Racial Prejudice, Partisanship, and White Turnout in Elections with Black Candidates


How does racial prejudice affect White turnout in elections with Black candidates? Previous research, which largely focuses on the relationship between prejudice and vote choice, rarely examines the relationship between prejudice and turnout, leading to an incomplete picture of the impact of prejudice on the fate of Black candidates. In this project, we examine a key condition under which partisanship and partisan strength moderate the effect of prejudice on electoral behavior. Specifically, we argue that when a prejudiced strong partisan shares the partisanship of a Black candidate, she is likely to experience a decision conflict—prejudice and partisanship point in opposing directions—increasing the likelihood that she stays home on Election Day. We test this argument through observational analyses of the 2008 presidential election. Our findings illuminate an additional barrier to Black electoral representation: racial prejudice undermines Black candidates’ efforts to mobilize strong partisans.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  1. 1.

    Based on reports from the Joint Center for Political and Economic Studies. Although the Joint Center shows a consistent, linear increase in Black elected officials at the local and state levels, the increases in the number of Black elected officials in the national House of Representatives and Senate are more minimal. In particular, at the national level since 1970 the only substantial increases in the number of Black elected officials occur in 1983 (a 16.7 % change) and 1987 (15.0 % change). Aside from these two years, the changes in the number of Black elected officials do not show a consistent or substantively large increase. Indeed, in some years the number of Black elected officials actually decreases at the national level.

  2. 2.

    As Weaver (2012) points out, “prior studies have … avoided party labels because of their potential to ‘swamp’ the results.”

  3. 3.

    The difference between a decision conflict and cognitive dissonance is that while a decision conflict is an individual’s inability to select between two equally important decision attributes, cognitive dissonance is the feeling of discomfort and tension which often arises from actually making the choice and selecting one decision alternative at the expense of the other. As a result, while some decision conflicts can produce cognitive dissonance, this is most likely to happen if an individual actually works to overcome the conflict and make a choice (Cummings and Venkatesan 1976). In our particular case, we will be considering cases where individuals cannot overcome decision conflicts.

  4. 4.

    Pasek et al. (2009) examine both vote choice and turnout, although their analysis does not specifically consider White individuals or the conditioning role of partisanship and partisan strength on the effect of prejudice.

  5. 5.

    Throughout the manuscript, given that our hypotheses are directional, all hypothesis tests are one-tailed.

  6. 6.

    Since we rely on a self-reported measure of voter turnout, it is worth noting that there is a possibility that over-reporting will affect the results. The extent to which over-reporting of turnout occurs is the subject of recent debate (Ansolabehere and Hersh 2012; Berent et al. 2011). If anything, we suspect that over-reporting results in a more conservative test; strong Democrats are more likely to falsely report voting (Belli et al. 2001), which may bias our estimates of the effect of prejudice on turnout among strong Democrats toward zero.

  7. 7.

    While this finding is suggestive, it is not dispositive. Interaction terms in logistic regressions do not account for an additional potential interaction resulting from the imposition of the sigmoidal function (Kam and Franzese 2007). The marginal effects plots are therefore better suited to show the differential effects of prejudice on turnout for strong Democrats and other Whites.

  8. 8.

    Given the various contextual factors that can influence prejudice, it is possible that some of the control variables are not causally prior to our critical variables; we thank an anonymous reviewer for raising this issue.

  9. 9.

    In order to estimate the total impact of prejudice on White strong Democrats, we also compare the mean predicted probability of turning out to vote using the independent variables’ actual values to the mean predicted probability of turning out to vote after setting the stereotype index to its midpoint, at which no prejudice is expressed. Using this method, we estimate that racial prejudice decreased turnout by 4 percentage points (from 91 to 87 %).

  10. 10.

    We obtain similar patterns when we estimate the marginal effects of an increase in prejudice using models where no controls are included. Using a model that includes all Whites, for example, an increase in prejudice among strong Democrats decreases the chance of turnout by 42 percentage points; an effect significant at p < 0.05 (one tailed). Among those who are not strong Democrats, an increase in prejudice has a null effect on turnout (+0.04 %, not significant).

  11. 11.

    We also estimate a version of Model 5 with the inclusion of candidate affect as a control, calculated as the differential between the thermometer scores for Obama and McCain. Our results are robust to the inclusion of this additional variable; the coefficient on the interaction is 5.45 (2.35), p < 0.05 (one-tailed).

  12. 12.

    See http://www.knowledgenetworks.com/ganp/election2008 for more information.

  13. 13.

    According to Pasek et al. (2009), the dates and response rates of the AP-Yahoo-Stanford survey are as follows: “A total of 2,779 individuals were invited to complete the Wave 6 questionnaire (August 27–September 6, 2008), and 2,012 individuals did so (completion rate = 72.4 %; cumulative response rate CUMRR1 = 10.4 %; see Callegaro and DiSagra 2008). 2,698 individuals were invited to complete the Affect Misattribution Procedure (August 27–September 6, 2008), and 1,688 of them did so (completion rate = 62.6 %; CUMRR1 = 9.2 %). A total of 2,742 individuals were invited to complete Wave 10 (November 4–18, 2008), and 1,989 did so (completion rate = 72.5 %, CUMRR1 = 10.4 %). 1,762 individuals who completed Wave 6 also reported turnout and candidate choice postelection.

  14. 14.

    The question wording is as follows: “We’re interested in how people feel about various groups. Please tell us whether you have a favorable or unfavorable impression of each of the following groups” (Blacks are included among the groups presented). The response options are: Extremely favorable, Very favorable, Somewhat favorable, Neither favorable nor unfavorable, Somewhat unfavorable, Very unfavorable, and Extremely unfavorable.

  15. 15.

    Although we do not make any direct hypotheses about the size of the marginal effects in Fig. 2c, d, as well as e and f, we do follow Cumming and Fidler (2005) and compare these effects, which are statistically different.

  16. 16.

    Democrats: Increase in differential due to an increase in partisan strength at low levels of prejudice is 18.7, significant at p < 0.001 (one-tailed). Increase in differential due to an increase in partisan strength at high levels of prejudice is 6.5, p = 0.511 (one-tailed).

  17. 17.

    Republicans: Increase in differential due to an increase in partisan strength at low levels of prejudice is 15.0, significant at p < 0.001 (one-tailed). Increase in differential due to an increase in partisan strength at high levels of prejudice is 21.99, p < 0.05 (one-tailed).


  1. Abelson, R., Kinder, D., Peters, M. D., & Fiske, S. (1982). Affective and semantic components in political person perception. Journal of Personality and Social Psychology, 42, 619–630.

    Article  Google Scholar 

  2. Ansolabehere, S., & Hersh, E. (2012). Validation: What big data reveal about survey misreporting and the real electorate. Political Analysis, 20, 437–459.

    Article  Google Scholar 

  3. Bartels, L. (2000). Partisanship and voting behavior: 1952–1996. American Journal of Political Science, 44, 35–50.

    Article  Google Scholar 

  4. Belli, R. F., Traugott, M. W., & Beckmann, M. N. (2001). What leads to voting over reports? Journal of Official Statistics, 17, 479–498.

    Google Scholar 

  5. Berent, M. K., Jon, A. K., & Arthur, L. (2011). The Quality of Government Records and ‘Overestimation’ of Registration and Turnout in Surveys: Lessons from the 2008 ANES Panel Study’s Registration and Turnout Validation Exercises. Working Paper no. nes012554. August 2011 Version. Ann Arbor, MI, and Palo Alto, CA: American National Election Studies. Retrieved March 2, 2014 from http://www.electionstudies.org/resources/papers/nes012554.pdf.

  6. Berry, W., Golder, M., & Milton, D. (2012). Improving tests of theories positing interaction. The Journal of Politics, 74, 1–19.

    Article  Google Scholar 

  7. Bobo, L., & Gilliam, F., Jr. (1990). Race, sociopolitical participation, and black empowerment. American Political Science Review, 84, 377–393.

    Article  Google Scholar 

  8. Campbell, A., Philip, E. C., Warren, E. M., & Donald, E. S. (1960). The American voter. Chicago: University of Chicago Press.

    Google Scholar 

  9. Canon, D. T. (1999). Race, redistricting, and representation. Chicago: University of Chicago Press.

    Google Scholar 

  10. Citrin, J., Green, D. P., & Sears, D. O. (1990). White reactions to black candidates: When does race matter? Public Opinion Quarterly, 54, 74–96.

    Article  Google Scholar 

  11. Cumming, G. and Fiona F. (2005) Interval estimates for statistical communication: Problems and possible solutions. International Statistics Institution, IASE Proceedings, IASE/ISI Satelite.

  12. Cummings, W. H., & Venkatesan, M. (1976). Cognitive dissonance and consumer behavior: A review of evidence. Journal of Marketing Research, 13, 303–308.

    Article  Google Scholar 

  13. Ehrlinger, J., Plant, E. A., Eibach, R. P., Columb, C. J., Goplen, J. L., Kunstman, J. W., et al. (2011). How exposure to the confederate flag affects willingness to vote for Barack Obama. Political Psychology, 32, 131–146.

    Article  Google Scholar 

  14. Einhorn, H. J., & Hogarth, R. M. (1981). Behavioral decision theory: Process of judgment and choice. Journal of Accounting Research, 19, 1–31.

    Article  Google Scholar 

  15. Gay, C. (2001). The effect of black congressional representation on political participation. American Political Science Review, 95, 589–602.

    Article  Google Scholar 

  16. Gerber, A., Green, D., & Shachar, R. (2003). Voting may be habit-forming: Evidence from a randomized field experiment. American Journal of Political Science, 47, 540–550.

    Article  Google Scholar 

  17. Green, D. P., Palmquist, B., & Shickler, E. (2002). Partisan hearts and minds: Political parties and the social identities of voters. New Haven: Yale University Press.

    Google Scholar 

  18. Grofman, B., Handley, L., & Lublin, D. (2001). Drawing effective minority districts: A conceptual framework and some empirical evidence. North Carolina Law Review, 79, 1383–1430.

    Google Scholar 

  19. Hajnal, Z. (2007). Changing white attitudes toward black political leadership. Oxford: Cambridge University Press.

    Google Scholar 

  20. Handley, L., & Grofman, B. (1994). The impact of the Voting Rights Act on minority representation. In C. Davidson & B. Grofman (Eds.), Quiet Revolution in the South. Princeton: Princeton University Press.

    Google Scholar 

  21. Highton, Benjamin. (2004). White voters and African American candidates for congress. Political Behavior, 26, 1–25.

    Article  Google Scholar 

  22. Highton, B. (2011). Prejudice rivals partisanship and ideology when explaining the 2008 presidential vote across the States. PS. Political Science & Politics, 44, 530–535.

    Article  Google Scholar 

  23. Huckfeldt, R., Levine, J., Morgan, W., & Sprague, J. (1999). Accessibility and the political utility of partisan and ideological orientations. American Journal of Political Science, 43, 888–911.

    Article  Google Scholar 

  24. Hutchings, V. L. (2009). Change or more of the same? Evaluating racial attitudes in the Obama era. Public Opinion Quarterly, 73, 917–942.

    Article  Google Scholar 

  25. Hutchings, V. L., & Piston, S. (2011). The determinants and political consequences of prejudice. In J. N. Druckman, D. P. Green, J. H. Kuklinski, & A. Lupia (Eds.), The Cambridge handbook of experimental political science. Cambridge: Cambridge University Press.

    Google Scholar 

  26. Hutchings, V. L., & Valentino, N. A. (2004). The centrality of race in American politics. Annual Review of Political Science, 7, 383–408.

    Article  Google Scholar 

  27. Kalmoe, N. P., & Piston, S. (2013). Is implicit prejudice against blacks politically consequential? Evidence from the AMP. Public Opinion Quarterly, 77(1), 305–322.

    Article  Google Scholar 

  28. Kam, C., & Franzese, R. (2007). Modeling and interpreting interactive hypotheses in regression analysis. Ann Arbor: University of Michigan Press.

    Google Scholar 

  29. Kinder, D. R., & Dale-Riddle, A. (2011). The end of race?. New Haven: Yale University Press.

    Google Scholar 

  30. Kinder, D. R., & Mendelberg, T. (1995). Cracks in American apartheid: The political impact of prejudice among desegregated Whites. Journal of Politics, 57, 402–424.

    Article  Google Scholar 

  31. Lau, R. R., & Redlawsk, D. P. (2006). How voters decide. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  32. Lewis-Beck, M., Tien, C., & Nadeau, R. (2010). Obama’s missed landslide: A racial cost? PS. Political Science and Politics, 43(1), 69–76.

    Article  Google Scholar 

  33. Manning, J. E., & Shogun, C. J. (2012). African American members of the United States Congress. Congressional Research Service Report 7–5700.

  34. Moskowitz, D., & Stroh, P. (1994). Psychological sources of electoral racism. Political Psychology, 15, 307–329.

    Article  Google Scholar 

  35. Pasek, J., Tahk, A., Lelkes, Y., Krosnick, J. A., Keith Payne, B., Akhtar, O., et al. (2009). Determinants of turnout and candidate choice in the 2008 U.S. presidential election. Public Opinion Quarterly, 73, 943–994.

    Article  Google Scholar 

  36. Petrow, G. A. (2010). The Minimal Cue Hypothesis: How black candidates cue race to increase white voting participation. Political Psychology, 31(6), 915–950.

    Article  Google Scholar 

  37. Piston, S. (2010). How explicit racial prejudice hurt Obama in the 2008 election. Political Behavior, 32, 431–451.

    Article  Google Scholar 

  38. Redlawsk, D. (2004). What voters do: Information search during election campaigns. Political Psychology, 25, 595–610.

    Article  Google Scholar 

  39. Redlawsk, D. P., Tolbert, C. J., & Franko, W. (2010). Voters, emotions, and race in 2008: Obama as the first black president. Political Research Quarterly, 63(4), 875–889.

    Article  Google Scholar 

  40. Reeves, K. (1997). Voting hopes or fears?. New York: Oxford University Press.

    Google Scholar 

  41. Rosenstone, S. J., & Hansen, J. M. (1993). Mobilization, participation, and democracy in America. London: Macmillan Publishing Company.

    Google Scholar 

  42. Schaffner, B. F. (2011). Racial salience and the Obama vote. Political Psychology, 32(6), 963–988.

    Article  Google Scholar 

  43. Sigelman, C. K., Sigelman, L., Walkosz, B. J., & Nitz, M. (1995). Black candidates, white voters. American Journal of Political Science, 39, 243–265.

    Article  Google Scholar 

  44. Tate, K. (2003). Black faces in the mirror. Princeton: Princeton University Press.

    Google Scholar 

  45. Terkildsen, N. (1993). When white voters evaluate black candidates. American Journal of Political Science, 37, 1032–1053.

    Article  Google Scholar 

  46. Tesler, M., & Sears, D. O. (2010). Obama’s race: The 2008 election and the dream of a post-racial America. Chicago: University of Chicago Press.

    Book  Google Scholar 

  47. Tversky, A., & Shafir, E. (1992). Choice under conflict: The dynamics of deferred decision. Psychological Science, 3, 358–361.

    Article  Google Scholar 

  48. Washington, E. (2006). How black candidates affect voter turnout. Quarterly Journal of Economics, 121, 973–998.

    Article  Google Scholar 

  49. Weaver, V. (2012). The Electoral consequences of skin color: The ‘Hidden’ side of race in politics. Political Behavior, 34, 159–192.

    Article  Google Scholar 

  50. Whitby, K. J. (1997). The color of representation. Ann Arbor: University of Michigan Press.

    Google Scholar 

Download references


We thank Salma Al-Shami, Vincent L. Hutchings, John E. Jackson, Nathan Kalmoe, Georgia Kernell, Donald R. Kinder, Adam Seth Levine, Arthur Lupia, John Barry Ryan, and David C. Wilson for their helpful comments. We also thank participants at the 2010 Midwest Political Science Association Annual National Conference, especially Andra Gillespie and Shanna Pearson-Markowitz. Finally, we thank participants in the University of Michigan’s Interdisciplinary Workshop in American Politics, especially Charles Doriean, Ashley Jardina, Pamela Clouser McCann, and David Thomas Smith.

Author information



Corresponding author

Correspondence to Spencer Piston.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 20 kb)

Appendix 1: Variable Coding

Appendix 1: Variable Coding

Below we include our coding, the ANES variable number and, in cases where we considered multiple codings of the variable, we note the other ways in which the variable was coded to ensure overall robustness. The descriptive statistics are across the entire sample. Stata do-files available upon request.

Dependent variable (turnout): Coded 1 if voted (V085036x, voted = 76.3 %).

Partisan strength: Coded 1 if strong (V083098a, strong = 35.43 %).

Prejudice scale: Construction discussed in text; relies on differences in ratings of Whites (V083207a, V083208a) and Blacks (V083207b, V083208b, mean = 0.57, median = 0.54, SD = 0.11).

Age: In years, recoded 0–1 (V083215x, mean = 0.42, median = 0.41, SD = 0.23; when in years mean = 49.7, median = 49, SD = 17.5).

Education: Highest grade of school completed, from 0 to 17, recoded 0–1(V083217, mean = 0.81, median = 0.76, SD = 0.13; when 0–17 mean = 13.7, median = 13, SD = 2.21).

Gender: Coded 1 if female (V081101, men = 56.13 %).

Income: Categorical range from less than $2,999 to $150,000, recoded from 0 to 1 (V083249, mean = 0.45, median = 0.46, SD = 0.26).

Married: Coded 1 if currently married (V083216x, married = 49.10 %).

South: Coded 1 if respondent lives in one of the following states: Florida, South Carolina, Alabama, Mississippi, Georgia, Louisiana, Texas, Virginia, Arkansas, Tennessee and North Carolina. Results robust to the (a) exclusion of Florida and (b) inclusion of Kentucky and Oklahoma (V081201a, south = 38.8 %).

Interest: Higher value reflects more interest in politics. Results robust to use of only V085072 and to a combination of both V085072 and V085073a (mean = 0.61, median = 0.67, SD = 0.31).

Contacted by Party: Coded 1 if respondent was contacted by someone from a major party, 0 otherwise (V085025, contacted = 47.6 %).

Affect for Candidate (used only in a robustness check, see footnote 8): Differential between the Obama thermometer (V083037a) and McCain thermometer (V083037a) (mean = 15.65, SD = 46.5). Also robust to the absolute value of the differential (mean = 39.6, SD = 28.9).

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Krupnikov, Y., Piston, S. Racial Prejudice, Partisanship, and White Turnout in Elections with Black Candidates. Polit Behav 37, 397–418 (2015). https://doi.org/10.1007/s11109-014-9268-2

Download citation


  • Race
  • Prejudice
  • Partisanship
  • Partisan strength
  • Turnout
  • Elections