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³ Erikson, MacKuen and Stimson, The Macro Polity; Stimson, James A., Public Opinion in America: Moods, Cycles, and Swings (Boulder, Colo.: Westview Press, 1991 and 1999)Google Scholar.

⁴ Erikson, MacKuen and Stimson, The Macro Polity, pp. 212–19; Stimson, James A., ‘The Micro Foundations of Mood’, in Kuklinski, James H., ed., Thinking about Political Psychology (Cambridge: Cambridge University Press, 2002), pp. 253–80.CrossRefGoogle Scholar

⁵ Erikson, MacKuen and Stimson, The Macro Polity, p. 219.

⁶ Erikson, MacKuen and Stimson, The Macro Polity, p. 5.

⁷ Erikson, MacKuen and Stimson, The Macro Polity, p. 219.

⁸ Page and Shapiro, The Rational Public, p. 316.

⁹ Aidt, Toke S., ‘Economic Voting and Information’, Electoral Studies, 19 (2000), 349–62CrossRefGoogle Scholar; Krause, George A. and Granato, Jim, ‘Fooling Some of the People Some of the Time?ATest of Weak Rationality with Heterogeneous Information Levels’, Public Opinion Quarterly, 62 (1998), 135–51Google Scholar.

¹⁰ Political sophistication is a broad term in the political science literature. Luskin, Robert C., ‘Explaining Political Sophistication’, Political Behavior, 12 (1990), 331–61CrossRefGoogle Scholar, p. 335, defines political sophistication as a function of three elements: (1) level of exposure to political information; (2) intellectual ability to retain and organize the encountered information; and (3) motivation to obtain and comprehend the political information. Zaller, John R., The Nature and Origins of Mass Opinion (New York: Cambridge University Press, 1992), p. 21CrossRefGoogle Scholar, substitutes the term ‘political awareness’ for political sophistication but offers a similar definition. He writes, ‘Political awareness… refers to the extent to which an individual pays attention to politics and understands what he or she has encountered’. Conceptually we agree with these definitions of political sophistication. Operationally we are limited by the available time-series data. Education level and vocabulary score are the only available measures of political sophistication across time. Fortunately, a strong association exists between education level and political sophistication level (see Althaus, Scott L., Collective Preferences in Democratic Politics (Cambridge: Cambridge University Press, 2003), pp. 15–17CrossRefGoogle Scholar. Converse, Philip E., ‘Some Priority Variables in Comparative Electoral Research’, in Rose, Richard, ed., Electoral Behavior: A Comparative Handbook (New York: The Free Press, 1974), p. 730Google Scholar, concludes that education is ‘probably the prime predictor of dependent variables reflecting political interest, participation, and mobilization’. Alvarez, R. Michael and Brehm, John, Hard Choices, Easy Answers (Princeton, N.J.: Princeton University Press, 2002), pp. 37 and 45CrossRefGoogle Scholar, use education level as one of their measures of chronic information and political sophistication. Luskin, ‘Explaining Political Sophistication’, questions the causal relationship between education level and political sophistication but suggests that the correlation exists because of the strong relationship between education and intelligence, occupation and interest in politics.

¹¹ Campbell, Angus, Converse, Philip E., Miller, Warren E. and Stokes, Donald E., The American Voter (New York: Wiley, 1960)Google Scholar; Converse, ‘The Nature of Belief Systems in Mass Publics’; Lazarsfeld, Paul F., Berelson, Bernard R. and Gaudet, Hazel, People's Choice: How the Voter Makes Up His Mind in a Presidential Campaign, 1st edn (New York: Columbia University Press, 1948)Google Scholar.

¹² E.g. Bartels, Larry M., ‘The American Public's Defense Spending Preferences in the Post-Cold War Era’, Public Opinion Quarterly, 58 (1994), 479–508CrossRefGoogle Scholar; Converse, Philip E., ‘Assessing the Capacity of Mass Electorates’, Annual Review of Political Science, 3 (2000), 331–54CrossRefGoogle Scholar; Delli Carpini and Keeter, What Americans Know About Politics and Why It Matters; Druckman, James N., ‘Does Political Information Matter’, Political Communication, 22 (2005), 515–19CrossRefGoogle Scholar; Schneider, Saundra A. and Jacoby, William G., ‘Elite Discourse and American Public Opinion: The Case of Welfare Spending’, Political Research Quarterly, 58 (2005), 367–79Google Scholar; Sniderman, Paul M., ‘The New Look of Public Opinion Research’, in Finifter, Ada W., ed., Political Science: The State of the Discipline II (Washington, D.C.: The American Political Science Association, 1993), pp. 219–45Google Scholar; Sniderman, Paul M. and Bullock, John, ‘A Consistency Theory of Public Opinion and Political Choice: The Hypothesis of Menu Dependence’, in Saris, William E. and Sniderman, Paul M., eds, Studies in Public Opinion (Princeton, N.J.: Princeton University Press, 2004), pp. 337–57CrossRefGoogle Scholar; Zaller, The Nature and Origins of Mass Opinion.

¹³ Zaller, The Nature and Origins of Mass Opinion.

¹⁴ Zaller, The Nature and Origins of Mass Opinion, p. 148.

¹⁵ Zaller, The Nature and Origins of Mass Opinion, p. 16.

¹⁶ It is important to note that in the rare instance when political messages are easy and ubiquitous – presidential elections may provide this type of message environment (see Converse, Philip E., ‘Information Flow and the Stability of Partisan Attitudes’, Public Opinion Quarterly, 26 (1962), 578–99CrossRefGoogle Scholar; Zaller, John, ‘Floating Voters in U.S. Presidential Elections, 1948–2000’, in Willem E., Saris and Sniderman, Paul M., eds, Studies in Public Opinion: Attitudes, Nonattitudes, Measurement Error, and Change (Princeton, N.J.: Princeton University Press, 2004)Google Scholar) – the RAS model predicts that the least informed will demonstrate the most responsiveness.

¹⁷ Erikson, MacKuen and Stimson, The Macro Polity; Page and Shapiro, The Rational Public.

¹⁸ Converse, Philip E., ‘Popular Representation and the Distribution of Information’, in Ferejohn, John A. and Kuklinski, James H., eds, Information and Democratic Processes (Chicago: University of Illinois Press, 1990), pp. 369–88, at p. 382Google Scholar.

¹⁹ Erikson, MacKuen and Stimson, The Macro Polity, p. 428–9.

²⁰ Page and Shapiro, The Rational Public; Erikson, MacKuen and Stimson, The Macro Polity.

²¹ Durr, Robert H., ‘What Moves Policy Sentiment?’ American Political Science Review, 87 (1993), 158–70Google Scholar; Erikson, MacKuen and Stimson, The Macro Polity.

²² Aidt, ‘Economic Voting and Information’; Conover, Pamela Johnston, Feldman, Stanley and Knight, Kathleen, ‘The Personal and Political Underpinnings of Economic Forecasts’, American Journal of Political Science, 31 (1987), 559–83Google Scholar; Duch, Raymond M., Palmer, Harvey D. and Anderson, Christopher, ‘Heterogeneity in Perceptions of National Economic Conditions’, American Journal of Political Science, 44 (2000), 635–52CrossRefGoogle Scholar; Holbrook, Thomas M. and Garand, James C., ‘Homo Economus? Economic Information and Economic Voting’, Political Research Quarterly, 49 (1996), 351–75CrossRefGoogle Scholar.

²³ Conover, Feldman and Knight, ‘The Personal and Political Underpinnings of Economic Forecasts’; Krause, George A., ‘Voters, Information Heterogeneity, and the Dynamics of Aggregate Economic Expectations’, American Journal of Political Science, 41 (1997); 1170–200CrossRefGoogle Scholar; Krause, George A. and Granato, Jim, ‘Fooling Some of the People Some of the Time? A Test of Weak Rationality with Heterogeneous Information Levels’, Public Opinion Quarterly, 62 (1998), 135–51Google Scholar.

²⁴ Erikson, MacKuen and Stimson, The Macro Polity, p. 447.

²⁵ In Rolling Nowhere, Ted Conover describes ‘Steamtrain’ Maury Graham, who claimed to be able to tell how the nation's economy was doing by the length of the cigarette butts he found on the sidewalk (see Conover, Ted, Rolling Nowhere: Riding the Rails with America's Hoboes (New York: Viking Press 1984), p. 9Google Scholar).

²⁶ DeBoef, Suzanna and Kellstedt, Paul M., ‘The Political (and Economic) Origins of Consumer Confidence’, American Journal of Political Science, 48 (2004), 633–49.CrossRefGoogle Scholar

²⁷ Conover, Pamela Johnston, Feldman, Stanley and Knight, Kathleen, ‘Judging Inflation and Unemployment: The Origins of Retrospective Evaluations’, Journal of Politics, 48 (1986), 565–88Google Scholar; Evan Parker-Stephen and Michael B. MacKuen, ‘Class Competence in the American Public’ (paper presented at the Annual Meeting of the Midwest Political Science Association, 2005).

²⁸ Zaller, The Nature and Origins of Mass Opinion; Zaller, John and Feldman, Stanley, ‘A Simple Theory of the Survey Response: Answering Questions and Revealing Preferences’, American Journal of Political Science, 36 (1992), 579– 616CrossRefGoogle Scholar.

²⁹ E.g. Converse, ‘The Nature of Belief Systems in Mass Publics’.

³⁰ Page and Shapiro, The Rational Public; Erikson, MacKuen and Stimson, The Macro Polity.

³¹ Erikson, MacKuen and Stimson, The Macro Polity; Stimson, ‘The Micro Foundations of Mood’.

³² The database has been augmented since the 1999 publication date; Stimson, Public Opinion in America. These numbers refer to the most recent publicly available dataset, dated 21 June 2004.

³³ Because the vocabulary test was not administered in every year, analyses based on this criterion will span the years 1974–2004.

³⁴ The algorithm first scales each series to a common metric and then uses a factor analytic approach to extract the common variance among survey questions to create the overall index. See Stimson, Public Opinion in America, pp. 133–7, and <http://www.unc.edu/~jstimson> for complete documentation.

³⁵ Figure A1 in Appendix 1 compares our GSS-based Mood measure with Stimson's Mood measure. Consistent with the high correlation, the two series move together over time.

³⁶ Clearly, the dividing points for the vocabulary test are somewhat arbitrary. We chose these values after looking at the distributions of scores over the years. These points divided the public, roughly, into three equal parts and are most stable in terms of their sizes from year to year.

³⁷ To ensure that we are not creating an artificial stability where none truly exists, all of the figures and analyses in this article were conducted with the exponential smoothing feature of Stimson's algorithm turned off.

³⁸ To further test the validity of using education as a measure of political sophistication we used eleven spending questions from the American National Election Surveys (ANES) to generate a second (biannual) measure of Mood from 1980 to 2004. Because the ANES asks respondents their education level and questions that reflect their political information level, it is possible to create and compare an ANES measure of policy Mood for the least educated and the least politically informed respondents. If the Mood of the least educated and the least politically informed correlate highly, we can be confident that there is significant overlap between these two subgroups, and using a measure of political information (if it was available) would not lead to different results from those obtained by using the education measure. However, if significant differences appear, we will have evidence that the opinions of education groups and political information groups move independently, indicating that education level may not be a valid proxy for political awareness level. First, we compare the ANES Mood measure with our GSS Mood measure to ensure that the two aggregate measures both capture the public's ‘Mood’. The two series correlate at r=0.88, suggesting that the two measures indeed capture the same concept. Next, we group ANES respondents by education and political information level. Following Zaller, The Nature and Origins of Mass Opinion,we create a thirteen-point index of political information based on: correctly identifying which political party controls the House, which party controls the Senate, and correct (relative) placement of the parties on defence spending, government service, aid to blacks, liberal/conservative scale, guaranteed jobs and health care (see Appendix 2 for question wording). Each correct response is coded as a 1. Respondents could also get 5 points based on the interviewer rating of respondent's level of political information. Although the biannual nature of the ANES does not permit time-series regression analysis, we can now compare the Mood of the least educated (those with less than a high school degree) with the Mood of the least politically informed. The percentage of respondents with less than a high school degree decreased over time, so each year we match the percentage or respondents who are ‘politically uninformed’ as closely as possible to the percentage with less than a high school degree. From 1980 to 2000, the Mood of the least educated and the least politically informed correlate at r=0.87. The two measures of sophistication – education and political information – are not one-and-the-same, but the similarities are clear. The high correlation suggests that education level is indeed a valid measure of political sophistication.

³⁹ Converse, ‘Popular Representation and the Distribution of Information’; Page and Shapiro, The Rational Public; Erikson, MacKuen and Stimson, The Macro Polity.

⁴⁰ Converse, ‘Popular Representation and the Distribution of Information’, p. 382.

⁴¹ Stimson, Public Opinion in America, p. 71; Erikson, MacKuen and Stimson, The Macro Polity, p. 208. The eleven question items asked in less than fifteen years follow the same first and second dimension patterns but, as we would expect, the shorter series show much more variability. Four items have similar coefficients across sophistication levels, matching the pattern in the top section of Table 3. For four items the coefficients of the middle and highest sophistication levels load differently from the least sophisticated and for three of the items the least and middle sophistication levels load similarly, and the most sophisticated are distinct.

⁴² Delli Carpini and Keeter, What Americans Know About Politics and Why It Matters.

⁴³ Erikson, MacKuen and Stimson, The Macro Polity, chap. 6.

⁴⁴ Parker-Stephen and MacKuen, ‘Class Competence in the American Public’.

⁴⁵ Erikson, MacKuen and Stimson, The Macro Polity, Table 6.4. Because the hypotheses posit directional predictions we use one-tailed tests.

⁴⁶ Tests for stationarity on these time series were inconclusive. In particular, when using the Augmented Dickey–Fuller test, we are unable to reject the null of a unit root; by contrast, when using the Kwiatkowski, Phillips, Schmidt and Shin test, we are unable to reject the null of level stationarity (see Kwiatkowski, Denis, Phillips, Peter C. B., Schmidt, Peter and Shin, Yongcheol, ‘Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root’, Journal of Econometrics, 54 (1992), 159–78CrossRefGoogle Scholar). Such findings should not be surprising with time-series as short as these, as both the ADF and KPSS tests have low power against the alternative hypothesis. In the face of such contradictory results, we opt to treat the series as stationary, which is the way that all previous analysts, including Erikson, MacKuen and Stimson (The Macro Polity), have treated these series.

⁴⁷ Zellner, Arnold, ‘An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias’, Journal of the American Statistical Association, 57 (1962), 348–68CrossRefGoogle Scholar; Zellner, Arnold, ‘Estimators for Seemingly Unrelated Regression Equations: Some Exact Finite Sample Results’, Journal of the American Statistical Association, 58 (1963), 977–92CrossRefGoogle Scholar.

⁴⁸ Binkley, James K. and Nelson, Carl H., ‘A Note on the Efficiency of Seemingly Unrelated Regression’, American Statistician, 42 (1988), 137–9Google Scholar. As might be expected, due to the small disturbance terms, estimating the equations individually produces nearly identical results. When the equations are estimated in separate regressions, all statistically significant relationships remain significant at p<0.10 or less.

⁴⁹ Converse, ‘Popular Representation and the Distribution of Information’, p. 382. To be certain that our findings do not result because of the specific items in our Mood index, we re-ran the analysis with several different Mood specifications. One potential concern is respondent redundancy between similarly worded spending questions in years when the GSS introduced new question wording. To account for this concern we re-estimated the Mood measure omitting the ‘Y’ version of each spending question. The results of the analysis were nearly identical, with all significant relationships remaining consistent at p<0.10 or less. We also ran the analysis using the Mood measure used by Ellis, Ura and Robinson, which includes only eleven spending items (see Ellis, Christopher R., Ura, Joseph Daniel and Robinson, Jenna Ashley, ‘The Dynamic Consequences of Nonvoting in American National Elections’, Political Research Quarterly, 59 (2006), 227–33Google Scholar). With this specification all relationships remained significant and Change in Unemployment became significant for the least sophisticated. The consistent pattern of results suggests that the analysis is not sensitive to the particular question items included in the Mood index.

⁵⁰ Consistent with our rationale for using the SUR framework, the residuals are strongly correlated across equations. However, with respect to autocorrelation, Breusch–Pagan LM tests performed separately on each equation show no clear evidence of autocorrelation.

⁵¹ Converse, Philip E., ‘Assessing the Capacity of Mass Electorates’, Annual Review of Political Science, 3 (2000), 331–54, p. 387.CrossRefGoogle Scholar

⁵² Erikson, MacKuen and Stimson, The Macro Polity.

⁵³ Zaller, The Nature and Origins of Mass Opinion; Bartels, ‘The American Public's Defense Spending Preferences in the Post-Cold War Era’; Converse, ‘Popular Representation and the Distribution of Information’; Converse, ‘Assessing the Capacity of Mass Electorates’; Erikson, MacKuen and Stimson The Macro Polity.

⁵⁴ Zaller, The Nature and Origins of Mass Opinion.

⁵⁵ Page and Shapiro, The Rational Public.

⁵⁶ Stimson, MacKuen and Erikson, ‘Dynamic Representation’; Erikson, MacKuen and Stimson, The Macro Polity, chap. 8.

⁵⁷ At least with GSS data, such an analysis is not possible, as their surveys occur the same time every year.