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Understanding Divergent Views on Redistribution Policy in the United States

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  • Louise C. Keely
  • Chih Ming Tan

Abstract

Particular demographic groups are often associated with distinct points of view across various dimensions of redistribution policy. In this paper, we investigate which demographic groups account for heterogeneity in views on welfare policy and views on appropriate levels of overall redistribution. Using data from the General Social Survey and classification tools, we find evidence that classifications of the population by race, socioeconomic status, and age have some predictive power. However, much heterogeneity in views on redistribution policy persists even within these demographic groupings and remains unexplained. Our results suggest that identity-based explanations for variations in these views have to be interpreted with caution.

Suggested Citation

  • Louise C. Keely & Chih Ming Tan, 2005. "Understanding Divergent Views on Redistribution Policy in the United States," Discussion Papers Series, Department of Economics, Tufts University 0515, Department of Economics, Tufts University.
    • Handle: RePEc:tuf:tuftec:0515
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    References listed on IDEAS

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    1. Daniel S. Hamermesh, 2004. "Subjective Outcomes in Economics," Southern Economic Journal, John Wiley & Sons, vol. 71(1), pages 1-11, July.
    2. Keely, Louise C. & Tan, Chih Ming, 2008. "Understanding preferences for income redistribution," Journal of Public Economics, Elsevier, vol. 92(5-6), pages 944-961, June.
    3. Lena Edlund & Rohini Pande, 2002. "Why Have Women Become Left-Wing? The Political Gender Gap and the Decline in Marriage," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(3), pages 917-961.
    4. Daniel S. Hamermesh, 2004. "Subjective Outcomes in Economics," Southern Economic Journal, John Wiley & Sons, vol. 71(1), pages 2-11, July.
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    Cited by:

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    2. Jehoshua Eliashberg & Sam K. Hui & Z. John Zhang, 2007. "From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts," Management Science, INFORMS, vol. 53(6), pages 881-893, June.
    3. Ioannides, Yannis M. & Soetevent, Adriaan R., 2007. "Social networking and individual outcomes beyond the mean field case," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 369-390.
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    5. Antoni Calvo-Armengol & Yannis M. Ioannides, 2005. "Social Networks in Labor Markets," Discussion Papers Series, Department of Economics, Tufts University 0517, Department of Economics, Tufts University.
    6. Darlene C. Chisholm & George Norman, 2006. "When to Exit a Product: Evidence from the U. S. Motion-Picture Exhibition Market," American Economic Review, American Economic Association, vol. 96(2), pages 57-61, May.

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    More about this item

    Keywords

    Data mining; classification and regression trees; random forests; redistribution preferences; welfare; identity;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • H50 - Public Economics - - National Government Expenditures and Related Policies - - - General
    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs

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