How to Code a Million Missions: Developing Bespoke Nonprofit Activity Codes Using Machine Learning Algorithms

Francisco J. Santamarina, Jesse D. Lecy, Eric Joseph van Holm

Research output: Contribution to journalArticlepeer-review

Abstract

National Taxonomy of Exempt Entities (NTEE) codes have become the primary classifier of nonprofit missions since they were developed in the mid-1980s in response to growing demands for a taxonomy of nonprofit activities (Herman in Nonprofit and Voluntary Sector Quarterly 19(3):293–306, 1990, Barman in Social Science History 37:103–141, 2013). However, the increasingly complex nature of nonprofits means that NTEE codes may be outdated or lack specificity. As an alternative, scholars and practitioners can create a bespoke taxonomy for a specific purpose by hand-coding a training dataset and using machine learning classifiers to apply the codes to a large population. This paper presents a framework for determining training set sizes needed to scale custom taxonomies using machine learning algorithms.

Original languageEnglish (US)
JournalVoluntas
DOIs
StateAccepted/In press - 2021

Keywords

  • Classification
  • Custom taxonomies
  • Machine learning
  • Nonprofit organizations

ASJC Scopus subject areas

  • Business and International Management
  • Sociology and Political Science
  • Public Administration
  • Strategy and Management

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