Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-22T15:51:34.142Z Has data issue: false hasContentIssue false
  • English
  • Français

All Economics is Local: Spatial Aggregations of Economic Information*

Published online by Cambridge University Press:  20 June 2016

David Fortunato ,
Clint S. Swift  and
Laron K. Williams
Get access Rights & Permissions [Opens in a new window]

Abstract

National economic indicators play a foundational role in political economic research, particularly in regards to electoral politics. Yet, scholars have failed to recognize that national economic indicators are simply aggregations of local economic information, and the manner in which they are aggregated may not be consistent with the process voters use to acquire, access, and incorporate economic information. We argue that the economic similarities among localities, and the way in which the media report on these similarities, provide more theoretically satisfying means of specifying how local information aggregates into an overall portrait of the national economy. We introduce a novel estimation procedure called the spatial-X ordered logit that offers the chance to model how voters’ evaluations respond to changes in contextualized economic information. Our results support our theory that voters incorporate economic information from other localities with similarly structured economies and in ways that are shaped by media messages. Furthermore, these two specifications offer greater explanatory power than national indicators and other geographical means of aggregating economic information. We conclude by offering a number of implications for research questions ranging from electoral accountability to spatial diffusion processes.

Type
Original Articles
Information
Political Science Research and Methods , Volume 6 , Issue 3 , July 2018 , pp. 467 - 487
Copyright
© The European Political Science Association 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

David Fortunato, Assistant Professor, Department of Political Science, Texas A&M University, 2010 Allen Building, 4348 TAMU, College Station, TX 77843-4348 ([email protected]). Clint S. Swift, PhD Candidate, Department of Political Science, University of Missouri, 103 Professional Building, Columbia, MO 65211-6030 ([email protected]). Laron K. Williams, Associate Professor, Department of Political Science, University of Missouri, 103 Professional Building, Columbia, MO 65211-6030 ([email protected]). Previous versions of this project were presented at the “Conference on Methodological Innovations in Comparative Politics,” Texas A&M University, 2015 and the annual meeting of the Midwest Political Science Association. The authors would like to thank Tom Hansford, Eric Neumayer, Thomas Plümper, Vera Troeger, and Guy D. Whitten for their extremely helpful comments. D.F. is grateful to the Hellman Fellows Fund for their generous support. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2016.26

References

Achen, Christopher. 1985. ‘Proxy Variables and Incorrect Signs on Regression Coefficients’. Political Methodology 11(3–4):299–316.Google Scholar
Anderson, Christopher J., Anderson, Silvia M., and Tverdova., Yuliya V. 2004. ‘Endogenous Economic Voting: Evidence from the 1997 British Election’. Electoral Studies 23:683–708.Google Scholar
Bartels, Larry. 2002. ‘Beyond the Running Tally: Partisan Bias in Political Perceptions’. Political Behavior 24:117–150.Google Scholar
Beck, Nathaniel, S. Gleditsch, Kristian, and Beardsley., Kyle 2006. ‘Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy’. International Studies Quarterly 50(1):27–44.Google Scholar
Cao, Xun. 2010. ‘Networks as Channels of Policy Diffusion: Explaining World Changes in Capital Taxation, 1998-2006’. International Studies Quarterly 54(3):823–854.Google Scholar
Chyzh, Olga. Forthcoming. ‘Keeping Up With Which Joneses: Spatial Diffusion of Rule of Law Through Economic International Organizations’. Foreign Policy Analysis (forthcoming).Google Scholar
Clarke, Kevin A. 2003. ‘Nonparametric Model Discrimination in International Relations’. Journal of Conflict Resolution 47(1):72–93.Google Scholar
Clarke, Kevin A. 2007. ‘A Simple Distribution-Free Test for Nonnested Model Selection’. Political Analysis 15(3):1–17.Google Scholar
Duch, Raymond M., Palmer, Harvey D., and Anderson., Christopher 2000. ‘Heterogeneity in Perceptions of National Economic Conditions’. American Journal of Political Science 44(4):635–652.Google Scholar
Duch, Raymond M., and Stevenson., Randolph T. 2008. Voting in Context: How Political and Economic Institutions Condition the Economic Vote. Cambridge: Cambridge University Press.Google Scholar
Duch, Raymond M., and Stevenson., Randolph T. 2010. ‘Context and Economic Expectations: When Do Voters Get it Right?’. British Journal of Political Science 41:1–31.Google Scholar
Ebeid, Michael, and Rodden, Johathan. 2006. ‘Economic Geography and Economic Voting: Evidence from the US States’. British Journal of Political Science 36(3):527–547.Google Scholar
Evans, Geoffrey, and Robert, Anderson. 2006. ‘The Political Conditioning of Economic Perceptions’. The Journal of Politics 68:194–207.Google Scholar
Evans, Geoffrey, and Mark, Pickup. 2010. ‘Reversing the Causal Arrow: The Political Conditioning of Economic Perceptions in the 2000–2004 US Presidential Election Cycle’. The Journal of Politics 72(4):1236–1256.Google Scholar
Fiorina, Morris P. 1978. ‘Economic Retrospective Voting in American National Elections: A Micro-Analysis’. American Journal of Political Science 22:426–443.Google Scholar
Gerber, Alan, and Donald, Green. 1999. ‘Misperceptions about Perceptual Bias’. Annual Review of Political Science 2:189–210.Google Scholar
Hays, Jude C., Kachi, Aya, and Robert J. Franzese, Jr. 2010. ‘A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application’. Statistical Methodology 7(3):406–428.Google Scholar
Karch, Andrew. 2007. ‘Emerging Issues and Future Directions in State Policy Diffusion Research’. State Politics and Policy Quarterly 7(1):54–80.Google Scholar
Kayser, Mark, and Peress, Michael. 2012. ‘Benchmarking Across Borders: Electoral Accountability and the Necessity of Comparison’. American Political Science Review 106(3):661–684.Google Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. 2000. ‘Making the Most of Statistical Analyses: Improving Interpretation and Presentation’. American Journal of Political Science 44:347–361.Google Scholar
Mooney, Christopher Z. 2001. ‘Modeling Regional Effects on State Policy Adoption’. Political Research Quarterly 54(1):103–124.Google Scholar
Neumayer, Eric, and Thomas, Plümper. 2016. ‘W’. Political Science Research and Methods 4(1):175–193.Google Scholar
Parker-Stephen, Evan. 2013. ‘Clarity of Responsibility and Economic Evaluations’. Electoral Studies 32:506–511.Google Scholar
Plümper, Thomas, and Neumayer, Eric. 2010. ‘Model Specification in the Analysis of Spatial Dependence’. European Journal of Political Research 49(3):418–442.Google Scholar
Plümper, Thomas, Troeger, Vera E., and Winner, Hannes. 2009. ‘Why is There No Race to the Bottom in Capital Taxation?’. International Studies Quarterly 53(3):761–786.Google Scholar
Powell, G. Bingham, and Whitten, Guy D.. 1993. ‘A Cross-National Analysis of Economic Voting: Taking Account of the Political Context’. American Journal of Political Science 37(2):391–414.Google Scholar
Stevenson, Randoph T., and Duch, Raymond. 2013. ‘The Meaning and Use of Subjective Perceptions in Studies of Economic Voting’. Electoral Studies 32:305–320.Google Scholar
Stimson, James A., Mackuen, Michael B., and Erikson, Robert S.. 1995. ‘Dynamic Representation’. American Political Science Review 89:543–565.Google Scholar
Van der Brug, Wouter, van der Eijk, Cees, and Franklin, Mark. 2007. The Economy and the Vote: Economic Conditions and Elections in Fifteen Countries. Cambridge, UK: Cambridge University Press.Google Scholar
Vega, Solmaria Halleck, and Elhorst, J. Paul. 2015. ‘The SLX Model’. Journal of Regional Science 55(3):339–363.Google Scholar
Williams, Laron K. 2015. ‘It’s All Relative: Spatial Positioning of Parties and Ideological Shifts’. European Journal of Political Research 54:141–159.Google Scholar
Williams, Laron K., and Whitten, Guy D.. 2015. ‘Don’t Stand So Close To Me: Spatial Contagion Effects and Party Competition’. American Journal of Political Science 59(2):309–325.Google Scholar
Williams, Laron K., Seki, Katsunori, and Whitten, Guy D.. 2016. ‘You’ve Got Some Explaining To Do: The Influence of Economic Conditions and Spatial Competition on Party Strategy’. Political Science Research and Methods 4(1):47–63.Google Scholar
Wlezien, Christopher. 1995. ‘The Public as Thermostat: Dynamics of Preferences for Spending’. American Journal of Political Science 39(4):981–1000.Google Scholar
Zaller, John R. 1992. The Nature and Origins of Mass Opinion. New York: Cambridge University Press.Google Scholar
Zhukov, Yuri M., and Stewart, Brandon M.. 2013. ‘Choosing Your Neighbors: Networks of Diffusion in International Relations’. International Studies Quarterly 57(2):271–287.Google Scholar
Supplementary material: PDF

Fortunato et al supplementary material

Fortunato et al supplementary material

Download Fortunato et al supplementary material(PDF)
PDF 138 KB