Skip to main content

The Effect of Social Information on Giving from Lapsed Donors: Evidence from a Field Experiment

Abstract

Using data from an experiment carried out by a large nonprofit organization, this paper finds that lapsed donors who received a solicitation letter referencing a relatively high donation made by another donor (high social information) were more generous in giving, but overall less likely to make a donation, relative to the baseline (low social information) group. After using the propensity score matching to correct for pretreatment differences in the two experimental groups, the estimated effect of high social information on the average donation amount is an increase of $14.95 (45 %). However, high social information is also found to reduce the probability a lapsed donor will give by 4.1 %. Thus, high social information can have potentially offsetting effects when applied to lapsed donors. Nonprofits should consider this trade-off when employing social information fundraising techniques to solicit donations from lapsed donors.

Résumé

À l’aide des données obtenues dans le cadre d’une expérience réalisée par une grande organisation à but non lucratif, cet article constate que les anciens donateurs ayant reçu une lettre de sollicitation mentionnant un don assez élevé réalisé par un autre donateur (informations sociales élevées) étaient plus généreux lorsqu’ils faisaient des dons, mais dans l’ensemble moins susceptibles d’en faire par rapport au groupe de référence (informations sociales faibles). Après avoir utilisé le score de propension correspondant pour corriger les différences de traitement préalable dans les deux groupes, l’effet estimé des informations sociales élevées sur le montant moyen des dons est en hausse de 14,95 $ (45 %). Toutefois, l’on découvre également que les informations sociales élevées réduisent la probabilité de don d’un ancien donneur de 4,1 %. Ainsi, les informations sociales élevées peuvent avoir d’éventuels effets compensatoires lorsqu’elles sont appliquées aux anciens donateurs. Les organismes à but non lucratif devraient envisager ce compromis lorsqu’ils utilisent des techniques de collecte de fonds d’informations sociales pour solliciter les dons d’anciens donateurs.

Zusammenfassung

Unter Verwendung der Daten aus einem Experiment einer großen Non-Profit-Organisation zeigt dieser Beitrag, dass ehemalige Spender, die einen schriftlichen Spendenaufruf erhielten, in dem auf eine relativ große Spende eines anderen Spenders verwiesen wurde (hohe soziale Information), großzügigere Spenden leisteten, die Wahrscheinlichkeit einer Spende jedoch insgesamt im Verhältnis zur Basislinie-Gruppe (niedrige soziale Information) geringer war. Nach Anwendung des Propensity Score Matching zur Korrektur von Pre-Treatment-Differenzen in den beiden Experimentgruppen entspricht der geschätzte Effekt hoher sozialer Informationen auf den durchschnittlichen Spendenbetrag einem Anstieg von 14,95 USD (45 %). Allerdings zeigt sich auch, dass hohe soziale Informationen die Wahrscheinlichkeit, dass ein ehemaliger Spender eine Spende leistet, gleichzeitig um 4,1 % reduzieren. Folglich können hohe soziale Informationen möglicherweise kompensierende Effekte haben, wenn sie ehemaligen Spendern überlassen werden. Gemeinnützige Organisationen sollten diesen Zielkonflikt berücksichtigen, wenn sie soziale Informationen als Mittel zur Spendensammlung bei ehemaligen Spendern einsetzen.

Resumen

Utilizando datos de un experimento llevado a cabo por una gran organización sin ánimo de lucro, el presente documento encuentra que los ex donantes que recibieron una carta de solicitud haciendo referencia a un donativo relativamente elevado realizado por otro donante (información social elevada) fueron más generosos dando, pero en general menos probables de realizar un donativo, con relación al grupo de base (información social baja). Después de utilizar la nivelación del grado de propensión para corregir las diferencias previas al tratamiento en los dos grupos experimentales, el efecto estimado de la información social elevada sobre el importe promedio del donativo es un aumento de 14,95 $ (45 %). Sin embargo, también se encontró que la información social elevada reduce la probabilidad de que un ex donante vuelva a dar en un 4,1 %. De este modo, la información social elevada puede tener efectos potencialmente compensatorios cuando se aplica a ex donantes. Las organizaciones sin ánimo de lucro deben considerar este compromiso cuando empleen técnicas de recaudación de fondos de información social para solicitar donativos de ex donantes.

通过查阅大量非盈利组织的试验数据,本文发现对于失效捐赠人来说,如果收到的宣传信引用了另一个捐赠人提供的较高金额捐赠(高社交信息),他们的捐助通常更多;但对于基准(低社交信息)小组,他们的整体捐赠更不太可能。使用倾向分数匹配修正两个试验小组的预处理差别后,高社交信息对平均捐赠金额的预计影响为增加$14.95 (45 %)。然而,试验还发现,高社交信息会降低失效捐赠人的概率4.1 %。由此,涉及失效捐赠人时,高社交信息会存在潜在的抵消影响。采用社交信息筹款技术吸引失效捐赠人进行捐赠时,非盈利组织应考虑这一权衡。.

本論文では、大規模な非営利組織が実施した実験データを用いて、資金提供者(高度社会情報)が行った高額寄付に関する文献を基にして資金提供終了者について調査するが、全体的なベースライン(低度社会情報)からは相対的な寄付する可能性が低い傾向がある。2 つの実験グループの事前治療の違いを補正するスコア・マッチングの使用後には、平均の寄付金額における高度社会情報の推定効果は $14.95(45 %)に増加した。ただし、高度社会情報では資金提供終了者のうち4.1 %に相当することがわかった。したがって、高度社会情報では、潜在的に終えた資金提供者に適用する場合の効果を相殺することができる。非営利団体は資金提供終了者からの寄付を勧誘する社会情報における資金調達手法を採用する際に、このトレードオフを検討する必要がある。.

بإستخدام بيانات من التجربة التي قامت بها منظمة كبيرة غير هادفة للربح، هذا البحث وجد أن المانحين الذين توقفوا عن التبرع الذين تلقوا رسالة إلتماس يتحولوا إلى تبرع عالي بالنسبة للذي قامت بها جهات مانحة أخرى (معلومات إجتماعية عالية) كانت أكثر سخاء في العطاء، لكن عموما˝ أقل إحتمال للقيام بالتبرع، نسبة إلى خط الأساس (معلومات إجتماعية منخفضة) المجموعة. بعد إستخدام محاولة التقدير لتصحيح الإختلافات قبل المعالجة في المجموعتين التجريبية، التأثير التقديري لمعلومات إجتماعية عالية على متوسط مبلغ التبرع هو زيادة قدرها 14.95$ (45٪). لكن، وجدت معلومات إجتماعية عالية أيضا˝ لتخفيض إحتمال أن المتبرع الذي يتوقف عن التبرع سوف يعطي بنسبة 4.1٪. هكذا، يمكن من أن معلومات إجتماعية عالية يكون لها آثار تعويض عندما تطبق على الجهات المانحة التي توقفت عن التبرع. يجب أن المنظمات الغير ربحية تنظرفي هذه المفاضلة عند إستخدام تقنيات المعلومات الإجتماعية لجمع التبرعات لإلتماس عطاء من الجهات المانحة التي توقفت عن التبرع.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    Another branch of this literature evaluates survey and focus group responses in an attempt to discover what affects individuals’ decision to continue or discontinue donating to an organization [e.g., Beldad et al. (2015), Beldad et al. (2012), Bennett (2009), Germain et al. (2007), Mathew et al. (2007), Sargeant and Jay (2004), Sargeant (2001a), and Sargeant (2001b)].

  2. 2.

    For simplicity, these variables are discretized into two categories, although the same pattern exists with more categories.

  3. 3.

    The results from this estimation and summary statistics for the estimated propensity scores are included in Appendix C. To ensure that the results would not be sensitive to different probit model specifications, variants of this model were estimated (e.g., including interactions), but the predicted probabilities were nearly identical to those from the original model (i.e., the correlation coefficient was never lower than 0.998).

  4. 4.

    This method of implementing the propensity score-matching estimator is known as the nearest-neighbor match. Kernel matching yields nearly identical results.

  5. 5.

    An alternative method of controlling for selection bias is using Heckman Two-Stage regression analysis (Heckman 1979), which accounts for the selection process that led to only a small number of the solicited donors making a contribution during this experiment. This method produces a very similar estimate of $14.58, which is significant at the 5 % level.

  6. 6.

    Although it seems like this is the most likely effect, it may also be true that because the low informational treatment is earlier in the letter, it could have had a greater impact on the giving behavior of potential donors than did the high treatment. In the extreme case of this possibility, the treatment effect would then measure the impact on donations of the low informational treatment relative to a control group that received no social information.

  7. 7.

    Note that n can be normalized to one without loss of generality. In general, the “breakeven” baseline response rate (i.e., the point at which low and high social information yield the same revenue) for any given average donation amount is computed as \(p = \frac{1}{14.95}(0.041 \times d + 0.61295)\). This is found by setting the expected revenue for low social information equal to that for high social information and solving for p.

References

  1. Aldrich, T. (2000). Re-activating lapsed donors: A case study. International Journal of Nonprofit and Voluntary Sector Marketing, 5, 288–293.

    Article  Google Scholar 

  2. Andreoni, J. (1998). Toward a theory of charitable fundraising. Journal of Political Economy, 106, 1186–1213.

    Article  Google Scholar 

  3. Andreoni, J. (2006a). Philanthropy. In L.-A. Gerar-Varet, S.-C. Kolm & J. Mercier Ythier (Eds.), The handbook of giving, reciprocity, and altruism, handbooks in economics (pp. 1201–1269). Amsterdam: North-Holland.

  4. Andreoni, J. (2006b). Leadership giving in charitable fund-raising. Journal of Public Economic Theory, 8, 1–22.

    Article  Google Scholar 

  5. Andreoni, J., & Petrie, R. (2004). Public goods experiments without confidentiality: A glimpse into fund-raising. Journal of Public Economics, 88, 1605–1623.

    Article  Google Scholar 

  6. Andreoni, J., & Rao, J. (2011). The power of asking: How communication affects selfishness, empathy, and altruism. Journal of Public Economics, 95, 513–520.

    Article  Google Scholar 

  7. Andreoni, J., & Vesterlund, L. (2001). Which is the fair sex? Gender differences in altruism. Quarterly Journal of Economics, 116, 293–312.

    Article  Google Scholar 

  8. Austin, P. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399–424.

    Article  Google Scholar 

  9. Barber, P. & Levis, B. (2013). Donor retention matters. Available from the Center on Nonprofits and Philanthropy: http://www.urban.org/UploadedPDF/412731-Donor-Retention-Matters.pdf. Accessed 4 April 2014.

  10. Beldad, A., Gosselt, J., Hegner, S., & Leushuis, R. (2015). Generous but not morally obligated? Determinants of Dutch and American donors’ repeat donation intention (REPDON). Voluntas: International Journal of Voluntary and Nonprofit Organizations, 26, 442–465.

    Article  Google Scholar 

  11. Beldad, A., Snip, B., & van Hoof, J. (2012). Generosity the second time around: Determinants of individuals’ repeat donation intention. Nonprofit and Voluntary Sector Quarterly, 20, 1–20.

    Google Scholar 

  12. Bennett, R. (2009). Factors influencing donation switching behaviour among charity supporters: An empirical investigation. Journal of Customer Behaviour, 8, 329–345.

    Article  Google Scholar 

  13. Bhattacharya, R., & Tinkleman, D. (2009). How tough are Better Business Bureau/Wise Giving Alliance financial standards? Nonprofit and Voluntary Sector Quarterly, 38, 467–489.

    Article  Google Scholar 

  14. Blackbaud. (2013). Reactivating lapsed donors: How to use loyalty and philanthropic segmentation to optimize donor reactivation. Charleston: Blackbaud.

    Google Scholar 

  15. Cadsby, C. B., & Maynes, E. (1998). Gender and free riding in a threshold public goods game: Experimental evidence. Journal of Economic Behavior & Organization, 34, 603–620.

    Article  Google Scholar 

  16. Charities Aid Foundation. (2011). World giving index 2011: A global view of giving trends. Kings Hill: Charities Aid Foundation.

    Google Scholar 

  17. Cnaan, R. A., Jones, K., Dickin, A., & Salomon, M. (2011). Nonprofit watchdogs: Do they serve the average donor? Nonprofit Management and Leadership, 21, 381–397.

    Article  Google Scholar 

  18. Cornelli, F. (1996). Optimal selling procedures with fixed costs. Journal of Economic Theory, 71, 1–30.

    Article  Google Scholar 

  19. Croson, R., & Shang, J. (2008). The impact of downward social information on contribution decisions. Experimental Economics, 11, 221–233.

    Article  Google Scholar 

  20. Eckel, C., & Grossman, P. (2008). Subsidizing charitable contributions: A natural field experiment comparing matching and rebate subsidies. Experimental Economics, 11, 234–252.

    Article  Google Scholar 

  21. Frey, B., & Meier, S. (2004). Social comparisons and pro-social behaviour: Testing “conditional cooperation” in a field experiment. American Economic Review, 94, 1717–1722.

    Article  Google Scholar 

  22. Germain, M., Glynn, S., Schreiber, G., Gelinas, S., King, M., Jones, M., et al. (2007). Determinants of return behavior: A comparison of current and lapsed donors. Transfusion, 47, 1862–1870.

    Article  Google Scholar 

  23. Giving, USA. (2013). The Annual Report on Philanthropy for the Year 2012. Chicago: Giving USA Foundation.

    Google Scholar 

  24. Grant, L. E. (2010). The response to third-party ratings: Evidence of the effects on charitable contributions (unpublished manuscript).

  25. Harbaugh, W. T. (1998). What do donations buy? A model of philanthropy based on prestige and warm glow. Journal of Public Economics, 67, 269–284.

    Article  Google Scholar 

  26. Heckman, J. (1979). Sample selection bias as specification error. Econometrica, 47, 153–162.

    Article  Google Scholar 

  27. Imbens, G. (2014). Matching methods in practice: Three examples. NBER working paper 19959.

  28. Jacobs, F. A., & Marudas, N. P. (2009). The combined effect of donation price and administrative inefficiency on donations to US nonprofit organizations. Financial Accountability & Management, 25, 33–53.

    Article  Google Scholar 

  29. Karlan, D., & List, J. (2007). Does price matter in charitable giving? Evidence from a large-scale natural field experiment. American Economic Review, 97, 1774–1793.

    Article  Google Scholar 

  30. Mathew, S., King, M., Glynn, S., Dietz, S., Caswell, S., & Schreiber, G. (2007). Opinions about donating blood among those who never gave and those who stopped: A focus group assessment. Transfusion, 47, 729–735.

    Article  Google Scholar 

  31. Parsons, L. M. (2003). Is accounting information from nonprofit organizations useful to donors? A review of charitable giving and value-relevance. Journal of Accounting Literature, 22, 104–129.

    Google Scholar 

  32. Piper, G., & Schnepf, S. (2008). Gender differences in charitable giving in Great Britain. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 19, 103–124.

    Article  Google Scholar 

  33. Prokopec, S., & De Bruyn, A. (2009). When asking for more leads to getting nothing: The impact of anchors on donor’s behavior. In Jean-Pierre Helfer & Jean-Louis Nicolas (Eds.), Proceedings of the 38th EMAC conference. European Marketing Academy: Nantes.

    Google Scholar 

  34. Reinstein, D., & Riener, G. (2012). Reputation and influence in charitable giving: An experiment. Theory and Decision, 72, 221–243.

    Article  Google Scholar 

  35. Roberts, R. D. (1984). A positive model of private charity and public transfers. Journal of Political Economy, 92, 136–148.

    Article  Google Scholar 

  36. Romano, R. (1991). When excessive consumption is rational. American Economic Review, 81, 553–564.

    Google Scholar 

  37. Romano, R., & Yildirim, H. (2001). Why charities announce donations: A positive perspective. Journal of Public Economics, 81, 423–447.

    Article  Google Scholar 

  38. Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  39. Sargeant, A. (2001a). Managing donor defection: Why should donors stop giving? New Directions for Philanthropic Fundraising, 32, 59–74.

    Article  Google Scholar 

  40. Sargeant, A. (2001b). Relationship Fundraising: How to keep donors loyal. Nonprofit Management and Leadership, 12, 177–192.

    Article  Google Scholar 

  41. Sargeant, A., & Jay, E. (2004). Reasons for lapse: The case of face-to-face donors. International Journal of Nonprofit and Voluntary Sector Marketing, 9, 171–182.

    Article  Google Scholar 

  42. Shang, J., & Croson, R. (2009). Afield experiment in charitable contribution: The impact of social information on the voluntary provision of public goods. Economic Journal, 119, 1422–1439.

    Article  Google Scholar 

  43. Silverman, W., Robertson, S., Middlebrook, J., & Drabman, R. (1984). An investigation of pledging behavior to a national charitable telethon. Behavior Therapy, 15, 304–311.

    Article  Google Scholar 

  44. Smith, S., Windmeijer, F., & Wright, E. (2013). Peer effects in charitable giving: Evidence from the (running) field. Economic Journal (forthcoming).

  45. Soetevent, A. (2005). Anonymity in giving in a natural context: A field experiment in 30 churches. Journal of Public Economics, 89, 2301–2323.

    Article  Google Scholar 

  46. Verhaert, G., & Van den Poel, D. (2011). Improving campaign success rate by tailoring donation requests along the donor lifecycle. Journal of Interactive Marketing, 25, 51–63.

    Article  Google Scholar 

  47. Vesterlund, L. (2003). The informational value of sequential fundraising. Journal of Public Economics, 87, 627–657.

    Article  Google Scholar 

  48. Vesterlund, L. (2006). Why do people give? In W. Powell & R. S. Steinberg (Eds.), The nonprofit sector: A research handbook (Vol. 2, pp. 568–587). New Haven, CT: Yale University.

    Google Scholar 

  49. Warr, P. G. (1982). Pareto optimal redistribution and private charity. Journal of Public Economics, 19, 131–138.

    Article  Google Scholar 

  50. Yetman, M. H., & Yetman, R. J. (2013). Do donors discount low-quality accounting information? The Accounting Review, 88, 1041–1067.

    Article  Google Scholar 

Download references

Acknowledgments

Sincere thanks to Marianne Bitler, Manisha Shah, Mike McBride, Tim Wong, and participants of the UC-Irvine Institute for Mathematical and Behavioral Science Lunch seminar for useful comments and suggestions. This paper benefitted greatly from the comments of four anonymous referees. All errors are my own.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Kristoffer Jackson.

Appendices

Appendices

Appendix A

High treatment letter for the NPO

Appendix B

Low treatment letter for the NPO

Appendix C

Probit regression to obtain predicted propensity scores.

A. Coefficients and standard errors

Dependent variable
Probability of being assigned into the high treatment group
Most recent donation 0.049***
(0.002)
Highest donation −0.044***
(0.002)
Gender −0.027
(0.021)
Intercept 0.021
(0.075)
N 15,166

B. Summary statistics for predicted probabilities (i.e., propensity scores)

  High treatment Low treatment Difference (high treatment–low)
Mean 0.530 0.487 0.042***
(0.002)
Minimum 0.002 0.0004 0.0016
Maximum 0.685 0.692 −0.007
N 7712 7454  
  1. Standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jackson, K. The Effect of Social Information on Giving from Lapsed Donors: Evidence from a Field Experiment. Voluntas 27, 920–940 (2016). https://doi.org/10.1007/s11266-015-9566-2

Download citation

Keywords

  • Charitable giving
  • Social information
  • Social influence
  • Lapsed donors
  • Field experiment
  • Nonprofit organizations