Inequity in Washington state covid-19-related crowdfunding

Abstract

The first seven months of the US COVID-19 pandemic saw a massive increase in COVID-19-related crowdfunding campaigns. Despite their popularity, these campaigns were rarely successful in reaching their monetary goals, with nearly 40% of them not receiving a single donation. Previous research has indicated that crowdfunding has increased inequities and disparities in wealth, and this study set out to examine the situation in Washington State, an area greatly divided socio-economically, culturally, and geographically. Using GIS-based Multi-Criteria Evaluation (MCE) models with Quantile classification, we created geospatial representations of composite independent variables representing the impacts of COVID-19 and social marginalization on Washington state counties. We then examined the relationships between these variables with the dependent variables, campaign prevalence and outcome (campaign median amount raised), and assessed for relationships through ANOVA tests. These representations allowed us to assess the possibility that both COVID-19 impact and social marginalization may be amplifying already present inequities in Washington state by influencing crowdfunding monetary outcomes and prevalence. Our research indicates that county-wide social marginalization is associated with COVID-19-related crowdfunding campaign prevalence, generally pointing towards counties with lower social marginalization producing more campaigns. We also found that COVID-19 impact is, surprisingly, not associated with campaign prevalence, and that there was no relationship between campaign outcome and marginalization or COVID-19 impact. These findings indicate that inequitable access to, and use of, technologies like crowdfunding may be further contributing to COVID-19 inequities within Washington state. Policy-makers should be advised to use crowdfunding information with extreme caution when considering it as a useful tool in assessing community needs within counties and allocating resources, because those with the highest need likely are not being represented in this data due to lack of access.