Where Ohio’s Opportunity Zone Money Went
The Ohio Opportunity Zone record answers a narrower question than “Did the program work?” It shows how many designated tracts received any observed investment, how the dollars were distributed among the funded places, and how those shares compared with population across different geographies. Reading those measures together supports a concrete judgment: Ohio’s investment footprint was not merely smaller than its designation map; it was concentrated inside a small set of places and especially in large metropolitan markets.
That conclusion depends on keeping the units separate. A tract can be designated without being funded. A funded tract can receive one project or a large share of total dollars. A region can account for a certain share of Ohio’s population but a very different share of projects or investment. Each comparison describes a different relationship between the policy map and private capital. Blending them would make the distribution appear more even—or more uniformly unsuccessful—than the evidence shows.
Participation is broad on paper, narrow in dollars
Ohio’s first result is a participation gap. The state had 320 census tracts nominated and designated in 2018; when changes in 2020 census tract boundaries and designations are taken into account, the study refers to 328 tracts. Between 2020 and 2024, only 33 percent of the originally designated Opportunity Zones received any investment. The study also identifies 153 tracts that received Opportunity Zone investment during the period, a total that includes both designated and nondesignated tracts.
The difference between those statements matters. The 33 percent figure asks: among the designated zones, how many received any investment? The 153 figure asks: how many tracts of either designation status appear as invested in the analyzed record? It would be incorrect to divide one by the other without preserving the study’s definitions. The numbers are useful precisely because they describe different stages of the distribution.
“Any investment” is the least demanding threshold for participation. A tract qualifies under that measure if the record shows some Opportunity Zone investment during the period; the measure does not say how much capital arrived, how many projects were involved, whether a project was completed, or whether local residents experienced a benefit. Even with that modest threshold, most designated Ohio zones did not register an investment in the data analyzed by the Urban Institute. The finding is evidence of limited reach in the observed period, not proof that those tracts could never attract a project or that designation had no other value.
The funded subset then narrows sharply when measured by dollars. The Urban Institute’s web summary reports that 60 percent of Ohio Opportunity Zone dollars flowed into only 2 percent of funded tracts. The PDF expresses the same concentration through a different comparison: more than half of the state’s investment went to nine designated Opportunity Zones, and more than 75 percent was concentrated in only 5 percent of tracts. These denominators should remain visible. “Two percent of funded tracts” and “five percent of tracts” are not necessarily the same base, and the nine-tract figure specifically refers to designated zones.
The measures nevertheless point in the same direction. Capital did not spread proportionately across the places that were eligible. A small group of tracts occupied the center of the dollar distribution, while many other designated tracts received nothing recorded in the period. The average funded tract would therefore be a poor description of where the money was concentrated. The distribution’s center is closer to a handful of high-receipt locations than to the typical place on the designation list.
This is a common interpretive trap in place-based investment data. A program can have a visible footprint—projects in 153 tracts, for example—without having a broad financial reach. Conversely, a program can be described as concentrated without implying that every funded tract received an unusually large amount. Participation and concentration must be read in sequence: first, identify the places with any observed investment; then ask how the total dollars were allocated within that subset.
The concentration also has a boundary that is easy to overlook. Two of the ten tracts with the greatest amount of Ohio Opportunity Zone investment were not designated Opportunity Zones. That fact does not mean that most dollars went outside the designated map, because the statistic is about the composition of the top ten tracts, not the share of total dollars. It does show that the locations at the top of the dollar distribution were not all selected from the official designation set.
The chart is not a performance ranking. It does not tell us whether the nine tracts were the most distressed, whether the projects generated better returns, or whether concentration improved or harmed surrounding communities. It is a distributional description. The fact that dollars gathered in a few places is important because it determines who actually encountered the program as an investment opportunity, but it is not by itself an evaluation of the program’s social return.
Population share is not investment share
The geographic comparison makes the concentration more legible. Rural and micropolitan areas represented 18 percent of Ohio’s population and 17 percent of its designated Opportunity Zones. Yet they accounted for only 4 percent of projects and 1 percent of total Opportunity Zone investment. The difference is not a matter of a few percentage points in a single measure. These areas were close to their population and designation shares but far below them in observed capital, especially when dollars rather than project counts are used.
Cleveland shows the reverse pattern. The Cleveland metropolitan area contained 18 percent of Ohio’s population but received 24 percent of the state’s Opportunity Zone projects and 49 percent of its dollars. Its project share exceeded its population share by 6 percentage points; its dollar share exceeded its population share by 31 percentage points. That gap between projects and dollars is revealing. Cleveland was overrepresented in both measures, but it was much more overrepresented in capital than in project count, indicating that the projects located there accounted for a particularly large portion of the state’s investment total.
Columbus also received a disproportionate share of investment. Smaller metropolitan areas, including Youngstown, Toledo, Akron, and Dayton, received lower shares of investment relative to their populations. The pattern is not simply “urban areas won and rural areas lost.” Large metropolitan areas attracted the strongest flows, while smaller metropolitan, micropolitan, and rural places were less represented than their population would suggest. Geography operated as a gradient of observed market reach, not a binary category.
The population comparison is useful but should not be mistaken for a fairness index. Population is a reference base, not a measure of investment need or opportunity. A region with 18 percent of the state’s people need not receive exactly 18 percent of the dollars if projects differ in size, housing type, or financing requirements. Nor does a dollar share below population share prove that a region was underserved by every relevant measure. What the comparison establishes is narrower: private capital was distributed differently from people and designated tracts, and the difference was especially large for rural and micropolitan areas.
The sequence of rural and micropolitan figures makes the gap more specific. These areas were close to their population share and designated-zone share, but their project share fell to 4 percent and their dollar share fell further to 1 percent. That pattern indicates that the divergence was not created only by a few unusually large projects; fewer projects appeared there in the first place, and the resulting capital total was smaller still. The figures do not identify whether the missing link was project supply, financing capacity, market demand, fund strategy, or some combination. They do show where a designation failed to translate into comparable observed investment reach.
Cleveland supplies the contrasting sequence. Its 24 percent project share was already above its 18 percent population share, but its 49 percent dollar share was much higher again. In other words, the metropolitan area was selected more often than its population alone would predict, and the selected projects carried a disproportionate financial weight. That is the distributional signature of two filters: which places receive projects and how much capital those projects attract. Neither comparison alone proves why the filters operated as they did, but together they show why a simple tract count cannot describe the financial geography.
Project shares and dollar shares also answer different questions. Project share tells us where the number of reported projects was located. Dollar share tells us where the financial weight was located. If a region has a modest project share but a much larger dollar share, its projects may be larger or more capital-intensive on average. If it has a project share higher than its dollar share, it may have more projects with smaller amounts. The Ohio summary gives enough evidence to identify the divergence, but not enough to calculate a complete project-size comparison for every geography. We should therefore describe the pattern without inventing an explanation for each region.
The geographic result is consistent with the broader mechanism already visible in the Ohio data: investors did not treat every eligible tract as interchangeable. The study reports that funded tracts were much denser in jobs than designated tracts without investment. Large metropolitan areas can contain more potential projects, deeper networks of developers and lenders, and more established demand, but the sources do not isolate the contribution of each factor. It is safer to say that market scale and job density were associated with where investment appeared than to claim that metropolitan status alone caused the flows.
This distinction matters for policy because a designation count can create a misleading sense of geographic inclusion. A rural county may have designated zones and therefore appear inside the program’s reach. But if the rural and micropolitan group contains 17 percent of designated zones and receives only 1 percent of dollars, designation has not translated into comparable financial exposure. The policy map records eligibility; the investment map records selection. Their divergence is the evidence that the latter cannot be inferred from the former.
It also matters for how concentration should be evaluated. A high dollar share in Cleveland is not automatically evidence of waste, just as a low dollar share in rural areas is not automatically evidence of successful targeting. The relevant judgment depends on the program’s objective. If the priority is to mobilize any private capital in low-income communities, investment in a job-dense metropolitan tract may count as a visible success. If the priority is to overcome financing barriers in places the market routinely overlooks, the same pattern raises a concern. The Ohio data show the choice that policymakers must confront; they do not settle the value judgment by themselves.
There is an additional reason to resist national extrapolation. Ohio is unusual in having state-level tax-incentive data that permit this kind of transaction-level analysis, but the records track projects using Ohio’s conforming incentive. Some Ohio Opportunity Zone projects may not have used that incentive and may therefore be absent. The study cleaned duplication across 2021 and 2022 records and adjusted tract boundaries to make comparisons more consistent. Those steps improve the evidence, yet the resulting percentages remain Ohio findings from a defined administrative record, not national rates.
A careful description of the map can now contain all three layers. At the eligibility layer, Ohio designated hundreds of tracts. At the participation layer, only 33 percent of originally designated zones received any observed investment during 2020–2024. At the dollar layer, more than half of the capital was concentrated in nine designated zones, with the web summary reporting 60 percent in 2 percent of funded tracts and the PDF reporting more than 75 percent in 5 percent of tracts. At the geographic layer, Cleveland’s 49 percent dollar share and rural and micropolitan areas’ 1 percent share differed sharply from their population and designation shares.
What this map cannot yet tell us is why the funded places were selected. It identifies the distributional outcome, not the full set of characteristics that made one tract more attractive than another. The next interpretive step is to compare funded and unfunded zones before the program began. That comparison can show whether capital followed places that were already gaining jobs, income, housing value, and educational attainment—evidence that would help explain the geography without confusing explanation with proof of causation.