Abstract
Previous work on information effects and preferences has used the technique of statistical imputation to estimate the impact of political ignorance on presidential preferences, suggesting that the electorate would vote differently if more informed. In this paper, I challenge that assertion by disputing the extent to which the changes in preferences generated by imputation are interpretable as information effects. Using data from the 1992–2008 National Election Surveys, I show that the changes in preferences resulting from imputation fail to support a number of hypothesized relationships between political knowledge and preferences. I suggest that the resulting shifts in preferences are most likely attributable to the psychological traits of the more informed rather than to information itself.
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Notes
As Kunda (1990) notes, people can engage in effortful processing and still be biased. What matters is that a reasonable case can be made for the biased conclusion. Many political judgments clearly fit this criteria.
(Redlawsk et al. 2010) do conclude that at some point, motivated reasoners “get it” and begin to respond more rationally to incongruent candidate information (i.e., information suggesting the candidate supports policies the subject dislikes). However, it is worth noting that even after encountering a large quantity of negative information, subjects still maintained positive affect towards their initially favored candidates. For example, subjects who were exposed to almost 80 % incongruent information about their preferred candidate still gave these politicians feeling thermometer ratings of just under 60 on a 0 – 100 point scale in which a score of fifty represents a “neutral” reaction (2010, Fig. 3).
The work of Kuklinski et al. (2000), Cohen (2003), and Nyhan and Reifler (2010) presents a rejoinder to Gilens’s finding (2001) that provision of specific policy facts changes attitudes. One possible explanation for the divergent results is that Gilens uses imputation on his experimental groups to assess the impact of providing policy specific facts, while the other works use more conventional analysis strategies.
Converse states this explicitly, arguing that differences in levels of observed political knowledge are “diagnostic of more profound differences in the amount and accuracy of contextual information voters bring to their judgments,” (Converse 2000, p. 333).
See, for example, Althaus (1998); over forty policy attitudes are analyzed, but no predictions are offered as to what fully informed preferences should look like. The mere existence of a significant difference in attitudes is considered evidence of an information effect.
As Bartels did in his original work, I did not consider third party candidates in order to facilitate comparisons both across election years and with the previous work on information effects. Only subjects who indicated a preference for the Republican or Democratic candidate were included in the analysis. Cases with missing values were excluded.
These demographic variables are: Age, Age2, Income, Education, Black, Female, Married, Homemaker, Homeowner, Retired, Clerical, Professional, Union Household, Urban, East, West, South, Protestant, Catholic, Jewish. At the time of this writing, full occupation data had yet to be coded for the 2008 NES, so that year’s model omits “Clerical” and “Professional.” For comments on the utility of such “kitchen sink” models, see Achen (2002) and Schrodt (2014).
These questions varied somewhat from survey to survey, but generally included recognition of political figures, awareness of which party controlled the House and/or Senate, and ideological placement of Presidential candidates and parties. Specific questions used are reported in the Appendix. Bartels is unique among the researchers using statistical imputation in employing the subjective measure of information (see Delli Carpini and Keeter 1996, Appendix Two, Althaus 1998, p. 548, and Gilens 2001, p. 382).
Bartels’s analysis spanned 1972–1992. I begin with a replication of 1992 to ensure that my model performed similarly to the original before proceeding with the subsequent years.
Complete model results are available in the Appendix.
The log-likelihood test employed by Bartels may be a too lenient one to judge the presence of information effects. I replaced the information variable in the model with another variable that has little theorized relationship to presidential vote: the number of survey-eligible adults in the household. I then checked to see if including this variable and its interactions would produce similar improvements in log-likelihood. The results are suggestive; in all five election years, adding this variable increased the log-likelihood of the model. In three out of five cases, the increases were significant (p values of 0.000, 0.013, 0.525, 0.231, and 0.014).
Predicated probabilities were generated using the “predict” function of the glm package in R. Note that this imputation procedure is more similar to that used by Delli Carpini and Keeter (1996) and Althaus (1998, 2003). Bartels’ approach involves estimating two sets of parameters, using the information variable and its interactions to produce “fully informed” preferences and then running a separate model employing 1 – information and its interactions to generate “uninformed” preferences (see Bartels 1996, p. 205). However, a standard interaction model estimates both informed and uninformed preferences simultaneously: the main effects of each variable occur when the information variable is at 0 (i.e., “uninformed preferences”), while the interactions represent the effect of each predictor when the information variable is set to 1 (i.e., “informed preferences”). The simpler model used in this analysis produces substantive results in line with those produced by Bartels’ model.
See the reports to the NES Board of Governors by Zaller (1985) and Delli Carpini and Keeter (1991) available at http://electionstudies.org/resources/papers/pilotrpt.htm.
Bartels suggests the hypothesis that uninformed voting likely favors the incumbent as people are reluctant to support candidates they are unfamiliar with (Bartels 1996, p. 201). Results from the subjective measure support this hypothesis, although this hypothesis offers no guidance as to how to interpret the findings in the years in which no incumbent ran.
Among the works using statistical imputation, Bartels (1996) expressly dismisses “non-empirical” attempts at determining a particular group’s political interests and simply asserts that low-information preferences are subject to bias or error. Delli Carpini and Keeter (1996) and Althaus (2003) are more explicit in arguing that more informed voters have a better grasp of their “true” interests, although precise determinants of those interests remains elusive. Gilens (2001) is silent on this question.
If the preferences take on two values (pro-Democrat or pro-Republican) and there are five years, there are 25 or 32 different ways to arrange preferences. Eight of these permutations, or 25 % of them, generate two reversals. Four (12.5 %) generate one reversal.
A possible objection to this line of inquiry is that values themselves are endogenous to political information levels; that is, as people become more informed, they will change their values as well. However, the values used in this analysis, authoritarianism and egalitarianism, are held to be fundamental human values (Schwartz 1994) and thus their crystallization is likely theoretically prior to development of political sophistication. Furthermore, since values provide the frame through which people interpret the world (Rokeach 1973; Schwartz 1994), it seems more probable that people will rationalize information in terms of these values rather than change their bedrock orientations (Rokeach 1973; Haidt 2001). Much more work is needed on the relationship between values and political sophistication before the assumption that citizens will change their values as they become more informed is justified.
The authoritarian values scale was not asked during the 1996 survey, so the subsequent analyses exclude data from that year. Responses were coded 0 if subjects choose the less egalitarian or authoritarian answer, 0.5 if they said “it depends”, and 1 if they selected the more egalitarian or authoritarian option.
Thus, the final models included political knowledge, authoritarianism, egalitarianism, and the twenty demographic variables. As detailed earlier, the information effects model interacts political knowledge with each of the demographic variables; the authoritarian model interacts authoritarianism with each of the demographic variables as well as with political knowledge, while the egalitarianism model interacts egalitarianism with the demographic predictors and political knowledge. Complete model results are available in the supplemental material. My thanks to an anonymous reviewer for suggesting this analysis.
I did attempt to gauge the vote probabilities when both egalitarianism and authoritarianism changed. However, the model failed to converge for the 2008 data. For the remaining years, the 95 % confidence intervals of the fully informed predictions and those of the values predictions overlapped in nine out of the twelve cases.
At the time the analysis were performed, the office recognition questions for 2008 were not yet coded. Results for these four questions were hand-coded by the author based on the open-ended responses provided by the respondents.
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Acknowledgments
My thanks to Richard Lau, David Redlawsk, Cesar Zucco, Michael Delli Carpini and several anonymous reviewers for helpful comments on earlier versions of this manuscript. Data used for this research is publicly available from The American National Election Studies (ANES; www.electionstudies.org). All analyses were carried out using the R statistical program (R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/). Errors and mistakes are solely the responsibility of the author.
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Appendix
Appendix
Political Knowledge Questions
The list below contains the questions used to construct the objective political knowledge scales used in the analyses.
1992 NES: V925113, V925951, V925952, V925915, V925916, V925917, V925918, V925919, V925920, V925921
1996 NES: V961010, V961072, V961073, V960379, V960380, V961189, V961190, V961191, V961192
2000 NES: V001210, V001356, V001357, V001382, V001383, V001447, V001450, V001453, V001456
2004 NES: V045089, V045090 V045160a, V045162, V045163, V045164, V045165, V045263, V045264
2008 NES: V085066, V085067, V085119a, V085190a, V085190b, V085120, V085121,V085122, V085123Footnote 21
Information Effects Models
Complete probit models for each of the survey years are presented here. Percentiles used for the income variable are as follows: 0–16th, 16–33rd, 33rd–67th, 67th–95th, above 95th.
Note that Bartels’ original analysis entailed estimating two sets of parameters: An “informed” vector created by interacting the political knowledge variable with each of the demographic variables and an “uninformed” vector generated by interacting 1—information with the predictors (see Bartels 1996, p. 205 as well as the Appendix). My replication model follows convention in that it includes both main effects, which are tantamount to “uninformed” preferences, and the interactions, which are equal to the “informed” preferences. This difference, as well as idiosyncratic differences in coding schemes, likely account for some of the divergent results in the replication. (Tables 5, 6, 7, 8, 9, 10)
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Pierce, D.R. Uninformed Votes? Reappraising Information Effects and Presidential Preferences. Polit Behav 37, 537–565 (2015). https://doi.org/10.1007/s11109-014-9281-5
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DOI: https://doi.org/10.1007/s11109-014-9281-5