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W

Published online by Cambridge University Press:  25 February 2015

Eric Neumayer  and
Thomas Plümper
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Abstract

In spatial econometrics, W refers to the matrix that weights the value of the spatially lagged variable of other units. As unimportant as it may appear, W specifies, or at least ought to specify, why and how other units of analysis affect the unit under observation. This article shows that theory must inform five crucial specification choices taken by researchers. Specifically, the connectivity variable employed in W must capture the causal mechanism of spatial dependence. The specification of W further determines the relative relevance of source units from which spatial dependence emanates, and whether receiving units are assumed to be identically or differentially exposed to spatial stimulus. Multiple dimensions of spatial dependence can be modeled as independent, substitutive or conditional links. Finally, spatial effects need not go exclusively in one direction, but can be bi-directional; recipients can simultaneously experience positive spatial dependence from some sources and negative dependence from others. The importance of W stands in stark contrast to applied researchers’ typical use of crude proxy variables (such as geographical proximity) to measure true connectivity, and the practice of adopting standard modeling conventions rather than substantive theory to specify W. This study demonstrates which assumptions these conventions impose on specification choices, and argues that theories of spatial dependence will often conflict with them.

Type
Original Articles
Information
Political Science Research and Methods , Volume 4 , Issue 1 , January 2016 , pp. 175 - 193
Copyright
© The European Political Science Association 2015 

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Footnotes

*

Department of Geography and Environment, London School of Economics, London WC2A 2AE, UK (email: [email protected]); Department of Government, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK (email: [email protected]).

References

Aldstadt, Jared, and Getis, Arthur. 2006. ‘Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters’. Geographical Analysis 38(4):327–343.Google Scholar
Aldstadt, Jared, and Getis, Arthur. 2002. ‘Under the Hood – Issues in the Specification and Interpretation of Spatial Regression Models’. Agricultural Economics 27(3):247–267.Google Scholar
Anselin, Luc, Le Gallo, Julie, and Jayet, Hubert. 2008. ‘Spatial Panel Econometrics.’. In László Matyas and Patrick Sevestre (eds), The Econometrics of Panel Data, Fundamentals and Recent Developments in Theory and Practice, 3rd ed., 624–660. Berlin Heidelberg: Springer-Verlag.Google Scholar
Baccini, Leonardo, and Dür, Andreas. 2012. ‘The New Regionalism and Policy Interdependence’. British Journal of Political Science 42(1):57–79.Google Scholar
Basinger, Scott J., and Hallerberg, Mark. 2004. ‘Remodeling the Competition for Capital: How Domestic Politics Erases the Race to the Bottom’. American Political Science Review 98:261–276.CrossRefGoogle Scholar
Beck, Nathaniel, Gleditsch, Kristian S., and Beardsley, Kyle. 2006. ‘Space is More Than Geography: Using Spatial Econometrics in the Study of Political Economy’. International Studies Quarterly 50:27–44.Google Scholar
Beenstock, Michael, and Felsenstein, Daniel. 2012. ‘Nonparametric Estimation of the Spatial Connectivity Matrix Using Spatial Panel Data’. Geographical Analysis 44:386–397.Google Scholar
Bennett, C. J. 1991. ‘What is Policy Convergence and What Causes It?’. British Journal of Political Science 21:215–233.Google Scholar
Brooks, S. M., and Kurtz, M. J.. 2012. ‘Paths to Financial Policy Diffusion: Statist Legacies in Latin America’s Globalization’. International Organization 66:95–128.Google Scholar
Cao, X., and Prakash, A.. 2010. ‘Trade Competition and Domestic Pollution: A Panel Study, 1980–2003’. International Organization 64:481–503.Google Scholar
Darmofal, David. 2006. ‘Spatial Econometrics and Political Science’. Working Paper. University of South Carolina, Columbia.Google Scholar
De Francesco, F.. 2012. ‘Diffusion of Regulatory Impact Analysis Among OECD and EU Member States’. Comparative Political Studies 45:1277–1305.Google Scholar
Debarsy, Nicolas, and Ertur, Cem. 2010. ‘Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model’. Regional Science and Urban Economics 40:453–470.Google Scholar
Dolowitz, David, and Marsh, David. 1996. ‘Who Learns What From Whom: A Review of the Policy Transfer Literature’. Political Studies 44:343–357.CrossRefGoogle Scholar
Dolowitz, David, and Marsh, David. 2000. ‘Learning from Abroad: The Role of Policy Transfer in Contemporary Policy-Making’. Governance – An International Journal of Policy and Administration 13:5–24.Google Scholar
Drukker, David M., Peng, Hua, Prucha, Ingmar, and Raciborski, Rafal. 2013. ‘Creating and Managing Spatial-Weighting Matrices with the spmat Command’. Stata Journal 13(2):242–286.Google Scholar
Elhorst, J. Paul. 2009. ‘Spatial Panel Data Models’. In M. M. Fischer and A. Getis (eds), Handbook of Applied Spatial Analysis, 377–407. Berlin, Heidelberg and New York: Springer.Google Scholar
Elhorst, J. Paul, Lacombe, Donald J., and Piras, Gianfranco. 2012. ‘On Model Specification and Parameter Space Definitions in Higher Order Spatial Econometric Models’. Regional Science and Urban Economics 42:211–220.Google Scholar
Elkins, Zachary, Guzman, Andrew T., and Simmons, Beth A.. 2006. ‘Competing for Capital: The Diffusion of Bilateral Investment Treaties, 1960–2000’. International Organization 60(4):811–846.Google Scholar
Faber, G., and Gerritse, M.. 2012. ‘Foreign Determinants of Local Institutions: Spatial Dependence and Openness’. European Journal of Political Economy 28(1):54–63.CrossRefGoogle Scholar
Flores, A. Q. 2011. ‘Alliances as Contiguity in Spatial Models of Military Expenditures’. Conflict Management and Peace Science 28:402–418.Google Scholar
Franzese, Robert, and Hays, Jude C.. 2006. ‘Strategic Interaction Among EU Governments in Active Labor-Market Policymaking: Subsidiarity and Policy Coordination Under the European Employment Strategy’. European Union Politics 7:167–189.Google Scholar
Franzese, Robert, and Hays, Jude C.. 2007. ‘Spatial-Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data’. Political Analysis 15(2):140–164.Google Scholar
Franzese, Robert, and Hays, Jude C.. 2008. ‘Spatial-Econometric Models of Interdependence’. Unpublished manuscript. Ann Arbor: University of Michigan.Google Scholar
Galton, Francis. 1889. ‘Discussion on Edward B. Tylor’s on a Method of Investigating the Development of Institutions, Applied to Laws of Marriage and Descent’. Journal of the Anthropological Institute of Great Britain and Ireland 18:270–272.Google Scholar
Garrett, Geoffrey. 1995. ‘Capital Mobility, Trade, and the Domestic Politics of Economic Policy’. International Organization 49(4):657–687.Google Scholar
Genschel, Philipp, and Plümper, Thomas. 1997. ‘Regulatory Competition and International Cooperation’. Journal of European Public Policy 4:626–642.Google Scholar
Gilardi, Fabrizio. 2010. ‘Who Learns from What in Policy Diffusion Processes? American Journal of Political Science 54:650–666.Google Scholar
Hall, Peter A. 1993. ‘Policy Paradigms, Social-Learning and the State – The Case of Economic Policy-Making in Britain’. Comparative Politics 25:275–296.Google Scholar
Kelejian, Harry H., and Prucha, Ingmar R.. 2010. ‘Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances’. Journal of Econometrics 157:53–67.Google Scholar
Kitschelt, Herbert, Lange, Peter, Marks, Gary, and Stephens, John D.. 1999. ‘Convergence and Divergence in Advanced Capitalists Democracies’. In Herbert Kitschelt et al. (eds), Continuity and Change in Contemporary Capitalism, 427–459. Cambridge: Cambridge University Press.Google Scholar
Lam, Clifford, and Souza, Pedro C. L.. 2013. ‘Regularization for Spatial Panel Time Series Using the Adaptive LASSO’, Working Paper. London School of Economics and Political Science, London.Google Scholar
Lee, Lung-fei, and Yu, Jihai. 2010. ‘Estimation of Spatial Autoregressive Panel Data Models With Fixed Effects’. Journal of Econometrics 154:165–185.Google Scholar
Leeson, P. T., and Dean, A. M.. 2009. ‘The Democratic Domino Theory: An Empirical Investigation’. American Journal of Political Science 53(11):533–551.Google Scholar
LeSage, James, and Pace, R. Kelley. 2009. Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
LeSage, James, and Pace, R. Kelley. 2011. ‘Pitfalls in Higher Order Model Extensions of Basic Spatial Regression Methodology’. Review of Regional Studies 41:13–26.Google Scholar
Linos, K. 2011. ‘Diffusion Through Democracy’. American Journal of Political Science 55(5):678–695.CrossRefGoogle Scholar
Manski, Charles F.. 1993. ‘Identification of Endogenous Social Effects: The Reflection Problem’. Review of Economic Studies 60(3):531–542.Google Scholar
Neumayer, Eric, and Plümper, Thomas. 2010a. Spatial Effects in Dyadic Data’. International Organization 64(1):145–166.Google Scholar
Neumayer, Eric, and Plümper, Thomas. 2010b. Galton’s Problem and the Spread of International Terrorism Along Civilizational Lines’. Conflict Management and Peace Science 27(4):308–325.Google Scholar
Neumayer, Eric, and Plümper, Thomas. 2012. ‘Conditional Spatial Policy Dependence: Theory and Model Specification’. Comparative Political Studies 47(5):819–849.Google Scholar
Neumayer, Eric, Plümper, Thomas, and Epifanio, Mariaelisa. 2014. ‘The Peer-Effect in Counterterrorist Regulations’. International Organization 68(1):211–234.Google Scholar
Olson, Mancur. 1965. The Logic of Collective Action. Cambridge, MA: Harvard University Press.Google Scholar
Olson, Mancur, and Zeckhauser, Richard. 1966. ‘An Economic Theory of Alliances’. Review of Economic Statistics 48(3):266–279.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, and Neumayer, Eric. 2015. ‘Free-Riding in Alliances: Testing an Old Theory with a New Method’. Conflict Management and Peace Science (forthcoming).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 53:761–786.Google Scholar
Quah, Danny. 1993. ‘Galton’s Fallacy and Tests of the Convergence Hypothesis’. Scandinavian Journal of Economics 95:427–443.CrossRefGoogle Scholar
Schmitt, Carina. 2011. ‘What Drives the Diffusion of Privatization Policy? Evidence from the Telecommunications Sector’. Journal of Public Policy 31(1):95–117.Google Scholar
Tobler, W. R. 1970. ‘A Computer Model Simulation of Urban Growth in the Detroit Region’. Economic Geography 46(2):234–240.Google Scholar
Ward, Michael D., and Gleditsch, Kristian. 2008. Spatial Regression Models. London: Sage.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