Skip to main content
SpringerLink
Log in

The Political Economy: Political Attitudes and Economic Behavior

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

Abstract

It has long been recognized that voters bring their political behaviors in line with economic assessments. Recent work, however, suggests that citizens also engage in economic behaviors that align with their confidence—or lack thereof—in the political system. This alignment can happen consciously or, as we suggest, unconsciously, in the same way that positivity carries over to other behaviors on a micro-level. Using monthly time series data from 1978 to 2008, we contribute further evidence of this relationship by demonstrating that political confidence affects consumer behavior at the aggregate level over time. Our analyses employ measures more closely tied to the theoretical concepts of interest while simultaneously accounting for the complex relationships between subjective and objective economic indicators, economic behavior, political attitudes, and the media. Our results suggest that approval of the president not only increases the electorate’s willingness to spend money, but also affects the volatility of this spending. These findings suggest that the economy is influenced by politics beyond elections, and gives the “Chief Economist” another avenue by which they can affect the behavior of the electorate.

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

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Ironically, as Kellstedt et al. (2015) point out, Katona argued that income and assets measure ability to spend and proposed the attitudinal ICS specifically as a measure of willingness (i.e., motivation). Yet four of the five questions focus on objective realities and ability (e.g., “would you say that you are better off or worse off financially than you were a year ago”); the one question closest to ascertaining motivation and willingness asks “do you think now is a good or bad time for people to buy major household items” rather than “do you plan” or “are people interested in buying” these items.

  2. Enns and Anderson (2009) examined whether mood could explain the observed changes in consumer behavior, and found no significant change in happiness after the election. However, they do not test whether happiness directly predicts spending and, in any case, the question is quite broad (i.e., “I’m very happy with my life as it is”) and does not speak to confidence in the political system.

  3. The complexity of the relationships described in Fig. 1 also raise concerns about endogeneity, a question we address in a later section.

  4. The data on total personal consumption expenditures were gathered from the Bureau of Economic Analysis’ “National Income and Product Accounts” (2009), publicly available online at http://www.bea.gov/national/. Measured in billions of dollars and seasonally adjusted, the data have also been adjusted for inflation to constant 2008 dollars.

  5. We nonetheless re-ran the model with the two business conditions questions (e.g., Kellstedt et al. 2015) and personal prospections (see footnote 1) one at a time in lieu of the index. The components exhibited unique relationships with spending, but in each case the results of interest were substantively and statistically unaffected. We thus use the index; results using the components are available from the authors upon request.

  6. These interventions were identified by examining the ARFIMA residuals. Only the most extreme events were included to avoid overfitting.

  7. This system has been used by other scholars and institutions as an indicator of media sentiment. Most prominently, beginning in the fall of 2007, the Annenberg National Election Study included daily summary data generated by Lydia to capture information about each presidential candidate and relevant issues, providing a snapshot of the campaign for the day respondents completed the survey. Studies of the system’s internal validity can be found in a variety of sources. An overview of the technical aspects of the process can be found in Bautin et al. (2010). For details about the aggregation process, see Bautin et al. (2009). For a discussion of the sentiment analysis, see Bautin et al. (2008) and Godbole et al. (2007). A technical discussion of the spatial analysis can be found in Bautin et al. (2010). Readers may also see Online Appendix A for a general discussion of the Lydia system.

    The media coverage variables also act as micro-event detectors, picking up events—political and otherwise—that relate to the president. This allows us to include changes in the political environment in a more parsimonious fashion, reducing the number of interventions in an already complex model.

  8. See Online Appendix B for integration tests and the values of d used to difference each series.

  9. Likewise, specifying the lag structure a priori requires assumptions about the behavior of the political and economic variables that are difficult to justify theoretically.

  10. Multicollinearity is certainly a concern for time series data, as variables may share a common trend over time. Although this is true when variables are included as regressors in their level form, differencing the data removes any time trend, thus limiting the correlation between variables (Gujarati 2004, p. 367).

  11. The model has a Durbin-Watson statistic of 2.07, indicating no significant autocorrelation remaining. The residuals are also white noise, with a Ljung-Box Q statistic of 14.6 at a lag of 20. The results for the models run using first differences instead fractional differencing techniques are presented in Online Appendix D. The results remain substantively unchanged.

  12. A table of cointegration tests can be found in Online Appendix C. We follow the procedures discussed in Grant and Lebo (2016) and Lebo and Grant (2016) to test for cointegration. The fractional differencing parameter for the FECM is 0.61. Although PCE and RDI do not meet traditional standards of cointegration in our data, they are treated as cointegrated in the economics literature and are included in our FECM (e.g., Davidson et al. 1978; Lettau and Ludvigson 2001). PCE and approval do not show evidence of cointegration and, as such, approval has been excluded from the FECM.

  13. To test for weak exogeneity, models were first specified for the marginal processes (e.g. consumer sentiment). These models were then estimated including an error correction mechanism (ECM) from the PCE model. If the marginal process is weakly exogenous to PCE, the ECM should be statistically insignificant. The next step is to include the residuals from the marginal process equation (estimated without the ECM) into the PCE model. A statistically insignificant coefficient for the residuals fails to reject the null hypothesis of weak exogeneity. Although not a component of the FECM, we included presidential approval in our weak exogeneity testing as a robustness check of our exclusion of this variable from the FECM. For a formal discussion of weak exogeneity, see Online Appendix E.

  14. The results for the models run using first differences instead fractional differencing techniques are presented in Online Appendix F.

References

  • Aisen, A., & Veiga, F. J. (2006). Does political instability lead to higher inflation? A panel data analysis. Journal of Money, Credit, and Banking, 38, 1379–1389.

    Article  Google Scholar 

  • Alesina, A., Lodregan, J., & Rosenthal, H. (1993). A model of the political economy of the United States. American Political Science Review, 87(1), 12–33.

    Article  Google Scholar 

  • Alesina, A., Özler, S., Roubini, N., & Swagel, P. (1996). Political instability and economic growth. Journal of Economic Growth, 1, 189–211.

    Article  Google Scholar 

  • Bautin, M., Vijayarenu, L., & Skiena, S. S. (2008). International sentiment analysis for news and blogs. Paper presented at 2nd international conference on weblogs and social media (ICWSM 2008).

  • Bautin, M., Ward, C. B., Patil, A., & Skiena, S. S. (2010). Access: News and blog analysis for the social sciences. Paper presented at 19th Int. World Wide Web conference (WWW 2010).

  • Bautin, M., Ward, C. B., & Skiena, S. S. (2009). A high-performance architecture for historical news analysis. Paper presented at 2nd Hadoop Summit.

  • Blinder, A. S. (1981). Inventories and sticky prices: More on the microfoundations of macroeconomics. Working Paper, National Bureau of Economic Research.

  • Brader, T. (2005). Striking a responsive chord: How political ads motivate and persuade voters by appealing to emotions. American Journal of Political Science, 49(2), 388–405.

    Article  Google Scholar 

  • Brody, R. A. (1991). Assessing the president: The media, elite opinion, and public support. Stanford, CA: Stanford University Press.

    Google Scholar 

  • Canes-Wrone, B., & Park, J.-K. (2012). Electoral business cycles in OECD countries. American Political Science Review, 106(1), 103–122.

    Article  Google Scholar 

  • Carroll, C. D. (1992). The buffer-stock theory of saving: Some macroeconomic evidence. Brookings Papers on Economic Activity, 2, 61–156.

    Article  Google Scholar 

  • Carroll, C., Fuhrer, J., & Wilcox, D. (1994). Does consumer sentiment forecast household spending? If so, why? The American Economic Review, 84(5), 1397–1408.

    Google Scholar 

  • Charemza, W., & Deadman, D. (1997). New directions in econometric practice: General to specific modelling, cointegration, and vector autoregression. Lyme: Edward Elgar.

    Google Scholar 

  • Chong, J., Halcoussis, D., & Phillips, M. (2011). Does market volatility impact presidential approval? Journal of Public Affairs, 11, 387–394.

    Article  Google Scholar 

  • Christiansen, C., Schmeling, M., & Schrimpf, A. (2012). A comprehensive look at financial volatility prediction by economic variables. Journal of Applied Econometrics, 27, 956–977.

    Article  Google Scholar 

  • Chung, M. (2013). Two out of five americans cut spending amid government shutdown. Bloomberg Business. 15 October. http://www.bloomberg.com/news/articles/2013-10-15/two-out-of-five-americans-cut-spending-amid-government-shutdown.

  • Cryder, C. E., Lerner, J. S., Gross, J. J., & Dahl, R. E. (2008). Misery is not miserly sad and self-focused individuals spend more. Psychological Science, 19, 525–530.

    Article  Google Scholar 

  • Cukierman, A., Edwards, S., & Tabellini, G. (1992). Seigniorage and political instability. The American Economic Review, 82(3), 537–555.

    Google Scholar 

  • Davidson, J. E., Hendry, D. F., Srba, F., & Yeo, S. (1978). Econometric modelling of the aggregate time-series relationship between consumers’ expenditure and income in the United Kingdom. The Economic Journal, 88, 661–692.

    Article  Google Scholar 

  • De Boef, S., & Kellstedt, P. (2004). The political (and economic) origins of consumer confidence. American Journal of Political Science, 48(4), 633–649.

    Article  Google Scholar 

  • Dhawan, R., & Jeske, K. (2006). How resilient is the modern economy to energy price shocks? Economic Review-Federal Reserve Bank of Atlanta, 91, 21.

    Google Scholar 

  • Dynan, K. E., Elmendorf, D. W., & Sichel, D. E. (2006). Can financial innovation help to explain the reduced volatility of economic activity? Journal of Monetary Economics, 53(1), 123–150.

    Article  Google Scholar 

  • Engle, R. F. (2003). Risk and volatility: econometric models and financial practice. Nobel lecture. Stockholm: Nobel Foundation.

    Google Scholar 

  • Engle, R. F., Hendry, D. F., & Richard, J.-F. (1983). Exogeneity. Econometrica, 51(2), 277–304.

    Article  Google Scholar 

  • Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating the time varying risk premia in the term structure: The ARCH-M model. Econometrica, 55(2), 391–407.

    Article  Google Scholar 

  • Enns, P. K., & C. J. Anderson. (2009). The American voter goes shopping: micro-foundations of the partisan economy. APSA 2009 Toronto Meeting Paper.

  • Ericsson, N. R., & Irons, J. S. (1995). Testing exogeneity. NewYork: Oxford University Press.

    Google Scholar 

  • Erikson, R., MacKuen, M., & Stimson, J. (2002). The macro polity. New York: Cambridge University Press.

    Google Scholar 

  • Evans, G., & Pickup, M. (2010). Reversing the causal arrow: The political conditioning of economic perceptions in the 2000–2004 U.S. presidential election cycle. Journal of Politics, 72(4), 1236–1251.

    Article  Google Scholar 

  • Fiorina, M. (1978). Economic retrospective voting in american national elections: A micro-analysis. American Journal of Political Science, 22, 426–443.

    Article  Google Scholar 

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

    Google Scholar 

  • Garner, A. (1991). Forecasting consumer spending: Should economists pay attention to consumer confidence surveys? Federal Reserve Bank of Kansas City Economic Review, 76, 57–71.

    Google Scholar 

  • Gelper, S., Lemmens, A., & Croux, C. (2007). Consumer sentiment and consumer spending: Decomposing the granger causal relationship in the time domain. Applied Economics, 39(1), 1–11.

    Article  Google Scholar 

  • Gerber, A. S., & Huber, G. (2009). Partisanship and economic behavior: Do partisan differences in economic forecasts predict real economic behavior? American Political Science Review, 103(3), 407–426.

    Article  Google Scholar 

  • Gerber, A. S., & Huber, G. (2010). Partisanship, political control, and economic assessments. American Journal of Political Science, 54(1), 153–173.

    Article  Google Scholar 

  • Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-scale sentiment analysis for news and blogs. In Proceedings of the international conference on weblogs and social media, pp. 219–222.

  • Granger, C. W. (1980). Long-memory relationships and the aggregation of dynamic models. Journal of Econometrics, 14(2), 227–238.

    Article  Google Scholar 

  • Grant, T., & Lebo, M. J. (2016). Error correction methods with political time series. Political Analysis, 24, 3–30.

    Article  Google Scholar 

  • Green, D., Palmquist, B., & Schickler, E. (2002). Partisan hearts and minds: Political parties and the social identities of voters. New Haven, CT: Yale University Press.

    Google Scholar 

  • Greene, S. (2004). Social identity theory and party identification. Social Science Quarterly, 85, 136–153.

    Article  Google Scholar 

  • Gronke, P., & Brehm, J. (2002). History, heterogeneity, and presidential approval. Electoral Studies, 21, 425–452.

    Article  Google Scholar 

  • Gujarati, D. N. (2004). Basic econometrics (4th ed.). New York: McGraw Hill.

    Google Scholar 

  • Han, S., Lerner, J. S., & Keltner, D. (2007). Feelings and consumer decision making: The appraisal-tendency framework. Journal of consumer psychology, 17, 158–168.

    Article  Google Scholar 

  • Katona, G. (1968). Consumer behavior: Theory and findings on expectations and aspirations. American Economic Review, 58(2), 19–30.

    Google Scholar 

  • Kellstedt, P. M., Linn, S., & Lee Hannah, A. (2015). The usefulness of consumer sentiment: Assessing construct and measurement. Public Opinion Quarterly, 79, 181–203.

    Article  Google Scholar 

  • Kernell, S. (1978). Explaining presidential popularity. American Political Science Review, 72(2), 506–522.

    Article  Google Scholar 

  • Keynes, J. M. (1936). The general theory of employment, interest, and money. New York: Harcourt, Brace, and World, Inc.

    Google Scholar 

  • Kiewiet, D. R. (1983). Macroeconomics and micropolitics: The electoral effects of economic issues. Chicago: University of Chicago Press.

    Google Scholar 

  • Kinder, D. R., & Kiewiet, D. R. (1979). Economic discontent and political behavior: The role of personal grievances and collective economic judgments in congressional voting. American Journal of Political Science, 23, 495–527.

    Article  Google Scholar 

  • Kinder, D. R., & Kiewiet, D. R. (1981). Sociotropic politics: The American case. British Journal of Political Science, 11, 129–141.

    Article  Google Scholar 

  • Kramer, G. H. (1983). The ecological fallacy revisited: Aggregate- verses individual-level findings on economic and elections, and sociotropic voting. American Political Science Review, 77(1), 92–111.

    Article  Google Scholar 

  • Kriner, D., & Schwartz, L. (2009). Partisan dynamics and the volatility of presidential approval. British Journal of Political Science, 39(3), 609–631.

    Article  Google Scholar 

  • Ladner, M., & Wlezien, C. (2007). Partisan preferences, electoral prospects, and economic expectations. Comparative Political Studies, 40, 571–596.

    Article  Google Scholar 

  • Lebo, M. J., & Grant, T. (2016). Equation balance and dynamic political modeling. Political Analysis, 24, 69–82.

    Article  Google Scholar 

  • Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart strings and purse strings: Carryover effects of emotions on economic decisions. Psychological Science, 15(5), 337–341.

    Article  Google Scholar 

  • Lettau, M., & Ludvigson, S. (2001). Consumption, aggregate wealth, and expected stock returns. The Journal of Finance, 56(3), 815–849.

    Article  Google Scholar 

  • Lockerbie, B. (1989). Change in party identification: The role of prospective economic evaluations. American Politics Research, 17(3), 291–311.

    Article  Google Scholar 

  • Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. Davidson, H. Goldsmith, & K. Scherer (Eds.), Handbook of affective science (pp. 619–642). New York: Oxford University Press.

    Google Scholar 

  • Ludvigson, S. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29–50.

    Article  Google Scholar 

  • MacKuen, M. B., Erikson, R. S., & Stimson, J. A. (1989). Macropartisanship. American Political Science Review, 83(4), 1125–1142.

    Article  Google Scholar 

  • MacKuen, M. B., Erikson, R. S., & Stimson, J. A. (1992). Peasants or bankers? The American electorate and the U.S. Economy. American Political Science Review, 6(3), 597–611.

    Article  Google Scholar 

  • Marcus, G., & MacKuen, M. (1993). Anxiety, enthusiasm, and the vote: The emotional underpinnings of learning and involvement during presidential campaigns. American Political Science Review, 87(3), 672–685.

    Article  Google Scholar 

  • Mobarak, A. M. (2005). Democracy, volatility, and economic development. Review of Economics and Statistics, 87, 348–361.

    Article  Google Scholar 

  • Nadeau, R., Niemi, R., Fan, D., & Amato, Timothy. (1999). Elite economic forecasts, economic news, mass economic judgments, and presidential approval. Journal of Politics, 61, 109–135.

    Article  Google Scholar 

  • National Income and Product Account. (2009). Personal Consumption Expenditures. National Income and Product Account.

  • Ozler, S., & Tabellini,G. (1991). External debt and political instability. No. w3772. National Bureau of Economic Research.

  • Poterba, J., & Samwick, A. (1995). Stock ownership patterns, stock market fluctuations, and consumption. Brookings Papers on Economic Activity, pp. 295–372.

  • Rick, S. I., Cryder, C. E., & Loewenstein, G. (2008). Tightwads and spendthrifts. Journal of Consumer Research, 34, 767–782.

    Article  Google Scholar 

  • Romer, C. (1990). The great crash and the onset of the great depression. Quarterly Journal of Economics, 105, 597–624.

    Article  Google Scholar 

  • Schwartz, J., Hoover, S., & Schwartz, A. (2008). The political advantage of a volatile market: The relationship between presidential popularity and the ‘Investor Fear Gauge’. Journal of Public Affairs, 8, 195–207.

    Article  Google Scholar 

  • Soroka, S. N., Stecula, D. A., & Wlezien, C. (2015). It’s (change in) the (future) economy, stupid: Economic indicators, the media, and public opinion. American Journal of Political Science, 59(2), 457–474.

    Article  Google Scholar 

  • Starr, M. (2008). Consumption, sentiment, and economic news. AU Department of Economics Working Paper Series, 2008–16.

  • Tullock, G. (1976). The vote motive: An essay in the economics of politics, with applications to the British Economy. London: Institute of Economic Affairs.

    Google Scholar 

  • Vuchelen, J. (2004). Consumer sentiment and macroeconomic forecasts. Journal of Economic Psychology, 25, 493–506.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Matthew Lebo, Janet Box-Steffensmeier, Jeffrey Segal, and Helmut Norpoth for their invaluable assistance with this project. Also, participants in the PRISM time series course and the four anonymous reviewers offered helpful comments that greatly improved the paper. The data and replication code can be found at https://dataverse.harvard.edu/dataverse/Key.

Author information

Authors and Affiliations

  1. Department of Government and Justice Studies, Appalachian State University, 350K Anne Belk Hall, Boone, NC, 28608-2107, USA

    Ellen M. Key

  2. Department of Political Science, St. John Fisher College, Basil 109, 3690 East Avenue, Rochester, NY, 14618, USA

    Kathleen M. Donovan

Authors
  1. Ellen M. Key
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathleen M. Donovan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ellen M. Key.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 56 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Key, E.M., Donovan, K.M. The Political Economy: Political Attitudes and Economic Behavior. Polit Behav 39, 763–786 (2017). https://doi.org/10.1007/s11109-016-9378-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11109-016-9378-0

Keywords

Search

Navigation