Skip to main content
SpringerLink
Log in

Blame, Responsibility, and the Tea Party in the 2010 Midterm Elections

  • Original Paper
  • Published:
Political Behavior Aims and scope Submit manuscript

Abstract

There is a general consensus both in the news media and scholarly research that 2010 was a highly nationalized election year. Reports have indicated that anti-Obama sentiment, the Democrats’ legislative agenda, the economy, and the Tea Party were all factors contributing to Democratic losses in the congressional elections. In this paper, we use data from 2010 Cooperative Congressional Election Study to examine the individual-level dynamics that contributed to the heightened nationalization of the 2010 congressional elections. Our analysis shows that Tea Party support and the attribution of blame and responsibility by voters are essential to understanding the 2010 election outcome, beyond what we would expect from a simple referendum model of midterm elections. Not surprisingly, Tea Party supporters blamed Democrats for the state of national affairs, disapproved of the Democrats’ policy agenda, and overwhelmingly supported Republican candidates in the congressional elections. However, our analysis shows that not all voters who supported Republican candidates were driven by high levels of opposition to President Obama and the Democrats. Another key group of voters blamed both Democrats and Republicans for the nation’s problems but ultimately held Democrats responsible in the voting booth by supporting Republican congressional candidates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Howard Dean interview on Morning Joe, MSNBC, October 7, 2010.

  2. Gallup, “Gallup Daily: Obama Job Approval,” http://www.gallup.com/poll/113980/Gallup-Daily-Obama-Job-Approval.aspx.

  3. It is important to note, however, that others have concluded that Tea Party endorsements did not improve the chances of electoral victory for Republican candidates (Bond et al. 2011; Jacobson 2011b; Karpowitz et al. 2011).

  4. Supporting this observation, a survey of Tea Party supporters found that while most Republicans expressed dissatisfaction with Washington politics, Tea Party supporters were more likely to express anger toward Washington. (New York Times/CBS News Poll, National Survey of Tea Party Supporters, April 5–12, 2010, http://documents.nytimes.com/new-york-timescbs-news-poll-national-survey-of-tea-party-supporters?ref=politics).

  5. Los Angeles Times Poll, October 17–19, 1994, http://articles.latimes.com/print/1994-10-21/news/mn-53022_1_times-poll.

  6. Respondents were selected from YouGov’s PollingPoint panel, an opt-in Internet panel, and then matched on a set of demographic and political characteristics to a random sample (stratified by age, gender, race, education, and region) from the 2005–2007 American Community Survey. The sample was weighted using propensity scores based on age, gender, race, education, news interest, voter registration, and non-placement on an ideology scale. This method produces a sample that looks similar to a probability sample on the matched characteristics, but may still differ in unknown ways on unobserved characteristics. Research comparing samples using this method to telephone and face-to-face surveys finds that such samples are similar in many ways (Yeager et al. 2011), although there remain concerns about generalizing results to a broader population (AAPOR 2010; Pasek and Krosnick 2010).

  7. Respondents were also given the option of selecting the answer “I’m not sure” in response to this question. Thirty-nine respondents selected this option, and they are omitted from our analysis.

  8. This quantity and those that follow are computed using team module weights.

  9. More detailed information is reported in the “Appendix” section. Because respondents who selected “blame both” were slightly less educated and knowledgeable about politics, we further tested the alternative hypothesis that these voters supported Republican candidates because of a lack of sophistication. We found several reasons to discount this alternative hypothesis. First, respondent education, political sophistication, and interest in politics are included as controls in all multivariable models. Second, because respondents were given the option of answering “I’m not sure” in response to the blame attribution question, there is little reason to believe that the respondents who selected the “blame both” option did so because they were unable to select a substantive answer. Third, as an additional robustness check, we compared these “I’m not sure” and “blame both” respondents across other questions in the survey to see if the “blame both” respondents were expressing non-attitudes when given the opportunity on other attitudinal and knowledge measures. Large percentages (around 45–60%) of those who answered “I’m not sure” on the blame question also selected “I’m not sure” in response to other attitudinal and knowledge questions, including questions asking them to place the Democratic and Republican Parties on an ideological scale and evaluate the Supreme Court and their states’ governors. The “blame both” respondents answered “I’m not sure” at much lower levels on these same questions (5–11 %), rates comparable to those who blamed either the Democrats or Republicans. Finally, closer scrutiny of the congressional vote by sophistication among the “blame both” respondents finds no evidence that the less sophisticated were simply casting a “nature of the times” vote for the GOP; rather, it was the most sophisticated respondents who were the most likely to vote GOP, suggesting they were cognitively capable of both blaming both parties but holding the Democrats responsible. We thank an anonymous reviewer for suggesting that we examine the relationship between blame attribution and political sophistication more closely. These results are available from the authors upon request.

  10. This analysis classifies partisan leaners as independents.

  11. We estimated a multinomial logit model predicting respondent selection into blame categories, with “blame Republicans” as the base category. In this model, party identification, Obama approval, issue attitudes, educational attainment, and interest in the campaign were predictive of blaming both parties for the state of the nation (relative to blaming Republicans). These model estimates are available from the authors.

  12. These differences are statistically significant at conventional levels (p < 0.05, one tailed).

  13. This quantity was computed by summing the number of pieces of legislation for which a CCES respondent expressed “support,” and dividing this quantity by the number of items for which each respondent offered a directional response (“support” or “oppose”).

  14. Herszenhorn and Hulse (2009).

  15. Krugman (2009, 2011).

  16. The correlation between Tea Party support and the “blame both” variable is −0.03. A majority of “blame both” respondents indicated that they were not supporters of the Tea Party movement (56 %). Among those respondents who “blamed both” and supported the Tea Party, most of these individuals did not participate in Tea Party events. These results can be found in the “Appendix” section.

  17. We also estimated models using separate issue attitude variables, measuring views toward taxes and spending, abortion, immigration policy, and gun control, but including these variables instead of the issue liberalism scale did not change our conclusions.

  18. The factor analysis yielded a single factor with an eigenvalue greater than 1. This primary factor accounted for 50 % of the variance in the eight policy attitudes used in the analysis. The latent dimension yields scores which range from −1.8 to 1.6, where higher values indicate more consistently liberal opinions on policy issues.

  19. We used a five category variable measuring responses to this item, which incrementally ranges from “Strongly Disapprove” to “Strongly Approve.” Those who selected the “Not sure” option were assigned to the intermediate category. We use separate dummy variables for Democratic and Republican incumbents.

  20. We estimated additional models using a 3-category, a 5-category (with categories for strong Republicans, weak and leaning Republicans, Independents, weak and leaning Democrats, and strong Democrats), and dummy variables for each party identification category. In each of these models we found results analogous to those reported below.

  21. The wording for the campaign interest item was "Some people seem to follow what's going on in government and public affairs most of the time, whether there's an election going on or not. Others aren't that interested. Would you say you follow what's going on in government and public affairs?" Response options included “hardly at all,” “only now and then,” “some of the time,” and “most of the time,” coded in that order. To measure political knowledge, we relied on an item from the pre-election wave which asked respondents to identify partisan majority control in four legislative chambers—the U.S. Senate, the U.S. House, the respondent’s state Senate and state House. Responses were summed from 0 to 4, with higher values indicating more correct answers.

  22. We explored several alternative specifications of the models reported in Table 4. Most notably, we included an indicator for freshmen Democratic members to see if newly elected members faced an especially difficult electoral environment. The variable was not statistically significant from zero and did not affect the parameters reported in the table. We also estimated the model with an indicator if the member voted for the ACA bill; and replicated with an interaction between a respondent’s issue preferences and Tea Party support. Again, these variables were not statistically significant.

  23. Wald test of joint statistical significance for both incumbency variables was statistically significant (\(\chi_{2}^{2} = 17.8, \;p < 0.05)\)).

  24. This result, and those that follow, were obtained by simulation using CLARIFY (King et al. 2000). Covariates were held at the following values: Obama approval (“somewhat disapprove”); Tea Party support (“not a supporter”); Policy attitude scale (“0”); blame variables (“blame both”); family income (“$50,000–$59,999”); education (“2 year degree”); age (“55”); sex (“male”); African-American racial identity (“non-black”). Simulations were based upon the model reported in column 2 of Table 4.

  25. The fact that presidential approval is not statistically significant in models 2 and 3 likely can be attributed to the large number of highly correlated variables in this model. Model 1 demonstrates that presidential approval is a significant predictor of vote choice.

  26. The more complex models in columns 2 and 3 significantly improve the model fit beyond the referendum model reported in column 1 (\(\chi_{11}^{2} = 35.2,\; p < 0.05\), column 2; \(\chi_{12}^{2} = 34.8, p < 0.05\), column 3).

  27. Again, given the potential alternative explanation that those who blamed both might be making a simple-minded “nature of the times” decision based on a lack of sophistication, these controls for these political sophistication variables are especially important. Although these measures are not statistically significant given the other variables in the model, we conducted a series of other analyses more closely examining the relationship between political sophistication, blame, and vote choice, summarized in footnote 9.

  28. For this calculation, we estimated the predicted vote of each respondent based on column 2 of Table 4. This yields a 56.1 % Republican share of the two-party vote. If we recalculate such that those who “blamed both” were evenly divided between voting for Democrats and Republicans, the Republican share of the two-party vote is predicted to be just 52.4 %.

  29. To obtain this result, we first used the model parameters from column 2 of Table 4 to predict in-sample vote choice. Voters whose Democratic vote probability was greater than 0.5 were classified as Democratic votes, while those with a probability of <0.5 were classified as Republican votes. For the counterfactual described in the text, we changed the coefficient for the “blame both” variable to zero. Then we used the remaining model parameters to generate an in-sample prediction for this scenario. Under the counterfactual described in the text, Democratic vote share moves from about 43 % to about 48 %.

  30. e.g., see Roberts (2010), Klein (2010), Salvanto and Gersh (2009).

References

  • AAPOR, Opt-In Online Panel Task Force. (2010). AAPOR report on online panels. Retrieved April 5, 2012 from http://www.aapor.org/AAPOR_Releases_Report_on_Online_Survey_Panes/2263.htm.

  • Abramson, P. R., Aldrich, J. H., & Rohde, D. W. (1995). Change and continuity in the 1992 elections (revised edition). Washington, D.C: CQ Press.

    Google Scholar 

  • Bailey, M. A., Mummolo, J., & Noel, H. (2012). Tea Party influence: A story of activists and elites. American Politics Research, 40(5), 769–804.

    Article  Google Scholar 

  • Bond, J. R., Fleisher, R., & Ilderton, N. A. (2011). Was the Tea Party responsible for the Republican victory in the 2010 House elections? Paper presented at the 2011 annual meeting of the American Political Science Association, Seattle, WA.

  • Brady, D. W., Fiorina, M. P., & Wilkins, A. S. (2011). The 2010 elections: Why did political science forecasts go awry? Political Science & Politics, 44(02), 247–250.

    Article  Google Scholar 

  • Campbell, J. E. (2010). The midterm landslide of 2010: A triple wave election. The Forum, 8(4), Article 3.

    Google Scholar 

  • Courser, Z. (2010). The Tea Party at the election. The Forum, 8(4), Article 5.

  • Fiorina, M. P. (1978). Economic retrospective voting in American national elections: A micro-analysis. American Journal of Political Science, 22(2), 426.

    Article  Google Scholar 

  • Fiorina, M. P. (1981). Retrospective voting in American national elections. New Haven, CT: Yale University Press.

    Google Scholar 

  • Gomez, B. T., & Wilson, J. M. (2003). Causal attribution and economic voting in American congressional elections. Political Research Quarterly, 56(3), 271–282.

    Article  Google Scholar 

  • Herszenhorn, D. M., & Hulse, C. (2009). Deal reached in Congress on $789 billion stimulus plan. New York Times. Retrieved February 11, 2009 from http://www.nytimes.com/2009/02/12/us/politics/12stimulus.html?_r=1&ref=us.

  • Jacobson, G. C. (2011a). Legislative success and political failure: The public’s reaction to Barack Obama’s early presidency. Presidential Studies Quarterly, 41(2), 220–243.

    Article  Google Scholar 

  • Jacobson, G. C. (2011b). The republican resurgence in 2010. Political Science Quarterly, 126(1), 27–52.

    Article  Google Scholar 

  • Jacobson, G. C. (2011c). The President, the Tea Party, and voting behavior in 2010: Insights from the Cooperative Congressional Election Study. Paper presented at the 2011 annual meeting of the American Political Science Association, Seattle, WA.

  • Jones, D. R., & McDermott, M. L. (2011). The salience of the Democratic Congress and the 2010 elections. Political Science & Politics, 44(02), 297–301.

    Article  Google Scholar 

  • Karpowitz, C. F., Monson, J. Q., Patterson, K. D., & Pope, J. C. (2011). Tea time in America? The impact of the Tea Party movement on the 2010 midterm elections. Political Science & Politics, 44(02), 303–309.

    Article  Google Scholar 

  • Kernell, S. (1977). Presidential popularity and negative voting: An alternative explanation of the midterm congressional decline of the president’s party. The American Political Science Review, 71(1), 44–66.

    Google Scholar 

  • Key, V. O., Jr. (1966). The responsible electorate: Rationality and presidential elections, 1936–1960. Cambridge: Belknap Press of Harvard University Press.

    Book  Google Scholar 

  • King, G., Tomz, M., & Wittenberg, J. (2000). Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science, 44(2), 341–355.

    Article  Google Scholar 

  • Klein, E. (2010). The 1982 election and pundit fallability. Retrieved March 1, 2010 from http://voices.washingtonpost.com/ezra-klein/2010/03/the_1982_election_and_pundit_f.html.

  • Koger, G., & Lebo, M. J. (2012). Strategic party government and the 2010 elections. American Politics Research, 40(5), 927–945.

    Article  Google Scholar 

  • Konisky, D. M., & Richardson, L. E., Jr. (2012). Penalizing the party: Health care reform issue voting in the 2010 election. American Politics Research, 40(5), 903–926.

    Article  Google Scholar 

  • Kramer, G. H. (1971). Short-term fluctuations in US voting behavior, 1896–1964. The American Political Science Review, 65(1), 131–143.

    Article  Google Scholar 

  • Krugman, P. (2009). Behind the curve. New York Times. Retrieved March 8, 2009 from http://www.nytimes.com/2009/03/09/opinion/09krugman.html.

  • Krugman, P. (2011). On the inadequacy of the stimulus. New York Times. Retrieved September 5, 2011 from http://krugman.blogs.nytimes.com/2011/09/05/on-the-inadequacy-of-the-stimulus/.

  • Nyhan, B., McGhee, E., Sides, J., Masket, S., & Greene, S. (2012). One vote out of step? The effects of salient roll call votes in the 2010 election. American Politics Research, 40(5), 844–879.

    Article  Google Scholar 

  • Pasek, J., & Krosnick, J. A. (2010). Measuring intent to participate and participation in the 2010 census and their correlates and trends: Comparisons of RDD telephone and non-probability sample internet survey data. Washington, D.C.: Statistical Research Division of the U.S. Census Bureau (#2010-15).

  • Peffley, M. (1984). The voter as juror: Attributing responsibility for economic conditions. Political Behavior, 6(3), 275–294.

    Article  Google Scholar 

  • Petrocik, J. R., & Steeper, F. T. (1986). The midterm referendum: The importance of attributions of responsibility. Political Behavior, 8(3), 206–229.

    Article  Google Scholar 

  • Roberts, C. (2010). November’s elections reminiscent of 1982 elections. Morning Edition. Retrieved September 6, 2010 from http://www.npr.org/templates/story/story.php?storyId=129676128.

  • Rudolph, T. J. (2003). Who’s responsible for the economy? The formation and consequences of responsibility attributions. American Journal of Political Science, 47(4), 698–713.

    Article  Google Scholar 

  • Salvanto, A., & Gersh, M. (2009). For Republicans, could 2010 be like 1994. Retrieved 24 September, 2009 from http://www.cbsnews.com/8301-503544_162-5335263-503544.html.

  • Stonecash, J. M. (2010). The 2010 elections: Party Pursuits, voter perceptions, and the chancy game of politics. The Forum, 8(4), Article 11.

    Google Scholar 

  • Tufte, E. R. (1975). Determinants of the outcomes of midterm Congressional elections. The American Political Science Review, 69(3), 812–826.

    Article  Google Scholar 

  • Weatherford, M. S. (2012). The wages of competence: Obama, the economy, and the 2010 midterm elections. Presidential Studies Quarterly, 42(1), 8–39.

    Article  Google Scholar 

  • Yeager, D. S., Krosnick, J. A., Chang, L., Javitz, H. S., Levendusky, M. S., Simpser, A., et al. (2011). Comparing the accuracy of RDD telephone surveys and internet surveys conducted with probability and non-probability samples. Public Opinion Quarterly, 75(4), 709–747.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Political Science, Duke University, Box 90204, Durham, NC, 27708, USA

    John H. Aldrich, Rebecca S. Hatch, D. Sunshine Hillygus & David W. Rohde

  2. Department of Political Science and the Institute for the Study of the Environment, Sustainability, and Energy, Northern Illinois University, DeKalb, IL, 60115, USA

    Bradford H. Bishop

Authors
  1. John H. Aldrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bradford H. Bishop
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rebecca S. Hatch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Sunshine Hillygus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David W. Rohde
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rebecca S. Hatch.

Appendix

Appendix

Blame category/Tea Party support

Educational attainment

Family income

Political knowledge

Campaign interest

Blame Democrats

3.89 (0.08)

9.05 (0.18)

3.12 (0.07)

2.85 (0.03)

Blame Republicans

4.30 (0.09)

8.76 (0.20)

2.97 (0.08)

2.62 (0.04)

Blame both

3.66 (0.07)

7.92 (0.16)

2.52 (0.07)

2.26 (0.05)

Tea Party Supporters

3.81 (0.07)

8.65 (0.15)

3.03 (0.06)

2.71 (0.03)

Average (weighted)

3.21 (0.05)

7.47 (0.11)

2.28 (0.05)

2.03 (0.04)

  1. Cell entries are mean values. Parentheses contain standard errors
  2. The bold entries highlight the mean values for respondents who blamed both Democrats and Republicans for the state of the nation

Blame category/Tea Party support

Not a supporter

Support, no participation

Support and participate

Total

Blame Democrats

9.0 % (24)

74.9 % (200)

16.1 % (43)

100 % (267)

Blame Republicans

97.1 % (297)

2.9 % (7)

0 % (0)

100 % (244)

Blame both

56.4 % (247)

38.6 % (169)

5.0 % (22)

100 % (438)

Average (unweighted)

54.6 % (540)

38.5 % (381)

6.9 % (68)

100 % (989)

  1. Cell entries are row percentages. Parentheses contain raw frequencies
  2. The bold entries highlight the percentages for respondents who blamed both Democrats and Republicans for the state of the nation

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aldrich, J.H., Bishop, B.H., Hatch, R.S. et al. Blame, Responsibility, and the Tea Party in the 2010 Midterm Elections. Polit Behav 36, 471–491 (2014). https://doi.org/10.1007/s11109-013-9242-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11109-013-9242-4

Keywords

Search

Navigation