nonprofit landscape in the u.s.

the nonprofit landscape in the US

Use these interactive charts to understand the nonprofit landscape in the US; based on analyzing the filings of nonprofits (501c3) with annual income over $50K, with the US Internal Revenue Service. It excludes Foundations, Trusts, and Religious Organizations.

For details, please visit Our Data.

  1. Nonprofits by Income Level
  2. Nonprofits by Software Spending
  3. Nonprofits by Geographic Location

Interactive: Select a state or a group of states in the map, or select an income level

Coming in Dec 2020: Exclusive Feature for our Premium Listing Solution Partners. – Access advanced functionality to create detailed reports with the information of the organizations that meet your filtering criteria: Organization Name, URL, Key contact person, Focus Area, and more.

[1] Excludes 800k nonprofit organizations with an annual income under $50K; it also excludes religious organizations and foundations or trusts

[2] This represents the percentage of the Annual Income that comes from selling services or products and does not include Charitable Donations or Government Grants

[3] Software Budget is based on actual income reported to the IRS by these Nonprofits, and applying a percentage of that income as IT spending. The applied percentages are different by income level and are informed by Statista (2019), Deloitte Insights (2018), (2017), Analysis by X4Impact in 2020, and Microsoft Tech for Social Impact Team insights (2020)

[4] One organization can be classified under one or many UN SDGs. For example, a Child Advocacy Center, helping children survivors of abuse and neglect, is classified under UN SDG #3 – Health and Well-Being, and under UN SDG #16 – Peace & Justice

limitations on our data aggregation, classification and analysis

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At this point, we are at 85% accuracy and we expect to continuously improve over time. Our approach is AI-assisted rather than AI-replaced. That means we aim at serving the needs of human experts but have complemented our experience with a private Wiki experience for our Challenges and Solutions to enable experts to augment, correct, and refine our content. For the automated labeling of data, such as classification under one or many of the UN Sustainable Development Goals, we conduct human audits to refine the machine learning process.

To learn more, please visit OUR DATA.

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Many niche services charge subscription fees for access to datasets such as private foundations activity, grants, private investment activity, compensation, and more. Private Beta users encouraged us to charge a subscription.

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