Sector Overview

What the Inland Northwest nonprofit sector looks like today

Key finding The Inland Northwest hosts 5,256 active nonprofits, and its composition closely mirrors both states’ averages — human services is the largest classified category (23% of all organizations, a third of those with a classified NTEE code), ahead of education and public benefit. The sector is financially bottom-heavy: most organizations report no revenue to the IRS at all, roughly three in ten filers bring in under $100,000, while just 73 organizations exceed $10 million and account for the bulk of the region’s $6.5 billion in nonprofit revenue.

GU = ({
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  red: "#c8102e",
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  s2: "#1baf7a",
  s3: "#c8102e",
  s4: "#eda100",
  ink: "#1a1a1a",
  muted: "#898781",
  grid: "#e1e0d9",
  seq: ["#cde2fb", "#9ec5f4", "#6da7ec", "#3987e5", "#256abf", "#184f95", "#0d366b"]
})

fnum = d3.format(",")

fmoney = (x) => x == null || isNaN(x) ? "—"
  : Math.abs(x) >= 1e9 ? "$" + d3.format(".2f")(x / 1e9) + "B"
  : Math.abs(x) >= 1e6 ? "$" + d3.format(".1f")(x / 1e6) + "M"
  : "$" + d3.format(",.0f")(x)

fpct = (x) => x == null || isNaN(x) ? "—" : d3.format(".1f")(x) + "%"

orgs = FileAttachment("data/orgs_active.csv").csv({typed: true})

subsectors = FileAttachment("data/subsector_counts.csv").csv({typed: true})

nteeMajor = FileAttachment("data/ntee_major.csv").csv({typed: true})

Composition by category

The NCCS NTEE v2 subsectors group nonprofits into broad fields of activity. About 30% of registered organizations — mostly small, older, or recently formed groups — carry no usable NTEE code and are shown as “Unclassified.”

Plot.plot({
  marginLeft: 200,
  marginRight: 70,
  marginBottom: 45,
  height: 420,
  width: 900,
  style: {fontSize: "13px"},
  x: {label: "Active organizations", labelAnchor: "center", grid: true, tickFormat: "~s"},
  y: {label: null},
  marks: [
    Plot.barX(subsectors.filter(d => d.geo === "Inland Northwest"), {
      x: "n",
      y: "subsector_name",
      sort: {y: "-x"},
      fill: GU.s1,
      rx: 3,
      insetTop: 2,
      insetBottom: 2,
      tip: true,
      title: d => `${d.subsector_name}\n${fnum(d.n)} organizations (${fpct(d.share * 100)} of sector)`
    }),
    Plot.textX(subsectors.filter(d => d.geo === "Inland Northwest"), {
      x: "n",
      y: "subsector_name",
      text: d => fnum(d.n),
      dx: 6,
      textAnchor: "start",
      fill: GU.ink,
      fontSize: 12
    }),
    Plot.ruleX([0], {stroke: "#c3c2b7"})
  ]
})

Source: NCCS Business Master File, June 2026 vintage, active organizations in the seven-county region.

How the region compares to state averages

Share of each state’s (or the region’s) active nonprofits by category. The regional mix tracks the Washington and Idaho statewide profiles remarkably closely — the Inland Northwest is not under- or over-built in any broad category relative to its states. The differences that do exist are small: slightly more religious congregations register here, and slightly fewer arts organizations than Washington overall.

Plot.plot({
  marginLeft: 200,
  height: 460,
  width: 900,
  style: {fontSize: "13px"},
  marginBottom: 45,
  x: {label: "Share of active nonprofits (%)", labelAnchor: "center", grid: true},
  y: {label: null, domain: subsectors.filter(d => d.geo === "Inland Northwest").sort((a, b) => b.n - a.n).map(d => d.subsector_name)},
  color: {
    legend: true,
    domain: ["Inland Northwest", "Washington (statewide)", "Idaho (statewide)"],
    range: [GU.s1, GU.s2, GU.s3]
  },
  marks: [
    Plot.dot(subsectors, {
      x: d => d.share * 100,
      y: "subsector_name",
      fill: "geo",
      r: 6,
      stroke: "#ffffff",
      strokeWidth: 1.5,
      tip: true,
      title: d => `${d.geo}\n${d.subsector_name}: ${fpct(d.share * 100)} (${fnum(d.n)} orgs)`
    }),
    Plot.ruleX([0], {stroke: "#c3c2b7"})
  ]
})

Dots that sit nearly on top of each other mean the region matches the state profile for that category. Exact values in the tooltip.

Size distribution

Revenue as most recently reported to the IRS. The majority of registered nonprofits — small clubs, PTAs, granges, all-volunteer groups — report no revenue at all (they fall below filing thresholds), so the chart below covers the 1,540 organizations with a reported revenue figure.

sizeBuckets = {
  const withRev = orgs.filter(d => d.revenue > 0);
  const buckets = [
    {label: "Under $100k", test: d => d.revenue < 1e5},
    {label: "$100k–$500k", test: d => d.revenue >= 1e5 && d.revenue < 5e5},
    {label: "$500k–$1M", test: d => d.revenue >= 5e5 && d.revenue < 1e6},
    {label: "$1M–$10M", test: d => d.revenue >= 1e6 && d.revenue < 1e7},
    {label: "Over $10M", test: d => d.revenue >= 1e7}
  ];
  return buckets.map(b => ({
    bucket: b.label,
    n: withRev.filter(b.test).length,
    revenue: d3.sum(withRev.filter(b.test), d => d.revenue)
  }));
}

Plot.plot({
  height: 300,
  width: 900,
  marginLeft: 60,
  style: {fontSize: "13px"},
  x: {label: null, domain: sizeBuckets.map(d => d.bucket)},
  y: {label: "Organizations", grid: true},
  marks: [
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      x: "bucket",
      y: "n",
      fill: GU.s1,
      rx: 3,
      insetLeft: 4,
      insetRight: 4,
      tip: true,
      title: d => `${d.bucket}\n${fnum(d.n)} organizations\nCombined revenue: ${fmoney(d.revenue)}`
    }),
    Plot.textY(sizeBuckets, {
      x: "bucket",
      y: "n",
      text: d => fnum(d.n),
      dy: -8,
      fill: GU.ink,
      fontSize: 12
    }),
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Plot.plot({
  height: 300,
  width: 900,
  marginLeft: 70,
  style: {fontSize: "13px"},
  x: {label: null, domain: sizeBuckets.map(d => d.bucket)},
  y: {label: "Combined revenue ($)", grid: true, tickFormat: d => d3.format("~s")(d).replace("G", "B")},
  marks: [
    Plot.barY(sizeBuckets, {
      x: "bucket",
      y: "revenue",
      fill: GU.s3,
      rx: 3,
      insetLeft: 4,
      insetRight: 4,
      tip: true,
      title: d => `${d.bucket}\n${fnum(d.n)} organizations\nCombined revenue: ${fmoney(d.revenue)}`
    }),
    Plot.textY(sizeBuckets, {
      x: "bucket",
      y: "revenue",
      text: d => fmoney(d.revenue),
      dy: -8,
      fill: GU.ink,
      fontSize: 12
    }),
    Plot.ruleY([0], {stroke: "#c3c2b7"})
  ]
})

The two charts share categories but tell opposite stories: organizations cluster at the small end, dollars at the large end. The 73 organizations over $10M — led by Kootenai Health, Gonzaga University, and the region’s credit unions and community health systems — account for the large majority of all nonprofit revenue in the region.

Explore every organization

Search and filter all 5,256 active nonprofits. Click column headers to sort.

viewof tblCounty = Inputs.select(
  ["All counties"].concat(Array.from(new Set(orgs.map(d => d.county))).sort()),
  {label: "County"}
)

viewof tblCategory = Inputs.select(
  ["All categories"].concat(Array.from(new Set(orgs.map(d => d.subsector_name))).sort()),
  {label: "Category"}
)

viewof tblSearch = Inputs.search(
  orgs.filter(d =>
    (tblCounty === "All counties" || d.county === tblCounty) &&
    (tblCategory === "All categories" || d.subsector_name === tblCategory)
  ),
  {placeholder: "Search names, cities…", columns: ["name", "city"]}
)

Inputs.table(
  tblSearch.map(d => ({
    Name: d.name,
    City: d.city,
    County: d.county,
    Category: d.subsector_name,
    NTEE: d.ntee ?? "—",
    "Reported revenue": d.revenue > 0 ? fmoney(d.revenue) : "—",
    Since: d.ruling_year ? String(d.ruling_year) : "—"
  })),
  {rows: 15, layout: "auto"}
)

“Since” is the year the IRS granted tax-exempt status. Revenue is the most recent figure reported to the IRS; “—” means the organization falls below filing thresholds or has not filed a financial return.