Measuring the Efficiency of Charitable Giving with Content Analysis and Crowdsourcing

Abstract

In the U.S., individuals give more than 200 billion dollars to over 50 thousand charities each year, yet how people make these choices is not well understood. In this study, we use data from CharityNavigator.org and web browsing data from Bing toolbar to understand charitable giving choices. Our main goal is to use data on charities' overhead expenses to better understand efficiency in the charity marketplace. A preliminary analysis indicates that the average donor is "wasting" more than 15% of their contribution by opting for poorly run organizations as opposed to higher rated charities in the same Charity Navigator categorical group. However, charities within these groups may not represent good substitutes for each other. We use text analysis to identify substitutes for charities based on their stated missions and validate these substitutes with crowd-sourced labels. Using these similarity scores, we simulate market outcomes using web browsing and revenue data. With more realistic similarity requirements, the estimated loss drops by 75%—much of what looked like inefficient giving can be explained by crowd-validated similarity requirements that are not fulfilled by most charities within the same category. A choice experiment helps us further investigate the extent to which a recommendation system could impact the market. The results indicate that money could be redirected away from the long-tail of inefficient organizations. If widely adopted, the savings would be in the billions of dollars, highlighting the role the web could have in shaping this important market.