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Optimal Targeting in Fundraising: A Machine-Learning Approach

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Abstract
Ineffective fundraising lowers the resources charities can use for goods provision. We combine a field experiment and a causal machine-learning approach to increase a charity’s fundraising effectiveness. The approach optimally targets fundraising to individuals whose expected donations exceed solicitation costs. Among past donors, optimal targeting substantially increases donations (net of fundraising costs) relative to benchmarks that target everybody or no one. Instead, individuals who were previously asked but never donated should not be targeted. Further, the charity requires only publicly available geospatial information to realize the gains from targeting. We conclude that charities not engaging in optimal targeting waste resources.

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  • Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
    • Handle: RePEc:jku:econwp:2021-08
    Note: English
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    as
    1. Adena, Maja & Huck, Steffen, 2022. "Personalized fundraising: A field experiment on threshold matching of donations," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1-20.
    2. Stephan Heblich & Alex Trew & Yanos Zylberberg, 2021. "East-Side Story: Historical Pollution and Persistent Neighborhood Sorting," Journal of Political Economy, University of Chicago Press, vol. 129(5), pages 1508-1552.
    3. Alvaro J. Name-Correa & Huseyin Yildirim, 2013. "A Theory of Charitable Fund-Raising with Costly Solicitations," American Economic Review, American Economic Association, vol. 103(2), pages 1091-1107, April.
    4. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    5. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    6. James Andreoni & Eleanor Brown & Isaac Rischall, 2003. "Charitable Giving by Married Couples Who Decides and Why Does it Matter?," Journal of Human Resources, University of Wisconsin Press, vol. 38(1).
    7. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    8. Alpizar, Francisco & Carlsson, Fredrik & Johansson-Stenman, Olof, 2008. "Anonymity, reciprocity, and conformity: Evidence from voluntary contributions to a national park in Costa Rica," Journal of Public Economics, Elsevier, vol. 92(5-6), pages 1047-1060, June.
    9. Aldashev, Gani & Marini, Marco & Verdier, Thierry, 2014. "Brothers in alms? Coordination between nonprofits on markets for donations," Journal of Public Economics, Elsevier, vol. 117(C), pages 182-200.
    10. Lei Dong & Carlo Ratti & Siqi Zheng, 2019. "Predicting neighborhoods’ socioeconomic attributes using restaurant data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(31), pages 15447-15452, July.
    11. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    12. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2023. "Offline Multi-Action Policy Learning: Generalization and Optimization," Operations Research, INFORMS, vol. 71(1), pages 148-183, January.
    13. James Andreoni & Lise Vesterlund, 2001. "Which is the Fair Sex? Gender Differences in Altruism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 293-312.
    14. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    15. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    16. Newman, George E. & Jeremy Shen, Y., 2012. "The counterintuitive effects of thank-you gifts on charitable giving," Journal of Economic Psychology, Elsevier, vol. 33(5), pages 973-983.
    17. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    18. Dufwenberg, Martin & Kirchsteiger, Georg, 2004. "A theory of sequential reciprocity," Games and Economic Behavior, Elsevier, vol. 47(2), pages 268-298, May.
    19. Aldashev, Gani & Verdier, Thierry, 2010. "Goodwill bazaar: NGO competition and giving to development," Journal of Development Economics, Elsevier, vol. 91(1), pages 48-63, January.
    20. Christopher R. Knittel & Samuel Stolper, 2019. "Using Machine Learning to Target Treatment: The Case of Household Energy Use," NBER Working Papers 26531, National Bureau of Economic Research, Inc.
    21. Meer, Jonathan, 2017. "Does fundraising create new giving?," Journal of Public Economics, Elsevier, vol. 145(C), pages 82-93.
    22. Drouvelis, Michalis & Marx, Benjamin M., 2022. "Can charitable appeals identify and exploit belief heterogeneity?," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 631-649.
    23. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
    24. Armin Falk, 2007. "Gift Exchange in the Field," Econometrica, Econometric Society, vol. 75(5), pages 1501-1511, September.
    25. repec:hal:pseose:halshs-01109513 is not listed on IDEAS
    26. Armin Falk & Anke Becker & Thomas Dohmen & Benjamin Enke & David B. Huffman & Uwe Sunde, 2017. "Global Evidence on Economic Preferences," NBER Working Papers 23943, National Bureau of Economic Research, Inc.
    27. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    28. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    29. Vesterlund, Lise, 2003. "The informational value of sequential fundraising," Journal of Public Economics, Elsevier, vol. 87(3-4), pages 627-657, March.
    30. Armin Falk & Anke Becker & Thomas Dohmen & Benjamin Enke & David Huffman & Uwe Sunde, 2018. "Global Evidence on Economic Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(4), pages 1645-1692.
    31. Susan Rose-Ackerman, 1982. "Charitable Giving and “Excessive†Fundraising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 97(2), pages 193-212.
    32. repec:aeg:report:2014-2 is not listed on IDEAS
    33. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
    34. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
    35. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    36. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    37. John A. List, 2011. "The Market for Charitable Giving," Journal of Economic Perspectives, American Economic Association, vol. 25(2), pages 157-180, Spring.
    38. John List & Michael Price, 2012. "Charitable Giving Around the World: Thoughts on How to Expand the Pie," Natural Field Experiments 00470, The Field Experiments Website.
    39. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    40. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    41. Jonah E. Rockoff & Brian A. Jacob & Thomas J. Kane & Douglas O. Staiger, 2011. "Can You Recognize an Effective Teacher When You Recruit One?," Education Finance and Policy, MIT Press, vol. 6(1), pages 43-74, January.
    42. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    43. John Morgan, 2000. "Financing Public Goods by Means of Lotteries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(4), pages 761-784.
    44. Okten, Cagla & Weisbrod, Burton A., 2000. "Determinants of donations in private nonprofit markets," Journal of Public Economics, Elsevier, vol. 75(2), pages 255-272, February.
    45. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2021. "Optimal Price Targeting," CESifo Working Paper Series 9439, CESifo.
    46. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    47. Natalie Jane de Vries & Rodrigo Reis & Pablo Moscato, 2015. "Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-28, April.
    48. Andreoni, James, 1988. "Privately provided public goods in a large economy: The limits of altruism," Journal of Public Economics, Elsevier, vol. 35(1), pages 57-73, February.
    49. James Andreoni & A. Abigail Payne, 2003. "Do Government Grants to Private Charities Crowd Out Giving or Fund-raising?," American Economic Review, American Economic Association, vol. 93(3), pages 792-812, June.
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    2. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2021. "Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs," Papers 2112.09850, arXiv.org.
    3. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2021. "Optimal Price Targeting," CESifo Working Paper Series 9439, CESifo.

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    More about this item

    Keywords

    Fundraising; charitable giving; gift exchange; targeting; optimal policy learning; individualized treatment rules;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • L31 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Nonprofit Institutions; NGOs; Social Entrepreneurship
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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