Stereotypes and false consensus: How financial professionals predict risk preferences

Journal of Economic Behavior & Organization 107 (2014) 553–565

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Journal of Economic Behavior & Organization journal homepage: www.elsevier.com/locate/jebo

Stereotypes and false consensus: How financial professionals predict risk preferences Benjamin Roth a,∗ , Andrea Voskort b,1 a b

Heidelberg University, Bergheimer Str. 58, 69115 Heidelberg, Germany BaFin (Federal Financial Supervisory Authority), 53117 Bonn, Germany

a r t i c l e

i n f o

Article history: Received 26 February 2013 Received in revised form 15 May 2014 Accepted 24 May 2014 Available online 13 June 2014 JEL classification: C91 D81 G02

a b s t r a c t We analyze the prediction of risk preferences of others using an artefactual field experiment with financial professionals and students. For their prediction, the subjects receive information on multiple demographic characteristics and a self-assessment of risk taking of the target. When analyzing the predictions we find three significant effects: subjects use the demographic information for stereotyping as well as the target’s self-assessment on risk taking, and we find a considerable false consensus effect. The false consensus effect is the strongest for experienced professionals. Regarding the prediction’s accuracy, we find that the forecasts of the professionals are more accurate than the forecasts of the students. © 2014 Elsevier B.V. All rights reserved.

Keywords: Risk preferences Financial advice Artefactual field experiment Behavioral finance

1. Introduction Confronted with a risky decision people prefer to be advised (Schotter, 2003). For example, when deciding on an investment product or a risky medical treatment. The recommendations of experts in the field of the decision (financial consultants, doctors) are seen as credible sources of information (Jungermann and Fischer, 2005). However, for the advisee it is vital that the prediction of their risk preference is accurate such that the recommendation they receive is unbiased and coincides with their willingness to tolerate risks. As financial literacy is limited, counseling is especially important for the financial sector (Rooij et al., 2011). Certainly, in the field of financial counseling, there are various agency problems that will affect the advisor’s recommendations. Nonetheless, an accurate prediction of the client’s preferences is a precondition to give meaningful advice (Bhattacharya et al., 2012). In this paper we explicitly abstain from these agency problems. Our objective is to start a step earlier. If the advisor’s only goal is to correctly gauge the risk preferences of the advisee, is the advisor able to do so? This also contributes to the current discussion on fee-based advisory, in which the agency problems, in contrast to commission-based advisory, are considerably reduced.

∗ Corresponding author. Tel.: +49 6221 542940; fax: +49 6221 543592. E-mail addresses: [email protected] (B. Roth), andrea.voskort@bafin.de (A. Voskort). 1 Formerly Leuermann. http://dx.doi.org/10.1016/j.jebo.2014.05.006 0167-2681/© 2014 Elsevier B.V. All rights reserved.

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Empirical evidence suggests that the behavior and risk perception of financial professionals is different from the general population (Nofsinger and Varma, 2007). At the same time, this group has particular impact on the financial decisions of clients (Kramer, 2012). In an artefactual field experiment, we thus study the predictions of risk preferences of professional financial consultants as well as of student subjects. When predicting the preferences of other individuals, two well documented factors come into play (Ames, 2004; Bonaccio and Dalal, 2006): first, the predictor projects his or her personal preferences on the judged target. This is known as the false consensus effect. Second, the predictor uses inferential strategies to assess the target’s preferences, such as stereotyping. This study investigates the simultaneous impact of the false consensus effect and of stereotyping when the beliefs on another person’s risk attitudes are formed. With regard to stereotyping, so far, only gender and ethnic stereotypes on risk preferences are reported (Eckel and Grossman, 2008; Hsee and Weber, 1999). However, risk preferences correlate with multiple demographic characteristics (Dohmen et al., 2011). Therefore, in our study predictors judge target profiles containing major demographic correlates: gender, age, education, income, parental and family status. Moreover, the profiles contain a measure of the target’s self-assessed risk preference. By this, we can test whether the predictors use information on risk preferences provided by the target. This experimental setup allows to estimate for each of these categories the marginal effect on the predicted risk preferences. Further, when comparing the predictors’ beliefs to a large-scale dataset we investigate whether the risk preferences of the targets are predicted correctly on a representative level. The results reveal evidence for stereotyping on demographic attributes as well as for false consensus of the predictions. Additionally, predictors include a self-stated risk of the target in their prediction. Therefore, this study shows that subjects indeed ground their beliefs on three different sources of information. Although, in the predictions of the experienced professionals, the false consensus effect has a large impact, it does not decrease the accuracy of their predictions. The remaining paper is structured as follows: Section 2 briefly discusses the literature. Section 3 outlines the experimental design, while Section 4 presents the results followed by concluding remarks in Section 5. 2. Literature There is a large body of literature that addresses the prediction of preferences in general (see Harvey and Fischer, 1997 or Fischer and Harvey, 1999). A commonly found phenomenon when making predictions2 is that subjects strongly rely on their personal preferences instead of focusing on the relevant information about the judged subject. This effect is known as the false consensus bias (Krueger and Clement, 1994; Bonaccio and Dalal, 2006). A false consensus does not necessarily decrease the accuracy of the predictions. It merely depends on the difference in the preference between the judging and judged subject (Hoch, 1987). Ames (2004) argues that besides using personal preferences, subjects use the inferential strategy of stereotyping to make their predictions. That is, the predictor incorporates a perceived correlation between a recognizable characteristic and the outcome of the prediction. Again, stereotyping can be a helpful strategy for accurate predictions as long as the perceived and the actual correlations coincide and the different sources (false consensus, stereotyping) are weighted properly. Restrictively, the study of Ames is executed as a hypothetical choice experiment without an incentivized risk measure. In terms of risk preferences, this is a crucial difference. With hypothetical questionnaires a researcher might find a correlation with behavior under risk, while incentivized measures elicit revealed risk preferences – or actual behavior under risk. For studying the false consensus effect, incentives are particularly important as Engelmann and Strobel (2000) report that the false consensus disappears under monetary incentives. There are several papers that investigate the relationship of the above projection strategies and risk attitudes. In terms of false consensus Chakravarty et al. (2011) show that the predicted and the personal preferences are not different and therefore establish a significant false consensus. This coincides with the findings of Hadar and Fischer (2008), who also find that the predictions are primarily based on the personal preferences. In a prospect theory framework, Faro and Rottenstreich (2006) also report a significant false consensus but find that the predictions are skewed toward risk neutrality. Regarding stereotypes, especially gender stereotypes have been investigated (Hsee and Weber, 1997; Siegrist et al., 2002; Daruvala, 2007; Eckel and Grossman 2008). Furthermore, Hsee and Weber (1999) find ethnical stereotypes (Chinese vs. American) in risk predictions. While gender stereotypes largely coincide with the actual observation that males are more risk tolerant, Hsee and Weber (1999) report that the stereotypes and the actual correlations fall apart as the Chinese are predicted to be more risk averse than the Americans, whereas the opposite is actually true. Although there are many papers which investigate the stereotype of a single demographic attribute, for the more realistic assumption that a predictor has to evaluate multiple characteristics, the literature is quiet. However, there are many demographic attributes which are correlated with risk taking. Prominent examples besides gender are age, parenthood, education or income (Barsky et al., 1997; Hartog et al., 2002 or Belzil and Leonardi, 2007). This is especially true for financial risk taking (Sung and Sherman, 1996; Hallahan et al., 2004), which is crucial as there is evidence that risk preferences are domain specific (Dohmen et al., 2011). When it comes to the prediction of risk preferences of a single individual, the predictor observes (e.g., in a counseling interview) multiple demographic characteristics. However, the different attributes correlate with financial risk taking in potentially different directions. For example, age is found to be negatively correlated with risk

2

‘Prediction’, ‘forecast’ and ‘belief’ are used interchangeably.

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Fig. 1. Experimental design: course of actions.

taking, while being male is positively correlated. So what do the stereotypes look like if we compare a young woman with an old man? This is where this paper adds to the literature. An important application for the prediction of preferences is the field of financial advice. In financial advisory there might be all kinds of agency conflicts which might influence the advice given to advisees. However, when considering the effect of advice on a client’s portfolio Bhattacharya et al. (2012) find unbiased advice to be a necessary (but not sufficient) condition for improving a client’s portfolio. Hence, an accurate prediction of the client’s risk attitude is vital. Due to their expert knowledge, professional advisors receive higher credibility from advisees. This makes their advice more influential (see Jungermann and Fischer, 2005) and therefore their ability to assess a client’s risk preference correctly more crucial. Previous studies suggest that financial professionals are less prone to behavioral biases, such as anchoring effects when forming expectations about long-term stock returns (Kaustia et al., 2008). They show a higher degree of analytical behavior than the general population (MacGregor and Slovic, 1999; Nofsinger and Varma, 2007). Furthermore, there is contradictory evidence regarding the degree of myopic loss aversion of financial professionals compared to student subjects (Haigh and List, 2005; Eriksen and Kvaloy, 2009). Roszkowski and Grable (2005) assess the ability of professional financial advisors to predict the risk preferences of clients but without using incentives. They argue that the subjects overweight the target’s demographic information and underweight the target’s self-assessment. This study contributes to the literature as we use incentives for the preference measures as well as for the predictions. The above papers study either the beliefs on certain populations or on random individuals whereas this experiment uses specifically chosen profiles. Moreover, we use an artefactual field experiment with professional financial advisors and non-professionals to explore potential differences in behavior between experienced and non-experienced predictors. Furthermore, we are able to disentangle the inferential strategies of stereotyping and of false consensus. 3. Experimental design This experiment is designed to study how predictors assess the risk preferences of target profiles of specific individuals. These profiles consist of seven attributes of the target: Six demographic characteristics and a self-stated preference measure. Since the different treatments manipulate the available information on the profiles, we are able to observe the stereotypes on all of these attributes, and, even more important, we can estimate the causal effect of the different demographic characteristics on the beliefs. The novelty of this design is that we vary multiple characteristics of the target profiles simultaneously. The design involves two distinct roles: subjects who form beliefs (predictors) and subjects about whom beliefs are formed (target profiles). Therefore our experimental setup consists of two main parts (Fig. 1).3 In a first part, we collect data with a web-based survey. From these data, we select eight observations that are presented to predictors in the second part as the target profiles. The second part is a computerized experiment with three treatments. When entering the lab the predictors are randomly assigned to a computer space. All treatments are played one after another without any interaction. We treat each predictor as an independent observation. To avoid learning effects, the payoffs of the whole experiment are displayed at the very end of the experiment. At first, the predictors complete the treatment SELF, in which we elicit the predictors’ risk preferences. In the second (RANK) and in the third treatment (PAY), the subjects predict the risk preferences of four profiles in each treatment. RANK and PAY differ in the way how the predictors acquire the information on the profiles. Before we describe the experiment in detail, we introduce the experimental risk measures. 3.1. Measures of risk aversion The experiment uses two incentivized risk measures: The D 100,000 question and the sMPL lottery. The D 100,000 task is worded as follows: D 100,000 question Please consider what you would do in the following situation: Imagine that you had won D 100,000 in the lottery. Almost immediately after you collect the winnings, you receive the following financial offer, the conditions of which are as follows: There is the chance to double the money. It is equally possible that you could lose half of the amount invested. You have

3

Please find all instructions in the Appendix.

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Fig. 2. sMPL task.

the opportunity to invest the full amount, part of the amount or reject the offer. What share of your lottery winnings would you be prepared to invest in this financially risky, yet lucrative investment? Your Decision D 100,000 – D 80,000 – D 60,000 – D 40,000 – D 20,000 – Nothing, I would decline the offer. This mechanism is an ordered lottery selection design in which subjects can invest up to D 100,000 into a lottery that doubles or halves the amount invested with equal probabilities.4 In order to provide incentives, we convert D 100,000 into D 2.50, D 80,000 into D 2, etc. for the actual payoff in the lab experiment. This elicitation mechanism is also part of the large-scale survey German Socioeconomic Panel (SOEP). It is an easily understandable mechanism because the structure is very clear. The D 100,000 question trades off a fixed payoff with an expected value of a binary lottery. All outcomes and choices have fixed and equal probabilities. An advantage of this risk measure is that we can relate our experimental data to this representative survey. Dohmen et al. (2011) validated this risk measure by using an experiment with real money at stake. The authors show that this measure nicely correlates with financial risk taking. As it does not capture preferences on the risk loving domain, the whole experiment is executed with a second risk task. Multiple price list The second measure is the multiple price list design of Holt and Laury (2002). In the following we refer to this task as HL-task or sMPL task. It is widely used in the experimental literature and is tested to measure risk attitudes outside the lab consistently (Harrison and List, 2004; Harrison et al., 2007) and captures a wide range of risk attitudes.5 Consider Fig. 2 for a graphical illustration for this mechanism. A subject faces ten choices between two lotteries (option A or option B). Option A pays D 2 in the first state and D 1.60 in the second state. Option B pays D 3.85 in the first and D 0.10 in the second state. The payoff of option A exhibits a lower variance than the payoff of option B. In the tenth row the expected payoff of option B strictly dominates the expected payoff of option A as the amount of D 3.85 is paid for sure. Hence, a rational individual prefers B over A at least in row ten. The higher the row number, the higher probability that the first state is paid out. The more rows a subject opts for option B, i.e., the earlier a subject switches from option A to option B, the higher the subject’s risk tolerance. For the subject’s payoff, one row is randomly chosen and a lottery according to the probability distribution of this row is played. In order to enforce monotonicity of the risk preferences, the subjects have to state the marginal row for switching from A to B (Andersen et al., 2006).6 Although there are some concerns that this elicitation mechanism might be prone to framing effects (see Harrison and Rutström, 2008; Levy-Garboua et al., 2011), a major advantage of the sMPL measure is its symmetry. A subject always compares two lotteries having equal probability distributions but different outcomes. In other tasks subjects face a trade-off between a lottery and a certainty equivalent (e.g., Gneezy and Potters, 1997 or Dohmen et al., 2011). This could potentially bias (e.g., less sophisticated) subjects toward the certainty equivalent as the single value is easier to evaluate than the more complex structure of the lottery.

4 5 6

Throughout the paper, the data is scaled in ‘D 10,000 not invested’ such that it ranges from 0 to 10 and larger number represents higher risk tolerance. The sMPL captures CRRA coefficients ranging from −0.95 to 1.37 (see Holt and Laury, 2002). Holt and Laury (2002) do not find any significant effects on the distribution of preferences when excluding subjects with non-monotonic answers.

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Table 1 Descriptive statistics: surveys and lab experiment. Variable

Part 1: Surveys Web

N Year born Female Partner Parent High incomea Uni degree Counsel. exp. (in years) Stated risk attitudeb D 100,000 questionc sMPLd a b c d

Part 2: Lab Experiment SOEP

Mean

SD

84 1979 0.57 0.41 0.20 0.02 0.59 – 3.54 7.62 5.30

– 10.0 0.56 0.62 0.40 0.15 0.50 – 1.81 2.70 1.78

Mean 20,750 1959 0.52 0.77 0.62 0.01 0.21 – 1.90 9.09 –

Non-prof.

Junior prof.

Senior prof.

SD

Mean

SD

Mean

SD

Mean

SD

– 17.71 0.50 0.42 0.49 0.07 0.41 – 2.13 1.98 –

77 1986 0.56 0.26 0.05 0 0.94 – 5.26 4.70 6.81

– 6.29 0.50 0.44 0.22 0 0.25 – 1.39 3.29 1.56

52 1989 0.46 0.23 0.02 0 1.00 1.02 5.08 6.00 6.33

– 1.06 0.50 0.43 0.14 0 0.00 1.07 1.52 2.44 1.78

38 1973 0.18 0.66 0.47 0.11 0.63 10.97 4.68 6.89 6.32

– 11.0 0.39 0.48 0.51 0.31 0.49 8.27 1.71 3.18 2.08

Monthly net income above D 6000 (approx. 8460$). Scale: 0 (= risk averse) to 10 (= fully prepared to take risks). Amount not invested in D 10,000. First row subject choose option B.

Both risk tasks are paid off with real money at stake. The use of incentive compatible risk elicitation mechanisms allows us to get full control on the risk a subject takes when facing a decision. By using these lotteries, the decision maker has ex-ante full information on the probability distribution and the potential outcomes of the task. 3.2. Experimental procedure The eight target profiles are chosen from a web-based survey.7 This allows us to generate profiles showing the necessary heterogeneity in their sociodemographic characteristics. Besides sociodemographics, we elicit their choices in the D 100,000 question and the sMPL. As a second survey we use the German Socioeconomic Panel (SOEP).8 This is a large-scale dataset that surveys approximately 20,750 subjects on a yearly basis and is therefore a powerful and representative tool for our purpose. In the final step of our analysis, we use the data from the SOEP to compute representative counterparts of the profiles from our web-survey. The experimental sessions took place between April 2011 and January 2012. In total, 167 subjects in the role of predictors participated. In the subject pool we have three types of subjects: senior professionals, junior professionals and non-professionals. In the existing literature there is evidence that the decision making of financial professionals is significantly different from the general population (MacGregor and Slovic, 1999; Nofsinger and Varma, 2007). We are interested whether and to which extent we can detect the behavioral biases in the decision making of subjects that give financial advice every day. The senior financial professionals have a counseling experience of approximately 11 years on average. As a third group we study junior professionals, which are similar to the non-professionals in their sociodemographics while at the same time they decided for the same profession as the senior professionals. The non-professionals were recruited via the AWI-lab at Heidelberg University where all sessions with non-professionals were run.9 The senior professional advisors were recruited from a large German financial advisory agency and from local banks. The junior advisors were recruited from a banking specific advanced training institution.10 After finishing high school, the junior professionals enter a study program in financial advisory at an applied university which contains practical counseling in up to 50% of time. Detailed information on the subject pool and descriptive statistics are given in Table 1. The experiment lasted approximately 50 min. The average payoff was D 11.92. In the following we present the three treatments (SELF, RANK, PAY).11 RANK and PAY differ in the way the information is provided to the subjects. The sociodemographic information in RANK and PAY is drawn from the following categories of the profiles’ sociodemographic characteristics: age, education, family status, income, gender, having children and selfassessment of risk-taking in financial matters. The possible realizations of these variables are shown in Table 2. We chose these categories as they are found to be the major correlates with risk attitudes (see Hallahan et al., 2004; Gaudecker et al.,

7 Participants were recruited via E-mail and were asked to further distribute the survey. Among all participants who completed the web-based survey we raffled off D 50. 8 See www.diw.de/soep for details. The D 100,000 question was included in the year 2009. 9 The experiment was programmed on a PHP-platform and accessible via a web browser. 10 We ran seven sessions with professionals – three in the lab and four on-site. 11 Between SELF and RANK, there is another treatment which is not reported, but we do not expect any interference as there is no feedback. For details see Roth and Voskort (2012).

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Table 2 Information in RANK and PAY. Age Education Family status Net income Gender Parenthood Risk index

Age in years University, advanced training, training, in training, no formal training Single, partner, married, divorced, living separated, widowed Up to D 1000, D 1001–D 3000, D 3001–D 6000, more than D 6000 Male, female Having children, having no children Self-assessment of risk with the question: Regarding financial matters, are you generally a person who is fully prepared to take risks or do you try to avoid taking risks? (0 = risk averse to 10 = fully prepared to take risks)

Table 3 Profiles in RANK and PAY. Age

Education

Family status

Net income (in D )

Gender

Parent

Risk indexa

100,000b

SOEP meanb

sMPLc

64 38 25 30 36 57 41 21

University Training Econ student Training Adv training University University Econ student

Married Single Partner Married Single Married Divorced Single

>6000 1001–3000 <1000 1001–3000 3001–6000 3001–6000 >6000 <1000

Male Female Male Male Male Female Female Female

Yes No No Yes No Yes No No

1 2 5 1 1 0 1 4

8 10 6 6 8 6 8 10

7.45 9.17 8.71 8.99 6.76 9.38 7.50 8.41

5 6 6 8 5 7 5 4

a b c

Scale: 0 (=risk averse) to 10 (=fully prepared to take risks) Amount not invested in D 10,000. First row subject choose option B.

2011; Dohmen et al., 2011). The regression models employed in the further analysis control for possible correlations between these categories. 3.2.1. Selection of profiles In total, eight profiles are used in RANK and PAY – four for each treatment. These profiles are chosen from the web-based survey and are displayed in Table 3. The choice of the profiles is important to achieve heterogeneity across the profiles and to gain variation in the beliefs. Therefore the set of profiles is a balanced and diversified sample over age, education, family status, income, gender, and parenthood (see Table 3). By using SOEP data, for the given characteristics of each profile it is possible to compute a representative answer for the D 100,000 question (see Table 3, column ‘SOEP mean’). To avoid a distortion of the incentives we chose the profiles in a way that they do not show any extraordinary risk attitudes relative to the individuals with identical characteristics in the SOEP. Where necessary in the analysis, we use the representative ‘SOEP mean’ instead of the profile choices. The profiles’ decisions are only used for the incentivation of the predictors in the experiment. 3.2.2. Treatment SELF The SELF treatment elicits the subjects’ sociodemographics and their risk preferences by using the measures introduced in Section 3.1. 3.2.3. Procedure treatment RANK In each of the treatments RANK and PAY, the subjects’ task is to predict the risk preferences of four profiles. Their order and the allocation to the treatments of the profiles are randomized. Each subject has to predict both risk measures for each profile. If the prediction is correct, the subject receives D 0.50 for each profile, otherwise nothing.12 Before making the prediction, the subject can influence which sociodemographic attributes of the profiles are uncovered. At the beginning of the RANK treatment, predictors state a ranking over the seven attributes (e.g., 1. age, 2. gender, 3. income, 4. risk index, etc.) of Table 2. For each profile the computer draws a random number which is applied to the ranking and determines how many categories are uncovered.13 Once the ranking is submitted to the computer, we show one profile after another, the subject predicts the risk preferences for each profile, for each with a new random number of attributes. The procedure attaches a higher probability to be uncovered on characteristics placed on a higher rank. Therefore the higher ranks reflect a higher informational value of a characteristic for the predictor. The ranking itself does not depend on a predictor’s risk preferences.

12 13

This payment procedure induces an incentive compatible elicitation of the predictions. The random number is drawn from a uniform distribution on the interval [1,7]. Hence, the category on the first rank is observed for sure.

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Fig. 3. Course of action in treatments RANK and PAY.

3.2.4. Procedure treatment PAY In the PAY treatment, predictors can freely choose whether a characteristic is uncovered. For each characteristic, and for each profile, the subject has to choose and pay separately (Fig. 3). The characteristics are priced according to a convex pricing rule. The first characteristic costs D 0.01 while buying all seven characteristics amounts to D 0.99 in total.14 We designed the treatments RANK and PAY to estimate the stereotypes on multiple demographics. For this, the treatments have to generate sufficient exogenous variation in these demographic characteristics for an estimation. The heterogeneous profiles already provide variation on the demographics, but if for all predictors all characteristics were visible, we could only draw conclusions on these specific target profiles. We achieve additional variation as in RANK and in PAY not all characteristics are necessarily uncovered. For example, we can estimate the stereotype on family status by comparing the profiles with the uncovered categories single woman and married woman. In this case the variable age does not play a role (it is invisible) although it is correlated with being married. Moreover, by varying the available information we can generalize our results and we overcome the fact that the subjects analyze certain profiles. In RANK, a random draw varies the number of visible characteristics and therefore guarantees that the number of visible attributes is exogenous to the predictor. This allows us to estimate causal marginal effects of more than one demographic attribute with a single profile. To explore the accuracy of the forecasts, the predictors need to see the relevant information on the profiles. In RANK, the predictors will see 3.5 characteristics on average. In the PAY procedure, the predictors can freely determine which information they want to use. The PAY mechanism leaves the predictor with a trade-off between the number of categories and the price. A potential concern is that the number of bought categories might correlate with the predictors’ risk preferences but in the data there is no such evidence.15 In the experiment there are uniform incentives applied to all groups of predictors. Therefore, the incentives might be distorted for high income predictors because the payoffs relative to their income are much lower. However, there is no significant rank correlation between income and success in RANK and PAY or experimental payoffs and income such that we can reject these concerns.16 Moreover, in none of the applied statistical tests there is evidence that subjects behave differently in the treatments RANK and PAY.17 Finally, the treatments SELF, RANK and PAY provide the setting to test the following aspects: • False consensus: For all predictions we observe the predictors’ risk preferences and the predictions. Having these data we can precisely estimate the false consensus. • Stereotyping: In the treatments RANK and PAY, the available sociodemographic information is varied when predicting the risk preferences of the profiles. This allows us to estimate the effect of the different pieces of information on the prediction. • Target’s preferences: The predictors can draw on a self-assessed risk measure of the target. We analyze whether predictors use this information. • Prediction errors: We can estimate the quality of the prediction by comparing the prediction with choices of the representative profiles. • All treatments are executed with three subject groups: non-professionals, junior professionals and senior professionals. By this design we can explore the differences in decision making between the groups. 4. Results After introducing the different treatments, the following section presents the results. Section 4.1 studies differences between the predictors’ self-assessments and their beliefs. Section 4.2 investigates how information on the profiles’ sociodemographic characteristics affects the predictor’s belief and Section 4.3 inspects whether the predictors’ beliefs are correct.

14 Price for the second characteristic: D 0.02, the third: D 0.03, the fourth: D 0.06, the fifth: D 0.12, the sixth: D 0.24, the seventh: D 0.50. As the minimum earnings in SELF amount to D 4, net losses are excluded. 15 The data show no significant correlations between the risk tasks and the number of bought categories (N = 167, 100,000: r = 0.023. p = 0.77; sMPL: r = −0.06, p = 0.44). 16 Rank correlation coefficients (N = 167): correct prediction in RANK, PAY & income: r = −0.055, p = 0.48; payoffs and income: r = 0.015, p = 0.85. 17 In the analysis, the data of RANK and PAY is pooled, as a Wilcoxon signed-rank test does not detect a statistical difference between the beliefs (p = 0.43, N = 167).

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Table 4 Predictor’s own risk preferences and predictions (Means).

D 100,000

sMPL

Self Belief Sign rank test Self Belief Sign rank test

Non-prof

Junior

Senior

4.70 6.89 N = 77, p < 0.001 6.81 7.27 N = 77, p < 0.05

6.00 7.69 N = 52, p < 0.001 6.33 7.31 N = 52, p < 0.001

6.89 7.34 N = 38, p = 0.717 6.32 6.45 N = 38, p = 0.711

Average choices in risk measures: predictors’ own choices (self) and predictions (belief) D 100,000: amount not invested in D 10,000. sMPL first row to choose option B.

4.1. Self-assessment and beliefs Table 4 displays, for the three predictor groups, the means of the predictors’ risk preferences for each of the two risk measures (rows ‘Self’). The rows ‘Belief’ show the means of the predictions – aggregated over RANK and PAY. We find statistically different distributions for the self-assessment and the belief at the 1%-level (respectively 5%-level) for the non-professionals and the juniors. Both groups, on average, take more risk in their own decisions compared to the beliefs about their targets’ risk preferences. In other words, the non-professional and junior predictors perceive their targets to be less risk tolerant. Other studies show ambiguous results regarding this question. Our finding is in line with Eckel and Grossman (2008), while Hsee and Weber (1997), Faro and Rottenstreich (2006) and Chakravarty et al. (2011) find the opposite correlation. In contrast, for the senior professionals, their self-assessment and the formed beliefs do not differ significantly. This finding might be interpreted as a false consensus bias, namely that they rely too heavily on their own risk preferences when assessing the profiles. This could be driven by several factors disregarded in the analysis above. The non-professionals as well as the junior professionals differ in terms of demographics (e.g., the targets are older on average, are parents more often, etc.) from the target population (see Tables 1 and 3). Hence, the deviance between the personal preferences and their predictions is not surprising (see Ames, 2004). In contrast, the senior professionals and the target profiles show more demographic similarities. In the below section we control for demographics and use fixed effects estimations to single out these effects. 4.2. Stereotyping and false consensus In order to analyze stereotyping and false consensus on the predictions, Table 5 presents six regression models – three for each risk measure. As these models control for the demographic attributes of the judged profile, we analyze how the stereotypes on these demographics find their way into the predictions. To estimate the false consensus effect we control for the predictor’s risk preferences. The regression analysis contains the pooled data of RANK and PAY. There are observations of 167 predictors. As each of them predicts the risk preferences of eight profiles we gain 1336 observations in total. To control for independence, in all analyses the standard errors are clustered on the subject level. Table 5 displays six regression models. In models (1), (2) and (3), the dependent variable is the inverted prediction in the D 100,000 question (amount not invested) while models (1a), (2a) and (3a) refer to the sMPL. The key aspect of these models is to control for the fact that the predictions rely on different sets of sociodemographic information. That is, the information differs with respect to what predictors see (e.g., male or female) and how much they see (i.e., how many categories). The number of available demographic attributes depends on the choice to buy (PAY) as well as the ranking and the random draw (RANK). To control for this issue the models have two sets of regressors. In the upper set (1{seen = 1}), dummy variables indicate a value of one if the corresponding characteristic is visible and zero otherwise. The variables in the part below (1{seen = 1} · {soc dem = / 0}) are interaction terms carrying the value of the variable (e.g., gender = male) and are interacted with the upper dummy variables. Thus the value of the characteristic shows up only if it is observable; otherwise the variable is zero. The upper set of dummy variables reflects the effect if the actual value of the variable is zero (e.g., the effect on male, as the gender dummy variable has a value of zero for male and one for female). To interpret the marginal effects of a demographic characteristic we have to consider the sum of the dummy variable (seen) and the interaction term (1 seen value). This total effect measures the effect of stereotyping (e.g., male vs. female) in the process of predicting others’ risk preferences. Table 6 presents the Wald tests for the joint significance. Consider the variable male in model (1). Since we report the amount not invested in the lottery, the prediction increases by D 6650 – on average – if a male is assessed. The investment decreases by D 11,130 for females. When summing up both coefficients to observe the gender stereotype, males are predicted to invest D 4480 more in the lottery than females. In model (1a), the effect points in the same direction and is significant as well. Hence, males are associated with a higher risk tolerance than females which is largely undisputed in the literature (Croson and Gneezy, 2009). When considering the effect of income, we find that the predictors expect individuals with a high income to be more risk tolerant. The effect is jointly significant for both measures (models 1 and 1a). However, the effect is driven by the profiles

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561

Table 5 Regression results: belief formation. Model

(1)

Dependent variable

100,000

1 {seen = 1}

Male Low income Year of birth No uni degree Single No children Risk index

/ 0} 1 {seen = 1} · {soc dem =

Female High income Year of birth Uni degree Partner Children Risk index

Risk pref.

(2)

(3)

(1a)

(2a)

(3a)

sMPL

−0.665*** (0.167) 0.00917 (0.22) 18.75 (15.18) −0.22 (0.238) −0.001 (0.194) −0.206 (0.188) 3.368*** (0.329)

−0.650*** (0.148) 0.08 (0.198) 13.98 (14.71) −0.17 (0.215) 0.0221 (0.171) −0.413** (0.173) 3.343*** (0.293)

−0.653*** (0.145) 0.0817 (0.197) 11.19 (14.34) −0.165 (0.206) 0.0288 (0.164) −0.415** (0.172) 3.389*** (0.279)

−0.246 (0.144) 0.0868 (0.156) 10.79 (12.95) −0.119 (0.19) −0.163 (0.184) −0.177 (0.163) 1.565*** (0.215)

−0.232* (0.14) 0.0473 (0.14) 11.98 (12.76) −0.271* (0.164) −0.183 (0.162) −0.156 (0.146) 1.668*** (0.212)

−0.204 (0.14) 0.0402 (0.144) 11.63 (12.53) −0.267 (0.162) −0.201 (0.163) −0.121 (0.145) 1.668*** (0.214)

1.113*** (0.213) −1.411*** (0.249) −0.00953 (0.00803) 0.0215 (0.242) 0.275 (0.233) 0.656*** (0.252) −0.886*** (0.115)

1.129*** (0.206) −1.430*** (0.242) −0.00711 (0.00773) 0.00338 (0.232) 0.193 (0.222) 0.750*** (0.239) −0.888*** (0.107)

1.155*** (0.205) −1.458*** (0.239) −0.00572 (0.00768) 0.0139 (0.231) 0.189 (0.22) 0.768*** (0.236) −0.878*** (0.105)

0.704*** (0.193) −0.633*** (0.235) −0.00566 (0.00723) 0.121 (0.212) 0.245 (0.201) 0.295 (0.228) −0.407*** (0.0765)

0.641*** (0.177) −0.610*** (0.215) −0.0062 (0.0065) 0.267 (0.192) 0.259 (0.184) 0.302 (0.209) −0.427*** (0.069)

0.642*** (0.176) −0.633**** (0.214) −0.00603 (0.00646) 0.26 (0.191) 0.281 (0.183) 0.291 (0.208) −0.428*** (0.0689)

0.183*** (0.0205)

0.186*** (0.0282) −0.143*** (0.0475) 0.103** (0.0518)

0.397*** (0.028)

0.350*** (0.033) −0.0326 (0.0597) 0.183*** (0.0693)

Self Self · junior Self · senior

Junior

0.667*** (0.117)

0.422*** (0.115)

1.278*** (0.319)

0.0232 (0.0983)

0.197** (0.0924)

0.386 (0.426)

Senior

0.654*** (0.154)

0.266* (0.147)

−0.439 (0.389)

−0.661*** (0.154)

−0.468*** (0.139)

1.644*** (0.499)

Rank

0.0711 (0.119)

0.0885 (0.113)

0.0886 (0.112)

0.0195 (0.105)

0.039 (0.0955)

0.0321 0.0953)

Constant

6.226*** (0.285)

5.425*** (0.297)

5.404*** (0.311)

6.934*** (0.246)

4.101*** (0.292)

4.423*** (0.33)

N R2 Adjusted R2 Profile FE

1,336 0.43 0.419 Yes

1,336 0.474 0.464 Yes

1,336 0.483 0.472 Yes

1,336 0.23 0.216 Yes

1,336 0.353 0.341 Yes

1,336 0.36 0.347 Yes

Standard errors clustered on subject level in parentheses. Dependent variable: beliefs in risk tasks. For a better readability, the belief in the D 100,000 question is denominated in ‘D 10,000 not invested’. 1{seen = 1} indicates a characteristic is visible. {soc dem} indicates the realization of the characteristic. The left-out category is 1{seen = 0}. The characteristics ‘income’, ‘education’ and ‘family status’ are binary variables. ‘Income’: high income (value = 1) and low income (value = 0), ‘education’: university degree (value = 1) and no university degree (value = 0), ‘family status’: having a partner (value = 1) and not having a partner (value = 0). Fixed effects control for the eight target profiles. * p < 0.1. ** p < 0.05. *** p < 0.01.

indicating an individual with a high income as the coefficient is highly significant, while the coefficient for low income in the upper part of Table 5 is not.18 This finding is in line with Dohmen et al. (2011) who report a positive correlation between risk taking and income.

18

High income refers to a monthly net income of D 6000 and more.

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Table 6 Wald tests on joint significance (p-values) in Table 5. Model

(1) (2) / 0} = 0 H0 : {socdem = 0} + {socdem =

Dependent variable

100,000

Gender Income Year of birth Education Family status Parenthood Risk index

0.002 0.000 0.421 0.389 0.228 0.140 0.000

(3)

(1a)

(2a)

(3a)

0.003 0.014 0.256 0.988 0.647 0.460 0.000

0.002 0.009 0.247 0.971 0.625 0.378 0.000

sMPL 0.005 0.000 0.316 0.346 0.235 0.169 0.000

0.002 0.000 0.420 0.389 0.228 0.140 0.000

0.003 0.014 0.256 0.988 0.647 0.460 0.000

Finally, the variable risk index is highly significant as well. On average the prediction increases by D 8860 for each point the profiles’ risk index variable increases. Both coefficients are jointly significant. This result is more surprising than it seems at a first glance. Krueger and Clement (1994) as well as Hadar and Fischer (2008) report that predictors primarily rely on their personal preferences instead of focusing on the target’s preferences. These findings show that if information on a target’s preferences and demographic information is given, the predictors draw on the different pieces of information but all of them are incorporated in the belief. The binary variables ‘Junior’ and ‘Senior’ identify the two groups of professionals while the non-professionals are left out. The coefficients for junior and senior professionals are significant and positive.19 For the D 100.000 question, both groups of professionals predict the profiles on average to be more risk averse than the non-professionals do. When considering the sMPL in models (1a) and (2a), on average, the senior professionals attach less risk aversion to the profiles relative to the non-professionals. For the junior professionals, we either find the effect of the D 100,000 question confirmed (model 2a) or the coefficient is insignificant (model 1a).20 Hence, over both measures, we find ambiguous results as the behavior of the professionals points in different directions. While models (1) and (1a) investigate stereotyping, the remaining models study a false consensus effect in addition. Therefore we include the predictors’ risk preferences denoted by ‘self’ into the models (2) and (2a). We interpret the correlation between the predictors’ risk attitudes and the prediction as a false consensus effect. The ‘self’ variable is highly significant in model (2) and in (2a) and of considerable magnitude. For example, in model (2) for every Euro the predictor invests in the lottery, the prediction increases by D 0.18. For the sMPL task, the ‘self’ coefficient is estimated to be 0.397. If the predictor increases the willingness to take risk by one row, the prediction increases by 0.397 rows – on average. In comparison, in model (2a), we estimate the gender effect to be 0.409 while the false consensus coefficient is 0.397. Hence, the effect on the prediction is nearly the same whether a predictor recognizes a female or chooses to switch one row away from the conditional mean. Therefore the results are in line with the findings of Chakravarty et al. (2011), who report similar findings for the false consensus effect in a sMPL. In contrast, the significant evidence contradicts Engelmann and Strobel (2000) as they report that the false consensus effect disappears when decisions are made with incentives. When comparing models (2) and (2a) to models (1) and (1a) the coefficients remain largely stable in magnitude and significance. We thus observe multiple effects on the beliefs simultaneously. Predictors use demographic information, the target’s self-assessment on risk attitude and their own risk attitude to form their beliefs. Models (3) and (3a) study whether the false consensus effect is of different magnitude in the three subjects groups. In these models ‘self’ refers to the false consensus of the non-professionals. ‘Self · junior’ denotes the false consensus of the junior relative to the non-professionals. The same is true for ‘Self · senior’.21 In model (3) junior professionals do not show a significant false consensus.22 In contrary, in model (3a) the false consensus of the junior professionals is not statistically different from the false consensus of the non-professionals. However, in both models the senior professionals show significantly higher false consensus. The estimated effect is of approximately twice the magnitude compared to the other groups. Generally, we would expect subjects who are concerned with the prediction of risk preferences regularly to develop strategies to assess others that go beyond the false consensus because of learning and feedback effects. Surprisingly, in our data we observe the opposite. 4.3. Prediction error The following section investigates whether the predictions meet the profiles’ risk preferences. For this, we combine the experimental data with data of the SOEP survey, which allows us to generalize our results and to make statements on a

19 20 21 22

Wald-tests (H0 : senior–junior = 0) do not reject for both models. For both models Wald-tests (H0 : senior–junior = 0) reject at p < 0.05. The omitted category is ‘Self non-professional’. A Wald test (H0 : self – self junior = 0) rejects with p < 0.001.

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Table 7 Regression results: prediction errors. Model dependent variable

(4) (belief-choice)2 Sum seen

−0.652*** (0.224) −0.945 (0.990) 1.823 (1.173) −2.105** (1.037) 0.970 (1.009) 0.322 (1.124) −0.101 (1.274) −8.943*** (1.826)

Year of birth 1 {seen = 1}

(5) (belief-choice)2

Education Family status Income Gender Children Risk index −4.599*** (1.253) −2.197 (1.819)

−4.011*** (1.118) −3.009* (1.747)

0.433 (0.671) 11.18*** (1.680)

−0.652 (0.640) 16.92*** (2.383)

N R2 Adjusted R2

1336 0.163 0.156

1336 0.222 0.211

Profile FE

yes

yes

Junior prof. Senior prof. Rank Constant

OLS regressions, standard errors clustered on subjects in parentheses, dependent variable: (belief-choice)2 , choice calculated from SOEP. 1{seen = 1} indicates if a characteristic is seen. * p < 0.1. ** p < 0.05. *** p < 0.01.

representative level. As discussed above, the SOEP contains data only for the D 100,000 question, which restricts the analysis to this risk measure. In the first step, we define our measure of prediction errors. When computing the risk choices of the profiles, we have to account for the fact that the predictors only observe a part of the demographic information of a certain profile when making their predictions. To compute the risk preferences for a representative profile, we condition on characteristics of the respective profile which are visible for the predictor. For example, if only male is uncovered, we compute the average investment in the D 100,000 question of the SOEP data conditioned on all males. If male and parenthood is visible, the mean is conditional on all fathers. Depending on the ranking and the random draw in RANK and the decision to buy in PAY, the characteristics vary for each prediction. To evaluate the quality of the prediction we define the prediction errors as the square difference between the predicted investment and the computed average investment of the profile.23 Table 7 displays the results of two regression models to investigate whether demographic information and if so, which demographic information helps to decrease the prediction errors. In both models the prediction errors are the dependent variable while the explanatory variables differ. In model (4) the variable ‘sum seen’ constitutes the number of uncovered sociodemographic characteristics when making the prediction. In model (5) the sum of visible characteristics is split up into the different categories. For each category a binary variable is included which indicates a one if the category is uncovered and a zero otherwise. A second set of regressors controls for the treatment and the subject type. The omitted category is ‘non-professional’. Both models include fixed effects for profiles and predictors. In model (4) the variable ‘sum seen’ is significant at the 1%-level. The negative sign indicates that if more categories are available, the precision of the prediction increases. The marginal effect of −0.652 is economically relevant as the mean of the squared prediction error amounts to approximately 8.7.

23 The results are qualitatively robust when using the absolute deviation between prediction and profile choice. We opt for the squared difference, which is stricter, because larger errors are mapped to a larger deviation.

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When considering model (5), the coefficient risk index is negative and significant. It indicates that if the profile’s selfassessed risk preference is visible, the prediction error decreases by approximately nine units. This confirms the significant predictive power of the variable risk index. This is also true for family status as it decreases the prediction error by 2.1 units on average. A further considerable result shows up with respect to the subject groups. The prediction error of the junior professionals is significantly lower compared to the (omitted) non-professionals. In model (4) this coefficient has a relevant impact with a value of −4.6. When comparing junior and senior professionals, the junior professionals have significantly lower prediction errors than senior professionals. The coefficient of the senior professionals is not significantly different from the coefficient of the non-professionals. In model (5) both groups of professionals perform significantly better compared to the non-professionals. In addition to the junior professionals, also senior professionals have a significantly lower prediction error. In summary, these models demonstrate that if more information is available the accuracy of prediction increases. The variables risk index and parenthood improve the prediction of risk preferences. Therefore, the target’s self-assessment turns out to be a major predictor of risk preferences. A main result is that professionals outperform non-professionals in making precise predictions. Interestingly, young professionals’ beliefs are even more precise than the beliefs of the senior professionals. 5. Conclusion This experimental design allows to test several inferential strategies to assess others’ risk preferences in a single setup. First, there is significant evidence for the false consensus effect. Interestingly, the effect is the strongest for senior financial professionals and the smallest for junior professionals. This holds true in terms of correlation as well as in absolute terms. This is an interesting result, as the subjects with the longest counseling experience show the largest false consensus. Second, we identify the target’s self-stated risk preference as a major correlate with the prediction. This clearly demonstrates that predictors are willing to consider the target’s preference when forming their beliefs. Third, the predictors use stereotypes on gender and income to make their prediction. The finding is surprising as this indicates that predictors do not rely entirely on the target’s self-assessment but also draw on other information about the target. When we investigate the accuracy of the predictions, junior and senior financial professionals are more successful in assessing others’ risk preferences than the non-professionals. 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