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The Dynamics of Aggregate Partisanship

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  • Box-Steffensmeier, Janet M.
  • Smith, Renée M.

Abstract

Despite extensive research on party identification, links between partisanship at the individual and aggregate level have largely been ignored. This leaves a gap in our understanding of the dynamics of aggregate partisanship. To remedy this, we identify a set of ideal types that capture the essential arguments made about individual-level party identification. We then combine the behavioral assumptions for each type with existing results on statistical aggregation to deduce the specific temporal pattern that each type implies for aggregate levels of partisanship. Using new diagnostic tests and a highly general time series model, we find that aggregate measures of partisanship from 1953 through 1992 are fractionally integrated. Our evidence that the effects of a shock to aggregate partisanship last for years—not months or decades—challenges previous work by party systems theorists and students of “macropartisanship.†Our arguments and empirical evidence provide a conceptually richer and more precise basis for theories of issue evolution or endogenous preferences—in which partisanship plays a central role.

Suggested Citation

  • Box-Steffensmeier, Janet M. & Smith, Renée M., 1996. "The Dynamics of Aggregate Partisanship," American Political Science Review, Cambridge University Press, vol. 90(3), pages 567-580, September.
    • Handle: RePEc:cup:apsrev:v:90:y:1996:i:03:p:567-580_20
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    Cited by:

    1. Maggie E. C. Jones & Morten Ørregaard Nielsen & Michał Ksawery Popiel, 2014. "A fractionally cointegrated VAR analysis of economic voting and political support," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(4), pages 1078-1130, November.
    2. Xenia Frei & Sebastian Langer & Robert Lehmann & Felix Roesel, 2020. "Electoral Externalities in Federations – Evidence from German Opinion Polls," Kyklos, Wiley Blackwell, vol. 73(2), pages 227-252, May.
    3. Morten Ørregaard Nielsen & Sergei S. Shibaev, 2015. "Forecasting daily political opinion polls using the fractionally cointegrated VAR model," Working Paper 1340, Economics Department, Queen's University.
    4. Richard C. Eichenberg & Richard J. Stoll & Matthew Lebo, 2006. "War President," Journal of Conflict Resolution, Peace Science Society (International), vol. 50(6), pages 783-808, December.
    5. James G. MacKinnon & Morten Ørregaard Nielsen, 2014. "Numerical Distribution Functions Of Fractional Unit Root And Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 161-171, January.
    6. Matthew J. Lebo & Janet M. Box‐Steffensmeier, 2008. "Dynamic Conditional Correlations in Political Science," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 688-704, July.
    7. Kirman Alan & Teyssière Gilles, 2002. "Microeconomic Models for Long Memory in the Volatility of Financial Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(4), pages 1-23, January.
    8. Eisinga, Rob & Franses, Philip Hans & Ooms, Marius, 1999. "Forecasting long memory left-right political orientations," International Journal of Forecasting, Elsevier, vol. 15(2), pages 185-199, April.
    9. Javier Hualde & Morten {O}rregaard Nielsen, 2022. "Fractional integration and cointegration," Papers 2211.10235, arXiv.org.
    10. Davidson James E. H. & Peel David A & Byers J. David, 2006. "Support for Governments and Leaders: Fractional Cointegration Analysis of Poll Evidence from the UK, 1960-2004," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(1), pages 1-23, March.
    11. Laura Mayoral & Juan J. Dolado & Jesús Gonzalo, 2003. "Long-range dependence in Spanish political opinion poll series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 137-155.
    12. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy (IfW Kiel).
    13. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    14. Avinash Dixit & Gene M. Grossman & Faruk Gul, 2000. "The Dynamics of Political Compromise," Journal of Political Economy, University of Chicago Press, vol. 108(3), pages 531-568, June.
    15. Alexander Boca Saravia & Gabriel Rodríguez, 2022. "Presidential approval in Peru: an empirical analysis using a fractionally cointegrated VAR," Economic Change and Restructuring, Springer, vol. 55(3), pages 1973-2010, August.
    16. Haldrup, Niels & Nielsen, Morten Orregaard, 2007. "Estimation of fractional integration in the presence of data noise," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3100-3114, March.
    17. Roy Cerqueti & Giulia Rotundo, 2015. "A review of aggregation techniques for agent-based models: understanding the presence of long-term memory," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1693-1717, July.
    18. Petar Sorić & Ivana Lolić & Marina Matošec, 2023. "The persistence of economic sentiment: a trip down memory lane," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 371-395, April.
    19. Goodell, John W. & McGroarty, Frank & Urquhart, Andrew, 2015. "Political uncertainty and the 2012 US presidential election: A cointegration study of prediction markets, polls and a stand-out expert," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 162-171.

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