by August Benzow

Social connections and trust — often referred to as "social capital" — play a significant role in economic well-being and social mobility. Individuals with higher educational attainment and economic prosperity tend to have stronger social networks, while those facing poverty and hardship often experience weaker network connections. The extent to which individuals can leverage the resources provided by social capital also varies significantly from one neighborhood to the next.

Communities of color, particularly Black and Hispanic neighborhoods, face additional historical and systemic barriers that limit their access to "bridging social capital," or the connections that span socioeconomic lines. While exceptions exist, like New Orleans and Washington, DC, the map of economic distress and social capital reveals a stark correlation with race and ethnicity.

A novel combination of zip code-level datasets reinforces the connections between prosperity and social capital

Social capital is broken down into two core types:

• bonding, which links individuals within groups, and
• bridging, which connects people across divisions such as race, class, and education.

Bridging social capital has the most significant impact on upward mobility.^[1] Building relationships that span socioeconomic divides can provide those disadvantaged at birth with access to the resources, opportunities, and support networks needed to ascend the economic ladder as they age. Children from low-income families could see a 20 percent average income increase in adulthood if they grew up in areas with economic connectedness typical of high-income families.^[2]

The ability to track and analyze these cross-class friendships at the neighborhood level has long been hampered by data constraints. Leveraging Facebook user data, a groundbreaking from Raj Chetty and Opportunity Insights unlocks the potential for exploring bridging social capital at the zip code level.

The dataset quantifies the level of bridging capital for most zip codes with an economic connectedness (EC) value. EC is calculated by taking the average proportion of friends with above-median socioeconomic status (SES)^[3] among people with below-median-SES and dividing it by 50 percent. The resulting value represents the degree to which people with a lower SES develop friendships with high SES individuals. A value of 0 indicates no friendships across the SES median, while a value of 1 suggests that low-SES individuals have an equal number of high- and low-SES friends. Any values above 1 indicate a higher prevalence of connections with high-SES individuals for those with lower SES.

To explore the reciprocal relationship between economic distress and social capital at the community level, this analysis combines this Opportunity Insights database with the Economic Innovation Group's Distressed Communities Index (DCI). The DCI sorts communities into five tiers of well-being (prosperous, comfortable, mid-tier, at risk, and distressed) based on seven economic indicators. When combined with the Opportunity Insights data, the DCI exposes how economic, social, and demographic forces shape the level of social capital in different communities.

High economic distress goes hand in hand with few cross-class friendships at the neighborhood level

Most distressed communities across America are doubly disadvantaged – not only economically but also in their isolation from opportunity. A nationwide zip code analysis uncovers a troubling trend: the more distressed a neighborhood, the fewer its residents' connections bridging the class divide. This absence of cross-class social capital compounds the uphill battle facing those struggling in society's most left-behind areas.

Tens of millions of Americans bear the burden of social and economic isolation. More than two-thirds (69 percent) of residents in distressed communities find themselves living in zip codes with the lowest levels of economic connectedness (bottom 20 percent). This translates to roughly 34 million people, representing approximately 10 percent of the US population. At the other end of the spectrum, prosperity and strong social connections often go together, although the connection isn't as absolute. Half of residents in prosperous communities reside in zip codes with the highest economic connectedness (top 20 percent), totaling 39 million individuals.

These findings underscore a sobering reality: It is exceedingly rare for the nation's most economically distressed communities to exhibit robust cross-class connectivity. At one extreme are prosperous communities with high rates of bridging social capital that facilitate upward mobility. At the other extreme are distressed communities with a scarcity of opportunity-catalyzing relationships across class boundaries. This divide reveals how the erosion of the American Dream occurs through the compounding forces of economic hardship and limited access to social networks that pave the way for advancement.

Thriving and struggling communities are worlds apart

The typical prosperous zip code has an EC value of 1.1 compared to 0.7 for the average distressed community. This gap is substantial, but even more striking is the vast difference between the extremes: the most prosperous and connected zip codes are worlds apart from the most distressed and least connected ones.

South Bay (33493) and Belle Glade (33430) in the Miami metro area suffer from exceptionally few economic connections between low- and high-income residents, ranking second and third lowest in the country. These mostly Black and Hispanic distressed communities also have some of the highest rates of poverty and prime-age adults out of work nationwide.

Despite technically belonging to a major metropolitan area, these agricultural communities bordering Lake Okeechobee face unique challenges. Separated by 25 miles of wetlands and farmland from the exurbs of Miami, they grapple with isolation and limited opportunities to build bridging social capital. These communities exemplify the struggle faced by many residents of deeply distressed communities who lack the connections crucial for upward mobility and well-being.

While zip codes experiencing deep economic hardship often lack connections across income levels, wealthier areas exhibit higher rates of cross-class friendships. Notably, all ten zip codes with the highest "economic connectedness" levels are clustered in California's Silicon Valley. Examples include Palo Alto (94301) and San Francisco's Marina District (94123), both characterized by high median incomes and educational attainment.

Bridging capital is not just concentrated at the top of the income distribution but also spatially. Opportunities for low-income residents to connect with higher-income individuals tend to be greater in areas with widespread prosperity, like Silicon Valley, compared to those facing deep economic hardship, like Belle Glade.

Stronger connectedness is linked to specific demographics and regions

Geographic disparities in bridging social capital become strikingly apparent when mapping the economic connectedness divide alongside levels of community distress. As the map below shows, zip codes in the bottom fifth for connectedness and classified as distressed by the DCI cluster heavily in the South. Conversely, prosperous zip codes ranking in the top 20 percent for connectedness concentrate in the Northeast, Upper Midwest, and Mountain West.

The Southeast, often heralded for its economic growth, appears to be a paradox when it comes to social mobility. Despite economic strides, a significant portion of the region lacks the social connections crucial for upward mobility. In five states – New Mexico, Louisiana, Mississippi, Alabama, and South Carolina – more than a quarter of zip codes languish in the bottom quintile for both economic connectedness and distress. Except for New Mexico, these states form a contiguous stretch across the Southeast.

As the map illustrates, this dearth of cross-class connectivity plagues predominantly rural areas throughout the Southeast. However, zooming in closer reveals urban cores are not immune either, with pockets of isolation pockmarking major urban centers. Memphis epitomizes this disconnect, with a staggering two-thirds of its zip codes ranking among America's most economically deprived areas while also scoring in the bottom fifth nationally for friendships across class lines.

The economic and social landscape outside the South paints a contrasting picture. In Utah and New Hampshire, a robust 40 percent of communities fall within the top quintile for both economic connectedness and prosperity. Massachusetts follows closely behind at 34 percent. The Midwest also exhibits pockets of economic potential, with Minnesota the leader. Nearly 28 percent of its zip codes rank in the highest tier for both metrics, with Wisconsin not far behind.

These regional bright spots align with prior research^[4] highlighting the Midwest's and Great Plains' abundance of social capital. The implication here is that strong social connections, regardless of their root cause, might act as a buffer against the detrimental effects of economic hardship on social capital, especially in regions with concentrated prosperity.

The role of race and ethnicity in explaining these regional variations in economic connectedness warrants further exploration. States with strong economic connections, like Wisconsin and New Hampshire, also have relatively homogenous populations that are nearly all non-Hispanic white. In contrast, states with fewer cross-class connections, like South Carolina, tend to be more diverse. This pattern tracks with research showing that high social capital tends to be found in racially homogeneous (mostly white) states. In contrast, states with high racial/ethnic diversity tend to have low levels of social capital.^[5]

While this correlation defies a straightforward explanation, the country's long history of inequality and segregation based on race and ethnicity clearly plays an important role. In both rural and urban settings, many low-income Black Americans have become stuck in economically distressed neighborhoods that offer few opportunities to build bridges across barriers of both race and social class. Other minority groups face similar challenges. Milwaukee's predominantly Black and Hispanic distressed zip codes, for example, exhibit below-average connectedness, diverging from the statewide trend.

Across the entire DCI spectrum, economic connectedness is lower in Black and Hispanic communities

Communities with a higher proportion of Black or Hispanic residents (1.5 times the national average) tend to have lower EC scores, regardless of their overall economic standing. At the very bottom of the economic well-being distribution, the average distressed community with a high Black share has an EC significantly lower than a distressed zip code that is primarily white or has a high Asian American and Pacific Islander (AAPI) population.

The typical low-SES resident of a mostly non-Hispanic white and prosperous community has an equal mix of low- and high-SES friends. Compare this to the average prosperous community with a high Black share, where its EC value dips below the median for all zip codes. While the correlation between individual race/ethnicity and EC value is weak (compared to factors like education and income), these findings point to disparities in economic connectedness across different demographics.

Even though majority Black communities tend to have fewer economic connections than their mostly white counterparts, there are notable exceptions. The following two local vignettes from Washington, DC, and New Orleans, LA, showcase distressed neighborhoods with large Black populations that also exhibit high levels of cross-class friendships.

Social connections can also vary widely across communities with similar economic status

While economic well-being often correlates with the level of social connections within a community, this relationship isn't always straightforward. While rare, some distressed areas exhibit surprisingly high levels of economic connectedness, while low-income residents in prosperous communities can have very few cross-class friendships.

While the extremes clearly demonstrate how prosperity and economic connectedness are linked, some zip codes defy expectations, showcasing the complex interplay of demographic and socioeconomic factors and how a community can succeed in spite of limited bridging social capital. For instance, while Hialeah, FL (33018), is a prosperous community in the Miami metro area, it has a meager EC value of .56–below the average for distressed communities.

What factors might explain this low value? Unlike many prosperous zip codes, Hialeah has lower educational attainment rates (comparable to the average for a mid-tier zip code), and 39 percent of its residents make less income than the state median. It is also an immigrant community: two-thirds of Hialeah's population identifies as Cuban, and almost the same share are foreign-born. Research shows that immigrants with less education tend to have lower social capital.^[6] Despite its low levels of cross-class friendships, the community ranks as prosperous in the DCI primarily due to high prime-age employment rates and robust growth in jobs and businesses.

On the other side of the country, zip code 94108, which includes parts of San Francisco's Nob Hill neighborhood and the city's Chinatown, continued to see its DCI score worsen in recent years. This downward slide was primarily due to job and business losses stemming from the COVID-19 pandemic and resultant policies. Despite ranking near the bottom of the distressed quintile, it maintains an especially strong EC value of 1.4.

Like many urban zip codes, pockets of prosperity in this neighborhood exist alongside significant economic hardship. Nearly half its inhabitants are immigrants, predominantly Chinese, many of whom lack a high school diploma. While nationally, Asian households tend to have higher average incomes than white households, data for this area reveals a different pattern, with white residents earning significantly more than Asian residents per capita ($102,500 vs. $33,900). Still, despite high poverty among its foreign-born population, this community exhibits over twice the economic connectedness score of prosperous Hialeah, one of Florida's immigrant hubs. In large part, this is likely because high earners are simply so pervasive in this dense urban area. Cross-class ties are strong in spite of entrenched racial income divides that, in other contexts, inhibit such bridging.

Washington, DC: the positive effects of Black prosperity on economic connections

In contrast to most other American cities, all four of the nation's capital's majority Black distressed and at risk zip codes score highly on economic connectedness. The average EC value across these four zip codes is .94, with the Langdon neighborhood (20018) having the highest value of .99. In contrast, neighboring Baltimore's 11 majority Black distressed or at risk zip codes have an average EC value of .72.

One possible explanation for the unexpected strength of economic connections among low-income residents of DC's distressed zip codes could be their proximity to Prince George's County, MD, one of the few prosperous majority Black counties in the United States. It leads the country in the number of Black households with incomes surpassing $100,000. Separated by the Anacostia River from other DC neighborhoods, the District's predominantly Black distressed zip codes may have strong social and economic ties over the county boundary instead. Friendships that bridge multiple divides (e.g., race and class) are rare in the United States.^[7] Consequently, a shared racial identity could facilitate economic connections between these distressed and prosperous communities. However, it is important to acknowledge that other factors, such as individual agency, geographic proximity, and existing social structures, also significantly shape economic opportunities and outcomes.

New Orleans: Building social capital in the wake of a natural disaster

Hurricane Katrina's impact on New Orleans wasn't solely natural; it was shaped by the city's socioeconomic landscape. Pre-existing vulnerabilities in impoverished communities amplified the disaster's destructive force. Political ineptitude and indifference contributed to the poor maintenance of levees and a bumbling response to the disaster.^[8] Katrina also demonstrated how vital bonding and bridging social capital are when resources and support are desperately needed. The tendency for residents of distressed communities to be isolated and lack strong connections to neighbors and the broader social fabric exacerbates the impact of tragedies like Katrina.

Based on the hardship New Orleans' low-income residents suffered due to the hurricane, it is surprising that many of the city's distressed zip codes have high EC values relative to their counterparts in other cities. The Lower Ninth Ward (70117), a community devastated by the catastrophic failure of the levees during Katrina, has an EC value of .82, well above the average for distressed zip codes. That number climbs to .9 in the neighboring zip code 70116, which is also distressed with a large share of Black residents. Other distressed Black neighborhoods scattered throughout the city have high EC values as well.

Katrina's impact on social capital was multifaceted. While the disaster disrupted existing networks and caused widespread displacement, it also catalyzed new connections. Some residents tapped into aid from local and national organizations, forged new bonds with displaced neighbors, and leveraged existing connections for survival.^[9] These developments may explain the high levels of bridging social capital, but the picture remains complex, especially without pre-Katrina data. It's plausible that low-income residents with weaker social networks were disproportionately displaced. The trauma of displacement, the dissolution of familiar communities, and the formation of new, geographically dispersed networks all reshaped the social capital landscape.

Conclusion

This analysis uncovers a stark truth about the American landscape: race, economic distress, and a lack of economic connectivity are intricately intertwined. Geographic and social segregation born from class, race, and ethnicity often serve to wall off distressed communities, significantly limiting their residents' opportunities to forge cross-class friendships.

While the link between strong cross-class connections and upward mobility is well-established, the relationship with broader community prosperity is complex. Prosperous communities typically boast robust bridging social capital, but whether this is a primary driver or a symptom of success remains unclear. Distressed communities with strong economic connections, like those in Washington, DC, and New Orleans, remain deeply impoverished despite higher upward mobility for individual residents.

The Distressed Communities Index is a powerful tool for understanding how and why the map of social capital varies and the impact that variation has on individuals and communities. By presenting these findings, we encourage further investigation into both the exceptions and the patterns that shape the nation's social and economic geography.

by August Benzow

This research brief explores how the prime-age employment gap (PAEG) and the official poverty measure (OPM) differ in identifying areas of economic hardship for federal aid allocation. While high-poverty counties often have high PAEGs, many high-PAEG counties do not have high poverty rates. This distinction results in different geographical patterns and identifies demographically distinct groups of distressed counties. As implemented in the Recompete program, the PAEG’s expansive eligibility criteria could pose challenges in the competitive grant allocation process due to limited funding.

Introduction

Enacted in August 2022, the CHIPS and Science Act created a new place-based policy specifically designed for deeply distressed areas: the . The Recompete program uses the prime-age employment gap (PAEG)—a new measure in federal place-based policy—to identify communities where employment among prime-age workers (25-54 years old) lags behind the national rate. With its distinctive focus on employment, Recompete breaks from the traditional place-based policy approach of using the Official Poverty Measure (OPM) as the primary diagnostic criteria.

Policymakers are familiar with the OPM and generally know what types of places it identifies and why, but are much less familiar with the PAEG and how this lens filters the country’s economic map. This brief will help guide federal decision-making by laying out how these two measures of geographic distress overlap and where they differ. It offers insights for EDA program administrators as they make near-term decisions on where to target scarce funding and guidance for Congress as it considers how to expand and improve upon place-based policy in the United States in the long term.

Despite its Shortcomings, the Official Poverty Measure has Dominated Federal Place-Based Policy for Over 50 Years

Developed in the mid-1960s, the Official Poverty Measure (OPM) has guided geographic targeting for most federal place-based policies. Its simplicity and ease of measurement make it an attractive option for policymakers, but it is not without its detractors. The federal poverty threshold is an inflation-adjusted benchmark based on the cost of food in 1963 and does not reflect how our understanding of poverty has evolved over the past 60 years. Importantly, the OPM:

• Does not consider cost of living differences between communities
• Only reflects pre-tax income
• Excludes some of the largest transfers to lower-income families
• Does not take into account relative changes in the experience of poverty over time^[1]

Despite its many shortcomings, the OPM has persevered as the targeting mechanism for many federal programs because it provides an easy way to quantify economic distress and is closely correlated with other indicators of economic disadvantage. For instance, regions of the country that are synonymous with concentrated disadvantage like the rural Deep South and Appalachia have persistently high poverty rates. Consequently, policymakers have historically used the OPM as a shorthand for a lack of economic opportunity.

The first large-scale use of the OPM to target federal funding to specific communities was in the 1990s, when Congress established the Empowerment Zone (EZ) program under the Empowerment Zones and Enterprise Communities Act of 1993. The EZ program leveraged tax incentives and block grants to drive economic investment in distressed areas of the United States, and used poverty rates alongside unemployment and general metrics of distress to identify eligible census tracts. As with many federal programs, a 20 percent poverty rate was the minimum benchmark a census tract needed to meet to qualify.

Authorized by the Community Renewal Tax Relief Act of 2000, New Market Tax Credits (NMTCs) deployed similar eligibility criteria with the OPM at its core. Designed to incentivize economic development through the use of tax credits, the policy targeted “low-income” census tracts, defined as a 20 percent poverty rate or a median family income below 80 percent of the surrounding area. Nearly one-quarter of the county’s 75,000 census tracts qualify under this criteria, but only a few thousand have received an investment. More recently, Opportunity Zones, which became law as part of the 2017 Tax Cuts and Jobs Act, pulled from the same criteria in designating communities in which certain private sector investments would carry beneficial tax treatment.

In 2009, the federal government took a different approach to addressing long-term economic distress with the designation of persistent-poverty counties through the so-called 10-20-30 provision as part of the American Recovery and Reinvestment Act. This provision requires certain federal agencies to direct 10 percent of their funding to counties with a poverty rate of 20 percent or more for at least 30 years, and marked a step forward in how the federal government thinks about economic distress by prioritizing persistent poverty; however, its use of counties instead of census tracts excluded most urban areas. Furthermore, it was only a targeting requirement, with no new programs created to address the specific economic challenges of distressed communities.^[2]

The newly-established shifts the focus of place-based policy to labor force participation and does not use the OPM at all in its targeting criteria (although a household income threshold is required for certain geographies). By focusing on participation, the PAEG captures not only those receiving formal unemployment benefits, but also those working-age adults who have exited the labor force entirely. In this sense, it addresses some of the root causes of poverty and economic distress; however, while poverty and PAEG are related, they are also distinct. The choice of indicator meaningfully changes the map of places that will be eligible for any given federal program.

Why the Choice of a Distress Metric Matters

Poverty and prime-age employment rates often diverge

On an individual level, the absence of gainful employment is causally linked to poverty, as breaking free from poverty often proves highly challenging without steady work.^[3] Both indicators can signal larger structural issues within a community or region, including weak labor markets, low median wages, and other economic disparities. The complexities of these structural issues mean that prime-age employment and poverty rates are not always aligned and may point to different challenges.

The scatterplot below shows this divergence with the prime-age employment gap for all U.S. counties along the y-axis and poverty rates along the x-axis. Counties with a high PAEG are highlighted in blue, those with a high-poverty rate are highlighted in gold, and those that meet both criteria are highlighted in gray. It clearly shows that most high-poverty counties (87 percent) also have a PAEG of 5 percentage points or greater. But the inverse is less true: only 42 percent of counties with a PAEG gap of five percentage points or more are also high poverty. Among the 1,207 counties with a PAEG of at least five percentage points (nearly 40 percent of all counties), just 507 have a poverty rate above 20 percent. Although high-poverty counties typically experience high PAEGs, the same cannot be said for high-PAEG counties—a majority of high-PAEG counties are not high-poverty.

The important takeaway from the large group of counties in the top left quadrant is that even setting a higher PAEG threshold—10 or 15 percentage points, perhaps—would still pull in many counties that are not high poverty. This raises questions as to whether economic or non-economic factors are driving elevated PAEG levels locally. There’s a strong case to be made that no place with a high PAEG is meeting its full economic potential, but whether the gap is responsive to policy intervention will depend heavily on whether it’s driven by more social/cultural factors (perhaps single-earner households are the norm) or a clear lack of economic opportunity (or something else). Consequently, policy interventions should carefully consider the driving factors for low prime-age employment when other indicators of economic distress are absent.

Pinal County, Arizona, near Phoenix, is one such country from the top left quadrant. Just 67 percent of adults in the country are employed, a gap of 12 percentage points compared to the national rate. Despite this high worklessness, the county’s poverty rate was 11.4 percent in 2021, higher than the typical county but well below the high-poverty definition embraced by the federal government. Forest County, Pennsylvania, is an even more extreme example—just 25 percent of adults are employed, a gap of 53.1 percentage points compared to the national rate. Despite this extremely high worklessness, the county’s poverty rate was 16.7 percent in 2021.

These examples demonstrate that a county’s PAEG can capture a type of distress overlooked by poverty rates alone. In some cases, and especially in rural areas, non-labor sources of income can reduce poverty rates^[4] even if employment opportunities are scarce. Other factors such as informal employment^[5] and declining male labor force participation can complicate the relationship between prime-age employment and poverty.

Different measurements of distress, different demographics

At the county level, both the PAEG and a high poverty rate capture a subset of communities that are more demographically diverse than the nation as a whole, and also more distressed on several key metrics. Educational attainment rates are equally low for both subsets of counties, and both are losing establishments on average while having significantly fewer high- and moderate-income jobs per capita than the nation.

On other characteristics, however, high-poverty counties diverge more significantly from the nation than those with a high PAEG. Whites comprise 51.4 percent of the population of the average high-poverty county versus 65.3 percent for the average high-PAEG county, above the national share of 59.5 percent. Not only is the poverty rate seven percentage points higher in the average high-poverty county compared to the average county with a high PAEG, but prime-age employment is also slightly lower. The slightly lower overall distress level in the high PAEG cohort is partly due to the larger number of counties in this group.

These statistics do not make the case for one indicator over the other as a more accurate measurement of economic distress, especially since a PAEG gap of five percentage points pulls in a much larger number of counties. Instead, each indicator identifies unique subsets of distressed communities that may require different policy interventions. Many questions about how these two indicators interact will have to be answered with additional analysis. It is enough to emphasize here that each of these indicators is intended to address a distinct economic challenge. While a lack of access to good jobs often operates in tandem with a lack of income for community residents, this is not always the case and different interventions may be needed depending on the goals of a particular program.

The Recompete Eligibility Map is Expansive

Nearly one-third of the U.S. population lives in a Recompete eligible community. This broad eligibility puts the onus on program administrators to identify the most worthy recipients to receive funding from a pilot program that has only received $200 million out of the $1 billion authorized by Congress.

It is important to note that there is an implicit arbitrariness in the selection of any targeting threshold. A PAEG of 5 percentage points or higher as an indicator of economic distress originated in research conducted by the economist Tim Bartik. He arrived at this number by estimating the per capita expenditure needed to halve that gap, which he found to be comparable to what was spent by the Tennessee Valley Authority (TVA), a massive New Deal program that began in the 1930s with the goal of transforming the job markets of distressed rural area.^[6] Additionally, a PAEG of 5 percentage points was seen as the right threshold to create a map of distress that was sufficiently expansive to generate broad political support and allow a large number of places to be eligible for participating in the grant competition while still targeting places that were unlikely to improve without support.

As Recompete made its way through Congress, the bar was lowered even more for certain geographies. The eligibility criteria for Core Based Statistical Areas (CBSAs) and Commuting Zones (CZs) was set at 2.5 percentage points. This decision alone expands the map of eligible communities even further. Ultimately, the most important test of a targeting metric is whether it is likely to support the goals and scope of a policy or program. The Recompete model was designed for depth/concentration—providing large grants to a small number of places in order to really move the dial—but it was saddled with an eligibility map more appropriate for breadth/participation.

Unlike other federal place-based policies, Recompete further expands eligibility by allowing communities to qualify for the program at multiple geographic levels. In addition to the generous CBSA and CZ qualification pathway, counties, cities, and groups of census tracts are all potentially eligible for the programs, albeit with a higher PAEG of 5 percentage points and a median household income (MHI) cutoff of no more than $75,000 tacked on, as well. All tribal lands qualify regardless of their PAEG.

In total, 1,420 counties are eligible for the program through 415 eligible CBSAs and 313 eligible CZs. Another 141 counties with a PAEG of at least five percentage points and a low MHI also qualify for the program, which means around half of U.S. counties are eligible. They are joined by 1,891 eligible municipalities and 4,308 census tracts.

The table below shows how a significantly higher number of every geography except census tracts and cities qualify for the Recompete program than would under an OPM-based measure of high poverty.

How expansive are the Recompete geographies compared to other place-based programs?

More communities are eligible for Recompete than almost any other place-based tool for targeting economic distress. The only other federal place-based program that casts as wide of an eligibility net as Recompete is the New Markets Tax Credit (NMTC), under which any census tract qualifies as a low-income community (LIC) if it has a poverty rate of 20 percent or higher or a median family income of 80 percent or less than the area it is benchmarked against. The chart below shows that 125.9 million people live in an LIC compared to 114.6 million in a Recompete-eligible geography. Opportunity Zones, which governors selected from the universe of LIC tracts along with a small number of contiguous tracts, only represent 31.5 million people. The most scoped targeting approach is through EDA-defined persistent-poverty counties, which must have had a poverty rate of 20 percent or higher for 30 years or more and cover just 8 percent of the population.

The Recompete eligibility map captures 31 percent of the country’s population. If a goal of the program is geographic diversity, then the Recompete targeting criteria achieves this. Nearly every state has at least one county that qualifies for the Recompete program, with much more extensive coverage in the western United States than is achieved with poverty-based measures. Not shown on the map are the contiguous groups of urban census tracts and cities that also qualify for Recompete funding, which expands eligibility for the program even further. While the Recompete program is among the few federal initiatives specifically targeted toward distressed communities, its expansive eligibility criteria encompasses areas that may not be considered distressed by other criteria.

In practice, the Economic Development Administration (EDA), which is tasked with administering Recompete, will evaluate applications and presumably award grants to communities that demonstrate a high need. However, this expansive map makes it more difficult to quickly determine which communities should receive limited funds from the pilot program. A well-scoped eligibility map can play an important role in filtering the communities that can apply for funding. The Recompete map does not accomplish this, which places more pressure on later stages of the selection process to grant a finite number of awards to places that truly have a large and meaningful PAEG to close.

Furthermore, the pitfalls of a map of high-poverty communities have been well researched (e.g., high poverty rates around college campuses because students generally don’t earn wages, other populations with little labor income, and the need for fine-grained, longitudinal poverty data). Referring back to the scatterplot above, researchers and program administrators are fairly knowledgeable about the types of places falling into the bottom right quadrant (high poverty rates but low PAEGs); we know a lot less about those in the top left. The PAEG/Recompete map is wholly new, in other words, and it will take a careful review of eligible communities to determine which ones stand to benefit the most from the program.

Conclusion

Neighborhoods, cities, and entire regions without adequate employment opportunities hold back national growth and perpetuate economic hardship for millions of Americans. Low prime-age employment indicates either a lack of jobs in a community or a high number of residents whose skills do not match those jobs, signaling a need for policy interventions that stimulate private-sector job growth and connect residents to employment opportunities.

The Recompete Pilot Program was designed to do exactly this. It marks an important milestone in the formulation of federal place-based policy: a new tool (flexible grants) designed to take on a specific challenge (boosting work) with awards guided by a custom-selected measure (prime age employment gap) intended to align the intervention from diagnosis to prescription. If adequately funded and executed, Recompete has tremendous potential to help the unemployed and those who have exited the labor force get back to work and serve as a model for future federal economic development policies. However, the funds allocated so far are disproportionately small relative to its ambitious goals and geographic breadth.^[7]

The current challenge is ensuring the success of a pilot program with limited resources and an expansive map of eligibility. Ultimately, the process of awarding grant money will be competitive, with only a small number of these distressed communities receiving Recompete dollars. In order to deliver on its mission and prove itself as a pilot, Recompete funds must be carefully allocated to communities that demonstrate both a high degree of need and a clear potential for success.