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Risk Attitudes and the Incumbency Advantage

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An Erratum to this article was published on 10 December 2013

Abstract

Explanations for the incumbency advantage in American elections have typically pointed to the institutional advantages that incumbents enjoy over challengers but overlook the role of individual traits that reinforce this bias. The institutional advantages enjoyed by incumbents give voters more certainty about who incumbents are and what they might do when (and if) they assume office. We argue that these institutional advantages make incumbents particularly attractive to risk-averse individuals, who shy away from uncertainty and embrace choices that provide more certainty. Using data from 2008 and 2010 Cooperative Congressional Election Study, we show that citizens who are more risk averse are more likely to support incumbent candidates, while citizens who are more risk accepting are more likely to vote for challengers. The foundations of the incumbency advantage, we find, lie not only in the institutional perks of office but also in the individual minds of voters.

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Notes

  1. There is some debate as to the size and source of individual level evidence of incumbency advantage in the early literature (see Born 1986; Eubanks 1985; Fiorina 1981; Johannes and McAdams 1981).

  2. See Born (1986) for an alternative perspective.

  3. Notably, risk aversion has been promoted as an explanation for an “incumbency advantage” in non-political decisions. For example, Muthukrishnan (1995) conducted a series of experiments to determine the factors that led people to choose a new consumer product over the one they were currently using. The study found that nearly 40 % of subjects who stayed with their incumbent brand did so despite reporting that they thought the challenging brand was superior. The author cites risk aversion as the likely reason that subjects would maintain loyalty to their brand despite recognizing that there appeared to be a better alternative.

  4. Prospect Theory offers one conditionality to this general argument. According to Prospect Theory, features of the contextual environment will make decision-makers more or less likely to engage in risky choice. Within the domain of losses, decision-makers are risk-seeking. Within the domain of gains, decision-makers are risk-averse. Quattrone and Tversky (2000) argue that voters will stick with the incumbent (the less risky candidate) in the region of gains (during good economic times) and will take the “political gamble” by backing challengers in the region of losses (during bad economic times).

  5. The CCES is a cooperative survey project that allows teams to purchase individual module surveys. The survey was conducted via the Internet by YouGov/Polimetrix using a matched random sample design. A subset of respondents recruited for online surveys were selected by matching them on a set of demographic characteristics to a randomly selected set of individuals from the population of American adults. Propensity score weights for the samples were developed so as to ensure that the sample represents the demographic characteristics of the adult population as reflected in the 2004 and 2008 Current Population Survey.

  6. The distribution of Risk Tolerance does not appear to be context-dependent. The distribution of Risk Tolerance in the 1996 PSID (during a relatively strong economy) is strikingly similar to that uncovered in the 2008 CCES (and in the 2010 CCES as well).

  7. Here, we utilize the three category summary measure (cc307) to ensure that we have enough respondents in the Independent category.

  8. The measure is based on cc302 and ranges from 0 (the economy has gotten “much better”) to 1 (the economy has gotten “much worse”). In 2008, over a majority of respondents though the economy had become “much worse” in the past year.

  9. Challenger quality is a dummy variable that takes on the value of 1 if a challenger held elective office previously and 0 otherwise. Data on challenger quality and candidate expenditures for 2008 and 2010 were compiled and generously shared by Gary Jacobson.

  10. We plot predicted probabilities for respondents in a Democratically held district who hold average economic assessments.

  11. Female is a dummy for female respondents (v208). Age is coded 0 (youngest) to 1 (oldest), based on v207. Education is a six category variable ranging from 0 (no high school degree) to 1 (advanced degree), based on v213. Income is a 14 category variable ranging from lowest (0) to highest (1), with refusals set to zero. Income Refused is a dummy for those who refused to report income. Married is a dummy for married respondents (v214). Partisanship is based on cc307a and consists of the seven-category measure, ranging from 0 (strong Democrat) to 1 (strong Republican). Ideology is a five-category self-placement measure based on v243, ranging from 0 (very liberal) to 1 (very conservative).

  12. We also reanalyzed the models using a variation of the challenger quality measure which differentiated the prior offices held by challengers (state legislators, other elective office holders, and former members of the House) with no substantive difference in results. We offer a handful of additional explanations for the insignificant results on challenger quality. The first has to do with the bluntness of the dependent measure. Our dependent measure is the dichotomous vote choice at the individual-level. Challenger quality is a significant predictor of incumbent vote share at the district-level, but its predictive power is fragile when it comes to the dichotomous measure of district-level incumbent victory. The second has to do with the merging of individual respondents to districts: we lose about 60 districts in the merge, given the design of the CCES and the voting patterns of the respondents. Third, the peculiarities of sampling anywhere between one and seven individual voters per district introduces more imprecision in estimating district-level effects compared with the aggregate election outcomes. Finally, weighting the individual-level survey data to be nationally representative allows us to make inferences about individuals, but results in weighting some districts more than others (in district-level, aggregate analysis, all districts are equally weighted). For these various reasons, we note that our results speak more persuasively to individual-level factors than district-level factors. We control for district-level factors to make our individual-level estimates more credible, but our dataset is not optimally crafted to adjudicate district-level effects.

  13. We construct our measure using variables cc317a, cc317k, and cc317l. Note that we lose about a quarter of our respondents, because they are unable or unwilling to place the incumbent on the ideological scale. Missingness is even more severe when it comes to the challenger: two-thirds of respondents fail to place the challenger on the ideological scale. The basic result holds when we only include a measure of challenger proximity: challenger proximity is highly significant and in the expected direction (b = 2.52, SE = 0.51, p < 0.01) and more importantly, for Risk Tolerance, b = −0.91, SE = 0.31, p < 0.01, but N plummets to 262.

  14. Specifically, 52.3 % of respondents are least risk tolerant, 16.7 % are somewhat risk intolerant, 11.6 % are more risk tolerant, and 19.3 % are maximally risk tolerant.

  15. It may also be the case that the effect of Risk Tolerance is conditioned by in-party versus out-party status. Our analyses provide little evidence to support this speculation. Finally, we examined whether the effect of Risk Tolerance differs across House and Senate elections. We suspect the effect would be weaker in higher-profile elections, such as senate and presidential races, where citizens might have other criteria upon which to base their votes. This is essentially what we found in our analysis of the 2008 and 2010 senate vote: a negative but statistically insignificant effect of Risk Tolerance on the likelihood of voting for the senatorial incumbent. In the interest of conserving space, the tables representing the analyses described above are omitted. They are available from the authors’ upon request.

  16. Our discussion is based on decision-making in single-shot elections, but the shadow of future elections is worth discussion. If a voter is forward-thinking, she may recognize that today’s challenger (if successfully elected) will be tomorrow’s incumbent, and her risk attitude should be weighted against future options and the her discount factor for the future. Such a rationale requires that voters be future-oriented, an assumption that may rely upon an overly optimistic view of myopic voters (e.g., Healy and Malhotra 2009).

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Acknowledgments

The authors would like to acknowledge the organizations that supported the 2008 and 2010 Cooperative Congressional Election Study data upon which this paper is based. The 2008 data was funded by the Center for Congressional and Presidential Studies at American University. The 2010 data was funded by the National Science Foundation along with contributions from our departments and colleges at the Florida State University, the University of Massachusetts, Amherst, and Vanderbilt University.

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Authors and Affiliations

  1. Department of Risk Management and Insurance, University of Georgia, Athens, GA, 30602, USA

    David L. Eckles

  2. Department of Political Science, Vanderbilt University, Nashville, TN, 37203, USA

    Cindy D. Kam

  3. Department of Political Science, Florida State University, Tallahassee, FL, 32306, USA

    Cherie L. Maestas

  4. Department of Political Science, University of Massachusetts, Amherst, Amherst, MA, 01002, USA

    Brian F. Schaffner

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  1. David L. Eckles
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Correspondence to Brian F. Schaffner.

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Eckles, D.L., Kam, C.D., Maestas, C.L. et al. Risk Attitudes and the Incumbency Advantage. Polit Behav 36, 731–749 (2014). https://doi.org/10.1007/s11109-013-9258-9

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