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Who Punishes Extremist Nominees? Candidate Ideology and Turning Out the Base in U.S. Elections — CORRIGENDUM

Published online by Cambridge University Press:  27 January 2025

ANDREW B. HALL  and
DANIEL M. THOMPSON
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Corrigendum
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American Political Science Review , Volume 119 , Issue 2 , May 2025 , pp. 1081 - 1084
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of American Political Science Association

The authors regret the inclusion of an error in the above article. At one point in the merging of data for our analyses, we used an incorrect district variable from the 2012 CCES (cdid rather than cdid13) that does not always accurately reflect redistricting. Correcting this error and updating our datasets results in minor changes to all estimates throughout the paper, and does not alter substantive takeaways. The article has been corrected. All updated tables and figures are included below. The authors have also updated the corresponding replication materials at the American Political Science Review Dataverse. The corrected online appendix is also available there.

Table 1.

Effect of Extremist Nominee on Party’s General-Election Vote Share and Victory, U.S. House, 2006–2014. (Corrected)

Note: Robust standard errors clustered by district in parentheses in columns 1-3 and 4-7; standard errors in columns 4 and 8 come from rdrobust package and are clustered by district. The running variable is the extremist primary candidate’s vote share winning margin in the primary.

Figure 2.

The Effect of Extremist Nominees on Validated Partisan Turnout in the General Election. U.S. House, 2006–2014. (Corrected)

Note: The dependent variable (on the vertical axis) is the share of all CCES respondents who turned out in a given general election who reported being strong, not very strong, or leaning supporters of the party holding the primary election (each observation is a district-year-primary). Points are averages in equal-sample-sized bins of the horizontal axis variable. Regression lines are from OLS estimated on raw data (not binned), separately on each side of the discontinuity. Shaded area represents 95% confidence interval.

Table 2.

Effect of Extremist Nominee on Party’s General-Election Turnout, U.S. House, 2006–2014. (Corrected)

Note: Robust standard errors clustered by district in parentheses in columns 1-3; standard error in column 4 comes from rdrobust package and is clustered by district. The running variable is the extremist primary candidate’s vote share winning margin in the primary.

Table 3.

Effect of Extremist Nominee on Party’s General-Election Turnout, U.S. House, 2006–2014. (Corrected)

Note: Robust standard errors clustered by district in parentheses in columns 1-3; standard error in column 4 comes from rdrobust package and is clustered by district. The running variable is the extremist primary candidate’s vote share winning margin in the primary.

Table 4.

Effect of Extremist Nominee on Party’s General-Election Turnout Across Scalings, U.S. House, 2006–2014. (Corrected)

Note: Robust standard errors clustered by district in parentheses in columns 1-3; standard errors in column 4 come from rdrobust package and are clustered by district. The running variable is the extremist primary candidate’s vote share winning margin in the primary. Each pair of rows represents the effect estimates from a different measure of ideology.

Figure 3.

RD Effect of Extremist Nominees on Vote Share and Partisan Share of Turnout, U.S. House, 2006–2014. (Corrected)

Table 5.

Effect of Extremist Nominee on Party’s General-Election Turnout in Midterm and Presidential Years, U.S. House, 2006–2014. (Corrected)

Note: Robust standard errors clustered by district in parentheses in columns 1-3 and 4-7; standard errors in columns 4 and 8 come from rdrobust package. The running variable is the extremist primary candidate’s vote share winning margin in the primary.

Table 6.

Effect of Extremist Nominee on Rates of Partisan General-Election Turnout, U.S. House, 2006–2014. (Corrected)

Note: Robust standard errors clustered by district in parentheses in columns 1-3 and 5-7; standard errors in columns 4 and 8 come from rdrobust package and are clustered by district. The running variable is the extremist primary candidate’s vote-share winning margin in the primary. In the first row, the dependent variable is the total number of CCES respondents in the party who turned out to vote divided by the total number of respondents identifying with the party. The second and third row dependent variables are the numerator and denominator of this ratio, respectively.

Figure 4.

Difference in Effect of Extremist Nominee on Turnout Rate, Opposing-Party Voters Minus Own-Party Voters. (Corrected)

Note: Line reflects RD estimates using 3rd-order polynomial of running variable. Shaded area represents 95% confidence interval from robust standard errors clustered by district.

References

REFERENCE

Hall, Andrew B. and Thompson, Daniel M.. 2018. “Who Punishes Extremist Nominees? CandidateIdeology and Turning Out the Base in US Elections.” American Political Science Review 112(3):509–524.CrossRefGoogle Scholar
Figure 0

Table 1. Effect of Extremist Nominee on Party’s General-Election Vote Share and Victory, U.S. House, 2006–2014. (Corrected)

Figure 1

Figure 2. The Effect of Extremist Nominees on Validated Partisan Turnout in the General Election. U.S. House, 2006–2014. (Corrected)Note: The dependent variable (on the vertical axis) is the share of all CCES respondents who turned out in a given general election who reported being strong, not very strong, or leaning supporters of the party holding the primary election (each observation is a district-year-primary). Points are averages in equal-sample-sized bins of the horizontal axis variable. Regression lines are from OLS estimated on raw data (not binned), separately on each side of the discontinuity. Shaded area represents 95% confidence interval.

Figure 2

Table 2. Effect of Extremist Nominee on Party’s General-Election Turnout, U.S. House, 2006–2014. (Corrected)

Figure 3

Table 3. Effect of Extremist Nominee on Party’s General-Election Turnout, U.S. House, 2006–2014. (Corrected)

Figure 4

Table 4. Effect of Extremist Nominee on Party’s General-Election Turnout Across Scalings, U.S. House, 2006–2014. (Corrected)

Figure 5

Figure 3. RD Effect of Extremist Nominees on Vote Share and Partisan Share of Turnout, U.S. House, 2006–2014. (Corrected)

Figure 6

Table 5. Effect of Extremist Nominee on Party’s General-Election Turnout in Midterm and Presidential Years, U.S. House, 2006–2014. (Corrected)

Figure 7

Table 6. Effect of Extremist Nominee on Rates of Partisan General-Election Turnout, U.S. House, 2006–2014. (Corrected)

Figure 8

Figure 4. Difference in Effect of Extremist Nominee on Turnout Rate, Opposing-Party Voters Minus Own-Party Voters. (Corrected)Note: Line reflects RD estimates using 3rd-order polynomial of running variable. Shaded area represents 95% confidence interval from robust standard errors clustered by district.

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