Six Facts about Risk Attitudes: Evidence from a Large, Representative, Experimentally-Validated...

Five Facts about Risk Attitudes: Evidence from a

Large, Representative, Experimentally-Validated

Survey ∗

Thomas Dohmen, Armin Falk, David Huffman, Uwe SundeJurgen Schupp, Gert Wagner

First DraftJanuary 28, 2005

Abstract

This paper marks an important step forward in the study of individ-ual risk attitudes. We use a novel set of survey measures, and a muchlarger sample than previous studies (22,000 individuals) to provide arepresentative picture of risk attitudes across an entire population. Oursurvey measures are attractive for studying determinants of risk atti-tudes, and also for testing fundamental assumptions about the stabilityof risk preferences across life domains. We also explore the impact ofchanges in question-framing on risk attitudes. The paper contributesan additional methodological innovation by testing the ability of thesurvey measures to predict real behavior under uncertainty, in a lab-oratory experiment. Ultimately, the analysis in the paper generatesfive robust facts about risk attitudes: (1) women are more risk aversethan men, at all ages during adulthood, across various domains of life,and independent of question-framing; (2) risk aversion increases withage, in various domains of life; (3) individuals with highly-educatedmothers are less risk averse; (4) the assumption of a single risk pref-erence, stable across domains, is a reasonable approximation; (5) thesurvey measures we use are valid predictors of actual behavior underuncertainty.

∗Corresponding Author: David Huffman, IZA Bonn, P.O. Box 7240, 53072 Bonn,Email: [email protected]

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1 Introduction

Wars are fought, fortunes are gained (or lost), and true love is pursued,

depending on attitudes towards risk. Risk is also pervasive in economic life.

Uncertainty is the rule rather than the exception when it comes to important

economic decisions. As a result, individual and household attitudes towards

risk are of fundamental importance in economics. Despite the importance

of attitudes towards risk, there is relatively limited evidence on what deter-

mines risk attitudes, how risk attitudes are distributed in the population,

and how risk attitudes should be measured.1

This paper makes a number of methodological contributions to the study

of risk attitudes. Survey studies have typically measured risk attitudes by

eliciting an individual’s certainty equivalent for a hypothetical lottery (Har-

tog, Carbonell, and Jonker, 2000; Guiso and Paiella, 2001; Guiso et al, 2002;

Serrano and O’Neill, 2004). Often, these studies have been limited by small

sample sizes, or samples that include only special groups of people. Exper-

imental studies have typically used small, real-stakes lotteries choices, but

have mainly studied the risk attitudes of college students (e.g. Holt and

Laury, 2002). We use a novel set of survey measures, collected for a much

larger sample than in previous studies: the data include roughly 22,000 in-

dividuals living in Germany. The sample is the 2004 wave of the German

Socio-Economic Panel (GSOEP), which is carefully constructed to be repre-

sentative of the population as a whole. The GSOEP measures are attractive

because they ask about risk attitudes in multiple ways. Respondents rate

their attitude towards risk in general, as well as their risk attitudes in the

context of six specific domains of life: driving, financial matters, sports,

career, health, and trusting others. Respondents also indicate how much1 In a recent contribution on risk, Gollier (2001) laments the lack of research on risk atti-

tudes: ”It is vital that we put more effort on research aimed at refining our knowledgeabout risk aversion. For unclear reasons, this line of research is not in fashion thesedays, and it is a shame.”

they would choose to invest in a specific, hypothetical investment scenario.

This variety of risk measures makes it possible to explore the stability of

risk attitudes across domains, and study the response of risk attitudes to

changes in framing, from abstract to more concrete. The paper contributes

an additional methodological innovation by testing the validity of all the

GSOEP survey measures with behavioral data on risk taking. The predic-

tive powers of the questions are tested in a laboratory experiment in which

subjects make real-stakes lottery choices.

The paper generates five facts about risk attitudes. The first fact is a

robust gender difference. Women are significantly more risk averse than

men, as measured by self-reported willingness to take risks. This difference

is present at all ages, from early adulthood until old age, although there is

some evidence that the gap closes among the elderly, for some domains of life.

The gender differenc is robust when controlling for differences in observables,

both in a multivariate regression sense, and in a standard Oaxaca-Blinder

decomposition. Women are also more risk averse in the context of all six

domains of life asked about in the GSOEP, as well as the more concrete

context of the hypothetical investment scenario. The finding that gender

differences persist even when context is added contrasts with results from

Schubert et al. (1999), showing that gender differences disappear with the

addition of context, but is consistent with Fehr-Duda et al. (2004). Also

noteworthy is the fact that the investment scenario gives explicit stakes

and probabilities, minimizing the scope for gender differences in pessimism

or optimism to explain the differences in risk aversion we measure. This

contrasts with Weber, Blais, and Betz (2002), who conclude that greater

female pessimism seems to explain gender differences in risk attitudes. The

gender difference we observe could reflect a difference in the way that women

and men weigh probabilities in decisions, consistent with the findings of

Fehr-Duda et al (2004)), or could possibly reflect a difference in attitudes

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towards risk per se.

The second fact is an age profile in willingness to take risks. Increasing

age is associated with increasing risk aversion. This pattern persists after

controlling for observable characteristics, and is evident for all six domains

of life considered in the GSOEP. Age also has a negative impact on will-

ingness to invest, in the hypothetical investment question. There has been

relatively little research on the impact of age on risk preferences. An excep-

tion is Harbaugh, Krause, and Vesterlund, (2002), which studies changes in

probability weighting with age and finds evidence of an age difference. One

caveat is that, although age is plausibly exogenous with respect to risk at-

titudes, a tendency for people who are more risk averse to live longer could

possibly explain part of the positive relationship we observe.

A third fact concerns the impact of family background on risk attitudes.

We are able to study family background by using information on parental ed-

ucation included in the GSOEP. The main result is that mothers with higher

levels of education tend to have children who are less risk averse, in most

domains of life. Notable exceptions are the domains of financial matters and

health. The impact of father’s education is more erratic across domains of

life, and also seems to interact with age. This evidence is partially consistent

with Hartog, Carbonell, and Jonker (2000), who find a similar significant

effect of mother’s education, in a survey of professional accountants in the

Netherlands.

A fourth fact is that individual risk attitudes are strongly correlated

across different domains of life. This is relevant for the debate over whether

risk attitude is in fact a single trait, stable across situations and contexts,

as is typically assumed by economists. Slovic (1964) was an early proponent

of the alternative view, that risk is not amenable to study out of context,

and similar concerns have been raised more recently by Eckel and Grossman

(forthcoming) and others. Based on a factor analysis, we find that most

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of the variation in risk attitudes is explained by a single underlying factor,

which suggests the standard economic assumption is not unreasonable. On

the other hand, all of the other six factors explain a non-trivial proportion of

the variation, suggesting that there is some value added from asking about

risk in different domains separately.

A fifth fact is that the risk measures we use do in fact have predictive

power when it comes to behavior. We report the results of a laboratory

experiment, in which subjects make real-stakes lottery choices, and also

answer the exact same questions used in the GSOEP survey. Reassuringly,

we find that the general risk question, the question about risk the domain of

financial matters, and the hypothetical investment scenario are all significant

predictors of lottery choices in the experiment.

After presenting the five main results on determinants and measurement

of risk attitudes, the paper reports correlations between risk attitudes and

other personal characteristics, such as net income, occupation, education,

marital status, employment status, etc.. These characteristics are likely to

be at least partly endogenous to risk attitudes, so we refrain from mak-

ing any type of causal inferences in this section. However, the correlations

are potentially useful in terms of raising questions for future research on

the determinants, and also the economic consequences, of individual risk

attitudes.

The paper ends with a discussion, in which we consider some possible

implications of our findings, as well as questions that are raised by this new

evidence on risk attitudes. The overarching, and most provocative question

is: what could be the mechanisms behind the transmission of risk attitudes?

The rest of the paper is organized as follows. Section 2 describes the

GSOEP and the risk measures. Section 3 presents our five main results.

Section 4 reports additional correlations, between risk attitudes and personal

characteristics that may be endogenous to risk attitudes. Section 5 presents

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a brief discussion and conclusion.

2 Data Description

The GSOEP is a representative panel survey of the German population, with

the initial wave in 1984.2 The GSOEP surveys the head of each household

in the sample, but also gives the full survey to all other household members

over the age of 17. The risk questions used in this paper were asked for the

first time in 2004. Accordingly, we use the 2004 wave of the GSOEP, which

includes 22,019 individuals, in 11,803 different households.

The GSOEP asks eight different questions about risk attitudes. The first

question asks for attitude towards risk in general, allowing respondents to

indicate their willingness to take risks on an eleven-point scale, with zero

indicating complete unwillingness to take risks, and ten indicating complete

willingness to take risks. The next six questions all use a similar scale,

but explore how risk attitudes vary across different domains of life: driving,

financial investments, sports, career, health, and trusting others. All of these

seven measures are characterized by ambiguity, rather than uncertainty,

in the sense that they leave it up to the respondent to infer the typical

probabilities, and stakes, involved in a given risk domain.

The eighth risk question is different, because it poses respondents with

a concrete, albeit hypothetical, investment choice, with explicit stakes and

probabilities:

Imagine you had won 100,000 Euros in a lottery. Almost imme-

diately after you collect, you receive the following financial offer

from a reputable bank, the conditions of which are as follows:

There is the chance to double the money within two years. It is

equally possible that you could lose half of the amount invested.2 The panel was extended to include East Germany in 1990, after reunification. For more

details on the GSOEP, see www.diw.de/english/.

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Respondents are then asked what fraction of the 100,000 Euros they

would choose to invest, and are allowed six possible responses: 0, 20,000,

40,000, 60,000 80,000, or 100,000. Whereas the first seven risk measures

are useful for exploring risk attitudes across various domains of life, this

more-narrow investment question is useful because it is explicit in terms of

the probabilities and stakes, and incorporates even stronger context, as a

concrete investment decision. The precise wording of all the risk questions

is available upon request, in German and English.

3 Determinants of Risk Attitudes

3.1 Risk attitudes in a representative sample

Figure 1 describes the distribution of general risk attitudes in our sample.

Each bar in the histogram indicates the fraction of individuals choosing a

given number on the eleven point risk scale. The modal response is 5, but

a substantial fraction of individuals answer anywhere in the range between

2 and 8. There is also a notable mass, roughly 7 percent of all individuals,

who choose the extreme of 0, indicating a complete unwillingness to take

risks, whereas only a very small fraction choose the other extreme. Cutting

the scale between 5 and 6, as a reasonable classification of individuals into

categories of risk averse and risk tolerant, we find that approximately 70 per-

cent of individuals in the sample are risk averse. This is roughly consistent

with the survey studies from other countries, cited above, which typically

measure risk attitudes using certainty equivalents to hypothetical lotteries.

Insert Figure 1 about here!

3.2 Exogenous factors: gender, age, and parental education

In searching for determinants of individual risk attitudes, it is only possible

to make causal statements about individual or background characteristics

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that are themselves exogenous to risk attitudes. There are at least three

characteristics that plausibly fall into this category: gender, parental back-

ground, and age.

As a first look at potential determinants of risk attitudes, the lower

panel of Figure 2 shows the difference between the fraction of women, and

the fraction of men, choosing each point on the risk scale. This difference

is positive for low numbers, and negative for high numbers, giving a first

indication that women, as a group, are relatively more likely to report a

reluctance to take risks.

Figure 2 shows the relationship between age and risk attitudes, sepa-

rately for each gender. The shading in the two panels represents the pro-

portions of respondents choosing each number on the eleven point risk scale,

for each age. Clearly, the proportion of individuals who are risk averse, i.e.

choose low numbers on the scale, increases strongly with age. For men, age

appears to cause a steady increase in risk aversion. For women, there is some

indication that risk aversion increases more rapidly from the late teens to

age thirty, and then remains flat, until it begins to increase again, from the

mid-fifties until the end of life. Comparing the panels for men and women,

it is also apparent that women are more risk averse than men throughout

the entire age range, although the gap may narrow somewhat among the

elderly.


Another noteworthy feature of Figure 2 is that the different shaded bands

track each other quite closely over the entire age range. This suggests that

aggregating the risk measure from ten categories to a smaller number of

categories is likely to preserve most of the information in the risk measure.

Indeed, the risk measure with ten categories is strongly correlated with a

binary risk measure (corr = 0.77), in which answers 0 through 5 are classified

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as risk averse and 6 through 10 are classified as relatively risk tolerant. This

observation will lead us to adopt this simple, binary classification of risk

attitudes in parts of the analysis later on.

Figure 3 presents histograms of general risk attitudes by parental educa-

tion. Other aspects of family background could be relevant for risk attitudes,

e.g. parental income, but only parental education is available in the data.

We use information on whether or not a parent passed the ”abitur”, an

exam that comes at the end of university-track high school in Germany and

is a prerequisite for attending university.3 The histograms in Figure 3 give

some indication that family background does play a role in determining risk

attitudes. In particular, the mass in the histogram for individuals with a

more highly-educated mother, as measured by completion of the abitur, is

clearly shifted to the right, indicating a greater willingness to take risks.

There is also some evidence that father’s education has a similar impact on

risk attitudes, although the difference is less striking.


To determine whether the relationships observed in the raw data are ro-

bust once we control simultaneously for different observable characteristics,

we now turn to regression analysis. We estimate simple probit regressions,

for which the dependent variable is the probability that an individual is risk

averse, in the sense of having chosen a number less than 6 on the risk scale.4

In these regressions, and all subsequent regressions, our significance tests use

robust standard errors, corrected for possible correlation of the error term

between individuals from the same household. The only sample restriction3 There are two types of high school in Germany, vocational and college-track. Only

about 30 percent of students attend college-track high schools, and pass their abitur,allowing them to attend college. Thus, completion of an abitur exam is an indicator ofrelatively high academic achievement.

4 An alternative is to use the full, eleven point scale as the dependent variable, and usean estimation procedure that corrects for censoring in interval data. We have triedthis approach for all of our regressions, but find that it makes little difference for thequalitative results, and thus we report the simpler, probit results.

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in the analysis is the omission of individuals who have missing values for

any of the variables in the regression.

Table 1 summarizes the baseline regressions on determinants of risk atti-

tudes, which use the most general risk measure as the dependent variable. In

the simplest specification, presented in the first column, females are signifi-

cantly more likely to be risk averse, and the probability of being risk averse

increases significantly with age. Having a mother or father who is relatively

highly educated, in the sense of having completed the abitur, significantly

reduces the likelihood of being risk averse.

Insert Table 1 about here!

The second column in Table 1 adds interaction terms terms between

all independent variables. The gender and age effects remain positive and

significant. The interaction term between age and female is not significant,

indicating that the gender gap in risk attitudes does not change with age.

On the other hand, mother’s education is no longer significant, and father’s

education switches sign, to become positive and significant. The interaction

between father’s education and age is also significant, indicating that the

impact of the father decreases with age, or alternatively, that age has a

weaker effect for individuals with an educated father.

The third column in the table presents results from a specification that

controls for other (potentially endogenous) personal characteristics: mari-

tal status, presence of children, employment status, nationality, occupation,

education, and subjective health status.5 The fourth column adds net house-

hold income as a final control.6 In both columns, we see the same significant

gender and age effect. Mother’s education is no longer significant in column5 Coefficients for all demographic controls are shown in full in Section 4.6 Household income is constructed by summing the net incomes of all household members.

Adding household income reduces the number of observations considerably, due to arelatively large fraction of missing values for the personal income variable. Accordingly,we present results with and without household income.

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four, but the interaction term with age is negative and significant. In column

five, mother’s education is again negative and significant. Father’s educa-

tion is positive and significant in both columns, and the interaction term

with age is negative and significant.

As an alternative approach to studying the gender difference in risk at-

titudes, we also perform a standard Oaxaca-Blinder decomposition. This

decomposes the difference in risk attitudes across gender into two different

components, one due purely to differences in observable characteristics, or

endowments, e.g. income and education, and the other due purely to differ-

ences in the way that endowments translate into behavior, i.e. differences in

the regression coefficients on income and education. This approach is more

flexible than the regression analysis above in the sense that it allows gender

to interact with all observable characteristics and not just age and parental

education. The results of the decomposition show that the gender difference

results from differences in the way that endowments impact behavior, rather

than differences in endowments themselves: the component for differences

in regression coefficients explains 90 percent of the gender gap.

In summary, women are more likely to be risk averse, and increasing

age leads to an increasing probability of risk aversion, in all specifications.

Having a mother who completed the abitur seems to lead to make risk tol-

erance more likely. The impact of father’s education is less consistent, with

a negative impact on the likelihood of risk aversion in some specifications

but a positive impact in the most complete specification.

3.3 Risk attitudes across different domains of life

The first section of Table 2 reports means and medians of individual risk

attitudes, across different domains of life. Judging by these statistics, there is

some variation in risk attitudes, indicating that it is meaningful to ask about

risk separately for different domains. Individuals are more risk averse in the

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domains of financial matters, driving, and health, and noticeably less risk

averse when it comes to taking risks in careers, sports, or trusting others.

Interestingly, risk aversion is weakest for the general risk measure, which

includes the least context, possibly because the lack of context makes risks

less salient. In terms of the variation across the six domains that include

more-concrete context, there are at least two possible explanations: people

may truly have different preferences, in different contexts, when it comes

to taking a gamble that is otherwise identical in utility terms; alternatively,

it could be that individuals indicate differences in their willingness to take

risks, simply because they believe that the typical probabilities and stakes

involved in taking risks differ across domains.


Turning to the gender comparison, it appears that women are more risk

averse than men, in all six life-contexts. As noted in the introduction, this

result contrasts to some extent with Schubert et al. (1999) , who find that

adding context eliminates gender differences in risk attitudes. A stronger

test in this regard will come later, when we look at our measure with the

most context, the hypothetical investment question.

The second section of Table 2 shows simple correlations between individ-

uals’ risk attitudes in different domains of life. Risk attitudes are far from

perfectly correlated across domains, but the correlations are still substan-

tial, typically in the neighborhood of 0.5, and all are highly significant. This

lends some support to the notion of risk attitude as an underlying, stable

trait of an individual.7 A more sophisticated, factor analysis of the seven

dimensions of risk attitudes shows that 57 percent of the variation in indi-7 Another way of assessing the stability of risk attitudes is to check what fraction of

individuals is consistently risk averse in all, or most, of the seven different domains.It turns out that almost 40 percent of individuals who are risk averse, i.e. choose anumber lower than 6, are risk averse in all seven domains, and 75 percent are risk averseacross five or more domains.

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vidual risk attitudes is explained by a single factor. This clearly points to

the existence of a single, underlying trait. Nevertheless, each of the other six

factors explains at least five percent of the variation, suggesting that there

is still some additional benefit to be had from asking about risk in different

domains separately.

In order to explore the determinants of risk attitudes in each of the seven

domains, Table 3 presents results from probit regressions, with the proba-

bility of an individual being risk averse as the dependent variable.8 A first

observation from Table 3 is that the gender difference is robust across all do-

mains, as is the positive impact of age on the probability of being risk averse.

The interaction between age and gender is negative in all domains besides

general and trust, indicating that in these domains, the gender difference

decreases, but does not disappear, as age increases.

The relationship between parental education and risk attitudes is less

consistent across domains. For the most part, having a parent who has

completed the abitur reduces the probability of being risk averse. A more

highly-educated mother makes individuals less risk averse in all domains,

except for general, financial matters, and health. A more highly-educated

father reduces the likelihood that daughters are risk averse, in career, health,

and trusting others, although the effect on risk aversion is positive, for both

genders, in the general domain. The interaction between parental education

and age suggests that a highly-educated mother reduces the impact of age

on risk aversion in the financial domain, but increases the impact of age in

the career domain. A more highly-educated father reduces the impact of

age in the general and trust domains.


8 For these regressions, we simplify by focusing on the linear approximation to the ageprofile.

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3.4 Alternative Measure of Risk Attitude: Hypothetical In-

vestment Decision

Section 2 described the hypothetical investment scenario in the GSOEP,

which allows respondents to choose how much of 100,000 Euros in lottery

winnings they wish to invest in a hypothetical asset. This asset returns

double the money invested, or one half of the money invested, in two years

time, with equal probability. Respondents can choose from six responses: 0,

20,000, 40,000, 60,000, 80,000, or 100,000.

We explore the determinants of this investment choice using regression

analysis, with the six-item response scale, ordered from 0 to 100,000, as the

dependent variable.9 Thus, a negative coefficient indicates a lower willing-

ness to invest and a higher degree of risk aversion. Our estimation procedure

accounts for the fact that the dependent variable is measured in intervals,

and hence is left and right censored.

Table 4 presents different specifications for the investment regression,

with each column adding progressively more controls, in exactly the same

manner as for the baseline regressions in Table 1. The salient feature of the

results is again the same: robust gender and age effects, in the direction of

increasing risk aversion. Mother and father’s education, on the other hand,

are for the most part not significant for the investment measure, especially

in the most complete specifications.


As discussed previously, these results strengthen the evidence that the

gender effect is robust to strong, contextual framing. The gender difference

for this question is also harder to explain by differences in pessimism or

optimism about probabilities and stakes, as these are given explicitly in the9 In this case we do not adopt a binary measure, as it is more difficult to choose a sensible

division of the scale.

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question. Potential explanations include differences in probability weighting

across gender, or differences in risk preferences.

3.5 Validation of Survey Measures

A serious concern with the use of hypothetical questions is that they might

not predict actual behavior. The standard argument is that these questions

are not incentive compatible, and thus respondents may give inaccurate

answers, perhaps due to strategic considerations, self-serving biases, or a lack

of attention. The evidence on this issue, however, is less than conclusive, and

there continues to be considerable debate over how accurate hypothetical

questions really are, and in what circumstances they are likely to perform

reasonably well (Camerer and Hogarth, 1999).

In order to test the performance of the GSOEP risk measures as pre-

dictors of actual behavior, we conducted a laboratory experiment. The

experiment was computerized, and was conducted with 160 University of

Bonn students as subjects. In the experiment, subjects answered all of the

risk questions asked in the GSOEP. They were also given a number of differ-

ent risky choices, for real stakes. In each choice situation they could either

pick a sure payment, or the following lottery: win 400 points with prob-

ability 0.5, or win nothing with probability 0.5 (the exchange rate in the

experiment was 1 point = 17 cents, implying a winning prize of 6.80 Euros).

Subjects made fifteen choices, with the sure payment varying from 25 to 375

points. Subjects were informed that one of their choices would be randomly

selected, and implemented, for real money, ensuring incentive compatible

responses. By observing the point at which subjects switched from choosing

the smaller, safe option to the lottery, it was possible to infer their certainty

equivalent for the lottery, and thus their individual degree of risk aversion

or risk lovingness.

Using this data we can test the validity of our risk measures, at least for

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university students, by looking at whether they predict the outcome of the

behavioral measure of risk aversion in the experiment. Reassuringly, Table

?? shows that the questions about general risk attitudes, risk in financial

matters, and the hypothetical investment question, are all significant pre-

dictors of the degree of risk lovingness exhibited in the experimental lottery

choices. This performance is quite good, considering the difference in fram-

ing between the survey and experimental measure, and in the case of the

hypothetical investment, the large difference in stakes. As expected, ques-

tions for risk attitudes in other domains of life, which are less closely related

to financial risks, are also weaker predictors of behavior in the experiment.

Importantly, we also find corroborating evidence on the gender difference

in risk attitudes: female subjects in the experiment are less likely to report

a willingness to take risks in the GSOEP questions, and are significantly

more risk averse (p-value = 0.008) as measured by their behavior in the

experimental lottery choices.


4 Risk attitudes and Potentially Endogenous Per-

sonal Characteristics

In this section we discuss the results of regressing risk attitudes on personal

characteristics, focusing on important economic variables, such as income,

occupation, education, and employment status. These are the same variables

included as controls in previous specifications, but now we report the coeffi-

cients in full. In interpreting these coefficients, we resist any temptation to

make causal statements, due to the clear endogeneity concerns. To take the

most obvious example, high income could be the cause of risk tolerance as

it cushions the individual from potential losses. Alternatively, risk tolerance

in these domains could lead to financial success and high income. Despite

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the difficulties of reverse causality, we investigate these correlations, because

they provide at least a starting point for thinking about future research on

the causes and consequences of risk attitudes.

Accordingly, Table 6 presents regressions of risk attitudes on our full

set of observables, focusing on the most economically relevant domains for

risk attitudes: general, financial, and career. As before, the regressions are

probits, with the probability of being risk averse as the dependent variable.

The results in Table 6 include a variety of intriguing correlations, but

particularly interesting are those for traditionally central economic variables.

Beginning with marital status, people who are married are more likely to be

risk averse, in all domains, although this is slightly weaker in the financial

domain. In terms of occupation, blue collar workers are significantly more

risk averse than white collar workers (the reference group in the regression),

and self-employed are significantly less likely to be risk averse than white col-

lar workers, in all domains. Civil servants are no different from white collar

workers in terms of risk attitudes. Educational attainment, as measured by

completion of the high school abitur (a substantial academic achievement),

is associated with a lower likelihood of being risk averse, in all domains.

Interestingly, net household income is associated with a lower probability of

being risk averse in general, and financial matters, but is not significantly

correlated with risk attitudes in career.


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5 Conclusion

This paper set out with the goal of creating a snapshot of individual risk

attitudes across a representative swath of the population. In so doing, we

have generated five stylized facts. In terms of determinants of risk atti-

tudes, we find an impact of three plausibly exogenous factors: a difference

in willingness to take risks by gender, with women being significantly more

likely to report that they are risk averse; an age profile for risk attitudes, in

which the probability of being risk averse increases with age; and an impact

of parental education, with the most clear-cut effect being a tendency for

more-educated mothers to have less risk averse children. In terms of mea-

surement, we find that risk attitudes vary across domains of life, but also

find that a single underlying trait explains most of this variation, indicating

that the typical assumption of cross-situational stability in risk attitudes is

a reasonable approximation. We also conclude, based on a laboratory ex-

periment, that the survey questions we use to measure risk attitudes have

significant predictive power when it comes to predicting real behavior under

uncertainty.

The evidence we find on the determinants of risk attitudes has potentially

important economic implications. A robust and pervasive gender difference

could play some role in explaining different labor market outcomes, and

investment behavior, observed for men and women. An age profile for risk

attitudes could have macroeconomic implications, e.g. demographic changes

leading to a large population of elderly, and thus a more conservative pool

of investors, could have an impact on economic growth. Although we find

that risk preferences are relatively stable across situations, an age profile

also raises questions about the stability of risk preferences over time. A role

for parental education in shaping the risk attitudes of children highlights a

potentially important, additional effect of subsidizing education.

An even bigger question raised by our findings is what are the mecha-

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nisms behind these determinants of risk attitudes? The gender difference we

find is consistent with different patterns of socialization, in the home and

in society in general. Conceivably the difference could also have an evolu-

tionary, or biological basis, as is discussed by Fehr-Duda et al (2004), but

evidence on gender differences in early childhood would be more relevant for

this question. Differences in risk attitudes over the life cycle could also be

socially constructed, e.g. risky behavior in driving, sports, and health could

be condoned at an early age but frowned upon later in life. But it is harder

to imagine the social forces behind increasing risk aversion in financial in-

vestments, so perhaps learning over the lifetime, or a shrinking time horizon

due to approaching death, could instead be the important mechanisms. The

impact of parental education on risk attitudes could reflect different atti-

tudes towards child rearing and appropriate life behavior, which are then

taught to children in the household. On the other hand, it is conceivable

that biological mechanisms could also play a role, i.e. as with many other

traits, risk attitudes could to some extent be genetically transmitted.

17

References

Camerer, C., and R. Hogarth (1999): “The effects of financial incen-tives in experiments: a review and capital-labor-production framework,”Journal of Risk and Uncertainty, pp. 7–42.

Diaz-Serrano, L., and D. O’Neill (2004): “The relationship betweenunemployment and risk-aversion,” IZA Discussion Paper No. 1214.

Eckel, C., and P. Grossman (????): “forthcoming Handbook of Experi-mental Results,” .

Fehr-Duda, Helga, M. d. G., and R. Schubert (2004): “Gender, Fi-nancial Risk, and Probability Weights,” Institute for Economic Researchworking paper, University of Zurich.

Gollier, C. (2001): The Economics of Risk and Time. MIT Press, Cam-bridge, Massachusetts, 1st edn.

Guiso, L., and M. Paiella (2001): “Risk-aversion, wealth, and back-ground risk,” CEPR Discussion Paper No. 2728.

Guiso, L., T. J., and L. Pistaferri (2002): “An empirical analysis ofearnings and employment risk,” Journal of Business and Economic Statis-tics, 20(2), 241–253.

Harbaugh, William, K. K., and L. Vesterlund (2002): “Risk atti-tudes of children and adults: choices over small and large probabilitygains and losses,” Journal of Experimental Economics, 5, 53–84.

Hartog, Joop, A. F.-i.-C., and N. Jonker (2000): “On a simple surveymeasure of individual risk aversion,” CESifo working paper no. 363.

Holt, C., and S. K. Laury (2002): “Risk aversion and incentive effects,”American Economic Review.

Schubert, Renate, M. B.-M. G., and H. Brachinger (1999): “Fi-nancial decision-making: Are women really more risk-averse?,” AmericanEconomic Review Papers and Proceedings, 89(2), 381–385.

Slovic, P. (1964): “Assessment of risk taking behavior,” PsychologicalBulletin, 61(3), 220–233.

Weber, Elke, A. R. B., and N. Betz (2002): “A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors,” Journalof Behavioral Decision Making, 15, 263–290.

18

A Tables

19

Table 1: Primary Determinants of General Risk Attitudes

Dependent Variable: General Risk Attitude(1) (2) (3) (4)

Female 0.459*** 0.510*** 0.578*** 0.546***(0.019) (0.057) (0.065) (0.083)

Age 0.015*** 0.016*** 0.011*** 0.009***(0.001) (0.001) (0.001) (0.002)

Female*Age -0.001 -0.002* -0.001(0.001) (0.001) (0.002)

High School Mother -0.171*** -0.121 0.100 0.078(0.044) (0.120) (0.139) (0.167)

High School Father -0.123*** 0.187** 0.325*** 0.473***(0.033) (0.095) (0.109) (0.135)

High School Mother * Female 0.006 0.010 0.040(0.086) (0.094) (0.103)

High School Father * Female -0.060 -0.108 -0.174**(0.064) (0.068) (0.077)

High School Mother * Age -0.002 -0.005** -0.005(0.003) (0.003) (0.004)

High School Father * Age -0.006*** -0.006*** -0.009***(0.002) (0.002) (0.003)

Constant -0.437*** -0.507*** -0.226* 0.054(0.033) (0.043) (0.121) (0.203)

Demographic Controls No No Yes Yes

Pseudo-R2 0.057 0.058 0.074 0.057log Pseudo-Likelihood -11248.9 -11239.4 -10126.8 -7547.9Obs. 18,934 18,934 17,520 12,256

Probit coefficient estimates. Dependent variable are binary risk measures forgeneral risk attitudes, where “1” indicates risk aversion (answers 0-5 in theoriginal data) and “0” indicates risk tolerance (answers 6-10 in the originaldata). Specifications in Column (3) and (4) include other controls, see Table6 and the discussion in the next section, and only differ in that specification(4) does not include household income, while specification (5) does. Robuststandard errors, allowing for clustering at the household level in parentheses,***, **, * indicate significance at 1-, 5-, and 10-percent level, respectively.

20

Tab

le2:

Cor

rela

tion

sB

etw

een

Ris

kA

ttit

udes

inD

iffer

ent

Dom

ains

ofLife

Gen

eral

Car

Dri

ving

Fin

anci

alSp

orts

Car

eer

Hea

lth

Tru

stM

atte

rsin

Oth

ers

Mea

n4.

419

2.92

62.

405

3.48

63.

604

2.93

43.

351

Med

ian

53

23

43

3M

ean

(Men

)4.

909

3.52

32.

882

3.96

14.

0389

3.31

73.

513

Med

ian

(Men

)5

33

44

33

Mea

n(W

omen

)3.

966

2.34

61.

962

3.04

33.

189

2.57

93.

201

Med

ian

(Wom

en)

42

23

32

3

Gen

eral

1.00

0C

arD

rivi

ng0.

489

1.00

0Fin

anci

alM

atte

rs0.

504

0.51

91.

000

Spor

ts0.

560

0.54

20.

499

1.00

0C

aree

r0.

609

0.50

70.

498

0.60

31.

000

Hea

lth

0.47

70.

504

0.45

60.

521

0.53

11.

000

Tru

st0.

415

0.33

70.

390

0.38

20.

406

0.43

61.

000

inO

ther

s

Cor

rela

tion

sar

eba

sed

onth

eor

igin

alm

easu

res

wit

h11

resp

onse

alte

rnat

ives

.

21

Tab

le3:

Pri

mar

yD

eter

min

ants

ofR

isk

Att

itud

esin

Diff

eren

tD

omai

nsof

Life

Dep

ende

ntV

aria

ble:

Ris

kA

ttit

ude

inth

edo

mai

nof

:G

ener

alD

rivi

ngFin

anci

alSp

orts

Car

eer

Hea

lth

Tru

stM

atte

rs(1

)(2

)(3

)(4

)(5

)(6

)(7

)

Fem

ale

0.51

0***

0.91

8***

0.72

3***

0.68

5***

0.48

7***

0.49

9***

0.18

6***

(0.0

57)

(0.0

62)

(0.0

75)

(0.0

62)

(0.0

56)

(0.0

66)

(0.0

62)

Age

0.01

6***

0.01

5***

0.01

1***

0.02

1***

0.00

3***

0.01

4***

0.00

8***

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

Fem

ale*

Age

-0.0

01-0

.015

***

-0.0

05**

*-0

.006

***

-0.0

05**

*-0

.003

***

-0.0

01(0

.001

)(0

.001

)(0

.001

)(0

.001

)(0

.001

)(0

.001

)(0

.001

)H

igh

Scho

olM

othe

r-0

.121

-0.2

68**

0.14

1-0

.288

**-0

.502

***

0.03

9-0

.327

***

(0.1

20)

(0.1

27)

(0.1

38)

(0.1

24)

(0.1

21)

(0.1

31)

(0.1

23)

Hig

hSc

hool

Fath

er0.

187*

*0.

101

-0.1

00-0

.230

**0.

100

-0.0

77-0

.073

(0.0

95)

(0.1

03)

(0.1

14)

(0.1

01)

(0.0

96)

(0.1

08)

(0.1

00)

Hig

hSc

hool

Mot

her

*Fe

mal

e0.

006

0.04

40.

089

0.10

10.

042

0.15

0-0

.014

(0.0

86)

(0.0

91)

(0.1

07)

(0.0

88)

(0.0

86)

(0.0

97)

(0.0

88)

Hig

hSc

hool

Fath

er*

Fem

ale

-0.0

60-0

.016

0.13

8***

-0.0

01-0

.138

**-0

.149

**-0

.091

(0.0

64)

(0.0

69)

(0.0

84)

(0.0

65)

(0.0

63)

(0.0

74)

(0.0

67)

Hig

hSc

hool

Mot

her

*A

ge-0

.002

0.00

4-0

.010

***

0.00

20.

008*

**-0

.004

0.00

3(0

.003

)(0

.003

)(0

.003

)(0

.003

)(0

.003

)(0

.003

)(0

.003

)H

igh

Scho

olFa

ther

*A

ge-0

.006

***

-0.0

01-0

.001

0.00

0-0

.003

0.00

2-0

.004

**(0

.002

)(0

.002

)(0

.002

)(0

.002

)(0

.002

)(0

.002

)(0

.002

)C

onst

ant

-0.5

07**

*-0

.059

0.55

2***

-0.4

17**

*0.

205*

**0.

235*

**0.

503*

**(0

.043

)(0

.046

)(0

.050

)(0

.046

)(0

.044

)(0

.047

)(0

.049

)

Pse

udo-

R2

0.05

80.

023

0.04

70.

075

0.01

00.

036

0.02

3lo

gP

seud

o-Lik

elih

ood

-112

39.4

-986

8.9

-618

6.4

-962

2.6

-118

24.5

-789

3.7

-885

1.4

Obs

.18

,934

18,9

3418

,934

18,9

3418

,934

18,9

3418

,934

Pro

bit

coeffi

cien

tes

tim

ates

.D

epen

dent

vari

able

are

bina

ryri

skm

easu

res

for

risk

atti

tude

sin

diffe

rent

dom

ains

,w

here

“1”

indi

cate

sri

skav

ersi

on(a

nsw

ers

0-5

inth

eor

igin

alda

ta)

and

“0”

indi

cate

sri

skto

lera

nce

(ans

wer

s6-

10in

the

orig

inal

data

).R

obus

tst

anda

rder

rors

,allo

win

gfo

rcl

uste

ring

atth

eho

useh

old

leve

lin

pare

nthe

ses,

***,

**,*

indi

cate

sign

ifica

nce

at1-

,5-

,an

d10

-per

cent

leve

l,re

spec

tive

ly.

22

Table 4: Primary Determinants of Investment in the Hypothetical InvestmentScenario

Dependent Variable: Amount Invested in the Hypothetical Asset(1) (2) (3) (4)

Female -0.432*** -0.495*** -0.567*** -0.596***(0.028) (0.086) (0.096) (0.122)

Age -0.020*** -0.021*** -0.011*** -0.008***(0.001) (0.001) (0.002) (0.003)

Female*Age 0.001 0.002 0.003(0.002) (0.002) (0.003)

High School Mother 0.096 -0.201 -0.359* -0.282(0.066) (0.179) (0.206) (0.246)

High School Father 0.336*** 0.288** 0.183 0.213(0.050) (0.145) (0.164) (0.201)

High School Mother * Female 0.216* 0.186 0.151(0.131) (0.141) (0.152)

High School Father * Female 0.001 0.045 0.111(0.097) (0.102) (0.112)

High School Mother * Age 0.005 0.007 0.004(0.004) (0.005) (0.006)

High School Father * Age 0.001 -0.001 -0.003(0.003) (0.003) (0.005)

Constant 0.583*** 0.638*** 0.761*** 0.434(0.054) (0.072) (0.216) (0.357)

log sigma 0.584*** 0.584*** 0.579*** 0.553***(0.011) (0.011) (0.012) (0.013)

log Pseudo-Likelihood -21083.6 -21080.7 -19271.7 -14361.2Obs. 18,804 18,804 17,428 12,209

Interval regression coefficient estimates. Dependent variable are investmentchoices for investment question, ranging from 0 (no investment), 1 (20,000=20%) etc. up to 5 (100,000 = 100%). Specifications in Column (3) and (4)include other controls, see discussion in the next section, and only differ inthat specification (4) does not include household income, while specification(5) does. Robust standard errors, allowing for clustering at the household levelin parentheses, ***, **, * indicate significance at 1-, 5-, and 10-percent level,respectively.

23

Tab

le5:

Val

idat

ion

ofSu

rvey

Ris

kM

easu

res

wit

hB

ehav

iora

lM

easu

reof

Ris

kP

refe

renc

es

Dep

ende

ntV

aria

ble:

Beh

avio

ralM

easu

reof

Ris

kP

refe

renc

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Gen

eral

8.14

3**

(1.9

09)

Car

Dri

ving

0.23

9(1

.366

)Fin

anci

alM

atte

rs5.

106*

*(1

.660

)Sp

orts

/Lei

sure

1.67

2(1

.659

)C

aree

r3.

750*

(1.8

38)

Hea

lth

1.22

8(1

.521

)Tru

st2.

067

(1.5

59)

Inve

stm

ent

Cho

ice

9.01

1**

(2.9

31)

Con

stan

t16

2.20

6**

205.

063*

*18

8.87

4**

196.

141*

*18

6.84

8**

201.

034*

*19

6.67

5**

193.

595*

*(1

0.92

0)(6

.137

)(6

.765

)(1

0.45

8)(1

0.13

6)(7

.216

)(8

.011

)(5

.586

)

Obs

erva

tion

s14

914

914

914

914

914

914

914

9R

-squ

ared

0.11

00.

060.

010.

030

0.01

0.06

OLS

regr

essi

ones

tim

ates

.E

stim

atio

nsba

sed

onda

taof

lott

ery

choi

ces

and

surv

eyre

spon

ses

of16

0pa

rtic

ipan

tsin

anex

peri

men

t.O

nly

data

for

the

149

subje

cts

wit

hm

onot

onou

spr

efer

ence

sar

eus

ed.

The

depe

nden

tva

riab

leis

the

smal

lest

valu

eof

the

save

opti

onth

atw

aspr

efer

red

toa

lott

ery

inw

hich

0or

400

poin

tsca

nbe

won

wit

heq

ual

prob

abili

ty.

The

regr

esso

rsin

colu

mns

(1)-

(7)

mea

sure

repo

rted

risk

atti

tude

sin

the

resp

ecti

vedo

mai

nson

scal

efr

om0

to10

.T

here

gres

sor

inco

lum

n(8

)m

easu

res

the

amou

ntin

vest

edin

ari

sky

asse

ton

asc

ale

from

0to

5,w

here

cate

gory

0co

rres

pond

sto

zero

inve

stm

ent

and

cate

gory

5to

inve

stm

ent

of10

0.00

0E

uro.

Stan

dard

erro

rsin

pare

nthe

ses;

one

aste

risk

deno

tes

stat

isti

calsi

gnifi

canc

eat

the

5%-lev

el,2

aste

risk

sde

note

stat

isti

calsi

gnifi

canc

eat

the

1%-lev

el.

24

Table 6: Primary Determinants of General Risk Attitudes

Dependent Variable: Risk Attitude in the domain of:General Financial Career

Female 0.546*** 0.699*** 0.448***(0.083) (0.112) (0.085)

Age 0.009*** 0.005** 0.008***(0.002) (0.002) (0.002)

Female*Age -0.001 -0.003 -0.002(0.002) (0.002) (0.002)

Abitur Exam Mother 0.078 0.274 -0.081(0.167) (0.190) (0.168)

Abitur Exam Father 0.473*** 0.019 0.313**(0.135) (0.162) (0.139)

Abitur Exam Mother * Female 0.040 0.053 -0.007(0.103) (0.129) (0.104)

Abitur Exam Father * Female -0.174** 0.153 -0.202***(0.077) (0.101) (0.079)

Abitur Exam Mother * Age -0.005 -0.010** 0.000(0.004) (0.004) (0.004)

Abitur Exam Father * Age -0.009*** -0.002 -0.005(0.003) (0.004) (0.003)

# Kids in HH (<16 yrs.) 0.039** 0.009 0.016(0.016) (0.019) (0.016)

Married 0.101** 0.092* 0.140***(0.041) (0.052) (0.042)

Divorced -0.046 0.100 -0.001(0.060) (0.077) (0.061)

Widowed 0.220** -0.111 0.236**(0.108) (0.138) (0.108)

Retired (Pension) 0.059 0.065 -0.301***(0.057) (0.076) (0.057)

Living & Working in East -0.083** 0.025 -0.086**(0.042) (0.057) (0.043)

German National -0.101** 0.094 -0.076(0.050) (0.065) (0.052)

Unemployed -0.021 0.142* -0.029(0.057) (0.079) (0.058)

Civil Servant 0.025 0.149** 0.065(0.051) (0.063) (0.051)

Blue Collar 0.197*** 0.137*** 0.288***(0.034) (0.044) (0.035)

Self-Employed -0.343*** -0.110** -0.447***(0.054) (0.064) (0.054)

25

Any Schooling Degree -0.131 0.078 -0.125(0.096) (0.169) (0.113)

Abitur Exam -0.157*** -0.226*** -0.174***(0.030) (0.037) (0.031)

General Health Condition 0.085*** 0.006 0.031**(0.015) (0.019) (0.015)

Smoker -0.170*** 0.037 -0.091***(0.027) (0.034) (0.027)

Body Mass Index 0.002 0.010*** 0.000(0.003) (0.004) (0.003)

log HH Income -0.047** -0.089*** -0.025(0.019) (0.027) (0.020)

Constant 0.054 0.960*** 0.292(0.203) (0.300) (0.217)

Pseudo-R2 0.057 0.062 0.040log Pseudo-Likelihood -7547.9 -4216.7 7244.5Obs. 12,256 12,256 12,256

Probit coefficient estimates. Dependent variable are binary risk mea-sures for risk attitudes in different domains, where “1” indicates riskaversion (answers 0-5 in the original data) and “0” indicates risk toler-ance (answers 6-10 in the original data). The abitur exam is completedat the end of university-track high-schools in Germany; passing theexam is a pre-requisite for attending university. Robust standard er-rors, allowing for clustering at the household level in parentheses, ***,**, * indicate significance at 1-, 5-, and 10-percent level, respectively.

26

B Figures

27

Figure 1: Willingness to Take Risks in General

0.0

5.1

.15

.2F

ractio

n

0 2 4 6 8 100=completely unwilling; 10=completely willing

Source: Calculations based on SOEP 2004, unweighted

All Respondents − SOEP 2004

General Risk Atttitudes

−.0

6−

.04

−.0

20

.02

.04

diffe

ren

ce

in

fra

ctio

n


Gender Differences

Notes: The top panel shows a histogram of responses to the question about general riskattitudes (measured on an eleven-point scale). The bottom panel shows the differencebetween the fraction of females and fraction of males choosing each response category,e.g. a positive difference for a given category indicates that relatively more femaleschoose that category.

28

Figure 2: Willingness to Take Risks in General, by age and gender

0.2

.4.6

.8F

ractio

n R

isk A

ve

rse

0.2

.4F

ractio

n R

isk T

ole

ran

t

20 30 40 50 60 70 80 90Age in Years

Risk Tolerant 10 9 8 7 6

Risk Averse 4 3 2 1 0

Men

0.2

.4.6

.8F

ractio

n R

isk A

ve

rse

0.2

.4F

ractio

n R

isk T

ole

ran

t

20 30 40 50 60 70 80 90Age in Years

Risk Tolerant 10 9 8 7 6

Risk Averse 4 3 2 1 0

Women

Notes: Each shaded band gives the fraction of individuals choosing a particular numberon the eleven-point response scale for the question about general risk attitudes. The darkband at the bottom corresponds to a choice of zero, with progressively lighter shadesindicating 1 through 4. The white band is the fraction choosing 5, and the progressivelydarker shades represent fractions choosing 6 through 10.

29

Figure 3: Willingness to Take Risks in General, by parental education

0.0

5.1

.15

.2F

raction


Father’s education: abitur not completed

0.0

5.1

.15

.2F

raction


Father’s education: abitur completed

0.0

5.1

.15

.2F

raction


Mother’s education: abitur not completed

0.0

5.1

.15

.2F

raction


Mother’s education: abitur completed

Note: completion of abitur exam is a prerequisite for university

Source: Calculations based on SOEP 2004, unweightedNotes: Each panel shows, for the indicated sub-sample, the histogram of responses tothe question about general risk attitudes (measured on an eleven-point scale). Abitur isprerequisite to go to university.

30

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