IDEAS home Printed from https://ideas.repec.org/a/taf/usppxx/v7y2020i1p69-86.html
   My bibliography  Save this article

A Computational Approach to Measuring Vote Elasticity and Competitiveness

Author

Listed:
  • Daryl DeFord
  • Moon Duchin
  • Justin Solomon

Abstract

The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states’ reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested.In this article, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states—Utah, Georgia, Wisconsin, Virginia, and Massachusetts—chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.

Suggested Citation

  • Daryl DeFord & Moon Duchin & Justin Solomon, 2020. "A Computational Approach to Measuring Vote Elasticity and Competitiveness," Statistics and Public Policy, Taylor & Francis Journals, vol. 7(1), pages 69-86, January.
  • Handle: RePEc:taf:usppxx:v:7:y:2020:i:1:p:69-86
    DOI: 10.1080/2330443X.2020.1777915
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/2330443X.2020.1777915
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/2330443X.2020.1777915?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sarah Cannon & Ari Goldbloom-Helzner & Varun Gupta & JN Matthews & Bhushan Suwal, 2023. "Voting Rights, Markov Chains, and Optimization by Short Bursts," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-38, March.
    2. Jeanne Clelland & Haley Colgate & Daryl DeFord & Beth Malmskog & Flavia Sancier-Barbosa, 2022. "Colorado in context: Congressional redistricting and competing fairness criteria in Colorado," Journal of Computational Social Science, Springer, vol. 5(1), pages 189-226, May.
    3. Swamy, Rahul & King, Douglas M. & Ludden, Ian G. & Dobbs, Kiera W. & Jacobson, Sheldon H., 2024. "A practical optimization framework for political redistricting: A case study in Arizona," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    4. Kiera W. Dobbs & Rahul Swamy & Douglas M. King & Ian G. Ludden & Sheldon H. Jacobson, 2024. "An Optimization Case Study in Analyzing Missouri Redistricting," Interfaces, INFORMS, vol. 54(2), pages 162-187, March.
    5. Amariah Becker & Dara Gold, 2022. "The gameability of redistricting criteria," Journal of Computational Social Science, Springer, vol. 5(2), pages 1735-1777, November.
    6. Benadè, Gerdus & Ho-Nguyen, Nam & Hooker, J.N., 2022. "Political districting without geography," Operations Research Perspectives, Elsevier, vol. 9(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:usppxx:v:7:y:2020:i:1:p:69-86. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uspp .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.