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Partition dependence in financial aid distribution to income categories

Author

Listed:
  • Chenmu Xing
  • Katherine Williams
  • Jamie Hom
  • Meghana Kandlur
  • Praise Owoyemi
  • Joanna Paul
  • Ray Alexander
  • Elizabeth Shackney
  • Hilary Barth

Abstract

When allocating resources, people often diversify across categories even when those categories are arbitrary, such that allocations differ when identical sets of options are partitioned differently (“partition dependence”). The first goal of the present work (Experiment 1) was to replicate an experiment by Fox and colleagues in which graduate students exhibited partition dependence when asked how university financial aid should be allocated across arbitrarily partitioned income brackets. Our sample consisted of community members at a liberal arts college where financial aid practices have been recent topics of debate. Because stronger intrinsic preferences can reduce partition dependence, these participants might display little partition dependence with financial aid allocations. Alternatively, a demonstration of strong partition dependence in this population would emphasize the robustness of the effect. The second goal was to extend a “high transparency” modification to the present task context (Experiment 2) in which participants were shown both possible income partitions and randomly assigned themselves to one, to determine whether partition dependence in this paradigm would be reduced by revealing the study design (and the arbitrariness of income categories). Participants demonstrated clear partition dependence in both experiments. Results demonstrate the robustness of partition dependence in this context.

Suggested Citation

  • Chenmu Xing & Katherine Williams & Jamie Hom & Meghana Kandlur & Praise Owoyemi & Joanna Paul & Ray Alexander & Elizabeth Shackney & Hilary Barth, 2020. "Partition dependence in financial aid distribution to income categories," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-13, April.
  • Handle: RePEc:plo:pone00:0231135
    DOI: 10.1371/journal.pone.0231135
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    References listed on IDEAS

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    1. Craig R. Fox & Robert T. Clemen, 2005. "Subjective Probability Assessment in Decision Analysis: Partition Dependence and Bias Toward the Ignorance Prior," Management Science, INFORMS, vol. 51(9), pages 1417-1432, September.
    2. Craig R. Fox & David Bardolet & Daniel Lieb, 2005. "Partition Dependence in Decision Analysis, Resource Allocation, and Consumer Choice," Springer Books, in: Rami Zwick & Amnon Rapoport (ed.), Experimental Business Research, chapter 0, pages 229-251, Springer.
    3. Eric Johnson & Suzanne Shu & Benedict Dellaert & Craig Fox & Daniel Goldstein & Gerald Häubl & Richard Larrick & John Payne & Ellen Peters & David Schkade & Brian Wansink & Elke Weber, 2012. "Beyond nudges: Tools of a choice architecture," Marketing Letters, Springer, vol. 23(2), pages 487-504, June.
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