Grants data fact sheet

Sources for Candid’s grants data

Every year, Candid processes data on approximately three million grants representing more than $180 billion in funding. We collect data from:

• Government agencies (such as the IRS or Canada Revenue Agency)
• Funders
• Other data sharing partners
• Organizations’ websites
• News sources

Data from these sources are cleaned, harmonized, and coded according to the Philanthropy Classification System (PCS).

Avoiding duplication of grants data

Candid uses a system of “survivorship” to determine which source of grants data gets precedence to avoid duplication. This system works as follows:

• Grants reported by funders themselves always override other sources. If we receive a complete grants list from a funder, it will block or replace data from annual returns, such as IRS Form 990 or 990-PF.
• Grants obtained from news sources—which are usually focused on a particular topic (such as a disaster)—are replaced by a complete source for that funders’ grantmaking once it is available for the year in question. Complete sources of grants data (i.e., ones that reflect the funders’ total giving) include data from the funder themselves or the 990-PF.

Data collected about grants

Candid needs the following to load data about a grant into our products:

• Funder name
• Recipient name
• Location¹
• Fiscal year
• Grant amount

Different types of sources contain varying levels of details about a grant. For example, grants sourced from government filings like the 990s tend to have limited descriptive text whereas data that a grantmaker shares directly often contains more information.

Candid’s grants reporting program offers funders the option of sharing additional details about their grants, including grant title, program area, grant description, and key characteristics, such as subject area, population served, and geographic area served. A complete list of fields and their definitions can be found on the grants reporting template.

Data recency

Annual return data from government sources—such as Form 990 or 990-PF—can take six months to two years to reach Candid from the date they were filed, while other sources may be quicker. Candid’s products are updated daily and reflect the latest data available.

Data completeness

Completeness of data can be thought of in two ways: the completeness of data for the sector (i.e., data that covers all funders’ giving) and the completeness of data for a specific funder. The timelier the data, the less likely it is to be complete at both levels.

Sector-wide data

United States: Candid’s data for a given year tends to be complete approximately two years after that year’s end (for example, data for 2022 will be complete by the end of 2024). Complete grants data for all foundations and grantmaking public charities is usually available from the U.S. government via the 990 and 990-PF within two years. More recent data is usually reported by the funders themselves or obtained by Candid from the news, funder websites, and other sources.

Other countries: Grants data from other countries is primarily self-reported by funders or contributed by Candid’s data partners, therefore, represents an unknown portion of grantmaking in those countries.

Specific funder data

If a funder reports on its grantmaking on a monthly or quarterly basis, Candid will only have partial data for the given year. Grants obtained from the news are also highly likely to represent only a small portion of a funder’s grantmaking.

Other factors impacting completeness of individual funder data

Candid is not always able to obtain a complete grants list for a funder.

Candid does its best to fill known gaps in the data by contacting government agencies supplying the data or requesting the information from funders, particularly larger U.S. funders, directly.

Coding grants

Candid’s Philanthropy Classification System consists of five facets:

• Subject
• Population served
• Support strategy
• Transaction type
• Organization type

Candid also collects and/or applies coding on geographic area served. Unless supplied directly to Candid by funders², taxonomic codes are applied to grants in four ways:

1. Rule-based coding

Candid’s system searches grant descriptions for phrases that exactly or very closely match some of our taxonomic codes and applies these codes to those grants. For example, a grant with a description of “For general support” would be assigned the general support code. All incoming grants go through this process.

2. Autoclassification

All grants are sent through Candid’s autoclassifier, which is intended to replicate how our expert staff would code grants. The autoclassifier is a machine learning model that bases its coding on text provided in any of the following fields:

• Grant title
• Grant description
• Program area

Grant title and program area are only available if a funder has shared its data with Candid directly and included those fields.

Candid’s autoclassifier is trained using manually coded grants and is periodically revised to continue improving its accuracy.

3. Manual coding

A portion of grants are reviewed and coded manually by Candid’s indexing team. This team reviews grants of $250,000 or more from 1,000 of the largest U.S. funders or for special projects. Indexers review approximately 25,000 grants representing $27 billion in giving per year. Candid’s autocoding process will never override coding applied by Candid staff or that supplied by funders who share their data.

4. Application of recipient organization coding

If the grant text available is so vague that it is not possible to auto- or manually code, the grant receives the coding of the recipient organization. If coding for a recipient does not exist for a given facet, the grant coding for that facet will remain blank.

Coding of recipient organizations happens in one of three ways:

• Review of the organization’s website by Candid staff
• Autoclassification based on the organization’s mission statement

The provision of codes by the organization itself through their Candid Nonprofit Profile

General notes about coding

The quality of the codes applied by either Candid’s autoclassifier or staff is directly related to the quality of the grants data we receive from funders or obtain from other sources. Staff are instructed not to make assumptions when coding grants; there must be evidence for a code in the text available for that code to be applied. We encourage funders to include detailed grant descriptions providing answers to the questions of who, what, where, and how in the data they share with Candid.

Special notes for researchers

Those intending to use Candid’s grants data to produce analyses about funding trends should keep the following in mind:

• Year-over-year comparability: The availability of grants data for a specific funder may vary from year to year, depending on whether Candid was able to source a grants list. Do not assume that available grants data reflects the full scope of an organization’s grantmaking.

To account and correct for these challenges, Candid created an annual research set, the Foundation 1000, which captures grants of $10,000 or more awarded by a set of the 1,000 largest U.S. funders for a given year. Grants in this set undergo additional cleaning and review to ensure an acceptable level of completeness and coding accuracy. Grants data based on the Foundation 1000 set is available for purchase.

Please contact sales@candid.org for more information about Foundation 1000 data or other grants datasets.

• Allocation of grant dollars where multiple codes exist: Grants may benefit multiple subjects, population groups, support strategies, and geographic areas served. In these cases, it is difficult to conclude the exact amount for each code, because we do not have sufficient information to specify the share of support that is intended for each.
• Population coding: In assigning population codes, neither Candid staff nor Candid’s autoclassifier consider the demographic makeup of the geographic area served. For example, even if a grant is intended to benefit a community with a largely Black/African population, the grant won’t receive that code unless that population is explicitly referenced in the available grant text Candid’s population served taxonomy also cannot capture intersectional identities. For example, a grant meant to benefit low-income seniors would be coded in the same way as a grant meant to benefit low-income people and seniors. Both grants would receive the following population served codes: “Seniors” and “Low-income people.” Our taxonomy does not have explicitly intersectional codes that apply, for example, only to “low-income seniors.”
• Authorized or paid grant amounts: Grant amounts may be either authorized, reflecting the full value of the grant the year it was made, or paid, representing the amount of funding disbursed to a recipient organization in the given fiscal year. Most grants in Candid’s database reflect amounts paid. Authorized amounts are generally only available if provided to Candid by the funder. If a grant is authorized for multiple years, the full value of the grant is attributed to the year in which it was authorized—i.e., a grant authorized in 2020 for $5 million to be distributed over the next five years will appear in Candid’s database as a single grant for $5 million for 2020. We record information about the grant duration, if available, separately.
• Double-counting of grants dollars: In some cases, grants dollars may be accounted for more than once in Candid’s database—i.e., when a funder awards a grant to an organization that then regrants those funds.

Candid removes grants awarded to organizations that also appear in the set as grantmakers when calculating overall totals (this approach also means that where a grant is awarded to support the capacity of an organization, rather than for regranting, it would be removed as well).

When reporting on grantmaking by funder, we take all grants into account.

[1] Candid recognizes that it is not always possible or advisable for funders to share recipient name and location due to privacy or security concerns. We offer funders several ways in which they can responsibly share their data with us in these contexts, detailed in the FAQs on our grants reporting page.

[2] Organizations must provide the exact codes—either the term (e.g., elementary education) or the code (e.g., SB030200) themselves—for Candid’s system to assign these codes to grants correctly. In some cases, organizations have sent Candid their taxonomy to be crosswalked to the PCS. These organizations then apply this crosswalk in their grants management system so the grants data they send Candid is already coded for the applicable facets.

If you are a researcher and have additional questions about Candid’s data, please contact sales@candid.org.