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Turnout and presidential coattails in congressional elections

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Estimating the impact of turnout on House election results is problematic because of endogeneity and omitted variable bias. The following study proposes an instrumental approach to correct for these problems by using a series of fixed effects two-stage least squares panel-data regression models covering three congressional apportionment cycles (1972–1980; 1982–1990; 1992–2000). The analysis tests whether voter participation decreases the House incumbent’s electoral support, regardless of the level of competition in the district. The study also aims to determine if an increase in participation benefits Democratic candidates and whether this effect is constant across apportionment cycles. The results show that the influence of turnout on incumbency vote share is conditional on the level of presidential support in the district. This finding is explained by the surge and decline thesis of Campbell (1960).

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  1. Studies generally find that nonvoters support the Democratic party more. However, this advantage is very small and not important enough to make a huge difference in election results (e.g., Sides et al. 2008; Martinez and Gill 2005).

  2. Although Gomez et al. (2007) have made their data available to researchers, their dataset does not contain midterm election weather information.

  3. Of course, one could argue that media markets do not follow district boundaries. Thus, campaign messages in one district could encourage participation in another nearby district. This is a valid argument, which unfortunately cannot be controlled for (Niemi et al. 1986). The consensus in the literature according to Prior (2006) seems to be that an incumbent benefits from television advertisement in districts where there is a poor overlap between media markets and district boundaries since challengers must compete with different incumbents to get media attention. Stratmann (2009) has also shown that campaign spending by an incumbent can increase electoral support, more so when the costs of media advertising in the district is low. If anything, congressional districts with overlapping media boundaries will be less likely to be affected by outside competition since challenger candidates will have a harder time communicating with the voters.

  4. I exclude states like Alaska and Wyoming where there is one congressional district only.

  5. Without loss of generality, I assume that the intercept is zero in the following equations.

  6. I removed districts where there was a significant amount of redistricting. See Carson et al. (2007) for a detailed explanation of this procedure.

  7. For a discussion of this procedure, see Sanchez (2011a, 2011b). See also Hansford and Gomez (2010) who use a similar approach. All of the bootstrapped standard errors in the second stage models are obtained from 1,000 repetitions.

  8. Like Adler (2000), I use linear interpolation. For example, I began by taking the 1980 census measure of the VAP in each congressional district. I then estimated the growth rate of the statewide population over 18 in each state between 1980 and 1990. Finally, I used this estimated growth value to calculate the VAP for each congressional district in the state in the five elections following the census (i.e., 1982, 1984, 1986, 1988, and 1990). I did the same for the 1972–1980 and 1992–2000 apportionment periods.

  9. One could argue that because the level of political participation in congressional elections is not correctly measured and since turnout is not necessarily decreasing in the United States (McDonald and Popkin 2001), any analysis of the relationship between political participation and election results will be biased because of systematic measurement errors. However, since I focus on the effects of fluctuating turnout rates in each congressional election, the fact that there is a measurement error associated with voter turnout in the study makes little difference because the error is systematic across the entire sample.

  10. Since open seat races are excluded from the analysis, the inclusion of a Republican incumbent dummy variable is not necessary (this is the baseline category in the model).

  11. In 1982, the 1980 presidential election had to be used. Because some district boundaries changed between 1980 and 1982, I removed 24 districts where there was a significant amount of redistricting. See Carson et al. (2007) for a detailed explanation of this procedure.

  12. 1972–1982–1992 would have to be excluded.

  13. This test included a one-year lagged observation on turnout in combination with the variables described above in Eqs. (6) and (7). For the 1972–1980 apportionment cycle, the Sargan-Hansen statistic is 0.67 (p-value=0.42). For the 1982–1990 apportionment cycle, the Sargan-Hansen statistic is 0.12 (p-value=0.73). For the 1992–2000 apportionment cycle, the Sargan-Hansen statistic is 1.67 (p-value=0.20).

  14. See Brambor et al. (2006) for a description on how to estimate these parameters.

  15. Notice also that, contrary to the previous analysis, several variables do not appear to reach the conventional level of significance because they have very large bootstrapped standard errors relative to their regression coefficients. However, since we measure the marginal effect of turnout on incumbent vote share, we need to consider the cumulative standard error of the interactive term which can be significant for different values of presidential vote even if this parameter is insignificant when it is set to zero (Brambor et al. 2006).

  16. This is an implication of the symmetry of interactive terms (Brambor et al. 2006; Berry et al. 2012).

  17. Although the effect of presidential support remains positive for the Democrats in the 1980s, this effect is stronger in presidential election years.

  18. A potential caveat is that the difference observed between the apportionment cycles could be explained by a lack of statistical power. However, the significant marginal effects of turnout and presidential vote share on incumbent support in the 1992–2000 apportionment cycle for Democrats and the confirmation of the basic patterns in all other cases provides sufficient evidence to the contrary (see the bottom plots of Figs. 1, 2).

  19. For example, the number of Democratic districts where the incumbent presidential candidate received less than 0.30 of the vote was 54 cases in the 1970s, 24 in the 1980s, and nine in the 1990s. And in this last decade, all of these cases were conservative Democrats in Congress: Bill Orton from Utah, Ralph Hall from Texas, Charles Stenholm from Texas, Earl Hutto from Florida, and Ben Erdreich from Alabama.

  20. As Jacobson (2005) explains, the results of the 2000 election helps demonstrate this point. In that election, Gore won the majority of the popular presidential vote, yet the distribution of House seats was 240 for Republicans and 195 for Democrats. In these data, the mean presidential vote share for Democratic incumbents is 0.54 with a standard deviation of 0.14. For Republicans, the mean is 0.47 with a standard deviation of 0.08.


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I would like to thank Brandice Canes-Wrone, Martial Foucault, Mathieu Turgeon and Steve Weldon for their helpful comments and suggestions. I would also like to thank Jamie Carson and Gary Jacobson for providing me with their data on congressional and presidential elections.

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Correspondence to Jean-François Godbout.

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Godbout, JF. Turnout and presidential coattails in congressional elections. Public Choice 157, 333–356 (2013).

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