An Experimental Study on Motivations for Socially Responsible Investment
Miwa Nakai,a* Tomonori Honda,b Nariaki Nishino,c and Kenji Takeuchi aaGraduate School of Economics, Kobe University, Japanb

The National Institute of Advanced Industrial Science and Technology, Japanc

School of Engineering, The University of Tokyo, Japan*

Corresponding author. E-mail: miwa.elisabetta@hotmail.com

Abstract:
This paper aims, through laboratory-based economic experiments, to shed light on thedecision-making process in SRI and how it differs from decision making by other types of investors.
We asked subjects to make decisions regarding stock investments on the basis of the three attributes ofreturn, variance, and CSR, and we estimated their utility function to classify the types of investors. We
also conducted a dictator game and lottery-choice experiments to measure subjects’ heterogeneity inregard to three psychological factors: altruism, risk aversion, and time preference. We used psychologicalfactors to apply a latent class model and we examined whether these factors affect investment in the stockof companies that actively promote CSR.
The main finding of this study is that there is heterogeneity among investors, even among SRI
investors. With the latent class model, we found conventional investors, SRI investors, and risk-loving
SRI investors among our subjects. We hypothesised three psychological factors behind this heterogeneity.
While the results support our altruistic hypothesis, the results under the risk-averse hypothesis and lower
time preference hypothesis depend on which investor group is treated as SRI investors and also on themodel specification. Although previous studies also found heterogeneity among SRI investors, no studies
have tried to reveal the psychological background of the heterogeneity.

Keywords: Behavioural Finance; Economic Experiment; Socially Responsible Investment
JEL Classification: M14; G02; C91

1

1. Introduction
The market size of Socially Responsible Investment (SRI) amounts to about 10 per cent in the
US and Europe, enough to have a potentially considerable impact on the whole financial market (US SIF,
2012; Eurosif, 2010). Despite the importance of SRI, what motivates people to invest in socially
responsible companies has yet to be identified. While many studies have compared the performances of
SRI funds and conventional funds, they have found no statistically significant difference between them
(Hamilton et al., 1993; Gregory et al., 1997; Goldreyer et al., 1999; Bauer et al., 2005; Kreander et al.,2005; Bauer et al., 2006; Bauer et al., 2007; Ziegler et al., 2007; Galema et al., 2008; Ziegler, 2009;
Gil-Bazo et al., 2010; Climent and Soriano, 2011). This means that economic gain cannot explain why
people invest in SRI funds.
Several studies on investment flow found that SRI investors do not follow economicallyrational behaviour, which chooses assets that can maximise return on investment given a certain risk.
Bollen (2007), for example, examined the relationship between fund flows and returns, using data on US
mutual funds for the period 1980 through 2002. He found that even though lagged returns were negative,
SRI investors did not sell as many funds as conventional investors rationally did. Benson and Humphrey(2008) provided a further insight into an SRI investor behaviour by incorporating both monthly andannual returns to see if investors react to current and/or past information and by incorporating lagged flow
to take into account the persistence of fund flow, using data from January 1991 to September 2005. They
found that SRI fund flows were less sensitive to returns than conventional funds, and SRI investors weremore likely to invest in a fund they already own. In line with the previous studies, Renneboog et al.
(2011), by using fund data from around the world, confirmed that SRI investors seemed to be moreinelastic regarding returns than conventional investors.
While findings of these studies suggest that the behaviour of SRI investors is different fromthat of rational investors as defined by classical economics, how different is still unclear. SRI investors do
not seem to make random decisions regarding investment, but they do seem to have a certain investment
decision-making process that evaluates both financial and non-financial factors. Recent developments inbehavioural finance suggest that psychological aspects might play a critical role in investor behaviour(Pompian, 2012). Focusing on these psychological aspects of investors would help in understanding the
behaviour of the SRI investor.
This paper aims to uncover the decision-making process in SRI and how it differs from that inother types of investment by using laboratory experiments. We asked subjects to make decisions on stock
investments with regard to three attributes: return, variance, and CSR (corporate social responsibility), so2

that we could estimate their utility function. We also conducted a dictator game and lottery-choice
experiments to measure subjects’ heterogeneity in regard to three psychological factors: altruism, risk
aversion, and time preference. We used the psychological factors to apply a latent class model andexamine the following hypotheses about the decision-making process of SRI investors. Hypothesis 1:
Investors who are more altruistic are more likely to obtain higher utility from the attribute of CSR since
they would expect CSR activities to contribute more to society. Hypothesis 2: Investors who are more risk
averse are more likely to obtain higher utility from the attribute of CSR since they would expect CSR
activities to bring lower risk to their stocks. Hypothesis 3: Investors who have a lower time preference
are more likely to obtain higher utility from the attribute of CSR since they would expect CSR activities tolead to greater corporate profit in the long run.
The main finding of this study is that there is heterogeneity among investors, and even among
SRI investors. Using the latent class model, we found conventional investors, SRI investors, andrisk-loving SRI investors among our subjects. We hypothesised that there are three psychological factors
behind this heterogeneity. While the results support the altruistic hypothesis, results under the risk-averse
hypothesis and the lower time preference hypothesis depend on which investor group is treated as SRI
investors and also on model specification. Although some previous studies likewise found heterogeneity
among SRI investors (Nilsson, 2008; Nilsson 2009; Bauer and Smeets, 2010; Cheah et al., 2011), no
studies have tried to reveal the psychological background of the heterogeneity. To the best of ourknowledge, our paper is the first attempt to identify the mechanism of diversities in investor behaviour,
including among SRI investors.
The rest of this paper is composed as follows. Section 2 reviews relevant literature. Section 3
explains our hypotheses. Section 4 presents our methodology, including our data, experimental design,and theoretical framework. The empirical results follow in Section 5. The implication of these estimation
results is discussed in Section 6. We then summarise our findings in Section 7.

3

2. Literature Review
There are many papers that analyse why some people invest in SRI and others do not. The
majority of existing studies concerning this matter focus on comparing the demographic attributes of SRI
and conventional investors. Some of these studies conclude that demographic information can explain the
difference between these two types of investors. Rosen et al. (1991) conducted a mail survey of 1,493
individual SRI investors in the United States and compared their demographic characteristics with thoseof conventional investors. Their findings were that SRI investors were younger and better educated but
had less income than general mutual fund investors. Similar results were obtained from a survey
conducted by Tippet and Leung (2001) of Australian investors, including 99 ethical investors. In addition,
they found that SRI investors in Australia were less likely to be retired investors, tended to be part-timeworkers, owned fewer stocks, and invested less money than conventional investors. Junkus and Berry(2010) conducted a large survey that had 5,391 responses from individual investors in the United States.
Their findings are also in line with the earlier studies just mentioned above, with the added informationthat single people were more likely to be SRI investors. On the other hand, McLachlan and Gardner(2004) found, using a sample of Australian investors, that there were no statistically significant
differences between 54 SRI investors and 55 conventional investors in such demographic attributes as age,education, and income. Williams (2007) obtained similar results; using a wide survey of investors acrossfive countries, he confirmed that demographics did little to distinguish SRI investors from conventional
investors. Although earlier studies suggest that demographic characteristics can explain the difference
between SRI investors and conventional investors, the results of more recent studies no longer support
such a conclusion.
In contrast to studies that focus on demographic variables, some studies have tried to explain
the characteristics of SRI investors from a psychological point of view. McLachlan and Gardner (2004)
concluded that differences between SRI and conventional investors were caused by cognitive, personality,and environmental factors. They found that SRI investors rated ethical issues as being more important intheir investment decisions than conventional investors did. Although the statistical significance wasmarginal, it was shown that SRI investors were more altruistic than conventional ones. Owen and Qian
(2008) surveyed 1,808 respondents from the United States and found that investors with higher
environmental concerns when they shopped were more likely to be SRI investors. Acting on thesefindings, we focus on psychological factors to explain why some investors choose SRI and others do not.
The contribution of our paper is twofold. First, we examine the diversity of SRI investors byapplying a latent class model to an estimation of the utility function of investors. This approach allows us

4

to capture the whole picture of the investment market, including potential varieties among SRI investors.
Second, we use methodologies that have been developed in studies in the field of experimental economics
to measure the extent of altruism, risk aversion, and time preference. Methodologies that have been usedin such studies are theoretically rigorous, thereby providing a more consistent estimate of thecharacteristics of investors.

5

3. Hypothesis Development
This section discusses our hypotheses, their backgrounds, and the literature that supports them.
Andersen et al. (2008) characterised utility functions in three dimensions, reflecting preferences over goods,
time, and uncertainty. Following this line of discussion we hypothesise that three psychological factors
might play a significant role in decision making in regard to SRI: altruism, risk aversion, and time
preference.

Altruism

Hypothesis 1: Investors who are more altruistic are more likely to obtain higher utility from the attribute ofCorporate Social Responsibility; in other words, more altruistic people tend to be SRI investors since theywould expect CSR activities to contribute more to society.

The first hypothesis is intuitive and straightforward. Since the aim of CSR activities is to takeresponsibility to society as a whole, this fact is expected to have a positive impact on members of thelocal and global community. Whether this attitude attracts potential investors depends on the extent towhich the investors care about the wellbeing of society as a whole. Therefore, investors with higher
altruism would have a higher tendency to be SRI investors.
As far as we know, no study comparing the degree of altruism of SRI investors andconventional investors has yet been published. However, Rosen et al. (1991) showed that SRI investors
could be a group of people who are altruistic. They conducted a mail survey of 1,493 individual SRI
investors in the United States and found that more than 80 per cent of their sample belonged to or donatedmoney to charitable causes. Even though they did not compare the percentages of similar people among
non-SRI investors, the results suggested that SRI investors were altruistic. Nilsson (2009) examined thedifference in degree of altruism among SRI investors by means of a survey of 563 individual SRI
investors in Sweden. He found that respondents who have higher proportions of SRI funds in theirfinancial assets are more altruistic than those who have fewer SRI funds. Although these studies did notestablish whether or not SRI investors are more altruistic than conventional counterparts, they confirmedthat altruism is associated in some way with being an SRI investor.

6

Risk aversion

According to the modern portfolio theory (Markowitz, 1952), investors attempt to minimiserisk for a given level of expected return. In other words, lower risk is one of the important factors indetermining investment. Researchers have discussed whether or not implementing CSR activities could
reduce management risk, which leads to a lower risk of stock values plunging (Heal, 2005). Hamilton(1995), for example, examined how an announcement of the Toxics Release Inventory (TRI) by the US
Environmental Protection Agency impacted on the stock values of TRI firms, using event study
methodology. The results showed that investor reactions to the news of the environmental pollution
caused by those firms was significantly negative; following a drop in their stock prices, an average loss instock value of $4.1 million was incurred by TRI firms. Flammer (2012) also showed that environmentally
unfriendly actions by firms led to negative impacts on the stocks of such firms. These findings show thatfirms’ socially irresponsible behaviour is assessed negatively in the financial market. Therefore, there is apossibility that investors might avoid investing in such firms, or be likely to invest in socially or
environmentally responsible firms because they are less risky investments. Indeed, Nakai et al. (2011)
found that funds holding stocks of firms actively engaged in CSR suffered less than conventional funds
from the global financial crisis. They examined the negative impact of the Lehman Brothers bankruptcy
on SRI funds and conventional funds in the Japanese market and found the drop in performance was lessin SRI funds on average—a result that showed SRI funds were less risky than conventional funds. Thesefindings lead to our hypothesis that SRI investors appreciate CSR activities because they contribute tolower risk in their investment. Rosen et al. (1991) also showed that SRI investors were somewhat risk
averse—a conclusion that they arrived at via a Likert-scaled statement (though they carried out nocomparison with conventional investors). Prompted by these findings, we hypothesise that SRI investors
appreciate CSR activities because they contribute to making their investment less risky.

Hypothesis 2: Investors who are more risk averse are more likely to obtain higher utility from theattribute of CSR; in other words, people with higher risk aversion tend to be SRI investors since theywould expect CSR activities to bring lower risk to their stocks.

Time preference

Another advantage of CSR sometimes discussed is that such activities by a firm sets its goods
or services apart from those of other firms, and this might contribute to better profits in the long run.

7

While companies are required to make significant investments in the short run when pursuing CSR, they
could gain financial return in the long run through improved corporate governance and competitiveness.
Thus one of the potential benefits of investing in a company with a higher CSR orientation can be such areturn in the long run if the investors have enough patience to wait until the gain is realised.
Cox et al. (2004) investigated the pattern of institutional investors in the UK and how it
related with CSR activities by companies, using a sample of over 500 UK companies. They found that the
proportion of a company owned by long-term investors (e.g., pension funds, life insurance funds, andcharitable funds) was significantly and positively related to company social performance. Benson andHumphrey (2008) showed that SRI fund investors were prone to invest longer than conventional fundinvestors. Winnett and Lewis (2000) examined popular models of financial markets in financial
journalism and concluded that much of the discussion suggests that ethical financial decisions werelinked to long-term prospects rather than to irresponsible short-term prospects. If SRI investors appreciate
the advantage of long-term investment, it can be concluded that they have a lower time preference.

Hypothesis 3: Investors who have a lower time preference are more likely to obtain higher utility from theattribute of CSR, since they would expect CSR activities to lead to greater corporate profit in the longrun.

8

4. Methodology
4.1 Data and Experimental Design
We conducted laboratory experiments on 22 December 2012 and 16 January 2013 at Kyoto
Sangyo University with 28 and 22 subjects, respectively. Those who participated in our experiments were
undergraduate students at Kyoto Sangyo University who were recruited through a website. The
experiments were carried out through a paper-based system on 22 December, and through a
computer-based system on 16 January; what the subjects were asked to do were much the same on eachdate except for the donation settings (discussed in more detail in Section 6.2). In the computer-based
system we used “SurveyMonkey”1 to have our subjects answer the questions and to collect their answers
immediately. Once subjects accepted the terms of the experiment and signed their consent forms, theexperimenters explained to them the schedule and the rewards the subjects were going to receive at theend of the entire experiment. Each subject received 1,000 Japanese yen (equivalent to US$10.03 as of 10
September 2013) for showing up and an additional amount of money that was dependent on theirperformance in the experiment. Each session included four experiments, the first of which was a choice
experiment to elicit subjects’ preferences regarding the return, risk, and CSR attributes of stock
investment. The other three were a dictator game and two lottery-choice experiments to estimate subjects’
psychological characteristics. We set a preparatory exercise for the choice experiment to ensure that
subjects fully understood what to do and how to answer. The experimenters had randomly selected inadvance two experiments out of four to determine participants’ rewards; the subjects, however, did notknow which two had been selected until all experiments were completed. The session took approximately
an hour and a half.
Our choice experiment was aimed at estimating people’s preferences over a set of alternatives
expressed as a bundle of attributes, called a choice set (Lancaster,1966; Rosen, 1974; McFadden, 1974);
an example is shown in Table 1. Each attribute has levels (presented in Table 2), so this makes a profile
different over alternatives. Subjects faced three alternatives and were asked to choose the one they
preferred the most. Through seeing their choices, we note how changes in the attribute levels affect theirchoices, and so we can estimate their utility functions.

1

SurveyMonkey is the world’s most popular online survey tool; it enables its users to send free surveys,
polls, questionnaires, customer feedback, and market research. (https://www.surveymonkey.com)

9

Table 1: An Example of a Choice Set in the Choice Experiment
Stock A

Stock B
-10 points

Possible Return on

0 points

0 points

Investment

20 points

20 points
30 points

Stock A nor
Stock B.

Corporate
Social

I invest in neither

10 points

0 points

Responsibility

Choose one and tick box	
  	
  	
 

□	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  □	
  	
  	
  	
  	
  	
  	
  	
  	
  □

Each respondent was asked to invest 100 points in one of the first two options (Stock A,
Stock B); or, if they choose the third option (“I invest in neither Stock A nor Stock B”), they would besaving 100 points. They were to make such a choice 12 times. In our study, we calculated the rewards(one point was regarded as one Japanese yen). The amount of money that subjects earned or lost byinvestment in each choice could not carry over to the next choice. We have three attributes: “Possible
Return on Investment”, “Variance”, and “Corporate Social Responsibility”. Possible return on investment
is the measure of the profit earned from each investment, and it has four patterns as shown in Table 3. Its
expected return is highest in Pattern A and lowest in Pattern D. Variance is a measure of risk, in that it
looks at which return will be forthcoming with how much likelihood. Although it was not shown
explicitly as an attribute in a choice set (see Table 1), subjects could know the event possibility for eachpossible return. If there are two possible returns appearing in a choice set (see Stock A in Table 1), eachof the two appears with a 50 per cent chance, which we regard as low risk. On the other hand, four
possible returns shown in a choice set (Stock B in Table 1) means a high risk, since each of the four
returns appears with a 25 per cent chance. The “Corporate Social Responsibility” attribute shows theamount of money that a firm donated to a non-governmental organisation. We specified thenon-governmental organisations in this experiment to be WWF Japan, UNESCO Japan, and Plan Japan10

(hereafter WWF, UNESCO, and Plan, respectively). A higher amount of donation by a company can beinterpreted as a greater effort directed toward environmental or social issues. With the settings such thatthe experimenters actually paid those amounts of donations to those organisations, subjects obtained
utility from investing in such firms. The point we should note here is that even though a subject chooses
to invest in a firm with a donation, his or her possible return is not reduced by the amount of donation;
this simplifies the interpretation of estimation results and reflects what normally happens: investment
return is not deducted even though people invest in firms implementing CSR activities.
We used all combinations of the attribute levels (4 * 2 * 3 = 24 combinations) as a profile andtwo of them were randomly selected, which became a choice set by adding the third option, which is “I
invest in neither Stock A nor Stock B”.

Table 2: Attributes, their Definitions, and Levels
Attribute

Definition

LevelsPossible Return on Investment

The measure of the profit earned

4 patterns (see Table 3)from each investment
Variance

The variance of possible return

None = 0

on investment

Low = 100
High = 250

Contribution to Environmental

The amounts of money that a

0 pointsGroupfirm donated to WWF Japan,

1 points

UNESCO Japan, or Plan Japan

10 points(return for a subject is NOT
lowered due to a contribution)

Table 3: Possible Return on Investment
Pattern

Possible Return on Investment

A

10 points

20 points

40 points

50 points

B

0 points

10 points

30 points

40 points

C-10 points

0 points

20 points

30 points

D-20 points-10 points

10 points

20 points

11

4.2 Latent Class Model
We use a latent class model in which respondents are classified into several groups according
to their utility functions; this captures their heterogeneity. This model enables us to explore which factors
make respondents’ utility functions different. It is a suitable methodology for our study since we would
like to know why some people prefer investing in CSR firms while others do not. The theoreticalframework of our latent class model is as follows. Respondent

i who belongs to classs ∈{1, 2,..., S} chooses one of several alternatives in choice set j ∈{1, 2,..., J } , and he repeats
making choices

Ti times. Under these circumstances, respondent i obtains the utility

U ij s = β s Xij + ε ij s ,
where

(1)

β s is a unique parameter depending on class s , which captures the preference heterogeneity;

Xij is an attribute vector that includes possible return on investment, variance, and CSR; ε ij s is the error
term and is identically independently distributed; it follows a Gumbel distribution. The probability ofi in class s can be shown byalternative j chosen out of h alternatives by a respondent

Pij s =(

exp β s Xij
J

) .

(2)

∑ exp ( β X )
s

ih

h =1

Next, we define a membership function as classifying investors into one of the latent classes withprobability

Pis (see Equation 4, below). Thus,
M *is = λs Z i + ξis ,

where

(3)

Z represents the observed attributes that explain the reason for heterogeneity in the utility

function. In our study, they are psychological factors: altruism, risk aversion, and time preference; ξ isthe error term that again is identically independently distributed across respondents and classes andfollows a Gumbel distribution. Hence, the probability that a respondent
expressed as

12i belongs to class s can bePis =

exp ( λs Zi )
R

,

(4)

∑(λ Z )
k

i

k =1

where

λk ( k ∈{1, 2,..., R}) is a unique parameter estimated to explain why a respondent is classified

into a particular class. If λ is estimated significantly positive, the observed attribute

i will be classified into class s . On the other hand, negative λprobability that a respondent
implies that

Zi increases theZi decreases the probability of a respondent i belonging to class s . The joint

probability of a respondenti choosing the alternative j in class s can be given by combining

equations (2) and (4) thus:#' #'%% exp β s Xij %% %% exp ( λ Z ) %%s i
Pijs = Pij s * ( Pis ) = $ J(*$ R(.
% ∑ exp ( β s Xih ) % % ∑ exp ( λk Z i ) %%& h =1%) %& k =1
%)

( )

(

)

(5)

We use altruism, risk aversion, and time preference as variables of a membership functionthat characterises a different class of utility function. We followed Forsythe et al. (1994) to measure
altruism by asking subjects to answer how much out of 500 Japanese yen they would give their partners(see further details in Experiment B of Appendix A). They do not know who their partners are, even after
the experiment. We used the amount of money they gave their partners as the measure of their altruism:
the more money they gave, the more altruistic they were. To measure their attitude toward risk, we
presented subjects with ten paired lottery-choice decisions (shown in Table 4) and asked them to choose
Lottery A or Lottery B in each of the ten options (Experiment C of Appendix A; Holt & Laury, 2002). In
both Lottery A and Lottery B, greater rewards are possible in the later options. Lottery A was defined as asafe choice, so people with more Lottery A choices can be considered to be more risk averse. See Table 5
to better understand the relationship between the number of safe choices and the risk preferenceclassification. We announced to the subjects that after the lottery-choice experiment we were going torandomly choose a number to decide which numbered option was going to be used to determine theirrewards, and then choose another number to determine how much the prize would be; by doing this, wegave subjects an incentive to choose all options carefully.

13

Table 4: Ten Paid Lottery-Choice Decisions
OptionLottery A

Lottery B

Your Choice1

1/10 of 200 yen, 9/10 of 160 yen

1/10 of 385 yen, 9/10 of 10 yen

2

2/10 of 200 yen, 8/10 of 160 yen

2/10 of 385 yen, 8/10 of 10 yen

3

3/10 of 200 yen, 7/10 of 160 yen

3/10 of 385 yen, 7/10 of 10 yen

4

4/10 of 200 yen, 6/10 of 160 yen

4/10 of 385 yen, 6/10 of 10 yen

5

5/10 of 200 yen, 5/10 of 160 yen

5/10 of 385 yen, 5/10 of 10 yen

6

6/10 of 200 yen, 4/10 of 160 yen

6/10 of 385 yen, 4/10 of 10 yen

7

7/10 of 200 yen, 3/10 of 160 yen

7/10 of 385 yen, 3/10 of 10 yen

8

8/10 of 200 yen, 2/10 of 160 yen

8/10 of 385 yen, 2/10 of 10 yen

9

9/10 of 200 yen, 1/10 of 160 yen

9/10 of 385 yen, 1/10 of 10 yen

10

10/10 of 200 yen, 0/10 of 160 yen

10/10 of 385 yen, 0/10 of 10 yen

Table 5: Risk Aversion Classifications Based on Lottery Choices
Number of Safe Choices

Range of Relative Risk

Risk Preference

Aversion forClassification

U ( x ) = x1− r (1 − r )0-1

r < −0.95

Highly Risk Loving

2−0.95 < r < −0.49

Very Risk Loving

3−0.49 < r < −0.15

Risk Loving

4−0.15 < r < 0.15

Risk Neutral

5

0.15 < r < 0.41

Slightly Risk Averse

6

0.41 < r < 0.68

Risk Averse

7

0.68 < r < 0.97

Very Risk Averse

8

0.97 < r < 1.37

Highly Risk Averse

9-10

1.37 < r

Stay in Bed

We conducted another lottery experiment suggested by Coller and Williams (1999) to measure the time
preference of subjects (Experiment D of Appendix A). Subjects were given the opportunity to obtain
either 5,000 yen in one month or somewhat more in three months. They needed to choose which payment
they preferred in each of a total of 15 options (Table 6). The amount given in three months varied but

14

would always be greater than 5,000 yen. Again we told subjects that a random number would be chosen
to decide which option would determine their rewards, and another number would be randomly chosen to
determine who the winner was. (Unlike the other experiments, for this experiment there would be only
one winner among the subjects.) Table 7 shows the relationship between the option at which subjects
switch from payment option A to payment option B and their rate of time preference. The less money thatpeople are willing to give up by obtaining payment in one month, the lower time preference they have.
We were concerned about possible significant correlations between variables in themembership function we used. Some studies (for example, Anderhub et al., 2001) showed a significant
negative relationship between risk aversion and time preference. We therefore examined correlations
among the three variables. The correlation coefficients between altruism and risk aversion, altruism andtime preference, and risk aversion and time preference turned out to be -7.1 per cent, -28.0 per cent, and7.1 per cent, respectively. Hence we concluded that no serious multicollinearity exists in our estimation.

Table 6: Fifteen Payment-Option-Choice Decisions
OptionPayment Option A

Payment Option B(pays amount below in(pays amount below in

one month)three months)

1

5,000 yen

5,016 yen25,000 yen

5,025 yen

3

5,000 yen

5,033 yen

45,000 yen

5,041 yen

55,000 yen

5,062 yen

6

5,000 yen

5,082 yen

7

5,000 yen

5,103 yen

8

5,000 yen

5,125 yen

95,000 yen

5,144 yen

10

5,000 yen

5,164 yen

11

5,000 yen

5,205 yen

12

5,000 yen

5,288 yen

13

5,000 yen

5,411 yen

14

5,000 yen

5,616 yen15

5,000 yen

5,822 yen

15

Your ChoiceTable 7: Time Preference Classification based on the Experiment
Option Where Switch Occurs

Rate of Time Preference

1

2.0%

2

3.0%

3

4.0%

4

5.0%

5

7.5%

6

10.0%

7

12.5%

8

15.0%

9

17.5%

10

20.0%

11

25.0%

12

35.0%

13

50.0%

14

75.0%

15

100.0%

16

5. Estimation Results
There are a few types of criteria to decide the optimal number of classes for discussing
estimated results in the latent class approach. The statistical criterion is one of them, and Table 8 involves
three such criteria: the McFadden pseudo R-squared, AIC (the Akaike Information Criterion), and BIC(the Bayesian Information Criterion). McFadden’s pseudo R-squared signals a better fit when it is higher,
while a smaller value indicates a better fit in AIC and BIC. When AIC and BIC show different results,
BIC is often adopted, since AIC tends to overestimate the number of classes. The calculated results inTable 8 suggest that a 2-Class model is optimal as its

ρ 2 is highest, and values in AIC and BIC are

lowest in three alternatives. However, when SRI investors are defined as subjects obtaining utility fromthe attribute of CSR, all subjects could be SRI investors in a 2-Class model (see the estimation results of2-Class, 3-Class, and 4-Class models presented in Table 9 and Appendix B). When we are dealing withthe real investment market, it is not reasonable to think all investors are SRI investors. On the other hand,
Swait (1994), Boxall and Adamowicz (2002), Scarpa and Thiene (2005), and Hynes et al. (2008) suggestthat it is also important to decide the optimal number of classes by having a regard for theoreticalconsistency. If we use a 2-Class model, it would mean that all investors in the financial market are SRI
investors, which is unlikely to reflect reality. In terms of statistical criteria, a 4-Class model does not seemto be the proper model to employ. For these reasons, we use a 3-Class model for interpretation in ourstudy, as shown in Table 9. For the estimation results of a 2-Class and a 4-Class model, see Appendix B.

Table 8: Comparison of Models with Different Number of Classes
Number of

Number of

Log-likelihood

ClassesParameters

at

ρ2

AIC

BIC

Convergence212-180.8580.702

385.716394.6193

21-185.1410.695

412.282427.862430-202.4860.666

464.972487.230

17

Table 9: Estimation Results in a 3-Class Model

Variable

Conventional Investors

SRI Investors

Risk-loving SRI Investors

Class 1

Class 2

Class 3

CoefficientStandard

Coefficient

Error

Standard

Coefficient

Error

Standard
Error

Utility Function
Return

.227*

.125

.193***

.035

.748

3.613

Variance

-.006

.006

-.004

.004

.009***

.003

CSR

-.223

.287

.169***.056.199***.062

Membership Function
Constant

-

-

-.021

4.555

.034

2.959

Altruism

-

-

.030

.036

.031

.036

Risk Aversion

-

-

-.011

3.090

.009

2.540

Time Preference

-

-

-.006

4.868

.012

2.576

D_Female

-

-

-.008

2.800

.006

1.882

D_Econ

-

-

-.007

3.333

.012

1.987

Latent Class

.224

.362

.414

Probability
Log-likelihood-185.141*** Statistically significant at the 1% level* Statistically significant at the 10% level

We define Class 1 as Conventional Investors since they obtain positive utility from return,and negative utility from variance and CSR. As we used this group as a baseline, their membershipfunction has no value. Class 2 is defined as SRI Investors since they gain positive utility from return and
CSR, and negative utility from variance. Compared with Conventional Investors, the SRI Investors groupis more altruistic, is less risk averse, and has a lower time preference. Class 3 is regarded as Risk-loving
SRI Investors as they obtain positive utility from all attributes including variance. Compared with
Conventional Investors, they are more altruistic, are more risk averse, and have a higher time preference.
We used two dummy variables as control variables: an economics student dummy and afemale dummy. Many studies (for example, Simmons and Emanuele, 2007) state that females are morealtruistic than males, hence we need to control gender effects on the altruism attribute. In the results of the18

SRI Investors group, both dummy variables show negative, which means that students not studying
economics and males are more likely to be SRI investors.
The existence of heterogeneity among SRI investors in our sample is confirmed, since there
are two SRI groups with different preferences linked with membership function (except for altruism),
which is in line with earlier findings (Nilsson, 2008; Nilsson 2009; Bauer and Smeets, 2010; Cheah et al.,2011). However, the estimation results shown in Table 9 do not show any statistical significance in thecoefficients of any membership function. This may be because we lose considerable freedom by using alatent class model. For this reason we applied a discrete logit model requiring less degrees of freedom:U = β 0 + β1Return + β 2 Variance + β3CSR + β 4CSR*Altruism+ β 5CSR*Risk Aversion + β 6 CSR*Time Preference(6)+ β 7CSR*Economics Student + β8CSR*Female
Variances ofβ 4 to β8 in equation 6 are all interaction terms. If β 4 is positive and statisticallysignificant, it can be interpreted that people with higher utility from CSR tend to obtain higher utility
from altruism. We can interpret β 5 toβ8 the same way. In this estimation, we obtain statisticalsignificance in altruism and time preference. From the estimated results obtained by the discrete logit
model, we can conclude that people who are more altruistic and have a higher time preference are more
likely to be SRI investors.

Table 10: Estimation Results from Discrete Logit Model
Variable

CoefficientStandard Error

Return.221***.015

Variance

-.001

.001

CSR

-.041

.050

CSR*Altruism.001***.000

CSR*Risk Aversion

.005

.043

CSR*Time Preference.125**.049

CSR*Economics Student

.003

.040

CSR*Female

-.011

.043

*** Statistically significant at the 1% level** Statistically significant at the 5% level196. Discussion
6.1 Hypothesis Testing
We now turn to compare the results estimated in the latent class model and the discrete logit
model. In the case of the first hypothesis, the two SRI groups (SRI Investors and Risk-loving SRI
Investors) studied with the latent class model and the discrete logit model show a positive value for thealtruism variable, although only the result estimated by means of the discrete logit model obtains
statistical significance at one per cent. For the second hypothesis, the SRI Investors group reveals less risk
aversion, while Risk-loving SRI Investors and SRI investors in the discrete logit model exhibit more risk
aversion. However, none of them are statistically significant. In the third hypothesis, the results in thelatent class model suggest that SRI Investors have a lower time preference, whereas Risk-loving SRI
Investors and SRI Investors in the discrete logit model have a higher time preference. Only the discrete
logit model shows a statistical significance at five per cent. For the control variables we have mixed
results, and none of them are statistically significant. While our specification of the membership function
does not show a strong statistical significance, the overall results suggest that there is heterogeneity
among SRI investors, as pointed out in earlier studies (Nilsson, 2008; Nilsson 2009; Bauer and Smeets,
2010; Cheah et al., 2011).

6.2 Choice of Destination for Donation
As was discussed in 4.1, when subjects decided to invest some money in a stock involving
CSR, the experimenters actually made that amount of donation to one of the environmental or social
projects carried out by WWF, UNESCO, or Plan.2 We assumed that a higher amount of points meant
greater efforts for CSR by that firm. We chose to donate to those three particular organisations because
this enabled subjects to choose from a variety of social activities. WWF is especially known for theirenvironmental activities, UNESCO has engaged in activities for further development and promotion ofculture, and Plan has been involved in social activities aimed at encouraging children’s rights andextending children's potential. Plan has also recently been active in programmes to help girls indeveloping countries receive a proper education so that they can obtain stable employment.
To investigate whether the possibility of choosing the destination of a donation affects the2

The list of projects to which we chose to make a donation is available upon request.

20

investor utility, we designed different donation settings in our experiment. In the first session conducted
on 22 December 2012, subjects were able to choose which project in which organisation their donation
would go to if they invested in stocks containing donations. We provided them with a list of projects in an
appendix attached to their instructions. When the experiment on investment choice was over, subjects
were asked to indicate on the sheet with their investment choices the name of one project to which theywould like their donations to be made. In the January session, however, we did not allow subjects tochoose a project, although they knew that their donations would go to one of the organisations. In order toexamine whether the different setting for donation led to greater or lower utility from CSR, we used thefollowing equation:

U = β 0 + β1return + β 2 variance + β 3CSR+β 4 ( CSR * dummy ) + u
where dummy = 1 if 1st session
dummy = 0 if 2nd session（7）

E "#U D = 0 $% = β 0 + β1return + β 2 variance + β 3CSR
E "#U D = 1$% = β 0 + β1return + β 2 variance + ( β 3 + β 4 ) CSR
Table 11: Estimation Results with Dummy Variable Distinguishing between 1st and 2nd Sessions
Variable

CoefficientStandard Error

Return.218***.014

Variance

-.000

.001

CSR

.010

.026

CSR*dummy.076**.035*** Statistically significant at the 1% level** Statistically significant at the 5% level

We pooled all data from the first and second sessions and estimated with a discrete logit model. There isan interaction dummy variable shown as CSR*dummy equal to one if subjects participated in the firstsession, and zero otherwise. The coefficient of CSR shows the utility obtained directly from CSR, since
CSR*dummy controls possible distinctions caused by the difference in option to choose the destination.
The coefficient of CSR*dummy tells us whether the design allowing subjects to choose their donation
destination changes their utility; since CSR*dummy has a positive coefficient with statistical significance,
the design that enables people to choose the donation projects actually increases their utility, as we see inTable 11. This suggests that reflecting investors' preferences in CSR activities would lead to a higher
evaluation of CSR efforts.

21

7. Conclusion
The aim of this paper was to reveal the decision-making process in individual investors,
especially focusing on SRI investors, by using economic experiments. We conducted an investment
experiment with undergraduate students to estimate their utility function and also conducted threeexperiments to measure three psychological factors: altruism, risk aversion, and time preference. We used
these psychological factors to apply a latent class model, and we examined whether these factors affectinvestment in the stock of companies that are actively promoting CSR, and how SRI investors differ fromother types of investors.
The findings of this paper can be summarized as follows. First, we confirm that there isheterogeneity among SRI investors, as previous studies found (Nilsson, 2008; Nilsson 2009; Bauer andSmeets, 2010; Cheah et al., 2011). We obtained a consistent result only for the first of our threehypotheses, which is that more altruistic subjects are more likely to be SRI investors—although astatistical significance is found only with a discrete logit model. The estimation results of the second andthird hypotheses depend on which investor group is treated as SRI investors, and also on model
specification. Second, we find that there exist stronger demands for SRI than its real market size. Ourestimation with a 3-Class model revealed that about 75 per cent of subjects are SRI investors, while thepercentage of SRI in the entire financial market in Japan is less than 1 per cent. While this implies a
potential for further development of the Japanese SRI market, it also prompts the research question,“Why is the potential not realised yet?” Finally, when subjects were allowed to choose the project for
their donation, they obtained significantly higher utility from the CSR attribute. This suggests thatreflecting investors' preferences in CSR activities would lead to a higher evaluation of CSR efforts.
We can point out some limitations of our study and important tasks for further research. Firstof all, even though our aim was to know more about investor behaviour in the financial market, ourexperiments were conducted with undergraduate students who might have less experience in investment
activities than investors, so perhaps their decision making does not accurately reflect that of active
investors. Second, the number of subjects for our experiments may have been insufficient since a latentclass model requires considerable degrees of freedom, for which a greater number of subjects would be
preferable. Third, we tried to identify fundamental psychological mechanisms behind SRI investment;
while we believe that this attempt partially succeeded, statistical support for our membership function is
not strong. Further investigation of these points remains to be done.

22Acknowledgements
This study is supported by The Mitsui & Co., Ltd., Environment Fund. We would like to acknowledge
the valuable comments and suggestions of Takahiro Tsuge, Shinpei Sano, and participants of the
Ishikawa-Takeuchi Seminar and Matsubayashi Seminar at Kobe University. The authors also thankparticipants at the seminar on the Role of Financial Industry toward a Sustainable Society and those at theseminar on Environmental Information, both of which were held in March 2013 at Kogakuin University,
for their helpful comments.

23

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27

Appendix A –Instructions for the ExperimentThank you very much for your participation in this economic experiment. You are kindly requested tonote the following important reminders:1.

You are not permitted to use your mobile phone during the experiment.

2.

You are not to have any conversation with other participants in this room. If you have any questions,please raise a hand and one of the experimenters will come to your desk to answer the question.

3.

You will find a letter of consent on your desk. Please read and sign it if you accept the terms andconditions. If you cannot accept them, you are asked to leave the room now.

4.

You are not permitted to have anything on your desk except materials provided by the experimenters.

5.

You are not to leave the room until the experiment is over. But please raise your hand if you need a
restroom break.

Your personal data, including the results of your experiment, will be kept completely confidential andaggregated only for research purposes. They will not be used for any other purpose than this experiment.
Your name will not be linked with your decisions when the results of the experiment are published. You
will be personally informed of the amount of your earnings and paid at the end of the experiment. The
reward consists of two parts: a 1,000-yen show-up bonus and an additional sum of money that isdependent on your choices during the experiments. The experimenters have randomly selected in advancetwo experiments out of the four (Experiment A, Experiment B, Experiment C, and Experiment D), as thebasis for calculating participants’ rewards. Which two experiments were selected will not be divulged
until the end of all experiments. The whole session will take two hours at most.

28

Experiment A (Investment Choice)
In Experiment A you are to make decisions on stock investment. As the diagram below indicates, you canchoose between “Stock A” and “Stock B”, or, if you prefer, you can choose “I invest in neither Stock A
nor Stock B.”
Sample Question
Stock A

Stock B

-20 points with 25%
Possible Return on-10 points with 25%±0 points with 50%

Investment+10 points with 25%+20 points with 50%+20 points with 25%

I invest in neither
Stock A nor
Stock B.

Corporate
Social10 points

1 point

Responsibility

Choose one and tick box

□	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  □	
  	
  	
  	
  	
  	
  	
  	
  	
  □

You will be presented with twelve such choices to make. Here we introduce “point” as a hypotheticalcurrency, as you can see in the sample question. In each question, you are provided with 100 points foryour decision making. If you choose “I invest in neither Stock A nor Stock B”, 100 points will be given
to you for certain, but you cannot carry over those points to the next investment choice. Also, when you
invest in either Stock A or Stock B, all 100 points will be invested (that is, you cannot invest partially in
Stock A and the rest in Stock B). All stocks differ in terms of “Possible Return on Investment” and“Corporate Social Responsibility”. Everything else is identical in Stock A and Stock B.

“Possible Return on Investment” is the expected return from your investment of 100 points. If two returns
are shown, as under Stock B in the sample question, one of the two appears with a 50 per cent chance. On
the other hand, if four possible returns are shown, as under Stock A in the sample, one of four returns
appears with a 25 per cent chance.

29

“Corporate Social Responsibility” shows the amount of points that the firm you would invest in donates
to one of the following organisations: WWF Japan, UNESCO Japan, and Plan Japan. Even though you
decide to invest in a firm with CSR activities, your investment return will not be deducted. A stock withzero points for CSR indicates that the firm does not implement any CSR activities, and so does not donate
to any of the above three organisations.

Once we make sure that all participants have finished making all twelve choices, the actual investment
returns are decided on the basis of random selection using a bingo cage containing balls. Your profits
from Experiment A will be translated into real rewards using a translation rate of 1 point =1 yen. If you
choose any stocks involving one point or more for the attribute “Corporate Social Responsibility”, thefirm you invested in would donate that sum of money. In this experiment, however, instead of the firm it
will be the experimenters who will make donations in accordance with your investment after translating
points into real money using a translation rate of 1 point =1 yen. You can decide whichorganisation/project the donation will go to. Please choose ONE of the projects listed in the appendix tothese instructions. If you are unable to select which project the donations should go to, select “Leave
entirely up to experimenters”. You can make sure that the experimenters have indeed donated money tothe project/organisation you choose by visiting our homepage, whose URL is enclosed in the envelope
containing the rewards you will be given at the end of the experiment.

You have a practice session for Experiment A. While you will face twelve choices in the real experiment,
you have three investment choices in this practice exercise. If you have any questions about ExperimentA, please raise your hand before the experiment begins.

30

Experiment B (Decision-making)You’re paired with a partner whose identity you will not know, neither during nor after the experiment.
500 yen is provided to each of you, and you can give your partner none, some, or all of this sum. Decide
how much of the 500 yen you will keep for yourself, and give your partner the rest.

Sample Question
The Amount of Money(1) The amount of money you keep for yourself

yen(2) The amount of money you give your partner

yen(3) Totalyen

Please write down the amount of money that you would like to keep for yourself on line (1) in the table,
and the amount of money that you will give your partner on line (2). Make sure the sums add up to 500
yen. In Experiment B, your reward will be (1) plus the amount of money that your partner gives you. If
you have any questions about this experiment, please raise your hand now.

31

Experiment C (Lottery-Choice Decisions 1)
You are required to choose either Lottery A or Lottery B in each of the ten options given. Depending on
your choices, the amount of the reward that you are going to obtain will differ.

Sample Question
OptionLottery A

Lottery B

Your Choice1

1/10 of 200 yen, 9/10 of 160 yen

1/10 of 385 yen, 9/10 of 10 yen○Lottery A○Lottery B

2

2/10 of 200 yen, 8/10 of 160 yen

2/10 of 385 yen, 8/10 of 10 yen○Lottery A○Lottery B

3

3/10 of 200 yen, 7/10 of 160 yen

3/10 of 385 yen, 7/10 of 10 yen○Lottery A○Lottery B

4

4/10 of 200 yen, 6/10 of 160 yen

4/10 of 385 yen, 6/10 of 10 yen○Lottery A○Lottery B

Choose either Lottery A or Lottery B in options 1 to 10. Note that the reward increases in the later optionsof both Lottery A and Lottery B; that is, the later options will give you more possibilities to obtain 200
yen in Lottery A and 385 yen in Lottery B. Once we are sure that all participants have completed making
choices for the ten questions, an experimenter will randomly produce a number from a bingo cage todecide which option is going to be used for your reward, and produce a second number to determine howmuch the prize will be. Raise your hand now if you have a question.

32

Experiment D (Lottery-Choice Decisions 2)
All of you have an opportunity to obtain 5,000 yen in Experiment D. Depending on whether you are paidin one month (Option A) or in three months (Option B), the amount you are going to receive will differ.

Sample Question
OptionPayment Option A

Payment Option B(pays amount below in(pays amount below in

one month)three months)

1

5,000 yen

5,016 yen25,000 yen

5,025 yen

3

5,000 yen

5,033 yen

45,000 yen

5,041 yenYour Choice

Choose Option A or Option B according to your preference. You are required to make such a decision
15 times. Please note that you will receive the Option B payment two months later than if you choose the
Option A payment, and that the amount of payment for Option A is always smaller than the one for
Option B. When all participants finish all 15 options, an experimenter will randomly produce a number
from a bingo cage to decide which option is going to be used for the reward, and produce a second
number to determine who the winner will be (in this experiment only one participant will be chosen to be
rewarded). Each of you has the same possibility to be the winner, so select each option carefully. Also,
the reward for Experiment D will not be given to the winner at the end of the experiment today. Rather,he/she will be paid in either one month or in three months, in accordance with the winner's actual choicein this experiment. If you have any questions, please raise your hand now.

33

Appendix B - Estimation Results in 2-Class Model and 4-Class Model under
Latent Class Model
Table a: Estimated Results in 2-Class ModelClass 1
Variable

CoefficientClass 2Standard

Coefficient

Error

Standard
Error

Utility Function
Return.112***.022.615***.159

Variance.003**

.002

-.004

.002

CSR

.078**.031.169***.036

Membership Function
Constant--

3.907*

2.216

Altruism

-

-

-.009

.008

Risk Aversion---1.968**.977

Time Preference---1.223

2.170

D_Female

-

-

-.532

1.307

D_Econ

-

-

-.773

1.165

Latent Class

.271

.729

Probability
Log-likelihood-180.858*** Statistically significant at the 1% level** Statistically significant at the 5% level* Statistically significant at the 10% level34

Table b: Estimated Results in 4-Class ModelClass 1
Variable

CoefficientClass 2Standard

Coefficient

Error

Class 3Standard

Coefficient

Error

Class 4Standard

Coefficient

Error

Standard
Error

Utility Function
Return.4502673.667.288142.67491.1924***.01570.782099.1017

Variance-.02124

1.6862.00242.09048-.00034-.17.03930.15612

CSR-.62071

166.62.123303.73968.0753***.02487.00936.6.703

Membership Function
Constant

-

-

.746

62.847.445

124.540.787

169.55

Altruism

-

-

-.015

3.337

.062

6.203

-.112

10.210

Risk Aversion

-

-

.789

61.349.954

67.159.132

94.615Time Preference

-

-

.655

39.932.737

46.596.12650.876

D_Female.064

52.982.447

94.676.557

58.156

D_Econ.324

32.982.0258

37.540.13142.853

Latent Class

.045

.247

.579

Probability
Log-likelihood-202.486*** Statistically significant at the 1% level35.128