Skip to content

· Public charity

China Summit Foundation Inc

To organize and support china-related summits, conferences, and other events events in the us and overseas.

$70k
Granted FY2018
2
Grants FY2018
—
States reached
$60k
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.

Community Improvement$140kInternational$10k
02FY2018 · 2 grants

Where the money goes

Grants by size, and the largest recipients.

By grant size · FY2018

  • $10k–50k1 grant · $10k
  • $50k–250k1 grant · $60k
$60,000
Median grant
—
States reached
$38k
Total assets
Largest grants
RecipientAmount
E-HOUSE CHINA$60,000
WEEDUCATION GROUP INC$10,000
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.

US giving: 13% of grant dollars ($20k). The need read here covers only this US portion; the 87% that went abroad ($130k) is mapped below.

show:

Dollar for dollar, its grants (FY17–17) land where the poverty rate runs at 16%, against an area that typically sits at 12%. 100% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 12%FACE HIRED INC: $10k → 14%CHINA YOUTH EDUCATION: $10k → 17%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.

Beyond the US: 87% of all-years giving ($130k)

1 country, by the recipient’s country on the filing.

grant dollars sentlessmore
China$130k · 3 grants
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. International giving is mapped by the recipient’s country from the same 990 filings; comparable need data isn’t available at that grain.
03Its field
04the grantee network

1 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.

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

1
Load-bearing (≥25% of a budget)
0
Early backer (in before they grew)
0/1
Grantees still filing
0/1
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 budgetCHINA SUMMIT FOUNDATION INC: $150,000 over 2y, 38% 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.

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.

Source object · view filing

Play a game based on China Summit’s filings →

More from the funding graph