (30, 25)</td><td></td><td>56.03</td><td>29.75</td><td>42.37</td><td>36.15</td><td>59.25</td><td>24.41</td><td>48.63</td><td>36.98</td><td>53.64</td><td>27.29</td><td>62.88</td><td>29.94</td></tr><tr><td>N (30, 25) vs B (40, 25)</td><td>1</td><td>54.95</td><td>30.50</td><td>47.56</td><td>38.52</td><td>62.22</td><td>27.56</td><td>51.70</td><td>38.93</td><td>59.77</td><td>27.40</td><td>60.70</td><td>32.29</td></tr><tr><td>N (30, 50) vs B (40, 50)</td><td></td><td>53.58</td><td>32.15</td><td>37.67</td><td>40.33</td><td>57.38</td><td>28.66</td><td>47.14</td><td>38.24</td><td>50.95</td><td>29.01</td><td>62.74</td><td>29.42</td></tr><tr><td rowspan="3">12</td><td>N (20, 25) vs B (30, 25)</td><td></td><td>56.15</td><td>32.05</td><td>41.68</td><td>34.69</td><td>62.17</td><td>27.30</td><td>49.17</td><td>36.53</td><td>58.61</td><td>27.86</td><td>65.85</td><td>29.08</td></tr><tr><td>N (30, 25) vs B (40, 25)</td><td>0</td><td>59.55</td><td>30.90</td><td>45.11</td><td>37.14</td><td>62.67</td><td>27.52</td><td>53.35</td><td>37.48</td><td>59.54</td><td>26.52</td><td>63.60</td><td>30.85</td></tr><tr><td>N (30, 50) vs B (40, 50)</td><td></td><td>53.67</td><td>33.54</td><td>43.29</td><td>36.13</td><td>58.17</td><td>30.15</td><td>49.26</td><td>37.39</td><td>58.38</td><td>26.05</td><td>60.23</td><td>31.78</td></tr><tr><td rowspan="3">13</td><td>N (20, 25) vs B (30, 25)</td><td></td><td>53.88</td><td>29.50</td><td>43.21</td><td>35.04</td><td>62.57</td><td>26.99</td><td>52.76</td><td>34.31</td><td>57.97</td><td>23.69</td><td>60.76</td><td>28.95</td></tr><tr><td>N (30, 25) vs B (40, 25)</td><td>-1</td><td>60.08</td><td>28.23</td><td>45.61</td><td>35.74</td><td>61.75</td><td>26.86</td><td>58.39</td><td>34.20</td><td>63.49</td><td>23.79</td><td>62.96</td><td>30.55</td></tr><tr><td>N (30, 50) vs B (40, 50)</td><td></td><td>61.83</td><td>26.56</td><td>46.49</td><td>32.80</td><td>63.84</td><td>25.04</td><td>57.99</td><td>31.00</td><td>59.21</td><td>22.37</td><td>59.86</td><td>29.72</td></tr><tr><td colspan="2">Overall</td><td></td><td>52.54</td><td>28.52</td><td>40.80</td><td>35.76</td><td>56.12</td><td>27.75</td><td>49.51</td><td>34.71</td><td>55.25</td><td>26.27</td><td>59.02</td><td>30.23</td></tr></tbody></table>
## C.2 Control tasks

Table 9 reports the mean of the variable of interest for each of the control tasks, for both samples, as well as a column that displays the difference between the two samples and a column that reports the p-value of a two-sided t-test (H0 is the two samples have an equal mean).

<table><caption>Table 9: SUMMARY STATISTICS FOR THE CONTROL TASKS</caption><thead><tr><th></th><th>Professionals</th><th>Students</th><th>Diff.</th><th>p-value</th></tr></thead><tbody><tr><td>Risk aversion</td><td>2.537</td><td>2.803</td><td>-0.266</td><td>.065</td></tr><tr><td>Prudence</td><td>3.079</td><td>3.480</td><td>-0.401</td><td>.009**</td></tr><tr><td>Temperance</td><td>2.405</td><td>2.642</td><td>-0.237</td><td>.145</td></tr><tr><td>SVO</td><td>20.517</td><td>18.083</td><td>2.434</td><td>.080</td></tr><tr><td>CRT</td><td>3.516</td><td>3.319</td><td>0.197</td><td>.306</td></tr><tr><td>Eleven-twenty (choice)</td><td>15.821</td><td>16.509</td><td>-0.688</td><td>.007**</td></tr><tr><td>Eleven-twenty (expected payoff)</td><td>18.994</td><td>19.869</td><td>-0.875</td><td>.005**</td></tr><tr><td>NEP</td><td>56.826</td><td>56.283</td><td>0.543</td><td>.417</td></tr></tbody></table>

** 1%, * 5%

Students seem to be more risk averse and prudent than professionals but there is no difference between the two samples in terms of temperance. A Pearson correlation test shows there is no correlation between risk aversion and prudence ($r = 0.048, p = .512$ for professionals and $r = -0.009, p = .877$ for students) but a positive correlation between prudence and temperance ($r = 0.280, p < .001$ and $r = 0.262, p < .001$, respectively).

The average SVO score of professionals is slightly higher compared to the students, the difference is significant at the 10% level. But this score is also used to determine the class of behaviour to which the participant belongs, ranging from competitive, individualistic, pro-social or altruistic (see Murphy et al., 2011, for a detailed explanation). Figure 10 reports the distribution of the scores for the two samples. None of the participants in either sample revealed to be altruistic. On the other end of the spectrum, financial professionals do not appear to be more competitive than students (4.21% of the professionals and 2.15% of the students). Then, professionals are equally divided between individualists (47.37%) and pro-social (48.42%), while 57.35% of students are individualists and 40.50% are pro-social. Overall, based on a Kolmogorov-Smirnov test, we can reject the hypothesis that the two distributions are identical ($p = .042$).

The average CRT score of professionals is slightly higher than that of students, but is more homogeneous in the latter sample, especially for the highest scores. A Kolmogorov-
<figure><img src="image_13.png" /><figcaption>Figure 10: DISTRIBUTION OF THE SVO SCORE IN BOTH SAMPLES</figcaption></figure>

Smirnov test does not reveal any significant difference, however ($p = .112$).

Finally, Figure 11 shows that both populations exhibit a similar distribution for the NEP score, meaning that they have similar concerns about environmental issues (Kolmogorov-Smirnov test, $p = .431$).

To summarize the results from the control tasks, professionals are less risk-averse and less prudent than students. They are more pro-social, and they are less successful at guessing the depth of reasoning of their cohort members. On the other hand, the two samples appear to have a similar profile in terms of temperance, cognitive abilities and environmental concern.

## C.3 Econometric analysis

### C.3.1 Regression analysis

As noted in Table 4, the regression method is as follows. We compute estimated coefficients in a random-effect (RE) Tobit panel regression with robust clustered standard errors at the participant level. This accounts for potential heteroskedasticity and serial correlation of errors (in parenthesis). Estimations were done with StataCorp software
<figure><img src="image_14.png" /><figcaption>Figure 11: DISTRIBUTION OF THE NEP SCALE SCORE, BY SAMPLE</figcaption></figure>

(2019).<sup>40</sup>

As usual, *, **, *** account for 10%, 5% and 1% significance level respectively. The last lines, $\chi^2$ and $p$-value, stand for the model non-null significance test.

In the analysis, the dependent variable is the proportion invested in the greener asset ($\lambda$). Context denotes screens used for estimation: *Green* context relates to screens 1,4,6,8,9, and 10; *Brown* context relates to screens 2,5,7,11,12, and 13. *All* combines *Green* and *Brown* (screen 3 is excluded from the analysis). *N* is the number of related observations (samples and selected screens).

Table 10 refers to the estimation in Section 2, while Table 11 refers to the estimation in Section 5.

The *Green* dummy variable refers to the *Green* context (*Green* = 1 for *Green* context and 0 for *Brown* context). The *Student* dummy variable controls for the POOL type (*Student* = 1 if the participant belongs to the STUD sample, and 1 if he belongs to the PRO sample). *Correlation* is a categorical variable which equals 0 for screens 1 to 7, 1 for screens 8 and 11, 2 for screens 9 and 12, and 3 for screens 10 and 13.

Variables specific to the PRO sample, such as finance jobs and investment strategies are presented in Table 6, while variables specific to the STUD sample, such as disciplines

<footnote n="40"><sup>40</sup>Stata: Release 16. Statistical Software. College Station, TX: StataCorp LLC. Further information on this class of models is available at https://www.stata.com/manuals/memetobit.pdf.</footnote>
are presented in Subsubsection 5.2.2.

In Table 11, all the variables present in Table 10 are taken as control variables, except SVO and NEP which are shown (cf. Appendix C.2 for descriptive statistics). In addition, Employer type (=0 if “bank”, =1 otherwise), Asset Class (categories: “Money market”, “Forex market”, “Equity”, “Bonds and FI”, “Others”), Market type (categories: “spot market”, “derivatives”, “both”, “others”)<fnref n="41" />, Age (in years), Gender (=0 for female, =1 for male) and Student level (in years)<fnref n="42" /> are hidden control variables in Table 11.

<footnote n="41">⁴¹All of these variables are for professionals.</footnote>

<footnote n="42">⁴²These last 3 variables are for students.</footnote>
<table><caption>Table 10: ESTIMATED COEFFICIENTS OF TABLE 4</caption><thead><tr><th>Sample<br>Screens<br>N</th><th>(1)<br>ALL<br>All<br>15008</th><th>(2)<br>ALL<br>Green<br>7504</th><th>(3)<br>ALL<br>Brown<br>7504</th><th>(4)<br>PRO<br>Green<br>3040</th><th>(5)<br>PRO<br>Brown<br>3040</th><th>(6)<br>STUD<br>Green<br>4464</th><th>(7)<br>STUD<br>Brown<br>4464</th></tr></thead><tbody><tr><td colspan="8"><strong>Asset characteristics</strong></td></tr><tr><td>Expected return<br>of greener asset</td><td>1.19**<br>(0.09)</td><td>0.92**<br>(0.11)</td><td>1.43**<br>(0.13)</td><td>0.66**<br>(0.15)</td><td>0.93**<br>(0.14)</td><td>1.13**<br>(0.15)</td><td>1.85**<br>(0.20)</td></tr><tr><td>Expected return<br>of browner asset</td><td>-0.81**<br>(0.09)</td><td>-0.70**<br>(0.11)</td><td>-1.01**<br>(0.11)</td><td>-0.56**<br>(0.13)</td><td>-0.57**<br>(0.12)</td><td>-0.82**<br>(0.16)</td><td>-1.38**<br>(0.19)</td></tr><tr><td>Standard deviation<br>of greener asset</td><td>-0.51**<br>(0.04)</td><td>-0.36**<br>(0.04)</td><td>-0.69**<br>(0.05)</td><td>-0.39**<br>(0.06)</td><td>-0.59**<br>(0.06)</td><td>-0.34**<br>(0.06)</td><td>-0.78**<br>(0.08)</td></tr><tr><td>Standard deviation<br>of browner asset</td><td>0.50**<br>(0.04)</td><td>0.54**<br>(0.04)</td><td>0.52**<br>(0.05)</td><td>0.50**<br>(0.06)</td><td>0.42**<br>(0.05)</td><td>0.57**<br>(0.07)</td><td>0.61**<br>(0.08)</td></tr><tr><td>Correlation 1</td><td>-10.01**<br>(1.28)</td><td>-15.44**<br>(1.64)</td><td>-3.94*<br>(1.64)</td><td>-7.79**<br>(2.08)</td><td>-0.78<br>(1.84)</td><td>-21.82**<br>(2.47)</td><td>-6.80*<br>(2.65)</td></tr><tr><td>Correlation 0</td><td>-8.66**<br>(1.26)</td><td>-15.02**<br>(1.56)</td><td>-1.67<br>(1.62)</td><td>-8.60**<br>(1.83)</td><td>-2.33<br>(1.76)</td><td>-20.36**<br>(2.44)</td><td>-5.28*<br>(2.63)</td></tr><tr><td>Correlation -1</td><td>-3.97**<br>(1.13)</td><td>-7.89**<br>(1.42)</td><td>0.32<br>(1.60)</td><td>-5.07**<br>(1.90)</td><td>3.87*<br>(1.87)</td><td>-10.05**<br>(2.07)</td><td>-2.74<br>(2.51)</td></tr><tr><td colspan="8"><strong>Sample characteristics</strong></td></tr><tr><td>Ranking treatment</td><td>7.41**<br>(2.41)</td><td>10.01**<br>(2.57)</td><td>3.99<br>(3.87)</td><td>4.56<br>(3.26)</td><td>2.53<br>(4.78)</td><td>14.09**<br>(3.91)</td><td>5.39<br>(5.87)</td></tr><tr><td>Tax treatment</td><td>16.04**<br>(2.12)</td><td>18.81**<br>(2.50)</td><td>12.69**<br>(3.74)</td><td>6.62*<br>(2.92)</td><td>-1.13<br>(4.35)</td><td>27.44**<br>(3.78)</td><td>24.46**<br>(5.85)</td></tr><tr><td>Student</td><td>-5.92**<br>(2.00)</td><td>-7.89**<br>(2.06)</td><td>-2.67<br>(2.95)</td><td></td><td></td><td></td><td></td></tr><tr><td>Green</td><td>-10.47**<br>(1.59)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="8"><strong>Control variables</strong></td></tr><tr><td>Intercept</td><td>37.38**<br>(9.45)</td><td>38.24**<br>(9.20)</td><td>25.44<br>(13.56)</td><td>55.08**<br>(12.53)</td><td>45.59**<br>(16.97)</td><td>16.89<br>(12.63)</td><td>1.93<br>(18.57)</td></tr><tr><td>Risk aversion</td><td>-1.16<br>(0.64)</td><td>-0.15<br>(0.69)</td><td>-2.32**<br>(1.04)</td><td>-0.29<br>(0.88)</td><td>-0.94<br>(1.32)</td><td>0.02<br>(1.01)</td><td>-3.64**<br>(1.51)</td></tr><tr><td>Prudence</td><td>0.44<br>(0.64)</td><td>-0.01<br>(0.62)</td><td>0.69<br>(1.01)</td><td>0.65<br>(0.87)</td><td>-0.49<br>(1.29)</td><td>-0.79<br>(0.86)</td><td>1.06<br>(1.49)</td></tr><tr><td>Temperance</td><td>-0.91<br>(0.58)</td><td>-1.10<br>(0.62)</td><td>-0.49<br>(0.94)</td><td>-0.81<br>(0.80)</td><td>-1.03<br>(1.23)</td><td>-1.40<br>(0.90)</td><td>0.11<br>(1.38)</td></tr><tr><td>SVO</td><td>0.24**<br>(0.06)</td><td>0.31**<br>(0.06)</td><td>0.17<br>(0.10)</td><td>0.11<br>(0.08)</td><td>0.24*<br>(0.11)</td><td>0.43**<br>(0.10)</td><td>0.01<br>(0.16)</td></tr><tr><td>NEP</td><td>0.37*<br>(0.15)</td><td>0.17<br>(0.15)</td><td>0.62**<br>(0.22)</td><td>-0.02<br>(0.20)</td><td>0.27<br>(0.28)</td><td>0.33<br>(0.21)</td><td>1.06**<br>(0.30)</td></tr><tr><td>CRT</td><td>-0.25<br>(0.48)</td><td>-1.67**<br>(0.52)</td><td>1.51<br>(0.81)</td><td>-1.53*<br>(0.62)</td><td>1.88*<br>(0.95)</td><td>1.60*<br>(0.77)</td><td>1.61<br>(1.23)</td></tr><tr><td>k-level</td><td>-0.26<br>(0.33)</td><td>-0.34<br>(0.42)</td><td>-0.99<br>(0.53)</td><td>0.23<br>(0.50)</td><td>-1.41*<br>(0.69)</td><td>-0.61<br>(0.61)</td><td>-0.93<br>(0.77)</td></tr><tr><td>σ² (Intercept)</td><td>369.73**<br>(38.76)</td><td>431.32**<br>(52.34)</td><td>1004.35**<br>(104.97)</td><td>257.15**<br>(33.26)</td><td>534.59**<br>(69.68)</td><td>560.91**<br>(97.65)</td><td>1411.10**<br>(200.70)</td></tr><tr><td>σ² (residual)</td><td>1162.16**<br>(63.55)</td><td>843.55**<br>(45.70)</td><td>902.87**<br>(53.69)</td><td>523.00**<br>(38.29)</td><td>509.43**<br>(40.72)</td><td>1125.58**<br>(79.38)</td><td>1266.65**<br>(97.26)</td></tr><tr><td>χ²</td><td>367.37</td><td>326.30</td><td>231.49</td><td>114.90</td><td>136.44</td><td>242.66</td><td>149.80</td></tr><tr><td>p-value</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr></tbody></table>
<table><caption>Table 11: ESTIMATED COEFFICIENTS OF TABLE 5</caption><thead><tr><th>Sample<br>Screen<br>N</th><th>(1)<br>PRO<br>All<br>6048</th><th>(2)<br>PRO<br>Green<br>3024</th><th>(3)<br>PRO<br>Brown<br>3024</th><th>(4)<br>STUD<br>All<br>8928</th><th>(5)<br>STUD<br>Green<br>4464</th><th>(6)<br>STUD<br>Brown<br>4464</th></tr></thead><tbody><tr><td>SVO</td><td>0.12<br>(0.08)</td><td>0.09<br>(0.08)</td><td>0.17<br>(0.11)</td><td>0.23**<br>(0.09)</td><td>0.43**<br>(0.10)</td><td>-0.01<br>(0.15)</td></tr><tr><td>NEP</td><td>0.05<br>(0.18)</td><td>-0.11<br>(0.18)</td><td>0.26<br>(0.25)</td><td>0.53*<br>(0.21)</td><td>0.29<br>(0.25)</td><td>0.79*<br>(0.35)</td></tr><tr><td><em>Finance jobs</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sales trading</td><td>-0.36<br>(3.38)</td><td>2.43<br>(3.32)</td><td>-3.41<br>(4.75)</td><td></td><td></td><td></td></tr><tr><td>Portfolio Manager</td><td>-7.21<br>(5.29)</td><td>-1.53<br>(5.46)</td><td>-13.19*<br>(6.44)</td><td></td><td></td><td></td></tr><tr><td>Analyst</td><td>15.75**<br>(4.47)</td><td>15.58**<br>(4.89)</td><td>16.49*<br>(6.54)</td><td></td><td></td><td></td></tr><tr><td>Support functions &amp; others</td><td>3.77<br>(4.48)</td><td>7.38<br>(4.82)</td><td>0.15<br>(6.78)</td><td></td><td></td><td></td></tr><tr><td><em>Investment strategies</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Value Investing</td><td>11.37*<br>(4.66)</td><td>6.99<br>(4.34)</td><td>16.30*<br>(6.43)</td><td></td><td></td><td></td></tr><tr><td>Mispricing</td><td>18.77**<br>(4.92)</td><td>13.32*<br>(6.02)</td><td>24.20**<br>(6.21)</td><td></td><td></td><td></td></tr><tr><td>Global Macro</td><td>10.87*<br>(4.33)</td><td>4.72<br>(3.89)</td><td>17.89**<br>(6.15)</td><td></td><td></td><td></td></tr><tr><td>None</td><td>1.20<br>(4.34)</td><td>-3.79<br>(4.33)</td><td>6.44<br>(5.39)</td><td></td><td></td><td></td></tr><tr><td>Market Making</td><td>11.63**<br>(3.70)</td><td>5.57<br>(3.53)</td><td>17.70**<br>(5.70)</td><td></td><td></td><td></td></tr><tr><td>Others</td><td>10.61*<br>(5.39)</td><td>5.68<br>(5.05)</td><td>17.74*<br>(8.12)</td><td></td><td></td><td></td></tr><tr><td><em>Disciplines</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Humanities</td><td></td><td></td><td></td><td>12.78<br>(9.18)</td><td>-2.43<br>(10.70)</td><td>24.89<br>(16.40)</td></tr><tr><td>Formal Sciences</td><td></td><td></td><td></td><td>7.51<br>(5.50)</td><td>1.83<br>(5.98)</td><td>13.92<br>(8.66)</td></tr><tr><td>Economics</td><td></td><td></td><td></td><td>6.70<br>(5.04)</td><td>1.38<br>(5.49)</td><td>12.07<br>(6.86)</td></tr><tr><td>Health</td><td></td><td></td><td></td><td>15.06**<br>(5.72)</td><td>7.37<br>(6.10)</td><td>21.62*<br>(9.39)</td></tr><tr><td>Natural Sciences</td><td></td><td></td><td></td><td>10.18<br>(6.71)</td><td>4.29<br>(6.64)</td><td>14.95<br>(9.01)</td></tr><tr><td>Social Sciences</td><td></td><td></td><td></td><td>9.53<br>(6.55)</td><td>5.67<br>(5.68)</td><td>15.38<br>(11.02)</td></tr><tr><td>Others</td><td></td><td></td><td></td><td>17.31*<br>(7.42)</td><td>13.64<br>(9.04)</td><td>21.64<br>(13.99)</td></tr><tr><td>Intercept</td><td>42.46**<br>(12.64)</td><td>48.51**<br>(12.12)</td><td>27.49<br>(17.13)</td><td>22.52<br>(20.62)</td><td>10.67<br>(23.27)</td><td>25.36<br>(33.79)</td></tr><tr><td>σ<sup>2</sup>(Intercept)</td><td>211.83**<br>(25.67)</td><td>200.12**<br>(24.97)</td><td>424.26**<br>(52.79)</td><td>406.77**<br>(60.42)</td><td>546.93**<br>(96.64)</td><td>1373.83**<br>(199.74)</td></tr><tr><td>σ<sup>2</sup>(residual)</td><td>636.82**<br>(40.74)</td><td>525.22**<br>(38.55)</td><td>512.31**<br>(40.95)</td><td>1649.37**<br>(119.87)</td><td>1125.48**<br>(79.37)</td><td>1266.40**<br>(97.23)</td></tr><tr><td>χ<sup>2</sup></td><td>213.28</td><td>176.14</td><td>174.06</td><td>276.77</td><td>268.98</td><td>160.56</td></tr><tr><td>p-value</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr></tbody></table>

### C.3.2 Coefficient analysis

Table 12 describes the tests that are performed on the estimates to test the equality of two parameters in the regression displayed in Table 10. Applied tests are Wald tests with $H_0$ assuming equality of two parameters in the same random-effect Tobit panel regression. The table displays p-values, with bold font when value is below 5%, indicating that the two parameters are significantly different at this level.
<table><caption>Table 12: TESTS OF PARAMETERS OF GREENER VS BROWNER ASSETS IN THE SAME REGRESSION</caption><thead><tr><th rowspan="2">Line and column</th><th colspan="2">P-values</th></tr><tr><th>Expected Return</th><th>Standard Deviation</th></tr></thead><tbody><tr><td>L1C1 vs - L2C1</td><td><strong>0.0000</strong></td><td></td></tr><tr><td>L1C2 vs - L2C2</td><td><strong>0.0010</strong></td><td></td></tr><tr><td>L1C3 vs - L2C3</td><td><strong>0.0000</strong></td><td></td></tr><tr><td>L1C4 vs - L2C4</td><td>0.2373</td><td></td></tr><tr><td>L1C5 vs - L2C5</td><td><strong>0.0003</strong></td><td></td></tr><tr><td>L1C6 vs - L2C6</td><td><strong>0.0014</strong></td><td></td></tr><tr><td>L1C7 vs - L2C7</td><td><strong>0.0000</strong></td><td></td></tr><tr><td>L3C1 vs - L4C1</td><td></td><td>0.8419</td></tr><tr><td>L3C2 vs - L4C2</td><td></td><td><strong>0.0000</strong></td></tr><tr><td>L3C3 vs - L4C3</td><td></td><td><strong>0.0000</strong></td></tr><tr><td>L3C4 vs - L4C4</td><td></td><td><strong>0.0119</strong></td></tr><tr><td>L3C5 vs - L4C5</td><td></td><td><strong>0.0001</strong></td></tr><tr><td>L3C6 vs - L4C6</td><td></td><td><strong>0.0000</strong></td></tr><tr><td>L3C7 vs - L4C7</td><td></td><td><strong>0.0036</strong></td></tr></tbody></table>

**Description:** LxCy stands for the line x and the column y in Table 4. Lines 1 and 2 refer to expected return of the greener asset and browner asset respectively, lines 3 and 4 to the standard deviation of the greener and browner asset respectively. Columns 1 to 7 are described at the beginning of section 3.

In Table 13, we study differences in specifications between groups. To achieve it, we test if the coefficients (slopes) of the variable under study (e.g., expected return of the greener asset, standard deviation of the browner asset) are significantly different between groups (green versus brown contexts, professional versus student population).

We follow the methodology proposed by Wooldridge (2016), a regression model with qualitative information, by investigating the interaction coefficient between (1) the variable under study and (2) the dummy variable which enables to distinguish the two groups under consideration (Dummy Group). By default, we let all the variables and constant differ between groups, by including all the interaction coefficients between the studied
groups and the explanatory variables. The general equation is the following:

$$
\begin{equation}
\begin{aligned}
\lambda ={}& \beta_0 + \beta_1 \text{ReturnGreener} + \beta_2 \text{ReturnBrowner} + \beta_3 \text{StandardDevGreener} \\
& + \beta_4 \text{StandardDevBrowner} + \beta_5 \text{Correlation0} + \beta_6 \text{Correlation1} \\
& + \beta_7 \text{Correlationminus1} + \beta_8 \text{Ranking} + \beta_9 \text{Tax} + \beta_{10} \text{Risk} \\
& + \beta_{11} \text{Prudence} + \beta_{12} \text{Temperance} + \beta_{13} \text{SVO} + \beta_{14} \text{NEP} \\
& + \beta_{15} \text{CRT} + \beta_{16} \text{LevelK} + \delta_0 \text{Group} + \delta_1 \text{Group.ReturnGreener} \\
& + \delta_2 \text{Group.ReturnBrowner} + \delta_3 \text{Group.StandardDevGreener} \\
& + \delta_4 \text{Group.StandardDevBrowner} + \delta_5 \text{Group.Correlation0} \\
& + \delta_6 \text{Group.Correlation1} + \delta_7 \text{Group.Correlationminus1} + \delta_8 \text{Group.Ranking} \\
& + \delta_9 \text{Group.Tax} + \delta_{10} \text{Group.Risk} + \delta_{11} \text{Group.Prudence} + \delta_{12} \text{Group.Temperance} \\
& + \delta_{13} \text{Group.SVO} + \delta_{14} \text{Group.NEP} + \delta_{15} \text{Group.CRT} + \delta_{16} \text{Group LevelK}
\end{aligned}
\tag{1}
\end{equation}
$$

Estimates are computed based on a random effects Tobit regression in a panel data framework, with robust cluster standard errors at the participant level, to account for the within-cluster error correlations and the heteroskedasticity, such as displayed in Section C.3.

The null hypothesis (H0) is that the two groups have coefficients (slopes) that do not significantly differ on the variable of interest. The alternative hypothesis (H1) is that the coefficients significantly differ between the two groups on the variable of interest. To perform this test, we observe the interaction coefficients between the dummy variable *Group* and the selected variable under study (for example if *ReturnGreener*, $\delta_1$ in Equation 1). If the interaction coefficient is significantly different from 0, then we can conclude that the two coefficients (slopes) differ between groups.
<table><caption>Table 13: TESTS OF PARAMETERS IN DIFFERENT REGRESSIONS</caption><thead><tr><th rowspan="2">Line and column</th><th colspan="6">Interaction coefficients and p-values (in parentheses)</th></tr><tr><th>EX RET<br>GR vs BR</th><th>SD DEV<br>GR vs BR</th><th>Asymmetry<br>neutral</th><th>EX RET<br>PRO vs STUD</th><th>SD DEV<br>PRO vs STUD</th><th>STUD<br>GR vs BR</th></tr></thead><tbody><tr><td>L1C2 vs L1C3</td><td>-0.45<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C4 vs L1C5</td><td>-0.25<br>(0.134)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C6 vs L1C7</td><td>-0.63<br>(0.003)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C2 vs L2C3</td><td>0.27<br>(0.037)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C4 vs L2C5</td><td>0.01<br>(0.953)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C6 vs L2C7</td><td>0.49<br>(0.019)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C2 vs L3C3</td><td>0.31<br>(0.000)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C4 vs L3C5</td><td>0.19<br>(0.006)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C6 vs L3C7</td><td>0.41<br>(0.000)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C2 vs L4C3</td><td>0.04<br>(0.481)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C4 vs L4C5</td><td>0.09<br>(0.105)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C6 vs L4C7</td><td>-0.01<br>(0.879)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C2 vs -L3C3</td><td>-0.14<br>(0.005)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C4 vs -L3C5</td><td>-0.09<br>(0.145)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C6 vs -L3C7</td><td>-0.18<br>(0.017)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C4 vs L1C6 (green)</td><td>0.39<br>(0.062)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L1C5 vs L1C7 (brown)</td><td>0.75<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C4 vs L2C6 (green)</td><td>-0.21<br>(0.312)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L2C5 vs L2C7 (brown)</td><td>-0.71<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C4 vs L3C6 (green)</td><td>0.07<br>(0.408)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L3C5 vs L3C7 (brown)</td><td>-0.12<br>(0.227)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C4 vs L4C6 (green)</td><td>0.04<br>(0.627)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L4C5 vs L4C7 (brown)</td><td>0.14<br>(0.126)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L8C2 vs L8C3</td><td>-4.40<br>(0.118)</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Interaction coefficients are displayed, and p-values are shown in parentheses.
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<footnote n="1">¹ CEE-M Working Papers / Contact : laurent.garnier@inrae.fr</footnote>

<footnote>• RePEc https://ideas.repec.org/s/hal/wpceem.html</footnote>

<footnote>• HAL https://halshs.archives-ouvertes.fr/CEE-M-WP/</footnote>
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