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Polarization and Issue Consistency Over Time

Abstract

The polarization of the political and social environment over the past four decades has provided citizens with clearer cues about how their core political predispositions (e.g., group interests, core values, and party identification) relate to their issue opinions. A robust and ongoing scholarly debate has involved the different ways in which the more polarized environment affects mass opinion. Using heteroskedastic regression, this paper examines the effect of the increasingly polarized environment on the variability of citizens’ policy opinions. We find that citizens today base their policy preferences more closely upon their core political predispositions than in the past. In addition, the predicted error variances also allow us to directly compare two types of mass polarization—issue distance versus issue consistency—to determine the independent effects each has on changes in the distribution of mass opinion.

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Notes

  1. 1.

    Data available online: <http://pooleandrosenthal.com/party_unity.htm>.

  2. 2.

    Issue evolution should also result in greater attitudinal “constraint” (Converse 1964), though the empirical evidence on constraint is fairly mixed (see, for instance, DiMaggio et al. 1996 and Abramowitz and Saunders 2008).

  3. 3.

    On the other hand, using the error variance means that issue consistency is defined relative to the demographic and party identification variables included in the mean equation. Any variables omitted from the mean equation that have become more or less correlated with the issue response would cause changes in the size of the error variance over time. Thus, “issue consistency” in this case means issue opinions that are consistent with the demographic and party identification variables included in the mean equation.

  4. 4.

    We use Table 2.2.1 on page 22 from Cohen (1988) to find the percent non-overlap. This number is then subtracted from 100 to give the percent overlap, which is the shaded region in Fig. 2.

  5. 5.

    The Cumulative File only included self-placement on the 7-point liberal-conservative scale for the 2002 midterm elections and does not include the 2006 midterm elections.

  6. 6.

    The regh STATA program created by Jeroen Weesie (Utrecht University) was used to estimate the linear heteroskedastic regression models.

  7. 7.

    Given that the dependent variables are categorical, our estimation approach implicitly assumes that the ANES issue scales are good approximations of continuous variables, i.e., adjacent positions on those scales can be treated as equidistant from each other in terms of the latent continuous utility or value comparison underlying the discrete survey responses. For the 7-point scales, we have no theoretical reason to doubt the validity of this assumption and imposing it if true makes our estimates more powerful (i.e., more likely to reject the null hypothesis of no relationship when it is false). For the 4-point abortion scale, however, there is good reason to question whether the positions reliably approximate a continuum since the middle choices of allowing abortion in circumstances of rape and incest or to save the life of the mother seem closer to each other than to their adjacent pro-choice/life response. As a robustness check, we re-estimated the Abortion model using heteroskedastic ordered probit and found no differences in our substantive results. For completeness sake, we did the same for the other issue models and all of those re-estimations produced findings consistent with the heteroskedastic regression results reported below. A summary of this auxiliary analysis is available from the authors upon request.

  8. 8.

    We also include two sets of interaction terms in the mean equation. First, church attendance is interacted with dummy variables for Protestant and Catholic. Second, upper income blacks might resent having their success attributed to policies such as government aid toward minorities, so the mean equation includes an interaction between family income and the dummy variable for black.

  9. 9.

    Past studies have used Poole and Rosenthal’s DW-NOMINATE ideology scores to estimate the Euclidean distance between the two party caucuses as a measure of party polarization (i.e., Hetherington 2001). Ideological distance is highly correlated with the party unity scores (r = .945) and the use of different measures does not produce substantially different results.

  10. 10.

    Results for the larger mean equations are available from the authors upon request.

  11. 11.

    One possible explanation for the contrary finding for Abortion is that our mean equation does not include an evangelical variable. More specifically, the temporal increase in the error variance could be due to omitting the distinction between evangelical and traditional Christians if this distinction has become more important over time. However, our mean equation does include church attendance and party identification variables, which should pick up some (if not most) of the effect of this distinction, thereby limiting any potential bias and making it an incomplete explanation at best. More generally, the inclusion of a large number of demographic variables in the model should likewise serve to limit the possible effects of omitted variables on our results.

  12. 12.

    We use Table 2.2.1 on page 22 from Cohen (1988) to find the percent non-overlap. This number is then subtracted from 100 to give the percent overlap, which is the shaded region in our Fig. 2.

  13. 13.

    To calculate Cohen’s d, the variances were converted to standard deviations.

  14. 14.

    We would like to thank anonymous reviewers for this suggestion.

  15. 15.

    Levendusky (2010), for instance, argues that elite cues help citizens to map their party identification issue positions and can perhaps lead to better vote choices (pp. 125–126).

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Acknowledgments

We would like to thank John Bruce and Bob Brown for helpful comments on an earlier version of this paper as well as the helpful comments of the anonymous reviewers.

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Correspondence to Andrew Garner.

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Garner, A., Palmer, H. Polarization and Issue Consistency Over Time. Polit Behav 33, 225–246 (2011). https://doi.org/10.1007/s11109-010-9136-7

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Keywords

  • Polarization
  • Partisan Sorting
  • Attitude consistency
  • Heteroskedastic regression
  • Uncertainty