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

Published online by Cambridge University Press:  01 January 2026

Francisco J. Santamarina Open the ORCID record for Francisco J. Santamarina [Opens in a new window] ,
Jesse D. Lecy  and
Eric Joseph van Holm
Francisco J. Santamarina*
Affiliation:
Evans School of Public Policy and Governance, University of Washington, 4105 George Washington Lane Northeast, Seattle, WA 98105, USA
Jesse D. Lecy*
Affiliation:
Watts College, Arizona State University, 411 N. Central Ave., Suite 750, Phoenix, AZ 85004-2163, USA
Eric Joseph van Holm*
Affiliation:
Department of Political Science, Urban Entrepreneurship and Policy Institute, The University of New Orleans, 256 Milneburg Hall, New Orleans, LA 70148, USA
*
fjsantam@uw.edu
jdlecy@asu.edu
evanholm@uno.edu
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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 However, the increasingly complex nature of nonprofits means that NTEE codes may be outdated or lack specificity.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.

Keywords

Nonprofit organizationsClassificationMachine learningCustom taxonomies

Information

Type
Research Papers
Information
Voluntas: International Journal of Voluntary and Nonprofit Organizations , Volume 34 , Issue 1 , February 2023 , pp. 29 - 38
Copyright
Copyright © International Society for Third-Sector Research 2021

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11266-021-00420-z.

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