# Risk-return trade-offs in the context of environmental impact: a lab-in-the-field experiment with finance professionals

Sébastien Duchêne, Adrien Nguyen-Huu, Dimitri Dubois, Marc Willinger

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Sébastien Duchêne, Adrien Nguyen-Huu, Dimitri Dubois, Marc Willinger. Risk-return trade-offs in the context of environmental impact: a lab-in-the-field experiment with finance professionals. 2022. [hal-03883121](https://hal.inrae.fr/hal-03883121)

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# How Environmental Attributes Shape Return and Risk Sensitivities in Portfolio Choices

Sébastien Duchêne, Adrien Nguyen-Huu, Dimitri Dubois, Marc Willinger

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Sébastien Duchêne, Adrien Nguyen-Huu, Dimitri Dubois, Marc Willinger. How Environmental Attributes Shape Return and Risk Sensitivities in Portfolio Choices. 2025. hal-03883121v2

HAL Id: hal-03883121

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Preprint submitted on 15 Oct 2025

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

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recherche français ou étrangers, des laboratoires
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Risk-return trade-offs in the context of
environmental impact

Sébastien Duchêne
Adrien Nguyen-Huu
Dimitri Dubois
Marc Willinger

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

CEE-M Working Paper 2022-12
How Environmental Attributes Shape Return
and Risk Sensitivities in Portfolio Choices

Sébastien Duchêne*

Adrien Nguyen-Huu†
Marc Willinger †

Dimitri Dubois†

October 15, 2025

Abstract

This paper investigates how financial return, risk, and environmental impact jointly shape portfolio choices in a lab-in-the-field experiment with more than 15,000 portfolio choices made by finance professionals and university students. Participants repeatedly allocated budgets between green, neutral or brown assets differing in expected return, risk. Asset color referred to environmental externalities which were modeled as real donations to a pro-environmental NGO (green) or to a fossil fuel association (brown).

Three robust findings emerge. First, environmental framing modifies financial sensitivities: participants respond more strongly to return changes in greener assets, while their responsiveness to risk does not differ by asset color. In high-risk contexts, portfolios shift toward higher-return brown assets, showing that standard return-risk trade-offs dominate when financial uncertainty rises. Second, preferences are asymmetric: aversion to brown assets dominates attraction to green ones, consistent with loss aversion and negativity bias. Third, both professionals and students exhibit pro-environmental tendencies, but for different reasons: professionals' choices reflect job roles and trading strategies, while students' allocations relate more to pro-social and environmental attitudes and respond more strongly to pecuniary and non-pecuniary incentives.

JEL Classification: G41; G11; C91

<footnote>*Montpellier Business School, Montpellier, France. Corresponding author: s.duchene@montpellierbs.com.</footnote>

<footnote>†CEE-M, Univ. Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.</footnote>
# Risk-return trade-offs in the context of environmental impact:

## a lab-in-the-field experiment with finance professionals

Sébastien Duchêne*

Adrien Nguyen-Huu†

Dimitri Dubois†

Marc Willinger †

December 1, 2022

## Abstract

We assess the impact of environmental externalities on portfolio decisions in a lab-in-the-field experiment on finance professionals and students. Participants are prone to accept lower returns for positive environmental impact but will not bear increased risk. They show asymmetric pro-environmental preferences depending on the sign of the externality. Finance professionals are more pro-environment than students, particularly regarding positive externalities, and less influenced by a ranking signal about environmental performance. Control tasks show that experimental measures of pro-social and environmental preferences have less influence on portfolios than market practices for professionals but are significant predictors for students.

<footnote>*Montpellier Business School, Montpellier, France. Corresponding author: s.duchene@montpellierbs.com.</footnote>

<footnote>†CEE-M, Univ. Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.</footnote>
# 1 Introduction

GREEN IS THE NEW BLACK of the finance industry. Environmental concerns, especially related to climate change, constitute a central element of socially responsible investment (SRI). A large body of evidence shows that social and environmental values increasingly shape investment decisions, both at the individual level and for institutional investors (Jansson and Biel, 2011; Riedl and Smeets, 2017; Briere and Ramelli, 2022; Bauer et al., 2021). Investors knowingly accept lower expected financial returns in exchange for nonpecuniary benefits from investing in assets with pro-social externalities. Pro-social preferences finds confirmation in empirical data (Fama and French, 2007; Jeffers et al., 2021), surveys (Brodback et al., 2019), field experiments (Brodback et al., 2021; Heeb et al., 2022), and laboratory experiments (Guenster et al., 2022; Bonnefon et al., 2025).

These trade-offs are unlikely to be fixed. The degree of pro-sociality may vary across individuals and evolve with market conditions. The Covid-19 shock illustrated this fragility: when confronted with financial losses, many investors reduced their ESG exposure (Döttling and Kim, 2021; Ferriani and Natoli, 2021). Such episodes suggest that the balance between return and externality is context-dependent rather than stable. Since changes in expected returns ultimately reflect varying market risk, this also raises the possibility of a trade-off between risk and environmental impact—one that may itself shift with circumstances. Examining these sensitivities is the central objective of this paper.

In this paper, we offer an incentivized portfolio experiment with a sample of 190 finance professionals in a lab-in-the-field setting, and replicate it in-the-lab with 279 student subjects, in order to explore the interaction of pro-environmental preferences with classical financial motives of return and risk. Participants repeatedly allocated budgets between two assets that systematically varied along three dimensions: expected return, risk, and environmental externalities. Finance professionals carried out the experiment within banks and asset management companies, providing a unique setting in which professionals were observed under controlled conditions that closely resembled their work environment.

Pooling our data, we analyze revealed sensitivities along three dimensions: the classic risk-return sensitivity, the return-impact sensitivity (impact defined as a positive environmental externality), and the risk-impact sensitivity. We confirm established re-
JEL Classification: G41; G11; C91

Acknowledgements

The paper circulated in a previous version under the title, "Why do finance profession-
als hold green or brown assets? A lab-in-the-field experiment." We thank Achraf Bouje-
maoui, Brice Corgnet, Felix Holzmeister, Michael Kirchler, Benoît Mulkay, Phu Nguyen-
Van, Sébastien Pouget, Patrick Sentis, participants of the SFB Research Seminar at the
University of Innsbruck, the 2021 Experimental Finance conference organized by the Uni-
versity of Innsbruck, the 2021 Western Economic Association International Conference,
the 2021 International Conference of the French Association of Experimental Economics,
the 2022 French Finance Association conference, and participants of the Paris School of
Economics seminar, Paris 8 seminar, University of Montpellier green finance seminar, for
feedback and suggestions. The usual disclaimer applies. We are particularly grateful to the
Moroccan Association of Trading Rooms (AMSM). Sébastien Duchêne acknowledges the
University of Montpellier for its funding and the support of the Institut Louis Bachelier,
Institut Europlace Finance. This work was supported by the French government under the
Programme d'Investissement d'Avenir, Initiative Sciences Innovation Territoires - MUSE,
and the University of Montpellier. Adrien Nguyen-Huu acknowledges the support of the
Chair Energy and Prosperity, under the aegis of "La Fondation du Risque."
sults on the first dimension and show that participants sacrifice part of their expected return for environmental impact. Our main contribution lies in documenting how environmental framing modifies financial sensitivities: participants respond more strongly to changes in the returns of greener assets, while their responsiveness to risk does not differ by asset color. When overall risk increases—through background or systematic exposure—participants rebalance toward higher-return, browner assets, and the environmental performance of portfolios declines. This echoes Guenster et al. (2022), who show that negative financial outcomes reduce pro-environmental investment, and Bansal et al. (2022), who argue that demand for SRI decreases in downturns. Our findings extend this logic by showing that the balance between environmental and financial considerations is itself sensitive to the risk environment: as financial uncertainty rises, standard return-risk trade-offs dominate, and the scope for environmental impact narrows.

A further novelty of our design is the symmetric modeling of positive and negative externalities. In line with delegated philanthropy experiments (Baron, 2007; Bénabou and Tirole, 2010; Guenster et al., 2022), investing in a green asset triggered a donation to an environmental NGO. Symmetrically, investing in a brown asset triggered a donation to a fossil fuel association, which we term “delegated misanthropy.” Brown assets also carried a premium to compensate for the moral cost of their negative externality.

This design allows us to compare sensitivities to positive and negative externalities in a unified framework. We find that participants are more reluctant to invest in brown assets than they are eager to invest in green ones. This asymmetry is consistent with prospect theory’s emphasis on loss aversion (Kahneman and Tversky, 1979; Tversky and Kahneman, 1991), as well as with the psychological literature on negativity bias, which shows that individuals react more strongly to negative than to positive stimuli (Baumeister et al., 2001; Rozin and Royzman, 2001). Our design thus reveals that negative externalities make the return-impact sensitivity particularly salient, amplifying portfolio adjustments. This complements recent work on asymmetries in SRI (Humphrey et al., 2021) and on sin assets (Chew and Li, 2021), while innovating by offering a fully symmetric setting with both positive and negative externalities.

We also test the robustness of our results through two incentive treatments designed to influence portfolio choices. First, a *ranking treatment* informed participants about their relative standing in terms of the proportion of their budget invested in the greener asset, drawing on social comparison theory (Festinger, 1954; Kirchler et al., 2018). Second, a *tax treatment* penalized brown allocations by reducing their net returns. We observe that students responded strongly to both treatments, particularly the tax, whereas finance
# 1 Introduction

GREEN IS THE NEW BLACK of the finance industry. Environmental concerns, especially related to climate change (Reboredo and Otero, 2021), constitute an important element of socially responsible investment (SRI). Much evidence shows that social and environmental values take a growing place in investing decisions, at the individual level (Jansson and Biel, 2011; Riedl and Smeets, 2017; Brière and Ramelli, 2020) and for various institutional investors (Jansson and Biel, 2011; Bauer et al., 2021; Barber et al., 2021). Investors would knowingly accept lower expected financial returns in exchange for nonpecuniary benefits from investing in assets with pro-social externalities (Coqueret, 2021). Pro-social preferences theory (e.g., Bénabou and Tirole, 2010) finds confirmation in empirical data (Fama and French, 2007; Jeffers et al., 2021), surveys (Brodback et al., 2019), field experiments (Brodback et al., 2021; Heeb et al., 2022), and laboratory experiments (Brodback et al., 2020; Bonnefon et al., 2022). Socially responsible signaling can even be strong enough to lower performance expectations from investors (Zhang, 2021) or misguide investors toward inefficient capital allocation in both financial and social returns (Green and Roth, 2021).

The picture becomes more complex when risk enters, as the three dimensions of risk, return, and impact are intertwined. Jeffers et al. (2021) show that impact funds have substantially lower market beta than venture capital funds, and Biasin et al. (2022) show that social impact investment must be seriously considered for portfolio variance minimization. The Covid-19 shock has also proven that pro-social preferences are more fragile under stress (Döttling and Kim, 2021), pushing investors to avoid an additional environmental, social, and governance (ESG) risk to financial losses (Ferriani and Natoli, 2021), even if SRI may have outperformed benchmarks in this period (Capelle-Blancard et al., 2021; Lööf et al., 2021). Those characteristics illustrate the difficulty of assessing the relationship between risk aversion, return-seeking and pro-social preferences separately.

In the present paper, we offer an experiment with a sample of 279 student subjects, and replicated it in a lab-in-the-field setting with a sample of 190 finance professionals, to explore the interaction of pro-environmental preferences with classical risk-return financial preferences. Participants faced repeated individual investment tasks where experimental assets vary along the dimensions of return, risk, and environmental externalities, the latter generated by investing in specific colored assets (green and brown). In particular,
professionals were largely unresponsive. This suggests that professionals' environmental allocations are less malleable and more anchored in structural norms and practices, while students' allocations are more elastic to pecuniary or social incentives.

The joint use of student and professional samples provides complementary forms of validity. The student lab sample ensures high internal validity, enabling us to isolate the three trade-offs in a controlled environment, while the lab-in-the-field sample with professionals offers high external validity by observing actors embedded in real financial institutions. We find that the portfolio trade-offs are qualitatively similar across both groups, but professionals exhibit somewhat stronger pro-environmental allocations than students. In their case, portfolio choices are more closely tied to job positions and trading strategies, whereas students' allocations are more directly associated with pro-social and pro-environmental preference scores. This suggests that professional roles and daily practices may weigh more heavily than personal attitudes in shaping environmental investment.

To account for individual heterogeneity, we also controlled for risk preferences, cognitive ability, and strategic reasoning using standard experimental tasks (Noussair et al., 2014; Frederick, 2005; Alaoui and Penta, 2016; Murphy et al., 2011; Dunlap et al., 2000). These controls capture part of the variation across participants but do not alter our main findings: environmental framing modifies sensitivities to returns but not to risk; aversion to brown assets dominates attraction to green ones; and professional allocations are primarily anchored in their roles and trading strategies.

The rest of the paper is structured as follows. Section 2 introduces the portfolio choice task and main predictions. Section 3 presents the data and the core results about the three trade-offs. Section 4 analyzes heterogeneity across professionals and students and the effects of treatments. Section 5 details the control tasks and provides robustness checks. Section 6 concludes.

## Relationship to Companion Paper

This article is a companion to XXXX, which tests a theoretical model of sustainable investment using solely baseline treatment data from our shared experimental study. To ensure accuracy and consistency, the description of the experimental protocol is therefore shared between both publications. The introductory context and the literature review also present some similarities, as both papers stem from the same research project.

However, the two articles pursue distinct objectives. While the companion paper
professionals participated in the experiment in conditions closely related to their work environment within banks and asset management companies, which provides a unique sample tested in a controlled environment.

Pooling our data, we analyze revealed preferences through three trade-offs: the risk-return trade-off, the return-impact trade-off (where impact means a relatively positive environmental externality) and, finally, the risk-impact trade-off. We confirm results found in the literature about the first two: participants naturally arbitrate between return and risk and show significant attraction to both return and environmental impact, arbitrating between the two dimensions in asset allocations. However, our principal contribution is to show that individuals do not bear a higher risk for improved environmental impact. In particular, when portfolios are more risky (e.g., in the presence of background, systematic risk), participants tend to compensate for the higher riskiness by seeking higher returns and thus diminish the environmental benefit. This finding echoes Brodback et al. (2020), who emphasize that bad outcomes generate less willingness to pay for SRI, and Bansal et al. (2021), who suggest that investors have a wealth-dependent preference for social responsibility, which leads to a decreased demand for SRI during bad times. The connection between bad financial outcomes and lower desirability of externalities extends to risky outcomes.

In the experiment with finance professionals, the core task was to allocate a fixed budget of 100€<fnref n="1" /> between two assets, which mimics a simple portfolio choice task. Each asset was characterized by three attributes: return (between 10% and 40%), standard deviation (between 0% and 50%) and environmental impact (explained below). The core task was repeated 35 times, by systematically varying each of the dimensions, to provide a rich data set that allowed us to identify the three trade-offs. We frame an asset's environmental impact, either as delegated philanthropy, or as delegated misanthropy. Delegated philanthropy (Baron, 2007; Bénabou and Tirole, 2010) is a standard experimental tool to simulate SRI or charitable giving (Heimann et al., 2011; Koppel and Regner, 2011; Eckel et al., 2017; Brodback et al., 2020; Humphrey et al., 2021). Participants are usually offered the chance to donate to a charity (e.g., the Red Cross or the WWF). In our setting, investing in a green asset leads to a donation to an environmentally virtuous third party by the experimenter. We take the mirror image of such donation for defining a brown asset, based on “delegated misanthropy”: investing in a brown asset leads to a donation to an environmentally vicious third party by the experimenter.

<footnote n="1">¹In practice we used the experimental currency "ecu", but the rate was 1 ecu = 1 euro for the professionals while it was 1 ecu = 0.2 euros for the students.</footnote>
focuses on model validation, the present work adopts an econometric framework to ana-
lyze the full dataset, including multiple experimental treatments (taxation and ranking),
control tasks and socio-demographic individual data. It provides new insights into how
environmental framing modifies financial sensitivities: participants react more strongly
to return changes in greener assets, while risk responsiveness does not vary with asset
color. The paper also documents a marked asymmetry in preferences (stronger aversion
to brown assets), and analyzes how investors respond to incentives. Finally, these be-
havioral patterns are cross-analyzed with individual characteristics to shed light on the
underlying drivers of such preferences.

## 2 Sensitivities to Return, Risk, and Environmental Fram-
ing

Our experimental portfolio choice task builds on a long tradition in experimental finance, from the seminal work of Gneezy and Potters (1997) to more recent studies such as Kirchler et al. (2018). Participants are asked to allocate a fixed budget between two assets. The main novelty of our design is that, in almost all tasks, one of the assets generates an environmental externality. Subsection 2.1 describes the task, subsection 2.2 details the protocol and sampling procedures, and subsection 2.3 states our main predictions.

## 2.1 Portfolio task

Participants allocate a fixed budget between two assets. Each asset has a random return modeled by a Bernoulli variable with expected return $\mu$ and standard deviation $\sigma$. Let $\lambda \in [0, 1]$ denote the share of the budget invested in one asset, the remaining $1 - \lambda$ being invested in the other. Participants composed such portfolios 35 times, following the order of the screens summarized in Table 1. For subsequent analyses, we define $\lambda$ as the proportion of the budget invested in the “greener” asset—namely, the green asset when facing a neutral–green allocation, and the neutral asset when facing a neutral–brown allocation.<fnref n="1" />

We deliberately implemented a non-random order of tasks.<fnref n="2" /> This choice allowed us to

<footnote n="1">¹There is no direct green-brown allocation in the design.</footnote>

<footnote n="2">²Although we cannot exclude the possibility that task order influenced decisions, there is no reason to believe that it biased choices in a systematic direction. Moreover, keeping the same ordering across</footnote>
By opposition to delegated philanthropy, which is based on the willingness to sacrifice material payoff to achieve social goals, we set a compensation mechanism that eases the moral cost of delegated misanthropy. We do this by introducing a “brown premium” on the returns of brown assets. Thereby, participants can either invest in a high-return brown asset that generates a negative externality on society<fnref n="2" /> or avoid it by waiving the brown premium and investing in a lower return asset with a better environmental externality.<fnref n="3" /> We find that participants are more reluctant to invest in brown assets than they are willing to invest in the corresponding green assets, *ceteris paribus*. Such difference in invested shares, between environmentally virtuous and vicious, may lie in different psychological and cognitive motives, akin to loss aversion in prospect theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1991).<fnref n="4" />

We combine experimental data from standard student subjects (hereafter the STUD sample) with data from finance professionals (hereafter the PRO sample) sampled in a lab-in-the-field setting. The key reason for using simultaneously conventional subjects and professional subjects, is that each type of subjects provides specific validity on a distinct dimension. The lab setting offers high internal validity, because it allows us to isolate the three trade-offs in a highly controlled environment, but at the cost of a low external validity. The lab-in-the-field setting, in which finance professionals performed the portfolio tasks in their natural environment, provides high external validity at the cost of a lesser control. In addition, finance professionals’ behavior is highly interesting as they advise clients, trade in the markets, invest in assets, being at the core of the financial system. In a complementary way, students are younger people who represent the new climate-sensitive generation (McDougle et al., 2011)<fnref n="5" />, thus offering important

<footnote n="2">²Delegated misanthropy is implemented by a donation to an association of the fossil energy industry.</footnote>

<footnote n="3">³Our mechanisms are somehow comparable to those of the “Click for a Charity” experiment of Ariely et al. (2009). The authors study how the donations depend on the beneficiary, either the Red Cross considered as a “good” cause by most participants, or the National Rifle Association considered as a bad cause by the majority of participants. Negative externalities were also studied in market experiments by Falk and Szech (2013) (killing mice) and by Bartling et al. (2015) and Bartling et al. (2019) (avoiding a CHF 60 donation to fund a surgery of a leprosy patient in India).</footnote>

<footnote n="4">⁴Asymmetry in SRI was recently investigated in Humphrey et al. (2021), and an analog with sin assets is studied in Chew and Li (2021), who show that aversion to vicious investment is larger than attraction to virtuous investment. Our results are in line with Humphrey et al. (2021), who show that negative externalities produce a larger impact on students’ investment decisions than positive externalities. However, in their paper, the negative externality is a deduction from a donation to non-profit organizations and does not produce a salient negative externality that can generate a direct negative loss onto the society, as in our design. Furthermore, their design does not offer the possibility of trading between two assets with positive returns, standard deviations and correlations that vary according to situations.</footnote>

<footnote n="5">⁵For a recent survey on climate concerns and generational differences, see https://news.gallup.com/poll/234314/global-warming-age-gap-younger-americans-worried.aspx.</footnote>
<table><caption>Table 1: ASSET CHARACTERISTICS IN THE PORTFOLIO CHOICE TASK</caption><thead><tr><th rowspan="2">Screen</th><th colspan="2">Situation 1</th><th colspan="2">Situation 2</th><th colspan="2">Situation 3</th><th rowspan="2">correl.</th></tr><tr><th>C(μ<sub>1</sub>, σ<sub>1</sub>)</th><th>C(μ<sub>2</sub>, σ<sub>2</sub>)</th><th>C(μ<sub>1</sub>, σ<sub>1</sub>)</th><th>C(μ<sub>2</sub>, σ<sub>2</sub>)</th><th>C(μ<sub>1</sub>, σ<sub>1</sub>)</th><th>C(μ<sub>2</sub>, σ<sub>2</sub>)</th></tr></thead><tbody><tr><td>1</td><td>N(15,0)</td><td>G(10,0)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td>N(15,0)</td><td>B(20,0)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3</td><td>N(15,0)</td><td>N(20,25)</td><td>N(15,0)</td><td>N(30,25)</td><td>N(15,0)</td><td>N(30,50)</td><td></td></tr><tr><td>4</td><td>N(15,0)</td><td>G(10,25)</td><td>N(15,0)</td><td>G(20,25)</td><td>N(15,0)</td><td>G(20,50)</td><td></td></tr><tr><td>5</td><td>N(15,0)</td><td>B(30,25)</td><td>N(15,0)</td><td>B(40,25)</td><td>N(15,0)</td><td>B(40,50)</td><td></td></tr><tr><td>6</td><td>N(20,25)</td><td>G(10,0)</td><td>N(30,25)</td><td>G(10,0)</td><td>N(30,50)</td><td>G(10,0)</td><td></td></tr><tr><td>7</td><td>N(20,25)</td><td>B(20,0)</td><td>N(30,25)</td><td>B(20,0)</td><td>N(30,50)</td><td>B(20,0)</td><td></td></tr><tr><td>8</td><td>N(20,25)</td><td>G(10,25)</td><td>N(30,25)</td><td>G(20,25)</td><td>N(30,50)</td><td>G(20,50)</td><td>1</td></tr><tr><td>9</td><td>N(20,25)</td><td>G(10,25)</td><td>N(30,25)</td><td>G(20,25)</td><td>N(30,50)</td><td>G(20,50)</td><td>0</td></tr><tr><td>10</td><td>N(20,25)</td><td>G(10,25)</td><td>N(30,25)</td><td>G(20,25)</td><td>N(30,50)</td><td>G(20,50)</td><td>-1</td></tr><tr><td>11</td><td>N(20,25)</td><td>B(30,25)</td><td>N(30,25)</td><td>B(40,25)</td><td>N(30,50)</td><td>B(40,50)</td><td>1</td></tr><tr><td>12</td><td>N(20,25)</td><td>B(30,25)</td><td>N(30,25)</td><td>B(40,25)</td><td>N(30,50)</td><td>B(40,50)</td><td>0</td></tr><tr><td>13</td><td>N(20,25)</td><td>B(30,25)</td><td>N(30,25)</td><td>B(40,25)</td><td>N(30,50)</td><td>B(40,50)</td><td>-1</td></tr></tbody></table>

**Description:** Up to three situations that required a single portfolio decision for each situation were displayed simultaneously in 13 successive screens. Notation $C(\mu_i, \sigma_i)$ refers to asset $i$ (1, 2) with color C (N = neutral, G = green, B = brown), gross expected return $\mu_i$ (in percent) and standard deviation $\sigma_i$ (in percent). Correlation between the two assets is provided if $\min(\sigma_1, \sigma_2) > 0$.

gradually increase task complexity and thereby control for learning effects. We expected participants to progressively adjust to the rising sophistication of the experiment, as they became more familiar with making trade-offs involving the experimental assets.

The experiment began in a very simple setting, without risk: in the first two tasks (screens 1 and 2), the assets were riskless and returns were known.

Next, we introduced risk on one asset only (screens 3 to 7). Here, participants faced a trade-off between a risk-free asset and a risky asset of any color. The 15 allocation situations varied in expected return, standard deviation, and color (see below for the experimental procedure). The portfolio's standard deviation was proportional to the amount invested in the risky asset, i.e., a linear function of $\lambda$. Participants thus had to deal simultaneously with risk and the environmental-return trade-off.

<footnote>subsamples and treatments (Section 4.2) ensures that induced effects are not related to specific sequences. From the third to the thirteenth screen, three situations were simultaneously displayed, differing only in returns and standard deviations. By presenting three situations on the same screen, we encouraged participants to arbitrate consistently as returns and standard deviations varied. All relevant information was updated in real time on the screen according to the participant's chosen allocation (see the protocol in Section 2.2).</footnote>
information on future investment behaviors. Besides, they represent a less specialized
population closer to the households, who account for a growing share of the market.<fnref n="6" />
Because the two settings offer valuable complementarities, combining them has become
a widespread practice in the experimental finance literature (see e.g., Haigh and List,
2005; Kirchler et al., 2018; Gajewski et al., 2020; Weitzel et al., 2020; Holmen et al., 2021;
Bottasso et al., 2022). We find that the three trade-offs are qualitatively similar in both
samples, but that finance professionals exhibit stronger pro-environmental preferences
than students, in particular regarding green assets.

In contrast to the STUD sample, the PRO sample contains some specific heterogeneity
related to jobs and investment strategies, which deserves special attention. The PRO
sample gathers professionals from investment banks and asset management companies,
who occupy different jobs. We invited therefore the finance professionals to respond to
a specific questionnaire at the end of the experiment about their profession, investment
strategy, and other finance-related questions. The outcome is a unique data set that
allows us to relate, investment strategies revealed in the experiment, to jobs and out-of-
the-lab investment strategies. We highlight that professionals' portfolio allocations in the
lab depend strongly on their kind of job and on the type of portfolio strategy they use. We
rely on categories that are similar to the fundamentalist vs. chartist opposition. It appears
that finance professionals who are used to considering extra-financial information and who
have longer decision-making horizons, tend to be more sensitive to the environmental
impact of their portfolio in the lab. This finding suggests that, beyond the personal
traits that are specific to finance professionals (Holmen et al., 2021), their position in
the industry and their daily practice are crucial determinants of their trade-offs in the
lab. This may be because some jobs and strategies attract specific profiles, i.e., there is
selection (Lagarde and Blaauw, 2014), or because they reinforce particular professional
norms (Cohn et al., 2014).

We test the robustness of our main finding by introducing two instruments that were
intended to promote trade-offs in favor of the greener asset: (1) a “ranking treatment”,
in which each participant was informed about her rank in terms of the proportion of the
budget invested in the greener asset, and (2) a “tax treatment” in which subjects had to
pay a tax in proportion to their poor environmental performance. The ranking treatment
aimed to trigger pride or shame through ranking information about the environmental
performance of one’s portfolio. According to social comparison theory (Festinger, 1954),

<footnote n="6">⁶For example, in the French market, according to the Autorité des marchés financiers (AMF), the number of active retail investors has doubled between 2019 and 2021 (Chatillon et al., 2021).</footnote>
Finally, the third stage of the experiment involved two risky assets in 18 different situations. In these cases, both assets had the same standard deviation for tractability, but an additional parameter entered: the correlation regime $\rho \in \{-1, 0, 1\}$. When $\rho = 1$, participants faced unavoidable risk; when $\rho = 0$, they could diversify to reduce risk; and when $\rho = -1$, they could fully eliminate risk. In this setting, participants simultaneously confronted return, risk, and environmental impact, with either diversifiable or non-diversifiable risk.<fnref n="3" />

## 2.2 Practical procedures and sampling

The full description of the experimental design, with screenshots and instructions, is provided in Appendix A.1 to A.3. The design was approved by the Institutional Review Board (IRB) of xxx (University of xxx, for blind review).

After a short introduction, participants faced 35 independent allocation tasks summarized in Table 1. In each task, they allocated 100 experimental currency units (ECUs) between two assets, knowing the conversion rate to euros in advance (different across samples). Screens displayed each asset's return, standard deviation, color, and beneficiary association. Once an allocation was chosen, the portfolio's expected return, standard deviation, outcome distribution, and donation were updated in real time.

**Payment and donation.** We used the random payment method: one of the 35 tasks was randomly selected for payment (Cubitt et al., 1998; Bardsley et al., 2009). In our design, environmental impact was directly linked to participants' portfolio choices. Investments in the green asset triggered a transfer of $50 \times \lambda$ ECUs to an environmental NGO<fnref n="4" />, whereas investments in the brown asset resulted in the same amount being donated to a fossil fuel

<footnote n="3">³Previous research shows that individuals may struggle to understand volatility and correlations (Enke and Zimmermann, 2019) and may instead rely on alternative metrics such as the probability of losses (Holzmeister et al., 2020). Two potential issues arise. First, participants could place greater weight on environmental donations if financial properties were less accessible. However, our primary sample consisted of finance professionals accustomed to handling such metrics in their daily work, making comprehension less problematic. Students and professionals who had difficulties could ask staff questions at any time, and all comprehension questions were systematically addressed. Second, professionals likely had a better grasp of volatility and correlations than students, potentially explaining part of the behavioral differences. Yet if such a gap exists in the lab, it almost certainly exists in real markets as well. This supports the inclusion of both professionals and students in our design to capture realistic heterogeneity in understanding and behavior.</footnote>

<footnote n="4">⁴The NGO is an international initiative supporting sustainable reforestation projects through crowdfunding. Further details for reviewers are provided in Appendix D.</footnote>
upward social comparisons improve individuals’ abilities and provide self-enhancement with new targets (List, 2006; Kirchler et al., 2018). The tax treatment was a penalty on the brown premium through a tax cut on expected profits that decrease with environmental performance. We observe that financial professionals are less sensitive to treatments than students, especially in contexts involving a negative environmental impact.

Throughout our analyses, we control for various behavioral traits, opinions, and socio-demographic data. Following the core investment task, participants took part in several control tasks and answered questionnaires. These tasks are taken from the mainstream literature to elicit five individual traits: risk preference (Noussair et al., 2014), cognitive ability (CRT, Frederick, 2005), strategic skill (the 11-20 game of Alaoui and Penta, 2016), pro-sociability (SVO, Murphy et al., 2011), and pro-environmental opinion (the NEP scale of Dunlap et al., 2000). While these tasks are mainly used as control variables to account for a likely heterogeneity between finance professionals and students, we are also specifically interested in their pro-social and pro-environmental preferences. As already shown by Riedl and Smeets (2017), these two dimensions may affect participants’ portfolio decisions. We show that this is the case with students, but the allocations of finance professionals are not correlated to their answers in those auxiliary tasks.

The rest of the paper is structured as follows. Section 2 introduces the portfolio choice task together with our main predictions. Section 3 presents the data and the core result about trade-offs. Section 4 then analyses the econometric patterns in the investment task in terms of colors (green vs. brown), samples (PRO vs. STUD), and the effect of treatments. Section 5 details the control tasks and the demographic data and redoes the analysis with that information to provide individual predictors of investment choices. Section 6 concludes.

## 2 Preferences over return, risk and environmental impact

The principle of the experimental portfolio choice task follows a long strand of experimental literature, from the seminal work by Gneezy and Potters (1997) to more recent work, e.g. Kirchler et al. (2018): participants are asked to allocate a fixed budget between two assets. The novelty of our experiment is that in almost all such tasks, one and only one asset generates an environmental externality. In subsection 2.1 we provide details about the task, in subsection 2.2 we state our core predictions, and in subsection 2.3 we detail the protocol and sampling procedures.
industry group<fnref n="5" />. For instance, allocating 70% of the budget to the green asset results in a 35 ECU donation to the environmental association. This symmetric mechanism ensured that every allocation decision carried an explicit positive or negative externality.

**Samples.** The experiment involved two groups: 190 finance professionals (PRO) in Casablanca, Morocco (October 2019) and 279 students (STUD) in xxx (2020). Professional sessions were organized in banks and asset management companies using tablets; student sessions were run at the Laboratory for Experimental Economics of xxx. Descriptive statistics are reported in Section 5 and Appendix B.1.

For the PRO sample, one participant out of ten was randomly selected for payment, resulting in high average earnings (€216.8 for selected participants). For the STUD sample, all participants were paid, with average earnings of €13.45. Remuneration levels in both samples were designed to provide strong monetary incentives and align with the standards of previous experiments with professionals (Cohn et al., 2015; Kirchler et al., 2018). Additional information on the incentive structures and benchmarks is available in Appendix D.

## 2.3 Predictions

Participants face three dimensions in their portfolio choice: return, risk, and environmental impact. Our research question is how these dimensions interact in portfolio allocations, and in particular whether environmental concerns lead participants to adjust their risk-return trade-offs.

As a by-product, and as expected, allocations also reveal a clear preference for higher returns and an aversion to risk. Since these findings are fully consistent with standard portfolio theory, we do not discuss them further.<fnref n="6" /> Our main focus lies in pro-environmental preferences—specifically, how the perception and valuation of return and risk are shaped by the environmental characteristics of assets.

**Hypothesis 1 (Pro-environmental preferences).** Participants prefer the greener over the browner asset in such a way that $\lambda$ decreases if a green (brown) asset is replaced by an equivalent neutral asset, that is, when a participant moves from a green (brown) versus neutral situation to a neutral versus neutral one, ceteris paribus.

<footnote n="5">⁵The association is an international consortium promoting the development and use of oil and gas. Further details for reviewers are provided in Appendix D.</footnote>

<footnote n="6">⁶See Appendix C.</footnote>
## 2.1 Portfolio task

Let us present the basic task. Participants are asked to allocate a fixed budget between two infinitely divisible assets: asset 1, with an expected return $\mu_1$ and a standard deviation $\sigma_1$, and asset 2, with an expected return $\mu_2$ and a standard deviation $\sigma_2$. If we denote by $\lambda \in [0, 1]$ the proportion of the budget invested in some asset, such as asset 1, the portfolio's expected return is $\mu_p = \mu_2 + \lambda(\mu_1 - \mu_2)$. Participants composed such portfolios 35 times, following the order of the screens summarized in Table 1. Asset 1 was always the neutral asset, while asset 2 was either green or brown (except for screen 3). For our subsequent analyses, we define $\lambda$ as the proportion of the budget invested in the “greenest” (or “least brown”) asset. In neutral versus green allocations, the greenest asset is the green asset, while in the neutral versus brown allocations, the greener asset is the neutral one.

<table><caption>Table 1: ASSET CHARACTERISTICS IN THE PORTFOLIO CHOICE TASK</caption><thead><tr><th rowspan="2">Screen</th><th colspan="2">Situation 1</th><th colspan="2">Situation 2</th><th colspan="2">Situation 3</th><th rowspan="2">correl.</th></tr><tr><th>C(μ<sub>1</sub>, σ<sub>1</sub>)</th><th>C(μ<sub>2</sub>, σ<sub>2</sub>)</th><th>C(μ<sub>1</sub>, σ<sub>1</sub>)</th><th>C(μ<sub>2</sub>, σ<sub>2</sub>)</th><th>C(μ<sub>1</sub>, σ<sub>1</sub>)</th><th>C(μ<sub>2</sub>, σ<sub>2</sub>)</th></tr></thead><tbody><tr><td>1</td><td>N(15,0)</td><td>G(10,0)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td>N(15,0)</td><td>B(20,0)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3</td><td>N(15,0)</td><td>N(20,25)</td><td>N(15,0)</td><td>N(30,25)</td><td>N(15,0)</td><td>N(30,50)</td><td></td></tr><tr><td>4</td><td>N(15,0)</td><td>G(10,25)</td><td>N(15,0)</td><td>G(20,25)</td><td>N(15,0)</td><td>G(20,50)</td><td></td></tr><tr><td>5</td><td>N(15,0)</td><td>B(30,25)</td><td>N(15,0)</td><td>B(40,25)</td><td>N(15,0)</td><td>B(40,50)</td><td></td></tr><tr><td>6</td><td>N(20,25)</td><td>G(10,0)</td><td>N(30,25)</td><td>G(10,0)</td><td>N(30,50)</td><td>G(10,0)</td><td></td></tr><tr><td>7</td><td>N(20,25)</td><td>B(20,0)</td><td>N(30,25)</td><td>B(20,0)</td><td>N(30,50)</td><td>B(20,0)</td><td></td></tr><tr><td>8</td><td>N(20,25)</td><td>G(10,25)</td><td>N(30,25)</td><td>G(20,25)</td><td>N(30,50)</td><td>G(20,50)</td><td>1</td></tr><tr><td>9</td><td>N(20,25)</td><td>G(10,25)</td><td>N(30,25)</td><td>G(20,25)</td><td>N(30,50)</td><td>G(20,50)</td><td>0</td></tr><tr><td>10</td><td>N(20,25)</td><td>G(10,25)</td><td>N(30,25)</td><td>G(20,25)</td><td>N(30,50)</td><td>G(20,50)</td><td>-1</td></tr><tr><td>11</td><td>N(20,25)</td><td>B(30,25)</td><td>N(30,25)</td><td>B(40,25)</td><td>N(30,50)</td><td>B(40,50)</td><td>1</td></tr><tr><td>12</td><td>N(20,25)</td><td>B(30,25)</td><td>N(30,25)</td><td>B(40,25)</td><td>N(30,50)</td><td>B(40,50)</td><td>0</td></tr><tr><td>13</td><td>N(20,25)</td><td>B(30,25)</td><td>N(30,25)</td><td>B(40,25)</td><td>N(30,50)</td><td>B(40,50)</td><td>-1</td></tr></tbody></table>

**Description:** Up to three situations that required a single portfolio decision for each situation were displayed simultaneously in 13 successive screens. Notation $C(\mu_i, \sigma_i)$ refers to asset $i$ (1, 2) with color C (N = neutral, G = green, B = brown), gross expected return $\mu_i$ (in percent) and standard deviation $\sigma_i$ (in percent). Correlation between the two assets is provided if $\min(\sigma_1, \sigma_2) > 0$.

We deliberately chose to carry the sequence of the tasks in a non-random order<sup>7</sup>, in

<footnote n="7"><sup>7</sup>Even if we cannot exclude the possibility that the order of the tasks influenced the participants, there is no reason to believe that it influenced the choice in a specific direction. Second, keeping the</footnote>
Hypothesis 1 can reflect a basic taxonomic preference, whereby participants favor green assets over neutral ones and avoid brown assets relative to neutral ones, *ceteris paribus*. This hypothesis captures a revealed ordering over asset color, independently of return or risk considerations. However, it also implicitly allows for potential *return-impact* or *risk-impact trade-offs*: if participants choose to allocate resources to a greener asset despite lower returns—or avoid a browner asset despite higher returns—they reveal a willingness to incur financial costs for environmental reasons. Similarly, if such allocations persist despite differences in risk level, this may reflect a tolerance for environmental risk. Hypothesis 1 thus serves as a foundational test of whether environmental externalities influence portfolio composition, even in the simplest settings where financial incentives point in the opposite direction.

**Hypothesis 2** (Environmental neutrality of sensitivities). *Participants respond equally to changes in return or risk, regardless of whether these changes affect the greener or the browner asset. Said otherwise, the responsiveness of $\lambda$ to variations in expected return or standard deviation is independent from asset color.*

Hypothesis 2 takes a conservative stance. Building on Hypothesis 1, it allows that an asset's color—green or brown—may carry intrinsic utility, such as the moral satisfaction of supporting an environmental cause or the disutility of financing a harmful one. However, this second hypothesis assumes that such utility does not interact with how participants evaluate changes in financial attributes like return or risk. In other words, taken together, Hypothesis 1 and Hypothesis 2 imply that environmental framing may shift the baseline utility of an asset, but it does not alter the way participants respond to its financial attributes such as return or risk.

This is, of course, a simplifying assumption. In practice, two competing mechanisms may operate. On one hand, if environmental externalities provide non-pecuniary utility that partially substitutes for financial utility, participants might care less about return or risk when assets are green—leading to dampened responsiveness (or sensitivity). In this view, greener assets provide an intrinsic value that reduces the effect of financial fluctuations on portfolio decisions. By contrast, environmental framing may amplify responsiveness by increasing the salience or moral weight of certain assets, inducing participants to monitor them more closely and to adjust their portfolios more aggressively in response to financial changes. The salience literature shows that more prominent attributes attract greater attention and weight (Bordalo et al., 2022, 2012), and in the environmental domain, framing effects have been shown to influence behavioral decisions via shifts in
order to increase the level of complexity gradually and smoothly, and control thereby
for learning. We suspected that participants would progressively adjust their skills to
the rising sophistication of the experiment, as they learned to make treadoffs with the
experimental assets. The experiment began very simply, without considering the risk
dimension. In the first two allocation tasks (screens 1 and 2), the assets are riskless,
and their returns are known. Those two cases lead to the trivial choice, $\lambda = 0$, for a
profit-maximizing participant indifferent to the externality of the assets. In contrast, a
participant who cares about the environmental externality, which is proportional to $\lambda$
(see the protocol in subsection 2.3), will refrain from investing the whole budget in the
more economically attractive browner asset and instead invest some in the greener asset,
that is, the green asset in screen 1 and the neutral asset in screen 2.

Then, integrating the risk dimension for one asset only (screens 3 to 7), participants
faced a trade-off between a risk-free asset and a risky asset of any color. In this second
phase of the experiment, the only risky asset is modeled by a Bernoulli fair lottery. The
15 allocation situations vary in expected return, standard deviation, and color. The
portfolio's standard deviation is simply proportional to the amount invested in the risky
asset, a linear function of $\lambda$. Participants thus had to deal with risk and the arbitrage
between return and environmental impact.

Finally, the third and last part is more complex as it involves two risky assets in 18
different situations. In these cases, the two assets have the same standard deviation for
convenience, but an additional parameter must be considered, which is the correlation
regime $\rho \in \{-1, 0, 1\}$ between returns. While $\rho = 1$ implies bearing an unavoidable
risk for participants, they can diversify their portfolio to reduce risk when $\rho = 0$, and
even cancel it when $\rho = -1$. In that setting, participants deal with return, risk and
environmental impact, with either diversifiable or non-diversifiable risk.<fnref n="8" />

<footnote>same ordering for the analysis of subsamples (Section 4.2) and treatments (Section 4.3) guarantees that induced effects are not related to specific orderings. Three situations were simultaneously displayed for each screen from 3rd to 13th, and the only differences among them were variations in returns and standard deviations. By offering three situations on the same screen, we encourage participants to arbitrate coherently, depending on changes in returns and standard deviations of the assets. All relevant information was displayed on the screen in real time, according to the allocation proposed by the participant (see the protocol in Section 2.3).</footnote>

<footnote n="8">⁸Regarding the complexity of the experiment, previous research has illustrated that individuals may have difficulties understanding volatility or correlations (Enke and Zimmermann, 2019) and sometimes rely on other risk metrics, such as the probability of experiencing losses (Holzmeister et al., 2020). This difficulty might generate two issues. First, participants could be confused about the information on these financial properties and, therefore, rely on more understandable data concerning donations. As a result, the importance of the externality could be overestimated. However, we emphasize that this experiment was mainly targeted at financial market professionals who deal with all these metrics on a daily basis in</footnote>
attribute salience (Homar and Cvelbar, 2021). Thus, the net effect of environmental framing on financial sensitivity is theoretically ambiguous, and empirical evidence is required to determine which mechanism dominates in our setting.

# 3 Main Results on Hypotheses

## 3.1 Pro-environmental preferences

Evidence for pro-environmental motives first emerges in the simplest tasks. In riskless situations (screens 1–2 in Table 1), most participants allocate a strictly positive share to the greener asset, with an average $\lambda$ of 51.80%, even though it offers a lower expected return. Likewise, when risk is unavoidable ($\rho$ = 1, screens 8 and 11), assets differ only by color and expected return. A purely return-maximizing agent would fully concentrate on the browner asset, yet the observed average $\lambda$ in these tasks is 48.53%$^7$. These descriptive patterns already suggest a willingness to sacrifice financial return in favor of environmental impact, in both riskless and risky settings.

To test Hypothesis 1, we analyze individual portfolio choice comparisons, which capture the effect of substituting between otherwise identical assets that differ only by color. Using *ceteris paribus* comparisons (Table 2), we find systematic differences: participants prefer green over neutral and avoid brown relative to neutral, with significance at the 1% level in all cases except for students in green-versus-neutral situations. This pattern confirms a taxonomic pro-environmental preference, as well as a more salient effect with brown assets than with green ones, in situations involving a return-risk tradeoff.

<table><caption>Table 2: WITHIN SUBJECT COMPARISON BY CHANGE OF COLOR (BASELINE TREATMENT)</caption><thead><tr><th rowspan="2">Sample</th><th colspan="3">Allocation 1</th><th colspan="3">Allocation 2</th><th rowspan="2">statistics</th><th rowspan="2">p-value</th></tr><tr><th>greener</th><th>browner</th><th>λ<sub>1</sub></th><th>greener</th><th>browner</th><th>λ<sub>2</sub></th></tr></thead><tbody><tr><td>PRO (n = 66)</td><td>G(20, 25)</td><td>N(15,0)</td><td>50.33</td><td>N(20, 25)</td><td>N(15, 0)</td><td>42.50</td><td>561.0</td><td>0.0035</td></tr><tr><td>PRO (n = 66)</td><td>N(15,0)</td><td>B(30, 25)</td><td>64.55</td><td>N(15,0)</td><td>N(30,25)</td><td>51.30</td><td>484.0</td><td>0.0065</td></tr><tr><td>STUD (n = 103)</td><td>G(20, 25)</td><td>N(15,0)</td><td>43.06</td><td>N(20, 25)</td><td>N(15, 0)</td><td>47.98</td><td>1552.0</td><td>0.3852</td></tr><tr><td>STUD (n = 103)</td><td>N(15,0)</td><td>B(30, 25)</td><td>57.79</td><td>N(15,0)</td><td>N(30,25)</td><td>44.17</td><td>981.0</td><td>0.0022</td></tr></tbody></table>

**Description:** p-value for the Wilcoxon-Mann-Whitney test with paired data, with $H_0: \lambda_1 = \lambda_2$ where $\lambda_i$ stands for $\lambda$ in allocation $i = 1, 2$ as denoted in the table. Notation $C(\mu, \sigma)$ stands for asset with the following characteristics: $C$ = for green, $=$ N for neutral and $=$ B for brown, with expected return $\mu$ and standard deviation $\sigma$. Hypothesis 1.1 expects $\lambda_1 > \lambda_2$ in all lines of the table.

<footnote>$^7$we refer to our companion paper for a full development.</footnote>
## 2.2 Predictions

Participants must deal with three dimensions: return, risk and environmental impact. They are confronted with a trade-off between a positive environmental impact and maximizing a portfolio's return. They are also expected to avoid or reduce the risk of their portfolio, along the line of standard portfolio theory (Kroll et al., 1984; Markowitz, 2010) and as usual in experiments involving lotteries (Holt and Laury, 2002).

Our core research question and our core result are how those three dimensions are treated together in a portfolio task. As suggested in the introduction, financial asset holders might be willing to give up part of the expected return for environmental benefits. This possibility must be confirmed. It is also unknown if investors are willing to accept a higher risk for such benefits. Furthermore, the tradeoffs between risk, return and environmental impact may depend on the type of subjects.

We build on two testable hypotheses regarding $\lambda$. For notational convenience, we identify the financial characteristics return and risk by $\mu_g$ and $\sigma_g$ for the greener asset and by $\mu_b$ and $\sigma_b$ for the browner asset. The first hypothesis (financial preferences) states that the overall proportion invested in the greener asset by all participants depends on the assets' returns, $\mu_g$ and $\mu_b$, and their standard deviations, $\sigma_g$ and $\sigma_b$.

**Hypothesis 1 (Regular financial 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.

The second hypothesis relates to pro-environmental preferences. Assuming that Hypothesis 1 holds, the perception and appreciation of return and risk characteristics of an asset may differ according to the color of that asset. First, it may impact $\lambda$ through a higher propensity to forgo larger returns to hold a higher proportion of greener assets,

<footnote>a trading room or a risk management department, which makes these concepts easier for them to grasp. Any university student and professional who had difficulty understanding the metrics could ask the staff questions at any time. We have systematically answered all questions of comprehension. Second, financial professionals may have a better understanding of volatility and correlations than students, which could potentially account for differences in behavior. However, if this gap exists in the laboratory, it is also likely to exist in the real world, as professionals are more familiar with these properties than laypeople. This fully supports the need to include professional and lay participants in an experimental setting when investigating these concepts and observing their impact on investment behavior.</footnote>
## 3.2 Environmentally framed preferences

Turning to Hypothesis 2, we compare responsiveness to expected returns. For that purpose, we estimate a panel Tobit on the full set of 15,008 portfolio choices<sup>8</sup>, reporting *average marginal effects* (AMEs) for interpretability. Technical details and complementary tests are provided in Appendix E.5. Table 3 summarizes the main estimates.

<table><caption>Table 3: IMPACT OF ASSETS CHARACTERISTICS ON $\lambda$ (ALL TREATMENTS)</caption><thead><tr><th>λ ~</th><th>μ<sub>g</sub></th><th>μ<sub>b</sub></th><th>σ<sub>g</sub></th><th>σ<sub>b</sub></th><th>{ρ = 1}</th><th>{ρ = 0}</th><th>{ρ = -1}</th></tr></thead><tbody><tr><td>Estimate</td><td>1.06</td><td>-0.73</td><td>-0.45</td><td>0.45</td><td>-8.93</td><td>-7.75</td><td>-3.59</td></tr><tr><td>Std. Dev.</td><td>(0.08)</td><td>(0.08)</td><td>(0.04)</td><td>(0.04)</td><td>(1.10)</td><td>(1.09)</td><td>(1.01)</td></tr><tr><td>p-value</td><td>&lt; .01</td><td>&lt; .01</td><td>&lt; .01</td><td>&lt; .01</td><td>&lt; .01</td><td>&lt; .01</td><td>&lt; .01</td></tr></tbody></table>

**Description:** average marginal effects (AME) in a random-effect Tobit panel regression with robust cluster standard errors at the subject level (provided in parenthesis), with $\lambda$ as the explained variable; 15,008 observations are used. Variables $\mu_b$, $\mu_g$, $\sigma_b$, $\sigma_g$ are the expected return and standard deviation of the greener and browner asset respectively. Correlation $\{\rho = 1\}$ (*resp* $\{\rho = 0\}$ and $\{\rho = -1\}$) is a categorical variable controlling for correlation between the two assets (= 0 for screens 1 to 7, = 1 for screens 10 and 13, = 2 for screens 9 and 12, = 3 for screens 8 and 11 in Table 1). Control variables tested but not included in the table: *Ranking treatment*, *Tax treatment*, *Green*, *Student*, *risk*, *Prudence*, *Temperance*, *SVO*, *NEP*, *k-level* and *CRT* (for details, see Section 4 and 5).

The AME of $\mu_g$ (the return of the greener asset) is significantly larger in magnitude than that of $\mu_b$ (the return of the browner asset), and a Wald test rejects the null of equality at the 1% level. This indicates that participants adjust their portfolios more strongly in response to changes in the greener asset's return than to equivalent changes in the browner asset's return. The rejection of Hypothesis 2 on returns therefore suggests that environmental framing modifies the strength of financial responsiveness.

The AMEs for $\sigma_g$ and $\sigma_b$ (the standard deviations of greener and browner assets) are of similar magnitude and opposite sign. This suggests that, unlike for returns, participants respond symmetrically to risk regardless of asset color. We therefore fail to reject the null hypothesis for risk responsiveness.

When both assets involve risk, correlation further shapes portfolio choices. The three dummies capturing correlation regimes—$\rho = 1$ (non-diversifiable), $\rho = 0$ (partially diversifiable), and $\rho = -1$ (fully diversifiable)—are all negative and significant. In these cases, correlation alters the overall risk profile of the portfolio. The negative coefficients show that, on average, participants reduce their allocation to the greener asset when facing correlated risks, particularly under non-diversifiable risk.

<footnote n="8"><sup>8</sup>The estimation pool includes both the baseline treatment and the two additional treatments, which are analyzed separately in Section 4.2. Their isolated effects are discussed in that section.</footnote>
and second, through a higher risk tolerance toward greener assets. In other words, partic-
ipants may have a higher willingness to pay, or a higher willingness to bear risk, or both,
to hold a larger share of green assets as they become greener.

**Hypothesis 2 (Pro-environmental preferences).**

Participants prefer the greener over the browner asset in such a way that:

1. Reluctance of asset substitution:

λ decreases if a green (brown) asset is replaced by an equivalent neutral asset, that is, when a participant moves from a green (brown) versus neutral situation to a neutral versus neutral one, ceteris paribus.

2. Return asymmetry:

A change of the expected return of the greener asset affects λ more than an equal change of the expected return of the browner asset.

3. Risk asymmetry:

A change of the standard deviation of the greener asset affects λ less than an equal change of the standard deviation of the browner asset.

Hypothesis 2.1 states pro-environmental preferences in a taxonomic manner, i.e., an intrinsic preference that an asset be green rather than neutral, or neutral rather than brown. It relies on the underlying mechanism that λ expresses both a preference for the greener asset and an aversion towards the browner asset. To establish empirically this property, we take as benchmarks the trade-offs involving two neutral assets (screen 3 decisions in Table 1), which we compare to the equivalent trade-offs for which one the two assets is colored (screens 4 and 5 in Table 1).

Hypothesis 2.2 states asymmetric pro-environmental preferences with respect to ex-
pected return: individuals' choices of λ reflect greater sensitivity to the greener asset's
expected return than to the return of the browner asset. In other words, for an individual
to be indifferent between greener and browner assets' expected returns, his required level
of the former needs to be lower than that of the latter. That is precisely what we express
as a revealed pro-environmental preference impact through asset returns. A similar pre-
diction applies to a change in the standard deviation (Hypothesis 2.3): λ is more resilient
to changes in the standard deviation of a green asset than that of a brown asset.

Notice that Hypothesis 1 contradicts either Hypotheses 2.2 or 2.3: investors cannot
hold all three together. Participants thus face a trilemma. In regard of the standard
risk-return trade-off, an increase in a portfolio's risk should lead participants to ask for a
compensating premium expressed in a higher return (Hypothesis 1), which is obtained by
These results should not be read as direct aversion to green assets, but rather as evi-
dence that environmental considerations lose salience once overall risk becomes harder to
manage. The observed reduction in greener asset holdings can be interpreted in two ways.
First, in risky environments participants may trade the higher level of risk against higher
financial returns, but not against improved environmental impact. Second, participants
may reallocate in order to diversify more effectively, thereby prioritizing risk reduction
over the greener asset’s environmental appeal. Both interpretations point to the same
conclusion: when risk constraints tighten, participants place greater weight on standard
return-risk trade-offs than on environmental framing.

In summary, our results demonstrate that environmental framing matters for financial responsiveness, but in an asymmetric way. Participants react more strongly to changes in the returns of greener assets, yet we find no evidence that risk responsiveness differs across asset colors. When overall risk rises—particularly under non-diversifiable conditions—allocations to the greener asset decline, showing that return-risk trade-offs are of main concern once financial risk increases. This pattern highlights the limits of a risk-impact trade-off and represents a central contribution of our study.

4 Refinements and Robustness

We now refine the central results along two dimensions. First, we distinguish between *green contexts* (green-neutral trade-offs) and *brown contexts* (neutral-brown trade-offs), as detailed in subsection 4.1, and then briefly compare the behavior of finance professionals (PRO) and students (STUD). Finally, subsection 4.2 tests the robustness of our findings by introducing two incentive treatments: a ranking treatment, providing a non-pecuniary reward for greener choices, and a tax treatment, providing a pecuniary incentive. Both are designed to reinforce the pro-environmental behavior observed in Section 2.

From a methodological perspective, this corresponds to a 2 × 2 factorial design, analyzed by replicating the estimates of Table 3 across subsamples and including treatment controls. Results appear in Table 4. Column (1) reports pooled estimates with subsample and treatment controls. Columns (2)–(3) split by context (green vs. brown), while columns (4)–(7) further separate PRO and STUD samples. Treatments, context, and sample dummies are listed under *Sample characteristics*.

The upper panel (*Asset Characteristics*) of Table 4 confirms standard portfolio preferences holds overall and within each subsample at the 1% level. We therefore focus below on the refinements that capture pro-environmental preferences across contexts, samples,
reducing $\lambda$. Mechanically, as $\mu_g < \mu_b^9$, the associated positive (negative) environmental impact is reduced (increased) if the value of $\lambda$ is lowered.

To what extent are these two channels, i.e., the sacrifice of expected return (Hypothesis 2.2) or the acceptance of increased riskiness (Hypothesis 2.3), equally likely to serve as a compensatory variation for the moral benefit of investing in the greener asset? Put differently, is environmental friendliness equally expressed in the acceptance of lower returns as in the acceptance of higher risks? Finally, is environmental friendliness expressed similarly in the PRO and STUD samples?

The latter question leads us to state our third hypothesis about the absence of a subject-pool effect concerning revealed preferences.

**Hypothesis 3** (No “green generation” effect).

*Students' revealed preferences are neither greener nor browner than those of finance professionals.*

Hypothesis 3 is justified on grounds of contradictory arguments. First, there are several reasons in favor of the students being greenest. The STUD pool contains younger subjects, who are more concerned, as a generation, by environmental issues, in particular with respect to their future lives, a fact that we name “green generation” effect. Supporting evidence is provided by Fransson and Gärling (1999) and by Dunlap et al. (2000). On top of that, the trade-offs made by financial professionals, reflect both their own preferences and the preferences and expectations of their customers or/and employers, even if indirectly. Through their professional experience, based on real portfolio choices or investments strategies, finance professionals might have integrated, maybe unconsciously, their customers’ and employers’ performance expectations, in terms of return, risk and environmental impact. Furthermore, the main issue of fiduciary duties is evolving. If up to now these have been focused on purely financial dimensions, the 2019 PRI report<fnref n="10" /> redefines the key fiduciary duties of the twenty-first century as “Understand and incorporate beneficiaries’ and savers’ sustainability-related preferences, regardless of whether these preferences are financially material.” The report also points out that investors who fail to integrate ESG issues are in violation of their fiduciary duties and are facing increased risk of legal claims. All of these elements can have an influence on professionals’ preferences.

<footnote n="9">⁹In this experiment $\mu_g$ is always lower than $\mu_b$.</footnote>

<footnote n="10">¹⁰with over 4000 financial institution signatories and 130 US trillion dollars under management: https://www.unpri.org/download?ac=9792</footnote>
and treatments.

## 4.1 Heterogeneity by Context and Sample

We now refine our core results by contrasting green and brown contexts. In green contexts, investing in the greener asset corresponds to delegated philanthropy (Bénabou and Tirole, 2010), where lower financial payoffs are partly offset by a warm-glow reward (Andreoni, 1990; Heimann et al., 2011; Guenster et al., 2022). In brown contexts, by contrast, investing in the browner asset amounts to delegated misanthropy: it delivers higher expected returns but generates negative externalities, potentially triggering guilt aversion (Battigalli and Dufwenberg, 2007; Greene and Haidt, 2002). This distinction allows us to test whether pro-environmental behavior is equivalently driven by attraction to green and aversion to brown.

Table 4, column (1), shows that allocations to the greener asset are significantly higher in brown than in green contexts: the *Green* dummy is negative and significant at the 1% level, implying that $\lambda$ is about 9.4 percentage points lower in green contexts. A similar contrast already appears in the very first riskless allocations: average $\lambda$ equals 42.85% in screen 1 (green context) and 60.74% in screen 2 (brown context; see Figure 1). These patterns indicate that aversion to brown outweighs attraction to green.

Turning to responsiveness, Table 4 confirms Hypothesis 2 in both contexts: the AME of the greener asset's expected return is larger in absolute value than that of the browner asset in almost all specifications (see the *Asset characteristics* panel). Moreover, we find that responsiveness to expected returns tends to be stronger in brown contexts, with the effect particularly pronounced among students. This suggests that sensitivity to financial incentives may be amplified when negative externalities are present. Such a pattern is consistent with the return-impact sensitivity documented in Section 3: although greener assets yield lower expected returns, participants adjust their portfolios strongly to return variations, and this responsiveness appears even more pronounced when choices involve negative externalities.<fnref n="9" />

With regard to risk, interpretation requires considering both variances and correlations, as before. Table 4 shows that sensitivity to the greener asset's standard deviation differs from that to the browner asset's standard deviation, indicating an asymmetry in

<footnote n="9">⁹As a robustness check, we also compared the sum of the absolute AMEs of the greener and browner return coefficients across contexts. This aggregate measure of responsiveness is consistently larger in the brown case, confirming that overall sensitivity to return variations is stronger when negative externalities are at stake. Detailed tests are reported in Appendix E.5.2.</footnote>
Second, there are arguments in favor of professionals being greenest. Indeed, the previous argument can be simply reversed: more and more customers are vigilant about the environmental impact of their financial investments, a fact that is necessarily reflected in the professionals' decisions, and which might also affect their own preferences. Because our mechanism relates on delegation, social preferences may interfere to some extent with environmental preferences. In that respect, some studies found that students are more selfish and less pro-social than non-student subjects (Engel, 2011; Belot et al., 2015). However, evidence about differences in social preferences is mixed. For instance, Exadaktylos et al. (2013) found no differences in three classical games (dictator, ultimatum and trust) between students and the general population, and Alm et al. (2015) found no difference in experiments on tax compliance between students and the general population. Fréchette and Schotter (2015) surveyed experimental papers that compare professionals and students behavior in exactly the same experimental setting: they report no difference with respect to theoretical predictions.

## 2.3 Practical procedures and sampling

The full description of the experimental design, with the corresponding screenshots and the instructions, is provided in Appendix A. The experimental design has been approved by the Institutional Review Board (IRB) of xxx (University of xxx, for blind review).

After an explanatory introduction, participants faced the 35 explicitly independent allocation situations summarized in Table 1. In each situation, participants had to allocate 100 experimental currency units (ECUs) between two assets. They only had to choose the (round) percentage to invest in the neutral asset. Participants were aware of the conversion rate between the experimental currency and euros at the beginning of the experiment. The conversion rate differed between the STU and the PRO samples (see the discussion below about actual payments). For each screen, participants were informed about the color of each asset and the corresponding beneficiary of their donation. The expected return and the standard deviation of each asset were also displayed. Once an allocation was proposed by a participant, the corresponding portfolio's characteristics were displayed in real time: its expected return<fnref n="11" />, standard deviation, realizable outcomes

<footnote n="11">¹¹Gross returns of each asset and net variables of the portfolio's characteristics (i.e., the expected net return, minimum and maximum net returns) were also displayed in the tax treatment to avoid any manual computations by the participants, and to simplify their comprehension of the tax. In the other treatments (without tax), there is no need to distinguish between gross and net returns.</footnote>
<table><caption>Table 4: IMPACT OF ASSETS AND SAMPLE CHARACTERISTICS ON $\lambda$</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr><tr><th>Sample</th><td>ALL</td><td>ALL</td><td>ALL</td><td>PRO</td><td>PRO</td><td>STUD</td><td>STUD</td></tr><tr><th>Context</th><td>All</td><td>Green</td><td>Brown</td><td>Green</td><td>Brown</td><td>Green</td><td>Brown</td></tr><tr><th>N</th><td>15,008</td><td>7,504</td><td>7,504</td><td>3,040</td><td>3,040</td><td>4,464</td><td>4,464</td></tr></thead><tbody><tr><td colspan="8"><em>Asset characteristics</em></td></tr><tr><td>Expected return of greener asset</td><td>1.06**<br>(0.08)</td><td>0.81**<br>(0.09)</td><td>1.30**<br>(0.11)</td><td>0.64**<br>(0.14)</td><td>0.90**<br>(0.13)</td><td>0.92**<br>(0.12)</td><td>1.59**<br>(0.17)</td></tr><tr><td>Expected return of browner asset</td><td>-0.73**<br>(0.08)</td><td>-0.61**<br>(0.09)</td><td>-0.92**<br>(0.10)</td><td>-0.53**<br>(0.13)</td><td>-0.55**<br>(0.11)</td><td>-0.67**<br>(0.13)</td><td>-1.19**<br>(0.16)</td></tr><tr><td>Standard deviation of greener asset</td><td>-0.45**<br>(0.04)</td><td>-0.32**<br>(0.03)</td><td>-0.63**<br>(0.05)</td><td>-0.37**<br>(0.05)</td><td>-0.57**<br>(0.06)</td><td>-0.28**<br>(0.05)</td><td>-0.67**<br>(0.07)</td></tr><tr><td>Standard deviation of browner asset</td><td>0.45**<br>(0.04)</td><td>0.47**<br>(0.04)</td><td>0.47**<br>(0.04)</td><td>0.48**<br>(0.05)</td><td>0.41**<br>(0.05)</td><td>0.46**<br>(0.05)</td><td>0.53**<br>(0.07)</td></tr><tr><td>Correlation ρ = 1</td><td>-8.93**<br>(1.10)</td><td>-13.56**<br>(1.37)</td><td>-3.56*<br>(1.48)</td><td>-7.47**<br>(1.98)</td><td>-0.75<br>(1.77)</td><td>-17.73**<br>(1.85)</td><td>-5.85**<br>(2.25)</td></tr><tr><td>Correlation ρ = 0</td><td>-7.75**<br>(1.09)</td><td>-13.21**<br>(1.30)</td><td>-1.52<br>(1.47)</td><td>-8.23**<br>(1.73)</td><td>2.25<br>(1.70)</td><td>-16.63**<br>(1.84)</td><td>-4.56*<br>(2.25)</td></tr><tr><td>Correlation ρ = -1</td><td>-3.59**<br>(1.01)</td><td>-7.08**<br>(1.26)</td><td>0.30<br>(1.46)</td><td>-4.88**<br>(1.83)</td><td>3.75<br>(1.81)</td><td>-8.52**<br>(1.71)</td><td>-2.38<br>(2.17)</td></tr><tr><td colspan="8"><em>Sample characteristics</em></td></tr><tr><td>Ranking treatment</td><td>6.52**<br>(2.13)</td><td>8.62**<br>(2.21)</td><td>3.57<br>(3.47)</td><td>4.35<br>(3.11)</td><td>2.45<br>(4.63)</td><td>11.07**<br>(3.07)</td><td>4.51<br>(4.92)</td></tr><tr><td>Tax treatment</td><td>14.41**<br>(1.88)</td><td>16.61**<br>(2.17)</td><td>11.58**<br>(3.39)</td><td>6.34*<br>(2.78)</td><td>-1.09<br>(4.19)</td><td>22.65**<br>(3.04)</td><td>21.40**<br>(5.07)</td></tr><tr><td>Student</td><td>-5.31**<br>(1.80)</td><td>-6.99**<br>(1.82)</td><td>-2.42<br>(2.68)</td><td></td><td></td><td></td><td></td></tr><tr><td>Green</td><td>-9.40**<br>(1.43)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

\*, \*\* account for 5% and 1% significance levels, respectively.

**Description:** average marginal effects (AME) in a random-effect Tobit panel regression with robust cluster standard errors at the subject level (provided in parenthesis). The explained variable is $\lambda$. Contexts denote those used for estimation: *Green* contexts denote screens 1, 4, 6, 8, 9 and 10 of Table 1; *Brown* contexts denote screens 2, 5, 7, 11, 12 and 13. *All* adds *Green* and *Brown* (screen 3 is excluded from analysis). *N* is the number of related observations (samples and situations in the context). The *Green* dummy variable controls for the subsets of screens (= 1 for Green screens and 0 for Brown). *Student* dummy variable controls for the type of sample (= 0 if subject belongs to PRO, = 1 if subject belongs to STUD). *Correlation* $\rho \in \{1, 0, -1\}$ is as in Table 3. Control variables tested but not included in the table: *risk*, *prudence*, *temperance*, *SVO*, *NEP*, *k-level* and *CRT* (for details, see Appendix A.5). We remove from the regression analysis the three situations in screen 3, for which participants were faced with two neutral assets. This leaves 32 situations with 469 participants, i.e., 15,008 observations.
with associated probabilities and the donated amount.

**Payment of the portfolio task and donation.** We relied on the “random payment method” (RPM thereafter): one of the 35 situations was randomly selected to be paid out in euros (conditionally on being eligible for payment; see the difference between the two samples below). The RPM is supposed to make each portfolio choice independent of the others (Cubitt et al., 1998). This incentive scheme removes the possibility of wealth and portfolio effects and suppresses hedging opportunities (Bardsley et al., 2009).

The externality was constructed as follows: in the case of a positive (respectively, negative) externality, the experimenter committed to donating $50 × \lambda$ ECUs to a pro-environmental association<fnref n="12" /> (respectively, an anti-environmental association, such as a fossil fuel association<fnref n="13" />). For example, investing 60% of the budget in the green asset leads to a donation of 30 ECUs to the pro-environmental association. The conversion rate was the same for the donation and the participants’ potential reward. Once the participant had entered a portfolio composition, the amount the experimenter proposed to donate was provided in real time. The amount of the donation for the association was calculated and then transferred to the association, based on the randomly selected situation for payment to the participant. Notice that the amount is fixed and independent of the colored asset’s random outcome, in contrast to Brodback et al. (2020).

**Samples.** Our experiment applies to two samples: the PRO sample, comprising 190 finance professionals, and the STUD sample, involving 279 students. The experiment with the PRO sample was carried out in October 2019 in Casablanca (Morocco), in major Moroccan banks and asset management companies, using a mobile laboratory with tablets. Students participated in sessions organized in 2020 at the Laboratory for Experimental Economics of xxx located in the University of xxx. All the lab sessions were conducted in compliance with the ethical code of conduct and the rules of the xxx, and the field sessions complied with the ethical rules of the employers of the PRO sample

<footnote n="12">¹²This association is a worldwide social enterprise enabling sustainable reforestation across the globe through crowdfunding. Their goal is to protect, rehabilitate, and develop forests around the world. More information is available upon request (name, weblink of the association, and amount transferred). Additional information for reviewers: the association is Reforest’Action: https://www.reforestaction.com/en.</footnote>

<footnote n="13">¹³An international association of producers of oil and gas, which is a consortium promoting the use of fossil fuels, such as gas from shale. More information is available upon request (name, weblink of the association, and amount transferred). Additional information for reviewers: the association is the International Association of Oil & Gas Producers: https://www.iogp.org/.</footnote>
risk responsiveness.<fnref n="10" /> Further evidence suggests that increases in the greener asset's risk are penalized more strongly in brown than in green contexts, whereas the response to the browner asset's risk remains statistically indistinguishable across contexts.<fnref n="11" /> Finally, the correlation dummies are large and negative—particularly in green contexts and among students—showing that a more adverse overall risk environment reduces λ beyond the effect of individual asset variances.

Taken together, these results imply no special tolerance for green risk; if anything, when negative externalities are involved, participants react more strongly to increases in the greener asset's volatility. This pattern reinforces our main conclusion: portfolio choice resolves the trilemma in favor of the standard return-risk trade-off, leaving no room for a distinct risk-impact trade-off.

Formal equality tests reported in Appendix E.5.2 confirm these findings: risk responsiveness differs significantly between greener and browner assets, and the coefficient on greener risk varies across contexts while that on browner risk remains comparatively stable. Thus, participants display no special tolerance for greener risk; on the contrary, greener risk is sanctioned more severely in brown contexts, reinforcing that portfolio choices are ultimately governed by the conventional return-risk trade-off, with no evidence of a distinct risk-impact trade-off.

A natural concern for external validity is whether students and finance professionals behave similarly. Although students are younger and often portrayed as more climate-sensitive (Fransson and Gärling, 1999; Dunlap et al., 2000), finance professionals may also internalize environmental concerns through client demand, fiduciary norms, or daily exposure to investment practice (Fréchette and Schotter, 2015). The literature provides mixed evidence: some studies report stronger prosociality among older or professional populations (Engel, 2011; Belot et al., 2015), while others find no systematic differences between students and non-students (Exadaktylos et al., 2013; Alm et al., 2015). We therefore have no specific assumptions on this point, especially since remuneration structures and cultural differences can have unexpected and unpredictable impacts.

In this experiment, we find that while both groups display pro-environmental preferences, students allocate less to greener assets in green contexts and exhibit a sharper green-brown asymmetry, especially in their responsiveness to expected returns. Full details, robustness checks, and a discussion of related literature are provided in Internet Appendix E.1.

<footnote n="10">¹⁰Equality tests confirming this result are reported in Appendix Table 12.</footnote>

<footnote n="11">¹¹Interaction estimates supporting this claim are reported in Appendix Table 13.</footnote>
participants. Detailed descriptive statistics about the two samples (PRO and STUD) are
provided in Section 5 and in the Internet Appendix B.1.

In the PRO sample, only one participant of 10 was randomly selected to be paid
out for real, generating high incentives to finance professionals. Twenty professionals
were selected, with an average payment of 216.81 euros. We used this scheme for two
reasons. First, transactions were managed by the Moroccan Association of Trading Rooms
(AMSM), which minimized the logistics burden. Second, many studies (Harrison et al.,
2007; Andersen et al., 2014; Beaud and Willinger, 2015; Charness et al., 2016) have
shown that “paying one,” that is, a subset of participants, is as effective as “paying all.”
To provide a strong monetary incentive, note that the average level of payment has
been designed to be higher than the remuneration of experienced Moroccan trading floor
professionals.<fnref n="14" /> Finally, relative to the hourly rate in the considered countries, the amount
aligns with payments made in previous experiments involving professionals, such as Cohn
et al. (2015) and Kirchler et al. (2018).<fnref n="15" />

In the STUD sample, all participants were paid 13.45 euros on average (including the
gratuity for participating).<fnref n="16" /> Additional information can be found in Internet Appendix
B. Like we did for professionals, we offered a very attractive remuneration, about 70%
higher than the French minimum net wage of 8.03 euros in 2020.

The differences between the two samples are discussed in detail in Section 4.2, partic-

<footnote n="14">¹⁴Being extremely conservative, we considered the net average level of salary in a trading room in Morocco for a professional with more than 10 years of experience (Computations are based on the 2019 Mickael Page remuneration study for Morocco, see https://casablancafinancecity.com/wp-content/uploads/2020/02/Michael_Page_2019_Morocco_Salary_Survey_Low_Res.pdf). Trading room professionals earn on average 36,812 dirhams per month. Additionally, we consider the Moroccan legal annual basis of 2288 hours and do not count overtime hours in the denominator as it would reduce the hourly wage. Finally, we apply the average euro/dirham exchange rate of October 2019. The net hourly wage is 18.23 euros, versus 21.68 euros of expected earnings for this 45-minute experiment, or 216.81 euros of average realized payment for the selected participants. Therefore, we are very confident in the level of motivation generated by this substantial, high level of remuneration.</footnote>

<footnote n="15">¹⁵As a comparison, Kirchler et al. (2018) paid, in Europe, one participant out of five with an average payment of 52 euros, which is around 260 euros for the selected participants, for a 45-minute experiment, approximately the same time spent during our experiment.</footnote>

<footnote n="16">¹⁶We could have selected only a subset, as we did for the professionals. However, the participants were part of the pool of volunteers registered to participate in experiments organized by the xxx Experimental Economics Laboratory. If possible (i.e., if it is not the research question or something mandatory in the experimental protocol), the rule is to avoid not paying a participant for participating in an experiment, as this could affect the participation rate for future experiments. Even if the “pay one” procedure does not affect motivation in an isolated experiment, it could adversely affect the volunteer pool and create sampling bias if only the subset of individuals intrinsically motivated by science enrolls in future experiments. Finally, given that many studies, such as Charness et al. (2016), do not find any behavior differences when “paying one” and “paying all,” we ensure that the data from the two samples are comparable from this perspective.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: AVERAGE $\lambda$ ACROSS ALLOCATIONS IN THE BASELINE TREATMENT</figcaption></figure>

**Notes:** The figure reports average $\lambda$ (share invested in the greener asset) by allocation across PRO and STUD participants in the baseline treatment. G and B denote green and brown contexts, followed by the difference in expected return and standard deviation between the greener and the browner asset. The upper panel displays screens 1–7 (no correlation), and the lower panel displays screens 8–13 with correlation $\rho \in \{-1, 0, 1\}$. See Table 1 for the mapping between screens and situations.

## 4.2 Incentive Treatments

The context (green or brown) and the participant pool (PRO or STUD) shape the strength of pro-environmental financial preferences documented in Section 2. This suggests that such preferences may be influenced by appropriate incentives.

We study two types of incentives outlined in Introduction: a non-pecuniary ranking feedback on portfolio externalities, and a pecuniary tax penalizing the return of the browner asset. In the PRO sample, 66 participants faced the baseline treatment, 63 the ranking treatment, and 61 the tax treatment; in the STUD sample, the corresponding figures are 103, 84, and 92. Treatments do not interact with contexts, asset characteristics, or the donation mechanism, which remain exogenous and are introduced only as
<table><caption>Table 2: IMPACT OF ASSETS CHARACTERISTICS ON $\lambda$</caption><thead><tr><th>λ ~</th><th>μ<sub>g</sub></th><th>μ<sub>b</sub></th><th>σ<sub>g</sub></th><th>σ<sub>b</sub></th><th>{ρ = 1}</th><th>{ρ = 0}</th><th>{ρ = -1}</th></tr></thead><tbody><tr><td>Estimate</td><td>1.06</td><td>-0.73</td><td>-0.45</td><td>0.45</td><td>-8.93</td><td>-7.75</td><td>-3.59</td></tr><tr><td>Std. Dev.</td><td>(0.08)</td><td>(0.08)</td><td>(0.04)</td><td>(0.04)</td><td>(1.10)</td><td>(1.09)</td><td>(1.01)</td></tr><tr><td>p-value</td><td>.00</td><td>.00</td><td>.00</td><td>.00</td><td>.00</td><td>.00</td><td>.00</td></tr></tbody></table>

**Description:** average marginal effects (AME) in a random-effect Tobit panel regression with robust cluster standard errors at the subject level (provided in parenthesis), with $\lambda$ as the explained variable; 15,008 observations are used. Variables $\mu_b$, $\mu_g$, $\sigma_b$, $\sigma_g$ are the expected return and standard deviation of the greener and browner asset respectively. Correlation $\{\rho = 1\}$ (resp $\{\rho = 0\}$ and $\{\rho = -1\}$) is a categorical variable controlling for correlation between the two assets ($= 0$ for screens 1 to 7, $= 1$ for screens 10 and 13, $= 2$ for screens 9 and 12, $= 3$ for screens 8 and 11 in Table 1). Control variables tested but not included in the table: *Ranking treatment*, *Tax treatment*, *Green*, *Student*, *risk*, *Prudence*, *Temperance*, *SVO*, *NEP*, *k-level* and *CRT* (for details, see Section 4 and 5).

ularly the question of the cultural difference between the two samples. The heterogeneity between samples is managed with a control variable in the econometric study of Section 3 below.

# 3 Testing predictions: main results regarding hypotheses

Presently, the portfolio task is tested on the pooled data, aggregating the observations from the PRO and STUD samples, and from various treatments (see subsection 4.3) which do not influence the results obtained here. Our analysis is grounded on econometric modeling of $\lambda$ as a function of the characteristics of financial situations (this involves 15,008 observations).

Because the share invested in an asset varies between 0% and 100%, participants' decisions are censored from below and above. In fact, 24% of all observed portfolios contained a single asset. This suggests that, if short-selling and leveraging had been available, both “negative” holdings and holdings above 100% might have been observed. To account for censuring, we rely on a Tobit model for panel data (technical details can be found in Appendix C.3). Table 2 summarizes the results that are discussed hereafter by providing the average marginal effects (AME) for each asset characteristic. All complementary tests of the section are displayed in Appendix C.3.2.

Table 2 first shows that Hypothesis 1 is validated unequivocally: our subjects have regular financial preferences. The coefficients for the expected return and the standard deviation are significantly non-zero at the 1% level, with signs as predicted by Hypothesis 1.1 and 1.2, for the greener and the browner asset respectively. Participants take a greater
categorical variables. Detailed treatment instructions appear in Internet Appendix A.3; Appendix E.3 provides the extended description, theoretical background, and main results.

**Ranking treatment.** Social comparison and social norms are long-standing drivers of behavior (Festinger, 1954), and rankings have proven particularly influential in financial trading and SRI contexts (Kirchler et al., 2018; Liu and Ma, 2020). We therefore expected that providing a real-time private ranking of $\lambda$ against a benchmark sample would act as a self-image incentive, stimulating competition on environmental performance.<fnref n="12" />

The ranking treatment increases $\lambda$ in the pooled sample, but significance vanishes in most subsamples. Effects are mainly concentrated in the STUD sample under green contexts, while estimates for other groups remain imprecise. This asymmetry is consistent with the fact that students in the baseline treatment already invest less in both green assets (in green contexts) and neutral assets (in brown contexts) than professionals. As a result, students need relatively less effort to secure a favorable ranking, which likely amplifies the treatment effect for them. In sum, rankings shift behavior only for students and only in green contexts, reinforcing the green-brown asymmetry.

**Tax treatment.** Environmental taxes are among the most established policy tools (Nordhaus, 1992; Bovenberg and De Mooij, 1994; Goulder, 2013). Here, the tax is generated in real time by portfolio choices: the browner asset's return is reduced according to a convex penalty, such that expected portfolio return becomes concave in $\lambda$.<fnref n="13" /> This design ensures that participants who initially allocate less to the greener asset face stronger marginal incentives to increase $\lambda$.

The tax treatment dummy is positive and significant in almost all regressions, with the exception of professionals in brown contexts, where the effect is weak and non-significant. The strength of the effect aligns with baseline allocations: since students invest less in green assets at baseline (40.8% vs. 52.5% for professionals), they respond more strongly to the tax. In 26 of 32 situations, students invest more in the greener asset than professionals under the tax treatment, reversing the baseline ranking. Both groups increase green allocations in green contexts, but only students do so in brown contexts. This again

<footnote n="12">¹²The benchmark consisted of 50 randomly selected baseline participants from the same pool (PRO or STUD), exposed to identical frames. Their decisions were used as the comparison group but were not collected under any ranking mechanism. See Internet Appendix A.3 for full instructions.</footnote>

<footnote n="13">¹³Formally, if $\Delta\mu = \mu_b - \mu_g$, then $\mu_p = \mu_g + (1 - \lambda)\Delta\mu - (1 - \lambda)^2\Delta\mu$.</footnote>
proportion of an asset if its expected return increases or if its standard deviation decreases.

Relying on that result, we then focus on the second hypothesis regarding pro-environmental preferences. The first piece of evidence relates to the claim made in subsection 2.1 that a profit-maximizing agent, with no environmental considerations, would invest his whole endowment in the browner asset if no risk is involved. In the first two non-risky situations (screens 1 and 2 in Table 1), most participants hold a strictly positive quantity of the greener asset: the average $\lambda$ for those two situations combined is 51.80%. Participants thus accept a fixed monetary loss as compensation for a higher positive (or lower negative) environmental externality. Similarly, in situations where the correlation between assets is $\rho = 1$, i.e., risk reduction is not feasible (screens 8 and 11), assets only differ in color and expected return. An agent seeking to maximize only the expected return would put all the initial endowment in the brown asset. The average $\lambda$ in those tasks is 48.53%, confirming the previous result.

The second piece of evidence treats Hypothesis 2.1, i.e., reluctance of asset substitution, specifically. The experiment provides several portfolio allocations that only differ in the color of available assets, that is, allowing *ceteris paribus* comparisons. This is what Table 3 presents, taking each sample and green or brown contexts separately. It shows that Hypothesis 2.1 is validated at the 1% level, except for students in green versus neutral situations.

<table><caption>Table 3: WITHIN SUBJECT COMPARISON BY CHANGE OF COLOR (BASELINE TREATMENT)</caption><thead><tr><th rowspan="2">Sample</th><th colspan="3">Allocation 1</th><th colspan="3">Allocation 2</th><th rowspan="2">statistics</th><th rowspan="2">p-value</th></tr><tr><th>greener</th><th>browner</th><th>λ<sub>1</sub></th><th>greener</th><th>browner</th><th>λ<sub>2</sub></th></tr></thead><tbody><tr><td>PRO (n = 66)</td><td>G(20, 25)</td><td>N(15,0)</td><td>50.33</td><td>N(20, 25)</td><td>N(15, 0)</td><td>42.50</td><td>561.0</td><td>0.0035</td></tr><tr><td>PRO (n = 66)</td><td>N(15,0)</td><td>B(30, 25)</td><td>64.55</td><td>N(15,0)</td><td>N(30,25)</td><td>51.30</td><td>484.0</td><td>0.0065</td></tr><tr><td>STUD (n = 103)</td><td>G(20, 25)</td><td>N(15,0)</td><td>43.06</td><td>N(20, 25)</td><td>N(15, 0)</td><td>47.98</td><td>1552.0</td><td>0.3852</td></tr><tr><td>STUD (n = 103)</td><td>N(15,0)</td><td>B(30, 25)</td><td>57.79</td><td>N(15,0)</td><td>N(30,25)</td><td>44.17</td><td>981.0</td><td>0.0022</td></tr></tbody></table>

**Description:** p-value for the Wilcoxon-Mann-Whitney test with paired data, with $H_0: \lambda_1 = \lambda_2$ where $\lambda_i$ stands for $\lambda$ in allocation $i = 1, 2$ as denoted in the table. Notation $C(\mu, \sigma)$ stands for asset with the following characteristics: $C = $ for green, $N$ for neutral and $B$ for brown, with expected return $\mu$ and standard deviation $\sigma$. Hypothesis 2.1 expects $\lambda_1 > \lambda_2$ in all lines of the table.

The last piece of evidence focuses on testing if either Hypothesis 2.2 (return asymmetry) or Hypothesis 2.3 (risk asymmetry) holds. This relies on comparing coefficients in Table 2.<fnref n="17" />

<footnote n="17">¹⁷This is possible because we chose to report average marginal effects (AME) in the core paper rather than coefficient estimates (reported in Appendix C.3). Marginal effects can then easily be interpreted</footnote>
highlights the green-brown asymmetry and underscores that students are more sensitive
to pecuniary incentives than professionals.

# 5 Determinants of Individual Preferences

In the previous sections, we focused exclusively on the portfolio task, analyzing how
situation parameters shape allocations between greener and browner assets. While ro-
bust aggregate patterns emerged, smaller differences appeared across contexts (green
vs. brown) and participant pools (PRO vs. STUD). A natural question follows: to what
extent can these differences be explained by individual characteristics?

To address this, we extend the econometric analysis by incorporating job status and
basic socio-demographic controls. This allows us to explore whether professional expe-
rience or demographic traits systematically account for the heterogeneity observed in
Sections 3 and 4. Other auxiliary tasks—risk preferences, cognitive ability, strategic rea-
soning, and pro-social or environmental attitudes—are presented in Appendix A.5 and
used only as controls.

Table 5 reports the average marginal effects (AMEs) from these extended specifica-
tions. We focus here on the role of job status and demographics, leaving the analysis of
other tasks to the appendix.

**5.1 Socioeconomic and demographic data**

The PRO and STUD samples reflect the socioeconomic traits of the populations they
were drawn from. Each sample took a final but specific survey after the experiment.
Therefore, the variables that are specific to each sample might also have a specific influence
on those participants’ portfolio choices.

5.1.1 The PRO sample

In partnership with the Moroccan Association of Trading Rooms (AMSM), which
facilitated access to major banks and asset management companies in Casablanca (the
leading African financial center according to the GFCI), we recruited our PRO partici-
pants. Ethical conditions were discussed in advance with the AMSM, and each institution
gave its approval before the sessions. Participation was voluntary, and professionals were
informed that the study aimed to understand investment behavior in financial markets.
Most expressed enthusiasm and a willingness to participate again.
Hypothesis 1 states that an increase in the expected return of any asset increases its proportion in the portfolio. However, if both assets in the portfolio undergo a similar increase in the expected return, effects compensate unless coefficients significantly differ. Table 2 shows that the AME for the expected return $\mu_g$ of the greener asset is larger (in absolute value) than the AME for the expected return $\mu_b$ of the browner asset. The Wald test rejects equality of AMEs ($p < 0.01$), and the values are as predicted by 2.2. Accordingly, under a systematic positive market shock, participants react by putting a greater proportion $\lambda$ into the greener asset. Participants are more sensitive to return when considering a greener asset than a browner one.<fnref n="18" />

Therefore, we showed that Hypothesis 2.1 holds: participants invest a larger share in the greener asset than they would if they were indifferent to externalities. We also showed that Hypothesis 2.2 holds: the return from a greener asset is more attractive than that of a browner asset by having a greater positive effect for each yield point. Relative to the trilemma, does that mean that Hypothesis 2.3 is invalidated?

Looking at the AMEs for the standard deviations in Table 2, which are sensibly equal (up to the sign) for the greener and the browner asset, it seems that participants are indifferent to the externality of assets when it comes to riskiness. However, this does not consider another risk parameter: the three correlation-regime AMEs.<fnref n="19" /> Because a majority of allocations with risky assets involve correlation (18 of 30), they capture effects that are specific to the presence of risk. The fact that those AMEs are all significantly and highly negative suggests that in the presence of two risky assets, participants invest much less in the greener asset than they do when one of the assets is riskless. It thus seems that an increase in portfolio risk leads participants to reduce their positive environmental externality or to increase their negative one. Another simultaneous effect to consider is the incentive to diversify the portfolio in the case of non-positive correlation regimes. Statistics<fnref n="20" /> indicate that participants show a portfolio composition very close to the minimal variance allocation ($\lambda = 50\%$), suggesting that the risk minimization goal dominates the positive environmental impact goal.

If participants have higher risk tolerance with respect to the greener asset, as put forth in Hypothesis 2.3, it is largely offset by the “regularity” of their financial preferences, which

<footnote>and compared to each other with a Wald test.</footnote>

<footnote n="18">¹⁸As explained previously, the estimated coefficients or AME read precisely as marginal (relative) effects but because the expected return variable is always positive, its coefficients can also be interpreted in the regression as absolute and indicate a preference for the greener asset over the browner asset.</footnote>

<footnote n="19">¹⁹Since two risky assets have the same standard deviation in situations involving correlation, the latter can easily be analyzed independently, as a specific regime, from standard deviation itself.</footnote>

<footnote n="20">²⁰See Appendix C.1 and Figure 1 below</footnote>
<table><caption>Table 5: IMPACT OF PRO-SOCIAL AND ENVIRONMENTAL PREFERENCES AND DEMOGRAPHICS</caption><thead><tr><th>Sample Context N</th><th>(1) PRO All 6048</th><th>(2) PRO Green 3024</th><th>(3) PRO Brown 3024</th><th>(4) STUD All 8928</th><th>(5) STUD Green 4464</th><th>(6) STUD Brown 4464</th></tr></thead><tbody><tr><td><strong>Preferences</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SVO</td><td>0.12 (0.08)</td><td>0.08 (0.07)</td><td>0.17 (0.11)</td><td>0.19** (0.07)</td><td>0.35** (0.08)</td><td>-0.01 (0.13)</td></tr><tr><td>NEP</td><td>0.05 (0.18)</td><td>-0.10 (0.18)</td><td>0.26 (0.24)</td><td>0.45* (0.18)</td><td>0.24 (0.21)</td><td>0.68* (0.30)</td></tr><tr><td><strong>Finance jobs</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sales Trading</td><td>-0.35 (3.23)</td><td>2.31 (3.15)</td><td>-3.29 (4.59)</td><td></td><td></td><td></td></tr><tr><td>Portfolio Manager</td><td>-6.81 (4.96)</td><td>-1.44 (5.14)</td><td>-12.54* (6.04)</td><td></td><td></td><td></td></tr><tr><td>Analyst</td><td>15.35** (4.36)</td><td>15.09** (4.62)</td><td>16.18* (6.57)</td><td></td><td></td><td></td></tr><tr><td>Support functions and others</td><td>3.63 (4.31)</td><td>7.07 (4.62)</td><td>0.15 (6.57)</td><td></td><td></td><td></td></tr><tr><td><strong>Investment Strategies</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Value investing</td><td>10.85* (4.47)</td><td>6.68 (4.17)</td><td>15.58* (6.17)</td><td></td><td></td><td></td></tr><tr><td>Mispricing</td><td>18.07** (4.75)</td><td>12.86* (5.84)</td><td>23.34** (5.97)</td><td></td><td></td><td></td></tr><tr><td>Global Macro</td><td>10.36* (4.14)</td><td>4.50 (3.71)</td><td>17.14** (5.91)</td><td></td><td></td><td></td></tr><tr><td>Market Making</td><td>11.10** (3.52)</td><td>5.32 (3.37)</td><td>16.95** (5.46)</td><td></td><td></td><td></td></tr><tr><td>None</td><td>1.13 (4.07)</td><td>-3.56 (4.04)</td><td>6.07 (5.08)</td><td></td><td></td><td></td></tr><tr><td>Others</td><td>10.12* (5.17)</td><td>5.43 (4.84)</td><td>16.99* (7.86)</td><td></td><td></td><td></td></tr><tr><td><strong>Disciplines</strong></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>10.61 (7.76)</td><td>-1.92 (8.40)</td><td>21.28 (14.62)</td></tr><tr><td>Formal Sciences</td><td></td><td></td><td></td><td>6.13 (4.45)</td><td>1.47 (4.80)</td><td>11.58 (7.18)</td></tr><tr><td>Economics</td><td></td><td></td><td></td><td>5.45 (4.04)</td><td>1.11 (4.40)</td><td>9.99 (5.60)</td></tr><tr><td>Health</td><td></td><td></td><td></td><td>12.58** (4.70)</td><td>6.05 (4.96)</td><td>18.35* (7.97)</td></tr><tr><td>Natural Sciences</td><td></td><td></td><td></td><td>8.38 (5.52)</td><td>3.49 (5.38)</td><td>12.48 (7.51)</td></tr><tr><td>Social sciences</td><td></td><td></td><td></td><td>7.83 (5.39)</td><td>4.63 (4.60)</td><td>12.85 (9.37)</td></tr><tr><td>Others</td><td></td><td></td><td></td><td>14.55* (6.33)</td><td>11.43 (7.71)</td><td>18.37 (12.28)</td></tr></tbody></table>

\*, \*\* account for 5% and 1% significance levels, respectively.

Average marginal effects (AME) in a random-effect Tobit panel regression with robust cluster standard errors at the subject level (provided in parenthesis). **Method and Analysis** are identical to Table 4. All variables included in the specifications of Table 4 are included as control variables. Variables for the PRO sample: *Finance jobs* and *investment strategies* are detailed in Table 6. Additional control variables included but not shown are *employer*, *asset class*, and *market type*, as defined in Table 6. Variables for the STUD sample: *Disciplines* are defined in Section 5.1.2. Control variables included but not shown are *age* (in years), *gender* (= 0 for female, = 1 for male), and *student level* (in years). One professional did not answer the questionnaire. Therefore, we lost 32 observations for the professional columns between this table and the previous one.
is established by the confirmation of Hypothesis 1. This is a crucial finding. In phase two of the experiment, participants arbitrate between risk and return by choosing $\lambda$, but in the experiment, the greener asset can be either the riskless or the risky asset, so that $\lambda$ is not pushed in any particular direction. In the third phase of the experiment, however, participants face two risky assets with the same standard deviation. The increase in the portfolio risk, in particular when $\rho = 1$, leads participants to seek compensation in return (Hypothesis 1), which imposes holding less of the greener asset. Such behavior clearly rejects hypothesis 2.3.

# 4 Testing predictions: Refinements and robustness

In this section, we challenge the above central result by varying two dimensions. The first and most important refinement, detailed in subsection 4.1, is the distinction between green and brown contexts, that is, green-neutral trade-offs versus neutral-brown trade-offs. The second dimension, detailed in subsection 4.2, concerns differences between PRO and STUD samples. In subsection 4.3, we challenge the robustness of the main results, by introducing two alterations of the baseline portfolio task: a ranking treatment and tax treatment. Both treatments provide an incentive towards green asset holding: a non-pecuniary incentive for the ranking treatment and a pecuniary incentive for the tax treatment. Those incentives are expected to reinforce the pro-environmental behavior we observed in Section 2, by incentivizing trade-offs in favour of a positive environmental impact.

This takes the form of a 2 × 2 full factorial design that is analyzed by repeating the analysis of Table 2 for subsamples and introducing a control variable for the treatments. The results are reported in Table 4 below. Column (1) takes back the results of Table 2 by adding the control variables for subsamples and treatments. Columns (2), (4), and (6) provide estimates only in green contexts, while columns (3), (5), and (7) provide estimates in brown contexts. Columns (2) and (3) contain the coefficients for the pooled samples PRO and STUD and for all treatments. Columns (4) and (5) contain the coefficients for the PRO sample only and columns (6) and (7) for the STUD sample only.

Treatments are listed as categorical variables in the *Sample characteristics* section of the table. That section of Table 4 also includes categorical variables for samples (PRO vs. STUD) and context (green vs. brown) to provide control in the analysis of pooled contexts (column (1)) and pooled samples (columns (1) to (3)).

A quick look at the upper part, (*Asset Characteristics*), of Table 4 confirms that
Our PRO pool is composed primarily of market practitioners. To characterize them, we collected information on job type (e.g., traders, risk managers), employer (bank, hedge fund, etc.), past experience, and investment strategies. For compliance reasons, institutions did not allow us to collect age and gender data. This omission does not appear critical: in our STUD sample these variables had no significant effect, and Tatarnikova et al. (2021) similarly found no gender or age differences in green vs. brown investment among professionals. The full questionnaire is reproduced in Appendix A.7.

<table><caption>Table 6: CATEGORIZATION OF THE PRO SAMPLE</caption><thead><tr><th>New category</th><th>includes</th></tr></thead><tbody><tr><td><strong>Finance Job & Past experience</strong></td><td></td></tr><tr><td><strong>Market Trading*</strong></td><td>Proprietary trading, market maker, jobs with trading activities</td></tr><tr><td>Sales trading</td><td>Sales trader, sales</td></tr><tr><td>Portfolio manager</td><td>Asset manager, portfolio manager</td></tr><tr><td>Analyst</td><td>Strategist, economist, financial analyst (buy/sell side), risk analyst, jobs with analyst skills</td></tr><tr><td>Support functions and others</td><td>Support and related functions, middle office job, business manager, trade finance, private banker, senior banker, other</td></tr><tr><td><strong>Employer</strong></td><td></td></tr><tr><td><strong>Bank*</strong></td><td>Bank</td></tr><tr><td>Other</td><td>Asset management company, trading company, broker, hedge fund, work for your own, private equity company, other</td></tr><tr><td><strong>Investment strategy</strong></td><td></td></tr><tr><td><strong>Pattern following*</strong></td><td>Technical analysis, trend following</td></tr><tr><td>Value investing</td><td>Value investing</td></tr><tr><td>Mispricing</td><td>Event driven, arbitrageur, scalping</td></tr><tr><td>Global macro</td><td>Global macro</td></tr><tr><td>Market making</td><td>Market making</td></tr><tr><td>None</td><td>None</td></tr><tr><td>Others</td><td>Merger arbitrage, others, no answer</td></tr><tr><td><strong>Asset class</strong></td><td></td></tr><tr><td><strong>Money market*</strong></td><td>Money market</td></tr><tr><td>Forex market</td><td>Forex market, currencies</td></tr><tr><td>Equity</td><td>Equity</td></tr><tr><td>Bonds</td><td>Bonds, fixed income and related derivatives</td></tr><tr><td>Others</td><td>Commodities, private equity, real estate, hedge fund, other</td></tr></tbody></table>

Note: * stands for the reference category in the regression. The distribution of the different variables are displayed in Appendix B.1, Figure 8.

We categorized jobs *ex ante* following common divisions in the finance industry (Table 6). Market operators were grouped under trading activities, while sales traders covered client-facing activities. Portfolio and asset managers correspond to fund management roles, and analysts cover positions focused on data analysis and risk monitoring rather
Hypothesis 1, i.e., regularity of financial preferences, holds overall and for any of the subsamples at the 1% level. From this basis, we thus concentrate on the specific pro-environmental preferences for various strata.

## 4.1 Green vs. brown contexts

As explained above, green and brown assets are connected to a donation from the experimenter to an association. For the case of green assets, it refers to the mechanism of delegated philanthropy (Bénabou and Tirole, 2010) in similar experimental protocols (Heimann et al., 2011; Koppel and Regner, 2011; Eckel et al., 2017; Brodback et al., 2020; Humphrey et al., 2021). The principle underlying the mechanism of delegated philanthropy is that the donor is compensated for the donation (i.e., a material loss) by an altruistic reward, such as a warm glow (Andreoni, 1990). However, from a practical point of view, there are three outcomes: a loss for the donor, a donation for the association, and a potential benefit for society if the revenue is invested.

This does not pose any challenge in green contexts but does in brown contexts. The counterpart for a brown asset that generates a negative externality is to provide material compensation to the donor to relieve the moral cost of guilt<fnref n="21" /> in inflicting a potential loss onto society. That is, by designing the brown asset as a symmetric asset to the green, we also create three potential outcomes: material compensation for the donor (a higher expected return), revenue for the association, and a potential loss for society if the revenue is invested.<fnref n="22" />

First, evidence shows that, on average, $\lambda$ is greater in brown than in green contexts. The Green dummy variable at the pooled samples level (column (1)) is significantly negative at the 1% level, providing a gross estimate of that result: participants put 9.40% less on average into the greener asset in green contexts than in brown contexts. The evidence put forth in Section 3 in favor of Hypothesis 2.1 shows several significant

<footnote n="21">²¹Battigalli and Dufwenberg (2007) develop a model of guilt aversion in games. More generally, there is a fundamental difference between green and brown situations: in a green situation, participants must arbitrate to generate positive externalities; in the brown situations, they must arbitrate to avoid negative externalities. Avoiding a negative consequence to a third party may trigger different cognitive processes than generating a positive consequence to a third party (Greene and Haidt, 2002; Krüger, 2015; Bialek and De Neys, 2017).</footnote>

<footnote n="22">²²With delegated philanthropy, participants are asked to make a direct transfer from the portfolio monetary loss to the philanthropic donation (Heimann et al., 2011; Koppel and Regner, 2011; Eckel et al., 2017). Such a mechanism is not valid with delegated misanthropy, which generates both a donation to the association revenue and a surplus profit. That is why we restrain ourselves from suggesting a monetary equivalence by decoupling the exact level of donation, fixed to half of the proportion invested in the colored asset, and the specific premium of colored assets.</footnote>
than direct trading. Analysts typically adopt longer-term, multidimensional perspectives, potentially leaving more room for environmental concerns. More generally, job characteristics and exposure to different business cultures may shape preferences (Cohn et al., 2014), while self-selection into specific roles may reinforce such differences (Lagarde and Blaauw, 2014).

We also distinguished investment strategies. The “pattern-following” category encompasses technical analysis and trend-following, typically short-term and based solely on price dynamics. The “mispricing” category refers to short-term error-correction or arbitrage strategies. “Value investing” and “global macro” involve longer horizons and integrate fundamental and macroeconomic information. The logic mirrors the fundamentalist vs. chartist distinction: the former relies on multidimensional signals, while the latter focuses narrowly on price patterns, largely excluding extra-financial considerations.

Given small subsamples and the exploratory nature of this analysis, we remain cautious in interpretation. New experiments will be needed to confirm these findings in a hypothesis-testing framework.

Substantial heterogeneity emerges across jobs and strategies. Analysts differ most clearly from traders: they allocate over 15 percentage points more to greener assets in both green and brown contexts. Portfolio and asset managers, by contrast, invest almost 29 percentage points more in brown assets than analysts, ceteris paribus.

Strategy differences are also marked. Pattern followers (technical analysis, trend following) invest significantly more in brown assets than other categories. In green contexts, only mispricing strategies (event-driven, arbitrage, scalping) differ significantly from pattern following.<fnref n="14" />

Overall, variation in λ is driven much more by job type and investment strategy than by individual traits such as SVO or NEP. For example, even the maximum SVO effect in brown contexts (13.6 points) is smaller than the differences observed across several job categories or trading strategies. This suggests that knowing a professional's role and trading style might be more informative about their environment-related financial decisions than auxiliary preference measures. The strong contrast between analysts and traders, or between pattern followers and fundamentalists, indicates that positions involving longer horizons and multidimensional information may both attract and foster greener preferences. This insight opens promising directions for future research.

<footnote n="14">¹⁴Although scalping is often classified under technical analysis, it also involves careful stock selection and fast arbitrage-like trades.</footnote>
<table><caption>Table 4: IMPACT OF ASSETS AND SAMPLE CHARACTERISTICS ON $\lambda$</caption><thead><tr><th>Sample Context N</th><th>(1) ALL All 15,008</th><th>(2) ALL Green 7,504</th><th>(3) ALL Brown 7,504</th><th>(4) PRO Green 3,040</th><th>(5) PRO Brown 3,040</th><th>(6) STUD Green 4,464</th><th>(7) STUD Brown 4,464</th></tr></thead><tbody><tr><td colspan="8"><em>Asset characteristics</em></td></tr><tr><td>Expected return of greener asset</td><td>1.06** (0.08)</td><td>0.81** (0.09)</td><td>1.30** (0.11)</td><td>0.64** (0.14)</td><td>0.90** (0.13)</td><td>0.92** (0.12)</td><td>1.59** (0.17)</td></tr><tr><td>Expected return of browner asset</td><td>-0.73** (0.08)</td><td>-0.61** (0.09)</td><td>-0.92** (0.10)</td><td>-0.53** (0.13)</td><td>-0.55** (0.11)</td><td>-0.67** (0.13)</td><td>-1.19** (0.16)</td></tr><tr><td>Standard deviation of greener asset</td><td>-0.45** (0.04)</td><td>-0.32** (0.03)</td><td>-0.63** (0.05)</td><td>-0.37** (0.05)</td><td>-0.57** (0.06)</td><td>-0.28** (0.05)</td><td>-0.67** (0.07)</td></tr><tr><td>Standard deviation of browner asset</td><td>0.45** (0.04)</td><td>0.47** (0.04)</td><td>0.47** (0.04)</td><td>0.48** (0.05)</td><td>0.41** (0.05)</td><td>0.46** (0.05)</td><td>0.53** (0.07)</td></tr><tr><td>Correlation ρ = 1</td><td>-8.93** (1.10)</td><td>-13.56** (1.37)</td><td>-3.56* (1.48)</td><td>-7.47** (1.98)</td><td>-0.75 (1.77)</td><td>-17.73** (1.85)</td><td>-5.85** (2.25)</td></tr><tr><td>Correlation ρ = 0</td><td>-7.75** (1.09)</td><td>-13.21** (1.30)</td><td>-1.52 (1.47)</td><td>-8.23** (1.73)</td><td>2.25 (1.70)</td><td>-16.63** (1.84)</td><td>-4.56* (2.25)</td></tr><tr><td>Correlation ρ = -1</td><td>-3.59** (1.01)</td><td>-7.08** (1.26)</td><td>0.30 (1.46)</td><td>-4.88** (1.83)</td><td>3.75 (1.81)</td><td>-8.52** (1.71)</td><td>-2.38 (2.17)</td></tr><tr><td colspan="8"><em>Sample characteristics</em></td></tr><tr><td>Ranking treatment</td><td>6.52** (2.13)</td><td>8.62** (2.21)</td><td>3.57 (3.47)</td><td>4.35 (3.11)</td><td>2.45 (4.63)</td><td>11.07** (3.07)</td><td>4.51 (4.92)</td></tr><tr><td>Tax treatment</td><td>14.41** (1.88)</td><td>16.61** (2.17)</td><td>11.58** (3.39)</td><td>6.34* (2.78)</td><td>-1.09 (4.19)</td><td>22.65** (3.04)</td><td>21.40** (5.07)</td></tr><tr><td>Student</td><td>-5.31** (1.80)</td><td>-6.99** (1.82)</td><td>-2.42 (2.68)</td><td></td><td></td><td></td><td></td></tr><tr><td>Green</td><td>-9.40** (1.43)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

\*, ** account for 5% and 1% significance levels, respectively.

**Description:** average marginal effects (AME) in a random-effect Tobit panel regression with robust cluster standard errors at the subject level (provided in parenthesis). The explained variable is $\lambda$. Contexts denote those used for estimation: *Green* contexts denote screens 1, 4, 6, 8, 9 and 10 of Table 1; *Brown* contexts denote screens 2, 5, 7, 11, 12 and 13. *All* adds *Green* and *Brown* (screen 3 is excluded from analysis). *N* is the number of related observations (samples and situations in the context). The *Green* dummy variable controls for the subsets of screens (= 1 for *Green* screens and 0 for *Brown*). *Student* dummy variable controls for the type of sample (= 0 if subject belongs to PRO, = 1 if subject belongs to STUD). *Correlation* $\rho \in \{1, 0, -1\}$ is as in Table 2. Control variables tested but not included in the table: *risk*, *prudence*, *temperance*, *SVO*, *NEP*, *k-level* and *CRT* (for details, see Section 5). We remove from the regression analysis the three situations in screen 3, for which participants were faced with two neutral assets. This leaves 32 situations with 469 participants, i.e., 15,008 observations.
### 5.1.2 The STUD sample

The STUD sample was drawn from a large pool of volunteers. Participants reported their age, gender, years of study, and discipline. Only discipline is used in the analysis, distinguishing health sciences, humanities, natural sciences, social sciences, business and management (reference), and economics and finance. Other variables had no significant effect.

Discipline matters only marginally. Health sciences students differ significantly from management students in brown contexts, while coefficients for other disciplines are mostly insignificant. Management students show the highest tendency to invest in the brown asset, whereas humanities is the only group with a negative coefficient in green contexts, though not significant. Age and gender play no role.

In sum, students' portfolio choices reflect pro-social and pro-environmental preferences, but these are not strongly related to their field of study. Instead, as shown in Appendix A.5, they are more directly associated with individual SVO and NEP scores. By contrast, professionals' choices are closely tied to their job positions and trading practices rather than to stated social or environmental leanings.

## 6 Discussion and conclusion

We investigated the interplay between return, risk, and environmental impact of assets in a 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 identify common patterns as well as disparities between finance professionals and students.

Before discussing the results, it is important to underline our scientific approach. The replicability of experimental results in economics and the social sciences has been extensively debated (Camerer et al., 2016, 2018). Recent work shows that preregistration improves effect size estimation and reproducibility in experimental economics (Strømland, 2019; Nosek et al., 2018). At the time this experiment was designed, preregistration was not common practice in economics. Nevertheless, the three core hypotheses detailed in Section 2.3, together with the main analysis and robustness tests (Sections 3 and 4), were formulated ex ante, and therefore fall within the realm of hypothesis testing. Some of these hypotheses were not confirmed (e.g., no risk asymmetry, no “green generation” effect, limited ranking treatment effect), while others were. By contrast, Section 5 adopts
differences when we separate green from brown contexts. The average $\lambda$ for the first allocation task (green context) is 42.85%, whereas it is 60.74% in the second task (brown context). A more detailed look is provided by Figure 1. The figure shows that investments are lower in green contexts than in brown ones for similar situations in terms of return spread and standard deviation. Table 3 already distinguished green and brown contexts with higher statistics in the latter than in the former.

<figure><img src="image_4.png" /><figcaption>Figure 1: AVERAGE $\lambda$ PER ALLOCATION IN THE BASELINE TREATMENT</figcaption></figure>

**Description of both panels:** average $\lambda$ for PRO and STUD participants separately in $S_{xy}$ where S and s stand for screen x and situation y, in reference to Table 1. Further information: G and B stand for green and brown contexts, followed by the difference in expected return and standard deviation between the greener and the browner assets. The upper panel shows the results from screens 1 through 7, and the lower panel shows the results from screens 8 through 13 involving a correlation $\rho$.

The econometric analysis of Hypothesis 2.2 and 2.3 can also be repeated column-wise to compare columns. If we turn to AMEs for the expected return first (Hypothesis 2.2), we notice three results. First, the result of Section 3 holds in all columns, that is, in both green and brown contexts and for both professionals and students. Column-wise,
an exploratory perspective, generating hypotheses that remain to be tested in future work (notably regarding job type and trading strategies among professionals). We stress this distinction between prediction and postdiction to clarify which findings are robust and which should be interpreted as exploratory.

Portfolio patterns observed in the experiment combine classical portfolio theory predictions (attraction to return, aversion to risk) with pro-environmental preferences. A first important finding is that many participants are willing to forego financial return to improve the environmental profile of their portfolio, and our design allows us to calibrate the extent of this sacrifice. At the same time, our most innovative result is that risk considerations tend to dampen pro-environmental investment: In risky environments, participants shift toward brown assets with higher expected returns. The fact that environmental preferences materialize through a measurable willingness to give up return, but weaken under risk, suggests a tension that future research should further investigate.

We also document a clear asymmetry: participants are more reluctant to hold brown assets than they are motivated to invest in green ones. This appears both in average allocations and in how they respond to changes in returns. Such behavior is consistent with prospect theory's emphasis on loss aversion (Kahneman and Tversky, 1979; Tversky and Kahneman, 1991) and with the psychological literature on negativity bias, which shows that negative stimuli trigger stronger reactions than positive ones (Baumeister et al., 2001; Rozin and Royzman, 2001). In our setting, negative externalities make the trade-off between return and impact more pressing, thereby magnifying adjustments in portfolio choices.

When comparing professionals and students, results must be interpreted with great caution. The two samples differ not only in professional experience but also in remuneration schemes, national contexts, and possibly in cultural background. With these caveats in mind, professionals tend to allocate more to greener assets in green contexts and are less influenced by incentive treatments. Their portfolio choices appear less connected to self-reported social or environmental preferences (SVO or NEP scores), and more closely linked to their job position and trading strategy. In contrast, students' portfolio choices are more responsive to both pecuniary and non-pecuniary incentives, and more strongly correlated with SVO and NEP. This suggests that professional practice and market roles may play a more decisive role in shaping investment behavior than stated preferences, though further research is needed to confirm this interpretation.

Our study also emphasizes the heterogeneity within the PRO sample. Analysts, for example, invest substantially more in green assets than market traders or trend followers,
the AME of the expected return of the greener asset (first line) is greater (in absolute value) than the AME of the expected return of the browner asset (second line).

Second, we compare the values in green and brown contexts, sample-wise (i.e., column (2) vs. (3), (4) vs. (5) and (6) vs. (7)). We observe that the AMEs for the expected return of the greener or the browner asset take greater (absolute) values in brown contexts than in green ones. Tests confirm this result for the ALL sample (5% level) and for students (5% level).<sup>23</sup> This shows that financial preferences show greater sensitivity in brown contexts than in green ones, especially for students. In other words, Hypothesis 1 is confirmed column-wise.

Third, the sum of AMEs (or the spread between absolute values) of the expected return of the greener and the browner asset, taken column-wise, is greater in brown contexts than in green ones (idem, column (2) vs. (3), (4) vs. (5) and (6) vs. (7)). This result has a clear interpretation; that is, an increase in the expected return of both assets will have a greater (and positive) impact on $\lambda$ in brown contexts than in green ones. In other words, the effect stated by Hypothesis 2.2 is stronger with a negative externality than with a positive externality.

We are left with Hypothesis 2.3 that was already disproved in aggregate. Comparing the AMEs of the standard deviations of the greener and browner assets in different contexts but sample-wise (i.e., column (2) vs. (3), (4) vs. (5) and (6) vs. (7)), we are left with the same conclusion: participants faced with an equivalent increase in the level of risk for both assets decrease the proportion of $\lambda$. The econometric analysis provides different rationales in green and brown contexts, however.<sup>24</sup>

The overall conclusion is a refinement of our core result of Section 2. Participants reveal an asymmetric perception of green and brown assets. Pro-environmental preferences appear (i.e., Hypothesis 2.1 holds), but they are stronger in brown contexts than in green ones. The trade-off between expected return and environmental impact remains (i.e., Hypothesis 2.2 holds) and is more salient in brown contexts. The predominance of the trade-off between expected return and risk over the trade-off between risk and envi-

<footnote n="23"><sup>23</sup>For column (2) vs. (3): $p \le .001$ when comparing the expected return of greener assets; $p \le .037$ when comparing the expected return of browner assets. For columns (4) vs. (5): $p \ge .13$ when comparing greener assets, $p \ge .95$ when comparing browner assets in both contexts. For column (6) vs. (7): $p \le .003$ when comparing greener assets; $p \le .019$ when comparing browner assets in both contexts. All tests are displayed in Internet Appendix C.3.2.</footnote>

<footnote n="24"><sup>24</sup>In green contexts, coefficients take values along what Hypothesis 2.3 suggests, if it was not the effect of correlation. We refer to the interpretation already provided in Section 3. There is no observable risk-environmental impact trade-off. The interpretation is more straightforward in brown contexts and relies only on standard deviation coefficients (correlation variable coefficients are non-significantly different from zero at any level).</footnote>
who focus on short-term price movements. These differences suggest that professional
specialization and investment horizons may shape the scope for integrating environmental
concerns into portfolio choices, a topic that deserves further investigation.

A distinctive feature of our design is the explicit modeling of negative externalities. While most SRI studies focus only on positive impacts, our “delegated misanthropy” framework introduces a symmetric mechanism to delegated philanthropy. This approach raises important questions: are participants reacting to the moral cost of harm, or to the salience of losses associated with donations in green contexts versus surpluses in brown contexts? The calibration of the externality at 50% was chosen to ensure meaningful trade-offs, but future work could systematically vary this parameter to map the “dose-response function” of externality levels.

In sum, our findings suggest that portfolio choices reflect a trilemma: return, risk, and environmental impact. Return and risk remain the primary drivers, while impact considerations appear asymmetrically—through stronger aversion to brown than attraction to green. Future research should deepen our understanding of how externalities, incentives, and professional practices shape this trilemma, and explore how robust these patterns remain across contexts and populations.

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## 4.2 Finance professionals vs. students

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The context (green or brown) and the participants' sample (PRO or STUD) reveal variations of the general pro-environmental financial preferences exhibited in Section 2. These variations in the strength of pro-environmental concern, suggest that it is possible to manipulate those preferences with appropriate incentives.

In this subsection we discuss the two types of incentives described in Section 2.1: a non-pecuniary incentive that targets individuals' self-image based on a ranking feedback about the level of portfolio externality, and a pecuniary incentive based on a tax function that penalizes the return on the browner asset. In the PRO sample, 66 participants faced the baseline treatment, 63 the ranking treatment, and 61 the tax treatment. In the STUD sample, 103 faced the baseline treatment, 84 the ranking treatment, and 92 the tax treatment. It is important to highlight that treatments do not interact with the situations and their order. Neither do they interact with the asset characteristics, environmental externalities (context), or the donation mechanism which are under the control of the experimenter, i.e., exogenously fixed. The characteristics are introduced in the analysis through a categorical variable only. Their complexity and potential policy application deserve a dedicated study that goes beyond the scope of the present paper.
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**Ranking treatment.** Social comparison and social norms ground a long-standing compo-
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information would act as an incentive for self-enhancement by stimulating competition in
the environmental dimension.

Presently, the ranking treatment provides a real-time private ranking of the partici-
pants’ choice of λ compared to a database of 50 pre-recorded values collected from the
same sample in the baseline treatment.<fnref n="25" /> This serves as a proxy for the relative environ-
mental performance of the portfolio choice of participants under this ranking treatment.
This information may serve as a social or moral reference when constructing one’s port-
folio.

The ranking treatment variable has a significantly positive effect on λ for the pooled
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a low rank (a better ranking): they can rise more easily to a low rank with a low λ, in

<footnote n="25"><sup>25</sup>In the ranking treatment, a section has been added to the instructions. In this section, we explained to the participants that the experiment had already been conducted previously with a sample of 50 participants and that their investment in green and brown assets would be compared to those in that sample, to rank their “environmental performance”. We provided two examples to explain how the ranking was calculated, based on the two assets involved in the frame (green vs. neutral or neutral vs. brown, see instructions in Internet Appendix A.3). To create the benchmark sample, we randomly selected 50 participants from the observations collected in the baseline treatment with the same category of participants (professionals or students). We kept the same benchmark sample in all sessions of the ranking treatment. Therefore, the decisions of all participants in the ranking treatment were compared to the decisions of a sample of 50 participants facing exactly the same frames and situations. The only difference is that the participants in the benchmark sample made their choices, without (i) knowing that their decisions would be used in another treatment, and (ii) without social comparison.</footnote>
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**Tax treatment.** Taxation is one of the straightest instruments to curb economic behavior. Environmental taxes are prevalent in environmental economics (Nordhaus, 1992; Bovenberg and De Mooij, 1994; Goulder, 2013). Presently, the tax is generated in real time by participants' decisions about $\lambda$. It is given by a linearly increasing tax rate on the browner asset's expected return, so that the final portfolio's expected return is a concave function of $\lambda$, increasing on $[0, 1/2]$ and decreasing after.<fnref n="26" /> This convex tax on financial return is expected to affect participants heterogeneously: the less participants are willing to initially put into the greener asset, the higher the incentive generated by the tax to increase $\lambda$. Convex taxes on financial transactions have been widely investigated in theory and practice (Green and Sheshinski, 1978; Subrahmanyam, 1998; Matheson, 2011; Burman et al., 2016). Sarkar (2008) shows that convex taxation is a credible financial representation of corporate taxes (see also Graham, 2003). It also constitutes an efficient incentive scheme, implemented in pricing design for principal-agent relations (Alasseur et al., 2020) or in the presence of behavioral biases undervaluing the costs of sin products (Gerster and Kramm, 2019).

The tax treatment dummy is positive and significant in all regressions, except for finance professionals in brown contexts (column (5)), for which it is negative and non-significant. Therefore, the hypothesis of tax sensitivity seems to be confirmed in almost all situations. There may be an inverse relationship between the size of the tax variable coefficient and the tax-free intention to invest in the greener asset because of the convexity of the tax. The average value of $\lambda$ in the baseline treatment is $\lambda = 52.54\%$ and $\lambda = 40.80\%$ for finance professionals and students respectively. This comports with the values obtained for the tax variable: the lower the $\lambda$ in the baseline treatment, the stronger the effect of the tax. As noted above, this effect is particularly strong on students because, in the tax treatment (26 of 32 situations), they invest a larger share in the greener asset

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

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than finance professionals invest. This reversal of positions, which does not appear with
the ranking treatment, shows that students are more sensitive to the tax treatment than
finance professionals. In the green context, students and finance professionals invest more
of their endowment in the greener asset in the tax treatment than in the baseline treatment
while only students do in the brown contexts. This provides additional arguments in favor
of the green vs. brown asymmetry and the difference of behaviors between pools.

**5 Exploratory analysis:**

**The determinants of individual preferences**

In the previous sections, we focused exclusively on the portfolio task by analyzing how
various situation parameters influence the composition of portfolios. Some clear, robust
patterns appeared at the aggregate level for the pooled samples, but small differences
appear when distinguishing between positive and negative environmental externalities
and between financial professionals and students. What are the reasons for these patterns
in portfolio choices? In the remainder of the paper, we aim to deepen our understanding
of the portfolio preferences exhibited in Sections 3 and 4, by conducting an exploratory
analysis that extends the econometric study to individual data. For that purpose, several
sets of variables are added to the basic model in the form of additional tasks.

After selecting the portfolio, all participants were asked to complete three tasks to
measure social preferences and potential predictors of investment behavior: (i) the social
value orientation task (SVO) to measure pro-sociability (Murphy et al., 2011), (ii) a
survey about environmental opinion based on the New Environmental Paradigm (Dunlap
et al., 2000), and (iii) a socioeconomic and demographic questionnaire.

Three control tasks were also included: (i) a lottery choice task to elicit risk preferences (Noussair et al., 2014), (ii) the one-shot 11–20 game to control for strategic skills (Arad and Rubinstein, 2012; Alaoui and Penta, 2016), and (iii) the cognitive reflection test (CRT) to measure cognitive ability (Frederick, 2005; Toplak et al., 2011; Korniotis and Kumar, 2010).

SVO, risk, cognitive and strategic tasks were incentivized in that one was randomly
selected to be paid out for real, in addition to the core investment task.<fnref n="27" /> All these tasks

<footnote n="27">²⁷Again, this incentive scheme offers the opportunity to eliminate any potential wealth and portfolio effects and remove hedging opportunities (Bardsley et al., 2009). The euro-ECU exchange rate (1 ECU = 1 euro for PRO, 1 ECU = 0.04 euros for STUD), and the probability of being paid (1 for STUD and 1/10 for PRO) for the supplementary task were the same as in the portfolio task for each sample.</footnote>
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are detailed in Appendix A.4 and A.5.

In subsection 5.1, we note pro-social (SVO) and environmental (NEP) preferences. Finally, in subsection 5.2, we focus on the socioeconomic and demographic questionnaire. The other individual data collected from other auxiliary tasks, such as risk, cognitive and strategic attitudes, are used as control variables.

The analysis consists in repeating the econometric analysis of Section 3 with the addition of questionnaire data. Table 5 provides the AME computed from estimates.<fnref n="28" />

The econometric results now consist of six regressions: columns (1) to (3) relate to the PRO sample and columns (4) to (6) to the STUD sample, with the respective decomposition in green (columns 2 and 5) and brown (columns 3 and 6) situations. For the sake of clarity, we show the AMEs for the SVO and NEP for discussion, but we hide all other variables that were analyzed in Section 3.

## 5.1 Pro-social and pro-environmental preferences

**Social Value.** As extensively discussed by Riedl and Smeets (2017), social preferences strongly influence SRI. Riedl and Smeets (2017) relied on the standard investment game of Berg et al. (1995) to elicit trusting behavior. We follow their track, but we rely on a different task to capture pro-sociability, the SVO score introduced by Murphy et al. (2011).

The SVO score is based on a distribution task between self and others. Each participant is asked to choose a payoff distribution, within a set of options, between himself and an anonymous counterpart randomly selected from the participants in the experiment. In the short version of the SVO elicitation task used in our experiment, the distribution choice was repeated six times with a different set of options in each round. In the PRO sample, pairs were formed at the end of the day, as the number of participants in a session was unknown in advance, whereas in the STUD sample, participants were randomly paired at the beginning of the corresponding task. One of the six screens presenting the choices was randomly selected for payment.

The six choices are averaged and aggregated to determine a score (see Internet Appendix A.5.2). Higher scores are associated with more pro-social individuals. Pro-sociability is likely to positively influence $\lambda$ because pro-social individuals care more about others' well-being.

<footnote n="28">²⁸Internet Appendix C.3 provides the estimates in another table.</footnote>
# Internet Appendix

<table><tr><td>A Experimental tasks</td><td>31</td></tr><tr><td>A.1 Portfolio task</td><td>31</td></tr><tr><td>A.2 Screenshot of a screen from the portfolio task</td><td>31</td></tr><tr><td>A.3 Internet links to the instructions for all treatments</td><td>32</td></tr><tr><td>A.4 Control tasks</td><td>34</td></tr><tr><td>A.5 Detailed description of the control tasks</td><td>35</td></tr><tr><td>A.6 Exploratory results: SVO and NEP</td><td>42</td></tr><tr><td>A.7 Financial market professional questionnaire</td><td>43</td></tr><tr><td>B Data collection</td><td>44</td></tr><tr><td>B.1 Samples</td><td>44</td></tr><tr><td>B.2 Practical procedures</td><td>46</td></tr><tr><td>C Predictions: Standard financial preferences</td><td>47</td></tr><tr><td>D Additional details on procedures and sampling</td><td>47</td></tr><tr><td>E Analysis</td><td>49</td></tr><tr><td>E.1 Portfolio task</td><td>49</td></tr><tr><td>E.2 Students vs. finance professionals</td><td>51</td></tr><tr><td>E.3 Treatments: detailed procedures and instructions</td><td>52</td></tr><tr><td>E.4 Control tasks</td><td>55</td></tr><tr><td>E.5 Econometric analysis</td><td>57</td></tr></table>
<table><caption>Table 5: IMPACT OF PRO-SOCIAL AND ENVIRONMENTAL PREFERENCES AND DEMOGRAPHICS</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th>Sample</th><th>PRO</th><th>PRO</th><th>PRO</th><th>STUD</th><th>STUD</th><th>STUD</th></tr><tr><th>Context</th><th>All</th><th>Green</th><th>Brown</th><th>All</th><th>Green</th><th>Brown</th></tr><tr><th>N</th><th>6048</th><th>3024</th><th>3024</th><th>8928</th><th>4464</th><th>4464</th></tr></thead><tbody><tr><td colspan="7"><strong>Preferences</strong></td></tr><tr><td>SVO</td><td>0.12<br>(0.08)</td><td>0.08<br>(0.07)</td><td>0.17<br>(0.11)</td><td>0.19**<br>(0.07)</td><td>0.35**<br>(0.08)</td><td>-0.01<br>(0.13)</td></tr><tr><td>NEP</td><td>0.05<br>(0.18)</td><td>-0.10<br>(0.18)</td><td>0.26<br>(0.24)</td><td>0.45*<br>(0.18)</td><td>0.24<br>(0.21)</td><td>0.68*<br>(0.30)</td></tr><tr><td colspan="7"><strong>Finance jobs</strong></td></tr><tr><td>Sales Trading</td><td>-0.35<br>(3.23)</td><td>2.31<br>(3.15)</td><td>-3.29<br>(4.59)</td><td></td><td></td><td></td></tr><tr><td>Portfolio Manager</td><td>-6.81<br>(4.96)</td><td>-1.44<br>(5.14)</td><td>-12.54*<br>(6.04)</td><td></td><td></td><td></td></tr><tr><td>Analyst</td><td>15.35**<br>(4.36)</td><td>15.09**<br>(4.62)</td><td>16.18*<br>(6.57)</td><td></td><td></td><td></td></tr><tr><td>Support functions and others</td><td>3.63<br>(4.31)</td><td>7.07<br>(4.62)</td><td>0.15<br>(6.57)</td><td></td><td></td><td></td></tr><tr><td colspan="7"><strong>Investment Strategies</strong></td></tr><tr><td>Value investing</td><td>10.85*<br>(4.47)</td><td>6.68<br>(4.17)</td><td>15.58*<br>(6.17)</td><td></td><td></td><td></td></tr><tr><td>Mispricing</td><td>18.07**<br>(4.75)</td><td>12.86*<br>(5.84)</td><td>23.34**<br>(5.97)</td><td></td><td></td><td></td></tr><tr><td>Global Macro</td><td>10.36*<br>(4.14)</td><td>4.50<br>(3.71)</td><td>17.14**<br>(5.91)</td><td></td><td></td><td></td></tr><tr><td>Market Making</td><td>11.10**<br>(3.52)</td><td>5.32<br>(3.37)</td><td>16.95**<br>(5.46)</td><td></td><td></td><td></td></tr><tr><td>None</td><td>1.13<br>(4.07)</td><td>-3.56<br>(4.04)</td><td>6.07<br>(5.08)</td><td></td><td></td><td></td></tr><tr><td>Others</td><td>10.12*<br>(5.17)</td><td>5.43<br>(4.84)</td><td>16.99*<br>(7.86)</td><td></td><td></td><td></td></tr><tr><td colspan="7"><strong>Disciplines</strong></td></tr><tr><td>Humanities</td><td></td><td></td><td></td><td>10.61<br>(7.76)</td><td>-1.92<br>(8.40)</td><td>21.28<br>(14.62)</td></tr><tr><td>Formal Sciences</td><td></td><td></td><td></td><td>6.13<br>(4.45)</td><td>1.47<br>(4.80)</td><td>11.58<br>(7.18)</td></tr><tr><td>Economics</td><td></td><td></td><td></td><td>5.45<br>(4.04)</td><td>1.11<br>(4.40)</td><td>9.99<br>(5.60)</td></tr><tr><td>Health</td><td></td><td></td><td></td><td>12.58**<br>(4.70)</td><td>6.05<br>(4.96)</td><td>18.35*<br>(7.97)</td></tr><tr><td>Natural Sciences</td><td></td><td></td><td></td><td>8.38<br>(5.52)</td><td>3.49<br>(5.38)</td><td>12.48<br>(7.51)</td></tr><tr><td>Social sciences</td><td></td><td></td><td></td><td>7.83<br>(5.39)</td><td>4.63<br>(4.60)</td><td>12.85<br>(9.37)</td></tr><tr><td>Others</td><td></td><td></td><td></td><td>14.55*<br>(6.33)</td><td>11.43<br>(7.71)</td><td>18.37<br>(12.28)</td></tr></tbody></table>

\*, \*\* account for 5% and 1% significance levels, respectively.

Average marginal effects (AME) in a random-effect Tobit panel regression with robust cluster standard errors at the subject level (provided in parenthesis). **Method and Analysis** are identical to Table 4. All variables included in the specifications of Table 4 are included as control variables. Variables for the PRO sample: *Finance jobs* and *investment strategies* are detailed in Table 6. Additional control variables included but not shown are *employer*, *asset class*, and *market type*, as defined in Table 6. Variables for the STUD sample: *Disciplines* are defined in Section 5.2.2. Control variables included but not shown are *age* (in years), *gender* (= 0 for female, = 1 for male), and *student level* (in years). One professional did not answer the questionnaire. Therefore, we lost 32 observations for the professional columns between this table and the previous one.
# 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.
**Environmental opinion.** Eliciting pro-environmental preferences raises a new challenge for researchers and survey designers (Lange et al., 2018). To our knowledge, an experimentally validated task for eliciting pro-environmental preferences is currently unavailable. However, a canonical survey, the New Environmental Paradigm (NEP scale) of Dunlap et al. (2000), has been used in thousands of studies for this purpose.

The NEP scale is based on 15 Likert-type questions related to five environmental dimensions (see Internet Appendix A.5.5). The standard approach quantifies each answer on a scale from 1 to 5. Therefore, the total score ranges from 15 to 75: the higher the score, the stronger the pro-environmental orientation.

**Results.** A look at variables related to preferences shows that measures of pro-social and pro-environmental leanings do not affect the portfolio choices of finance professionals. Interestingly, these variables are highly significant for students, and they have the expected sign. However, the SVO is positively correlated with the share invested in the greener asset only in green contexts, while the NEP is positively correlated only in brown contexts. No significant effects were found in the converse situations. A careful look at the NEP questionnaire in Internet Appendix A.5.5 shows that it is particularly oriented to the human origin of environmental degradation and is less oriented toward attenuation or repairing impacts. In contrast, the SVO task is about sharing a monetary endowment with another subject. This relates strongly to the delegated philanthropy mechanism in the green context, where the donation carries a positive moral connotation. For students at least, this may show that the SVO task may not be suited for testing negative social and or environmental externalities, and the experimental literature is still in search of a protocol for measuring environmental preferences.

As we said earlier, control tasks do not substantially modify the results of Table 4. In short, let us mention that the variables are rarely significant at the 5% level in more than one context and one sample.

## 5.2 Socioeconomic and demographic data

The PRO and STUD samples reflect the socioeconomic traits of the populations they were drawn from. Each sample took a final but specific survey after the experiment. Therefore, the variables that are specific to each sample might also have a specific influence on those participants' portfolio choices.
<figure><img src="image_4.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
5.2.1 The PRO sample

In partnership with the Moroccan association of trading rooms (AMSM), which sup-
ported us during the experimental sessions with professionals, we had access to many
large banks and asset management companies operating in Casablanca (the first financial
center in the African continent on the Global Financial Centers Index (GFCI)). We first
discussed the ethical conditions of the experiment with the AMSM and then organized
our visit to the different institutions, following their respective agreements. Professionals
volunteered to participate. At the end of the experiment, we explained to the profession-
als that we were interested in understanding their investment behavior in the financial
markets. The vast majority of the professionals we met were enthusiastic about partici-
pating in the experiment and said they would volunteer to do it again in the future. A
particular characteristic of our PRO pool of participants is that it was mostly drawn from
practitioners of financial markets. Therefore, we asked multiple-choice questions about
the type of job (traders, risk managers, etc.), the employer (bank, hedge fund, etc.), past
experience, and investment strategy. For ethical and compliance reasons, banks and asset
management companies did not allow us to collect data on age and gender. However, we
found that these two control variables had no significant effect on investments in green
or brown assets in our student sample. In addition, Tatarnikova et al. (2021) showed
in an investment task that there were no gender and age effects in the proportions of
investment in green and brown assets among financial professionals. The professionals'
questionnaire is provided in Internet Appendix A.6.

We categorized *a priori* our PRO sample according to the participants’ jobs. The proposed categorization is reported in Table 6. The categories provided in that table are not arbitrary. They obey informal rules relative to the knowledge of the *a priori* categories. We used the following logic for finance jobs and past experience categorical variables. Operators of financial markets are categorized under market trading activities, whereas sales trading refers to client-related activities. Portfolio and asset managers manage funds of assets (stocks, bonds, derivatives, commodities, and forex) for clients such as individuals, companies, and banks. The “analyst” category pools all the jobs that mainly require analyzing large sets of financial and economic data to make predictions or control risky positions in support of decision making and trading. These jobs mainly rely on analytical skills with multiple dimensions, using some macroeconomic and financial fundamentals. They do not directly intervene in financial markets.

Analysts are practitioners who consider much more long-term and multidimensional
### A.3.3 Tax treatment

http://leem.umontpellier.fr/files/appendix/stranded_asset/screenshots_PRO_tax.pdf
<table><caption>Table 6: CATEGORIZATION OF THE PRO SAMPLE</caption><thead><tr><th>New category</th><th>includes</th></tr></thead><tbody><tr><td><em>Finance Job & Past experience</em></td><td></td></tr><tr><td><strong>Market Trading*</strong></td><td>Proprietary trading, market maker, jobs with trading activities</td></tr><tr><td>Sales trading</td><td>Sales trader, sales</td></tr><tr><td>Portfolio manager</td><td>Asset manager, portfolio manager</td></tr><tr><td>Analyst</td><td>Strategist, economist, financial analyst (buy/sell side), risk analyst, jobs with analyst skills</td></tr><tr><td>Support functions and others</td><td>Support and related functions, middle office job, business manager, trade finance, private banker, senior banker, other</td></tr><tr><td><em>Employer</em></td><td></td></tr><tr><td><strong>Bank*</strong></td><td>Bank</td></tr><tr><td>Other</td><td>Asset management company, trading company, broker, hedge fund, work for your own, private equity company, other</td></tr><tr><td><em>Investment strategy</em></td><td></td></tr><tr><td><strong>Pattern following*</strong></td><td>Technical analysis, trend following</td></tr><tr><td>Value investing</td><td>Value investing</td></tr><tr><td>Mispricing</td><td>Event driven, arbitrageur, scalping</td></tr><tr><td>Global macro</td><td>Global macro</td></tr><tr><td>Market making</td><td>Market making</td></tr><tr><td>None</td><td>None</td></tr><tr><td>Others</td><td>Merger arbitrage, others, no answer</td></tr><tr><td><em>Asset class</em></td><td></td></tr><tr><td><strong>Money market*</strong></td><td>Money market</td></tr><tr><td>Forex market</td><td>Forex market, currencies</td></tr><tr><td>Equity</td><td>Equity</td></tr><tr><td>Bonds</td><td>Bonds, fixed income and related derivatives</td></tr><tr><td>Others</td><td>Commodities, private equity, real estate, hedge fund, other</td></tr></tbody></table>

Note: * stands for the reference category in the regression.

The distribution of the different variables are displayed in Appendix B.1, Figure 8.

extra-financial (or economic) information than market traders (or sales traders) usually do. Taking care of such considerations probably leaves more space for environmental concerns. More generally, the nature of the job and the finance professionals' exposure to different business cultures might shape their preferences (Cohn et al., 2014). Second, reverse causality may also apply, revealing (self-)selection to specific financial jobs and positions (Lagarde and Blaauw, 2014).

In terms of investment strategies, “pattern following” refers to trading strategies focusing on time series configurations of quoted prices, such as following trends or technical analysis, on a time scale that can include intraday trading (a somewhat chartist approach). The mispricing category includes strategies based on short-term error-adjustment dynamics in prices and volatility. They can be grounded on financial knowledge other than price
## A.4 Control tasks

Participants completed a series of additional tasks designed to capture heterogeneity in social, environmental, risk-related, and cognitive preferences. These tasks were used as control variables in the econometric analysis. Importantly, regressions excluding these variables yield coefficients almost identical to those in Table 4, confirming that our main results are robust to individual heterogeneity. Still, following Holmen et al. (2021), it is reasonable to expect that finance professionals perform differently on some of these tasks compared to students.

Task 1 was intended to measure higher-order risk preferences, following the methodology of Noussair et al. (2014). Respondents answered 15 binary-choice questions involving compound lotteries to elicit risk aversion, prudence, and temperance. These three dimensions relate respectively to the second, third, and fourth moments of the underlying risk distribution. While skewness and kurtosis are absent from the portfolio task itself, Bottasso et al. (2022) showed that risk, prudence, and temperance are linked in finance professionals’ decisions, suggesting that they may provide useful information on portfolio choices.

Task 2 was the Social Value Orientation (SVO) task introduced by Murphy et al. (2011). It provides a continuous score between -20° and 60°, classifying participants from competitive to altruistic. This task captures pro-social preferences and is particularly relevant to test whether pro-sociability affects the share invested in the greener asset.

Task 3 was the 11–20 game, originally designed by Arad and Rubinstein (2012) and modified by Alaoui and Penta (2016). This one-shot, two-player game assesses each participant’s depth of strategic reasoning (level-k thinking), in a manner similar to the beauty contest game (Brañas-Garza et al., 2012).

Task 4 was the Cognitive Reflection Test (CRT) (Frederick, 2005; Toplak et al., 2011; Korniotis and Kumar, 2010), consisting of seven numerical questions with correct or intuitive answers. The CRT score ranges from 0 to 7 and serves as a proxy for reasoning and numeracy. Prior work shows that such skills predict investor performance (Corgnet et al., 2018).
dynamics. “Value investing” and “global macro” refer to longer time horizons, and they
include more extra-financial information (fundamentalist approaches based on corporate
valuation and macroeconomic outlook, respectively).

The interpretation follows the one proposed for jobs. Technical analysis and trend
following (pattern-following category) are the main strategy categories that rely exclu-
sively on short-term price movements and exclude *a priori* any asset picking based on
corporate finance data, macroeconomic analysis, or extra-financial information. Indeed,
focusing exclusively on price dynamics without integrating other parameters may explain
why finance professionals using these strategies could not integrate environmental exter-
nalities and, instead, focus only on the risk and profitability of the asset. In brief, the
categorization of jobs and investment strategies follows the usual division between the
fundamentalist and the chartist approaches relatively closely.

Without any *a priori* hypothesis on the impact of questionnaire data on portfolio
choices, we refrain from over-interpreting the possible effects of the variables described
in subsection 5.2, and remain very cautious in the discussion of these first findings.
Moreover, sub-samples in each job and strategy are relatively small. New experiments
will therefore be required to assess these results in an hypothesis-testing approach. The
two samples were drawn from different populations in different countries, and they must
be analyzed separately.

**Results.** Substantial differences in the level of $\lambda$ can be found for different jobs and investment strategies. AMEs show that analysts are the professionals who differ significantly from market traders in both green and brown situations; they invest significantly more in the greener assets (above 15 percentage points in both green and brown contexts). Portfolio and asset managers invest almost 29 percentage points more in the brown assets than analysts, all things being equal.

Looking at investment strategies, participants who rely on technical analysis and trend
following (pattern-following category) invest significantly more in the browner asset than
all other defined types of strategies. However, in the green context, only event-driven,
arbitrage and scalping strategies (mispricing category) are significantly different from
technical analysis and trend following.<fnref n="29" />

Together, jobs and strategies reflect a very high heterogeneity of investment in the

<footnote n="29">²⁹Scalping is often referred to as a technical analysis practice. However, it relies on careful stock selection and repeated fast buy-sell orders and order book dynamics to be profitable. Therefore, it is also closely related to arbitrage methods.</footnote>
Task 5 was a survey based on the New Environmental Paradigm (NEP) scale (Dunlap et al., 2000), consisting of 15 Likert-type questions covering environmental orientations. Scores range from 15 to 75, with higher scores indicating stronger pro-environmental preferences.

Two of these tasks are of particular interest for our hypotheses on green preference. Following Riedl and Smeets (2017), we focus on the influence of revealed pro-sociability (SVO) and expressed pro-environmental preferences (NEP) on $\lambda$. Detailed descriptions of all tasks are provided below.

## 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
greener asset by finance professionals. This heterogeneity is prominent over the possible variations in revealed pro-social and pro-environmental preferences measured by the SVO or the NEP scales.<fnref n="30" /> This result indicates that an experimenter may 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. This has great consequences for further studies.

### 5.2.2 The STUD sample

The STUD sample was drawn from a large participant pool of voluntarily registered student participants. Participants were asked standard questions about age, gender, years of study, and discipline. We use the A.M. discipline categories of health and the folk-known division of liberal arts (humanities, formal sciences, natural sciences, social sciences). We also distinguish business and management (the reference category, hereafter management) on one side and economics and finance (hereafter economics) on the other side. This serves a possible explanatory purpose: those two categories are an important recruiting pool in the finance profession and represent reasonably large subsamples. We use only the disciplines in the analysis and put aside the other variables as controls because age, gender and years of study are not significant.

**Results.** As with jobs and strategies, students’ disciplines show a small effect, which could be the result of (i) the process and content of learning that differs across disciplines, and (ii) student (self-)selection based on the choice of specific disciplines to study. Indeed, health sciences is significantly different from management in brown situations exclusively. The only singular effect, although it is not significant, is that all coefficients are positive, except for humanities in a green context. That means that management students show the highest propensity to hold the brown asset, that is, the most profitable one. Students’ disciplines could have shown a similar heterogeneity between categories if the variance

<footnote n="30">³⁰For example, the SVO value has an amplitude of 80. Multiplied by the AME of the SVO, the average effect on λ in brown contexts, for example, is 13.6 between a participant with a minimal SVO score and another participant with a maximal SVO score. This effect is less than the effect generated by different job categories.</footnote>
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.

<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.