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The Political Implications of American Concerns About Economic Inequality

Abstract

This article presents a national measure of Americans’ level of concern about economic inequality from 1966 to 2015, and analyzes the relationship between this construct and public support for government intervention in the economy. Current research argues that concerns about economic inequality are associated with a desire for increased government action, but this relationship has only been formally tested using cross-sectional analyses. I first use a form of dynamic factor analysis to develop a measure of national concern over time. Using an error correction model I then show that an increase in national concern about economic inequality does not lead to a subsequent increase in support for government intervention in the economy. Instead there is some evidence that, once confounding factors are accounted for, an increase in concern could lead to reduced support for government intervention.

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Fig. 1
Fig. 2

Notes

  1. Benabou (2000, p. 100) notes that “at high enough levels of inequality…the standard skewness effect eventually dominates: there are so many poor that they impose redistribution no matter what its aggregate impact may be.” See also Fig. 1 in Benabou (2000), which shows support for redistribution declining in response to increased inequality only when B > 5 (i.e. when redistribution generates gains in ex ante efficiency) and Δ (the measure of inequality) is between 0 and 0.2.

  2. In this paper the algorithm is implemented using the program “Wcalc,” version 6. See Stimson (2015) for additional details.

  3. Available at http://laits.utexas.edu/policymoods/.

  4. See Table 5 in the Appendix for list of variables and loadings. Because overall policy mood includes many items that do not explicitly touch on redistribution, a second policy specific mood variable was also created, which included only questions that concerned macroeconomics, health and labor. However, the dynamics of the resulting construct were not appreciably different from overall mood in any of the analyses performed here.

  5. Mackinnon critical values can be calculated by hand using Eq. (26) and the theta coefficients presented in Tables 2–5 in Ericsson and Mackinnon (2002). The critical value for a given ECM depends on the total number of variables k, which is used to determine the appropriate theta coefficients, and the total sample size \(T_{i}\), which is used to calculate the adjusted sample size \(T_{i}^{a}\) (where \(T_{i}^{a} = T_{i} - \left( {2k - 1} \right) - 1\) for equations with a single constant term).

  6. It is possible that some respondents’ answers to these “factual” may nevertheless be driven more by their levels of concern about inequality rather than their factual judgement (e.g. a respondent may report that inequality has increased because he or she is especially concerned about it). However, there is no current data on the extent to which respondents interpret such questions in this way.

  7. For detailed information on the question wording, recoding scheme, and date range of these questions see Table 6 in the online appendix.

  8. The total eigenvalue here is not a whole number, as it would be in traditional principle components analysis where it always equals the number of items included. This is because any cases with missing data (all of them in this situation) contribute less than unit variance (and usually much less) to the solution. See Stimson (2015) for more details.

  9. As can be seen in Fig. 1, the Gallup item increases from 57% in 2011, to 59% in 2013, to 63% in 2015, while the CBS item increases from 60% in 2012 to 63% in 2014 to 65% in 2015. The most notable difference in their behavior is a rapid decline from 66% to 60% between 2011 and 2012 for the CBS item, while the Gallup item increases slightly during that period.

  10. During the time period being investigated here (1966–2015), the percentages of House and Senate seats controlled by Democrats in a given year are positively correlated with one another (ρ = 0.73) but appear to be correlated with party control of the Presidency in opposite directions. A logistic regression model on party control of the White House finds that, in a given year, Democratic control of the White House is negatively correlated with the percentage of the Democratic House seats (odds ratio 0.82, p < 0.05), but appears to be positively correlated with the percentage of Democratic senate seats (odds ratio 1.2, p < 0.1).

  11. All analyses were also run using the percentage of income earned by the top 1% of Americans, with the overall results being substantively identical. The top 10% measure was chosen because it reflects the level of inequality across a broader segment of the American economy. In addition, all models were also run substituting the US Gini coefficient for the top decile’s income share as a measure of economic inequality. The results of these alternate analyses (which can be found in Table 7 and model 3 in Table 11 in the Appendix) were not substantively different from those reported here, with some exceptions that are discussed in subsequent footnotes.

  12. See Table 8 in the Appendix for complete results of Augmented Dicky–Fuller and Phillips–Perron tests for unit-root and Kwiatkowski, Phillips, Schmidt, and Shin tests for trend and level stationarity on all variables. Attempts were also made to control for the overall economic situation in America. However, univariate tests were unable to reject the hypothesis of stationarity for either the US unemployment rate (the measure used by KE & LG) or the Michigan consumer sentiment index. Since the inclusion of a stationary variable alongside integrated or fractionally integrated data could produce an unbalanced model neither measure of overall economic performance is included in the models presented here. However, alternative models (presented in Tables 9 and 10 in the Appendix) which did include these measures did not find either consumer sentiment or unemployment rate to have a significant short or long term relationship with concern, meaning that their omission should not bias estimates of the relationship between concern and mood. Confirmation of this is shown in Model 4 in Table 11 in the Appendix.

  13. As discussed in footnote 5 supra, these critical values were calculated by hand for the given values of k (number of variables) and \(T_{i}^{a}\) (adjusted sample size), following Ericsson and Mackinnon (2002).

  14. The positive (long-term) relationship between economic inequality and policy mood is also statistically significant at p < 0.001 when inequality is measured using the Gini coefficient, rather than the top decile’s income share (Table 7 in the Appendix).

  15. Conversely, a change in the Gini coefficient is not significantly related to concern, either in the short- or long-term (see Table 7 in the Appendix), suggesting that, as McCall (2013) argues, Americans are more aware of changes at the top of the income distribution than changes in the overall distribution of income in the country.

  16. See Table 11 in the Appendix for these alternative models of overall mood. An additional alternative model of mood which includes an additional control for the US unemployment rate alongside the variables included in Table 4, also finds the long-term concern coefficient to be negative and significant at p < 0.05.

  17. Notwithstanding the other efforts made here to respond to Grant and Lebo’s criticism of the use of error correction models, a failure to reject the null hypothesis also obviates their claim that incorrect usage of these models can lead to unacceptable rates of Type 1 error.

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Acknowledgements

The author thanks James Stimson, Leonard Saxe, Michael Doonan, Grant Ritter, and the anonymous reviewers for their helpful comments and suggestions. Replication files for all results presented in this paper can be found at https://dataverse.harvard.edu/dataverse/polbehavior.

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Correspondence to Graham Wright.

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Wright, G. The Political Implications of American Concerns About Economic Inequality. Polit Behav 40, 321–343 (2018). https://doi.org/10.1007/s11109-017-9399-3

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Keywords

  • Public opinion
  • Economic Inequality
  • Time-series analysis
  • Policy mood
  • Error correction models