Skip to main content

Fit for the Job: Candidate Qualifications and Vote Choice in Low Information Elections

Abstract

Cues and heuristics—like party, gender, and race/ethnicity—help voters choose among a set of candidates. We consider candidate professional experience—signaled through occupation—as a cue that voters can use to evaluate candidates’ functional competence for office. We outline and test one condition under which citizens are most likely to use such cues: when there is a clear connection between candidate qualifications and the particular elected office. We further argue that voters in these contexts are likely to make subtle distinctions between candidates, and to vote accordingly. We test our account in the context of local school board elections, and show—through both observational analyses of California election results and a conjoint experiment—that (1) voters favor candidates who work in education; (2) that voters discriminate even among candidates associated with education by only favoring those with strong ties to students; and (3) that the effects are not muted by partisanship. Voters appear to value functional competence for office in and of itself, and use cues in the form of candidate occupation to assess who is and who is not fit for the job.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    The author did not provide standard errors and so we cannot calculate whether these coefficients, which are very close in magnitude, are statistically different from one another.

  2. 2.

    Per the California Code of Regulations, Ballot Designations. http://www.sos.ca.gov/administration/regulations/current-regulations/elections/ballot-designations/#section-20714.

  3. 3.

    A copy of the ballot designation worksheet can be found here: http://elections.cdn.sos.ca.gov/ballot-designation-worksheet/ballot-designation-worksheet.pdf.

  4. 4.

    These two features make it seem less plausible that candidates can stretch the reality of their backgrounds for political gain. Yet, decisions about ballot designations are certainly likely to be influenced by the attributes of their opponent. In our setting, highlighting a background in education would seem to be most electorally beneficial when the opposing candidates come from different, less-relevant fields. Future research should certainly address the strategic nature of ballot designations, and its consequences for election outcomes.

  5. 5.

    It is possible that these three categories reflect both experience working with students as well class differences. For example, we may simply expect candidates in the “high” and “medium” education category to fare better than those in the “low” education category simply because the jobs in the former are viewed more generally by society as “better” jobs, reflecting higher status. We return to this point later in the manuscript as we interpret our results.

  6. 6.

    We used the following as a database of female first names: http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/nlp/corpora/names/female.txt.

  7. 7.

    We recognize that using a Hispanic surname match is not a perfect indicator of whether a respondent is Hispanic or not. Some candidates, especially married women, with Hispanic surnames may not identify as Hispanic. Nevertheless, this is a reasonable proxy. And perhaps most importantly, regardless of whether the candidate is Hispanic-born or not, our coding reflects the information that voters may use in evaluating candidates.

  8. 8.

    We also run a model which excludes those candidates who ran unopposed. These results are presented in Table 2 of the Appendix, and are substantively and statistically similar to these presented below.

  9. 9.

    The 95% CI for the difference between the “high” education group and “business owner” is [2.79, 4.27], and the 95% CI for the difference between the “medium” education group and “business owner” is [0.79, 2.27].

  10. 10.

    This study was approved by the Office of the Institutional Review Board at the University of New Mexico (1129029-1) and the Office of the Human Research Protection Program at the University of California, Los Angeles (IRB#17-001659).

  11. 11.

    The order in which the attributes appeared was randomized in each of the three tasks.

  12. 12.

    We also calculated three conditional AMCEs (i.e., an exploration of heterogeneous treatment effects) of occupation on vote choice: one where the party of the candidate and the party of the respondent are the same (i.e., Republican and Republican/Republican and lean-Republican, Democrat and Democrat/Democrat and lean-Democrat, or no party (candidate) and neither party (voter)), one where the two differ, and one where the candidate had no party affiliation. These analyses re-run the same model, but include an interaction between the occupation attribute and indicator for shared party affiliation and allow us to determine whether the effects uncovered so far appear only when the candidate and respondent share a party affiliation. We find that that the effect of occupation does not differ dramatically depending on whether the respondent shares the party of the candidate. Figure 4 in the Appendix presents the results of this analysis.

References

  1. Abramowitz, A. I., & Webster, S. (2016). The rise of negative partisanship and the nationalization of U.S. elections in the 21st century. Electoral Studies,41(1), 12–22.

    Article  Google Scholar 

  2. Ahler, D. J. (2014). Self-fulfilling misperceptions of public polarization. Journal of Politics,76(3), 607–620.

    Article  Google Scholar 

  3. Atkeson, L. R., & Partin, R. W. (2001). Candidate advertisements, media coverage, and citizen attitudes: The agendas and roles of senators and governors in a federal system. Political Research Quarterly,54(4), 795–813.

    Article  Google Scholar 

  4. Barreto, M. A., Villarreal, M., & Woods, N. D. (2005). Metropolitan Latino political behavior: Voter turnout and candidate preference in Los Angeles. Journal of Urban Affairs,27(1), 71–79.

    Article  Google Scholar 

  5. Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Analysis,20(3), 351–368.

    Article  Google Scholar 

  6. Bianco, W. T. (1984). Strategic decisions on candidacy in U.S. congressional districts. Legislative Studies Quarterly,9(2), 351–364.

    Article  Google Scholar 

  7. Bond, J. R., Fleisher, R., & Talbert, J. C. (1997). Partisan differences in candidate quality in open seat House races, 1976-1994. Political Research Quarterly,50(2), 182–299.

    Article  Google Scholar 

  8. Box-Steffensmeier, J. M., Jacobson, G. C., & Grant, J. T. (2000). Question wording and the House vote choice: Some experimental evidence. Public Opinion Quarterly,64(3), 257–270.

    Article  Google Scholar 

  9. Bullock, C. S., III. (1994). Section 2 of the Voting Rights Act, districting formats, and the election of African Americans. Journal of Politics,56(4), 1098–1105.

    Article  Google Scholar 

  10. Bullock, C. S., III, & Campbell, B. A. (1984). Racist or racial voting the 1981 Atlanta municipal elections. Urban Affairs Quarterly,20(2), 149–164.

    Article  Google Scholar 

  11. Butler, D. M., & Powell, E. N. (2014). Understanding the party brand: Experimental evidence on the role of valence. Journal of Politics,76(2), 492–505.

    Article  Google Scholar 

  12. Cadei, E. (2018). Why an election tradition is banned in other states. Sacramento Bee. Retrieved June 9, 2018, from, http://www.sacbee.com/news/politics-government/capitol-alert/article207850079.html.

  13. Cox, G. W., & Katz, J. R. (1996). Why did the incumbency advantage in U.S. House elections grow? American Journal of Political Science,40(2), 478–497.

    Article  Google Scholar 

  14. Crawford, E. (2018). How nonpartisan ballot design conceals partisanship: A survey experiment of school board members in two states. Political Research Quarterly,71(1), 143–156.

    Article  Google Scholar 

  15. Downs, A. (1957). An economic theory of democracy. New York: Harper & Row.

    Google Scholar 

  16. Dubois, P. L. (1984). Voting cues in nonpartisan trial court elections: A multivariate assessment. Law and Society Review,18(3), 395–436.

    Article  Google Scholar 

  17. Erikson, R. S. (1971). The advantage of incumbency in congressional elections. Polity,3(3), 395–405.

    Article  Google Scholar 

  18. Ferreira, F., & Gyourko, J. (2009). Do political parties matter? Evidence from U.S. cities. Quarterly Journal of Economics,124(1), 399–422.

    Article  Google Scholar 

  19. Ferreira, F., & Gyourko, J. (2014). Does gender matter for political leadership? The case of U.S. mayors. Journal of Public Economics,112, 24–39.

    Article  Google Scholar 

  20. Gelman, A., & King, G. (1990). Estimating incumbency advantage without bias. American Journal of Political Science,34(4), 1142–1164.

    Article  Google Scholar 

  21. Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014). Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. Political Analysis,22(1), 1–30.

    Article  Google Scholar 

  22. Hersh, E. D., & Schaffner, B. F. (2013). Targeted campaign appeals and the value of ambiguity. Journal of Politics,75(2), 520–534.

    Article  Google Scholar 

  23. Huddy, L. (1994). The political significance of voters’ gender stereotypes”. In M. X. Delli Carpini, L. Huddy, & R. Y. Shapiro (Eds.), Research in micropolitics: New directions in political psychology. Greenwich: JAI Press.

    Google Scholar 

  24. Huddy, L., & Terkildsen, N. (1993). The consequences of gender stereotypes for women candidates at different levels and types of offices. Political Research Quarterly,46(3), 503–525.

    Article  Google Scholar 

  25. Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science,59(3), 690–707.

    Article  Google Scholar 

  26. Jacobson, G. C., & Kernell, S. (1981). Strategy and choice in congressional elections. New Haven: Yale University Press.

    Google Scholar 

  27. Kirkland, P. A., & Coppock, A. (2017). Candidate choice without party labels: New insights from conjoint survey experiments. Political Behavior. https://doi.org/10.1007/s11109-017-9414-8.

    Article  Google Scholar 

  28. Klein, D., & Baum, L. (2001). Ballot information and voting decisions in judicial elections. Political Research Quarterly,54(4), 709–728.

    Article  Google Scholar 

  29. Koch, J. W. (2000). Do citizens apply gender stereotypes to infer candidates’ ideological orientations? Journal of Politics,62(2), 414–429.

    Article  Google Scholar 

  30. Krasno, J. S., & Green, D. P. (1988). Preempting quality challengers in House elections. Journal of Politics,50(4), 920–936.

    Article  Google Scholar 

  31. Lien, P. (1998). Does the gender gap in political attitudes and behavior vary across racial groups? Political Research Quarterly,51(4), 869–894.

    Article  Google Scholar 

  32. Longoria, T., Jr. (1999). The impact of office on cross-racial voting: Evidence from the 1996 Milwaukee mayoral election. Urban Affairs Review,34(4), 596–603.

    Article  Google Scholar 

  33. Lublin, D. I. (1994). Quality, not quantity: Strategic politicians in U.S. Senate elections, 1952-1990. Journal of Politics,56(1), 228–241.

    Article  Google Scholar 

  34. Lupia, A., & McCubbins, M. D. (1998). The Democratic dilemma: Can citizens learn what they need to know?. New York: Cambridge University Press.

    Google Scholar 

  35. Matson, M., & Fine, T. S. (2006). Gender, ethnicity and ballot information: Ballot cues in low-information elections. State Politics and Policy Quarterly,6(1), 49–72.

    Article  Google Scholar 

  36. McDermott, M. L. (1997). Voting cues in low-information elections: Candidate gender as a social information variable in contemporary United States elections. American Journal of Political Science,41(1), 270–283.

    Article  Google Scholar 

  37. McDermott, M. L. (1998). Race and gender cues in low-information elections. Political Research Quarterly,51(4), 895–918.

    Article  Google Scholar 

  38. McDermott, M. L. (2005). Candidate occupations and voter information shortcuts. Journal of Politics,67(1), 201–219.

    Article  Google Scholar 

  39. Mechtel, M. (2013). It’s the occupation, stupid! Explaining candidates’ success in low-information elections. European Journal of Political Economy,33(2), 53–70.

    Google Scholar 

  40. Mondak, J. J. (1993). Public opinion and heuristic processing of source cues. Political Behavior,29(2), 167–192.

    Article  Google Scholar 

  41. Mueller, J. E. (1970). Choosing among 133 candidates. Public Opinion Quarterly,34(3), 395–402.

    Article  Google Scholar 

  42. Nakanishi, M., Cooper, L. G., & Kassarjian, H. H. (1974). Voting for a political candidate under conditions of minimal information. Journal of Consumer Research,1(2), 36–43.

    Article  Google Scholar 

  43. Partin, R. W. (2001). Campaign intensity and voter information: A look at gubernatorial contests. American Politics Research,29(2), 115–140.

    Article  Google Scholar 

  44. Pomper, G. M. (1975). Voters’ choice: Varieties of American electoral behavior. New York: Dodd Meade.

    Google Scholar 

  45. Popkin, S. L. (1994). The reasoning voter: Communication and persuasion in presidential campaigns. Chicago: University of Chicago Press.

    Google Scholar 

  46. Rahn, W. M. (1993). The role of partisan stereotypes in information processing about political candidates. American Journal of Political Science,37(2), 472–496.

    Article  Google Scholar 

  47. Rapoport, R. B., Metcalf, K. L., & Hartman, J. A. (1989). Candidate traits and voter inferences: An experimental study. Journal of Politics,51(4), 917–932.

    Article  Google Scholar 

  48. Rosenstone, S. J., & Hansen, J. M. (1993). Mobilization, participation, and democracy in America. New York: Macmillan.

    Google Scholar 

  49. Sanbonmatsu, K. (2006). Where women run: Gender and party in the American states. Ann Arbor: University of Michigan Press.

    Book  Google Scholar 

  50. Schaffner, B. F., Streb, M., & Wright, G. (2001). Teams without uniforms: The nonpartisan ballot in state and local elections. Political Research Quarterly,54(1), 7–30.

    Google Scholar 

  51. Sniderman, P. M. (2017). The democratic faith: Essays on democratic citizenship. New Haven: Yale University Press.

    Google Scholar 

  52. Sniderman, P. M., Brody, R. A., & Tetlock, P. E. (1991). Reasoning and choice: Explorations in political psychology. New York: Cambridge University Press.

    Book  Google Scholar 

  53. Squire, P. (1989). Challengers in U.S. Senate elections. Legislative Studies Quarterly,14(4), 531–547.

    Article  Google Scholar 

  54. Squire, P., & Smith, E. (1988). The effect of partisan information on voters in nonpartisan elections. Journal of Politics,50(1), 169–179.

    Article  Google Scholar 

  55. Trounstine, J. (2011). Evidence of a local incumbency advantage”. Legislative Studies Quarterly,36(2), 255–280.

    Article  Google Scholar 

  56. Vanderleeuw, J. M. (1990). A city in transition: The impact of changing composition on voting behavior”. Social Science Quarterly,71(2), 326–338.

    Google Scholar 

Download references

Acknowledgements

We thank Kathleen Hale, participants in UCLA’s Political Psychology lab and at MPSA 2018, as well as the anonymous reviewers for helpful comments on earlier drafts. Brian Hamel acknowledges the National Science Foundation Graduate Research Fellowship Program for support. Replication code and data are available at https://doi.org/10.7910/DVN/1HPT9N. All errors are our responsibility.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Brian T. Hamel.

Appendix

Appendix

See Tables 2 and 3 and Fig. 4.

Table 2 Descriptive Statistics
Table 3 Effect of working in education on vote share—excluding unopposed candidates
Fig. 4
figure4

Effect of effect of working in education on vote choice—conditional on shared candidate-respondent party

Survey Instrument

What year were you born (e.g., 1980)?

What is your gender?

  • Male

  • Female

  • Other [Please Specify]

What racial or ethnic group best describes you?

  • White

  • Black or African-American

  • Asian

  • Hispanic or Latino

  • Other [Please Specify]

To the best of your knowledge, what was your total family income before taxes in 2016?

  • Below $21,000

  • $21,000–$41,999

  • $42,000–$59,999

  • $60,000–$79,999

  • $80,000–$99,999

  • $100,000 or more

  • Don’t know

What is the highest level of school you have completed or the highest degree you have received?

  • Less than high school

  • High school graduate—high school diploma or equivalent (for example: GED)

  • Some college but no degree

  • Associate degree in college—occupational/vocational program

  • Associate degree in college—academic program

  • Bachelor’s degree (for example: BA, AB, BS)

  • Graduate degree (for example: MA, MS, MEng, MBA, MD, DVM, JD, Phd)

  • Other [Please Specify]

Generally speaking, do you usually think of yourself as a Democrat, Republican, an independent, or what?

  • Democrat

  • Republican

  • Independent

  • Other [Please Specify]

(if Democrat or Republican was selected)—Would you say that is strong or weak?

  • Strong

  • Weak

(if Independent was selected)—Would you say that you lean toward one of the parties?

  • Lean Democrat

  • Lean Republican

  • Neither

We hear a lot of talk these days about liberals and conservatives. Here is a seven-point scale on which political views that people might hold are arranged from extremely liberal to extremely conservative. Where would you place yourself on this scale or haven’t you thought much about this?

  • Extremely liberal

  • Liberal

  • Slightly liberal

  • Moderate; middle of the road

  • Slightly conservative

  • Conservative

  • Extremely conservative

  • Haven’t thought much about this

Conjoint Attributes

Gender:

  • Female

  • Male

Incumbency Status:

  • No

  • Yes

Occupation:

  • Attorney

  • Business Owner

  • School Guidance Counselor

  • School Janitor

  • School Teacher

Party:

  • Democrat

  • No Party

  • Republican

Race:

  • Asian

  • Black or African-American

  • Hispanic or Latino

  • White

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Atkeson, L.R., Hamel, B.T. Fit for the Job: Candidate Qualifications and Vote Choice in Low Information Elections. Polit Behav 42, 59–82 (2020). https://doi.org/10.1007/s11109-018-9486-0

Download citation

Keywords

  • Low information elections
  • Voting behavior
  • Heuristics
  • Occupation
  • Conjoint experiments