Geospacial Social Science Analysis*

*New course running in Methods School 2025!

Space is crucial to explaining many political phenomena. Think about how the strategies of political parties in one county are informed by the successes and failures by parties in other countries. Or consider how protesters in autocracies often learn from protesters elsewhere about what tactics may help them gain concessions from or even overthrow an autocrat. However, dynamics such as these are often left out of statistical models meaning that there is a disconnect between our theories about how the world works and the models that we estimate.

This course provides participants with the knowledge and tools to apply spatial econometric models to their research. This is a hands-on course where participants will gain experience of estimating various spatial models to help them answer theoretically significant and policy-relevant questions.

 

Dates

This one week, 17.5-hour course runs Monday-Friday, 7 - 11 July, 2025. The course is scheduled for 9:00 am - 12:30 pm.

 

Classroom Location

Faculty of Arts and Social Sciences

 

Instructor

Edward Goldring, The University of Melbourne

 

Detailed Description

Spatial econometric models have grown in popularity in the social sciences over the last three decades, especially as scholars grapple with estimating models that match the spatial complexity of their theories. This five-day course is designed to provide participants with an introduction to the use of spatial econometric models. It prepares participants to carefully theorize about these spatial processes and more effectively test theoretical expectations about patterns of spatial interdependence.

We begin the course by exploring how prominent theories of social science (e.g., policy diffusion, party competition, civil war spillovers, democratization, etc.) argue that the processes of nearby or similar units are related. In the second and third days we focus on issues of model specification that are unique to these models; this includes correctly specifying the manner in which the units (i.e., individuals, states, countries, etc.) are spatially related, and whether the spatial dependence occurs among variables, the errors, or the outcome itself. The fourth day emphasizes how to estimate a variety of simple spatial econometric models (e.g., spatial lag, spatial error, and spatial-X). In the final day, we explore techniques to provide meaningful quantities of interest from these models.

This is a hands-on course, meaning that a major goal is to have participants learn about techniques by putting them to work with statistical software. Participants are encouraged to develop their own research questions about spatial processes and to bring their own data sets. This is not a requirement though; participants will also be able to work with data provided by the instructor. The main statistical software program that we will use for the labs is Stata though at times we will also utilize GeoDa and R, both of which are free software.

 

Prerequisites

While there are no formal prerequisites, it would be beneficial if participants have an understanding of regression analysis. (cf. Regression Analysis)

 

Requirements

Participants are expected to have access to an internet-connected computer and to be able to use the statistical software Stata for this course.

 

Core Readings

Beck, Nathaniel, Kristian Skrede Gleditsch, and Kyle Beardsley. 2006. “Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy.” International Studies Quarterly 50(1): 27-44.

Neumayer, Eric, and Thomas Plumper. 2016. “W.” Political Science Research and Methods 4(1): 175-193.

Vega, Solmaria Halleck, and J. Paul Elhorst. 2015. “The SLX Model.” Journal of Regional Science 55(3): 339-363.

Ward, Michael D., and Kristian Skrede Gleditsch. 2008. Spatial Regression Models. Sage Publications.

 

Suggested Readings

Darmofal, David. 2015. Spatial Analysis for the Social Sciences. Cambridge University Press.

Fortunato, David, Clint S. Swift, and Laron K. Williams. 2018. “All Economics is Local: Spatial Aggregations of Economic Information.” Political Science Research and Methods 6(3): 467-487.

Franzese, Robert J., and Jude 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.

Gailmard, Sean. 2014. Statistical Modeling and Inference for Social Science. Cambridge University Press.

Goldring, Edward, and Sheena Chestnut Greitens. 2020. “Rethinking Democratic Diffusion: Bringing Regime Type Back In.” Comparative Political Studies 53(2): 319-353.

Kayser, Mark Andreas, and Michael Peress. 2012. “Benchmarking Across Borders: Electoral Accountability and the Necessity of Comparison.” American Political Science Review 106(3): 661-684.

Kellstedt, Paul M., and Guy D. Whitten. 2009. The Fundamentals of Political Science Research. Cambridge University Press.

Moore, Will H., and David A. Siegel. 2013. A Mathematics Course for Political & Social Research. Princeton University Press.

Plumper, Thomas, and Eric Neumayer. 2010. “Model Specification in the Analysis of Spatial Dependence.” European Journal of Political Research 49(3): 418-442.

Williams, Laron K. 2015. “It’s All Relative: Spatial Positioning of Parties and Ideological Shifts.” European Journal of Political Research 54(1): 141-159.

Williams, Laron K., and Guy D. Whitten. 2015. “Don’t Stand So Close to Me: Spatial Contagion Effects and Party Competition.” American Journal of Political Science 59(2): 309-325.

Williams, Laron K., Katsunori Seki, and Guy D. Whitten. 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.

Zhukov, Yuri, and Brandon M. Stewart. 2013. “Choosing Your Neighbors: Networks of Diffusion in International Relations.” International Studies Quarterly 57(2): 271-287.