Measuring individual risk preferences

How economists approach risk aversion

The rational actor model is pervasive in economics, and has had some success invading other social sciences. In this model, an actor is assumed to have consistent preferences, and to make choices that best satisfy those preferences subject to existing constraints. Measuring individual risk preferences with incentivized tasks involves constructing sets of decisions for subjects to make. In order for a measure to accurately capture individual preferences, the decisions that make up the measure should have real financial consequences, so that subjects experience the associated gains and losses in payoffs. For example, given a choice between a risky, higher-expected-payoff gamble (say a 50/50 chance of winning $100 or $0) and a less risky, lower-payoff gamble (say, a 50/50 chance of winning $70 or $20), a choice of the latter indicates greater risk aversion. Theory guides the construction of the decision set so that choices reveal a subject's underlying risk tolerance. The decisions that subjects make in a constructed set of these tasks then demonstrate a “revealed” preference, and can be used to estimate risk tolerance in a utility-function framework.

As mentioned above, preferences are measured with two main types of procedures: survey questions and incentivized tasks. In the academic fields of psychology, sociology, and political science, the most common approach is the survey method. Hypothetical questionnaires are also the method of choice for the financial services industry. For example, risk tolerance might be assessed by asking a subject to report his past risky behaviors, or his perceptions of his own desire to take risks, and then constructing an index from a series of answers. Reliability and validity are assessed by testing how survey items collectively form a coherent measure, by repeating the test on different samples, and by examining the correlation between such measures and the behaviors they should predict. Thus, survey measures are fundamentally empirical, developed by finding questions that are correlated with one another that, intuitively, seem to capture the desired construct.

By contrast, economists have a preference for theoretically elegant measures (see e.g. [1]) but may fail to notice the difficulty facing a subject (lacking training in economics and mathematics) who must understand the incentive structure in a complex procedure in order to make good decisions [2].

Economists tend to prefer these incentivized measures on the grounds that, in the absence of incentives, subjects may misrepresent their preferences. Economists typically prefer so-called “incentive compatible” mechanisms that induce truth-telling. Indeed, without incentives there is little cost to misrepresenting one's preferences. One can imagine that a subject may lie to make other subjects or the experimenter think well of him, or even to bolster his own self-image. But when money is at stake in the decision, misrepresentation becomes more costly. Clearly, incentives are likely to make a substantial difference in the outcomes of preference-elicitation procedures under conditions where choices have a social-desirability element. To make this clear, measuring altruism is a case where social desirability could play a role. If a person were asked how kind or altruistic they are on a scale of 1–10, they might claim to be a great benefactor of others. But if that person were given $100 and asked how much they wish to donate to a charitable organization or another individual, his claimed altruism might evaporate. In the latter task, the person must actually give up money in order to indicate altruistic preferences.

It is not immediately clear that risk-taking has the same kind of social desirability, but there are instances where it is the case. Accordingly, men have been shown to take greater risks when observed by attractive women, for example.

Now, suppose one wants to measure an individual's propensity to take risks. A survey might ask subjects to self-report previous risky behavior, or rate their own preferences for risky options. Alternatively, incentivized elicitation procedures place subjects in carefully controlled settings where they make choices involving substantial stakes. In order to exhibit risk aversion, the subject must pay for it in the form of lower expected earnings. This kind of reasoning makes a skeptical social scientist believe that the incentivized task is more valid and reliable. However, there are very few studies that attempt to empirically test which measures of preferences are superior to others, or that even explore criteria by which to judge superiority.

Survey measures

Several survey measures can be divided into single-question measures and multi-question indexes of preferences. Both have been used extensively. The most widely adopted single question was developed for use in the German Socio-Economic Panel (GSOEP) survey, and asks subjects to rate themselves on a scale from 0 to 10 as to whether they prefer to avoid risks (=0) or enjoy taking risks (=10); a similar question is used in the UK Household Longitudinal Survey. These domain-general questions have been shown to correlate positively with self-reported behavior and preferences across many domains and with behavior in incentivized tasks [3], [4], [5].

The Zuckerman Sensation Seeking Scale, an early multi-question survey measure, assesses risk preferences with subscales for thrill and adventure seeking, experience seeking, disinhibition, and boredom susceptibility, and has been used in hundreds of studies. To explore further the variability of preferences across domains, a 2002 study subsequently developed a Domain-Specific Risk-Taking (DOSPERT) scale that explicitly considers several domains where risk is likely to matter [6]. It assesses both risk perceptions and risk preferences in the domains of financial decisions (separately for investing versus gambling), health/safety, recreational, ethical, and social decisions. This survey uses rating scales asking subjects to indicate how risky they believe a given activity to be (e.g. betting a day's income at the horse races), and then to report how likely they are to engage in the activity. The answers are combined to create domain-specific and general indices.

Incentivized measures

Several incentivized measures have been developed using different strategies for determining an individual's degree of risk aversion. These methods include valuation tasks, choice tasks, and framed incremental tasks.

Comparing different measures

In general, it is easy to implement survey methods for measuring risk aversion, and subjects seem to be able to answer them without difficulty. The main shortcoming of survey methods seems to be that economists do not believe them, because there is nothing at stake for subjects. They also do not yield sufficiently precise information to allow estimation of parameters of a utility function, thus limiting their usefulness in structural modeling.

While both psychologists and economists think of risk aversion as a stable trait, which then applies across domains, it is easy to imagine someone who makes conservative investment choices but enjoys risky sports or health behaviors. For that reason, a domain-general measure may not predict well across domains. This is the appeal of the DOSPERT scale. It is worth mentioning, though, that even domain-specific measures are no panacea. While the scale has been tested on both student and adult populations, the relevant domains might vary across populations, so that the questions and even the domains may require tinkering to be population-relevant.

Incentivized measures developed by economists are both clever and promising, but their use is not without controversy. All of these tasks are based on the idea that people value money, but that the subjective value of an additional dollar of income falls as income increases. This assumption implies that a decision maker will be risk-averse. But risk aversion has a special definition in this context: it means that a decision maker will avoid variance; i.e. for a given average return, subjects will prefer gambles or investments with less variability in outcomes. A variety of ingenious tasks have been developed, some simple and some more complex, and there is a trade-off between the ease of comprehension and the sophistication or theoretical precision of the measure. Perhaps the biggest problem with these methods is that they often fail to take into account how people (who are not academics) make decisions. A method that might be very appealing to economists on theoretical grounds nevertheless has to be explained to subjects in a way that allows them to undertake the kind of reasoning that the designers expect of them. While college students do this relatively easily, it can be a major challenge for individuals with lower levels of mathematical proficiency or less familiarity with basic economic concepts. In addition, the question of whether these structured, incentivized methods are indeed more valid, in the sense of predicting risky behavior, is still being explored.

For example, beginning with valuation tasks, the complex, two-stage BDM method may confuse even relatively sophisticated subjects. While the method is designed so that subjects have an incentive to truthfully reveal the monetary value they place on a given gamble, they may not understand that this is true. Individuals are accustomed to stating prices as a starting point in a negotiation, and so may overstate their value for a lottery, leading to an inference of risk-seeking preferences. Indeed, this method routinely produces risk-seeking estimates of preferences, and no other method does this. It is possible that the second-stage price generation leads the subjects to believe that their valuations should depend on the range of possible prices, rather than just the properties of the lottery they are faced with, biasing valuations upward.

Choice tasks appear to avoid this problem. The choice methods mentioned above are relatively easy for subjects to understand, with the possible exception of HL. The average subject in a choice task is risk averse—some say “too” risk averse—and few subjects are risk-seeking. Comparing the choice-based tasks, HL and B/EG, the easier B/EG method is a coarser measure. Its small number of options means that subjects are categorized into fewer “bins,” but with perhaps greater accuracy given the narrower scope for misunderstanding. HL has a finer classification, but since it requires a greater understanding of probabilities, it may produce more errors, making differences across individuals and groups more difficult to detect. To make matters worse, the errors that result from more complex tasks may not only generate neutral noise, but also bias elicited preferences. Of course, MPL methods can be designed to be simple or complex, and to have few or many categories. They can also be presented in an iterative framework, where the grid becomes finer in subsequent tasks.

Finally, incremental tasks tend to be more intuitive for subjects because of their contextual frame. The frame can make the decisions easier for subjects to process cognitively than the more neutrally framed MPL tasks. The GP task is framed as an investment in a risky asset, and so is likely to be correlated with actual investment behavior simply due to the frame. The bomb and balloon tasks are also framed as real tasks, and have been shown to correlate with risk taking in a variety of fields (such as health or recreation). However, behaviors in these tasks require multiple rounds of computerized testing, and the strong framing of the decisions (involving bombs or balloons) may mean that behavior is less likely to be related to financial decision making or other important economic decisions. The domain of the task may affect its ability to generalize to behavior in other domains. Thus, framing may help lower the cognitive load for subjects making decisions, but at the same time limit their generalizability [13].

An important puzzle emerges from studies that compare different methods. While no one has conducted a full meta-analysis comparing all elicitation methods, the partial attempts (such as [12] and papers in the Additional references online) yield an odd result: Different measures—whether survey- or task-based—seem to give different answers. Not only are the levels different, but the correlations across measures tend to be quite low. To clarify, suppose that a group of subjects completes several different incentivized and survey measures of risk aversion. Some time later the same subjects return to repeat the tasks. Because all of the tasks are designed to measure the curvature of the utility function, the measures should all give the same answer, both across measures and across time. But there is considerable evidence that they do not.

A 1962 paper (see [2] for details) was the first to show that risk tolerance of student subjects varies depending on the elicitation procedure and response mode. This study was followed by a number of others in the 1980s and 1990s documenting the inconsistency across measures. The profession has been slow to get the message. The typical response by non-experimental economists is that something must have been wrong with the experiment, yet the result has been replicated again and again. One might well ask whether this evidence means that economists’ conception of risk aversion needs a little work.

A study from 2010 conducted several different risk-aversion measures with a sample of about 1,000 Canadian adults [2]. The B/EG and HL methods were implemented, both of which involve choices among lotteries, and so have similar response modes, with similar stakes averaging about $75. A highly significant correlation of 0.38 between the two measures was found. While this is encouraging, the correlation is significantly different from 1, the value it should take if the two tasks are measuring the same underlying utility function.

Individual differences

Risk preferences vary systematically by gender and age, as well as cultural environment. Women are, in general, more risk averse than men, though the difference is not large. The gender difference is found across measures, though it is stronger in some than others. The gender difference in risk preferences has been associated with choice of college major and occupation; gender differences in investment portfolios and retirement savings are attributed to differences in risk aversion. Older decision makers are often more risk-tolerant in investments, but that is hard to separate from differences in wealth. It seems clear that wealthier individuals should be more risk tolerant, though this is perhaps attributable to a greater capacity to tolerate losses than a difference in risk aversion per se.

There is accumulating evidence that cultural differences importantly shape preferences [4]. Evidence is also emerging that women and men in matrilineal cultures switch roles, with women taking more risks than men. These studies suggest that culture may play an important role in shaping risk tolerance, and downplays the nature portion of the perpetual nature versus nurture debate.

Cognitive ability may also play a role in shaping elicited risk preferences in incentivized tasks. Many studies find that greater risk aversion is associated with lower cognitive ability. A recent study reviews these findings and presents new data showing that correlation between risk aversion and cognitive ability may be a kind of illusion caused by the types of errors that are made with different elicitation methods [13]. This reinforces the idea that care must be taken in preference-elicitation to ensure that subjects are cognitively capable of understanding the tasks. The evidence suggests that if subjects fully understand the task, cognitive ability is unlikely to matter [2].