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· Public charity

PTA Oregon Congress Beverly Cleary PTA

Beverly cleary pta provides school support and student enrichment for the approximately 550 students, kindergarten through 8th grade, at two school campuses in portland, oregon.

$52k
Granted FY2018
3
Grants FY2018
1
States reached
$23k
Largest
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 2018.

Education$42kArts & Culture$10k
02FY2018 · 3 grants

Where the money goes

Grants by size, and the largest recipients.

How this is measured
The 3 grants below total $45,549, the rows itemized in this filing. The $51,668 headline is the total grant expense on the return; the other $6,119 is giving the schedule does not break out (grants under the $5,000 itemization floor, grants to individuals, grants on other schedules). Every figure below describes the itemized rows only.

By grant size · FY2018

  • Under $10k1 grant · $10k
  • $10k–50k2 grants · $36k
$13,075
Median grant
1
States reached
$67k
Total assets
Largest grants
RecipientAmount
BEVERLY CLEARY SCHOOL$22,948
BEVERLY CLEARY SCHOOL$13,075
BEVERLY CLEARY SCHOOL$9,526
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 (FY17–18) land where the poverty rate runs at 12%, against an area that typically sits at 10%. 100% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 10%BEVERLY CLEARY SCHOOL: $23k → 12%BEVERLY CLEARY SCHOOL: $13k → 12%BEVERLY CLEARY SCHOOL: $10k → 12%BEVERLY CLEARY SCHOOL: $6k → 12%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.
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 field
04the grantee network

0 grantees tracked through their own filings, 2017–2018.

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 2018, not grant rows in a single year, so this will not match the grant count on the cover. 0 of the 1 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)
0
Early backer (in before they grew)
0/0
Grantees still filing
0/0
Grew since first funded

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

25%50%75%100%grantee revenue →↑ funder's share of their 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.

04Through Plinth

Warm introductions · Powered by PlinthPlus

How do I get to PTA Oregon Congress Beverly Cleary PTA?

Find your warmest path to PTA Oregon Congress Beverly Cleary PTA 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.

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 1 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 2018, released 2018. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

Possibly out of date. A more recent filing (FY2025) is on record and reports $12k of grant expense, but none of it to a named recipient. On a Form 990 that can mean grants abroad, filed by region only (Schedule F), grants under $5,000, which are not itemized, or a schedule referenced as an attachment the e-file does not carry. The figures above are therefore from FY2018, the most recent year this funder named its grantees.

Source object · view filing

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