Nonprofit Mission Classifiers


This project represents a collaboration between:

Jesse Lecy
Associate Professor, Arizona State Univ.
Nonprofit Studies and Data Science

Ji Ma
Assistant Professor, UT Austin LBJ School
Philanthropic and Nonprofit Studies

Pamela Paxton
Professor, UT Austin

Francisco Santamarina
PhD Student, Univ. of Washington
Data Analytics & Nonprofit Capacity

Eric Van Holm
Assistant Professor, Univ. of New Orleans
Entrepreneurship and Public Policy

Nonprofit Open Data Collective collaborators at large:

Nathan Grasse
Associate Professor, Carleton University Canada
Nonprofit Finance & Management

Julia Carboni
Associate Professor, Syracuse Unversity
Nonprofit Studies and Collaborative Governance

David Borenstein, Ph.D.
Heather Kugelmass, Ph.D.
Founders of Open990

Current Manuscripts

Messamore, A., & Paxton, P. (2021). Surviving Victimization: How Service and Advocacy Organizations Describe Traumatic Experiences, 1998–2016. Social Currents, 8(1), 3-24.

Santamarina, F. J., Lecy, J. D., & van Holm, E. J. (2021). How to Code a Million Missions: Developing Bespoke Nonprofit Activity Codes Using Machine Learning Algorithms. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 1-10. [ CODE ]

Ma, J. (2021). Automated Coding Using Machine Learning and Remapping the US Nonprofit Sector: A Guide and Benchmark. Nonprofit and Voluntary Sector Quarterly, 50(3), 662-687.

LePere-Schloop, M. (2021). Nonprofit role classification using mission descriptions and supervised machine learning. Nonprofit and Voluntary Sector Quarterly, 08997640211057393.

Paxton, P., Velasco, K., & Ressler, R. (2019a). Form 990 Mission Glossary v.1. [Computer file]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor].

Paxton, P., Velasco, K., & Ressler, R. (2019b). Form 990 Mission Stemmer v.1. [Computer file]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor].

Lecy, J., Ashley, S. & Santamarina, F. (2019). “Do Nonprofit Missions Vary by the Political Ideology of Supporting Communities? Some Preliminary Results.” Public Performance and Management Review. [ PDF ]

Related Work

Ren, C., & Bloemraad, I. (2022). New Methods and the Study of Vulnerable Groups: Using Machine Learning to Identify Immigrant-Oriented Nonprofit Organizations. Socius, 8, 23780231221076992. [ PDF ]

Ashley, S. & Boyd, C. (2021) Addressing Racial Funding Gaps in the Nonprofit Sector Requires New Data Approaches. [ Urban Institute Blog ] [ Racial Equity Analytics Lab ]


The Aspen Institute has been instrumental for their efforts to advocate for better data and promote the work of open data.

Many people and groups have worked hard to make IRS 990 e-filing possible, new data sources accessible, and new partnerships productive.