Strategic and Non-Strategic Pro-Social Behavior in Financial Markets

Transcription

1 Strategic and Non-Strategic Pro-Social Behavior in Financial Markets Arno Riedl Maastricht University Paul Smeets Maastricht University October 2011 Preliminary Version: Please do not cite or distribute Abstract We investigate pro-social behavior by linking unique administrative investor data to fully incentivized internet based experiments on cooperative and trustworthy behavior. First, we find that a majority of individual investors behaves pro-socially, even in the absence of reputation benefits, which is consistent with previous evidence using student samples. Second, self-reported pro-social behaviors in different field contexts are significantly related: investors who have a socially responsible mutual fund in their portfolio are more likely to be registered as an organ donor and to do voluntary work. Third, overall prosocial behavior in the public goods and trust game experiments is unrelated to socially responsible investing and self-reported pro-social behavior in the field. We argue that this discrepancy is likely due to strategically motivated reputation effects in field pro-social behavior, a motivation excluded by design in the Internet experiments. Specifically, we show that investors who buy socially responsible mutual funds for non-strategic reasons are also more trustworthy in the trust game. Our results suggest that without accounting for different motivations for pro-social behavior in the field, studies underestimate the relation between lab experiments and field behavior. Key words: pro-social behavior, trust game, public goods game, field experiment, individual investors Corresponding author: Paul Smeets. Maastricht University School of Business and Economics, Department of Finance (LIFE) and European Centre for Corporate Engagement, PO Box 616, 6200 MD Maastricht, The Netherlands. pm.smeets@maastrichtuniversity.nl. Tel.: +31 (0) Arno Riedl: Maastricht University, School of Business and Economics, Department of Economics, CESifo and IZA, a.riedl@maastrichtuniversity.nl 1

2 1. Introduction There is a large body of experimental evidence showing that people do not only maximize personal wealth, but also behave pro-socially (e.g. Andreoni (1995), Berg, Dickhaut and McCabe (1995), Ledyard (1995), Glaeser et al. (2000), Sobel (2005), Egas and Riedl (2008), Fischbacher and Gachter (2010)). However, there is a lively discussion on the usefulness of laboratory experiments like the public goods and trust game to predict field behavior (Levitt and List (2007), Falk and Heckmann (2009)). Some studies show that there is a relation between behavior in these laboratory experiments and behavior in the field (Karlan (2005), Benz and Meier (2008), Baran, Sapienza and Zingales (2010) Fehr and Leibbrandt (2011)). Other studies find that there is no such relation (List, 2006). There are important differences between behavior measured in the lab and in the field. In laboratory experiments on pro-social behavior, subjects are highly anonymous, the games are one-shot and hence there are no reputation effects. In contrast, in the field people can be motivated to behave prosocially because of signaling (Glazer and Konrad, 1996), social pressure (Dellavigna, List and Malmendier, 2009) and reputation (List, 2006). These fundamental differences between non-strategic pro-social behavior in the lab and potentially strategic pro-social behavior in the field, can explain why studies might not find a relation between pro-social behavior in the lab and in the field. It is ultimately an empirical question whether pro-social behavior in the field is strategic or non-strategic. We combine administrative data on pro-social behavior in the field, a survey and experimental data to shed light on this. We define true (non-strategic) pro-social behavior as an action taken by an individual that benefits others and bears a financial cost to the individual without giving strategic reputational benefits. The context of our study is the market for financial products. According to Levitt and List (2007), the financial market provides a good setting to study the impact of social preferences on behavior. To the best of our knowledge, this study is the first to directly relate laboratory experiments on pro-social behavior to investment behavior in financial markets. We acquired unique administrative investor data to test whether investors who buy socially responsible mutual funds contribute more in a public goods game and are more trustworthy in a trust game. Socially responsible mutual funds can be perceived as a good with both a private and a public component. The private component is the returns it provides, which is the case for all mutual funds. In contrast to conventional mutual funds, socially responsible funds also provide a public good component, because they focus on the broader society by investing in companies that take care of human rights, employee relations, environmental protection, etc. (Social Investment Forum (2010), Bauer and Smeets (2011)). We show that investors on average expect lower returns on socially responsible mutual funds compared to conventional funds and therefore in expectation pay a price to contribute to a public good. Investors also believe that they positively contribute to a better world by buying socially responsible mutual funds and thus create a positive externality. Our administrative data cover the investment behavior of individuals at the largest mutual fund provider in the Netherlands. This mutual fund provider offers both a large variety of socially responsible and conventional mutual funds. All investors are personally responsible for their investment choices and buy funds directly on-line. 2

3 The data have some major advantages. First, we use administrative investment data to measure pro-social behavior. Therefore, we do not suffer from problems of self-reported social behavior in surveys that potentially could be biased. Second, we have a large set of control variables, including gender, age, wealth, income and an incentivized lottery experiment to measure risk preferences. The large variation that we have in demographic background is absent in studies using a student sample. Third, we explicitly measure beliefs in the public goods game and incentivize investors for their estimates of the behavior of others. Our main findings can be summarized as follows. First, we find that there is a large degree of true (non-strategic) pro-social behavior of individual investors. That is, investors on average show a relatively large degree of cooperation in the public goods game and trustworthiness in the trust game. The level of pro-social behavior of investors is comparable, or slightly higher than that of students in previous studies. We show that pro-social behavior of investors is largely conditional in nature. That is, investors who believe that others contribute more in the public goods game, also contribute more themselves. This is consistent with studies using a student sample (Fischbacher and Gachter, 2010). Second, we find that self-reported pro-social behavior in different field contexts is related. Whereas 47% of the regular investors are registered as an organ donor, 60% of the socially responsible investors are and this is significantly different. Similarly, 42% of the regular investors do voluntary work, but this is 53% for socially responsible investors. However, the fact that socially responsible investors also behave more pro-socially in other contexts does not tell us whether this is because of strategic or non-strategic reasons. The results from the public goods game and trust game show that socially responsible investors are not more cooperative or trustworthy in a context-free environment without reputation benefits. Behavior in the two social experiments is also unrelated to donations to charity, being registered as an organ donor and doing voluntary work. True pro-social behavior is unable to explain why investors buy socially responsible mutual funds, donate to charity or do voluntary work. This finding supports the idea that laboratory experiments on pro-social behavior are not informative about behavior in the field (List (2006) and Levitt and List (2007)). However, the finding can also be consistent with mixed motives to pro-social behavior in the field. Sobel (2005) distinguishes between strategic and non-strategic prosocial behavior. Our survey allows us to understand the effect of different reasons for behaving pro-socially in the field. We asked socially responsible investors why they buy socially responsible mutual funds and let investors choose from a list that includes financial and non-financial reasons. The financial reasons are tax benefits 1, a higher expected return, diversification benefits and a better risk-return trade-off. The non-financial reasons include improving the environment and social reasons. We find that investors, who buy socially responsible mutual funds for a non-financial reason, are significantly more likely to be registered as an organ donor, do voluntary work and donate more to charity. Our most important finding is that investors who buy socially responsible mutual funds for a 1 Investors can choose from a fund menu that includes both socially responsible mutual funds with tax benefits and socially responsible funds without tax benefits. In the administrative data we can see which type of funds they hold. The tax benefit can be at a maximum 2.5% of the amount invested. 3

4 non-financial reason are also significantly more trustworthy in the trust game. On average, they send back about 13% more to the trustee than conventional investors. In contrast, investors who buy socially responsible funds for a financial reason send back about 31% less than conventional investors and 39% less than socially responsible investors with a non-financial reason. Overall, there is no difference in the trustworthiness and contribution in the public goods game between socially responsible and conventional investors. However, we show that this finding should not lead to the conclusion that laboratory experiments are not informative about behavior in the field. It is important to explicitly consider that there are different types of people with varying reasons to behave pro-socially. Neglecting this heterogeneity masks the fact that there also exist non-strategic pro-social types. This finding has important consequences for the discussion on the usefulness of lab experiments on pro-social behavior (Levitt and List (2007), Falk and Heckmann (2009)). We show that without considering the different reasons for pro-social behavior in the field, studies underestimate the relation between lab behavior and field behavior. Laboratory experiments offer a controlled environment in which researchers can carefully rule out reputation effects. Lab experiments and field studies are complements and can shed light on the distinction between strategic and non-strategic pro-social behavior. This paper complements studies on pro-social behavior with students in the lab. Fischbacher and Gachter (2010) identify different types of students in the lab. There are also a couple of recent papers focusing on strategic and non-strategic pro-social behavior in the lab with students (Cabral, Ozbay and Schotter (2011), Dreber, Fudenberg and Rand (2011), Reuben and Suetens (2011)). Taken together, these studies show that cooperation in repeated games is largely strategic in nature and there is no relation between strategic and non-strategic pro-social behavior. We explicitly consider different types of investors and strategic and non-strategic reasons to behave pro-socially in the field. Our research differs from other studies that link laboratory experiments on pro-social behavior to field behavior. For instance, Karlan (2005) conducted experiments with microfinance borrowers in Peru and Fehr and Leibbrandt (2011) did experiments with fishermen around a lake in northeastern Brazil. The most obvious difference with our study is that we conduct experiments with investors in the Netherlands. However, not only the context and country is different, but also the social distance between subjects. It is highly probable that the Peruvian microfinance borrowers know each other, just like the fishermen living around the same lake and it can (subconsciously) create a group identity. This group identity can undermine the observation of true pro-social behavior, because subjects can (subconsciously) perceive the situation to be non-anonymous and would care about their reputation (Hagen and Hammerstein, 2006). Moreover, Glaeser et al. (2000) show that larger social distance decreases trustworthiness in a trust game. Because we conduct anonymous on-line experiments with investors spread over the whole Netherlands, we substantially reduce this problem. List (2006) also investigates strategic and non-strategic pro-social behavior in the lab and in the field. However, he does not link laboratory experiments directly to field behavior. Rather, sports card traders participate in a gift exchange experiments in the lab with and without context. In addition, he runs gift exchange field experiments with these traders. He finds that pro-social field behavior is 4

5 primarily due to reputation, which is consistent with our finding that pro-social field behavior is mostly strategic in nature. We can directly test whether considering strategic and non-strategic motivations in the field, strengthens the relation between lab experiments and field experiments. Benz and Meier (2008) find a relation between donation behavior in the lab and donation behavior in the field. They compare the same context (donations) in a lab and field setting, but we relate context-free (true social preference) experiments to context-rich field behavior (with other motives like signaling). Our paper also fits into literature on the influence of pro-social behavior on investment decisions. Hong and Kostovetsky (2011) find that democratic mutual fund managers are more likely to buy stocks that score higher on social responsibility criteria than republican fund managers. Bollen (2007) shows that pro-social behavior can result in investor loyalty. Our research shows that investors can have mixed motives for buying socially responsible mutual funds, which include true pro-social motives and signaling. This finding is in line with Bauer and Smeets (2011) who show that there is large heterogeneity in the preferences of socially responsible investors that affects their loyalty to the bank and the revenue they generate for the bank. Our findings have important consequences for practice. First, banks and mutual funds can benefit from distinguishing between strategic and non-strategic pro-social behavior in their marketing. They can use the fact that many investors buy socially responsible mutual funds for reputational reasons. There is anecdotal evidence of one Dutch socially responsible banks that provides debit and credit cards which display a large whale to signal to others that someone is a client of the socially responsible bank. In addition, banks can organize social events to support charity, where socially responsible investors can bring friends. Second, the Dutch government decided in 2011 that the tax benefits that exist on certain types of socially responsible mutual funds will be reduced from a maximum of 2.5% to 1.2%. Investors who buy socially responsible mutual funds for strategic reasons will be more likely to sell these funds than investors who buy them for non-strategic reasons. 2. Field Experiments and Administrative Data In this section, we first describe the field setting and give some general background information. Second, we describe the public goods game and risk preference lottery. Third, we explain the experimental procedure. 2.1 Field Experiments with Individual Investors We ran large scale field experiments with individual investors from the largest mutual fund family in the Netherlands in spring A mutual fund family is one provider of a collection of mutual funds, like Vanguard, Fidelity and ishares in the United States. The mutual fund family in our study 5

6 offers a wide range of investment funds, including equity funds, bond funds and mixed funds. Within each of these categories they offer a list of funds like global funds, sector funds and socially responsible funds. This paper uses administrative data on the fund holdings of individual investors to measure prosocial behavior in the field. Specifically, we observe whether an investor holds a socially responsible mutual fund. We define a socially responsible investor as an individual that holds at least one socially responsible mutual fund. According to Levitt and List (2007), the financial market is a good place to study the impact of pro-social behavior, because the stakes are high, actors are highly anonymous and there is little scrutiny of their behavior. Socially responsible mutual funds can be perceived as a good with both a private and a public good component. The private component is the returns it provides, which is the case for all mutual funds. In contrast to conventional mutual funds, socially responsible funds also provide a public good component, because they focus on the broader society by investing in companies that take care of human rights, employee relations, environmental protection, etc. (Social Investment Forum (2010), Bauer and Smeets (2011)). Socially responsible investing is widespread in the Netherlands, with one of every six investors holding a socially responsible mutual fund (Millward Brown, 2009). Also in the United States and the rest of Europe, socially responsible investing is growing fast (Social Investment Forum (2010), EUROIF (2010)). To test whether investors really perceive socially responsible investing a product with a public goods component, we ask investors two questions. First, we ask investors to rate their agreement to the statement I believe that socially responsible investing contributes to a better society. Socially responsible investors believe that they positively contribute to a better world by buying socially responsible mutual funds (4.98 on a 1-7 scale) and thus create a positive externality. Second, we ask investors about their beliefs on the returns of SRI mutual funds compared to conventional mutual funds. About 60% of investors expect a lower return on SRI mutual funds compared to conventional mutual funds. Investors therefore in expectation pay a price to contribute to a public good. To measure pro-social behavior in a controlled way with complete anonymity and a one-shot setting, we invited both socially responsible and conventional investors to participate in a couple of laboratory experiments. Next, we explain the trust game, public goods and risk preference experiments we conducted. 2.2 Measuring Trustworthiness To measure trustworthiness, we use a variant of the standard trust game introduced by Berg, Dickhaut and McCabe (1995). Both the first mover and the second mover are endowed with 50 euro. The first mover decides on the amount he or she wants to send to the second mover, which can be any multiple of 5 euro. The amount sent will be tripled. The second mover decides how much money to return to the first mover and this amount will not be tripled. Hence, the earning of the first mover is 50 euro minus the amount sent plus the amount returned by the second mover. The earning of the second mover is 50 6

7 euro plus triple the amount sent by the first mover minus the money he sends back. Before the experiment starts, we first ask investors a couple of comprehension questions. We use the strategy method (Selten, 1967) for second movers, which means that for each of the 11 possible amounts sent by the first mover ranging from 0 euro to 50 euro the second mover decides how much to send back. We randomly match each second mover to a first mover and only the amount actually send by the first mover will determine the earnings. We use the strategy method because of practical reasons and to get a more comprehensive measure of the behavior of these investors. This method is similar to Baran, Sapienza and Zingales (2010) and Falk and Zehnder (2010). One can use several ways to measure trustworthiness based on the data from the strategy method. Throughout the paper, we will use the amount returned by the second mover for the maximum transfer of 50 euro by the first mover. We argue that this is the best measure of trustworthiness because the stakes are the largest for this decision (see also Baran, Sapienza and Zingales (2010). For robustness we also run all analyses with the average return ratio over all the possibilities in the strategy method. 2.3 Measuring Voluntary Contributions To measure voluntary contribution behavior, we conducted a one-shot three-person linear public goods experiment. We use a design that is very similar to Egas and Riedl (2008) and Fehr and Leibbrandt (2011). Each investor was endowed with 40 euro and could decide to contribute nothing, everything, or a part of this money to the public good. Contributions to the public good earned a 50% return to all three group members (i.e., the marginal per capita return (MPCR) was 0.5). Thus, the value of each euro contributed to the public good was increased to 1.5 euro and then equally divided among the subjects. The earnings of an individual investor were the sum of the euro kept (i.e. the euro not invested in the public good) plus the return from the public good. We informed investors that they were randomly matched to two other individual investors at the same bank, who would stay anonymous during and after the experiment. Investors also knew that they would stay anonymous themselves. To ensure that participants understood the game well, we asked them a couple of comprehension questions. After investors made their choice about how much to contribute to the public good, we elicited investors beliefs about the contributions of the other two group members. We use the interval scoring rule to measures these beliefs (Schlag and Van der Weele, 2009). Investors gave a confidence interval on their expectations of the contributions of the other two group members. So, they gave a range of the minimum and maximum expected contribution. If the prediction was right (i.e. is within the interval), investors could at maximum earn an extra 5 euro. The amount they earned decreased proportionally to the size of the interval. The advantages of the interval scoring rule are that investors are incentivized for their predictions and take the decision more seriously. Second, we have both a measure of their expectation as well as their confidence in this prediction in contrast to asking investors for a single point prediction. Third, the interval scoring rule is easier to understand and takes less time for subjects than several other measures of beliefs. For instance, when using the quadratic scoring rule, individuals have 7

8 to make a prediction about the probability of each possible action taken by other participants in the experiment. 2.4 Measuring Risk Preferences Investors who are conditionally cooperative might see the choice of how much to contribute to the public good as a risky one, because there is uncertainty about the contribution of others. Therefore, it is important to control for risk preferences. We measure risk preferences with incentivized multiple price list lotteries, in line with Holt and Laury (2002), Andersen et al. (2008) and Dohmen et al. (2011). Investors faced 20 different decisions and for each decision they decided between receiving a specific sure amount and a lottery with a 50% chance of winning 300 euro and a 50% chance of winning nothing. The choices presented to investors in the experiment are identical to Dohmen et al. (2011) and can be found in Table A.1 in the appendix. The sure amount was at minimum 0 euro and at maximum 190 euro and increased in each decision with 10 euro. It is determined randomly which of the 20 decisions is relevant for the earnings. The choices made by individuals in each of the 20 decisions, allow us to estimate the risk preference. We investigate the point at which individuals switch between the gamble and the certain choice. A risk neutral individual will switch at 150 euro for sure, which is the expected value of the gamble. A risk-averse individual will switch at an amount below 150 euro, whereas a risk seeking individual requires a certain payment above 150 euro. 2.5 Experimental Procedure Investors participated in the experiments on-line in spring We randomly selected 38,305 investors from the 150,000 in our database. We made one exception to the random selection and invited all socially responsible investors to participate, because we wanted to increase the power of our statistical tests. In total, we invited 3,382 socially responsible investors. All selected investors received an that contained a link to the on-line survey. All investors participate in three experiments and a questionnaire. For every investor, we measure risk preferences and time preferences (not reported in this paper). We randomize the order of these two experiments, so that about half of the investors first complete the risk preference task and the other half first gets the time preference task. In addition, each investor takes part either in the public goods game or trust game, which is randomly determined. We inform investors that at the end of the survey, we randomly determine with a chance of one out of ten whether they get paid. Those who are selected for payment get one of the three experiments (risk, time or social) paid out at random. We pay investors via bank transfer at the first working day after they completed the survey. All payments were guaranteed by Maastricht University, which we told at the beginning of the survey. 8

9 We use a unique identification number to link the choices in the experiments and responses to the survey to our administrative data. The abstract identification number ensures the anonymity of the participants. We purposely did not inform subjects that we check whether they are a socially responsible investor or a conventional investor. We want to ensure that investors do not act in the public goods or trust game out of a desire for consistency in their pro-social behavior. The introduction to the on-line survey explained the general procedure, including an explanation about the earnings. In the first part of the survey, we asked about general investment issues like the assets held, the number of investment accounts held and investment goals. In this first part, investors also participated in the risk aversion lottery, but the public goods and trust game are conducted afterwards. We asked all survey items regarding socially responsible investing and social behavior after the public goods experiment or trust game, so that we did not prime subjects. Subjects were aware that there were three experiments in total, but they did not know what the experiments were about until they actually participated in them. The analyses in this paper are all based on behavior in the public goods game and the decisions of the second person in the trust game. We also have data on the behavior of first movers in the trust game, but keep this out for brevity and because it is not a direct measure of pro-social behavior. In total, we have 2,957 investors that completed the on-line questionnaire, equating to a response rate of about 8%. Within the public goods and trust game, we also ran different between-subject treatments, of which participants were not aware. In the control group, investors are matched to another investor at random and they are aware of this. In this paper, we only focus on behavior in the control group. This means that the total number of people that participated in the experiments reported in this paper is more than Hypothesis Development There is a large body of evidence that in anonymous, one-shot experiments subjects behave prosocially. Observing pro-social behavior in the lab is relevant, because decisions have real economic consequences and subjects experience real emotions (De Quevrain et al. (2004), Xiao and Houser (2005), Falk and Heckman (2009)). Because of the tight laboratory control, there is no strategic incentive for subjects to behave pro-socially in the form of reputational benefits and subjects have to pay a monetary price for their pro-social behavior. We define this type of behavior as true pro-social behavior. There are a variety of models that explain why people behave truly pro-socially, according to our definition (Rabin (1993), Fehr and Schmidt (1999), Bolton and Ockenfels (2000)). We are largely agnostic about the exact social preferences that would result in pro-social behavior, but we do distinguish between true pro-social behavior and strategic pro-social behavior. The majority of studies on true pro-social behavior have been conducted with university students (e.g. Benz and Meier (2008), Baran, Sapienza and Zingales (2010), Fischbacher and Gachter (2010)). 9

10 There are a couple of studies that use a non-student sample, which include Fehr and List (2004), Egas and Reidl (2008), Falk, Meier and Zehnder (2011). These studies also find evidence of true pro-social behavior among these non-student participants. We investigate a selective group that consists of individual investors. Kaustia and Torstila (2010) find that left-voting individuals are less likely to participate in the stock market than right-voters. They argue that left-voting people see a mismatch between their own values and the values of the stock market. Given the evidence that investors have a different set of values than non-investors, the question is whether we find the same level of true prosocial behavior among investors as among other subject groups. Hypothesis 1 Individual investors show the same level of true pro-social behavior as subjects in comparable studies After we investigate the prevalence of true pro-social behavior among individual investors, we are interested in the drivers of this behavior. There are several studies showing that cooperative behavior is largely conditional in nature For instance, Fischbacher and Gachter (2010) find that there are several types of individuals and the most common type is by far the conditional cooperator. Conditional cooperation means that individuals are more willing to cooperate if they believe that others do the same. Our explicit measure of beliefs allows us to test the extent to which true pro-social cooperative behavior of investors is conditional in nature. With the exception of the study of Frey and Meier (2004) and Fehr and Leibbrandt (2011) 2, there is little evidence on conditional cooperation with a non-student subject pool. Hypothesis 2 True cooperative behavior by investors is largely conditional in nature There is not only evidence of pro-social behavior in the lab, but also in the field. For instance, Falk (2007) shows that donors reciprocate to gifts by donating more. There is a lively discussion among psychologists about the question whether pro-social behavior is a stable personality trait, or it varies across different contexts (for an overview see Ross and Nisbett (1991) and Borghans et al. (2008)). In the context of our paper, the question is whether socially responsible investors are also more likely to be registered as an organ donor, do voluntary work, etc. Previous studies typically do find correlations between pro-social behavior in different context that are at maximum (e.g. Ross and Niesbett (1991), Benz and Meier (2008)). We therefore hypothesize that pro-social behavior in the different contexts of our study is (imperfectly) related. The empirical analysis will show whether there is at least 2 Frey and Meier (2004) do not measure beliefs. Moreover, the social distance between subjects in the study by Fehr and Leibbrandt (2011) is much smaller than the social distance between investors in our study. It therefore remains an open question whether in a situation of more social distance cooperative behavior is conditional in nature. 10

11 some stable component in pro-social behavior, whether it is true pro-social behavior or status seeking, which results in a correlation between behaviors in different contexts. Hypothesis 3 Pro-social behavior in different field contexts is (imperfectly) related In the field, there can be many different motives behind pro-social behavior next to what we defined as true pro-social behavior. For example, Glazer and Konrad (1996) show that signaling is an important determinant of charitable donations. List (2006) shows that sports-card traders only behave reciprocal if it has consequences for their reputation. Dellavigna, List and Malmendier (2009) show that social pressure is an important driver of giving in door-to-door fund raising. It is therefore likely that investors buy socially responsible mutual funds not because of true pro-social behavior, but because of signaling benefits. Hypothesis 4 Investors buy socially responsible mutual funds because of strategic reasons and do not behave truly pro-social in experiments without reputation effects. To test this hypothesis, investors participate randomly in either the trust game or public goods game. If socially responsible investors behave truly pro-social, they should have a significantly higher level of trustworthiness in the trust game and a higher level of cooperation in the public goods game. We choose for two different experiments to test the robustness of our findings 3. The decision of the second mover in the trust game is non-interactive and the only uncertainty about the earnings for the second mover is that he does not know the amount sent by the first mover. However, this should not matter according to economic theory. In contrast, the public goods game is interactive and there is uncertainty about the contributions of other investors. Therefore, beliefs can play an important role in the decisions made in the public goods game. The study by Fischbacher and Gachter (2010) shows that in a laboratory experiment with students, several types of people exist. The most common type is the conditional cooperator (about half) and the second most common type is a free-rider. This heterogeneity is also likely to have an effect on field behavior. In fact, Bauer and Smeets (2011) show that there exist various types of socially 3 There would be other experiments on pro-social behavior that we could have conducted next to the public goods game and trust game. However, because we wanted to have enough power, we decided to conduct these two experiments. We preferred the trust game over the dictator game, because we believe that reciprocity is a fundamental component of human behavior. We will also use the behavior of the first mover in the trust game in future studies. 11

12 responsible investors who exhibit different levels of loyalty to their bank and therefore generate different levels of revenue for the bank. We hypothesize that there are socially responsible investors who truly behave pro-socially and investors that buy socially responsible mutual funds because of signaling. If we were only to compare the true pro-social behavior of conventional investors and socially responsible investors and ignore investor heterogeneity, the aggregate behavior might not show that an investor type with true pro-social behavior exists. We therefore explicitly look at different types of investors. Hypothesis 5 There are several types of socially responsible investors. Investors who buy socially responsible mutual funds for a financial reason will not behave more pro-socially in the trust game and public goods game, but those who buy the funds for a non-financial reason will. 4. Results 4.1 Variable Definitions and Summary Statistics Table 1 presents the summary statistics of our sample of individual investors. A majority of 85% are men, the average and median age is 59 and 50% of investors have a university degree. N Mean Median Std. Min Max Deviation Male Age University Degree Low Income High Income Low Wealth High Wealth Married No of Kids Risk Preferences Table 1 Low income is a dummy variable that has a value of one if the investor has a before tax income below 40,000 euro per year and the high income dummy is one for an after-tax income above 60,000 euro per year. Low wealth is a dummy that takes on a value of one if the investor has liquid wealth 12

13 (exclusive real estate) below 50,000 euro and high wealth corresponds to having a wealth above 100,000 euro. 4 Kids is a variable representing the number of kids of the individual. Risk preferences correspond to the switch amount that determines the level of risk aversion in the risk preference lottery, measured in euro. Table 1 shows that the average investor switches at euro (S.D. = 42.39), where the risk neutral switching point is at 150 euro. Figure 1 presents the distribution of the risk preferences of investors in our sample. The figure shows that there is large heterogeneity in risk preferences, similar to Barsky et al. (1997) and Dohmen et al. (2011). There are two peaks in the distribution at 100 euro and at the risk neutral switching point of 150 euro. We control for risk preferences in the upcoming analyses. 4.2 The True Pro-social Behavior of Investors Figure 1 Do individual investors behave truly pro-social? Figure 2 depicts the behavior of investors in the trust game. It shows the amounts returned by the second mover for a maximum transfer by the first mover of 50 euro. On average, investors return euro (S.D. = 34.90) to the first mover. Hence, there is a large degree of trustworthiness among investors and first movers on average earn an almost 50% return (24.57 euro) on sending their endowment of 50 euro to the second person. In fact, the most common back-transfer is 100 euro, which corresponds to an equal split of the earnings. The level of trustworthiness found among investors is similar to (and even slightly higher) that found among other subject pools (Glaeser et al. (2000), Fehr and List (2004), Baran, Sapienza and Zingales (2010), Falk and Zehnder (2010)). 4 We use self-reported total wealth on all investment accounts and bank accounts of an investor together, instead of using the wealth held at the broker that we can observe in the administrative data. The reason is that investors can have several investment accounts and this would make the observation of the wealth in one of these accounts (the administrative data) a noisy proxy for the total wealth. 13

14 Figure 2 Figure 3 shows the behavior of investors in the public goods game. The results of the public goods game are in line with those for the trust game and indicate that there exists a large degree of cooperation among investors. On average, investors contribute euro (S.D. = 14.05) to the public good, which corresponds to 42.2% of their endowment. Moreover, 72% of the investors contribute a positive amount to the public good and only 28% are pure free-riders. This level of contributions is similar to that found in comparable studies (Egas and Riedl (2008), Fischbacher and Gachter (2010)). Figure 3 We also looked at the beliefs about the contributions of others. Investors expect that the two other group members on average contribute euro to the public good. Investors thus expect that others contribute slightly more than they do themselves (16.88 euro). The average range in beliefs is euro, which for instance could arise if investors in the interval scoring rule expect others to contribute between 10 euro and euro. 14

15 Taken together, we find support for hypothesis 1 that there is a large degree of truly pro-social behavior among investors. The patterns in pro-social behavior of investors are similar to those found in comparable studies with students and other samples. 4.3 The Determinants of True Pro-social Behavior Given the finding that investors generally do behave pro-socially, it is important to understand what is driving this behavior. Previous research shows that demographic variables can only explain a very small part of the variation in true pro-social behavior. However, research has identified a few variables that are related to the level of trustworthiness in the trust game and the degree of cooperation in the public goods game. Sutter and Kocher (2007) find that older people are more trustworthy than younger people. In the public goods game, Egas and Reidl (2008) find that older people contribute slightly more than younger people. Beliefs about the behavior of others plays a far more important role in determining cooperative behavior than demographic factors (Fischbacher and Gachter (2010), Fehr and Leibbrandt (2011)). Column (1) of Table 2 presents the results of an OLS regression of the trustworthiness of the second-mover in the trust game on a variety of demographic variables and a measure of risk aversion from the risk preference lottery. 5 The dependent variable is the amount of money returned by the second mover for a maximum transfer of 50 euro by the first mover in the strategy method. 6 All independent variables have been defined in section 4.1. The results indicate that investors with kids are significantly more trustworthy than investors without kids. For each additional kid, the investors returns 4.52 euro extra in the trust game (t = 3.201, p = 0.000). Investors who are more risk tolerant are also more trustworthy. A risk neutral investor sends back 6.90 euro more (t = 3.501, p = 0.000) than a risk averse investor who switches at 100 euro in the risk preference task (mode). The finding that risk preferences are related to trustworthiness is quite surprising given that there is no theoretical reason to expect this relation. We also find that older investors are less trustworthy than younger investors. An investor who is 10 years older, returns 3.83 euro less in the trust game (t = , p = 0.02). This finding is in contrast to Sutter and Kocher (2007) who find that older people are more trustworthy. We believe that this difference is due to the fact that investors on average are older than the average person in the Netherlands. We therefore measure differences in age on the upper side of the distribution instead of over the full age distribution. 5 We also ran Tobit regressions that give similar results. Therefore, we decided to report the OLS regressions throughout the paper, because the interpretation is easiest. 6 For robustness, we also constructed an average return ratio over all the 11 choices made by second movers in the strategy method trust game. In unreported regressions, we find similar results. 15

16 (1) (2) (3) (4) Trust Public Goods Constant [5.884] [5.125] [2.097] [2.249] Beliefs [17.935]. 880 [16.691] University [.845] [-2.497] [-2.093] Male [-.801] [-.043] [.531] Risk Preferences. 138 [3.501].017 [.965] [1.832] Married [-.741].490 [.274] [-.615] Low Income [-1.116] [1.408] [1.180] High Income [-1.001].602 [.304] [.321] Low Wealth [.488] [-3.302] [-3.583] High Wealth [-.437] [-1.671] [-1.473] Age.383 [-2.192] [-1.521] [-2.184] Kids [3.201].746 [1.160] [.704] n obs Table 2 Column (2) of Table 2 presents OLS regressions with the level of contributions in the public goods game as dependent variable. We use the same independent variables as in the regressions for the trust game. Investors with a university degree contribute 3.67 euro less to the public good (t = , p = 0.01). This is a quite large effect, given that the average contribution to the public good is about 17 euro. The significant coefficients on low and high wealth also show that wealth is an important variable to control for. There seems to be a curve linear relation between wealth and contributions to the public good. Low wealth investors contribute the least; people with medium wealth are most cooperative, followed by high wealth investors. Most studies on pro-social behavior do not control for wealth or have little variation in wealth because of using a student sample. The main difference with the behavior of the second mover in the trust game is that behavior in the public goods game is interactive. A subject is uncertain about the contributions of others and beliefs about the contributions of others therefore play an important role if people are conditionally cooperative. We therefore control for beliefs by including the average midpoint of the belief interval given by 16

17 investors in the interval scoring rule. That is, if an investor indicates that he expects the other investors to contribute between 10 euro and 20 euro, the midpoint is 15 euro 7. The coefficient on beliefs in column (3) is highly significant (t = , p = 0.000). Investors who believe that the other two group members on average contribute one euro more, contribute euro extra themselves. The R-squared of this regression is 0.431, which is much higher than the R- squared of the regression including many demographic variables and risk preferences, but omitting beliefs (R 2 = 0.054). This finding supports hypothesis 2 that cooperative behavior of investors is mainly conditional in nature. This finding is in line with previous studies with students (Fischbacher and Gachter, 2010) and implies that using student subject pools to study condition cooperation is valid. In column (4), we regress contributions in the public goods game on beliefs and demographics. These results show the impact of demographic characteristics on conditional cooperation. That is, how cooperative are investors after taking into account their beliefs about others. The coefficient on beliefs is nearly identical to that in the regression without demographics. Interestingly, risk preferences and age turn significant after controlling for beliefs, in contrast to the regression in column (2). Overall, pro-social behavior is largely conditional in nature and demographic variables only have a minor role as explanatory variables. There is some indication that risk tolerant and younger investors behave more pro-socially than risk averse and older investors. 4.4 Pro-social Behavior in Different Field Contexts In the previous two sections, we looked at true pro-social behavior of investors in the public goods and trust game. In this section, we look at pro-social behavior in the field. Specifically, we investigate whether pro-social behavior in different field contexts is related. Do investors who behave pro-socially by buying socially responsible mutual funds also donate more to charity, do more voluntary work and are they more likely to be registered as an organ donor? We complement our administrative data on pro-social investment behavior with a survey in which we ask investors to report on pro-social field behavior. The variable organ donor is a dummy that has a value of one if the investor is registered as organ donor and zero if she is not. 8 The variable voluntary work is a dummy that has a value of one if the investor does voluntary work at least several times per month and is zero otherwise. The variable donations is the self-reported amount that investors donate to charity per year. We exclude investors who answer do not know or do not wish to answer. Figure 4 shows that socially responsible investors are indeed more likely to be registered as an organ donor. Whereas 47% of conventional investors is registered as an organ donor, this is 60% for 7 There are always three group members in the public goods experiment. We ask investors to give a separate interval for the beliefs of the contributions of subject B and subject C. In the analysis, we take the average of the midpoint of beliefs about subject B and subject C. We also ran the regression by only including the midpoint of subject B and only including those for subject C. This gives only a slightly lower coefficient on the beliefs variable, probably because there is more noise. 8 We classify investors who do not want to answer as not being registered as an organ donor. The results are robust to leaving out these investors. The same holds for the voluntary work dummy. 17

18 socially responsible investors (t = 3.67, p = 0.000). Even a large percentage of conventional investors is registered as an organ donor compared to 27.5% in the Netherlands overall (Johnson and Goldstein (2003)). Moreover, Figure 5 shows that 53% of socially responsible investors do voluntary work regularly, but this is only 42% for conventional investors (t = 3.41, p = 0.000). The median socially responsible investor donates 500 euro to charity per year, but this is 300 euro for conventional investors 9. Figure 4 Figure 5 These results show that pro-social behavior in different field contexts is (imperfectly) related and this support hypothesis 3. So, there is at least some stable component in pro-social behavior that results in this relation. Benz and Meier (2008) find a relation between donations to charity in the field and in the lab and our study complements theirs by looking at pro-social behavior in different field contexts. 4.5 True Pro-social Behavior of Conventional and Socially Responsible Investors The previous section showed that socially responsible investors also behave more pro-socially in other field contexts. However, the question remains open whether this is truly pro-social behavior or strategic pro-social behavior. In this section, we compare true pro-social behavior between conventional and socially responsible investors. Figure 6 compares the behavior of conventional and socially responsible investors in the trust game and Figure 7 present the results for the public goods game. The figures show that the true prosocial behavior of socially responsible investors is not higher than that of conventional investors. In the 9 Average donations to charity are not significantly different between conventional investors and socially responsible investors. However, the distribution is skewed. After taking logs, the difference becomes highly significant (t = 3.02). Socially responsible investors on average donate 1,006 euro to charity per year and this is 833 euro for conventional investors. 18

19 public goods game, socially responsible investors even contribute slightly less. This is the first indication that investors buy socially responsible mutual funds mainly because of reputational benefits. Figure 6 Figure 7 Table 3 analyses the difference in true pro-social behavior between socially responsible investors and conventional investors in a multivariate setting. Column (1) and (2) present the results of OLS regressions of the trustworthiness of investors in the trust game. The dependent variable is the amount returned by the second mover for a transfer of 50 euro by the first mover. Socially responsible investor is a dummy that takes on a value of one if the investor holds a socially responsible mutual fund and zero otherwise. The coefficient on socially responsible investor is insignificant (t = 0.452), showing that there is no difference in the true trustworthiness of socially responsible and conventional investors. Column (3) in Table 3 report OLS regressions of contributions in the public goods game on the dummy socially responsible investors. There is no significant difference in the cooperativeness of socially responsible and conventional investors (t = ). In column (4), we control for demographics and most important for beliefs about the contributions of others. That is, we investigate the difference in the level of conditional cooperation between conventional investors and socially responsible investors. We find that socially responsible investors even are less cooperative than conventional investors, because controlling for their beliefs, they contribute 2.59 euro less to the public good. However, this effect is only marginally significant (t = , p = 0.025). 19

20 (1) (2) (3) (4) Trust Public Goods Constant [43.374] [5.807] [25.940] [2.473] Socially Responsible Investor [.418] [.452] [-.619] [-1.783] Beliefs [16.795] University [.846] [-1.985] Male [-.820] [.501] Risk Preferences [3.504] [1.778] Married [-.718] [-.791] Low Income [-1.133] [1.223] High Income [-.987] [.363] Low Wealth [.487] [-3.733] High Wealth [-.460] [-1.538] Age [-2.147] [-2.322] Kids [3.185] [.801] n obs Table 3 The results in this section strongly suggest that investors buy socially responsible mutual funds because of signaling instead of true pro-social behavior. If socially responsible investors make economic choices in a completely anonymous, one-shot situation, they are not more likely than conventional investors to behave pro-socially. Hence, we find support for hypothesis 4. In unreported results, we also find that organ donation, donation to charity and voluntary work are unrelated to true pro-social behavior in the public goods and trust game. So, also these pro-social behaviors tend to be strategic in nature. 4.6 Heterogeneity in investor types: identifying true pro-social investors The previous section shows that in general, socially responsible investors behave pro-socially because of strategic reasons. However, due to investor heterogeneity, there might be a true pro-social type (non-strategic) that behaves both pro-socially in laboratory experiments and in the field. Hypothesis 20

21 5 states that there are several types of socially responsible investors. Those who buy socially responsible mutual funds because of financial reasons will not behave truly pro-social, but those who buy them for non-financial reasons will. To test hypothesis 5, we ask investors for their most important reason to buy socially responsible mutual funds. Investors could select one of the presented answer options or they could choose other and fill out a reason not listed, which we exclude from our analysis. We focus on the distinction between listed financial reasons and non-financial reasons to buy socially responsible mutual funds. We create a dummy variable that has a value of one if someone invests in socially responsible mutual primarily for a financial reason and a zero for a non-financial reason. Financial reasons comprise tax advantages, higher expected returns, a better return-risk trade-off and diversification benefits. Non-financial reasons include contributing to the environment and social reasons. Figure 8 divides investors into three groups, which are conventional investors, socially responsible investors with a financial reason and socially responsible investors with a non-financial reason. The figure shows that socially responsible investors with a financial reason are less likely to be registered as an organ donor than conventional investors, but this difference is not statistically significant. However, socially responsible investors with a non-financial reason are significantly more likely to be registered as an organ donor, with 59% of them being registered as an organ donor compared to 44% for socially responsible investors with a financial reason (p-value = 0.024). Figure 8 Figure 9 21

22 Figure10 Figure 9 shows that 51% of socially responsible investors with a non-financial reason do voluntary work regularly, compared to 42% of conventional investors (p-value = 0.007). Moreover, Figure 10 shows that socially responsible investors with a non-financial reason on average donate more to charity (1.319 euro per year) than d conventional investors (783 euro), which is significantly different (p-value = 0.000). Figure 11 shows that investors who buy socially responsible mutual funds for a non-financial reason are more trustworthy than conventional investors and socially responsible investors with a financial reason. Figure 12 shows that socially responsible investors with a financial reason are less cooperative in the public goods game, but this difference is not statistically significant. Figure 11 Figure 12 Next, we run a set of regression to test whether the results hold in a multivariate setting. Column (1) of Table 4 shows that investors who buy socially responsible mutual funds for a financial reason send back euro less in the trust game (t = , p = 0.025). This is an economically large effect, because the average back transfer is euro. Importantly, once we specifically account for the fact 22

23 that there are socially responsible investors with a financial motive; the dummy on socially responsible investors becomes positively significant. That is, investors with a non-financial reason to buy socially responsible mutual funds send back 9.94 euro extra (t = 2.056, p = 0.040). These results remain significant when adding control variables, as can be seen in column (2) of Table 4. Constant Socially Responsible Investor SRI with Financial Reason (1) (2) (3) (4) Trust Public Goods ,059 19,057 8,747 [43,918] [5,548] [25,841] [2,134] 9,940 7,838 -,531-1,362 [2,056] [1,653] [-,221] [-,745] 23,153 [-2,254] 19,144 [-1,921] -,443 [-,120] -2,050 [-,742] Beliefs - - -, 875 [16,625] University - 2,710 [,832] - 2,144 [-1,912] Male - -2,049 [-,402] -,541 [,310] Risk Preferences -, 133 [3,307] -, 029 [2,092] Married - -2,292 [-,608] - -1,021 [-,748] Low Income - -5,140 [-,888] - 2,330 [1,169] High Income - -3,778 [-,833] -,619 [,413] Low Wealth - 1,744 [,312] - 6,814 [-3,694] High Wealth - -1,422 [-,334] - -2,415 [-1,578] Age -,398 [-2,274] -,115 [-2,102] Kids - 4,573 [3,239] -,404 [,827],013,074,000,463 n obs Table 4 Column (3) and (4) of Table 4 regress contributions to the public goods game on the dummy for socially responsible investors and a dummy for buying socially responsible mutual funds for a financial reason. In contrast to the results for the trust game, investors with a financial reason do not contribute less to the public good. Neither do socially responsible investors with a non-financial motive contribute more to the public good. It is striking that there is such a large difference between the results of the trust game and public goods game There can be several reasons for this. First, the trust game might be better in measuring pro-social behavior. Second, the trust game measures trustworthiness of the second mover, but the public good game measures conditional cooperation. It 23

24 5. Conclusion This paper shows that many investors behave truly pro-socially by reciprocating in an anonymous one-shot trust game or by contributing in a public goods game. This is consistent with comparable studies using student samples. However, the true pro-social behavior measured in laboratory experiments does not drive decisions to buy socially responsible mutual funds, become an organ donor or to do voluntary work. Pro-social behavior in the field tends to be strategic in nature and is used for reputational benefits and status. The great importance of status and reputation in life might trigger people to behave pro-socially. It is probably more socially acceptable to build up status by behaving pro-social than to buy luxury goods like a Rolex watch (Glazer and Konrad, 1996). The finding that pro-social behavior in the field is strategic is true for the average individual. However, there can be heterogeneity among individuals in the reasons to behave pro-socially. The majority might buy socially responsible mutual funds or donate to charity for signaling, but a significant minority could truly behave pro-socially in the field. We indeed find evidence for the existence of several types of people. Those people who invest socially responsible for a non-financial reason are both more likely to show pro-social behavior in the field and true pro-social behavior in a controlled trust game experiment. Future research can investigate which kind of pro-social behavior in the field is true in nature and which behavior is strategic. The lab gives the advantage of a tight control and rules out reputation effects. Research can benefit from linking laboratory experiments to field behavior and vary the context, stakes and level of anonymity. It is also important to take the heterogeneity in different types into account, because otherwise the relation between behavior in the lab and in the field is underestimated. could be that trustworthiness is more related to pro-social behavior in the field than conditional cooperation. Karlan (2005) also finds that the pro-social behavior in the field (the repayment of a microfinance loan) is related to trustworthiness in the trust game, but not to behavior in the public goods game. Baran, Sapienza and Zingales (2010) do find a relation between second-player trust game behavior and donations to a charity fund, but there is no correlation between behavior in an 8- person prisoner s dilemma game (quite similar to a public goods game) and donations to the charity. They find a negative insignificant coefficient. 24

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29 Table 1 Summary Statistics This table presents the summary statistics of our sample. Low income is a dummy variable that has a value of one if the investor has a before-tax income below 40,000 euro per year and the high income dummy is one for an after-tax income above 60,000 euro per year. Low wealth is a dummy that takes on a value of one if the investor has liquid wealth (exclusive real estate) below 50,000 euro and high wealth corresponds to having a wealth above 100,000 euro. Kids is a variable representing the number of kids of the individual. Risk preferences corresponds to the switch amount that determines the level of risk aversion in the risk preference lottery, measured in euro. N Mean Median Std. Min Max Deviation Male Age University Degree Low Income High Income Low Wealth High Wealth Married No of Kids Risk Preferences

30 Table 2 True pro-social behavior in the trust game and public goods game This table presents OLS regressions of behavior in the trust and public goods game on investor characteristics. The dependent variable in column (1) is the amount returned by the second mover in the trust game (using the strategy method) for a maximum transfer of 50 euro by the first mover. The dependent variable in column (2)-(4) is the amount contributed in the public goods game. All demographic variables are explained in Table 1. Beliefs about the contributions of others in the public goods game is measured with the interval scoring rule and is the midpoint of the interval predicted by the investor. Constant (1) (2) (3) (4) Trust Public Goods [5.884] [5.125] [2.097] [2.249] Beliefs University [.845] Male [-.801] Risk Preferences. 138 [3.501] Married [-.741] Low Income [-1.116] High Income [-1.001] Low Wealth [.488] High Wealth [-.437] Age Kids.383 [-2.192] [3.201] [-2.497] [-.043].017 [.965].490 [.274] [1.408].602 [.304] [-3.302] [-1.671] [-1.521].746 [1.160] [17.935]. 880 [16.691] [-2.093] [.531] [1.832] [-.615] [1.180] [.321] [-3.583] [-1.473] [-2.184] [.704] n obs *** 1% ** 5% * 10% significance level T-stats in parentheses 30

31 Table 3- True pro-social behavior of socially responsible and conventional investors This table presents OLS regressions of behavior in the trust and public goods game on investor characteristics and a dummy indicating whether an investor is a socially responsible investor (owns a socially responsible mutual fund) or a conventional investor. The dependent variable in column (1) and (2) is the amount returned by the second mover in the trust game (using the strategy method) for a maximum transfer of 50 euro by the first mover. The dependent variable in column (3) and (4) is the amount contributed in the public goods game. All demographic variables are explained in Table 1. Beliefs about the contributions of others in the public goods game is measured with the interval scoring rule and is the midpoint of the interval predicted by the investor. Constant (1) (2) (3) (4) Trust Public Goods [43.374] [5.807] [25.940] [2.473] Socially Responsible Investor [.418] [.452] [-.619] [-1.783] Beliefs [16.795] University [.846] [-1.985] Male [-.820] Risk Preferences [3.504] Married [-.718] Low Income [-1.133] High Income [-.987] Low Wealth [.487] High Wealth [-.460] Age [-2.147] [.501] [1.778] [-.791] [1.223] [.363] [-3.733] [-1.538] [-2.322] Kids [3.185] [.801] n obs *** 1% ** 5% * 10% significance level T-stats in parentheses 31

32 Table 4 Different motives for pro-social behavior in the field This table presents OLS regressions of behavior in the trust and public goods game on investor characteristics and a dummy indicating whether an investor is a socially responsible investor (owns a socially responsible mutual fund) or a conventional investor. We also introduce a dummy variable that takes on a value of one if an investor is a socially responsible investor and has a financial reason to be one. This financial reason can be tax benefits, a higher expected return or a better risk-return trade-off. The dependent variable in column (1) and (2) is the amount returned by the second mover in the trust game (using the strategy method) for a maximum transfer of 50 euro by the first mover. The dependent variable in column (3) and (4) is the amount contributed in the public goods game. All demographic variables are explained in Table 1. Beliefs about the contributions of others in the public goods game is measured with the interval scoring rule and is the midpoint of the interval predicted by the investor. Constant Socially Responsible Investor (1) (2) (3) (4) Trust Public Goods ,059 19,057 8,747 [43,918] [5,548] [25,841] [2,134] 9,940 7,838 -,531-1,362 [2,056] [1,653] [-,221] [-,745] SRI with Financial Reason 23,153 [-2,254] 19,144 [-1,921] -,443 [-,120] -2,050 [-,742] Beliefs - - -, 875 [16,625] University - 2,710 [,832] - 2,144 [-1,912] Male - -2,049 [-,402] -,541 [,310] Risk Preferences -, 133 [3,307] -, 029 [2,092] Married - -2,292 [-,608] - -1,021 [-,748] Low Income - -5,140 [-,888] - 2,330 [1,169] High Income - -3,778 [-,833] -,619 [,413] Low Wealth - 1,744 [,312] - 6,814 [-3,694] High Wealth - -1,422 [-,334] - -2,415 [-1,578] Age -,398 [-2,274] -,115 [-2,102] Kids - 4,573 [3,239] -,404 [,827],013,074,000,463 n obs *** 1% ** 5% * 10% significance level T-stats in parentheses This table is made for subgroup 1368=1 and for those investors who completed the risk preference lottery. So, those investors that have a value in the SPSS file in the risk preference experiment columns. 32

33 Figure 1 - Risk Preferences This figure presents the distribution of risk preferences of investors in our sample. The euro amounts in the graph correspond to switch amounts in the risk preference lottery and is the risk-free amount that subjects prefer over a 50% chance to win 300 euro or 0 euro. The risk neutral switching point is at 150 euro. 33

34 Figure 2 - Trustworthiness in the Trust Game This figure presents the distribution of the level of trustworthiness of investors in our sample. Trustworthiness is measured by the amount send back by the second mover in the trust game (strategy method) for a maximum transfer of 50 euro by the first mover. 34

35 Figure 3 - Contributions in the Public Goods Game This figure depicts the distribution of contributions to the public goods game of investors in our sample. Investors were endowed with 40 euro. 35

36 Figure 4 - Comparison of the Percentage of Organ Donors between Conventional and Socially Responsible Investors Figure 5 - Comparison of the Percentage of Voluntary Work between Conventional and Socially Responsible Investors Figure 6 - Comparison of Trustworthiness in the Trust Game between Conventional and Socially Responsible Investors Figure 7 - Comparison of Contributions in the Public Goods Game between Conventional and Socially Responsible Investors 36

37 Figure 8 - Organ Donor Figure 9 - Voluntary Work Figure 10 - Donation to Charity 37

38 Figure 11 Trustworthiness in the Trust Game Figure 12 - Contribution in the Public Goods Game 38

39 Table A.1 Choices in Risk Preference Experiment Source: Dohemn et al. (2011) 39