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Gypsy Moth Slow the Spread Foundation at a glance

· Public charity

Gypsy Moth Slow the Spread Foundation

The purpose is to implement the slow the spread strategy.

$2.4M
Granted FY2025still arriving
11
Grants FY2025still arriving
—
States reached
$576k
Largest
01What it fundsRead
02The needRead
03Its granteesRead
04Its fieldRead
01What it funds
01100% classified

What it funded, over time

Every grant placed by its stated purpose and the recipient’s mission, by year, FY2017 to 2025.

Food & Nutrition$29MAnimals$984kEnvironment$810kEducation$492k
02FY2025 · 11 grants

Where the money goes

Grants by size, and the largest recipients.

By grant size · FY2025

  • $10k–50k2 grants · $51k
  • $50k–250k3 grants · $238k
  • $250k+6 grants · $2.1M
$257,584
Median grant
—
States reached
$686k
Total assets
Largest grants
RecipientAmount
MINNESOTA DEPARTMENT OF AGRICULTURE$575,918
VIRGINIA DEPARTMENT OF AGRICULTURE AND CONSUMER SERVICES$348,972
WISCONSIN DEPARTMENT OF AGRICULTURE TRADE & CONSUMER PROTECTION$317,156
OHIO DEPARTMENT OF AGRICULTURE$306,907
WEST VIRGINIA DEPARTMENT OF AGRICULTURE$302,063
ILLINOIS DEPARTMENT OF AGRICULTURE$257,584
IOWA DEPARTMENT OF NATURAL RESOURCES$93,959
INDIANA DEPARTMENT OF AGRICULTURE$79,673
NORTH CAROLINA DEPARTMENT OF AGRICULTURE$64,527
UNIVERSITY OF KENTUCKY$34,776
TENNESSEE DEPARTMENT OF AGRICULTURE$16,151
02The need
03

Does the money reach high-need areas?

Each grant placed by the hardship in its grantee’s ZIP, against the area’s typical level.

show:

Dollar for dollar, its grants (FY17–21) land where the poverty rate runs at 15%, against an area that typically sits at 12%. 94% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 12%MINNESOTA DEPT OF AGRICULTURE: $652k → 14%STATE UNIVERSITY OF NEW YORK ESF COLLEGE FOUNDATION INC: $3k → 14%Cornell University Dept of Entomology: $24k → 16%IOWA DEPT OF NATURAL RESOURCES: $202k → 10%TENNESSEE DEPARTMENT OF AGRICULTURE: $41k → 14%INDIANA DEPT OF NATURAL RESOURCE DIVISION OF ENTOMOLOGY & PLANT PATHOLOGY: $263k → 16%OHIO DEPT OF AGRICULTURE: $617k → 15%WEST VIRGINIA DEPT OF AGRICULTURE: $383k → 18%VIRGINIA COMMONWEALTH UNIVERSITY: $78k → 22%MINNESOTA DEPT OF AGRICULTURE: $570k → 14%CORNELL UNIVERSITY DEPT OF ENTOMOLOGY: $6k → 16%IOWA DEPT OF NATURAL RESOURCES: $86k → 10%TENNESSEE DEPARTMENT OF AGRICULTURE: $41k → 14%INDIANA DEPT OF NATURAL RESOURCE DIVISION OF ENTOMOLOGY & PLANT PATHOLOGY: $173k → 16%OHIO DEPT OF AGRICULTURE: $615k → 15%WEST VIRGINIA DEPT OF AGRICULTURE: $333k → 18%VIRGINIA COMMONWEALTH UNIVERSITY: $58k → 22%MINNESOTA DEPT OF AGRICULTURE: $514k → 14%IOWA DEPT OF NATURAL RESOURCES: $76k → 10%INDIANA DEPT OF NATURAL RESOURCE DIVISION OF ENTOMOLOGY & PLANT PATHOLOGY: $159k → 16%OHIO DEPT OF AGRICULTURE: $524k → 15%WEST VIRGINIA DEPT OF AGRICULTURE: $301k → 18%VIRGINIA COMMONWEALTH UNIVERSITY: $20k → 22%MINNESOTA DEPT OF AGRICULTURE: $485k → 14%IOWA DEPT OF NATURAL RESOURCES: $62k → 10%INDIANA DEPT OF NATURAL RESOURCE DIVISION OF ENTOMOLOGY & PLANT PATHOLOGY: $121k → 16%OHIO DEPT OF AGRICULTURE: $437k → 15%WEST VIRGINIA DEPT OF AGRICULTURE: $261k → 18%UNIVERSITY OF RICHMOND DEPARTMENT OF BIOLOGY: $6k → 22%MINNESOTA DEPT OF AGRICULTURE: $348k → 14%IOWA DEPT OF NATURAL RESOURCES: $53k → 10%INDIANA DEPT OF NATURAL RESOURCE DIVISION OF ENTOMOLOGY & PLANT PATHOLOGY: $73k → 16%OHIO DEPT OF AGRICULTURE: $419k → 15%WEST VIRGINIA DEPT OF AGRICULTURE: $180k → 18%0%20%40%50%more need →
grant to an above-average-need area below average· circle size = grant amount
How this is measured
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty line in the grantee’s ZIP, averaged from the tracts in that ZIP.

Which US states its grants reach

MN
IL
WI
MI
NY
IA
IN
OH
KY
WV
VA
TN
NC
How this is measured
US grants placed by each grantee’s ZIP, which can differ from where services are delivered. Need from public data (U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA), shown as context about the area, never attributed to the funder’s giving.
03Its grantees
repeat funding

Who it funds again

Organizations funded in more than one year, against those funded once.

100%of every dollar goes to organizations it has funded before.
$31M · 16 repeat orgs$9k to everyone else

16 repeat relationships: 11 still active in FY2025, 5 since wound down.

How the two cohorts compare

Re-uppedFunded once

Organizations

16
2

Total granted

$31M
$9k

Median revenue growth · since first grant

+59%
+131%

Still filing today

19%
100%

New vs renewed · share of each year

In FY2025, 100% of grant dollars renewed an existing relationship; $0 went to new ones.

50%100%’17’18’19’20’21’22’23’24’25
RenewedFirst-time
How this is measured
First-time means a grantee’s first year in the filing window; renewed means funded in an earlier year too. The renewed share climbs naturally as a portfolio matures, so the signal is a year that bucks it (a wave of new grantees). The earliest year is left-censored: relationships that predate the data read as new. Theme is the grantee’s IRS cause.

Backed again, and grew

  • UO
    University of Kentucky Research Foundation
    9× · 2017–2025 · $404k · revenue +66%
  • TR
    The Research Foundation for The State University of New York
    5× · 2019–2024 · $51k · revenue +55%
  • Cornell University
    2× · 2017–2018 · $29k · revenue +59%

Funded once

  • UO
    UNIVERSITY OF RICHMOND
    one grant, 2018 · $6k · revenue +19%
  • EC
    ESF COLLEGE FOUNDATION INCgraduated
    one grant, 2018 · $3k · revenue +131%
How this is measured

Repeat means funded in two or more distinct years. Growth and survival are read from each grantee’s own subsequent IRS filings.

04Its field
04the grantee network

5 grantees tracked through their own filings, 2017–2025.

Each grant beside the grantee’s revenue from every source, year by year.

How this is measured

Counted here: distinct organizations funded across 2017 to 2025, not grant rows in a single year, so this will not match the grant count on the cover. 5 of the 18 grantees resolved in that span have returns of their own we could reconcile.

Each grantee is resolved to its own IRS returns. Association, dated; never a causal claim.

0
Load-bearing (≥25% of a budget)
1
Early backer (in before they grew)
5/5
Grantees still filing
5/5
Grew since first funded

Where the money sits, by cause and then by grantee

Wisconsin Dept of Agriculture Trade & Consumer Protection: $6,565,150 · OtherWisconsin Dept of Agriculture Trade & Consumer ProtectionMINNESOTA ZOO: $5,350,089 · OtherMINNESOTA ZOOVIRGINIA DEPT OF AGRICULTURE & CONSUMER SERVICES: $4,916,380 · OtherVIRGINIA DEPT OF AGRICULTURE & CONSUMER SERVICESOHIO DEPT OF AGRICULTURE: $4,143,478 · OtherOHIO DEPT OF AGRICULTUREWEST VIRGINIA DEPARTMENT OF AGRICULTURE: $3,060,875 · OtherWEST VIRGINIA DEPARTMENT OF AGRICULTUREILLINOIS DEPT OF AGRICULTURE: $2,748,548 · OtherILLINOIS DEPT OF AGRICULTURE+7 more: $3,713,841 · Other+7 moreUniversity of Kentucky Research Foundation: $403,897 · EducationThe Research Foundation for The State University of New York: $50,745 · EducationCornell University: $29,002 · Education+2 more: $8,656 · Education
Other$30,498,361Education$492,300

Each grantee by its size and the funder’s share of it; top left is load-bearing

25%50%75%100%$100Mgrantee revenue →↑ funder's share of their budgetUniversity of Kentucky Research Foundation: $403,897 over 9y, 0.0% of budgetThe Research Foundation for The State University of New York: $50,745 over 5y, 0.0% of budgetCornell University: $29,002 over 2y, 0.0% of budgetUNIVERSITY OF RICHMOND: $5,904 over 1y, 0.0% of budgetESF COLLEGE FOUNDATION INC: $2,752 over 1y, 0.1% of budget
Grantee by grantee: a decade per organization, before and after funding

A decade per grantee: revenue shaded, grants as bars, all rows on one timeline.

  • Who funds University of Kentucky Research Foundation ↗
  • Who funds The Research Foundation for The State University of New York ↗
  • Who funds Cornell University ↗
  • Who funds UNIVERSITY OF RICHMOND ↗
  • Who funds ESF COLLEGE FOUNDATION INC ↗

Co-funders

Grantmakers whose grantees overlap with this funder’s far more than size alone predicts. Closer means stronger alignment; arcs show where they also fund each other. Here they form a set of overlaps that mostly run through this funder rather than between each other.

US Livestock Genetics…
US Livestock Genetics Exports IncWI13.6× affinity4 shared granteesties to 0 of 0Hover any node to trace its alignments.Compare side by side →

Open a dossier: US Livestock Genetics Exports Inc

How this is measured
Affinity is a Gamma-Poisson posterior co-funding rate, re-centered on the typical rate, so thin evidence shrinks toward no signal. Overlap is association, not proof of shared intent.

How much its grantees rely on government

Each dot is one organization Gypsy Moth Slow the Spread Foundation funds: left to right is the share of its income from government, bottom to top the share from Gypsy Moth Slow the Spread Foundation. Bigger dots raise more.

2025
’20’21’22’23’24’25
no gov · 00%0%1%3%5%funder's share of their income ↑0%23%45%68%90%share of the org’s income from government →
    no gov moneyreceives it· size = income
    0get no government money at all
    0rely on government for over half their income
    ⤢ axis zoomed · 0–90%
    typical government reliance, FY20–25
    How this is measured
    Government income is each org’s traced federal awards (USASpending grants and contracts) plus state payments (open checkbooks) as a share of its total revenue (IRS Form 990); the self-reported government-grant line (990 line 1e) is carried for cross-check: a grantee that reports government grants we could not trace to a source is left off the chart rather than shown as receiving none. An association, not a claim that a grant caused the public funding. Coverage is precision-first, a floor rather than a census; state records exist for 9 states, so a grantee outside them shows no state figure (dimmed) rather than a false zero. Federal award amounts are obligations, which can span years, so a single-year share is indicative. 990 filings lag 12 to 24 months.

    On method. Every financial figure here is read from IRS e-file XML, the funder’s own 990/990-PF, and from the multi-year returns of the 18 grantees we resolved, each linked to its source. Grantee outcomes are each organization’s own program-service reporting (Form 990, Part III), read as association with funding, never as caused by it. Low-confidence name matches are suppressed rather than guessed. How we build these →

    How this is measured
    Each figure carries one of six labels. Filed is what the funder reported on its return. Official is another government record about an organization, such as a federal award or a charity register, joined by name where no shared identifier exists. Resolved is an identity we worked out where the filing named a recipient without an EIN, kept only above a measured confidence threshold. Computed is arithmetic over those, like themes, portfolio clusters and co-funder strength. Context is a statistic about a place rather than about an organization, which is what the need overlay is: it describes the area a grantee’s address sits in, not where its work lands. Inferred is drawn by a model from text, like the partnerships read out of public news and organization websites. Each is labeled where it appears.

    Generated from the IRS Form 990 e-file return for fiscal year 2025, released 2025. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

    Source object · view filing

    Play a game based on Gypsy Moth Slow the Spread’s filings →

    More from the funding graph

    The unnamed billions
    grants reported with no recipient in the public record
    The regranting layer
    51% of grant dollars land in the regranting layer
    The DAF merry-go-round
    $9.7B circulates between donor-advised funds
    The endowment market
    who holds foundation money, and what just moved
    The camps
    funders cluster by geography, not ideology
    By state
    where grant money is based, and what stays home