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Measuring State and District Ideology with Spatial Realignment

Published online by Cambridge University Press:  15 April 2015

Abstract

We develop a new approach for modeling public sentiment by micro-level geographic region based on Bayesian hierarchical spatial modeling. Recent production of detailed geospatial political data means that modeling and measurement lag behind available information. The output of the models gives not only nuanced regional differences and relationships between states, but more robust state-level aggregations that update past research on measuring constituency opinion. We rely here on the spatial relationships among observations and units of measurement in order to extract measurements of ideology as geographically narrow as measured covariates. We present an application in which we measure state and district ideology in the United States in 2008.

Type
Original Articles
Copyright
© The European Political Science Association 2015 

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Footnotes

*

James E. Monogan III, Assistant Professor, Department of Political Science, University of Georgia, Athens, GA 30602 ([email protected]). Jeff Gill, Professor, Departments of Political Science, Biostatistics and Surgery, Washington University, One Brookings Drive, Seigle Hall, St. Louis, MO 63130-4899 ([email protected]). The authors thank Robert S. Erikson, Bradley P. Carlin, Guy D. Whitten, Stephen Ansolabehere, Robert J. Franzese, Jude Hays, Paulo J. Ribeiro Jr., and the anonymous reviewers for their helpful assistance. This study was supported in part by resources and technical expertise from the Georgia Advanced Computing Resource Center, a partnership between the University of Georgia’s Office of the Vice President for Research and Office of the Vice President for Information Technology. Questions may be directed to James Monogan as corresponding author. Complete replication information and our estimates of ideology in 2008 are available at our Dataverse page: http://hdl.handle.net/1902.1/22006. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2015.5

References

Ansolabehere, Stephen. 2011. ‘CCES, Common Content, 2008, Version 4’. Available at http://hdl.handle.net/1902.1/14003, accessed April 18, 2013.Google Scholar
Ansolabehere, Stephen D., Snyder, James M., and Stewart, Charles. 2001. ‘Candidate Positioning in U.S. House Elections’. American Journal of Political Science 45:136159.Google Scholar
Banerjee, Sudipto, Carlin, Bradley P., and Gelfand, Alan E.. 2004. Hierarchical Modeling and Analysis for Spatial Data. New York, NY: Chapman & Hall.Google Scholar
Berry, William D., Ringquist, Evan J., Fording, Richard C., and Hanson, Russell L.. 1998. ‘Measuring Citizen and Government Ideology in the American States, 1960–93’. American Journal of Political Science 42:327348.CrossRefGoogle Scholar
Beyle, Thad, Niemi, Richard G., and Sigelman, Lee. 2002. ‘Gubernatorial, Senatorial, and State-Level Presidential Job Approval Ratings: The U.S. Officials Job Approval Ratings (JAR) Collection’. State Politics and Policy Quarterly 2:215229.Google Scholar
Box-Steffensmeier, Janet M., and Jones, Bradford S.. 2004. Event History Modeling: A Guide for Social Scientists. New York, NY: Cambridge University Press.Google Scholar
Brace, Paul, Arceneaux, Kevin, Johnson, Martin, and Ulbig, Stacy G.. 2004. ‘Does State Political Ideology Change Over Time?Political Research Quarterly 57:529540.Google Scholar
Brace, Paul, Sims-Butler, Kellie, Arceneaux, Kevin, and Johnson, Martin. 2002. ‘Public Opinion in the American States: New Perspectives Using National Survey Data’. American Journal of Political Science 46:173189.CrossRefGoogle Scholar
Cohen, Jeffery E. 2006. Public Opinion in State Politics. Stanford, CA: Stanford University Press.Google Scholar
Cressie, Noel A. C. 1991. Statistics for Spatial Data. New York, NY: Wiley.Google Scholar
Cressie, Noel A. C.. 1993. Statistics for Spatial Data, rev. ed. New York, NY: Wiley.Google Scholar
Cressie, Noel A. C., and Wikle, Christopher K.. 2011. Statistics for Spatio-Temporal Data, 1st ed.New York, NY: Wiley.Google Scholar
Cressie, Noel A. C., and Hawkins, Douglas M.. 1980. ‘Robust Estimation of the Variogram: I’. Mathematical Geology 12(2):115125.Google Scholar
DeLeon, Richard E., and Naff, Katherine C.. 2004. ‘Identity Politics and Local Political Culture: Some Comparative Results from the Social Capital Benchmark Survey’. Urban Affairs Review 39(6):689719.Google Scholar
Diggle, Peter J., and Ribeiro, Paulo J. Jr. 2002. ‘Bayesian Inference in Gaussian Model-Based Geostatistics’. Geographical & Environmental Modeling 6(2):129146.CrossRefGoogle Scholar
Diggle, Peter J., and Ribeiro, Paulo J. Jr.. 2007. Model-Based Geostatistics. New York, NY: Springer.Google Scholar
Djupe, Paul A., and Sokhey, Anand E.. 2011. ‘Interpersonal Networks and Democratic Politics’. Political Science and Politics 44(1):5559.Google Scholar
Elazar, Daniel J. 1966. American Federalism: A View from the States. New York, NY: Thomas Y. Crowell.Google Scholar
Enders, Walter. 2009. Applied Econometric Time Series, 3rd ed.New York, NY: Wiley.Google Scholar
Erikson, Robert S., and Wright, Gerald C.. 1980. ‘Policy Representation of Constituency Interests’. Political Behavior 2:91106.Google Scholar
Erikson, Robert S., Wright, Gerald C., and McIver, John P.. 1993. Statehouse Democracy: Public Opinion and Policy in the American States. New York, NY: Cambridge University Press.Google Scholar
Erikson, Robert S., Wright, Gerald C., and McIver, John P.. 2006. ‘Public Opinion in the States: A Quarter Century of Change and Stability’. In Jeffrey E. Cohen (ed.), Public Opinion in State Politics, 229253. Stanford, CA: Stanford University Press.Google Scholar
Finke, Roger, and Scheitle, Christopher P.. 2005. ‘Accounting for the Uncounted: Computing Correctives for the 2000RCMS Data’. Review of Religious Research 47(1):522.Google Scholar
Fischer, David Hackett. 1989. Albion’s Seed: Four British Folkways in America. New York, NY: Oxford University Press.Google Scholar
Franzese, Robert J. Jr., and Hays, Jude. 2007. ‘Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data’. Political Analysis 15(2):140164.Google Scholar
Garreau, Joel. 1981. The Nine Nations of North America. Boston, MA: Houghton Mifflin.Google Scholar
Gastil, Raymond D. 1975. Cultural Regions of the United States. Seattle, WA: University of Washington Press.Google Scholar
Gelman, Andrew, and Hill, Jennifer. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York, NY: Cambridge University Press.Google Scholar
Gelman, Andrew, and Little, Thomas C.. 1997. ‘Poststratification into Many Categories Using Hierarchical Logistic Regression’. Survey Methodology 23:127135.Google Scholar
Gill, Jeff. 2008. ‘Is Partial-Dimension Convergence a Problem for Inferences from MCMC Algorithms?Political Analysis 16(2):153178.Google Scholar
Gill, Jeff. 2014. Bayesian Methods for the Social and Behavioral Sciences, 3rd ed. New York, NY: Chapman & Hall/CRC.Google Scholar
Gill, Jeff, and Womack, Andrew J.. 2012. ‘The Multilevel Model Framework’. In Brian Marx, Marc Scott, and Jeff Simonoff (eds), The Sage Handbook of Multilevel Modeling, 320. Thousand Oaks, CA: Sage.Google Scholar
Gimpel, James G., and Schuknecht, Jason E.. 2003. Patchwork Nation: Sectionalism and Political Change in American Politics. Ann Arbor, MI: University of Michigan Press.Google Scholar
Gray, Virginia. 1976. ‘Models of Comparative State Politics: A Comparison of Cross-Sectional and Time Series Analysis’. American Journal of Political Science 20:235256.Google Scholar
Gujarati, Damodar N., and Porter, Dawn C.. 2009. Basic Econometrics, 5th ed.New York, NY: McGraw-Hill/Irwin.Google Scholar
Hobert, James P., and Casella, George. 1996. ‘The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models’. Journal of the American Statistical Association 91:14611473.Google Scholar
Huckfeldt, Robert, and Sprague, John T.. 1995. Citizens, Politics, and Social Communication: Influence in an Election Campaign. New York, NY: Cambridge University Press.Google Scholar
Jackson, John E. 1989. ‘An Errors in Variables Approach to Estimating Models with Small Area Data’. Political Analysis 1:157180.Google Scholar
Jackson, John E. 2008. ‘Endogeneity and Structural Equation Estimation in Political Science’. In Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, 404431. New York, NY: Oxford UniversityPress.Google Scholar
Jackson, John E., and King, David C.. 1989. ‘Public Goods, Private Interests, and Representation’. American Political Science Review 83(4):11431164.Google Scholar
Jacoby, William G., and Schneider, Saundra K.. 2001. ‘Variability in State Policy Priorities: An Empirical Analysis’. Journal of Politics 63:544568.Google Scholar
Jones, Dale E., Doty, Sherry, Grammich, Clifford, Horsch, James E., Houseal, Richard, Lynn, Mac, Marcum, John P., Sanchagrin, Kenneth M., and Taylor, Richard H.. 2002. Religious Congregations and Membership in the United States 2000: An Enumeration by Region, State and County Based on Data Reported for 149 Religious Bodies. Nashville, TN: Glenmary Research Center.Google Scholar
Kellsall, J., and Diggle, P. J.. 1995. ‘Non-Parametric Estimation of Spatial Variation in Risk’. Political Analysis 14:23352342.Google Scholar
Kernell, Georgia 2009. ‘Giving Order to Districts: Estimating Voter Distributions with National Election Returns’. Political Analysis 17:215235.Google Scholar
Lawson, A. B., and Williams, F.. 1993. ‘Application of Extraction Mapping in Environmental Epidemiology’. Statistics in Medicine 12:12491258.Google Scholar
Lax, Jeffrey R., and Phillips, Justin H.. 2009. ‘How Should We Estimate Opinion in the States?American Journal of Political Science 53:107121.Google Scholar
Lieberman, Robert C., and Shaw, Greg M.. 2000. ‘Looking Inward, Looking Outward: The Politics of State Welfare Innovation Under Devolution’. Political Research Quarterly 53:215240.Google Scholar
Lieske, Joel. 1993. ‘Regional Subcultures of the United States’. Journal of Politics 55(4):888913.Google Scholar
Matheron, G. 1963. ‘Principles of Geostatistics’. Economic Geology 58:12461266.Google Scholar
Minnesota Population Center. 2011. National Historic Geographic Information System: Version 2.0 [Machine-Readable Database]. Minneapolis, MN: University of Minnesota.Google Scholar
Norrander, Barbara. 2001. ‘Measuring State Public Opinion with the Senate National Election Study’. State Politics and Policy Quarterly 1:113127.Google Scholar
Pacheco, Julianna 2011. ‘Using National Surveys to Measure Dynamic U.S. State Public Opinion: A Guideline for Scholars and an Application’. State Politics and Policy Quarterly 11:415439.Google Scholar
Park, David K., Gelman, Andrew, and Bafumi, Joseph. 2004. ‘Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls’. Political Analysis 12:375385.Google Scholar
Park, David K., Gelman, Andrew, and Bafumi, Joseph. 2006. ‘State-Level Opinions from National Surveys: Poststratification Using Multilevel Logistic Regression’. In Jeffrey E. Cohen (ed.), Public Opinion in State Politics, 209228. Stanford, CA: Stanford University Press.Google Scholar
Pool, Ithiel de Sola, Abelson, Robert P., and Popkin, Samuel L.. 1965. Candidates, Issues, and Strategies. Cambridge, MA: MIT Press.Google Scholar
Putnam, Robert D. 1966. ‘Political Attitudes and the Local Community’. American Political Science Review 60(3):640654.Google Scholar
Putnam, Robert D.. 1993. Making Democracy Work: Civic Traditions in Modern Italy. Princeton, NJ: Princeton University Press.Google Scholar
Ravishanker, Nalini, and Dey, Dipak K.. 2002. A First Course in Linear Model Theory. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Ruggles, Steven, Alexander, J. Trent, Genadek, Katie, Goeken, Ronald, Schroeder, Matthew B., and Sobek, Matthew. 2010. Integrated Public Use Microdata Series: Version 5.0 [Machine-Readable Database]. Minneapolis, MN: University of Minnesota.Google Scholar
Shor, Boris, and McCarty, Nolan. 2011. ‘The Ideological Mapping of American Legislatures’. American Political Science Review 105(3):530551.Google Scholar
Sinclair, Betsy. 2012. The Social Citizen: Peer Networks and Political Behavior. Chicago, IL: University of Chicago Press.Google Scholar
Tam Cho, Wendy K., and Gimpel, James G.. 2007. ‘Prospecting for (Campaign) Gold’. American Journal of Political Science 51(2):255268.Google Scholar
Tausanovitch, Chris, and Warshaw, Christopher. 2013. ‘Measuring Constituent Policy Preferences in Congress, State Legislatures, and Cities’. Journal of Politics 75(2):330342.CrossRefGoogle Scholar
United States Census Bureau. 2002. ‘Census 2000 Gazetteer Files: Zip Code Tabulation Areas’. Available at http://www.census.gov/geo/maps-data/data/gazetteer2000.html, accessed April 20, 2013.Google Scholar
United States Department of Agriculture (USDA). 2004. ‘2003 Rural–Urban Continuum Codes’. Available at http://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx, accessed July 24, 2013.Google Scholar
Weber, Ronald E., Hopkins, Anne H., Mezey, Michael L., and Munger, Frank. 1972. ‘Computer Simulation of State Electorates’. Public Opinion Quarterly 36:549565.Google Scholar
Weber, Ronald E., and Shaffer, William R.. 1972. ‘Public Opinion and American State Policy-Making’. Midwest Journal of Political Science 16:683699.Google Scholar
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