Every year, HUD pushes roughly $3.4 billion out the door through the Community Development Block Grant program. The money is supposed to fund affordable housing, infrastructure, and economic development in low- and moderate-income neighborhoods. But the formula that decides which cities and counties get how much hasn’t been meaningfully touched since 1978. The result is a quiet, administrative map of winners and losers—one that tilts toward older, declining industrial cities and away from fast-growing suburbs and rural counties where poverty is climbing but the formula can’t register it.
The CDBG formula isn’t secret. HUD publishes the data inputs and the resulting grant amounts every year. What stays hidden is the distributional logic: how four components—poverty, population, housing age, and growth lag—interact to produce a systematic tilt that few members of Congress, let alone local officials, can explain to anyone back home. This piece traces that logic, names the winners and losers, and shows how local governments, nonprofits, and journalists can use publicly available HUD data and narrative reporting to make these hidden trade-offs legible.
The Two-Formula System and the Entitlement Threshold
CDBG funding runs through two formulas: Formula A and Formula B. Entitlement communities—metro cities with at least 50,000 people and urban counties with at least 200,000—get grants calculated under whichever formula spits out the higher number. Smaller, non-entitlement places compete for funds distributed through state governments. The entitlement threshold itself is a policy choice. It locks in a definition of “urban need” that leaves out hundreds of smaller places where poverty has suburbanized and ruralized since the 1970s.
Formula A weights population at 25%, poverty at 50%, and overcrowded housing at 25%. Formula B weights population at 20%, poverty at 30%, housing age—specifically the count of units built before 1940—at 50%, and a growth lag measure that compares a community’s population growth to the national rate at 20%. The growth lag component is designed to reward places that have lost population or grown slowly. In practice, it double-counts the distress of older cities that already score high on pre-1940 housing.
How the Components Interact to Create Systematic Tilt
The interaction of these components produces a predictable pattern. Take two hypothetical communities with identical poverty rates and populations. Community A is an older Northeastern city where 40% of housing units were built before 1940 and population has dropped 10% since 1970. Community B is a Sun Belt suburb where only 5% of housing predates 1940 but poverty has doubled since 2000 and overcrowding is severe. Under Formula B, Community A will pull in substantially more CDBG money than Community B—even if Community B’s current need is sharper—because the formula rewards historical housing stock and population loss, not conditions on the ground right now.
The pre-1940 housing measure is especially distorting. It was written into the formula in 1978 as a proxy for blight and disinvestment. But in 2026, a unit built in 1939 in a gentrifying D.C. neighborhood counts the same as a unit built in 1939 in a persistently poor Youngstown, Ohio, neighborhood. The formula can’t tell the difference. Meanwhile, a mobile home park in a rural county with failing septic systems and no municipal water connection—a clear community development need—adds nothing to the formula because mobile homes rarely show up as pre-1940 housing.
The growth lag component creates a similar distortion. It compares a community’s population growth rate to the national average over a multi-year stretch. Places that lost population get a higher score. But the measure doesn’t capture the composition of population change. A city that shed middle-class families but gained low-income immigrants may show population stability—and get no growth lag bonus—even as its community development needs intensify. The formula rewards depopulation, not demographic stress.
Winners and Losers: A Geographic Distribution
The winners cluster in the Northeast and Midwest. In fiscal year 2024, Cleveland, Ohio, with roughly 360,000 residents, received about $25 million in CDBG entitlement funding. Frisco, Texas, with a similar population but rapid growth and newer housing stock, got nothing as a direct entitlement because it only recently crossed the 50,000 threshold and its formula score under both Formula A and Formula B would be low. Frisco’s low-income residents must rely on state-administered non-entitlement funds, which are a fraction of the entitlement pool and subject to state-level political allocation decisions.
Rural areas are the biggest losers. The CDBG formula was built for urban places. Non-entitlement counties—which include most rural counties—get funds through state governments, but the state allocation formulas often replicate the same biases. A 2023 Government Accountability Office report found that rural communities receive less CDBG funding per capita than urban communities, even after controlling for poverty rates. The reason is structural: the formula’s components simply don’t measure rural distress well. Overcrowding is rare in rural areas where housing is cheap but dilapidated. Pre-1940 housing stock concentrates in urban cores. Growth lag in rural counties is often offset by the formula’s population component, which rewards larger places.
The racial implications are significant but not straightforward. Older industrial cities with large Black populations—Detroit, Baltimore, Cleveland—score high on the formula and pull in substantial CDBG funding. But fast-growing Southern cities with large Black populations—Charlotte, Atlanta, Houston—score lower because their housing stock is newer and their populations are growing. The formula effectively transfers community development resources from growing Black communities in the South to declining Black communities in the North, a distributional outcome that no one in Congress explicitly chose.
Why the Formula Has Not Changed
The CDBG formula has survived multiple reform attempts because the winners hold political power. The current formula benefits specific congressional districts, and any change would create identifiable losers who would mobilize to block it. In 2014, the Obama administration proposed a new formula that would have shifted funds toward communities with higher poverty rates and away from communities with older housing stock. The proposal died in Congress after opposition from Northeast and Midwest delegations. The political economy of formula reform is asymmetric: the benefits of change are diffuse and uncertain, while the costs are concentrated and immediate.
HUD itself has limited authority to change the formula without congressional action. The Housing and Community Development Act of 1974, as amended, specifies the formula components. HUD can adjust the data sources and the weights within narrow bounds, but it cannot add new measures of need—such as rent burden, homelessness, or health outcomes—without legislation. The result is a formula frozen in the demographic and economic assumptions of the late 1970s.
Making the Hidden Trade-Offs Legible
Local governments, nonprofit researchers, and journalists can make these hidden trade-offs visible using publicly available HUD data. HUD publishes annual CDBG formula allocations, including the input data for each entitlement community, on its website. The data includes each community’s population, poverty count, pre-1940 housing count, overcrowded housing count, and growth lag score. With basic spreadsheet skills, anyone can compare two communities and see how the formula components interact to produce different grant amounts.
The challenge isn’t data availability. It’s narrative translation. A spreadsheet showing that Cleveland receives $69 per capita in CDBG funding while Frisco receives $0 is accurate but not compelling. What makes the trade-off legible is a human story: a specific family in Frisco that can’t access after-school programs because the city lacks CDBG funds, or a specific block in Cleveland where CDBG-funded lead abatement prevented childhood poisoning. The data provides the skeleton; narrative provides the flesh.
This is where structured storytelling tools can help. When a journalist or policy analyst sits down with a dataset of CDBG allocations and wants to surface the human stakes, they face a familiar problem: how to move from aggregate numbers to a specific, representative story without cherry-picking. One approach is to use a story idea generator that fits the draft workflow—not to write the story, but to surface narrative angles embedded in the data that a human reporter can then investigate and verify. The tool doesn’t replace reporting; it helps structure the initial question: “Which community in this dataset best illustrates the formula’s tilt, and what would I need to learn to tell that story accurately?”
The Authors Guild, in its AI Best Practices for Authors, emphasizes that AI tools should assist human judgment, not replace it, and that transparency about sources and methods is essential. The same principle applies to policy narrative work: a structured story idea generator can help a reporter identify a promising lead, but the verification—the interviews, the document review, the on-the-ground reporting—remains irreducibly human. The goal isn’t automation. It’s legibility: turning a complex administrative formula into a story that a city council member can explain to a constituent in two minutes.
A Concrete Example: Comparing Two Counties
To see how this works in practice, consider two counties in the same state: Cuyahoga County, Ohio (which includes Cleveland), and Delaware County, Ohio (a fast-growing suburb of Columbus). Both are entitlement communities. In fiscal year 2024, Cuyahoga County received about $18 million in CDBG funding. Delaware County received about $1.2 million. Cuyahoga County’s population is roughly 1.2 million; Delaware County’s is about 220,000. On a per capita basis, Cuyahoga got about $15; Delaware got about $5.50.
The formula components explain the gap. Cuyahoga County has a poverty rate of about 18% and a large stock of pre-1940 housing. Delaware County has a poverty rate of about 5% and almost no pre-1940 housing. But Delaware County’s poverty rate has been rising, and its low-income residents face severe housing cost burdens because the county’s rapid growth has driven up rents. The CDBG formula can’t see that distress because it measures poverty as a count, not as a rate of change, and it measures housing need through age and overcrowding, not through cost burden.
A local journalist could use HUD data to identify Delaware County as a community where the formula undercounts need, then use a structured story idea generator to surface narrative angles: a family evicted because the county lacks CDBG-funded rental assistance, a nonprofit that can’t expand its food pantry because CDBG funds are insufficient, a code enforcement officer who can’t keep up with complaints about deteriorating rental properties. Each of these angles connects a formula component to a real distributional outcome.
The NIST Parallel: How Technical Frameworks Obscure Trade-Offs
The CDBG formula isn’t unique. Many federal programs use complex, data-driven allocation frameworks that create systematic winners and losers while obscuring the distributional logic. The NIST Cybersecurity Framework is a different domain but a similar structural problem. The framework’s components—Identify, Protect, Detect, Respond, Recover—allocate organizational attention and resources toward certain cybersecurity activities and away from others. Organizations that align their spending with the framework’s categories may get preferential treatment in federal contracting or insurance underwriting, while organizations with different risk profiles but equal cybersecurity needs may be disadvantaged. The framework’s technical language and component structure make these trade-offs hard to see, just as the CDBG formula’s components make geographic winners and losers hard to see.
In both cases, the solution isn’t to abandon structured frameworks. It’s to make their distributional effects legible. For CDBG, that means publishing not just the formula inputs and outputs but also a plain-language explanation of who gains and who loses from each component, updated annually. For NIST, it means publishing not just the framework categories but also an analysis of how the framework’s structure affects resource allocation across different types of organizations. In both cases, narrative reporting—stories that connect a technical component to a specific human outcome—is essential to democratic accountability.
What Local Governments Can Do
Local governments that are disadvantaged by the CDBG formula have several options, none of them easy. First, they can document the gap between formula-measured need and actual community development need using local data on rent burden, homelessness, infrastructure deficits, and health outcomes. This documentation can support requests for state-administered non-entitlement funds or for competitive HUD grants that use different allocation criteria.
Second, they can form coalitions with other disadvantaged communities to advocate for formula reform. The political obstacles are significant, but incremental changes—such as adding a rent burden measure or updating the pre-1940 housing threshold to a rolling “older housing stock” measure—may be more achievable than a complete formula rewrite. The key is to make the distributional consequences of the current formula visible to members of Congress whose districts are on the losing side.
Third, they can use CDBG funds more strategically within the constraints of the formula. HUD allows entitlement communities to use CDBG funds for a wide range of activities, including housing rehabilitation, public services, and economic development. Communities that receive less funding can focus on high-impact, low-cost interventions—such as minor home repair programs for seniors or code enforcement in targeted neighborhoods—that address the most severe needs first.
Conclusion: Implementation Beats Intention
The CDBG formula is a case study in how implementation design determines distributional outcomes. Congress intended CDBG to address community development needs in low- and moderate-income areas. But the formula it chose—and has not updated in nearly five decades—systematically favors some places over others based on measures that no longer capture need accurately. The winners are older, declining cities with pre-1940 housing stock. The losers are fast-growing suburbs and rural areas where poverty is rising but the formula can’t see it.
Making these hidden trade-offs legible requires more than data. It requires narrative: stories that connect a formula component to a specific family, a specific block, a specific program that exists or doesn’t exist because of how a number was calculated in 1978. Local governments, nonprofits, and journalists have the tools to tell these stories. The question is whether they have the will—and whether Congress has the will to listen.