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Risk Orientation, Risk Exposure, and PolicyOpinions: The Case of Free Tradepops_774 657..684

Sean EhrlichFlorida State University

Cherie MaestasFlorida State University

This research offers a general framework for thinking about how individual dispositiontowards risk influences public policy opinions. Affinity for or aversion to risk is, in part, astable personality characteristic that interacts with risk and reward messages in complexpolicy debates. We examine the implications of this for public opinions about free trade withextensions to immigration policy. We argue and find that opinions about policy dependjointly upon one’s exposure to potential gains or losses and one’s risk orientation. Thefindings have implications for crafting and framing public policies because they highlighthow individual characteristics are likely to shape the public response to policy proposals.Our findings suggest that there may be limits, in the aggregate, to the degree to which elitescan alter the level of support for policies through framing or through offering risk-mitigating policy provisions.

KEY WORDS: Risk orientation, Risk aversion, Risk acceptance, Personality, Public policy opinions,Free trade, Foreign policy opinions, Trade opinions, Immigration opinions

Public policies—economic, social, foreign, regulatory, environmental, andmyriad others—create winners and losers in society, but the likelihood of winningor losing, for any individual, is often uncertain under the terms of a policy. In fact,some policies exacerbate individual-level uncertainty because they lead to variablerather than fixed outcomes. For example, market-based solutions to public policyproblems (e.g., opening markets to free trade, privatizing social security, orderegulating businesses) leave citizens vulnerable to random market shocks and,therefore, more at risk and more uncertain about potential gains or losses. More-over, it is common for media and elite to present multiple and conflicting accounts

Political Psychology, Vol. 31, No. 5, 2010doi: 10.1111/j.1467-9221.2010.00774.x

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of potential losses or gains from a policy, magnifying public uncertainty. Thatgains and losses are expected but not certain is central to the story of policyopinion formation because it highlights that opinions about policy are reallyopinions about risky and uncertain outcomes.

We argue that individual risk orientation—one’s general degree of comfortwith facing uncertain gains or losses—must be considered fundamental to ourunderstanding of public policy opinions. A broad literature that spans multipledisciplines recognizes that individuals vary substantially in their response to risks,including physical risks, gambling risks, decision-making risks, and investmentchoices (Filbeck, Hatfield, & Hovarth, 2005; Kowert & Hermann, 1997; Meertens& Lion, 2008; Zaleskiewicz, 2001). Individual-level risk orientation has beenlinked to stable personality characteristics and, more recently, to genetic charac-teristics (Carducci & Wong, 1998; Cesarini, Dawes, Johannesson, Lichetenstein,& Wallace, 2009; Filbeck et al., 2005; Kowert & Hermann, 1997; Nicholson,Soane, Fenton-O’Creevy, & Willman, 2005; Rosier et al., 2009; Zuckerman &Kuhlman, 2000). Taken together, these studies suggest that orientation towardsrisk is, at least in part, an underlying trait rather than wholly dependent uponcontext or framing. Despite mounting evidence that individuals vary substantiallyin how they grapple with risk, very few studies in political science examine howvariation in risk orientation shapes mass opinions about public policy issues(but see Peterson & Lawson, 1989; Schaffner & Eckles, 2009). Instead, researchin this area typically centers on questions related to vote choice and participation(Berinsky, 2000; Berinsky & Lewis, 2007; Helmke, 2009; Morgenstern &Zechmeister, 2001; Nadeau, Martin, & Blais, 1999; Peterson & Lawson, 1989).

We draw from these and other studies to develop a general account of how riskorientation influences policy opinions through sensitizing individuals to their risksof loss or gain. In doing so, we diverge somewhat from the common account of riskorientation in policy preferences, prospect theory, which conceptualizes risk aver-sion or risk acceptance as an outcome of the characteristics of the decision context(Kahneman & Tversky, 1979; Tversky & Kahneman, 1981).1 In testing prospecttheory, few studies have considered whether trait-based risk orientation operateseither independently or interactively with the framing and domain contexts, butthose that do are suggestive. Kam and Simas (2010) demonstrate that risk orien-tation predicts consistent preferences for risky or certain policies regardless of theloss or gain frames in the hypothetical “Asian disease” policy problem. Likewise,Kowert and Hermann (1997) find evidence that personality characteristics, some ofwhich are clearly associated with risk orientation, work independently and inconjunction with framing to alter preferences for risky versus certain hypotheticalpolicies. It remains to be seen, however, whether risk orientation influences policy

1 In prospect theory, risk orientation is defined as the expression of a preference for a risky versuscertain outcome and depends upon the probabilistic framing of gains and losses as well as anindividual’s status-quo position relative to expected gains and losses (Kahneman & Tversky, 1979).

658 Ehrlich and Maestas

support in the context of a nonhypothetical policy in which individuals facepersonal risks under the terms of the policy and one in which they are exposed tomultiple and competing elite messages about potential gains and losses.

We extend research on risk orientation into the realm of real-world policy andcontend that individuals faced with similar risks of loss or gain under the terms ofa policy may still differ substantially in their support for the policy based simplyon the differences in their trait-based response to risk. Specifically, we argue thatrisk orientation serves to moderate the connection between expected risks orrewards and policy support. Our account speaks to broader questions in politicalpsychology of how individual traits interact with the political environment toinfluence attitudes and behaviors (e.g., Lavine et al., 2005; Slothuus, 2008; Zaller,1992) and to recent calls for renewed study of the role of personality in politics(Mondak, 2010; Mondak & Halperin, 2008). Like these and other studies, we seekto move beyond a “one model fits all” approach by recognizing that policy debatesthat center on risk-related messages may trigger very different responses, depend-ing upon one’s risk aversion or affinity.

We formulate a general framework for understanding how risk orientationshapes policy opinion by examining one particular foreign economic policy—freetrade. Trade policy is an attractive venue to study the effects of risk orientationbecause there is already a strong body of research that has established who insociety is likely to win and lose when trade is liberalized and how variation inexpected gains influences trade policy opinions (Baker, 2003; Mayda & Rodrik,2005; Scheve & Slaughter, 2001). However, much of this literature developeddivorced from the broader literatures on opinion formation in political science andpsychology. This article, therefore, also contributes to the goal of providing stron-ger behavioral and psychological underpinnings for trade policy opinions (see alsoHiscox, 2006; Mansfield & Mutz, 2009).

Trade policy opinion is an area in which risk orientation should play animportant role in shaping opinions because individual-level outcomes are drivenby markets, not governments, thus, individual gains and losses are not certain. Thismakes it an ideal “test case” for considering the effects of risk orientation onopinions. We argue that risk orientation causes individuals to respond selectivelyto the possible gains and losses associated with trade policy and that this condi-tional response shapes policy support. Using data from a national U.S. survey in2006, we directly test the hypotheses derived from this account and find novelresults in the factors that support free trade. We also apply our theory to anadjacent policy opinion—immigration liberalization—as a second means oftesting whether our general argument applies in other policy areas.

Distributional Effects of Trade and the Role of Risk Exposure in Opinions

Theoretical and empirical models of public opinions about trade policy drawmaterially from political-economy theories of trade because they offer predictions

659Risk Orientation and Policy Opinions: The Case of Free Trade

about how different portions of society are likely to fare in a liberalized tradeenvironment. The Stolper-Samuelson theorem, a leading economic theory detail-ing the distributional effects of trade, predicts that one’s factor endowment willdetermine whether one is helped or hurt by expanded trade, with those holdingabundant factors helped and those holding scarce factors hurt (Rogowski, 1989).In developed economies like the United States or Western Europe, capital andskilled labor are abundant, while unskilled labor is scarce. In this circumstance,free trade will increase exports of products made with skilled labor and capital,since the rest of the world will demand these products from the United States, andincrease imports of products made with unskilled labor, since the rest of the worldhas excess supply of these products. Thus, the Stolper-Samuelson Theorem pre-dicts that holders of capital and skilled labor, usually the well-off in society, willbenefit from trade while holders of unskilled labor, usually the poorer, less edu-cated portions of society, will be hurt by trade.2

Numerous public opinion studies have explored whether this differentialincome effect influences trade policy preferences. Scheve and Slaughter (2001),for example, find that skill ownership (measured by education) is one of theprimary predictors of trade policy preferences in the Unites States, while Maydaand Rodrik (2005) and O’Rourke and Sinnott (2001) find that this result isgeneralizable to all developed countries. However, although the predictions fromthe Stolper-Samuelson Theorem are usually presented in determinative terms, i.e.,all low-skilled workers lose from trade and all high-skilled workers gain fromtrade, in reality, low-skilled workers are only at increased risk of losing from trade.In other words, trade increases the probability that a low-skilled worker will losetheir job or suffer reduced wages while increasing the probability that a high-skilled worker will realize increased wages. Thus, expanded trade generates notguaranteed income effects but, merely, changes in the probability of these effects.As a result, free trade has a differential impact on the risk exposure of low-skilledand high-skilled workers.

Broadening the theoretical story to include risk orientation focuses attentionon how exposure to uncertain outcomes (risks of job loss) interacts with person-ality characteristics is an important first step toward understanding the mecha-nisms that influence public support for trade policy. In fact, the literature on theembedded liberalism thesis already approaches the trade dilemma in just this way,although often implicitly. The embedded liberalism thesis argues that policymakers increase support for free trade by creating policies that compensateworkers if they lose their job due to increased imports (Ruggie, 1982), therebyreducing market-driven risks. These policies typically include unemploymentinsurance, job retraining programs, and general welfare policies. The implicit

2 See Hiscox (2002) for more on Stolper-Samuelson and the alternative Ricardo-Viner theorem, whichargues that trade opinions are driven by industry of employment. We lack data on respondents’industry, but our argument works identically, regardless of the source of risk.


argument is that workers may oppose trade because it increases their risk of jobloss. Therefore, when insurance or compensation is provided to offset this risk,workers should be less opposed to free trade. Hays, Ehrlich, and Peinhardt (2005)provide evidence that these compensatory policies are, in fact, effective in increas-ing support for free trade among workers likely to face increased risk because oftrade. However, a better understanding of how individual-level characteristicswork in conjunction with risk exposure to influence trade preferences is necessaryto determine whether such policies work to increase the support broadly, orwhether such policies only move a select subset of the population at risk.

Risk, Trade, and Risk Orientation

The question of whether individual-level risk orientation shapes trade prefer-ences or, indeed, public policy preferences more generally has been largelyneglected. In the area of trade opinions, only one cross-national study explores thistopic: Mayda, O’Rourke, and Sinnott (2007) found that risk-averse individualsare less supportive of free trade. Although a valuable contribution, the analysisis hampered by two problems. First, it employs a blunt and truncated proxy tocapture risk orientation. Respondents were classified as “risk averse” if theybelieved that governments should take responsibility for providing jobs or socialservices to all individuals. While risk aversion may encourage a respondent tosupport government provision of social insurance, there are numerous otherreasons why a respondent might support a government safety net. Moreover, thedichotomous variable taps only one side of the risk-orientation continuum becauseit collapses risk-neutral and risk-acceptant individuals into the “not risk averse”category. Their results may understate the importance of risk orientation becausethey do not make comparisons between individuals from both ends of the “riskaverse–risk acceptant” continuum.

The second limitation of the study, we contend, is theoretical in nature. Maydaet al. (2007) assume that risk aversion should have unconditional effects on tradepolicy opinions for all individuals, regardless of the nature of risk exposure underfree trade. But risk aversion should not have a uniform effect—instead, the effectshould differ depending upon whether one expects a potential loss or gain underthe terms of the policy, i.e., risk aversion should not matter for those who are notat risk of a loss.

Risk Orientation

Risk orientation can be conceived of as a generalized individual-level affec-tive response to facing the uncertain possibility of a gain or loss (a risk).3 In this

3 See Slovic, Finucane, Peters, and MacGregor (2004) for a related and nuanced discussion of the roleof affect in the perception of risk.


sense, it is a personality trait or disposition that is distinct from one’s perceptionof the level of risk involved in an activity or choice (Weber, Blais, & Betz, 2002)and from one’s risk-taking actions, i.e., the behavioral choice to engage in activi-ties that yield uncertain gains or losses (Zuckerman & Kuhlman, 2000). Althoughthere has been some debate about whether people possess a general dispositiontoward risk (see Shoemaker, 1993; Weber et al., 2002), research demonstrates thatrisk orientation and risk-taking behavior are both linked to a number of stable andwell-studied personality traits including extroversion, openness, agreeableness,sensation seeking, conscientiousness, achievement orientation, and “Type A” per-sonalities (Carducci & Wong, 1998; Kowart & Hermann, 1997; Nicholson et al.,2005; Filbeck et al., 2005; Zuckerman & Kuhlman, 2000), and, recently, linked togenetic characteristics (Cesarini, 2009; Rosier et al., 2009). A full examination ofthe relationship between risk orientation and personality is beyond the scopeof this paper; however, the appendix offers a brief analysis demonstrating thislink.

That risk taking and risk attitudes are linked to personality traits supportsthe idea that risk propensity is a general disposition that is not completelydependent upon context. Here, we depart in one important way from a morecommon psychological approach to thinking about how individuals respondto risky policy choices. Prospect theorists label “risk attitudes” as one’spreference for a policy that produces a probabilistic (risky) outcome, dependingupon one’s position relative to some status quo point and the framing ofthe choice (see Quatterone & Tversky, 1988, p. 722). While we do notdispute the central findings of prospect theory that framing and domainsmatter to choices, we disagree with the assertion that framing and domain com-pletely define one’s affinity or aversion to risk, and recent experimental worksupports this contention (Kam & Simas, 2010). We draw from research thatsuggests risk orientation is an exogenous and stable personality trait and weexpect it to influence how one defines, subjectively, the choice problem. Indeed,it is essential to understand how risk orientation influences one’s sensitivity torisk and rewards if we are to understand opinions in real-world settings whereelite frequently offer conflicting stories about risks and reward under the samepolicy.

From this perspective, our work speaks directly to the role of personality inpolitics by considering how individuals with different traits respond to the samepolicy debate. Personality traits are central to explaining a range of politicalopinions and traits often interact with contextual features of the political environ-ment to produce heterogeneous individual-level responses to political stimuli(Mondak, 2010; Mondak & Halperin, 2008). Logically, a similar process shouldoccur in the formation of trade opinions, a point we develop more fully in the nextsection.


Risk Orientation and Selective Response to Risk Exposure

We argue that risk orientation selectively directs one’s attention to certaintypes of information about complex policies, thus one’s risk orientation magnifiesthe importance of policy’s risks or rewards. Most policies have the potential toproduce both positive and negative outcomes and, thus, can be framed in termsof either losses or gains. Indeed, it is not unusual for elite and media to offercompeting frames, arguments, or messages about the same topic (Cobb & Kuk-linski, 1997; Chong & Druckman, 2007a, 2007b; Jerit, 2009; Zaller, 1992). Intrade policy, liberalizing trade has been framed as an opportunity for growth aswell as a risk to America’s jobs and economic health (Baker, 2003; Cobb &Kuklinski, 1997; Hiscox, 2006). The former emphasizes how trade is beneficial toconsumers, workers, and producers by allowing the free flow of products acrossborders, lowering prices on goods for consumers, providing new markets fordomestic producers, and increasing job opportunities. The latter emphasizes thatfree trade could result in losses of American jobs and industries through compe-tition with countries that enjoy cheap labor and fewer regulatory standards.

These competing policy arguments about trade differ in relevance for indi-viduals along two dimensions: personal economic circumstances (risk exposure)and risk orientation. Recall that the Stolper-Samuelson theory suggests that indeveloped economies, high-skill workers and capital owners will gain from tradewhile low-skill workers face job and income loss. Thus, positive messages abouttrade are most relevant to the abundantly skilled, while negative messages are mostrelevant to the low skilled. Therefore, we would generally expect low-skill indi-viduals to be less favorably disposed towards free trade than high skilled, andprevious research has found this to be the case (Scheve & Slaughter, 2001; Mayda& Rodrik, 2005).

However, one’s risk orientation should also determine how influential thesemessages are in shaping opinions. Individuals selectively take from a complexinformation environment that information that is most consistent with their under-lying predispositions and resist inconsistent information (Zaller, 1992). Predispo-sitions such as political awareness, partisanship, and political ideology arecommonly found to moderate the influence of framing (Chong & Druckman,2007a; Haider-Markel & Joslyn, 2001; Slothuus, 2008; Zaller, 1992) and, in acompetitive framing environment, predispositions—core values—influence thepersuasiveness of one frame compared to another (Sniderman & Theriault, 2004).We argue that personality traits should work the same way by signaling to theindividual which information in a complex, competitive environment is mostrelevant, personally, to their opinions. Lavine et al. (2005) suggest that “situationalforces activate corresponding personality dispositions from memory, thus render-ing them temporarily salient” (p. 222) when forming opinions. Since trade policyhas direct bearing on the prospects of future financial gains and losses, we expectpersonality traits or predispositions that are most closely tied to managing


economic and financial risks to serve as strong moderators of opinions in thisdomain. Risk-averse individuals should be especially sensitive to informationabout risk of losses while risk-acceptant people should be sensitive to informationabout opportunities for gains.4

Our argument thus far suggests unique influences on trade opinions for eachof four conditions outlined in Table 1: (1) risk averse facing probable loss, (2) riskaverse facing probable gain, (3) risk acceptant facing probable loss, and (4) riskacceptant facing probable gain. We expect those exposed to risk of loss (low-skillworkers) to be receptive to information about potential losses and, therefore,weight that information heavily in forming policy opinions. We expect risk-acceptant individuals who face probable losses to filter negative messages abouttrade and, therefore, discount potential losses when forming trade opinions. Con-versely, those facing potential gains from trade—high-skill workers—shouldexhibit the opposite pattern. Risk-averse, high-skilled workers should filter infor-mation about potential gains because it is inconsistent with their predispositions,while those who are risk acceptant should be receptive to information aboutpotential gains. These expectations lead to the following set of hypotheses:

• Hypothesis 1: Among low-skilled individuals, greater risk aversion willreduce support for free trade.

• Hypothesis 2: Among low-skilled individuals, greater risk acceptance willmake no difference in the level of support for free trade policy.

• Hypothesis 3: Among high-skilled individuals, greater risk aversion will makeno difference in the level of support for free trade policy.

• Hypothesis 4: Among high-skilled individuals, greater risk acceptance willincrease the level of support for free trade policy

4 We rely on an assumption about the causal mechanism in order to deduce empirically testablehypotheses, but two studies offer justification for this assumption. Sniderman and Theriault (2004)find that subjects faced with competing frames about allowing a public rally by an extremist groupwere most persuaded by frames that matched their underlying predispositions toward individualfreedoms. Lavine et al. (2005) found that the trait authoritarianism interacted with threat conditionsto cause subjects to choose attitude-congruent messages.

Table 1. Expected Effect of Risk Orientation and Risk Exposure Combinations onTrade Policy Considerations

Risk Exposure from Trade Policy

Probable income loss (low skill class) Probable income gain (high skill class)

Risk Averse Receptive to and heavily weightsinformation about losses

Filters and/or places little weight oninformation about gains

Risk Acceptant Filters and/or places little weight oninformation about losses

Receptive to and heavily weightsinformation about gains


Data and Methods

To test these hypotheses we use public opinion data from the 2006 Coopera-tive Congressional Election Survey (CCES), which included a set of relevantquestions on trade policy opinion and risk orientation.5 The dependent variable,Trade Support, measures how supportive or opposed a respondent is to limitingtrade and adopts the question wording most commonly used in research on tradeopinions. Respondents were asked to indicate how much they agreed with thefollowing statement: “The United States should limit imports of foreign productsin order to protect the U.S. Economy” and response categories ranged from 1(strongly agree) to 5 (strongly disagree). Higher values on this scale indicategreater support for free trade.6 Overall, the balance of respondents tips towards theprotectionist side of the scale. Nearly 14% strongly agreed with this statement,another 29% agreed and almost 31% expressed neutrality—they marked “neitheragree nor disagree.” Only 26% of the sample favored free trade, with 18% dis-agreeing and the other 8% strongly disagreeing. This distribution is similar to otherstudies. For instance, Scheve and Slaughter (2001) find that, depending on exactquestion wording, pluralities or small majorities of respondents oppose free trade.

We measure risk orientation through a trait-based survey question. Psychol-ogy research demonstrates that survey questions can elicit valid and reliableindicators of personality traits (Connolley et al., 2007; McCrae & Costa, 1987)and that key personality traits can be captured with a single survey question(Gosling, Rentfrow & Swann, 2003; Nadeau et al., 1999; Denissen, Geenen,Selfhout, & Van Aken, 2008). We adopt this approach rather than the behavioraleconomic method which observes subjects’ choices among gambles. Gambles arenot always good predictors of orientation towards the more general types of risksencountered in daily life because they tend to disproportionately tap a singledimension of risk taking that is associated with sensation or thrill-seeking behavior

5 Data are drawn from 1,000 randomly selected respondents to the post election wave of the 2006Cooperative Congressional Elections Survey administered via internet. Subjects for the larger study(n = 38,443) were selected from an opt-in internet panel based on a propensity matching techniquedesigned to mirror the national voting age population in the United States. Details of the studymethodology can be found at http://web.mit.edu/polisci/portl/cces/index.html and http://www.polimetrix.com/documents/Polimetrix_Whitepaper_Sample_Matching.pdf. Univariate and bivariatefrequencies are weighted to correct for response biases in age, sex, race, education, and geographiclocation while multivariate analyses include appropriate covariates to account for biases. Notably, thetype of biases prevalent in this type of survey—an absence of low-skilled individuals—would serveto attenuate the coefficients and bias our results towards a null finding.

6 This standard question wording is essentially identical to the often-studied trade question included onthe International Social Survey Program (see, for example, Mayda & Rodrik, 2005). The question hasa negative bias in that it focuses on potential costs from trade without balancing it with potentialbenefits; therefore we might expect to see stronger effects for risk-averse individuals compared to riskacceptant individuals. We account for this in the analysis. Nevertheless, studies that use balancedquestions, such as Scheve and Slaughter (2001), have found similar results to those that use negativelyframed questions. See Hiscox (2006) for more on framing in trade questions.


rather than the dispositional aspects of risk orientation (Meertens & Lion, 2008;Lion & Meertens, 2001).

We asked respondents the following question: “In general, people often haveto take risks when making financial, career, or other life decisions. Overall, howwould you place yourself on the following scale?” The 7-point response scale wasanchored at one end by “extremely comfortable taking risks” and at the other by“extremely uncomfortable taking risks.” The phrase “neither comfortable noruncomfortable taking risks” anchored the middle category (see the appendix forthe visual presentation of the question). Figure 1 shows that nearly 39% placedthemselves in the risk-acceptant range of the scale while 32% placed themselveson the risk-averse side of the scale. The remaining respondents located themselvesin the “risk neutral” category. Like other studies of risk propensity, we find widevariation in individual-level orientation towards risk, and this variation is associ-ated with other measures that are well known to be related to risk such as stockownership, gender, income, age, education, marital status, and other personalitytraits (see the appendix for validity analyses).

Empirically, it is plausible that the effects of risk aversion and risk acceptancewould be asymmetric for two reasons. First, risk aversion may simply exert astronger effect if individuals fear potential losses more than they welcome poten-tial gains, a result commonly found in lab experiments (Tversky & Kahneman,1981). Second, the question wording for the dependent variable emphasizes the

0 5 10 15 20 25 30 35

1. Extremely comfortable takingrisks

2.

3.

4. Neither comfortable oruncomfortable taking risks

5.

6.

7. Extremely uncomfortable takingrisks

Percent

Risk Averse

Risk Acceptant

Figure 1. Distribution of Risk Orientation


negative aspects of trade which could create a stronger “trigger” for risk-averseindividuals. To account for this possibility, we split the risk scale into three parts:a risk-aversion scale, a risk-acceptance scale, and a risk-neutrality indicator whichserves as the reference point in the models. Using the full 7-point scale imposes aconstraint that the effects of a unit difference in risk aversion and risk acceptanceare identical. Splitting the scale into separate measures of risk aversion and riskacceptance relaxes that constraint and allows the strength of the effect to beestimated separately for each orientation.

The risk-aversion scale ranges from a low of 0 (not risk averse) to a high of 3(extremely uncomfortable taking risks). Everyone who categorized themselves asrisk neutral or risk acceptant scored a 0 on this measure. Those who placedthemselves on the risk-averse side of the scale scored a 1, 2, or 3 on the risk-aversion measure depending on their level of discomfort with risk. Thus, someonewho placed themselves at the lowest possible level of risk aversion (a 5 on the7-point scale) is scored a 1 on our risk-aversion scale; someone who placedthemselves in the middle risk-aversion category (a 6 on the 7-point scale) is scoreda 2; and someone who placed themselves at the highest level of risk aversion (a 7on the 7-point scale) is scored a 3. We followed the same procedure for creating therisk-acceptance scale, with risk-neutral and risk-averse individuals given a score of0 and those on the risk-acceptant side given a score of 1, 2, or 3, depending on theirlevel of comfort with risk. For example, those who scored a 3 reported that theywere extremely comfortable taking risks. The excluded category that serves as abaseline in our model is the risk-neutral category because the respondent is, bydefinition, risk neutral when the risk-aversion and risk-acceptant scales are bothequal to zero.

The second step in testing our hypotheses is to construct interactions betweenskill level and risk propensity. Skill is difficult to measure directly, and there hasbeen significant debate about the best ways to measure it indirectly. The mostcommon measure is to use education with college graduates typically assumed tobe high skilled and those without college degrees assumed to be low skilled.7 Wefollow the same convention by separating respondents into high-skill and low-skillcategories based upon whether they hold a college degree. We include individualswho graduated from two-year colleges as well as those with four-year degrees inour measure of “high-skill.” Two-year colleges are traditionally focused on pro-viding skill-oriented degrees, many of which are service oriented, such as healthcare, police work, construction, and hospitality management, jobs not easily sub-stituted even with free trade.

7 This is the technique, for instance, of Scheve and Slaughter (2001), Mayda and Rodrik (2005), andmany other public opinion studies of trade policy preferences. Studies of legislative voting on trade,such as Bailey and Brady (1998), also frequently use level of education in a legislator’s district as ameasure of the skill composition of that district. However, this measure is not without controversy.Hainmueller and Hiscox (2006), for instance, argue that education measures exposure to protradearguments and cosmopolitanism and not skill. See also Mansfield and Mutz (2009).


We multiply both risk-orientation scales, risk averse and risk acceptant, bythe “high skill” variable to create the interaction and include five variables in themodel to test our theory: high skill, (high-skill ¥ risk averse), (high-skill ¥ riskacceptant), risk acceptant, and risk averse. Fortunately, risk orientation and skilllevel are not strongly correlated, so we have ample variation in risk orientation forhigh- and low-skilled workers.8 The coefficients for risk averse and risk acceptantwill pick up the marginal effect of each orientation on support for free trade amongthose with low skills (less than a two-year degree). We expect the coefficient forrisk averse to be negative and significant because low-skilled workers who are riskaverse are likely to be most sensitive to potential losses from trade. We expect thecoefficient for risk acceptant to be zero because low-skill workers stand to gainlittle from trade policy, and they filter information about losses. In contrast, weexpect the coefficients for (high-skill ¥ risk acceptance) and (high-skill ¥ riskaversion) to both be positive. High-skill individuals have opportunities to gainunder trade, and those comfortable with risk should be especially sensitive to thepotential for gains. Finally, we expect a positive coefficient for the high-skill riskaverse. This might seem counterintuitive at first glance but keep in mind that,based on Hypothesis 3, we expect no effect of risk aversion on the predictedprobability for high-skill workers. The total marginal effect of risk aversion forhigh-skilled workers is the sum of the coefficients for the risk-averse variable andthe (high-skill ¥ risk aversion) variable. Thus, the coefficient for (high-skill ¥ riskaversion) must offset the negative coefficient we expect for risk aversion.

In addition to our substantive variables of interest, we include a number ofcontrol variables drawn from previous public opinion studies of trade policypreferences such as Scheve and Slaughter (2001), Mayda and Rodrik (2005), andHays et al. (2005). Because policy preferences may be tied to underlying politicalideology such as a belief in free markets, we include a measure of ideology that iscoded higher for conservatives.9 Unemployment is a dummy variable measuringwhether the respondent is currently unemployed (out of a job and seeking work)or not. Income measures family income with a 14-point scale: higher-incomerespondents have consistently been found to be more supportive of free trade inprevious studies.10 Age measures the age of the respondent in years. Older respon-dents have been found to be less supportive of trade, although the theoretical

8 The joint distribution is: Probable Loss and Risk Acceptant, 19%; Probable Loss and Risk Neutral,14%; Probable Loss and Risk Averse, 16%; Probable Gain and Risk Acceptant, 24%; Probable Gainand Risk Neutral 12%; Probable Gain and Risk Averse, 15%.

9 Ideology is measured on a 5-point scale ranging from very liberal to very conservative. We alsoreestimated the analysis using partisanship with no major changes to the main results. Since elitepartisan preferences in policy debates about trade policy have not been clear, we do not have a clearpartisan hypothesis, thus we opted to include ideology rather than party.

10 Income is a 14-category variable ranging from “below 10,000” to “150,000 or more.” Thirteenpercent of the respondents refused to answer the income question so we imputed values based onage, education, race, gender, home ownership, and retirement. Estimating the model using thenonimputed version of the income variable reduces the number of cases from 914 to 787; however,


reasons for this are not clear. Since it is possible that the negative effects of tradedecline over time due to retirement or other factors, we also include the squaredvalue of age to capture non-linear effects. Married equals 1 if the respondent iscurrently married and 0 otherwise. Married respondents are expected to be lesssupportive of trade because they will be more sensitive to the costs associated withjob loss. Female equals 1 if the respondent is female and 0 if the respondent ismale: women have consistently been found to be less supportive of free trade,although, as with age, the theoretical reasons are unclear. Finally, we also includethree indicator variables to control for possible sampling bias as discussed abovein footnote 7. Black and Hispanic equal 1 if the respondent is of the stated race and0 otherwise. The survey was conducted prior to the 2006 congressional election sothe salience of trade policy might have been greater in competitive races. There-fore, we also include a dummy variable to indicate whether the respondent lived ina competitive House district. We report the model both with and without incomesince income might confound the results of our analysis. Income is sometimesused as an alternative measure of skill and, not surprisingly, income and educationare highly correlated, although conceptually, education has a closer link to jobskills. Substantively, income may also measure the tastes for consumption ofimportable goods as suggested by Baker (2005).11

Results

Table 2 presents three ordered probit models where the dependent variable iscoded from 1 to 5, with 5 indicating the strongest support for free trade and 1indicating the strongest opposition. The first model demonstrates that the newsurvey data used here is comparable to existing data by replicating the standardmodels in the literature and finding similar results. However, based on our argument,the effects of skill level should be conditioned by risk orientation, with risk aversionand risk acceptance having asymmetric conditioning effects depending on the levelof skill. Model 2, our main model, supports this expectation and demonstrates thatthe magnitude of the independent effect of skill level drops appreciably andbecomes insignificant once we have controlled for the moderating effects of riskorientation. This model shows that risk orientation is a statistically significantpredictor of opinions after controlling for a number of alternative causes. Model 3demonstrates that the results in Model 2 are robust to including the strong control ofhousehold income as discussed above.Among the highly skilled, only those who are

the substantive results are similar to Model 3 with only slight attenuation of the coefficients. Thecoefficients for risk aversion and the interaction between risk aversion and high skill remainsignificant at p < .10.

11 Baker (2005) argues that because of consumption effects, the poor, who are more sensitive to lowerprices for imported goods, might also have reason to support free trade. However, within the UnitedStates, the skill effect seems to more than offset this consumption effect: Baker finds income to bepositively, but not significantly, related to support for free trade while all other studies have foundincome to be positively and significantly related.


risk acceptant are more likely to support trade, while among the low skilled, onlythose who are risk averse are more likely to oppose trade.

A better way to assess both the statistical and substantive significance of thehypotheses, however, is to examine the effects of risk orientation on the predictedprobabilities of supporting free trade by skill level, depending upon whether one isrisk averse (Figures 2a) or risk acceptant (Figures 2b).12 Because the substantive

12 The predicted probabilities were generated in Stata 9.0 using Model 2 in Table 2. We generated arandom draw of 1000 of each of the coefficients drawn from normal distribution with a mean ofb and a standard deviation equal to the standard error of the coefficient. The predicted probabilitiesare calculated based on the 1000 “models” of simulated coefficients so the reported probability isthe mean of the 1000 predictions. The upper and lower bounds of the 95% confidence intervalscorrespond to the 2.5 and 97.5 percentiles of the distribution of the predicted probabilities.

Table 2. The Effect of Risk and Risk Exposure on Support for Free Trade

Model 1 Model 2 Model 3

B SE B SE B SE

High Skill (College degree) 0.393 0.076** 0.163 0.141a 0.100 0.147a

High Skill ¥ Risk Averse – – 0.290 0.193a 0.241 0.203a

High Skill ¥ Risk Acceptant – – 0.282 0.135*a 0.249 0.142*a

Risk Averse – – -0.127 0.053**a -0.116 0.055*a

Risk Acceptant – – 0.041 0.055a 0.028 0.055a

ControlsIncome – – – – 0.059 0.010**Unemployed -0.538 0.165** -0.532 0.162** -0.368 0.169*Ideology (Conservative) -0.001 0.047 -0.003 0.047 -0.003 0.049Age -0.024 0.012* -0.023 0.013* -0.036 0.013**Age squared 0.000 0.000* 0.000 0.000 0.000 0.000**Female -0.376 0.066** -0.328 0.072** -0.301 0.072**Married 0.019 0.062 0.006 0.063 -0.102 0.065Black -0.133 0.183 -0.134 0.171 -0.114 0.155Hispanic -0.115 0.129 -0.153 0.130 -0.149 0.127Competitive House race -0.018 0.081 -0.041 0.084 -0.049 0.083

m1 -1.926 0.310** -1.952 0.326** -1.817 0.343**m2 -0.956 0.300** -0.967 0.321** -0.821 0.334**m3 -0.162 0.294 -0.164 0.316 -0.007 0.332m4 0.808 0.316** 0.818 0.344** 0.990 0.358**

N 922 917 917Wald chi2 147.6** 296.1** 420.7**lnL -1351.4 -1333.6 -1323.1

*p < .05 **p < .01 (one-tailed tests); a: joint test of significance of interactions, p < .001.The model is ordered probit with robust standard errors, clustered on state to address the potentialwithin-unit correlations of cases due to similar exposure to risks or benefits from liberalized trade.The dependent variable is the level of agreement or disagreement with the statement The UnitedStates should limit imports of foreign products in order to protect the U.S. Economy and responsecategories ranged from 1 (strongly agree) to 5 (strongly disagree).


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2a: Predicted Probability of Supporting Free Trade Among Risk Averse

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2b: Predicted Probability of Supporting Free Trade Among Risk Acceptant

High-Skill

Low-Skill

High-Skill

Low-Skill

Figure 2. The Effect of Risk Orientation on Supporting Free Trade, by Skill LevelNote: Predicted probabilities are based on Table 2, Model 2, holding constant all other variables at

their mean or mode. Figures 3a and 3b show the predicted probability of falling in the category“disagree” or “strongly disagree” and 95% confidence intervals are shown in both figures.


concept of interest is “support” for free trade, we predict the probability that arespondent falls in the category “disagree” or “strongly disagree” when given thestatement “the U.S. should limit imports to protect the economy.”

The results in Table 2 and Figure 2 support all four hypotheses. Greater riskaversion among low-skilled workers is associated with a reduced probability ofsupporting free trade (Hypothesis 1) while the effect of greater risk aversionamong high-skilled workers is statistically insignificant (Hypothesis 3). In con-trast, risk acceptance is associated with a higher probability of support amonghigh-skill workers (Hypothesis 4) but not low-skill workers (Hypothesis 2).

Low Skill. The probability of supporting free trade is not high for low-skilledindividuals, generally, but it is lowest among those who are risk-averse. Thepredicted probability drops from .23 to .13 as the level of risk aversion increasesfrom risk neutral to extremely risk averse (Figure 2a). Instead, opposition to freetrade rises from a predicted probability of .48 among those who are risk neutral to.63 for those who are extremely risk averse (not shown in graph), indicating thatamong the most risk averse, nearly two in three oppose free trade at leastsomewhat.

In contrast, the level of risk acceptance for low-skilled individuals makes nodifference to the likelihood of supporting or opposing free trade. The predictedprobability of supporting trade changes minutely as the level of risk acceptanceincreases, hovering near .23 across the entire range of strength of risk acceptance(Figure 2b). Likewise, opposition to free trade varies little, regardless of the degreeof risk acceptance felt by the low-skill worker, where the predicted probabilityranges from .44 to .42 and the differences is statistically insignificant.

That we observe a change in probability of support with the intensity of riskorientation for those in the risk-averse category and not those in the risk-acceptantcategory is telling. Our theory predicts that risk aversion sensitizes one to lossinformation, with the degree of sensitivity to loss increasing as intensity of riskaversion increases. In contrast, risk acceptance serves as a filter on loss messageswhile sensitizing one to gain messages. However, in the context of trade, thosewith low skills expect little gain so the increased sensitivity to gains makes littledifference to opinions. Accordingly, we should observe intensity effects among theaverse, not the acceptant, and this result is apparent in Figures 2a and 2b. Con-ceptualizing risk orientation along a continuum that ranges from extremely riskacceptant to extremely risk averse helps to highlight the dual role that orientationplays in filtering and sensitizing individuals to potential gains and losses frompolicies.

High Skill. High-skill individuals exhibit a different pattern entirely comparedto low-skill individuals. Among the risk acceptant high-skill workers, the predictedprobability of supporting free trade rises from about .30 to .62 as the strength ofrisk acceptance shifts from risk neutral to extremely risk acceptant (Figure 2b),while opposition to free trade drops to just over .2 (not shown in graph). Theprobability of supporting trade also increases among the highly skilled risk averse,


but the confidence interval is so large that the differences in probability betweenthe risk averse and the risk neutral are not statistically discernible. We cannot ruleout the possibility that support stayed the same or even declined slightly. Thispossible increase in support for trade among high-skilled risk-averse respondentsis somewhat puzzling, although we offer a tentative (and admittedly post hoc)suggestion for why this might be the case. The Stolper-Samuelson theorem pre-dicts that low-skill workers bear the brunt of the risk and uncertainty associatedwith free trade. If the mechanism we posit is correct, and risk-averse individualsare more attuned to negative or threatening messages, then high-skill individualswould learn from those messages that they are not the group at risk. Rather, theylikely learn that the risks are relatively small for skilled workers relative to thepotential benefits. If so, then attention to negative messages could produce theparadoxical result of boosting support for trade among this group. With thatsaid, of course, there is no way to assess this claim in the context of these data.But we suggest this as an interesting avenue to pursue in the context ofexperimental research where information about the policy risks and rewards can bemanipulated.

Taken together, these results strongly suggest that risk orientation and expo-sure are important determinants of trade policy preferences, but they must beconsidered jointly because risk orientation moderates the effects of risk exposure.Risk aversion matters only to those who are exposed to risks of loss and riskacceptance matters only to those who are exposed to the possibility of gain. Theprobability of supporting free trade among the most risk-averse, low-skilled indi-viduals is less than one in six, while the probability of supporting free trade amongthe most risk-acceptant, high-skilled individuals is nearly two in three. In contrast,nearly two in three low-skilled, highly risk-averse individuals oppose free trade,while less than one in six high-skilled risk-acceptant individuals are likely tooppose it.

A Brief Look at Immigration Policy Opinions

Two charges could be leveled at the results presented thus far. First, onemight wonder whether our argument generalizes to other policy opinions.Second, one could critique the indirect measure of risk exposure in the trademodel as being too general to be certain we are capturing a joint effect of riskorientation and risk exposure. We, too, recognize the limits of the skill measure,though it is one that plagues nearly all published studies of trade policy opin-ions. Fortunately, we can examine policy opinions in another policy area that issimilar to trade but has a more direct measure of risk exposure—support forliberalizing immigration laws.

Immigration policy is akin to trade policy in that policies that liberalizeimmigration requirements, particularly those that expand amnesty to undocu-mented immigrants, can alter the size and composition of employment sectors.


Economic analyses of immigration often treat immigration similarly to trade,viewing it as the movement of people rather than goods across borders, and findincome effects similar to those in trade policy (Scheve & Slaughter, 2001).

Although a full analysis of immigration policy opinions is well beyond thescope of this paper, we can take a cursory look at whether perceived risk expo-sure and risk orientation jointly influence public opinion about extending citi-zenship to illegal immigrants. Prior research shows that individuals employed insectors with threatened occupations are less supportive of immigration (Citrin,Green, Muste, & Wong, 1997) so we have some a priori evidence that job riskdrives opinions in this area. Our argument would suggest that this effect shouldbe most pronounced for those who feel threatened and are risk averse. We testthis argument using data from the 2006 CCES that asked individuals whetherthey supported granting citizenship to illegal immigrants.13 In addition, respon-dents were asked whether they believed that a “job like theirs” could be filled byan illegal immigrant.14 To be fair, the question is not a perfect measure of jobthreat because it does not ask whether illegal immigrants who are granted citi-zenship pose a threat, but it is a reasonable proxy since it taps the feeling thatone’s job is at risk from the entry of low-skill workers.15 We interact thismeasure of risk exposure with our risk orientation scales with an expectationthat an increase in risk exposure would reduce support for extending citizenshipamong those who are risk averse, but not among those risk who are acceptant.We control for sociotropic economic evaluations (national and state economy),race, income, gender, marital status, education, employment status, and politicalideology.16

Figure 3 shows that the results of the model support our argument. Thepredicted probability of supporting the policy drops dramatically as job threat risesfor those who are risk averse but not among those who are risk acceptant. In fact,

13 Question wording from 2006 CCES: “Another issue is illegal immigration. One plan considered bythe Senate would offer illegal immigrants who already live in the U.S. more opportunities to becomelegal citizens. Some politicians argue that people who have worked hard in jobs that the economydepends on should be offered the chance to live here legally. Other politicians argue that the plan isan amnesty that rewards people who have broken the law. What do you think? If you were faced withthis decision, would you vote for or against this proposal?”

14 Question wording: “Please indicate how likely it is that over the next 12 months that . . . a job likeyours could be filled by an illegal immigrant.”

15 Notably, our theory would also predict that those who are more risk averse would feel more “at risk.”In an ordered probit model, risk orientation has a positive and significant effect (p(t) < .05) on theprobability of feeling one’s job could be filled by an illegal immigrant, but this effect is lower amonghigh-skill workers. Controls in the model include race, gender, age, marital status, and retirementstatus. Results are available from authors upon request.

16 The coefficients from the probit model predicting support for offering citizenship are as follows(*p < .05, one-tailed). Pr(Y = 1) = -1.97 -.13 (immigrant job threat) + .24*(risk averse scale) -.15*(job threat ¥ risk averse scale) - .03(risk acceptant scale) + .07(job threat ¥ risk acceptant scale)- .02(state economic performance - .22*(national economic performance - .45*(conservative scale)+ .29(Black) + .65* (Hispanic) - .05(female) - .08(married) -.18(retired) - .09(unemployed)-.01(income) + .07*(education). N = 817. Wald Chi square = 198.5.


variation in the perceived risk of job loss is not a significant predictor of opinionsabout immigration for either risk-acceptant or risk-neutral individuals. Only risk-averse individuals tie their opinions on immigration policy to their subjectivefeelings of risk exposure. Thus, although we have focused on the case of trade inthis paper, the evidence from this second policy area suggests that the theoryapplies in other areas as well and works as we would expect when using a moredirect measure of risk exposure.

Discussion and Conclusions

That risk orientation moderates the effects of risk exposure on policy supportis important; free trade and immigration are but two of many policy domains inwhich individuals are exposed to an uncertain mix of gains and losses under theterms of a proposed policy. We’ve argued that risk orientation, as a personalitytrait, predisposes individuals to be sensitive to certain types of information aboutpolicies and the risks they pose. As a result, not everyone exposed to risk under the

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Figure 3. Effect of Risk Exposure’s on Support for Extending Citizenship to Illegal Immigrants,by Risk Orientation

Note: Predicted probabilities are based on the coefficients from the model reported in footnote 16with remaining values set at their mean or modal value. Dashed and dotted lines are 95%

confidence intervals. For the risk acceptant and risk aversion predictions, risk orientation was set tothe maximum value and interacted with the level of job risk.


policy is necessarily responsive to risk- or loss-based arguments nor is everyoneanticipating gains from the policy sensitive to gain-based arguments. This hasimplications for future directions of research, including the need to incorporatemeasures of risk orientation more regularly into empirical studies of publicopinion.

With regard to free trade, our findings augment the accounts of Scheve andSlaughter (2001) and Mayda and Rodrik (2005) by showing how risk orientationand risk exposure interact in shaping preferences for trade, and we build upon thework of Hiscox (2006) and Mansfield and Mutz (2009) to provide better behav-ioral and psychological foundations to the trade policy preference literature. Thisresearch also provides evidence for the microfoundations of the embedded liber-alism literature which argues that policymakers can increase support for tradeamong those at risk by providing insurance against loss and compensating thosewho do lose from trade (Hays et al., 2005). Because risk aversion is a key deter-minant of trade policy support, such policies are likely to be most effective atswaying those who are jointly “at risk” and risk averse. Hays et al. (2005) foundthat compensation can increase support for trade among low skilled workers, andthese findings help to identify the mechanism through which this occurs. However,we also suggest that further examination is warranted to determine whether theembedded liberalism thesis effects occur most strongly among the risk-aversesubgroup of low-skilled workers.

More generally, this research highlights the importance of considering riskorientation as an explanatory factor in models of political opinions, particularlyopinions about market-based policies that expose individuals to risky or uncertainoutcomes. Political science and economic theories that draw from an expectedutility framework often assume that individuals are risk averse but results fromsurvey questions and lab experiments consistently refute the validity of thisassumption (Berinsky & Lewis, 2007; Grable, 2000; Fellner & Maciejovsky,2007). Prospect theory offers a counterpoint to expected utility by suggesting thatrisk orientation is dependent upon the context of framing and domain (Kahneman& Tversky, 1979), but recent research suggests that trait-based risk orientationoperates independent of context (Kam & Simas, 2010). Thus, we suggest analternative approach to incorporating the concept of risk orientation into theoriesof public opinion by considering how risk orientations might serve as a filter forrisk and reward information.

We argue that traits exogenous to a decision context help individuals define,subjectively, their prospects of gain and loss and help connect these prospects totheir opinions. It is common for individuals to encounter contradicting or alterna-tive frames about prospective gains and losses from policies (see Jerit, 2009), butwhich frame becomes most relevant to an individual likely depends at least partlyupon individual predispositions or characteristics. Chong and Druckman (2007b)distinguish between “frames in communication” and “frames in thought” wherethe former refers to framing by speakers relaying information and the latter refers


to the “individual’s cognitive understanding of a given situation” (p. 101). Wecontend that personality traits, and risk orientation in particular, help to definethese frames in thought. This is akin to prospect theory’s “editing phase” in whichindividuals define the contours of the choice problem for themselves, includingwhether they are in the domain of loss or gain (Levy, 1992; Kahneman & Tversky,1979).

Admittedly, the analysis reported here does not provide a direct test of thecausal mechanism we have proposed. However, our models provide supportiveevidence for the four hypotheses that are logically deduced from such anaccount, so further investment in this line of research seems warranted. To date,studies of individual-level response to competitive framing are rare (Chong &Druckman, 2005; Jerit, 2009). Only two that we are aware of bring direct evi-dence to bear on this question. Sniderman and Theriault (2004) show that valuespredispositions shape the influence of frames in a competitive environment,while Lavine et al. (2005) demonstrate that personality traits (authoritarianism)and context (threat) jointly influence subjects’ selection of information whenoffered competing views of a policy. We view our findings as complementary tothese studies as we provide additional support for the interaction between indi-vidual characteristics and political context in shaping opinions in the context ofa salient policy problem.

The conclusions from this study are also similar to Nadeau et al. (1999) inthat they (and we) suggest that the risk averse and the risk acceptant use dif-ferent measuring sticks when forming preferences. Our measure, however,allows respondents to express greater variation within each category of risk aver-sion and shows that variation within categories is influential as well. Somerespondents locate themselves quite close to risk neutral, while others have moreextreme reactions to risk; those at the extreme ends of the scale are most sen-sitive to the potential risks or rewards from free trade. This has implications bothfor our theoretical conceptualization of risk orientation and our empirical mea-sures of risk orientation. Not only should we be attentive to differences amongcategories of risk orientation, but we should also consider differences withincategories.

Measures of risk orientation are not regularly included in survey instru-ments, but they should be, and, when they are included, they should be sensitiveenough to distinguish intensity. We’ve done this using a single survey question,thereby demonstrating that including a valid measure of risk orientation need notbe costly in terms of survey space or respondent time. However, we recognizethat there is a trade-off in cost and accuracy since a single-item measure willhave greater error than a multi-item scale. Whenever possible, multi-item scaleswould be preferable. Meertens and Lion (2008) offer a short seven-item set ofrisk orientation questions that they combine into a reliable Risk Propensity Scale(RPS) that would be suitable for testing theories related to trait-based risk ori-entation on political opinions. Like our single-item measure, the items in the


RPS are dispositional rather than behavioral (selecting among gambles), so theytap the underlying concept of risk orientation as affect rather than choicesamong framed alternatives.17 However, when space and time are at a premium,the measure used in this paper offers a reasonably valid single-item indicator ofthe concept.

Incorporating risk orientation into public opinion studies can also help illu-minate the potential for aggregate public policy shifts by helping us to betterunderstand the distribution of risk aversion and risk acceptance in the popula-tion. Our argument and results have implications for the extent to which poli-ticians can use risk- and loss-based arguments to shape public support forpolicies. Public support for policy change is actively cultivated by elected offi-cials because aggregate public opinion sets boundaries for the direction andmagnitude of policy change (Page & Shapiro, 1992). Cobb and Kuklinski (1997)argue that there may be an individual level bias towards overweighting loss-based arguments in forming opinions which could result in a bias in aggregateopinions that favor the status quo. Nearly all policy proposals involve some sortof loss for at least some people, thus politicians who wish to trigger concernsabout losses should find the task easy. However, our argument, if correct, sug-gests that the aggregate impact of loss-based arguments might be smaller thanexpected. Individual-level opinions depend on expectations of gains and lossesfrom proposed policies, but also on the sensitivity of individuals to those expec-tations. If risk orientation biases how expected outcomes affect opinions, thosebiases become writ large in the aggregate. Expectations of losses (through expo-sure to risk) exert a strong influence on opinions, but risk aversion, as a per-sonality trait, is far from ubiquitous. Nearly 39% of our sample indicated thatthey were at least somewhat risk acceptant, with another 29% professing riskneutrality. Only 32% of our sample characterized themselves as risk averse, andamong those, only half fell into the group of people considered “at risk” of jobloss from free trade. Thus, while some individuals were very sensitive to antici-pated losses, they were a distinct minority in our sample—just over 15%. Risk-acceptant individuals who tend to discount potential losses made up more thanhalf of those in the low-skill category. Although loss-oriented political argu-ments may exert great power over the opinions of some individuals, the effec-tiveness of this type of argument may be limited to the subset of people who areselectively attentive to such information—the risk averse. Politicians canmanipulate the sense of risk that the public feels about a policy, but they cannot

17 For scholars working with existing datasets that lack direct measures of risk orientation, wesuggest using correlates of risk aversion such as stock ownership, attitudes towards gambling,age, and gender. Measures such as these, while not perfect, provide a starting point for testingtheories that account for variation in risk orientation and help to provide a richer account of publicopinion, particularly for policies that subject some sectors of the public to risky and uncertainoutcomes.


force everyone to care about that risk. Likewise, they can manipulate perceptionsof gains, but not everyone will be persuaded of the potential benefits.

Appendix

Single-Question Measure of Risk Orientation

Strategies for measuring risk orientation are numerous and vary across disci-plines, with most developed for use in experimental settings where the use of a largenumber of survey items to build indices of risk orientation is feasible. This strategyhas produced high-quality measures, but, practically, this approach is too costly toimplement in a typical-length public opinion survey. We demonstrate that it ispossible to capture the essential characteristic of risk orientation using a singlequestion, much like we do with political ideology or political partisanship.Single-question measures of personality have been used to successfully tapother personality dimensions, particularly those of the “five-factor” model ofpersonality—agreeableness, extroversion, conscientiousness, neuroticism, andopenness to new experiences (see, for example, Denissen et al., 2008; Gosling,Rentfrow, & Swann, 2003). Moreover, Nadeau et al. (1999) argue that risk propen-sity can be tapped by a direct self-rating question, noting that such self ratings areoften the highest loading item in factor analyses used to create risk-propensityscales.

The construct validity of the 7-point risk orientation measure stems from thenature of the question wording and from its relationship to other variables wellknown to be associated with risk orientation. On its face, the question asks for ageneralization of risk attitudes over a range of life-domains to capture an overallorientation rather than a domain specific orientation and contains clear anchors forthe scale.

“In general, people often have to take risks when making financial, career, or otherlife decisions. Overall, how would you place yourself on the following scale?”

Extremelycomfortabletaking risks

Neithercomfortablenoruncomfortabletaking risks

Extremelyuncomfortabletaking risks

0 0 0 0 0 0 0

Variation in responses to this item is related to other variables in ways that wewould expect given previous research using more complicated indicators of riskorientation. Moreover, we can observe whether these relationships hold acrossmultiple surveys and time periods. Identical risk orientation measures were


included on both the 2006 and 2008 Cooperative Congressional Elections Study(CCES) and a 5-point version was included in the 2008 Cooperative CampaignAnalysis Project (CCAP). This permits us to examine the relationship of each ofthese items to demographics known to be associated with risk orientation. Previousstudies have found that gender, age, income, marital status, and education shapeattitudes towards risk (see Dahlback, 1991; Grable, 2000; Nadeau et al., 1999;Slovic, 1964) Ordered probit models show statistically significant results for allbut one of these well-established relationships for both the CCAP and CCESstudies (see Table A1). Only marital status failed to significantly influence riskorientation.

Both the 2008 CCAP and CCES contain batteries of personality trait itemsthat have been shown to relate to risk-taking propensity that should relate directlyto risk orientation. Table A2 replicates findings from a psychological study thatrelates risk-taking propensity to the five-factor model of personality traits: extra-version, emotional stability, openness, agreeableness, and conscientiousness(Nicholson et al., 2005). We draw from each survey common measures of thepersonality dimensions for the five-factor model (see Gosling, 2003; Mondak,2010; Mondak & Halperin, 2008), but only the CCAP offers measures for each ofthe five dimensions. The CCES provides measures of two of the five dimensions,but it also provides other personality type measures that are typically associatedwith risk orientation: impulsivity, enjoyment of gambling, and uncertainty aver-sion. The results of the CCAP model are substantively identical to Nicholson et al.,with extraversion and openness negatively related to risk aversion, while agree-ableness, conscientiousness, and neuroticism are positively related to risk aver-sion. The CCES model provides additional evidence that characteristics such asenjoying gambling, making impulsive decisions, discomfort with uncertainty, andoptimism are all also related to risk aversion in predictable ways.

Finally, we demonstrate that the model has predictive validity through usingthe risk-orientation measure to predict stock ownership. Even after controlling forincome, age, and gender, we find statistically significant (P < .05) negative effectsof both the 5-point scale the 7-point scale.18 This is reassuring since a vastliterature in finance and economics relates investment behavior to risk orientation(see Carducci & Wong, 1998; Fellner & Maciejovsky, 2007; Grable, 2000; Keller& Siegrist, 2006).

Taken together, these analyses suggest that the single-question 7-pointmeasure is a valid indicator of individual-level risk orientation. Although multi-item scales are less prone to error, this analysis suggests that a single itemapproach can be used successfully in situations where cost and survey space are anissue.

18 The coefficients and standard errors for the 2008 CCAP Model: Income .17 (.02), Age .017 (.004),Female -.07 (.112), risk aversion 5 pt -.11 (.05). The coefficients and standard errors for the 2006CCES model: Income .12 (.02), Age .011 (.004), Female -.16 (.13), risk aversion 5 pt -.10 (.045).


Table A1. Ordered Probit Analysis, Risk Orientation, and Demographic Characteristics(risk aversion is high value)

2006 CCES 2008 CCAP (5 pt scale) 2008 CCES

B SE B SE B SE

Female 0.40 0.07*** 0.42 0.08*** 0.48 0.07***Age 0.01 0.00*** 0.01 0.00*** 0.01 0.00***Married 0.04 0.07 -0.03 0.08 0.07 0.08Income -0.03 0.01*** -0.03 0.01*** -0.04 0.01***Education -0.08 0.02*** -0.09 0.03*** -0.13 0.03***

m1 -1.96 0.17 -1.41 0.20 -1.40 0.17m2 -1.05 0.16 -0.30 0.20 -0.80 0.17m3 -0.24 0.15 0.74 0.20 -0.17 0.17m4 0.44 0.15 1.50 0.20 0.56 0.17m5 1.07 0.16 1.02 0.17m6 1.54 0.16 1.33 0.17

N 963 837 931LR(chi2) 67.59 86.22 132.10

**p < .05 ***p < .01The dependent variable ranges from 1 (extremely comfortable taking risks to (5 or 7) extremelyuncomfortable taking risks.

Table A2. The Relationship between Personality Traits and Risk Orientation(scaled so that risk aversion is high value)

5 Factor Traits: CCAP 2008 (5 point scale) CCES 2008 (7 point scale)

Survey Item Survey Item

Extroversion Extravert -0.14 0.03*** Shy 0.094 0.02***Neuroticism Anxious 0.05 0.03** Worry 0.067 0.03***Openness (Un)conventionala -0.10 0.03*** –Agreeableness Sympathetic 0.12 0.04*** –Conscientiousness Dependable -0.07 0.04** Impulsive -0.148 0.03***

Optimism -0.11 0.03***Enjoy Gambling -0.056 0.02***Dislike Uncertainty 0.122 0.03***

ControlsFemale 0.30 0.09*** Female 0.39 0.08**Age 0.01 0.00*** Age 0.015 0.00***Married -0.03 0.10 Married -0.033 0.09Income -0.02 0.01 Income -0.032 0.01**Education -0.08 0.03** Education -0.108 0.03***

m1 -1.95 0.39 -1.34 0.32m2 -0.80 0.38 -0.674 0.32m3 0.35 0.38 0.054 0.32m4 1.19 0.38 0.836 0.32m5 1.367 0.32m6 1.691 0.32N 602 725LR Chi2 118.07 231.4

**p < .05 ***p < .01 (one-tailed tests)The dependent variable ranges from 1 (extremely comfortable taking risks) to 5 or 7 (extremelyuncomfortable taking risks).a. The variable “conventional” was rescaled so that high values indicate strong disagreement that the word“conventional” described the respondent.


ACKNOWLEDGMENTS

We have received numerous helpful comments on this and previous drafts ofthe paper. We especially thank the anonymous reviewers, the editor, Daniel Kono,John Sides, Phil Paolino, Matthew Eshbaugh-Soha, Jacqueline DeMeritt, ShaunBowler, Martin Johnson, and Will Pollock. Support for this project and for par-ticipation in the CCES was provided by the College of Social Sciences and theDepartment of Political Science at Florida State University. Correspondence con-cerning this article should be sent to Cherie Maestas, Department of PoliticalScience, Florida State University, 569 Bellamy Building, Tallahassee, FL 32306-2230. E-mail: [email protected]

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