Studies of gender-ideology stereotypes suggest that voters evaluate male and female candidates in different ways, yet data limitations have hindered an analysis of candidate ideology, sex, and actual election outcomes. This article draws on a new dataset of male and female primary and general election candidates for the U.S. House of Representatives from 1980 to 2012. I find little evidence that the relationship between ideology and victory patterns differs for male and female candidates. Neither Republican nor Democratic women experience distinct electoral fates than ideologically similar men. Candidate sex and ideology do interact in other ways, however; Democratic women are more liberal than their male counterparts, and they are advantaged in primaries over Republican women as well as Democratic men. The findings have important implications for contemporary patterns of women’s representation, and they extend our understanding of gender bias and neutrality in American elections.
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The magnitude of the effect of candidate sex on perceptions of candidate ideology is much smaller than that of factors like respondent ideology, feeling thermometer ratings, and perceptions of party ideology (Koch 2002, p. 421). Moreover, the size of the effect of actual candidate ideology on voter perceptions of her ideology is between two and four times that of candidate sex (Koch 2002, p. 421).
The DIME dataset includes candidates who filed with the FEC. Candidates who do not exceed the $5000 threshold of campaign fundraising are not required to file. Those who are excluded are thus more likely to be long-shot candidates, but it is not clear that they are more likely to be ideologues or moderates. Even so, these excluded candidates comprise only 6% of primary winners and 0.02% of general election winners, so they are highly unlikely to have an influence on policy outcomes or women’s representation. Furthermore, these data provide the best publicly available measures of the ideological positions of congressional winners and losers over time.
Bonica suggests that contribution records may even offer a more complete measure of ideology than does legislative voting. Contributors can consider factors beyond candidates’ voting behavior, such as policy goals, endorsements, or cultural values. Yet Bonica’s goal is not to replicate DW-NOMINATE scores, and he notes that the two measures should be viewed as complementary.
These averages include races in which candidates do and do not face opposition.
Ideology could also be measured as liberalism, but I opted to use this measure in light of the above research on whether conservatism has a stronger effect on election outcomes for female candidates.
Candidate sex was unable to be identified in 13 cases.
Pettigrew et al.’s (2014) data and the data presented here are virtually identical. There are very minor discrepancies due to the omission of a few states and/or districts in a handful of years, but the fact that they were collected independently provides further validation to both datasets.
I calculated the total number of primary candidates by party, congressional district, and year. The number of candidates was calculated by congressional district and year in states where the top two vote getters advance to the general, regardless of party (i.e., CA, LA, and WA in various cycles).
I also ran the models with controls for partisan eras (1980–1992; 1994–2004; 2006–2012), and the results are virtually identical to those presented below (see Table 3). In addition, I account for whether it was a presidential election year and whether the candidate was running in the South, and the results are the same (see Table 4). Standard errors are clustered by race in the primary models.
Like Lawless and Pearson (2008), I exclude primary and general election candidates who are unopposed. Of the 17,639 primary candidates with CFscores, 7606 (43%) were unopposed; of the 12,632 general election candidates with CFscores, 916 were unopposed (7%). This figure is higher than the percentage of unopposed candidates in the full sample of 24,125 primary candidates (35%), because those with CFscores were more likely to run unopposed than those without CFscores (43% and 12%, respectively). Since the focus is on the interaction between candidate ideology and sex, the candidates with ideology scores are of primary interest here.
I also ran logistic regression models, and these results are presented in Table 5. I also present the most basic specification of the models with ideology, sex, the interaction term, and incumbent (Table 6). Lastly, I ran the models with primary and general election vote share as the dependent variable (Table 7), but I opted to focus on victory rates because they are of ultimate relevance for patterns of women’s representation. Across specifications, the results remain largely the same, and the interaction term does not reach conventional levels of significance. The sole exception is the general election model for Democrats in Table 7, but again, the size of the coefficient is small, and the overwhelming pattern indicates statistical and substantive insignificance across models.
It is possible that the pooled models mask variation over time and that sex and ideology were associated with victory rates in the 1980s and early 1990s. To examine this question, I ran separate models for each election year (see Lawless and Pearson 2008 and Frederick 2009 for similar empirical approaches). The results are presented in Fig. 6. In general, the relationship between ideology and election outcomes does not differ for men and women across this 30-year period.
All other variables are set at their mean or mode so these values are for non-incumbents.
Candidate quality has long been a key factor in congressional elections as well, but data limitations prevent its inclusion here. Pettigrew et al.’s (2014) dataset includes the previous political experience of primary candidates from 2000 to 2010, and this variable is correlated with campaign receipts at 0.60 and with incumbent at 0.82 so I am confident the models are capturing a key dimension of quality. Yet I also ran the models with this measure of candidate quality among non-incumbents from 2000 to 2010, and the interaction term is insignificant (see Table 8).
Male and female candidates were statistically indistinguishable in all five categories. The ideology category controls are not shown in Table 2, but the coefficients are not significant.
These values are calculated from the models in Table 1. Additional models that exclude the interaction term are provided in Tables 9 and 10. In the primary models, candidate sex is insignificant for Republicans but positive and significant for Democrats, with and without the inclusion of ideology. In the general election models, candidate sex is insignificant for Republicans but negative and significant for Democrats, with and without the inclusion of ideology.
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Previous versions of the article were presented at the annual meetings of the Midwest Political Science Association and the Southern Political Science Association. I am grateful to the Dirksen Congressional Center and the Political Parity Project for their support of the data collection. I thank Rosalyn Cooperman, Melissa Deckman, Chris Faricy, Shana Gadarian, Andy Hall, Katherine Michelmore, and three anonymous reviewers for their helpful feedback. I am grateful to Spencer Piston for his comments on multiple drafts of the article.