· Public charity
Green Chips DBA Impact NV
Founded in 2008, ImpactNV has served as an independent nonprofit comprised of Nevadas public and private entities and NGOs.
What it funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year, FY2023 to 2025.
Where the money goes
Grants by size, and the largest recipients.
| Recipient | Amount |
|---|---|
| Las Vegas Area Council | $20,000 |
Does the money reach high-need areas?
Each grant placed by the hardship in its grantee’s ZIP, against the area’s typical level.
Dollar for dollar, its grants (FY23–25) land where the poverty rate runs at 12%, against an area that typically sits at 11%. 67% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.
How this is measured
How this is measured
3 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. 3 of the 4 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.
Where the money sits, by cause and then by grantee
Each grantee by its size and the funder’s share of it; top left is load-bearing
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.
How much its grantees rely on government
Each dot is one organization Green Chips DBA Impact NV funds: left to right is the share of its income from government, bottom to top the share from Green Chips DBA Impact NV. Bigger dots raise more.
How this is measured
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How do I get to Green Chips DBA Impact NV?
Find your warmest path to Green Chips DBA Impact NV through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.
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