Prof Jude Hays, University of Pittsburgh
4 - 15 August (two week course / 35 hrs)
Jude C. Hays is a Professor of Political Science at the University of Pittsburgh. Before going to Pittsburgh, he was a Professor of Political Science and a research fellow at the Cline Center for Democracy at the University of Illinois (2005-2010) and a Professor in both the Political Science Department and the Gerald R. Ford School of Public Policy at the University of Michigan as well as a faculty associate at the Center for Political Studies in the Institute for Social Research (2000-2005). His research has been supported by the National Science Foundation, and he has published articles in journals such as Comparative Political Studies, Political Analysis, European Union Politics, International Organization, World Politics, International Studies Quarterly, the American Journal of Political Science, and the Journal of Economic Behaviour and Organization. His first book, Globalization and the New Politics of Embedded Liberalism, was published by Oxford University Press in 2009.
- Spatial interdependence is ubiquitous throughout the social sciences. This is particularly true when we expand our conception of space beyond physical geography to include cultural, political and economic notions of distance. The likelihood and outcomes of demonstrations, riots, coups, and revolutions in one country almost certainly depend in substantively crucial ways on such occurrences in other countries (e.g., through demonstration effects or snowballing). Election outcomes and candidate qualities or strategies in some contests surely depend on those in others, and representatives’ votes in legislatures certainly depend on others’ votes or expected votes. In micro-behavioral research, long-standing and recently surging interest in contextual or network effects often refers to the effects on each individual’s behavior or opinion from sets of other individuals’ opinions or behaviors; e.g., a respondent’s opinion on some policy likely depends on the opinions of her state, district, community, or social group. In international relations, states’ entry decisions in wars, alliances, and organizations, e.g., heavily depend on how many and who else enters and how. In comparative and international political economy, globalization, i.e., international economic integration, implies strategic (and non-strategic) interdependence in national-level macroeconomic policymaking. This course introduces spatial and spatiotemporal econometric models for continuous and limited dependent variables that can address such interdependence, with an emphasis on social-science applications.
- The main objective of this course is to teach students how to incorporate the interdependence implied by most social scientific theories into their empirical analysis. Participants will learn inter alia how to 1) diagnose spatial patterns in their data, 2) estimate the structural parameters of spatial and spatiotemporal regression models, 3) calculate and present spatial and spatiotemporal effects, and 4) use spatial modelling to discriminate between the multiple sources of spatial correlation common exposure, interdependence, and selection and, when it exists, evaluate the nature of this interdependence (e.g., strategic free-riding behaviour, learning, coercion) among units of observation.
- Students should have a basic understanding of matrix algebra, probability theory, first-year calculus, and regression as well as some familiarity with a software package that can be used for spatial analysis (e.g., STATA, MATLAB, or R).
- Ward, M.D. and K.S. Gleditsch. 2008. Spatial Regression Models. Thousand Oaks, CA: Sage.
- Franzese, R. and J. Hays. 2008. “Contagion, Common Exposure, and Selection: Empirical Modelling of Theories and Substance of Interdependence in Political Science.” Concepts & Methods, 2008, 4(2): 2-8. Newsletter of the IPSA Committee on Concepts and Methods, Eds. B. Kittel and D. Raess. http://www.concepts-methods.org/newsletters/20090119_30_C&M%20Newsletter%202008%202.pdf
Representative Backround Reading
- Anselin, L. 2006. Spatial Econometrics. In T.C. Mills and K. Patterson, eds., Palgrave Handbook of Econometrics: Volume 1, Econometrics Theory. Basingstoke: Palgrave Macmillan, pp. 901-941.
- Franzese, R. and J. Hays. 2008. “Empirical Models of Spatial Interdependence” In Oxford Handbook of Political Methodology, Eds. Janet Box-Steffensmeier, Henry Brady, and David Collier, pp. 570-604, Oxford U.K.: Oxford University Press.
- Beck, N., K. Gleditsch, and K. Beardsley. 2006. “Space is More than Geography: Using Spatial Econometrics in the Study of Political Economy.” International Studies Quarterly 50: 27-44.
- Anselin, Luc. 1995. “Local Indicators of Spatial Association – LISA.” Geographical Analysis 27: 93-115.
- Elhorst, J.P. 2001. “Dynamic Models in Space and Time.” Geographical Analysis 33:119-140.
- Franzese, R.J and J.C. Hays. 2007. “Spatial-Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15(2): 140-164.