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Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting

Published online by Cambridge University Press:  20 May 2022

Kevin DeLuca Open the ORCID record for Kevin DeLuca [Opens in a new window]  and
John A. Curiel Open the ORCID record for John A. Curiel [Opens in a new window]
Kevin DeLuca*
Affiliation:
Ph.D. Candidate in Political Economy and Government, Harvard Kennedy School, 79 John F. Kennedy Street, Cambridge, MA 02138, USA. E-mail: kevindeluca@g.harvard.edu
John A. Curiel
Affiliation:
Assistant Professor of Political Science, Ohio Northern University, Hill Memorial 204B, Ada, OH 45810, USA. E-mail: j-curiel@onu.edu
*
Corresponding author Kevin DeLuca
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Abstract

Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological inference methods in identifying voter race. In this article, we test the viability of employing BISG to redistricting under two posterior allocation methods for race assignment: plurality versus probabilistic. We validate these methods through 10,000 redistricting simulations of North Carolina and Georgia’s congressional districts and compare BISG estimates to actual voter file racial data. We find that probabilistic summing of the BISG posteriors significantly reduces error rates at the precinct and district level relative to plurality racial assignment, and therefore should be the preferred method when using BISG for redistricting. Our results suggest that BISG can aid in the construction of majority-minority districts during the redistricting process.

Keywords

redistrictingracerepresentationBayesian estimationBISG

Information

Type
Letter
Information
Political Analysis , Volume 31 , Issue 3 , July 2023 , pp. 465 - 471
Copyright
© The Author(s) 2022. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

References

Abott, C., and Magazinnik, A.. 2020. “At-Large Elections and Minority Representation in Local Government.” American Journal of Political Science 64 (3): 717–733.CrossRefGoogle Scholar
Alvarez, R. M., Katz, J. N., and Kim, S.-y. S.. 2020. “Hidden Donors: The Censoring Problem in U.S. Federal Campaign Finance Data.” Election Law Journal 19 (1): 1–18.CrossRefGoogle Scholar
Clark, J. T., Curiel, J. A., and Steelman, T.. 2021. “Minmaxing of Bayesian Improved Surname Geocoding and Geography Level Ups in Predicting Race.” Political Analysis: 1–7. https://doi.org/10.1017/pan.2021.31 Google Scholar
Conroy, M., and Green, J.. 2020. “It Takes a Motive: Communal and Agentic Articulated Interest and Candidate Emergence.” Political Research Quarterly 73 (4): 942–956.CrossRefGoogle Scholar
Crabtree, C., and Chykina, V.. 2018. “Last Name Selection in Audit Studies.” Sociological Science 5: 21–28. https://doi.org/10.15195/v5.a2 CrossRefGoogle Scholar
Curiel, J. A., and DeLuca, K.. 2022. “Replication Data for: Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting.” Harvard Dataverse, https://doi.org/10.7910/DVN/LXGDWZ CrossRefGoogle Scholar
Curiel, J. A., and Steelman, T. S.. 2018. “Redistricting Out Representation: Democratic Harms in Splitting Zip Codes.” Election Law Journal 17 (2): 328–353.CrossRefGoogle Scholar
Elliott, M. N., Fremont, A., Morrison, P. A., Pantoja, P., and Lurie, N.. 2008. “A New Method for Estimating Race/Ethnicity and Associated Disparities Where Administrative Records Lack Self-Reported Race/Ethnicity.” Health Services Research 43 (5p1): 1722–1736. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-6773.2008.00854.x (accessed October 26, 2020).CrossRefGoogle Scholar
Enos, R. D., Kaufman, A. R., and Sands, M. L.. 2019. “Can Violent Protest Change Local Policy Support? Evidence from the Aftermath of the 1992 Los Angeles Riot.” American Political Science Review 113 (4): 1012–1028.CrossRefGoogle Scholar
Fifield, B., Imai, K., Kawahara, J., and Kenny, C. T.. 2020a. “The Essential Role of Empirical Validation in Legislative Redistricting Simulation.” Statistics and Public Policy 7 (1): 52–68.CrossRefGoogle Scholar
Fifield, B., Kenny, C. T., McCartan, C., Tarr, A., and Imai, K.. 2020b. “redist: Simulation Methods for Legislative Redistricting.” Available at the Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=redist.Google Scholar
Fraga, B. L. 2015. “Redistricting and the Causal Impact of Race on Voter Turnout.” Journal of Politics 78 (1): 19–34.CrossRefGoogle Scholar
Goodman, L. 1953. “Ecological Regressions and the Behavior of Individuals.” American Sociological Review 18: 663–666.CrossRefGoogle Scholar
Grumbach, J., Sahn, A., and Staszak, S.. 2020. “ Gender, Race, and Intersectionality in Campaign Finance .” Political Behavior 44: 319–340.CrossRefGoogle Scholar
Henderson, J. A., Sekhon, J. S., and Titiunik, R.. 2016. “Cause or Effect? Turnout in Hispanic Majority-Minority Districts.” Political Analysis 24 (3): 404–412.CrossRefGoogle Scholar
Hicks, W. D., Klarner, C. E., McKee, S. C., and Smith, D. A.. 2018. “Revisiting Majority-Minority Districts and Black Representation.” Political Research Quarterly 71 (2): 408–423.CrossRefGoogle Scholar
Imai, K., and Khanna, K.. 2016. “Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.” Political Analysis 24 (2): 263–272.CrossRefGoogle Scholar
King, G. 1997. A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton, NJ: Princeton University Press.Google Scholar
Lu, C., et al. 2019. “Examining Scientific Writing Styles from the Perspective of Linguistic Complexity.” Journal of the Association for Information Science and Technology 70 (5): 462–475.10.1002/asi.24126CrossRefGoogle Scholar
Rhinehart, S., and Geras, M. J.. 2020. “Diversity and Power: Selection Method and Its Impacts on State Executive Descriptive Representation.” State Politics and Policy Quarterly 20 (2): 213–233.CrossRefGoogle Scholar
Shah, P. R., and Davis, N. R.. 2017. “Comparing Three Methods of Measuring Race/Ethnicity.” Journal of Race, Ethnicity, and Politics 2 (1): 124–139.CrossRefGoogle Scholar
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