Human Services Focus

The organizations that meet basic community needs — food, housing, employment, youth, safety

Key finding 768 active human services nonprofits (NTEE groups I, J, K, L, O, P) serve the region, generating about $734 million a year — led by Second Harvest Inland Northwest, Goodwill Industries, and Spokane United Methodist Homes. The mix skews heavily toward general human services and youth development; employment services (44 orgs) is the smallest category, and housing & shelter (75 orgs) remains thin relative to the scale of housing need — housing organizations are among the region’s smallest by revenue.

GU = ({
  navy: "#002967",
  red: "#c8102e",
  s1: "#1c5cab",
  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})

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

countySummary = FileAttachment("data/county_summary.csv").csv({typed: true})

hsLetters = new Map([
  ["I", "Crime & Legal-Related"],
  ["J", "Employment"],
  ["K", "Food, Agriculture & Nutrition"],
  ["L", "Housing & Shelter"],
  ["O", "Youth Development"],
  ["P", "Human Services (multipurpose)"]
])

hsOrgs = orgs.filter(d => hsLetters.has(d.ntee_major))

What counts as “human services” here

This page drills into six NTEE major groups that correspond most directly to basic community needs:

NTEE	Category	Examples in the region
I	Crime & Legal-Related	legal aid, victim services, offender reentry
J	Employment	job training, vocational rehabilitation
K	Food, Agriculture & Nutrition	food banks, meal programs
L	Housing & Shelter	shelters, affordable-housing developers
O	Youth Development	mentoring, scouting, youth programs
P	Human Services	multipurpose agencies, family services, seniors

Organizations and dollars by category

hsByLetter = Array.from(hsLetters, ([letter, label]) => {
  const rows = hsOrgs.filter(d => d.ntee_major === letter);
  return {
    letter,
    label: `${label} (${letter})`,
    n: rows.length,
    revenue: d3.sum(rows, d => d.revenue > 0 ? d.revenue : 0)
  };
})

Plot.plot({
  marginLeft: 240,
  marginRight: 70,
  marginBottom: 45,
  height: 280,
  width: 900,
  style: {fontSize: "13px"},
  x: {label: "Active organizations", labelAnchor: "center", grid: true},
  y: {label: null},
  marks: [
    Plot.barX(hsByLetter, {
      x: "n",
      y: "label",
      sort: {y: "-x"},
      fill: GU.s1,
      rx: 3,
      insetTop: 2,
      insetBottom: 2,
      tip: true,
      title: d => `${d.label}\n${fnum(d.n)} organizations\nCombined revenue: ${fmoney(d.revenue)}`
    }),
    Plot.textX(hsByLetter, {
      x: "n", y: "label", text: d => fnum(d.n),
      dx: 6, textAnchor: "start", fill: GU.ink, fontSize: 12
    }),
    Plot.ruleX([0], {stroke: "#c3c2b7"})
  ]
})

Plot.plot({
  marginLeft: 240,
  marginRight: 80,
  marginBottom: 45,
  height: 280,
  width: 900,
  style: {fontSize: "13px"},
  x: {label: "Combined reported revenue ($)", labelAnchor: "center", grid: true, tickFormat: d => d3.format("~s")(d).replace("G", "B")},
  y: {label: null},
  marks: [
    Plot.barX(hsByLetter, {
      x: "revenue",
      y: "label",
      sort: {y: "-x"},
      fill: GU.s3,
      rx: 3,
      insetTop: 2,
      insetBottom: 2,
      tip: true,
      title: d => `${d.label}\n${fnum(d.n)} organizations\nCombined revenue: ${fmoney(d.revenue)}`
    }),
    Plot.textX(hsByLetter, {
      x: "revenue", y: "label", text: d => fmoney(d.revenue),
      dx: 6, textAnchor: "start", fill: GU.ink, fontSize: 12
    }),
    Plot.ruleX([0], {stroke: "#c3c2b7"})
  ]
})

Food & nutrition revenue is dominated by a single organization — Second Harvest Inland Northwest ($152M), one of the largest food banks in the Northwest. Youth development has the second-most organizations (150) but the least combined revenue, a sign of a category built on small, volunteer-driven groups.

Human services coverage by county

Organizations per 10,000 residents. Spokane County — the region’s population and need center — has the lowest per-capita human services coverage in the region, a pattern examined in depth on the Service Gaps page.

Plot.plot({
  marginLeft: 130,
  marginRight: 70,
  marginBottom: 45,
  height: 300,
  width: 900,
  style: {fontSize: "13px"},
  x: {label: "Human services orgs per 10,000 residents", labelAnchor: "center", grid: true},
  y: {label: null},
  marks: [
    Plot.barX(countySummary, {
      x: "hs_per_10k",
      y: "county",
      sort: {y: "-x"},
      fill: GU.s1,
      rx: 3,
      insetTop: 2,
      insetBottom: 2,
      tip: true,
      title: d => `${d.county}\n${fnum(d.n_human_services)} HS orgs / ${fnum(Math.round(d.population))} residents\n${d3.format(".1f")(d.hs_per_10k)} per 10k`
    }),
    Plot.textX(countySummary, {
      x: "hs_per_10k", y: "county", text: d => d3.format(".1f")(d.hs_per_10k),
      dx: 6, textAnchor: "start", fill: GU.ink, fontSize: 12
    }),
    Plot.ruleX([0], {stroke: "#c3c2b7"})
  ]
})

Formation trend: is capacity keeping up?

New human-services organizations formed per year across the region, among organizations still active today (IRS ruling dates; dissolved organizations are not counted, so early years understate true formation activity).

hsHist = {
  // formations.csv is aggregated across all categories, so recompute from the
  // active-org file for the human services subset (active orgs only).
  const rows = orgs.filter(d => hsLetters.has(d.ntee_major) && d.ruling_year >= 1970 && d.ruling_year <= 2025);
  const roll = d3.rollups(rows, v => v.length, d => d.ruling_year)
    .map(([year, n]) => ({year, n}))
    .sort((a, b) => a.year - b.year);
  for (let i = 0; i < roll.length; i++) {
    const w = roll.slice(Math.max(0, i - 4), i + 1);
    roll[i].ma5 = d3.mean(w, d => d.n);
  }
  return roll;
}

Plot.plot({
  height: 340,
  width: 900,
  marginLeft: 45,
  marginBottom: 45,
  style: {fontSize: "13px"},
  x: {label: "Year of IRS ruling", labelAnchor: "center", tickFormat: "d"},
  y: {label: "New HS organizations (still active)", grid: true},
  marks: [
    Plot.lineY(hsHist, {x: "year", y: "n", stroke: GU.s1, strokeWidth: 1.5, curve: "monotone-x"}),
    Plot.lineY(hsHist, {x: "year", y: "ma5", stroke: GU.red, strokeWidth: 2.5, curve: "monotone-x"}),
    Plot.ruleY([0], {stroke: "#c3c2b7"}),
    Plot.tip(hsHist, Plot.pointerX({
      x: "year", y: "n",
      title: d => `${d.year}\n${fnum(d.n)} new HS orgs\n5-yr average: ${d3.format(".1f")(d.ma5)}`
    }))
  ]
})

md`<span style="color:${GU.muted}; font-size:0.85rem;"><span style="color:${GU.s1}">━</span> annual &nbsp;&nbsp;<span style="color:${GU.red}">━</span> 5-year moving average. Counts only organizations still active in 2026, so early years understate true formation activity.</span>`

Directory: every human services organization

viewof hsCat = Inputs.select(
  ["All six categories"].concat(Array.from(hsLetters.values())),
  {label: "Category"}
)

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

viewof hsSearch = Inputs.search(
  hsOrgs.filter(d =>
    (hsCat === "All six categories" || hsLetters.get(d.ntee_major) === hsCat) &&
    (hsCounty === "All counties" || d.county === hsCounty)
  ),
  {placeholder: "Search names, cities…", columns: ["name", "city"]}
)

Inputs.table(
  hsSearch.map(d => ({
    Name: d.name,
    City: d.city,
    County: d.county,
    Category: hsLetters.get(d.ntee_major),
    NTEE: d.ntee ?? "—",
    "Reported revenue": d.revenue > 0 ? fmoney(d.revenue) : "—"
  })),
  {rows: 15, layout: "auto"}
)

Source: NCCS Business Master File, June 2026 vintage. Revenue is the most recent figure reported to the IRS.