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Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence

Author

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  • Burnett, Wesley

    (West Virginia University)

  • Lacombe, Donald J.

    (West Virginia University)

Abstract

Ordinary least squares econometric approaches to estimating election vote outcomes potentially ignore spatial dependence (or autocorrelation) in the data that may affect estimates of voting behavior. The presence of spatial autocorrelation in the data can yield biased or inconsistent point estimates when ordinary least squares is used inappropriately. Therefore, this paper puts forward a spatial econometric model to estimate the vote outcomes in the 2004 presidential election. We contribute to the literature in two ways. One, we extend the voting behavior literature by considering newly developed spatial specification tests to determine the proper econometric model. The results of two different spatial specification tests suggest that a spatial Durbin model provides a better fit to the data. Two, we offer a richer interpretation of the spatial effects, which differ from standard ordinary least squares estimates, of the county-level vote outcome for the 2004 presidential election.

Suggested Citation

  • Burnett, Wesley & Lacombe, Donald J., 2012. "Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence," The Review of Regional Studies, Southern Regional Science Association, vol. 42(1), pages 75-89, Spring.
    • Handle: RePEc:rre:publsh:v:42:y:2012:i:1:p:75-89
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    References listed on IDEAS

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    1. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 557-579, September.
    2. Ray C. Fair, 1996. "Econometrics and Presidential Elections," Journal of Economic Perspectives, American Economic Association, vol. 10(3), pages 89-102, Summer.
    3. Kan, Kamhon & Yang, C C, 2001. "On Expressive Voting: Evidence from the 1988 U.S. Presidential Election," Public Choice, Springer, vol. 108(3-4), pages 295-312, September.
    4. Eisenberg Daniel & Ketcham Jonathan, 2004. "Economic Voting in U.S. Presidential Elections: Who Blames Whom for What," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 4(1), pages 1-25, August.
    5. Prasanna, P.H.S.N. & Gunaratne, L.H.P & Withana, W.D.R.S., 2004. "Economic Analysis of Paddy Threshing Methods," Sri Lankan Journal of Agricultural Economics, Sri Lanka Agricultural Economics Association (SAEA), vol. 6, pages 1-17.
    6. Donald J. Lacombe & Timothy M. Shaughnessy, 2007. "Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote," Public Finance Review, , vol. 35(4), pages 480-499, July.
    7. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    8. Abrams, Burton A & Butkiewicz, James L, 1995. "The Influence of State-Level Economic Conditions on the 1992 U.S. Presidential Election," Public Choice, Springer, vol. 85(1-2), pages 1-10, October.
    9. Shively, W. Phillips, 1969. "“Ecological” Inference: The Use of Aggregate Data to Study Individuals," American Political Science Review, Cambridge University Press, vol. 63(4), pages 1183-1196, December.
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    Cited by:

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    2. Elena Anatolyevna Podkolzina & Olga Anatolyevna Demidova & Lada Evgenyevna Kuletskaya, 2020. "Spatial Modeling of Voting Preferences in Russian Federation," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 2, pages 70-100.
    3. Panagiotis Artelaris & George Mavrommatis, 2021. "The role of economic and cultural changes in the rise of far‐right in Greece: A regional analysis," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(2), pages 353-369, April.
    4. Fırat Gündem, 2023. "Beliefs, economics, and spatial regimes in voting behavior: the Turkish case, 2007–2018," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    5. Sebastien Bourdin & André Torre, 2023. "Geography of contestation: A study on the Yellow Vest movement and the rise of populism in France," Journal of Regional Science, Wiley Blackwell, vol. 63(1), pages 214-235, January.
    6. Panagiotis Artelaris & Yannis Tsirbas, 2018. "Anti-austerity voting in an era of economic crisis: Regional evidence from the 2015 referendum in Greece," Environment and Planning C, , vol. 36(4), pages 589-608, June.

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    More about this item

    Keywords

    spatial econometrics; spatial Hausman test; 2004 presidential election;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • H10 - Public Economics - - Structure and Scope of Government - - - General

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