Crowdfunding platforms like Kickstarter are expected to democratize funding by increasing the availability of capital to traditionally underrepresented groups, but there is conflicting evidence about racial disparities in success rates. This paper contributes to the information systems literature on crowdfunding by examining the racial dynamics in the Kickstarter platform. In particular, we study three (sometimes conflicting) cues that allow potential backers to infer race: fundraiser photo, project photo, and textual content of project description. We create a novel set comprised of project characteristics; the race of project and fundraiser photo subjects, determined using facial recognition software; and the full text of project descriptions. Our analysis results in three main findings. First, there are substantial differences in the textual content of project descriptions across racial groups. It is possible to predict with high accuracy the race of those associated with a Kickstarter project based only on the words in the description. Second, projects with Black fundraisers or subjects in project photos face significantly lower success rates, even when controlling for observable project characteristics and the textual content of project descriptions. Third, we address cases when the racial cues are not aligned. Race in the fundraiser photo has a greater effect on success probability than does race in the project photo; visually-identifiable race in general has a greater effect on success probability than does textually-identifiable race.The results expand information systems theory on crowdfunding, identity, and discrimination, utilize novel big data techniques, and yield empirical results that demonstrate bias in an important online platform. This work has important practical implications both for online platform designers and for users of said platforms.
Read full abstract