Identifying essential nonprofits with a novel NLP Method

Abstract

Abstract In this research note, we propose a classification method for identifying whether a 501c3 nonprofit organization is considered essential for economic recovery. During the first few months of the COVID‐19 pandemic, many nonprofit organizations experienced negative financial effects from the economic recession. While these nonprofits saw increased demand for their services, the weakness in the overall economy led to a decline in donations. Fiscal assistance by local, state, and federal government to essential organizations was a critical element to an economic recovery, and governments needed to prioritize aid to the most essential organizations first. By identifying essential nonprofit organizations in advance, these organizations could quickly and efficiently receive financial assistance. Using descriptive text data provided by Ohio nonprofit organizations in their IRS tax filings, we propose a novel natural language processing (NLP) technique to measure the degree of “essentialness” to a nonprofit's work. We show that our model offers an improvement to the classification system known as the National Taxonomy of Exempt Organizations (NTEE). Our machine learning model is also compared to an independent evaluation of a nonprofit's essentialness produced by human researchers.

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