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Measuring Geographic Distribution for Political Research

Published online by Cambridge University Press:  18 March 2019

Dong Wook Lee
Affiliation:
Department of Political Science, Adelphi University, Garden City, NY 11530, USA. Email: [email protected]
Melissa Rogers*
Affiliation:
Department of International Studies, Claremont Graduate University, Claremont, CA 91711, USA. Email: [email protected]

Abstract

Political scientists are increasingly interested in the geographic distribution of political and economic phenomena. Unlike distribution measures at the individual level, geographic distributions depend on the “unit question” in which researchers choose the appropriate political subdivision to analyze, such as nations, subnational regions, urban and rural areas, or electoral districts. We identify concerns with measuring geographic distribution and comparing distributions within and across political units. In particular, we highlight the potential for threats to inference based on the modifiable areal unit problem (MAUP), whereby measuring concepts at different unit aggregations alters the observed value. We offer tangible options for researchers to improve their research design and data analysis to limit the MAUP. To help manage the measurement error when the unit of observation is unclear or appropriate data are not available, we introduce a new measure of geographic distribution that accounts for fluctuations in the scale and number of political units considered. We demonstrate using Monte Carlo simulations that our measure is more reliable and stable across political units than commonly used indicators because it reduces measurement fluctuations associated with the MAUP.

Type
Articles
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Authors’ note: The authors thank Daniel Bochsler for help with construction of the geographic distribution measure. The authors greatly benefited from the feedback from Editor Jeff Gill, three anonymous reviewers, Ryan Bakker, Scott Desposato, Indridi Indridiason, Stephanie Rickard, Guillermo Rosas, and participants at SCCPI at UC San Diego, Faculty Research Colloquium at CSU Long Beach, and MPSA 2017 and 2018. Replication data are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/DKY4DY

Contributing Editor: Jeff Gill

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