CREAT: Census Research Exploration and Analysis Tool

Employees in the US Nonprofit Sector

May 2026

Working Paper Number:

CES-26-33

Abstract

The nonprofit sector employs roughly 10% of the American workforce, making it the third largest workforce behind the retail and manufacturing sectors. Despite this, relatively little is known about its employees. This paper is the first to use comprehensive administrative tax data, covering the near-universe of workers in the US, to quantify and explain the causes of the nonprofit pay differential. Unconditionally, we find the nonprofit earnings penalty to be 12% relative to for-profit workers. Estimating an 'AKM' worker-firm job ladder model, we show that most of the penalty is causal and not driven by selection. We also document considerable heterogeneity across industries, both in terms of earnings premia/penalties and worker selection, and show that nonprofit and for-profit earnings have been converging over time.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
payroll, quarterly, earnings, employee, employ, employed, labor, proprietor, expenditure, profit, revenue, workforce, tax, wage variation, socioeconomic, irs, earner, wage earnings, earnings employees, earnings workers, employment firms

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Internal Revenue Service, Standard Industrial Classification, Employer Identification Numbers, North American Industry Classification System, American Community Survey, Longitudinal Employer Household Dynamics, AKM, Employment History File, Individual Characteristics File, W-2, Federal Statistical Research Data Center, Form W-2

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