Articles

S(k)in in the game: Investing Responsibly in the Face of Risk

Abstract

This letter deals with responsible decision making: Will model presence and risk influence investment in socially responsible funds? We conducted a choice-based experiment in which participants were invited to allocate their portfolio across three investments: a socially responsible fund, a sin fund or a safe account. The risk of the SRI and sin fund was manipulated across treatment (high or low), as well as the presence of a model face (woman, man or without). We find that SRI choice was positively influenced by the presence of a masculine face. A higher level of risk of the sin fund relative to the SRI one favors greater investment in SRI. On the opposite, a higher level of risk of the SRI relative to the sin fund reduces investment in SRI, apart for highly ethically-minded consumers, who invest in SRI and shun sin stock without consideration for risk.

1. Introduction

Socially responsible investments (SRI) have become increasingly popular these last years amongst institutional investors. SRI now amounts to $35.3 trillions globally, an amount which represent over a third of all professionally managed assets (GSIA, 2020). However, retail investors lag. While they are 52 to 80% to express their willingness to hold at least a part of their portfolio in SRI, they are only around 5% to do so (see BNP Paribas 2018 survey over 5,000 retail investors from various European countries: Italy, France, Belgium, Germany and the Netherlands). There is thus a need to understand the drivers of investment in SRI for retail investors (Capelle-Blancard and Monjon, 2012, Riedl and Smeets, 2017). However, little is known about the effect of investor/fund ethical congruence and gender congruence. We consider the potential nudge of fund ethical positioning, its risk level and the human figure used to promote it. We investigate this issue along three lines.

A first dimension of interest is the ethical character of SRI. SRI funds select their products not only based on the risk-return trade-off, but also on Environmental, Social and Governance criteria, giving, for example, the reassurance that investments exclude companies in the tobacco, weapons, or gambling industries. On the opposite, some funds called “sin funds” invest exclusively in these industries. While behavioral research on SRI is flourishing, research on sin investing is limited (Salaber, 2007).

Second, studies have traditionally investigated willingness-to-pay in SRI investing by varying the expected return of the SRI funds (Apostolakis et al., 2018, Brodback et al., 2019), showing that investors are on average willing to pay a premium to invest in an SRI fund. However, the risk level of the fund should theoretically have an impact as well.

Third, presentation surrounding the funds matters, and can act as a priming nudge. It is well established that face presence (versus no presence) in ad influences positively costumers' cognitive responses (Sajjacholapunt and Ball, 2014, Sato and Kawahara, 2015). Furthermore, some studies revealed a stereotype effect of ad model's gender on observer's responses (Knoll et al., 2011; Åkestam et al., 2017). According to Niessen-Ruenzi & Ruenzi (2018), investors may prefer male fund managers to female ones because of gender stereotypes bias.

Our paper unfolds as follow. We first depict the methods used, before moving on to univariate results. Multivariate results from our regressions are then presented, before concluding and presenting paths for future research.

2. Materials and Methods

Participants

446 adults (55% men) aged 16 to 61 years (M=21.73, SD=7.05) were surveyed between October and November 2020. The participants were recruited from a business school initial and lifelong learning programs (79% enrolled in the 5-years Master program of the school)1.

Experimental design

We designed a between subject design 3 (Face) x 2 (Risk of SRI) x 2 (Risk of sin) experiment. We customized the test header to manipulate face presence (see figure 1): woman face, man face, and no face conditions. We used two models (young Caucasian man/woman), unknown, to avoid celebrity endorsement effect. Facial expressions (positive) and head orientation were identical. We defined two conditions for the risk of each fund: high (6) and low (3) (on a scale rated from 1 extremely low to 7 extremely high). In total, 12 versions of the survey were generated, randomizing the 12 different conditions of the manipulated independent variables. Respondents were asked to evaluate their feelings regarding the model on a likert scale from 1 to 5. We used in the analysis the extent to which respondents felt the model was “considerate”.

Procedure

The participants were exposed to an investment game scenario (see figure 2, inspired from Gajewski et al., 2021) with a header showing one of the three conditions for face presence. The main task of this experiment was to allocate $10.000 between three investment vehicles: an SRI fund, a sin fund, and a safe account. The risk and expected return of each fund were rated from 1 to 7, as in the real world.

Based on Riedl and Smeets (2017), we tested different control variables which could influence SRI investment decision: 1) We measure risk preferences, based on a modified version (Desmoulins-Lebeault et al., 2018) of the Eckel and Grossman (2008) risk preferences task, 2) cognitive reflection score (Frederick, 2005), 3) financial literacy level (Lusardi and Mitchell, 2008), 4) Altruism scale (Goldberg et al., 2006), and 5) Ethically Minded Consumer Behavior scale (EMCB, Sudbury-Riley and Kohlbacher, 2016). We display in Table 1 below descriptive statistics regarding our variable, alongside a short description of them.


Variable

Description

Mean

Std. Dev.

Min

Max

Model Woman

Dummy equal to 1 if the respondents saw the female model.

0.341

0.475

0

1

Women

Dummy equal to 1 if the respondents is a woman.

0.448

0.498

0

1

Model Man

Dummy equal to 1 if the respondents saw the male model.

0.314

0.465

0

1

High Risk (HR) Both

Dummy equal to 1 if both investments were high risk.

0.253

0.435

0

1

High Risk (HR) SRI

Dummy equal to 1 if only the SRI investments was high risk.

0.231

0.422

0

1

High Risk Sin

Dummy equal to 1 if only the Sin investments was high risk.

0.233

0.423

0

1

Scale EMCB

Ethically Minded Consumer Behavior scale (EMCB, Sudbury-Riley and Kohlbacher, 2016).

35.422

7.338

11

50

Risk Tolerance

Risk preferences, based on a modified version (Desmoulins-Lebeault et al., 2018) of the Eckel and Grossman (2008)

2.453

1.394

1

5

CRT

Number of correct answers to the Cognitive reflection score (Frederick, 2005), out of 3 questions.

1.639

1.150

0

3

Altruism

Altruism scale (Goldberg et al., 2006)

9.475

5.311

-9

20

Financial Literacy

Number of correct answers to the Financial literacy test of Lusardi and Mitchell, 2008, out of 3 questions.

1.087

0.764

0

3

Age

Age of the participant.

21.740

7.065

16

61

Ever Invested

Dummy equal to 1 if individual has invested on the finacnial market (currently or in the past)

1.729

0.445

1

2

Currently Working

Dummy equal to 1 if currently employed

0.253

0.435

0

1

Master Program

Dummy equal to 1 if enrolled in the Master Program

0.794

0.405

0

1

Considerate

Likert scale – “I found the model presented to me to be considerate.”

3.410

0.877

1

5

Table - Descriptive Statistics

3. Results

The SRI Fund - Across all treatments, respondents allocated 53% of their investment to the SRI fund, 17% to the sin fund, and 30% to the safe account. We thus already observe a deviation from the classic 1/n allocation, indicating a clear preference for SRI over the sin fund (p<0.01, Figure 3).

Face: Gender Effect - Simple T-test underlines that respondents being confronted with the male model invest significantly more in SRI than the control group seeing no model or to the group seeing a female model (marginal significance, p<10% in both cases, Figure 4). Women who have seen a male model invest significantly more in SRI than women having seen a female model (p<5%, simple t-test). The male model indeed appeared more considerate, to the female part of our sample (p<10% for the whole sample, p<10% for the women-only sample, non-significant in the men-only sample).

Risk effect It is likely that relative risk - the risk of the SRI fund relative to the one of the sin funds – is the one that matters to participants, as opposed to the level of risk of each fund in absolute term. We analyse the results in that direction. As highlighted in Figure 5, when both funds are low or high risk, we do not observe any change. However, the SRI Fund being high risk leads to significantly lower (p<1%, t-test) investment in SRI compared to control, while the sin fund being high risk leads to significantly higher investment in SRI (p<1%, t-test, Figure 5).

Regressions

Face: Gender effect - Performing regressions (Table 1), we observe in the first regression a significant positive effect of male model on SRI investing (p<10%). This effect turns non-significant (p=10.4%) once we include how considerate the human model was perceived (regression 2). Perceiving the model as more considerate increase investment in SRI (p<10% in regression 2, p<5% in the third). We introduce interaction in regression 3. We observe that women who have seen the female model invest less in SRI, compared to seeing no face or the male model (marginal significance, p<10%). The addition of this interaction enables to highlight that women who do not see the female model (and thus see either no face or the male model) invest more in SRI than their men counterparts (p<10%).

Risk Effect and interaction with EMCB - Respondents who were exposed to a condition with a higher risk SRI fund invested less in SRI (around 11%, p<1%, see regression 1 and 2), while those who were exposed to a higher risk sin fund invested more in SRI (around 12%, p<1%).

Older respondents and respondents engaged in the 5-years program of the school invested more in SRI, across all regression models (p<1%). Those scoring higher on the financial literacy scale (p<5%) and those scoring higher on the EMCB scale (p<1%) also invest more in SRI, across all models.

In regression 3, we observe a significant interaction between seeing the risky SRI funds and score on the EMCB scale. This interaction shows that non-ethically minded respondents reduce their investment in the SRI fund when this one displays a worse risk-return profile. These respondents prefer a socially responsible investment but are willing to swap it for a better risk-return profile. On the opposite, ethically minded respondents will allocate a similar portion of their portfolio to the SRI fund, even when this fund displays a worse risk-return profile (p<1%, Figure 6). Thus, strongly ethically minded respondents maintain their level of investment in SRI, independently of the excess return they could earn by switching to the sin fund. One could say that their engagement in favor of ethics is “not for sale.”

The Sin Fund

Interestingly, a significant proportion (49.5% across all treatments) of participants refused to invest in the sin fund. Female participants, older participants, participants engaged in the 5-years program of the school were more likely to refuse to invest in the sin fund. Participants scoring higher on the CRT test or the EMCB and preferring lower risk lotteries were more likely to refuse to invest in the sin fund (see Table 2 for a logistic regression).

Such a zero investment in the sin funds could be interpreted as a complete rejection of the industries supported by this investment vehicle. However, here, we see again an effect of risk: participants being confronted with a high-risk SRI fund treatment were less likely to refuse to invest in the sin fund. On the opposite, participants confronted with the high-risk sin funds were more likely to refuse to invest in it (Figure 7). It thus again seems that moral values are, for most, malleable in the face of higher financial risk.

However, we again observe an interaction between scores on the EMCB scale and a higher risk for the SRI funds. For strongly ethically minded consumers, there is again no impact of the risk of the SRI funds on their decision to boycott the sin fund (Figure 8).

Figure 1 - Illustrative example of headers in the “Woman condition”, “Man condition” and “No-Face Condition”

4. Discussion

This study shows that participants exposed to a male model or to a low-risk SRI fund compared to the sin one invested more in SRI. It was previously demonstrated that the use of feminine or masculine figures in ads activates certain mental schemes in consumer's mind (Åkestam et al., 2017). We found that male figures induced higher investment in SRI Our results corroborated Niessen-Ruenzi and Ruenzi (2018) gender stereotype effect in an SRI investment context.

We also found that a high EMCB score influence positively the investment in the SRI fund in particular when its risk is high relative to the sin fund. This result is in adequation with previous studies findings that social value orientations impact responsible behavior, independent of the personal costs (Cameron et al., 1998). Our results also corroborate previous studies showing a higher responsible purchasing intention among highly educated individuals and women (Nilsson, 2008).

We explored the effect of human presence and risk on SRI. Future researches may further considerate the gender congruence between the model and the observer, potentially using eye-tracking measures to assess which stimuli catches more visual attention and may nudge investors toward SRI. International studies, enabling for cross cultural comparisons, would also prove a promising avenue for future research.

Acknowledgements

We wish to thank participants of the School on Behavioral and Neuroscientific Research for Economics, Finance and Accounting as well as colleagues of the Experimental Workshop for their fruitful insights on the paper. We would also like the editor, Joël Petey, as well as two anonymous referees, for their constructive comments.

Appendix

Figure 2 – Illustrative example of the main task instructions

Table - OLS: Percentage invested in SRI


 

 

 

 

 

 

 

 

 

 

Regression 1 - OLS

 

Regression 2 - OLS

 

Regression 3 - OLS

Coef.

P>|t|

Coef.

P>|t|

Coef.

P>|t|

Model Woman

0.124

0.962

0.075

0.977

3.727

0.252

Women

1.122

0.636

1.394

0.557

4.759*

0.093

ModWoman#Women

-8.563*

0.054

Model Man

4.699*

0.072

4.256

0.104

3.568

0.170

High Risk (HR) Both

-1.218

0.676

-1.481

0.611

-1.553

0.591

High Risk (HR) SRI

-10.016***

0.001

-10.245***

0.001

-45.524***

0.001

Scale EMCB

0.671***

0.001

0.665***

0.001

0.453***

0.009

HR SRI#ScaleEMCB

0.978***

0.008

High Risk Sin

11.489***

0.001

11.262***

0.001

11.527***

0.001

Risk Tolerance

0.066

0.934

0.129

0.871

0.065

0.934

CRT

-0.382

0.694

-0.369

0.703

-0.514

0.592

Altruism

-0.009

0.966

-0.025

0.908

-0.103

0.631

Financial Literacy

3.170**

0.035

3.049**

0.043

3.142**

0.035

Age

0.531***

0.008

0.540***

0.007

0.530***

0.007

Ever Invested

0.595

0.813

0.431

0.864

0.355

0.887

Currently Working

3.700

0.205

3.820

0.190

4.472

0.123

Master Program

8.697***

0.007

8.690***

0.007

8.262***

0.010

Considerate

2.130*

0.079

2.673**

0.028

Constant

3.889

0.677

 

-2.768

0.783

 

3.176

0.761

N

446

446

446

AIC

BIC

0.184

4045.29

4110.89

 

0.190

4044.08

4113.79

 

0.210

4036.91

4114.82

Table - Logistic Regression: Refusing to Invest in the sin Fund

We checked for VIF, heteroscedasticity and non-normality of residuals for all regressions and did not find any point of concern. Regarding variable coding, CRT and Financial literacy are scores ranging from 0 to 3 to the respective standard test. For EMBC and altruism constructs we computed a general score adding their scales' items. Risk Tolerance range from 1 to 5, and represents the lottery chosen by participants, 5 being the highest risk lottery. High Risk (HR) Both, High Risk (HR) SRI, High-Risk Sin are three dummy variables representing our experimental treatments regarding risk (base category being thus low risk both). Similarly, Model Woman and Model Man are dummies coding for respondents having seen these models (base category being thus no human model).*,** and *** respectively indicate p<10%, p<5% and p<1%.


  

 

  

 

 

Logistic Regression 

AME 

Coef. 

P>|z| 

dy/dx

P>|z|

Model Woman 

-0.263 

0.312 

-0.066

0.310

Women 

0.509** 

0.033 

0.126**

0.031

Model Man 

-0.049 

0.852 

-0.012

0.852

High Risk (HR) Both 

-0.293 

0.300 

-0.073

0.297

High Risk (HR) SRI 

-4.382** 

0.014 

Scale EMCB 

0.042** 

0.015 

HR SRI#c.ScaleEnv 

0.090* 

0.053 

High Risk Sin 

0.839*** 

0.005 

0.205***

0.003

Risk Tolerance 

-0.145* 

0.069 

-0.036*

0.069

CRT  

0.174* 

0.080 

0.044

0.080

Altruism 

-0.025 

0.262 

-0.006

0.262

Financial Literacy 

0.001 

0.993 

0.000

0.993

Age 

0.102*** 

0.001 

0.025***

0.000

Ever Invested 

0.283 

0.276 

0.071

0.273

Currently Working 

0.376 

0.202 

0.094

0.198

Master Program 

0.664* 

0.055 

0.163**

0.046

Considerate 

0.161 

0.187 

0.040

0.187

Constant 

-5.073*** 

0.001 

 

 

446 

R² 

0.156 

LL 

-260.76 

AIC 

557.53 

BIC 

631.34 

  Regarding variable coding, CRT and Financial literacy are scores ranging from 0 to 3 to the respective standard test. For EMBC and altruism constructs we computed a general score adding their scales' items. Risk Tolerance range from 1 to 5, and represents the lottery chosen by participants, 5 being the highest risk lottery. High Risk (HR) Both, High Risk (HR) SRI, High-Risk Sin are three dummy variables representing our experimental treatments regarding risk (base category being thus low risk both). Similarly, Model Woman and Model Man are dummies coding for respondents having seen these models (base category being thus no human model ).*,** and *** respectively indicate p<10%, p<5% and p<1%.The average marginal effects were estimated at the means. We do not display these marginal effects for the interaction in this table, as they present specific complexity, and instead display them in Figure 8.

Notes

  • While our sample is essentially composed of students, recent evidence tends to underline that they are a reasonable proxy for (even professional) investors (see Gajewski and Meunier, 2020).

References

  • Eckel, C. C., & Grossman, P. J. (2008). Men, women and risk aversion: Experimental evidence. Handbook of experimental economics results, 1, 1061-1073.

Authors


Luc Meunier

luc.meunier@essca.fr

Affiliation : ESSCA School of Management

Country : France


Safaa Adil

Country : France


Marjorie Tendero

Country : France

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