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.
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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.
Per the California Code of Regulations, Ballot Designations. http://www.sos.ca.gov/administration/regulations/current-regulations/elections/ballot-designations/#section-20714.
A copy of the ballot designation worksheet can be found here: http://elections.cdn.sos.ca.gov/ballot-designation-worksheet/ballot-designation-worksheet.pdf.
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.
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.
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.
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.
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.
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].
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).
The order in which the attributes appeared was randomized in each of the three tasks.
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.
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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.
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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
- Low information elections
- Voting behavior
- Conjoint experiments