I. Introduction
Decades of research in economics and psychology has identified a large number of behavioral regularities—specific patterns of behavior present in the choices of a large fraction of decision-makers—that run counter to the standard model of economic decision-making. This has led to an enormous amount of research aimed at understanding each of these behaviors. However, significantly less work has gone into linking these regularities with each other, either theoretically or empirically. Instead, most regularities have been studied in isolation, with specific models developed for each one. This has led to concerns about model proliferation in behavioral economics.
1 As Fudenberg (
2006, 698) notes, “[B]efore behavioral theory can be integrated into mainstream economics, the many assumptions that underlie its various models should eventually be reduced to the implications of a smaller set of more primitive assumptions.”
In this paper, we study the pattern of correlations across a large number of behavioral regularities, with the goal of creating an empirical basis for more comprehensive theories of decision-making. We use an incentivized survey to elicit 21 behaviors from a representative sample of the US population (
). These
econographics—a neologism describing measures of trait-like behaviors related to economic decision-making—cover broad areas of social preferences (eight measures), attitudes toward risk and uncertainty (nine measures), overconfidence (three measures), and time preferences (one measure). Whenever possible, our elicitations are incentivized: the compensation participants receive depends on their choices. We also include two measures of cognitive abilities and several demographic variables. Moreover, we took steps to limit the effects of measurement error by eliciting many of our measures twice and using the “obviously related instrumental variables” (ORIV) technique (Gillen, Snowberg, and Yariv
2019).
Overall, our main finding is that there is a clear and relatively simple structure underlying our data. We can summarize 21 econographics with six components, as shown in
section IV and summarized in
table 1. These components can easily be seen in the correlational structure of the data—for example, the correlation between the measures in the Generosity component range from 0.34 to 0.86. Two of these components underlie social preferences and beliefs about others, and two underlie risk and uncertainty. One component has explanatory power in both the domains of social and risk preferences. A final component underlies overconfidence measures. Time preferences are spread across a number of components but load most heavily on the Punishment component of social preferences. Each econographic, except for time preferences, loads heavily on only one of the six components. As we detail below, existing models predict some of the relationships we observe, but to our knowledge none match the overall pattern, suggesting the need for new theories.
A. Approach and Limitations
We focus on a specific set of measures and study their empirical relationships in order to create an empirical basis that can aid the development of comprehensive theories of decision-making. Our approach is complementary to others in the literature. One standard approach is to focus on specific theoretical links (e.g., Chakraborty, Halevy, and Saito
2020, which links time and risk preferences). Others start with a specific theoretical mechanism and then study—theoretically and, in some cases, also empirically—which behaviors it can generate. This is the case of popular modern research programs on rational inattention (Stevens
2020; Woodford
2020; Khaw, Li, and Woodford
2021; Frydman and Jin
2022), salience (Bordalo, Gennaioli, and Shleifer
2012,
2013; Li and Camerer
2022), limited strategic thinking (Camerer, Ho, and Chong
2004; Crawford, Costa-Gomes, and Iriberri
2013; Farhi and Werning
2019; García-Schmidt and Woodford
2019), incomplete preferences (Masatlioglu and Ok
2014; Cerreia-Vioglio, Dillenberger, and Ortoleva
2015), preference imprecision (Butler and Loomes
2007,
2011), and cognitive uncertainty (Enke and Graeber
2019). While these important programs are complementary to our approach, our investigation is primarily empirical and does not seek to relate a large number of behaviors to a single underlying mechanism. Rather, it aims to construct a basis for theoretical modeling by exploring the empirical structure of our data, which will turn out to be more complex than what can be explained by a single mechanism.
Examining all links between econographics, as we do—including many not studied by theories that seek to link different behaviors—has a number of benefits, especially when compared with the standard approach of theorizing about a particular connection between two behaviors and testing it. First, as correlations are not transitive, identifying underlying structures requires observing all links (or lack thereof) between behaviors.
2 Second, once that structure is identified, its components can be examined to see whether they are associated with existing constructs—such as demographics or cognitive ability. Third, by measuring all behaviors simultaneously in a representative sample, we ensure that the patterns we identify are not due to shifting participant populations between studies.
3The empirical structure we uncover could have taken one of three forms, with different implications. First, there may be no discernible structure, suggesting the continued usefulness of examining behaviors in isolation. Second, there may be a structure that matches well with some existing theoretical approach. Third, it could be that a structure exists but does not match any extant theory. Our results are somewhere between the second and third possibilities: extant theories match some of the correlations we observe, but no theory explains the patterns in a specific domain, let alone the overall patterns we observe. This suggests the need for further theoretical exploration, for which we hope we give an empirical basis.
There are inherent constraints on the number and type of behaviors we could elicit on an incentivized survey. This meant that we had to leave out many interesting phenomena. While we would like to have been able to capture many other behaviors, making choices was unavoidable. Some measures were infeasible in our context: for example, measuring time preferences using a real-effort task (Augenblick, Niederle, and Sprenger
2015; Cohen et al.
2020) or preferences for competition (Niederle and Vesterlund
2007); such measures, while interesting, would have taken as much time as approximately a half-dozen of our other elicitations combined. Other measures—such as inattention, character, and some noncognitive skills—lack clear gold-standard elicitations. There are standard measures of cognitive skills (IQ and cognitive reflection) and personality (see, for early examples in economics, Almlund et al.
2011; Becker et al.
2012). We choose to focus on cognitive skills because of the possibility of their central role in behavioral economics (see, e.g., Benjamin, Brown, and Shapiro
2013; Stango and Zinman,
forthcoming). Future studies can and should focus on alternative sets of behaviors. Indeed, as we discuss in
section VII, some contemporaneous studies already are.
B. Analysis and Results
Describing the 378 correlations between 21 econographic variables, as well as between cognitive and demographic variables, is a daunting task. However, we are aided by the fact that many of the econographics fall into clusters of variables—featuring high intracluster correlations and low intercluster correlations. To summarize them, we make use of principal components analysis (PCA), a statistical technique that produces components—linear combinations of variables—that explain as much variation in the underlying behaviors as possible, as discussed in
section III.
4In our analysis, discussed at length in
section IV, we are aided by a fact that was not ex ante obvious: risk and social preferences are largely—but, as we will see below, not completely—independent. Thus, we first study social and risk preferences separately, before combining them with measures of time preferences and overconfidence.
Our results show that there is significant scope for representing our eight measures of social preferences in a more parsimonious way. The behaviors we measure break down into three clusters, as summarized in
table 1. In particular, altruism, trust, and two different types of reciprocity form a cluster. Pro-social punishment and antisocial punishment constitute a second cluster. The third cluster is formed by two different types of inequality aversion—dislike of having more than another person and a dislike of having less. As we discuss below, these results are not in line with any of the existing theories of social preferences.
Risk preferences show a structure that is less parsimonious than standard theory. Our nine measures form three clusters, as summarized in
table 1. Two separate ones contain different measures of risk attitudes toward ordinary lotteries. One cluster is formed by the willingness to accept (WTA) for a lottery ticket, together with risk aversion for lotteries with gains, losses, and gains and losses (see
sec. II.B) as measured by certainty equivalents. The other cluster is formed by the willingness to pay (WTP) for a lottery ticket, along with risk aversion as measured by lottery equivalents. While the members of each cluster are highly related to each other, they are largely independent across clusters, suggesting the presence of two separate and independent forms of risk attitude toward lotteries.
5 These two components have different relationships with other features (e.g., age), as shown in
section V. The third cluster consists of aversion to compound lotteries and ambiguity aversion. These behaviors are highly correlated, consistent with previous studies (Halevy
2007; Dean and Ortoleva
2019; Gillen, Snowberg, and Yariv
2019). Our richer data allow us to document that they are largely unrelated to other aspects of risk preferences.
Analyzing risk and social preferences together—along with overconfidence and time preferences—results in six components, as summarized in
table 1. Two social components and two risk components are largely unchanged. The three overconfidence measures form a new component.
6 Patience loads most heavily (and negatively) on the punishment component of social preferences. However, one social component—inequality aversion—and one risk component—related to WTP—combine with each other, showing the possibility of further parsimony in the representation. Of course, the generic idea of a connection between risk and inequality aversion has been suggested before. For example, from behind the “veil of ignorance” more inequality creates more risk (Carlsson, Daruvala, and Johansson-Stenman
2005). Our results indicate, on the one hand, that this has some empirical support (unlike other possible connections that have been suggested). On the other hand, they show how this holds for only one of the two components of risk attitudes toward ordinary lotteries (in particular, the one related to WTP), not the other (the one related to WTA).
We also examine the relationships between the components we identify and cognitive abilities and demographics, in
section V. Four of the six components are correlated with cognitive abilities: higher cognitive ability is associated with a lower propensity to punish and greater patience, lower overconfidence, and higher generosity. Moreover, we document a relationship between cognitive abilities and risk preferences, but with only one of the two components linked with risk aversion—the one joined with inequality aversion. Connecting with demographics, the strongest links are with education and income; moreover, these variables do not seem to be simply proxying for cognitive ability but have their own individual effect.
C. Relation to Theory and Literature
Finally, we draw out the implications of our data for existing theories, in
section VI, some of which were alluded to above. Broadly speaking, we show that while there is some overlap between our data and common theories of social and risk preferences, no theory explains the overall patterns well, even within a specific domain. For social preferences, the three outcome-based models we consider—altruistic preferences, social welfare, and inequality aversion—predict that many of the measures that make up the Generosity component (described in
table 1) should be positively correlated.
7 However, these theories also predict a relationship with inequality aversion, which we do not observe. For risk preferences, models delineating risk and uncertainty do not match our data. Moreover, although reference points are clearly important in explaining the split between risk-aversion measures associated with WTA and those with WTP, common theories of reference dependence make additional predictions that are not consistent with our data.
Our study is uniquely suited to our aim of understanding the empirical basis for more parsimonious behavioral models across the risk, social, time, and confidence domains. There is a significant literature, summarized in
section VII, that examines the correlation between two or three behavioral measures and/or cognitive ability. These more limited sets of relationships are not able to recover all of the nuanced structure we document here. There are a small number of studies that do measure more behaviors (Burks et al.
2009; Dohmen et al.
2018; Falk et al.
2018; Dean and Ortoleva
2019; Stango and Zinman,
forthcoming). As discussed in
section VII, these studies differ from ours in terms of behaviors examined, incentivization, representativeness, and/or how they deal with measurement error.
II. Design and Econographics
Four design decisions followed from our goal of providing an empirical basis for the underlying structure of theories of behavioral decision-making. While the most important was the selection of behaviors to elicit, we return to this discussion after introducing the behaviors we measure.
Our second decision was to study behavioral measures rather than using those measures to estimate model parameters. For example, when measuring risk aversion, we elicit certainty equivalents for lotteries and use (a linear transform of) them in our analyses, rather than trying to identify a parameter of some utility function (e.g., a constant relative risk aversion, or CRRA, utility function). This allows our results to be used to inspire theories that connect behaviors without committing to specific functional forms, which are almost surely misspecified and for which precise individual-level parameter estimation is difficult in a short survey.
8Two final design decisions—that our study is incentivized and representative—are easy to justify. Much of the literature we build on comes from laboratory studies in economics, which are almost always incentivized. While there are sometimes good reasons to use nonincentivized measures, these reasons are largely related to feasibility and credibility. These concerns were not substantial in our case, and we were motivated to move past them. In order to make our empirical basis representative of a broad range of people, rather than just specific subgroups, we used a representative sample. These two design decisions drove and constrained a number of implementation details discussed in the next section.
A challenge of using a representative sample is that some participants will be poorly educated (relative to convenience samples of college students). For generalizability, this sampling is a feature, not a bug; but it does mean that elicitations must be simple and designed to have good internal validity for all Americans. Consequently, many of our behavioral measures are based on the same elicitation technique: indifference elicited using a multiple price list (MPL) method. This was chosen because it allows for more efficient estimation of indifference points than asking individual binary choices—which, for an experiment of our scope, would be infeasible—and it is seen as easier for participants to understand than incentivized pricing tasks (Cason and Plott
2014). A training period, with examples and supervised trial sessions, preceded the actual survey.
9 Other common issues that arise in trying to draw representative samples are addressed in
section III.A. The techniques we used to (statistically) deal with measurement error, detailed in
section III.B, further helped in recovering valid estimates from these low-education populations.
Appendix A gives implementation details omitted here. The specific question wordings, screenshots, and other details of experimental design can be found in our replication bundle (Chapman et al.
2022a).
A. Social Preferences
There are many examples where people’s actions take into account the preferences and beliefs of others, even in nonstrategic settings. The motivating factors behind these acts are often given the broad term of social preferences.
Altruism is defined as giving to strangers while expecting nothing in return. In experimental economics, it is usually measured using the dictator game (Forsythe et al.
1994; Falk et al.
2013), in which one participant decides unilaterally how to split money between themselves and another person. Following this literature, we measure Altruism as the amount given to another person in the dictator game.
Trust and reciprocity are intertwined. To understand why, imagine that a stranger asks for money for a sure-thing investment. In order to provide money in such a venture, you must trust that he will give some of the proceeds back to you. The act of giving money back is
reciprocation, which may depend on how much money you gave to the stranger. We measure these concepts through a standard trust game: one participant (the sender) decides how much of an endowment to send to a second (the receiver). This amount is doubled by the experimenter, and then the receiver decides how much to send back—which is also doubled (Berg, Dickhaut, and McCabe
1995). We measure Trust as the amount sent by the participant when they are in the role of the sender. The original sender will also take the role of receiver in a different interaction: Reciprocity: Low corresponds to the amount sent back when receiving the lowest amount, and Reciprocity: High corresponds to the amount sent back when receiving the maximum amount.
10People are willing to punish others for what they perceive as bad behavior—even when that behavior does not directly affect them—and punishment is costly. To measure this, we allow participants to observe a trust game in which the sender gives all the money they have and the receiver returns nothing. We give each participant a stock of points to punish the receiver—that is, to pay a cost to reduce the points the receiver gets: the amount used is Prosocial Punishment. Prior studies document that a significant minority of people—the percent possibly depending on culture—also punish the sender (Herrmann, Thöni, and Gächter
2008). Thus, we also give a separate stock of points that can be used to punish the person who sent all their money. The amount used to punish the sender is Antisocial Punishment.
Many people seem uncomfortable with having a different amount (greater or less) than others, a phenomenon known as
inequality aversion (Fehr and Schmidt
1999; Charness and Rabin
2002; Kerschbamer
2015). Dislike Having Less is how much a person is willing to forgo in order to ensure that they will not have less than another person. Dislike Having More is how much a person is willing to forgo in order to ensure that they will not have more than another person.
B. Measures of Risk Attitudes
To measure attitudes toward risk and uncertainty, we elicit the valuation of various prospects. All lotteries involve only two possible payoffs, and most assign 50% probability to each.
Following the standard approach, we identify the behavioral manifestation of risk aversion as valuing a lottery at less than its expected value. Extensive research shows that the patterns of valuation depend on whether a lottery contains positive payoffs, negative payoffs, or both a positive and a negative payoff.
11 Thus, we include three measures of risk aversion: Risk Aversion: Gains elicits a participant’s certainty equivalent for a lottery containing nonnegative payoffs, Risk Aversion: Losses elicits a participant’s certainty equivalent for a lottery with nonpositive payoffs, and Risk Aversion: Gain/Loss elicits a participant’s certainty equivalent for a lottery with one positive and one negative payoff (Cohen, Jaffray, and Said
1987; Holt and Laury
2002). The difference between the expected value of the lottery and a participant’s value is used in the analysis, so larger numbers indicate more risk aversion.
The
endowment effect is the phenomenon that, on average, people value a good more highly if they possess, or are endowed with, it. In our implementation, WTP is the amount a participant is willing to pay for a lottery ticket, and WTA is the amount the participant is willing to accept for the same ticket when she or he is endowed with it. The difference between WTA and WTP is the Endowment Effect (Kahneman, Knetsch, and Thaler
1990). We discuss this as a risk preference because the object being bought and sold is a lottery ticket, but more importantly, because of the patterns revealed in the analysis of
section IV.C. However, as the pattern of correlations in our data suggests that WTA and WTP are also fundamental behaviors, we primarily examine these rather than the endowment effect.
Risk attitudes often change when one of the available options offers certainty, as demonstrated through the common-ratio effect (Allais
1953). Under expected utility, when the winning probabilities of two lotteries are scaled down by a common factor, a person’s ranking over those lotteries should not change. However, this is often not the case. To capture this effect, we ask the participant to make two choices, one that measures risk aversion with a certain alternative and another in which both options are risky. In Risk Aversion: CR Certain (where CR = Common Ratio), we elicit the amount
b such that the participant is indifferent between a certain amount
a and a lottery paying
b with probability α (and zero otherwise): that is, a lottery equivalent of a sure amount. In Risk Aversion: CR Lottery, we elicit the amount
c such that the participant is indifferent between a lottery paying
a with probability
(and zero otherwise) and
c with probability
(and zero otherwise). Under expected utility,
; the Common Ratio measure is then
(Dean and Ortoleva
2019). In keeping with our treatment of the endowment effect, we enter the constituent measures in most analyses.
12Ambiguity aversion is a preference (or beliefs that lead to a preference) for prospects with known probabilities over those with unknown probabilities. To measure it, we use an
ambiguous urn filled with balls of two different colors: one color gives the participant a positive payoff, and the other gives them zero. Participants do not know the proportions of the different colors of balls in the urn but are allowed to choose which color gives a positive payoff. They are then asked for their certainty equivalent for a draw from this urn. If participants have a prior over the composition of the urn, they must believe that a draw from the urn has a winning probability of at least 50%, yet many participants prefer a 50/50 lottery with known odds. The difference between the certainty equivalents for draws from the
risky urn—with a known composition of 50% of each color—and the ambiguous urn is Ambiguity Aversion.
13 Similarly, a draw from a risky urn is usually more highly valued than one in which the number of balls is unknown but drawn from a uniform distribution—that is, a compound lottery. The difference between the certainty equivalents for draws from the risky urn and a compound urn is Compound-Lottery Aversion (Halevy
2007).
C. Overconfidence
Overconfidence can be divided into three types. Overestimation refers to a person’s estimate of her performance on a task (vs. her actual performance). Overplacement refers to her perceived performance relative to other participants (vs. her real relative performance). In order to measure these phenomena, we ask participants to complete two tasks: on one, we ask them to estimate their performance, and on the other, we ask them to estimate their performance relative to others taking the survey. The difference between these subjective estimations and actual performance, in absolute or percentile terms, gives us Overestimation or Overplacement, respectively (Moore and Healy
2008).
Overprecision refers to a belief that one’s information is more precise than it actually is. We ask participants to estimate a number (such as the year the telephone was invented) and then tell us how close they think they were to the correct answer. To difference out Overprecision from justified precision, we regress how close the participant thought they were on a fourth-order polynomial of their accuracy (Ortoleva and Snowberg
2015a,
2015b).
14D. Patience (Time Preferences)
A payoff sometime in the future is generally seen as less valuable than a payoff of the same size today. The value today of a fixed future payoff is Patience (Andersen et al.
2008).
15E. Choice of Measures
The choice of measures to include was driven largely by a desire to focus on “the basics” and by time constraints inherent in the study design. This general logic still left room for many subjective choices. In this subsection, we try to lay out how this logic was applied and how subjective considerations ultimately led to the set of measures we focused on.
Most of our measures are simple deviations from the standard neoclassical model of a selfish expected-utility maximizer. Behavior under that model is relatively simple: choices involving risk and uncertainty are driven by the curvature of the utility function. Adding time adds a discount rate. Choices involving other individuals are no different than choices involving the market: all result in maximizing the welfare of the individual, with no regard for the welfare of others. Finally, complex choices are processed no differently than simple ones. In the domain of risk, deviations from this model allow for distortions in the way these choices are perceived, including reference points and violations of independence. In the domain of social choice, deviations allow for caring about the welfare of others. Deviations in the domain of cognitive limitations include biased cognitive processing, how those biases affect strategic interactions, and so on.
We focused mainly on preferences in the risk and social domains. These areas have generated enormous interest both within and beyond behavioral economics. Arguably, they constitute the areas where behavioral economic models have been most extensively applied in other fields (e.g., finance and development economics). Each area has generated multiple empirical regularities that are inconsistent with the standard model of economic decision-making, leading to model proliferation. As different modeling approaches suggest different empirical relations between these regularities, it is particularly useful to establish the correlational structure between these behaviors to guide future modeling work.
Within each domain, the measures we included were guided by a combination of theory and importance to the literature. In the case of risk, we first focused on the two classic violations of independence: common-ratio and ambiguity-aversion measures. Both have been central to generating huge literatures on non–expected utility theory. Moreover, both have been explained as manifestations of probability weighting (e.g., Quiggin
1982; Segal
1987), as has aversion to compound lotteries (Segal
1990). Probability weighting has also been advanced as a possible explanation for small-stakes risk aversion.
We then focus on the second major behavioral force in choice under risk: reference dependence. Since the seminal work of Kahneman and Tversky (
1979), the ideas of reference-dependent risk attitudes and loss aversion have been central to understanding risky choice. The key behavioral patterns associated with reference dependence are differing risk attitudes in the gain-and-loss domain and an increase in risk aversion for lotteries that include both gains and losses, thus motivating our three risk-aversion measures. At the same time, the idea of loss aversion has been used to explain the endowment effect (see, e.g., Kahneman, Knetsch, and Thaler
1990; Kőszegi and Rabin
2007). This motivated us to also include a measure of the endowment effect in our study.
We would argue that our nine measures of risk attitude span the behaviors that have been most influential in guiding the development of models of risky choice and are the most obvious manifestations of the three key theoretical constructs in the literature—ambiguity attitudes, nonlinear probability weighting, and reference dependence.
In the case of social preferences, we selected a set of measures that both reflect standard experiments and connect to most widespread theories of social preferences in a parsimonious way. In virtually all models of social preferences, people account for the other’s well-being. This is most directly captured by our measures of altruism and trust, which are both associated with classical measures in their own right. Common approaches, most prominently that of Fehr and Schmidt (
1999), add the role of inequality aversion, with a specific distinction between preferences over advantageous and disadvantageous inequality. Other theories point toward the importance of reciprocity and different types of punishment. Importantly, different theories make different predictions about the distribution and correlation of these behaviors.
We made less of an attempt to cover behaviors thought to be driven by biases or mistakes, largely because of time constraints on the survey. Moreover, many of these judgment biases are carefully considered in Stango and Zinman (
forthcoming), which can be seen as a companion to our more intensive focus on preferences. However, we did have space for a couple of such behaviors and chose to focus on overconfidence (Moore and Healy
2008) and level-
K thinking (Nagel
1995). Overconfidence was motivated by a longer-term interest in the relationship between economic and political behavior, and prior results suggest that overconfidence is an important link between the two (Ortoleva and Snowberg
2015a,
2015b). Level-
K thinking has long been a subject of interest for one of our coauthors, although our elicitations did not seem to produce reliable results; see
figure E.1.
Naturally, there are other areas arguably of equal interest to behavioral economists that we have ruled out on various grounds. While there are many interesting behavioral phenomena and traits in strategic settings—for example, quantal response and competitiveness—the types of questions needed to measure these behaviors comprehensively would be too time-consuming for our survey. For similar reasons, we were able to measure only time preferences using money, rather than, say, real-effort tasks. Given the well-known issues with this approach, we restricted ourselves to a single time-preference measure. In the interests of parsimony, we measured reference dependence within the context of risk preferences, rather than separately, and did not measure violations of the independence of irrelevant alternatives, such as the compromise or asymmetric dominance effect. Overall, time constraints in the survey forced us to make choices and to leave out many measures that would be interesting to explore in future work.
IV. Relationships between Econographics
The next two sections attempt to explain, as succinctly as possible, the relationships between 21 econographics variables.
24 We begin by examining each econographic separately. Next, we study the relationships between econographics through a visual inspection of correlation matrices, followed by PCAs to verify the observed patterns. This leads to our central finding that the 21 econographics are well summarized by six principal components. In the next section, we examine the relationship between these principal components and cognitive abilities and demographics. As noted in the
introduction, there are many ways one might summarize these 378 relationships. Our approach is driven by the desire to create an empirical basis for an underlying structure of more comprehensive theories of behavioral decision-making and by the hope that this is a relatively straightforward way to do so.
We describe the relationships between econographics in three steps. We first examine social preferences, then examine risk preferences, and then combine social and risk preferences with overconfidence measures and patience. This is done for simplicity and because each of the first two types of preferences may be of independent interest. Moreover, as our results are largely driven by clusters of correlations, these clusters will not disappear when additional measures are added to the analysis (although they may be augmented).
A. Summary Statistics
The summary statistics in
table 2 show that behavior in our data is consistent with standard findings in the laboratory. In addition to summary statistics, we also show the percentage of participants whose responses are in the “expected” direction.
25 Surveying the information in this column, the majority of participants are risk averse or risk neutral over gains, risk loving over losses, and inequality averse; exhibit an endowment effect and the common-ratio effect; are ambiguity averse; have a negative reaction to compound lotteries; and are overconfident. (A majority of participants are not all of these things simultaneously.) Note that although patience in this table is represented as a discount rate, in the correlation analysis we code the variable as discussed in
section II.D. Either coding gives the same (directional) interpretation to correlations. However, the coding in
section II.D is linear in a participant’s answer, allowing for the measurement error correction discussed in
section III.B.
Our data exhibit fairly standard levels of noise. As discussed in Gillen, Snowberg, and Yariv (
2019) and Snowberg and Yariv (
2021), the correlation of duplicate measures—subtracted from 1—gives the level of noise in a particular elicitation. Gillen, Snowberg, and Yariv (
2019) report correlations of around 0.65 between duplicate measures, using data from Caltech undergraduates. In most cases, the correlations we observe—in the final column of
table 2—are somewhat higher. This implies that our data are less noisy than similar data obtained from Caltech undergraduates. The exceptions are the overconfidence measures, which are noisier than the rest.
26 When there is no correlation listed, we have only one elicitation of that behavior.
B. Links between Social Preferences
There is ample opportunity to create a more parsimonious representation of the social preferences we measure: altruism, trust, anti- and prosocial punishment, and distributional preferences. These measures fall into three clusters shown in correlation
table 3: one formed by the two measures of reciprocity, and altruism and trust; a second formed by the willingness to punish pro- and antisocial behavior; and a third formed by our two measures of inequality aversion. These clusters are characterized by high within-cluster correlations and low correlations between measures in different clusters. To make these clusters visually apparent, we present the correlation matrix in the form of a “heat map,” where the shade of red indicates the magnitude of the correlation.
27
The first correlation in the first cluster—0.86 between the two reciprocity measures—is a useful example for interpretation. There are distinctions between those who are more and less reciprocal when a partner is more or less generous, resulting in a less than perfect correlation. Yet the predominant behavioral distinction, reflected in the very high correlation, is how reciprocal someone is in all conditions. This overarching behavior is (empirically) related to both trust and altruism, although more closely to the former. We note that although some readers may have anticipated some of these correlations, the fact that the literature distinguishes between, say, different forms of reciprocity, indicates that these anticipations are not universally shared.
The second cluster contains the two punishment measures.
28 Like other clusters, they are highly correlated with each other but with few other measures. However, these measures are characterized by both an extensive and an intensive margin. The extensive margin is whether or not someone punishes and the intensive margin is how much punishment a person metes out. This leads to obvious questions about how these two margins contribute to the overall relationship. The correlations on both margins are roughly equal. About two-thirds of participants engage in prosocial punishment, whereas only one-third engage in antisocial punishment. However, almost everyone who engages in antisocial punishment also engages in prosocial punishment. Of those who engage in both types of punishment, there is a correlation of ∼0.4 in the amount they choose to spend on punishment of both parties. Both the extensive and intensive margins are poorly related to other measures of social preferences.
The third cluster contains the distributional preferences measures. It features the weakest intracluster correlation. Moreover, both measures in this cluster—Dislike Having More and Dislike Having Less—are moderately correlated with other econographics. These moderate correlations extend to some measures in the risk domain, leading to this component combining with risk preferences in the analysis of all 21 econographics in
section IV.D.
Before turning to the PCA, it is worth noting that there is very little in the literature that indicates which specific correlations we should, and should not, find. This could lead to a lot of plausible storytelling. For example, one might believe that the dislike of having more (one form of inequality aversion) entirely motivates altruism and that a dislike of having less (another form) motivates punishment. Yet these patterns are not present in our data. Alternatively, one might expect that the predominant feature of distributional preferences is the presence or absence of a preference for equality, which would generate the observed correlation between Dislike Having More and Dislike Having Less. Whatever one’s priors (or lack thereof), these results should be informative. We discuss what we believe are the biggest takeaways for theory in
section VI.
The PCA of these correlations shows the same patterns: results appear in
table 4. Three components are suggested for inclusion under parallel analysis—see
figure C.1 for the scree plot. Together, these three components explain 68% of the variation in the eight measures of social preferences we explore here.
These three components have fairly obvious interpretations. The first is generosity in behaviors that directly influence the well-being of another person. The second is a general affinity for punishment. The third and final component captures both types of inequality aversion. The components are thus named accordingly: Generosity, Punishment, and Inequality Aversion.
Overall, these results show that there is a clear structure to the connections between different social preferences. These measures tend to group in clusters with high within-cluster correlations and low across-cluster correlations, leading to the latent structure shown by the PCA.
C. Links between Risk Attitudes
In this subsection, we show that models of decision-making under risk and uncertainty may be too parsimonious. As shown by the clear clusters in
table 5, Ambiguity Aversion and Compound-Lottery Aversion group together, blurring the line between risk and uncertainty. Moreover, we find two clusters of risk attitudes: one related to WTA and the other to WTP.
The easiest cluster to interpret contains Ambiguity Aversion and Compound-Lottery Aversion.
29 This high correlation is consistent with extant empirical work (Halevy
2007; Dean and Ortoleva
2019; Gillen, Snowberg, and Yariv
2019) and theoretical observation (Segal
1990; Dean and Ortoleva
2019). Note that both of these measures are essentially unrelated to measures of risk attitudes toward ordinary lotteries, suggesting either a delineation between risk and uncertainty, with compound lotteries grouped with ambiguity aversion, or the lack of a clean line between risk and ambiguity.
The two remaining clusters both contain risk attitudes and suggest two separate aspects of risk preferences. The first contains Risk Aversion: Gains, Risk Aversion: Losses, Risk Aversion: Gain/Loss, and WTA, and the second includes Risk Aversion: CR Certainty, Risk Aversion: CR Lottery, and WTP. Crucially, there are low correlations between the measures in different clusters (with the exception of the relationship between Risk Aversion: Losses and WTP—a correlation of 0.3, meaning that risk aversion using WTP is negatively correlated with risk aversion over losses). This suggests that there are two separate aspects of risk aversion that are largely unrelated to each other or to preferences under uncertainty. Note that these two aspects are both within a fairly narrow domain: the valuation of risky lotteries. Thus, unlike psychology research, which has shown differences in risk preferences across outcome domains, these patterns suggest differences in preferences within the domain of money lotteries—the most studied domain in choice theory and experimental and behavioral economics.
These two separate aspects of risk preference align with WTA and WTP, respectively, giving this division substantive and theoretical importance. We find that certainty equivalents fall into one cluster while lottery equivalents fall into another. This is in line with prior research finding that different fixed elements in an MPL lead to different degrees of risk aversion. Here, as in the literature, this is consistent with the fixed element of the MPL acting as a reference point (Sprenger
2015). Our results add, first, that these groups are largely uncorrelated with each other and, second, that they naturally align with WTA and WTP. WTA—where the lottery is explicitly the reference point—naturally aligns with certainty equivalents: measures where the lottery is fixed. Similarly, WTP aligns with measures with a fixed monetary amount. At the same time, as we discuss in
section VI and more in depth in Chapman et al. (
2022b), our data are difficult to reconcile with current theoretical explanations. The most visible signature of this difficulty in
table 5 is the lack of an obvious component of loss aversion positively related to WTA and negatively related to Risk Aversion: Gain/Loss.
Before proceeding, it is worth discussing the two measures in the correlation table that we do not include in the clusters or in the following PCA: the endowment effect and the common-ratio effect. Both measures are the difference of two other measures we already include: for example, the endowment effect is the difference between WTA and WTP. Thus, including all three measures in a PCA makes little sense. As described above, our own recent research compiles extensive evidence that WTA and WTP should be treated as separate behavioral measures (Chapman et al.
2022b). Thus, we include the component measures (WTA and WTP) rather than the endowment effect. We take the same approach with the common-ratio measures: entering the constituent measures into the correlation and PCA.
30Once again, the PCA in
table 6 confirms the visual patterns in the correlation table. The first three components explain 66% of the variation in the nine measures of risk preferences considered here. The first and the second clearly capture different aspects of risk attitudes. Following the discussion above, we use the names Risk Aversion: WTA and Risk Aversion: WTP to describe them. The third encompasses more complex lotteries, which may induce uncertainty: hence, we use the name Uncertainty.
D. Putting It All Together
Analyzing all 21 econographics together leads to six components. The structure of components from the risk and social domains is preserved; however, one social and one risk component combine. The sixth component is comprised of the three overconfidence measures. Time preferences load on several components, most heavily on the Punishment component.
Analyzing risk and social preferences, together with overconfidence and time preferences, is straightforward because there are few important relationships between risk and social preferences. However, they are not completely unrelated. One of the social preference components (Inequality Aversion) combines with a risk-preference component (Risk Aversion: WTP). Otherwise, the structure in the previous two subsections is largely unaltered.
The first and second components in
table 7 are similar to the first components in the social and risk analyses, respectively. Thus, we retain the same names. Patience loads moderately on both components: more generous and less risk-averse people are more patient.
The third component is of particular interest. It combines the measures included in the second risk component, Risk Aversion: WTP, with those of the third social component, Inequality Aversion. This suggests that some aspects of social and risk preferences are distinct, while others are more related. Moreover, this relationship follows a clear pattern building upon the previously identified components: Inequality Aversion combines with Risk Aversion: WTP. We note that both of these components deal with aversion to spreads—in possible payoffs or distributional assignments. Thus, we conjecture that a similar form of caution may lead participants to both dislike entering conditions of risk and generically dislike unequal allocations. Note, however, that this pertains only to entering a situation of risk (WTP for a lottery), rather than leaving one (WTA). In line with its constituent parts, we call this component Inequality Aversion/WTP.
The fourth component loads heavily on all three overconfidence measures; naturally, we call it Overconfidence. Given existing work on the conceptual distinctions between different types of overconfidence, the fact that they would load on a single component may not have been ex ante obvious (Moore and Healy
2008).
The fifth is the social component Punishment, to which (a bit of) time preferences have been added. People who score highly on this component enjoy both pro- and antisocial punishment and are impatient. Thus, we dub this new component Impulsivity.
The sixth component is essentially the same as the third risk component. Thus, we continue to call this component Uncertainty.
There are a number of ways to examine the robustness of our results. We discuss two here—changing the number of components and using other methods to extract latent variables—and another—adding and removing variables—in
section VIII.
The Inequality Aversion/WTA component is robust to adding or subtracting components. Reducing the number of components to five, as shown in
table E.3, causes the Uncertainty component to combine with both the Overconfidence and Impulsivity components but otherwise leaves the structure of the components qualitatively unchanged. Perhaps more interesting is what happens when we add components. With either seven or eight components—the latter recommended by the Kaiser criterion—the Overconfidence component splits into two, as shown in
tables E.4 and E.5. Both of these overconfidence components contain overprecision, but one contains overestimation, and the other, overplacement. Adding an eighth component further splits the Generosity component into two, with the reciprocity measures on one component and the altruism and trust measures on the other. The rest of the components are largely unchanged by the inclusion of additional components.
Using other ways of computing the correlation matrix or other latent dimension recovery techniques has very little effect on our results, as shown in
tables E.6–E.9. In particular, our results are robust to using unweighted measures or the average of measures (rather than ORIV) to compute the correlation matrix. Moreover, one can use Spearman rank-order correlations, which are robust to nonlinear relationships and outliers, to compute the correlation matrix, and this also results in very little change to the structure shown in
table 7. Finally, one can use factor analysis (on the average of measures), rather than PCA. This, too, produces qualitatively similar results.
Overall, the underlying structure of our data can be summarized with four points. First, six interpretable and separate components explain 60% of the variance of our 21 variables. Second, the social and risk components do not change from the earlier analyses in tables
4 and
6, but two components—Risk Aversion: WTP and Inequality Aversion—collapse into one. Third, time preferences are captured by several of the existing components, possibly signaling that they are related to many elements, although none particularly strongly. Fourth, overconfidence measures are largely separate and are captured by a single component.
VII. Literature
Before reviewing the few papers that, like ours, examine the correlations between large sets of behavioral regularities, we note that there is a small but significant literature focusing on the connections between only two or three of them. These papers primarily focus on the relationships between different risk preferences or between risk and time preferences; a couple also study the links between social preferences and risk. Broadly speaking, where these studies overlap with our work, they find similar relationships. We discuss this literature in greater detail in
appendix D.
Another group of papers study the relationship between risk and/or time preferences and cognitive ability (Burks et al.
2009; Dohmen et al.
2010; Benjamin, Brown, and Shapiro
2013—see Dohmen et al.
2018 for a recent review). In aggregate, the literature suggests that higher cognitive ability is associated with less risk aversion. Our results suggest some nuance, as we find different relationships between cognitive ability and the two components of ordinary risk attitudes we identify.
Our work is differentiated from the studies described above by its large representative sample and measures of multiple behavioral regularities. This allows us to identify the complete correlational structure without worries about confounds from differing study designs or populations. However, two contemporaneous and complementary projects also have multiple behavioral measures and use representative populations. Stango and Zinman (
forthcoming) measure a broad set of 17 behavioral factors in a representative sample of US adults to study, in part, the relationships between them. Falk et al. (
2018) survey 80,000 adults across 80 countries to document patterns of six behaviors. Two additional studies use multiple elicitations in nonrepresentative populations.
40 These studies differ from ours in both purpose and implementation: they study different measures, do not use incentives, and do not take the steps that we do to eliminate the attenuating effects of measurement error.
Contemporary with our work, Stango and Zinman (
forthcoming) also study the correlation between behavioral economic phenomena, as well as that between those phenomena, demographics, and life outcomes. They measure behavioral biases in six classes. Two of these—risk and uncertainty biases and overconfidence—are closely related to measures in our analysis, while the other four classes are not.
41 Stango and Zinman also measure what they call “behavioral inputs,” three of which have equivalents in our analysis: cognitive skills, risk aversion, and patience. Unlike our study, Stango and Zinman’s does not measure social preferences.
Despite various differences in approach, there is significant consistency between our results and those of Stango and Zinman concerning concepts modeled in both studies.
42 Both find strong correlation between three types of overconfidence: overestimation, overprecision, and overplacement. In the risk domain, Stango and Zinman measure ambiguity aversion, loss aversion (similar to our mixed risk), and preference for certainty (similar to our common-ratio measure). As with our study, they find them to be weakly, and in some cases negatively, correlated. In both studies, patience shows no strong correlation with either risk-aversion measures or overconfidence. Both studies also find cognitive skills to be negatively related to overconfidence and patience and only weakly related to risk preferences.
On the basis of their factor analysis, Stango and Zinman propose a structure with four factors: two “processing” factors—related to biased beliefs and choice mistakes—and two “preference factors”—related to discounting and risk attitudes. They find that cognitive skills are strongly and negatively associated with the two processing biases, weakly associated with the discounting factor, and unrelated to the risk factor. Some similar patterns emerge in our study. Our overconfidence factor is a subset of the biased-belief factor of Stango and Zinman and is also negatively associated with cognitive skills. Our impulsivity factor is similar to their discounting factor, and we also find a negative relation between it and cognitive skills, albeit a stronger one than do Stango and Zinman. Unlike Stango and Zinman, we find two risk components.
Two other recent papers have some overlap with our work. Falk et al. (
2018) collect a subset of the measures we include here: patience, risk, positive reciprocity, punishment, altruism, and trust. Each is measured through a combination of qualitative self-reports and hypothetical money questions. Three of the four measures of social preferences—“altruism,” “positive reciprocity,” and “trust”—are highly correlated, a result that is reproduced in our data.
43 The purposes of that paper are simply different from ours. For example, they are interested in a huge range of 80 countries, differences between countries, and cross-country associated variables (such as language and religion). Their measures are also unincentivized.
An immediate predecessor of this paper is Dean and Ortoleva (
2019), which studies relationships between many of the same behaviors in a sample of 180 Brown undergraduates. These students are likely to be less heterogeneous on some dimensions, such as cognitive ability. Dean and Ortoleva focus more on risk and time and less on social preferences. Broadly speaking, where these two studies use similar measures, they tend to find similar relationships. However, these correlations represent a limited subset of the 210 we examine here: 45 for Dean and Ortoleva and 15 for Falk et al. (
2018). An exception to these similarities is that Dean and Ortoleva find a strong positive relationship between the endowment effect and loss aversion for risky choice, while for the closest measure of loss aversion (Risk Aversion: Gain/Loss) in our data, we find a strong negative relationship. This is discussed in great detail in Chapman et al. (
2022b).
In recent years, others have collected information on basic economic preferences and personality and noncognitive skill variables (see, e.g., Becker et al.
2012; Jagelka
2020).
44 These investigations follow an increase in the links between personality and economic outcomes (Almlund et al.
2011; Heckman, Jagelka, and Kautz
2019). There are still open questions in this evolving empirical literature about how to correct for measurement error and how strongly correlated measures of all these variables are across time (Morris et al.
2021 show low cross-time correlations for many noncognitive skills). Including personality and noncognitive skills is obviously important, and we are eager to see the accumulation of knowledge from other efforts, like those above, that include more and different variables.
As all of these studies are trying to understand a complicated question of first-order importance to behavioral economics—How are many different behaviors related?—it is natural that there would be some overlap. Because this question is both fundamental and complicated and involves a large number of possible measures, it is not likely that there will be a single, definitive, study soon. Instead, we believe that these many studies trace out different features of this complicated enterprise. The ideal cumulative scientific process, in our opinion, is what this area of research is currently working toward: creating a set of studies, all with distinctive strengths, that can be generally assessed as results are published.
VIII. Conclusion
We elicit 21 econographics from a representative sample of 1,000 US adults in order to create an empirical basis for an underlying structure of more comprehensive theories of behavioral decision-making. We identify six interpretable econographic components that explain a large fraction of the variance in these 21 econographic variables: Generosity, Risk Aversion: WTA, Inequality Aversion/WTP, Overconfidence, Impulsivity, and Uncertainty. These components suggest that representations more parsimonious than current theories of social preferences are possible but that canonical theories of risk preferences are perhaps too parsimonious. Moreover, they suggest limited, and nuanced, connections between risk and social preferences. By studying the relationship between the components we identify and cognitive measures and demographics, we document several stylized facts that may be useful for theorizing.
A strength of our study is the number of behaviors included in our analysis. However, the behaviors we included were limited both by survey time and by the current literature. These, in turn, present limits for our analyses. A nuanced view of these limitations comes from thinking about what would happen if we had included more, or fewer, measures, which we do in the remainder of this paper. This exercise also speaks to the robustness of our results.
Including an elicitation extremely similar to “Risk Aversion: Gains” has little qualitative effect on our conclusions, as shown in
table E.10. This extremely similar measure loads heavily on the second component in
table 7, and this component now becomes the first (in terms of percent of variation explained).
45 Thus, the ranking of components may respond to the inclusion or exclusion of measures. Consequently, we have not attached meaning to the ordering in the text.
Removing Dislike Having More reduces the number of components in
table 4 to two but has little qualitative effect on the overall analysis of
table 7, as shown in
table E.11.
46 Dislike Having Less still combines with the variables in the Risk Aversion: WTP component to form Inequality Aversion/WTP. Thus, it appears that minor perturbations are not particularly consequential, although these exercises have little to say about larger changes.
The robustness here stems from the fact that most correlations between measures—displayed, for example, in tables
3 and
5—are either large or close to zero.
47 There are a few middling correlations—such as those between Dislike Having More and other social preference measures. These are the likely sources of fragility in PCA. Adding a variable with a number of middling correlations may cause parallel analysis to suggest the inclusion of an additional component, and this inclusion may lead to extensive changes in existing components. Consequently, the components are useful for making sense of the large correlation matrices in our analysis. However, the correlation matrices themselves are the most robust—and consequential—part of the analysis, as these are the fundamental patterns upon which latent variable models, such as PCA, are built.
Overall, this discussion suggests that our main conclusion—that there is an underlying structure to our measures that is informative for theorizing—is robust. However, we note that our findings relate to fairly specific domains of economic behaviors: choices over money lotteries, time, the distribution of resources between two people, and beliefs about oneself and others. Adding more measures might create broader clusters that increase the average number of measures per component or lead to a more diffuse set of underlying dimensions. Moreover, examining the relationship between clusters and consumer behavior, as in Stango and Zinman (
forthcoming), might, in the context of specific applications, increase the salience of particular relationships and/or the importance of some components. Whatever the outcome of such explorations, it is worth noting that a failure to reduce all economically relevant behaviors to just a handful of components is not a failure of behavioral economics. Chemistry has been incredibly successful with more than 100 elements. In econographics, we have barely started down that path. It should be our goal to accurately and adequately describe economic behavior with no more, and no fewer, components than necessary. This study takes a step in that direction.
