Measuring Normative Risk Preferences
∗
Gosse A.G. Alserda
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Erasmus School of Economics, Erasmus University Rotterdam
February 7, 2017
Abstract
The results of eliciting risk preferences depend on the elicitation method. Different methods of
measuring the same variable tend to produce different results. This raises the question whether nor-
mative risk preferences can be elicited at all. Using two types of manipulation, I assess the normative
value of risk preference elicitation methods. Following IRT, the results of the multiple lottery choice
method are combined with two qualitative methods into a composite score. The responses of 9,235
pension fund members to a dedicated survey indicate this composite score approximates the latent
variable normative risk preferences better than individual method responses do, substantially reduc-
ing measurement noise and method-specific biases. Analysis of the manipulations shows that both
the results and the normative value of the risk preference elicitation methods depend on the specific
amounts, order, and endowment chosen. Combining simpler methods with more advanced methods
framed closely to the relevant situation increases the normative value of elicited risk preferences.
Keywords: Normative Risk Preferences, Composite Score, Multiple Lottery Choice, Item Re-
sponse Theory, Manipulations
Words: 7,963
∗
I thank Benedict Dellaert, Rogier Potter van Loon, and Fieke van der Lecq for valuable suggestions and the participants
at the ESA World Meeting 2016 for their comments. I am grateful to Korn Ferry Hay Group NL and several employers for
their fruitful cooperation on this project.
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1 Introduction
Risk preferences are relevant to a large number of decisions, including financial decisions, which often
involve trading off risk and return. Research has shown that risk preferences tend to differ between
individuals (Harrison et al., 2007; Holt and Laury, 2002; Weber et al., 2002) and that differences in
risk preferences affect optimal choices (Campbell et al., 2003; Viceira, 2001). Risk preferences can vary
between persons for a number of reasons, including parents’ risk taking (Levin and Hart, 2003), genetic
variation (Zyphur et al., 2009), nationality (Hsee and Weber, 1999), age (Yao et al., 2011), and gender
(Jianakoplos and Bernasek, 1998).
Individuals often make choices without explicitly knowing (the quantitative value of) their risk pref-
erences. However, in the case of delegated decisions, risk preferences need to have explicit value for the
delegated decision makers to make decisions that maximize value for the relevant person(s). Since most
individuals are unaware of their risk preferences, these must be elicited indirectly. Many risk preference
elicitation methods are cited in the literature, including the multiple lottery choice (MLC) method (Holt
and Laury, 2002), the betting game choice method (Gneezy and Potters, 1997), and the willingness-to-pay
(Becker et al., 1964) and self-description methods (e.g. Kapteyn and Teppa (2011)). However, different
risk preference elicitation methods tend to yield distinct results for the same variable and within the
same domain (Alserda et al., 2017). Even the same method can provide different results due to framing
(Harrison et al., 2007; L´evy-Garboua et al., 2012), the domain of the question (Weber et al., 2002), or
noisy behaviour (Dave et al., 2010).
This raises the question whether it is possible to measure normative risk preferences and, if so, how.
Normative preferences are described as ‘preferences that represent an economic agent’s true interests’
(Beshears et al., 2008, p. 3), as opposed to revealed preferences, which are ‘preferences that rationalize
an economic agent’s observed actions’ (p. 2). In the case of measurement noise or biases in the elicitation
of risk preferences, revealed preferences can differ from normative preferences. The extent to which
revealed risk preferences correspond to normative risk preferences indicates the method’s normative value.
However, normative risk preferences cannot be measured; therefore, normative risk preferences are a latent
variable and need to be approximated using revealed preferences.
Delegated decision makers are increasingly expected to ensure that decisions reflect individual pref-
erences (EIOPA, 2013; Frijns, 2010; Rozinka and Tapia, 2007). Delegated decision makers should thus
become familiar with the normative preferences. The use of revealed risk preferences that are not in
line with normative preferences will lead to suboptimal decisions, which will lower individuals’ welfare
(Viceira, 2001).
A fundamental trade-off within risk preference elicitation methods is that of simplicity versus prac-
tical usefulness. More complicated methods involving monetary amounts and probabilities have better
predictive accuracy (Dave et al., 2010) and can be easily translated to relative risk aversion (RRA), a
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quantitative measure of risk aversion. Preferences can thus be easily applied in practical situations, as in
setting an asset allocation (Viceira, 2001). However, more complicated methods can also induce noisier
behaviour, especially for subjects with lower numeracy skills, who may not fully understand their options
(Dave et al., 2010). The normative value of more complicated methods may therefore be lower.
The simplest methods include self-description questions. In these methods, respondents are asked to
describe themselves, normally in comparison to others. The respondents do not need to be financially
literate to understand such questions; however, the expectations of others may not be constant and true
risk preferences may not be known. The results are thus hard to quantify and therefore difficult to apply
to real-life situations, as in asset allocation (Alserda et al., 2017).
Van Rooij et al. (2011) find that large proportions of the population have only a basic understanding
of financial topics. Lusardi and Mitchell (2007) confirm this finding and show that only half of older
Americans can correctly answer questions about compounding, inflation, and risk diversification. Fi-
nancial literacy influences financial decision making (Van Rooij et al., 2011) and financially illiterate
individuals have difficulty making adequate retirement plans (Lusardi and Mitchell, 2007). Intuitively,
respondents who have difficulty understanding basic financial topics cannot be expected to perfectly un-
derstand complicated risk elicitation questions. Therefore, one should be careful interpreting the results
of more complicated risk elicitation methods.
Beshears et al. (2008) confirm this intuition and show that complexity increases the effect of biases in
the elicitation of risk preferences. If the number of investment options increases, experimental subjects
are shown to be more likely to choose simple, risk-free investment options compared to complex, risky
investment options (Iyengar and Kamenica, 2006). Greater complexity in the form of more options also
tends to reduce pension plan participation (Beshears et al., 2013; Iyengar et al., 2004).
In this paper, I elicit risk preferences with three different methods, with two variations of the last
method: an augmented version of the MLC method, an investment choice question, and two self-
description questions. Using item response theory (IRT), the results of the three methods are combined
into a single composite risk aversion score. This composite score is the closest available approximation
to the latent variable normative risk preferences (Menkhoff and Sakha, 2016) and can benefit from both
the simplicity of the self-description method and the quantitative value of the MLC method.
Two kinds of manipulation are added to the risk preference elicitation methods. First, the MLC
method is manipulated in terms of order, amounts, and starting probability. The composite score is
used as a reference point to compare the normative value of the four manipulations of the MLC method.
Second, the subjects are split into a group where the (risk-free) base pension is included in the question
and a group where it is excluded. Both manipulations indicate the extent to which framing effects
influence the normative value of elicitation methods.
Risk preferences are elicited in the pension domain. The pension domain involves a prominent case of
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delegated decision making, with an investment manager allocating the assets of pension fund participants
(e.g. pension capital). The optimal mix of assets depends strongly on risk preferences, which must
therefore be known to optimize the asset mix (Campbell et al., 2003; Viceira, 2001).
Elicitation of the risk preferences of 9,235 pension fund participants confirms that different elicita-
tion methods result in different elicited risk preferences. Combining multiple risk preference elicitation
methods reduces measurement noise and method-specific biases. The composite score of risk preferences
therefore provides a more reliable estimation of normative risk preferences. Of the individual methods,
the augmented MLC method, especially with a manipulated starting point, provides the most useful
quantitative information in the domain of pension income. The absence of a strong effect of the inclusion
or exclusion of the base pension shows that it is important to elicit risk preferences in a situation (i.e.,
endowment) as closely as possible to the observed reality for members, since individuals have difficulty
processing these kinds of effects. Risk preferences are dependent on sociodemographic information, such
as income, age, and education level.
2 Method
Risk preferences are elicited with three different methods and a number of variations on these methods.
The methods include the MLC method (Hardy, 2001), the self-description method (Kapteyn and Teppa,
2011), and the investment choice method (Van Rooij et al., 2007).
2.1 MLC
In line with previous research (Anderson and Mellor, 2009; Croson and Gneezy, 2009; Dohmen et al.,
2011), the MLC method of Holt and Laury (2002) is used as the primary method for eliciting risk
preferences in the pension domain. The MLC method presents respondents a series of choices between
two lotteries. The first lottery is safe and has a smaller difference between the good state and the bad
state. The second lottery is riskier and has a larger difference between both states. At the start, the
probability of the good state is low and the safe lottery is dominant for all but extremely risk-seeking
individuals. In subsequent choices, the probability of the good state increases and the risky lottery
becomes increasingly attractive. At a certain point, the respondents will switch from the safe lottery to
the risky lottery, the switching point thereby revealing their risk preferences.
However, I implement a number of improvements to cope with much observed irregularities. First,
in line with Weber et al. (2002), the questions are adjusted to the relevant domain (i.e. pensions).
Second, the range of possible RRA values is increased to deal with the higher risk aversion expected
in the pension domain (Van Rooij et al., 2007). To keep the maximum number of questions constant
and to reduce the predictability of the subsequent questions, the presented possibilities of the good state
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are changed compared to those of Holt and Laury (2002) (see Table 1). To prevent respondents from
changing between the safe and risky lottery more than once, the online survey is programmed so that
when respondents change from the safe lottery to the risky lottery, they must confirm their choice. After
confirmation, their switching point is recorded and they proceed to the next survey question. Respondents
can return to a question and change their choice until they have confirmed it. An example of such a
question is presented in Figure 1.
In addition, prior to the effective question, respondents must answer an introductory MLC question.
This question introduces the respondents to the concept of this method and allows them to make three
choices between two lotteries concerning holiday trips. The results of this question are not used but
should increase understanding of the question and reduce previously observed biases. (Holt and Laury,
2002; Harrison et al., 2007).
Figure 1: Example of an adjusted MLC question
You have indicated that your after-tax monthly income is between €1,800 and €2,000. The amounts in this question are
based on this income level. They represent monthly net income levels, including the state old age pension. The
probabilities changes with your choice.
At which probability would you switch from Plan A to Plan B?
Plan A Plan B
Your guaranteed income is:
$1,290
$860
In addition, you have a probability of
of receiving additional income of:
10%
$220
10%
$1,080
So you have a 10% probability of a
total pension income of:
$1,510
$1,940
Plan A
Plan B
Notes: Example for a participant with a net monthly income of $2,150. This example represents the first
choice out of a sequence of 10 in which the probability (highlighted in red) systematically increases for
the additional pension income. All amounts are converted to US dollars.
2.2 Manipulations
To assess the effect of theoretically neutral variations in the presentation of the MLC questions, two
kinds of manipulation are added. First, 70% of the population are presented the baseline question, using
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