Signaling and herding in reward-based crowdfunding

Abstract

This paper investigates how signaling and herding behavior interact in crowdfunding markets to give raise to an information cascade, even when there are no identifiable experts, which is the typical case in reward-based crowdfunding. Using daily funding data for on all the projects launched on Kickstarter during one month, we find that during the initial phase of the campaign, the funding decisions of a reduced number of early backers are based on information and quality signals offered by the creator. However, during the second phase, signaling is substituted by the herding behavior of a large number of late backers, imitating early backers. The results suggest that, even in the absence of identifiable experts, backers self-select into early or late backers depending on their ability to process the information, so that herding after signaling generates an information cascade that ameliorates asymmetric information problems. The findings are relevant for (i) creators, that will obtain better results by targeting their crowdfunding campaigns at better informed potential contributors, and (ii) regulators, that can expect backers’ self-selection and herding to work together to protect uninformed backers from fraud and deception even when participation is not restricted.