Hostname: page-component-848d4c4894-jbqgn Total loading time: 0 Render date: 2024-06-24T03:40:32.396Z Has data issue: false hasContentIssue false

Measuring Constituency Ideology Using Bayesian Universal Kriging

Published online by Cambridge University Press:  16 April 2021

Jeff Gill*
American University, Washington, DC, USA
Jeff Gill, Department of Government, American University, Washington, DC 20016, USA. Email:


In this article, we develop and make available measures of public ideology in 2010 for the 50 American states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian universal kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP Code Tabulation Areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state, congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

Original Article
© The Author(s) 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Amos, Brian, McDonald, Michael P., Watkins, Russell. 2017. “When Boundaries Collide: Constructing a National Database of Demographic and Voting Statistics.” Public Opinion Quarterly 81 (Suppl. 1): 385400.CrossRefGoogle Scholar
Ansolabehere, Stephen D. 2011. “CCES, Common Content, 2008” Ver. 4. (accessed June 1, 2020).Google Scholar
Ansolabehere, Stephen D., Snyder, James M., Stewart, Charles. 2001. “Candidate Positioning in U.S. House Elections.” American Journal of Political Science 45:136–59.CrossRefGoogle Scholar
Banerjee, Sudipto, Carlin, Bradley P., Gelfand, Alan E. 2015. Hierarchical Modeling and Analysis for Spatial Data. 2nd ed. New York: Chapman & Hall/CRC.Google Scholar
Berry, William D., Ringquist, Evan J., Hanson, Richard C. Fording Russell L. 1998. “Measuring Citizen and Government Ideology in the American States, 1960-93.” American Journal of Political Science 42:327–48.CrossRefGoogle Scholar
Beyer, Kirsten M. M., Schultz, Alan F., Rushton, Gerard. 2008. “Using ZIP Codes as Geocodes in Cancer Research.” In Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research, and Practice, eds. Rushton, Gerard, Armstrong, Marc P., Gittler, Josephine, Greene, Barry R., Pavlik, Claire E., West Dale L., Michele M., Zimmerman, . New York: CRC Press, 3768.Google Scholar
Cressie, Noel A. C. 1993. Statistics for Spatial Data. Rev. ed. New York: John Wiley.Google Scholar
DeLeon, Richard E., 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.CrossRefGoogle Scholar
Diggle, Peter J., Ribeiro, Paulo J. Jr. 2007. Model-Based Geostatistics. New York: Springer.Google Scholar
Djupe, Paul A., Sokhey, Anand E. 2011. “Interpersonal Networks and Democratic Politics.” PS: Political Science & Politics 44 (1): 5559.Google Scholar
Elazar, Daniel J. 1966. American Federalism: A View from the States. New York: Thomas Y. Crowell.Google Scholar
Erikson, Robert S., Wright, Gerald C. 1980. “Policy Representation of Constituency Interests.” Political Behavior 2:91106.CrossRefGoogle Scholar
Erikson, Robert S., Wright, Gerald C., McIver, John P. 1993. Statehouse Democracy: Public Opinion and Policy in the American States. New York: Cambridge University Press.Google Scholar
Fischer, David Hackett. 1989. Albion’s Seed: Four British Folkways in America. New York: Oxford University Press.Google Scholar
Garreau, Joel. 1981. The Nine Nations of North America. Boston: Houghton Mifflin.Google Scholar
Gastil, Raymond D. 1975. Cultural Regions of the United States. Seattle: University of Washington Press.Google Scholar
Gelman, Andrew, Little, Thomas C. 1997. “Poststratification into Many Categories Using Hierarchical Logistic Regression.” Survey Methodology 23:127–35.Google Scholar
Gimpel, James G., Schuknecht, Jason E. 2003. Patchwork Nation: Sectionalism and Political Change in American Politics. Ann Arbor: University of Michigan Press.CrossRefGoogle Scholar
Grammich, Clifford, Hadaway, Kirk, Houseal, Richard, Jones, Dale E., Krindatch, Alexei, Stanley, Richie, Taylor, Richard H. 2012. 2010 U.S. Religion Census: Religious Congregations & Membership Study. Association of Statisticians of American Religious Bodies. (accessed June 1, 2020).Google Scholar
Grubesic, Tony H. 2008. “Zip Codes and Spatial Analysis: Problems and Prospects.” Socio-Economic Planning Sciences 42 (2): 129–49.CrossRefGoogle Scholar
Grubesic, Tony H., Matisziw, Timothy C. 2006. “On the Use of ZIP Codes and ZIP Code Tabulation Areas (ZCTAs) for the Spatial Analysis of Epidemiological Data.” International Journal of Health Geographics 5:58.Google ScholarPubMed
Hanretty, Chris, Lauderdale, Benjamin E., Vivyan, Nick. 2018. “Comparing Strategies for Estimating Constituency Opinion from National Survey Samples.” Political Science Research and Methods 6 (3): 571–91.CrossRefGoogle Scholar
Huckfeldt, Robert, Sprague, John T. 1995. Citizens, Politics, and Social Communication: Influence in an Election Campaign. New York: Cambridge University Press.Google Scholar
Jackson, John E. 1989. “An Errors in Variables Approach to Estimating Models with Small Area Data.” Political Analysis 1:157–80.Google Scholar
Jackson, John E. 2008. “Endogeneity and Structural Equation Estimation in Political Science.” In The Oxford Handbook of Political Methodology, eds. Box-Steffensmeier, Janet M., Brady, Henry E., Collier, David. New York: Oxford University Press, pp. 404–431.Google Scholar
Kernell, Georgia. 2009. “Giving Order to Districts: Estimating Voter Distributions with National Election Returns.” Political Analysis 17:215–35.CrossRefGoogle Scholar
Lax, Jeffrey R., Phillips, Justin H. 2009. “How Should We Estimate Opinion in the States?American Journal of Political Science 53:107–21.CrossRefGoogle Scholar
Lieske, Joel. 1993. “Regional Subcultures of the United States.” Journal of Politics 55 (4): 888913.Google Scholar
McCarty, Nolan M., Poole, Keith T., Rosenthal, Howard. 1997. Income Redistribution and the Realignment of American Politics. American Enterprise Institute Studies on Understanding Economic Inequality. Washington, DC: AEI Press.Google Scholar
Minnesota Population Center. 2011. National Historic Geographic Information System: Version 2.0. Minneapolis: University of Minnesota.Google Scholar
Monogan, James E. III, Gill, Jeff. 2016. “Measuring State and District Ideology with Spatial Realignment.” Political Science Research and Methods 4 (1): 97121.CrossRefGoogle Scholar
Monogan, James E. III, Konisky, David M., Woods, Neal D. 2017. “Gone with the Wind: Federalism and the Strategic Location of Air Polluters.” American Journal of Political Science 61(2):257–70.Google Scholar
Park, David K., Gelman, Andrew, Bafumi, Joseph. 2004. “Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls.” Political Analysis 12:375–85.Google Scholar
Park, David K., Gelman, Andrew, Bafumi, Joseph. 2006. “State-Level Opinions from National Surveys: Poststratification Using Multilevel Logistic Regression.” In Public Opinion in State Politics, ed. Cohen, Jeffrey E. Stanford: Stanford University Press, pp. 209–228.Google Scholar
Pool, Ithiel de Sola, Abelson, Robert P., Popkin, Samuel L. 1965. Candidates, Issues, and Strategies. Cambridge: MIT Press.Google Scholar
Poole, Keith T., Rosenthal, Howard. 1997. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.Google Scholar
Putnam, Robert D. 1966. “Political Attitudes and the Local Community.” American Political Science Review 60(3):640–54.CrossRefGoogle Scholar
Putnam, Robert D. 1993. Making Democracy Work: Civic Traditions in Modern Italy. Princeton: Princeton University Press.Google Scholar
Ravishanker, Nalini, Dey, Dipak K. 2002. A First Course in Linear Model Theory. Boca Raton: Chapman & Hall/CRC.Google Scholar
Selb, Peter, Munzert, Simon. 2011. “Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information.” Political Analysis 19 (4): 455–70.CrossRefGoogle Scholar
Shor, Boris, McCarty, Nolan. 2011. “The Ideological Mapping of American Legislatures.” American Political Science Review 105 (3): 530–51.Google Scholar
Sinclair, Betsy. 2012. The Social Citizen: Peer Networks and Political Behavior. Chicago: University of Chicago Press.Google Scholar
Tam Cho, Wendy K, Gimpel, James G. 2007. “Prospecting for (Campaign) Gold.” American Journal of Political Science 51(2):255–68.CrossRefGoogle Scholar
Tausanovitch, Chris, Warshaw, Christopher. 2013. “Measuring Constituent Policy Preferences in Congress, State Legislatures, and Cities.” Journal of Politics 75 (2): 330–42.Google Scholar
Tobler, Waldo R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46 (2): 234–40.CrossRefGoogle Scholar
United States Department of Agriculture. 2013. “2013 Rural-Urban Continuum Codes.” (accessed June 1, 2020).Google Scholar
Weber, Ronald E., Hopkins, Anne H., Mezey, Michael L., Munger, Frank. 1972. “Computer Simulation of State Electorates.” Public Opinion Quarterly 36:549–65.CrossRefGoogle Scholar
Weber, Ronald E., Shaffer, William R. 1972. “Public Opinion and American State Policy-Making.” Midwest Journal of Political Science 16:683–99.Google Scholar
Supplementary material: File

Gill supplementary materials

Online Appendix

Download Gill supplementary materials(File)
File 23 KB