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Ecological Regression and Voting Rights

Author

Listed:
  • David A. Freedman

    (University of Californca, Berkeley)

  • Stephen P. Klein

    (PAND Corporation)

  • Jerome Sacks

    (University of Illinois, Urbana-Champaign)

  • Charles A. Smyth

    (University of Illinois, Urbana-Champaign)

  • Charles G. Everett

    (Applied Automated Engineering Corporation)

Abstract

Ecological regression is a statistical mainstay in litigation brought under the Voting Rights Act of 1965. The technique is discussed in the context of a suit against the County of Los Angeles that came to trial in 1990. Ecological regression depends on very strong assumptions about political behavior The authors identify these assumptions and show that they are not supported by the data. Also described is an alternative "neighborhood model, " which is a priori more plausible and fits the facts better. The neighborhood model leads to quite different conclusions about voting behavior.

Suggested Citation

  • David A. Freedman & Stephen P. Klein & Jerome Sacks & Charles A. Smyth & Charles G. Everett, 1991. "Ecological Regression and Voting Rights," Evaluation Review, , vol. 15(6), pages 673-711, December.
    • Handle: RePEc:sae:evarev:v:15:y:1991:i:6:p:673-711
    DOI: 10.1177/0193841X9101500602
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    References listed on IDEAS

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    3. Denton, N.A. & Massey, D.S., 1988. "Residential Segregation Of Blacks, Hispanics, And Asians By Socioeconomic Status And Generation," University of Chicago - Economics Research Center 88-2, Chicago - Economics Research Center.
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    1. Lorien Rice & Mark Henderson & Margaret Hunter, 2017. "Neighborhood Priority or Desegregation Plans? A Spatial Analysis of Voting on San Francisco’s Student Assignment System," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(6), pages 805-832, December.
    2. Olga Orlanski & Günther G. Schulze, 2017. "The Determinants of Islamophobia - An Empirical Analysis of the Swiss Minaret Referendum," CESifo Working Paper Series 6741, CESifo.
    3. D. James Greiner & Kevin M. Quinn, 2009. "R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 67-81, January.
    4. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    5. Lisa M. George & Joel Waldfogel, 2006. "The New York Times and the Market for Local Newspapers," American Economic Review, American Economic Association, vol. 96(1), pages 435-447, March.
    6. Enora Belz & Arthur Charpentier, 2019. "Aggregated Data and Compositional Variables: Methodological Note [Données Agrégées et Variables Compositionnelles : Note Méthodologique]," Working Papers hal-02097031, HAL.
    7. Irene L. Hudson & Linda Moore & Eric J. Beh & David G. Steel, 2010. "Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 185-213, January.
    8. Zax Jeffrey S., 2012. "Single Regression Estimates of Voting Choices When Turnout is Unknown," Statistics, Politics and Policy, De Gruyter, vol. 4(1), pages 1-22, October.

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