Nonprofit Role Classification Using Mission Descriptions and Supervised Machine Learning

Abstract

Scholars have used both quantitative and qualitative approaches to empirically study nonprofit roles. Mission statements and program descriptions often reflect such roles, however, until recently collecting and classifying a large sample has been labor-intensive. This research note uses data on United Ways that e-filed their 990 forms and supervised machine learning to illustrate an approach for classifying a large set of mission descriptions by roles. Temporal and geographic variation in roles detected in mission statements suggests that such an approach may be fruitful in future research.