在线慈善众筹中匹配捐款的动态影响:理论与实证

The Impact of Matching Fund on Charity-based Online Crowd-funding:Dynamic Analysis and Empirical Evidence

在线众筹平台可以利用网络汇聚慈善性捐助以辅助公益性项目融资,近年来得到了长足发展,获得了社会的普遍关注,在基于社会协调机制的“第三次分配”的实践中起到了积极推动作用。在线慈善众筹过程中,平台可以借助企业和基金会的大额捐款对个人捐款进行匹配,即匹配捐款机制。匹配捐款能否有效刺激个人捐款,是一个值得探讨的重要问题。一方面,经典的公共品理论指出,公共品的非排他性导致个人捐助者有“搭便车”的动机,企业的匹配捐款会“挤出”个人捐款。另一方面,动态公共品博弈产生的“跨时策略互补”效应会激发个体多捐助。本文采用一个离散公共品的动态自发供给模型来分析匹配捐款如何影响个人捐款额。模型分析显示,在“搭便车”动机与“跨时策略互补”效应的共同作用下,匹配捐款是否产生“挤出”效应取决于公益项目是否具有较高的内在质量。项目质量较高时,即达成目标筹款金额的内生预期时间较短时,则匹配捐款可以提升个人捐款额。本研究利用慈善众筹网站Kiva的项目捐款微观数据,建立预测模型和构造项目质量的动态测量。实证分析结果支持基于理论模型分析得到的假说。

How to attract participation and elicit contributions in charity-based online crowdfunding has remained a challenging problem for a long time.Leveraging on corporate or individual funds to set up a matching fund mechanism has been widely adopted as a popular approach to motivate lenders'donations.This study examines how the matching fund mechanism affects individual lenders'contribution,theoretically and empirically.On the one hand,the classical tenet of public economics claims that each individual relies on peers to contribute more and enjoy the project.Hence,an outsider's contribution via the matching fund mechanism should decrease individual lenders'contribution due to the"free-riding"problem.On the other hand,the"strategic complements across time"effect in the framework of dynamic provision of public goods results in more contributions from individual lenders.Our theoretical model illustrates that which of the two opposing effects dominates hinges crucially on the intrinsic quality of a project.When the endogenous completion time of a project is short,it has high intrinsic quality and getting matched is more likely to crowd-in individual lenders'contribution.Otherwise,for a project with low intrinsic quality,geting matched may crowd-out individual lenders'contribution.Using realtime data from Kiva.org,a well-known charity-based crowd-funding platform,we find that the empirical results provide supportive evidence for the theoretical predictions.