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The Effect of Political Competition on Democratic Accountability

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An Erratum to this article was published on 11 December 2012

Abstract

Representing uncompetitive, homogeneous constituencies is increasingly the norm for American legislators. Extensive research has investigated how competition affects the way representatives respond to their constituents’ policy preferences. This paper explores competition’s effect on the other side of representation, how constituents respond to their legislators’ policy record. Combining multiple measures of state competitiveness with large-N survey data, I demonstrate that competition enhances democratic accountability. Voters in competitive states are more interested in politics, more aware of the policy positions their U.S. senators have taken, and more likely to hold them accountable for those positions at election time. Robustness checks show that these effects are not due to the intensity of campaigning in a state: general competition, not particular campaign activities, drives citizens’ response. The recent increase in uncompetitive constituencies has likely lessened the degree to which legislators are held accountable for their actions in office.

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Notes

  1. Political competition refers to the degree of (potential) conflict of preferences between groups within a constituency (Bishin 2003, p. 1). Previous research has not settled on a common terminology for this competition, also referring to it as “complexity” (Ensley et al. 2009), “discord” (Fitzgerald and Curtis 2012), “diversity” (Putnam 2007), or “heterogeneity” (Anderson and Paskeviciute 2006; McAtee and Wolak 2011). Throughout this paper, I use the terms “political competition”, “diversity”, and “heterogeneity” interchangeably.

  2. Common Cause, see http://www.commoncause.org/site/pp.asp?c=5nJCJQPvEhKUE&b=7461781; the League of Women Voters, http://ca.lwv.org/lwvc/action/redistrict/; Americans for Redistricting Reform, http://www.americansforredistrictingreform.org/html/redistricting_reform_principle.html, all accessed July, 2011.

  3. A separate literature explores how media coverage of politics—and in particular, of incumbent legislators’ records—varies across contexts (e.g. Arnold 1990; Hutchings 2003). Assessing the quality of the information communicated by the media in competitive states is beyond the scope of this paper. Instead, I focus on the narrower claim from social arena studies that political competition drives interest in politics, and thus increases information-seeking.

  4. Concerns about the representativeness of Internet sampling are significant but less germane to this research design, which rests on comparing differences between respondents in competitive and uncompetitive states rather than measuring absolute levels of knowledge or vote choice in the electorate. Nonetheless, the sample represents the electorate of 2006 very closely in vote choice and demographic characteristics (Vavreck and Rivers 2008).

  5. Residents of Hawaii and Indiana were also surveyed but are excluded from these analyses. Measures of ideological disagreement at the state level are only available for the 48 contiguous states. In Indiana, incumbent Senator Dick Lugar faced no major party challenger and won 87 % of the vote, making the state a distinct outlier in the vote choice models.

  6. Updated measures of ideological disagreement using a more recent survey such as the 2004 NAES are not yet available. Levendusky and Pope (2010) also estimate the same measure using the 2006 CCES survey data, including the seven roll call vote questions used here. To avoid problems of endogeneity in using the same survey source, I rely on their estimates from the 2000 NAES. Empirically, the measures are correlated at .48 for the states in this analysis, suggesting that using the 2000 measure should not significantly bias the results.

  7. Table A1 in the Online Appendix gives the rank and value of each measure for the states in this analysis.

  8. Specifically, comparing the sum of squared residuals from a multi-level Poisson model to the residual degrees of freedom results in a ratio of 1.1, with a p-value of .001 based on the χ2 distribution.

  9. As a robustness check to assess whether one item among the seven roll call votes may be influencing the results unduly, Table A3 in the Online Appendix presents the results from a series of regression models that replicate the analysis in Table 2, dropping one of the roll call votes from the scale each time. The estimated effects of state competition on knowledge of the incumbent’s record are consistent across all of these specifications, offering little evidence that individual items in the aggregate scale are skewing the results.

  10. The substantive results obtained from models that use actual levels of congruence rather than perceptions are near-identical to those presented here, as we might expect given the strong correlation between them shown in Table 3.

  11. Additionally, estimating models that include the campaign intensity variables but exclude the state competition variables does not suggest any significant relationship between the two. Incumbent fundraising is very weakly but positively related to the weight constituents place on policy congruence (.08, SE = .04, p = .08) but overall there is little suggestion that campaign factors alter the structure of vote choice (see Table A6 in the Online Appendix). As a final robustness check, I estimated models that allowed all of the individual-level coefficients to vary by state. If the interest-priming hypothesis is correct, then political competition should prime citizens to weigh their policy interests more heavily, but should not alter the weights they place on other elements (party congruence and retrospective evaluations of the economy and of Iraq). The results from these models, available in Table A4 in the Online Appendix, show no significant effect of political competition on the other individual-level covariates. The fact that political competition affects the importance of policy congruence, but not party or retrospective evaluations, is more evidence for H2. I am grateful to one of the anonymous reviewers for suggesting this additional test of the theory.

  12. Note that these results are independent of the overall level of policy congruence in the electorate. That is, the first differences show the differences between a voter who perceives 25 % congruence and a voter who perceives 75 % congruence in each of these types of states. As Table A1 in the Online Appendix shows, significant numbers of voters perceive high or low levels of policy congruence in all types of states, with high and low levels of political competition.

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Acknowledgments

I thank Steve Ansolabehere, Barry Burden, Claudine Gay, Jason Mycoff, Joe Pika, Meg Rithmire, Sid Verba, and in particular Ben Bishin for helpful comments on previous versions of this paper. I am also grateful to the journal’s three anonymous reviewers who helped clarify and strengthen the argument substantially. All errors are, of course, my own.

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  1. Department of Political Science and International Relations, University of Delaware, 347 Smith Hall, Newark, DE, 19716, USA

    Philip Edward Jones

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Appendix: Model Specifications

Appendix: Model Specifications

Interest model

In the models predicting constituents’ interest in politics (shown in Table 1), all of the coefficients except for the intercept are fixed. The individual-level model can be written as follows:

$$ \begin{aligned} \hbox{Political interest}_{ij} =& \beta_{0j} + \beta_{1j}(\hbox{Female})_{ij} + \beta_{2j}(\hbox{Age})_{ij} + \beta_{3j}(\hbox{Income})_{ij} + \beta_{4j}(\hbox{Black})_{ij} + \beta_{5j}(\hbox{Hispanic})_{ij} + \beta_{6j}(\hbox{Other race})_{ij}\\ &\quad + \beta_{7j}(\hbox{High school})_{ij} + \beta_{8j}(\hbox{Some college})_{ij} + \beta_{9j}(\hbox{College})_{ij} + \beta_{10j}(\hbox{Post-college})_{ij} + r_{ij} \end{aligned} $$

where i indexes individuals, j indexes states, and r ij represents the residual for individual i in state j. At the state level, β0j is modeled as a function of several state-level variables (as an example, for Model 1(a) in Table 1):

$$ \begin{aligned} \beta_{0j} =& \gamma_{00} + \gamma_{01}(\hbox{Turnout 2004})_{j} + \gamma_{02}(\hbox{High school graduates})_{j} + \gamma_{03}(\hbox{Electoral competition})_{j} + \mu_{0j}\\ \hbox{and} & \quad \beta_{pj} = \gamma_{p0}\quad \hbox{for}\,\, \hbox{p}=1-10 \end{aligned} $$

The full model is obtained by substituting the second model into the first one. Since the dependent variable is a categorical variable, I use an ordered logistic regression model (see Bauer and Sterba 2011). The knowledge models in Table 2 and the congruence models in Table 3 take the same approach, using a Poisson and least squares estimator respectively.

Vote choice model

In the models predicting constituents’ vote choice for or against the incumbent senator (shown in Table 4), all of the coefficients except for the intercept and policy congruence are fixed. The individual-level model can be written as follows:

$$ \begin{aligned} \hbox{Vote choice}_{ij} =& \beta_{0j} + \beta_{1j}(\hbox{Policy congruence})_{ij} + \beta_{2j}(\hbox{Co-partisan})_{ij} + \beta_{3j}(\hbox{Independent})_{ij} + \beta_{4j}(\hbox{Don't know senator's party})_{ij}\\ &\quad+ \beta_{5j}(\hbox{Economy gotten worse})_{ij} + \beta_{6j}(\hbox{Economy stayed same})_{ij} + \beta_{7j}(\hbox{Economy gotten better})_{ij} \\ &\quad + \beta_{8j}(\hbox{Economy gotten much better})_{ij} + \beta_{9j}(\hbox{Don't know economy})_{ij} + \beta_{10j}(\hbox{Iraq war not a mistake})_{ij} \\ &\quad+ \beta_{11j}(\hbox{Don't know Iraq war})_{ij} + r_{ij} \end{aligned} $$

where i indexes individuals, j indexes states, and r ij represents the residual for individual i in state j. At the state level, β0j and β1j are modeled as a function of several state-level variables (as an example, for Model 1(a) in Table 4):

$$ \begin{aligned} \beta_{0j} =& \gamma_{00} + \gamma_{01}(\hbox{Turnout 2004})_{j} + \gamma_{02}(\hbox{High school graduates})_{j} + \gamma_{03}(\hbox{GOP senator})_{j} + \gamma_{04}(\hbox{Electoral competition})_{j} + \mu_{0j}\\ \beta_{1j} =& \gamma_{10} + \gamma_{11}(\hbox{Turnout 2004})_{j} + \gamma_{12}(\hbox{High school graduates})_{j} + \gamma_{13}(\hbox{GOP senator})_{j} + \gamma_{14}(\hbox{Electoral competition})_{j} + \mu_{1j}\\ \hbox{and}& \quad \beta_{pj} = \gamma_{p0}\quad \hbox{for}\,\, \hbox{p}=2-11 \end{aligned} $$

The full model is obtained by substituting the second model into the first one.

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Jones, P.E. The Effect of Political Competition on Democratic Accountability. Polit Behav 35, 481–515 (2013). https://doi.org/10.1007/s11109-012-9203-3

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