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Dynamic Pie: A Strategy for Modeling Trade‐Offs in Compositional Variables over Time

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  • Andrew Q. Philips
  • Amanda Rutherford
  • Guy D. Whitten

Abstract

The substance of politics involves competition that evolves over time. While our theories about competition emphasize trade‐offs across multiple categories, most empirical models tend to oversimplify them by considering trade‐offs between one category and everything else. We propose a research strategy for testing theories about trade‐off relationships that shape dynamic compositional variables. This approach improves current methods used to analyze compositional dependent variables by addressing two limitations. First, although scholars have considered compositional dependent variables, they have done so in contexts that were not dynamic. Second, current approaches toward graphical presentations become unwieldy when the compositional dependent variable has more than three categories. We demonstrate the utility of our strategy to expand current theories of party support and political budgeting. In both cases, we can extend trade‐offs across pairs of alternatives (e.g., prime minister versus all other parties or spending on defense versus everything else) to competition across multiple alternatives.

Suggested Citation

  • Andrew Q. Philips & Amanda Rutherford & Guy D. Whitten, 2016. "Dynamic Pie: A Strategy for Modeling Trade‐Offs in Compositional Variables over Time," American Journal of Political Science, John Wiley & Sons, vol. 60(1), pages 268-283, January.
  • Handle: RePEc:wly:amposc:v:60:y:2016:i:1:p:268-283
    DOI: 10.1111/ajps.12204
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    Cited by:

    1. Ali Kagalwala & Andrew Q. Philips & Guy D. Whitten, 2021. "What About the Rest of the Pie? A Dynamic Compositional Approach to Modeling Inequality," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1534-1552, July.
    2. Khan, Zaid Ashiq & Koondhar, Mansoor Ahmed & Tiantong, Ma & Khan, Aftab & Nurgazina, Zhanar & Tianjun, Liu & Fengwang, Ma, 2022. "Do chemical fertilizers, area under greenhouses, and renewable energies drive agricultural economic growth owing the targets of carbon neutrality in China?," Energy Economics, Elsevier, vol. 115(C).
    3. Prelorentzos, Arsenios-Georgios N. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Xidonas, Panos & Goutte, Stephane & Thomakos, Dimitrios D., 2024. "Introducing the GVAR-GARCH model: Evidence from financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    4. Hanretty, Chris, 2021. "Forecasting multiparty by-elections using Dirichlet regression," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1666-1676.

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