Public Data Sources for Needs Statements

Public data for a grant needs statement comes from federal statistical agencies, program agencies, and state open-data portals, all free. The Census Bureau covers population and income, the CDC covers health, and the Department of Education covers schools. Geography, not topic, is the usual constraint.

Current figures — verified 2026-08-11

Item	Value	Source
ACS 1-year estimates geography threshold	areas with populations of 65,000 or more	Census Bureau
ACS 1-year supplemental estimates threshold	areas with populations of 20,000 or more	Census Bureau
ACS 3-year estimates	discontinued; releases through 2011–2013 remain available	Census Bureau
Census QuickFacts place coverage	cities and towns of 5,000 or more	Census QuickFacts
CDC PLACES measure count	49 measures, including 9 drawn from the ACS	CDC PLACES
USDA Food Access Research Atlas structure	two applications: SNAP-authorized Retailer Access Map and Large Retailer Access Map	USDA ERS

These figures change. Verify against the linked source before relying on them. Report an outdated figure

Key takeaways

• Federal statistical data is free, citable, and organized by geography.
• Match the geography of the data to the geography of the project.
• Small-area estimates are modeled, not counted; label them.
• Cite the product, table, and vintage — not “Census data.”
• State and local sources are often more current than federal ones.

What is a public data source for a needs statement?

A public data source for a needs statement is any free, publicly published dataset that quantifies the problem a grant proposal proposes to address, for the geography and population the project will serve. Federal statistical agencies produce most of it, program agencies publish administrative data alongside it, and states and localities fill in what national surveys cannot resolve.

Needs data answers a different question from funding data. Sources describing where money is and who controls it are covered separately in free grant and funder data sources. The directory below covers the other half — the population, health, education, housing, economic, and environmental statistics that support a claim about need. Within evidence, evaluation, and data, needs data is the first evidentiary obligation a proposal takes on.

Selection criteria state the standard directly. Department of Education criteria ask reviewers to weigh “the data presented (including a comparison to local, State, regional, national, or international data) that demonstrates the issue, challenge, or opportunity to be addressed by the proposed project” (34 CFR 75.210). A raw number with no comparison geography does not satisfy that language.

Three properties determine whether a source is usable for a given proposal: the smallest geography it publishes, how often it refreshes, and whether its estimates are direct measurements or statistical models. Each entry below states the first two. The third is flagged wherever it applies, because presenting a modeled estimate as a count is a reliable way to lose credibility with a reviewer who knows the source.

Where do you find population, income, and poverty data?

Population, income, and poverty data comes primarily from the Census Bureau, which publishes down to the census tract and block group through the American Community Survey and models sub-state poverty separately for program allocation. Six sources cover nearly every demographic claim a proposal needs to make.

• American Community Survey via data.census.gov — income, poverty, educational attainment, employment, language spoken at home, disability, housing cost burden, vehicle access, internet subscription, and commuting. Smallest geography: census block group, through the 5-year estimates. Updated annually. The Census guidance on 1-year versus 5-year estimates is the source of record for which product covers which geography.
• Census QuickFacts — one-screen demographic and economic profiles for the nation, states, counties, and larger places. Smallest geography: place, above a published population floor (see current figures). Updated as component estimates release. Useful for orientation, never sufficient as a sole citation.
• Small Area Income and Poverty Estimates — model-based estimates of people in poverty, children in poverty, and median household income. Smallest geography: school district. Updated annually. School-district child-poverty estimates here are produced specifically to implement Title I of the Elementary and Secondary Education Act, which makes them the most defensible education-need statistic available.
• Small Area Health Insurance Estimates — uninsured counts and rates by age, sex, race, and income category. Smallest geography: county. Updated annually.
• IRS Statistics of Income ZIP Code data — returns filed, adjusted gross income, wages, and Earned Income Tax Credit claims by income bracket. Smallest geography: ZIP code. Updated annually. Strong where survey estimates for small areas are too noisy to use.
• IRS Statistics of Income migration data — county-to-county and state-to-state inflows and outflows with associated income. Smallest geography: county. Updated annually. The cleanest evidence for population loss or in-migration pressure.

The Census Bureau also publishes a developer API covering the American Community Survey, the decennial census, and economic programs, which is worth the setup cost only if you build needs assessments repeatedly.

Where do you find labor market and economic data?

Labor market and economic data comes from the Bureau of Labor Statistics for employment and wages, and the Bureau of Economic Analysis for income and output. Both publish to the county level, and the choice among them turns on whether the claim is about workers, jobs, or the regional economy.

• Local Area Unemployment Statistics — labor force, employment, unemployment, and unemployment rate by place of residence. Smallest geography: county and many cities. Updated monthly, with routine revisions.
• Quarterly Census of Employment and Wages — employment and wages reported by employers, covering the large majority of U.S. jobs, by industry. Smallest geography: county by industry. Updated quarterly. The right source for sector-decline and sector-strategy claims.
• Occupational Employment and Wage Statistics — employment levels and wage percentiles by occupation. Smallest geography: metropolitan and nonmetropolitan area. Updated annually. The standard basis for in-demand-occupation and wage-adequacy arguments in workforce proposals.
• BEA regional GDP and personal income — real gross domestic product and personal income with annual growth rates. Smallest geography: county. Updated annually. Useful for framing regional economic distress without relying on a survey estimate.
• Census LEHD OnTheMap — where workers in an area live and where residents of an area work, by age, earnings, and industry. Smallest geography: census block. Updated annually. Uniquely suited to jobs-housing mismatch, commuting burden, and transportation access arguments.
• O*NET OnLine — occupational tasks, skills, knowledge requirements, and work context, crosswalked to standard occupation codes. Not geographic. Updated on a rolling basis. Used to justify curriculum alignment rather than to establish need.

Where do you find health data for a needs statement?

Health data for a needs statement comes from the Centers for Disease Control and Prevention for disease and mortality, the Health Resources and Services Administration for workforce shortage designations, and the Substance Abuse and Mental Health Services Administration for behavioral health. Several reach the census tract, which is unusual among health sources.

• CDC PLACES — model-based small-area estimates of chronic disease outcomes, prevention practices, disabilities, risk behaviors, health status, and health-related social needs. Smallest geography: census tract. Updated annually. The only national source of tract-level chronic disease prevalence; always label the estimates as model-based.
• CDC WONDER — queryable mortality, natality, cancer incidence, and population files. Smallest geography: county. Updated as underlying files release. The authoritative source for mortality-rate claims, with small counts suppressed for confidentiality.
• Behavioral Risk Factor Surveillance System — the annual adult health behavior survey underlying many modeled estimates. Smallest geography: state, with selected metropolitan estimates. Updated annually. Use when a reviewer will want a direct estimate rather than a modeled one.
• CDC/ATSDR Social Vulnerability Index — a composite index built from American Community Survey variables across socioeconomic status, household characteristics, minority status, and housing and transportation. Smallest geography: census tract. Updated on a multi-year cycle. Well suited to defining and defending a target geography.
• HRSA Data Warehouse — health center service areas, workforce data, and designation status, including Health Professional Shortage Areas and Medically Underserved Areas. Smallest geography: designated service area, often sub-county. Updated continuously. Designation status is frequently an eligibility criterion, so check it before writing rather than after.
• SAMHSA data — substance use and mental illness prevalence from the National Survey on Drug Use and Health. Smallest geography: substate region. Updated annually. The default citation for behavioral health need.
• County Health Rankings & Roadmaps — a compiled county profile spanning health outcomes and health factors, with state ranks and downloadable measure files. Smallest geography: county. Updated annually. Each measure names an upstream source in the methodology documentation; cite that source alongside the ranking.
• AHRQ Social Determinants of Health Database and KFF State Health Facts — pre-assembled social determinants variables at county, ZIP, and tract level, and state-level health policy and coverage indicators respectively. Both save assembly time; KFF is a non-governmental source and should be labeled as such.

Where do you find education data?

Education data comes from the National Center for Education Statistics for the universe of schools and colleges, and from the Department of Education’s program offices for accountability and civil rights collections. School and district identifiers connect them, so start with the universe file.

• Common Core of Data — the universe file of every public school and district: enrollment by grade and demographic group, staffing counts, locale codes, and district finance. Smallest geography: individual school. Updated annually. The starting point for any K-12 proposal.
• EDGE — school and district boundary files, district-level demographic profiles built from American Community Survey data, and locale assignments. Smallest geography: school district and school point. Updated annually. Essential for mapping and for documenting rurality where a notice offers rural priority.
• ED Data Express — state and district assessment proficiency, adjusted cohort graduation rates, and accountability identifications under the Every Student Succeeds Act. Smallest geography: school and district. Updated as states report. Establishes whether a target school is federally identified for improvement, which is often a priority or eligibility criterion.
• Civil Rights Data Collection — discipline actions, restraint and seclusion, advanced course access, and chronic absenteeism, disaggregated by race, sex, disability, and English learner status. Smallest geography: individual school. Collected on a multi-year cycle. The definitive source for equity-gap claims.
• IPEDS — enrollment, completions, graduation rates, student financial aid, price, faculty, and finance for institutions participating in federal student aid. Smallest geography: individual institution. Updated annually across interrelated survey components.

State report cards supplement all five. Every state education agency publishes an accountability report card required under federal law, and those files frequently carry current-year results and locally defined measures months before the equivalent federal collection appears.

Where do you find housing and homelessness data?

Housing and homelessness data comes from the Department of Housing and Urban Development, which publishes custom census tabulations of housing problems, the income and rent standards that define affordability, and annual counts of people experiencing homelessness. Census construction statistics cover supply.

• CHAS data — custom Census tabulations of housing problems, including cost burden, severe cost burden, overcrowding, and lacking complete kitchen or plumbing facilities, crossed by HUD income categories, tenure, and household type. Smallest geography: census tract. Updated on a multi-year release cycle. The definitive source for a claim about cost-burdened households in a service area.
• Income Limits — the income thresholds that define low, very low, and extremely low income by household size and area. Smallest geography: metropolitan area and non-metropolitan county. Updated annually. These are the definitions a housing funder will use, so use them too.
• Fair Market Rents — rent standards by bedroom count, including small-area rents by ZIP code. Smallest geography: ZIP code for small-area rents. Updated annually. Pair with income limits to quantify an affordability gap arithmetically rather than rhetorically.
• Point-in-Time and Housing Inventory Counts — sheltered and unsheltered homelessness counts and bed inventory. Smallest geography: Continuum of Care service area. Updated annually. The standard homelessness citation; acknowledge that a single-night count understates the annual population.
• Census Building Permits Survey — new privately owned residential units authorized by building permits. Smallest geography: permit-issuing place. Updated monthly and annually. The cleanest evidence that housing supply is or is not responding to demand.

Where do you find food, agriculture, and rural data?

Food, agriculture, and rural data comes from the Department of Agriculture’s Economic Research Service, which publishes tract-level food access measures and the standard classifications used to establish rurality. One widely used food insecurity source is philanthropic rather than federal.

• Food Access Research Atlas — indicators combining low income with low access to food retailers, measured by both straight-line and road-network distance. Smallest geography: census tract. Updated on an irregular cycle as component applications release. Cite the specific low-income and low-access flag rather than the informal phrase “food desert,” which is not a variable in the data.
• Food Environment Atlas — store and restaurant availability, food assistance participation, local food indicators, and related health outcomes. Smallest geography: county. Updated periodically.
• Rural-Urban Continuum Codes — the standard classification of counties by metropolitan status and adjacency. Smallest geography: county. Updated after each decennial census. The citable way to document rurality when a notice offers rural priority points.
• County Typology Codes — county economic dependence type and policy types including persistent poverty, low education, and population loss. Smallest geography: county. Updated on a decennial cycle. A persistent-poverty designation is often worth more in a proposal than any single statistic.
• Map the Meal Gap — food insecurity rates and meal cost estimates, including child food insecurity. Smallest geography: county and congressional district. Updated annually. Produced by Feeding America, a nonprofit; label it as a non-governmental estimate.

Where do you find justice and public safety data?

Justice and public safety data comes from the Bureau of Justice Statistics for surveys and correctional statistics, and from the Federal Bureau of Investigation for offense and arrest counts reported by law enforcement agencies. Both carry coverage caveats that a careful proposal states rather than hides.

• Bureau of Justice Statistics — victimization, corrections, courts, and law enforcement statistics with documentation and analysis tools. Smallest geography: state for most series, with some jurisdiction-level collections. Updated as individual series release.
• FBI Crime Data Explorer — offense, arrest, and incident data submitted by law enforcement agencies. Smallest geography: reporting agency. Updated annually with rolling quarterly releases. Agency participation varies, so state the reporting coverage for your jurisdiction alongside any rate you quote.

Local sources usually beat both for a city-scale proposal. Police department open-data portals, county sheriff dashboards, and court administrative offices publish incident-level data with far more geographic precision than any national compilation, and citing them signals familiarity with the jurisdiction.

Where do you find environmental data?

Environmental data comes from the Environmental Protection Agency, which publishes ambient air quality measurements, drinking water compliance records, facility-level enforcement history, and integrated community screening layers. Coverage is by monitor, facility, or water system rather than by population geography, which changes how the data must be handled.

• EnviroAtlas — ecosystem services, land cover, and community-level environmental and demographic indicators. Smallest geography: census block group in community components. Updated as component layers release.
• Outdoor Air Quality Data — monitored concentrations and design values for criteria pollutants, plus nonattainment designations. Smallest geography: monitoring site. Updated continuously, with annual certified summaries.
• AirNow — current and forecast Air Quality Index by location. Smallest geography: reporting area. Updated hourly. Better for public communication than for a needs statement.
• Safe Drinking Water Information System reporting — violations, enforcement actions, and compliance status for public water systems. Smallest geography: individual water system. Updated quarterly.
• ECHO — inspection, violation, and enforcement history for regulated facilities. Smallest geography: individual facility. Updated weekly. The right tool for a claim about a specific polluting facility near a service area.

EJScreen, the agency’s environmental justice screening and mapping tool, combines environmental and demographic indicators at small-area geography and is widely cited in place-based proposals. Its public hosting has changed more than once, so confirm the live EPA landing page before citing it, and pair it with the Social Vulnerability Index if a stable second source is needed.

Where do you find business and economic development data?

Business and economic development data comes from the Census Bureau for establishment counts and from the Small Business Administration for lending and program activity. Both are used to establish that a local business base exists, is concentrated in particular sectors, or is underserved by capital.

• County Business Patterns — establishment counts, employment, and payroll by industry and establishment size class. Smallest geography: ZIP code. Updated annually. The standard source for sector density and small-business composition claims.
• SBA open data — loan approvals, program activity, and agency datasets published as open data. Smallest geography: varies by dataset, commonly borrower location. Updated on dataset-specific schedules. Use for capital access gaps rather than for demographic need.
• FCC National Broadband Map — reported broadband availability by technology and speed tier. Smallest geography: individual serviceable location. Updated on a semiannual filing cycle. Pair availability data with American Community Survey subscription data, which measures adoption — a distinction reviewers notice.

Where do you find nonprofit and philanthropic data?

Nonprofit and philanthropic data comes from Internal Revenue Service filings, which exempt organizations must make available for public inspection, and from intermediaries that render those filings searchable. In a needs statement, the use is landscape analysis: showing what services exist, who provides them, and where the gap is.

• IRS Form 990 series downloads — machine-readable filings from electronically filed returns, organized by year with index files. Smallest unit: individual organization. Updated on a periodic release schedule. The raw source, best for building a dataset rather than answering one question.
• ProPublica Nonprofit Explorer — the same filings rendered searchable, with multi-year financial summaries and full-text search. Smallest unit: individual organization. Updated as filings release. The fastest route from an organization name to its finances.
• Candid — nonprofit and foundation profiles, research, and directories, with free access available at partner locations. Smallest unit: individual organization. A commercial nonprofit intermediary; label it as such.

A duplication analysis built from these sources answers a question reviewers ask silently: if the need is real, why is no one else meeting it? Naming the organizations that operate nearby, and stating precisely what they do not do, is stronger than asserting that a gap exists.

Where do you find state and local data?

State and local data comes from state open-data portals, state agency reporting systems, and locally produced assessments. For education, corrections, child welfare, and vital records, state sources are frequently more current and more granular than the federal compilations built from them.

Five patterns find nearly all of it. First, check the state open-data portal, which usually sits at a data.<state>.gov address — California, New York, and Texas are representative. Second, go directly to the state agency that administers the policy area, since state education agencies, labor market information offices, vital statistics offices, and Medicaid agencies publish detailed files that never reach a portal. Third, check Data.gov, the federal open-data catalog, for agency datasets that are hard to locate otherwise.

Fourth, use locally produced assessments. Nonprofit hospitals publish community health needs assessments on a recurring cycle as a condition of tax exemption, and those documents contain primary survey data for a defined service area. Regional councils of government, workforce boards, and continuums of care publish similar planning documents.

Fifth, use sources built specifically to describe local hardship. United For ALICE publishes county-level household survival budgets and the share of households above the poverty line but below the cost of basics. Opportunity Atlas publishes tract-level estimates of children’s adult outcomes by parental income, drawn from the research data library at Opportunity Insights. Local 211 call volume, available from the regional 211 operator, is unmatched for demonstrating unmet demand because it measures people who asked for help and did not get it.

How do you match data geography to your service area?

Matching data geography to a service area means finding the smallest published geography that contains your service area without diluting it, and saying explicitly how the two relate. A county statistic used to describe three census tracts understates concentrated need; a tract statistic used to describe a multi-county region is too noisy to defend.

Survey estimates carry sampling error, and the smaller the area, the larger it gets. American Community Survey tables publish a margin of error alongside every estimate. Reporting a tract-level estimate without its margin of error, or to a precision the margin cannot support, is a technical error a reviewer with statistical training will catch. Where the margin is wide, the honest moves are to aggregate several tracts, use the 5-year product, or use a modeled small-area source built for the purpose.

The Census Bureau states the tradeoff between the two main products plainly. Its guidance describes 5-year estimates as best used when “precision is more important than currency” and 1-year estimates as best used when “currency is more important than precision” (Census Bureau). Population thresholds decide availability rather than preference; those thresholds appear in the current figures above.

Model-based estimates deserve their own sentence in the proposal. CDC PLACES, the Small Area Income and Poverty Estimates, and the Small Area Health Insurance Estimates are statistical models that combine survey data with administrative records and population estimates — not counts. Describing them accurately costs one clause and buys credibility that is difficult to recover once lost.

How do you cite a data source in a grant proposal?

Citing a data source in a grant proposal means naming the publisher, the specific product, the table or measure, the geography, and the vintage — enough that a reviewer could reproduce the number. “Census data” is not a citation. “U.S. Census Bureau, American Community Survey 5-year estimates, Table S1701, [county], [years]” is.

Five elements belong in every citation. The publisher establishes authority and separates federal statistics from advocacy compilations. The product distinguishes among the many datasets a single agency publishes. The table or measure identifier makes the figure reproducible. The geography makes the comparison auditable. The vintage — the reference period the data describes, which is not the release date — is what keeps the claim honest as time passes.

Two conventions matter beyond the citation itself. Always benchmark: report your area against the county, the state, and the nation, since the selection criteria explicitly reward comparison across geographies. And always cite the underlying source rather than the aggregator; a commercial mapping platform that pre-joins federal data is a convenience, not a source.

Primary data collection supplements public data where no public source reaches the question — service waitlists, intake records, participant surveys, focus groups, and partner referral data. Primary data is strongest when it quantifies unmet demand that no national survey measures, and weakest when it substitutes for a public statistic that exists. The credible pattern pairs both: public data establishes the scale of the problem, and your own data establishes that the problem shows up at your door. Where that data will later become a performance measure, design the collection once, as described in performance measurement and indicators.

What goes wrong with needs-statement data?

Needs-statement data fails in a small number of repeatable ways, and reviewers who read many proposals recognize each one immediately. Two failures do most of the damage: a national statistic used to describe a local problem, and old data presented as though it described the present.

Six failure modes account for most of the rest:

• Geography substitution. A national or state rate quoted as though it characterized a neighborhood, usually because the local number was inconvenient or unavailable.
• Stale vintage, unstated. A figure describing a reference period years in the past, presented without its vintage so the reader cannot tell.
• No benchmark. A number with no comparison to county, state, or national values, leaving the reviewer unable to judge whether it is high.
• Modeled estimates presented as counts. Small-area model output reported to a precision it does not have, with no acknowledgment of the method.
• Margins of error dropped. Tract-level survey estimates reported as exact, when the interval is wide enough to include no problem at all.
• Need described, service gap not. Statistics establishing that a problem exists, with nothing establishing that existing providers are not already addressing it.

One more failure is subtler and harder to fix in revision: data that does not connect to the intervention. A needs statement that documents one problem while the project design addresses another leaves reviewers to reconcile the two, and they generally will not. The statistics in the needs section should be the same constructs that appear in the statement of need, the logic model, and the measures the project later reports.

Frequently asked questions

What is the difference between ACS 1-year and 5-year estimates?

The 1-year estimates use twelve months of collected data and cover larger geographies; the 5-year estimates pool sixty months and cover all areas, including census tracts and block groups. The Census Bureau frames the choice as currency versus precision. Population thresholds, not preference, determine which product is published for a given place.

Is county-level data good enough for a neighborhood project?

Usually not. County estimates average conditions across areas that may differ sharply, which systematically understates concentrated need. Use tract-level sources — the American Community Survey 5-year product, CDC PLACES, the Social Vulnerability Index, or HUD CHAS — and report county and state values alongside as benchmarks.

What is a margin of error and do you have to report it?

A margin of error is the range around a survey estimate that reflects sampling uncertainty. American Community Survey tables publish one for every estimate. Reporting it is not always required, but presenting a small-area estimate as an exact count when the interval is wide is a factual overstatement a technical reviewer will flag.

Are model-based estimates acceptable in a needs statement?

Yes, and for many small-area health and poverty questions they are the only option. The requirement is disclosure. Name the source, describe the estimate as model-based, and avoid language implying that anyone counted the people in question. Modeled estimates paired with a direct measure from another source are stronger still.

Should you use state data instead of federal data?

Use both. State education, corrections, child welfare, and vital statistics systems typically publish more recent and more granular figures than the federal collections built from them, and citing them signals local competence. Federal sources remain necessary for national and cross-state benchmarking, which selection criteria frequently require.

How recent does needs data have to be?

Recency requirements come from the notice, not from a general rule. The workable standard is to use the most recent release of the most appropriate product, state its reference period, and explain any known change since. A carefully labeled older figure is defensible; an undated figure implying currency is not.

Related topics

• Evidence, Evaluation, and Data — the hub covering logic models, evaluation design, measurement, and data
• Levels of Evidence in Grant Funding
• Writing an Evaluation Plan
• Writing a Statement of Need
• Free Grant and Funder Data Sources

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