JOURNAL ARTICLE

Are Political and Charitable Giving Substitutes? Evidence from the United States.

  • Published In: Management Science (INFORMS), 2024, v. 70, n. 11. P. 8030 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Yildirim, Pinar; Simonov, Andrei; Petrova, Maria; Perez-Truglia, Ricardo 3 of 3

Abstract

This article examines the relationship between political giving and charitable giving in the United States, using microdata from the American Red Cross (ARC) and the Federal Election Commission (FEC) through two natural experiments. The first experiment analyzes the impact of foreign natural disasters as positive shocks to charitable donations, finding that a 34.9% increase in ARC donations corresponds with an 18.8% decline in political contributions, implying a crowding-out elasticity of 0.53. The second experiment exploits geographic discontinuities in political advertising markets, showing that a 10% increase in political ad spending raises political donations by about 0.20% but reduces charitable donations by approximately 0.14%, yielding a crowding-out elasticity of 0.59. These findings suggest that political and charitable giving are close substitutes rather than complements, a conclusion supported by robustness checks using IRS aggregate charitable deduction data and donations to Catholic Relief Services. The study highlights implications for nonprofit managers, political campaigns, and researchers, emphasizing that shifts in one type of giving can significantly affect the other, with potential consequences for disaster relief and electoral outcomes.

Additional Information

  • Source:Management Science (INFORMS). 2024/11, Vol. 70, Issue 11, p8030
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2021.00845
  • Accession Number:180699479
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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