Federal place-based policy and the geography of inequality in the United States, 1990–2019

Abstract

This paper assesses the growth and spatial distribution of federal place-based policies in the United States. Using a novel dataset of federal place-based policies from 1990 to 2019, we show how the dual forces of fiscalization and financialization have fueled a substantial increase in federal place-based funding to communities via competitive tax credit and grant programs. We consider whether federal place-based funding has been distributed in a compensatory way by prioritizing more disadvantaged communities or whether it has compounded neighborhood inequalities by prioritizing more advantaged communities. We find that federal place-based funding has gone overwhelmingly to communities experiencing economic disadvantage, as intended, but at the same time such policies have compounded other forms of spatial inequality via disproportionate investment in areas with more nonprofit organizations and stronger housing markets. Economically disadvantaged neighborhoods that are spatially embedded within counties with strong housing markets and robust nonprofit sectors received the most federal place-based funding. These organizational and housing market inequities are strongest for tax credit and competitive grant programs, precisely the forms of funding that have grown most over this period. The funding trends reveal a pattern of cumulative advantage, as poor communities with initial funding advantages in the 1990s went on to receive the vast majority of federal place-based funding in the subsequent decades, leading to growing divergence among high-poverty communities in the distribution of federal place-based resources over time.

Introduction

Areas of concentrated neighborhood disadvantage are durable features of the US landscape, resulting from multi-generational patterns of discrimination and disinvestment (Sampson 2012; Sharkey 2013; Wilson 1987; Massey and Denton 1993). Racially and economically exclusionary public policies have played a significant role in creating, institutionalizing, and reinforcing such disparities (Fullilove 2004; Hillier 2003; Hyra 2008; Rothstein 2017). Communities with concentrated disadvantage—marked by substandard housing, under-resourced schools, limited employment opportunities, hazardous health conditions, and greater exposure to crime—can produce adverse outcomes for residents (Sampson, Morenoff, and Gannon-Rowley 2002; Sharkey and Faber 2014). Collectively, these practices have contributed to extreme wealth inequality and place-based inequities in children's prospects for upward mobility (Chetty et al. 2020, 2014; Alvarado 2016; Oliver and Shapiro 1995).

In the late twentieth century, the ascendance of neoliberal governance and erosion of the social safety net led to a retrenchment of direct aid to low-income Americans (Danziger and Gottschalk 1995; Hacker 2004). Yet, during this same period, geographically targeted public investments—such as place-based policies—emerged as politically popular tools to address poverty. We follow other scholars in defining "place-based" policies as those in which eligibility and implementation are tied to a specific geographic area (Neumark and Simpson 2015). Such spatially targeted subsidies are typically channeled through public and private entities to improve local economic opportunities, housing, and other institutions and amenities (Dobbie and Fryer 2011; Dunning 2022; Glaeser and Gottlieb 2008; Kline and Moretti 2014). Although these efforts may improve neighborhood conditions, like other hallmarks of neoliberalism that favor the private market they may also lead to uneven geographic patterns of investment and displacement (Freeman 2005; Fullilove 2004; Marcuse 1985). Because place-based investments can alter neighborhood context, resident outcomes, and patterns of neighborhood change, they offer a window into questions of longstanding sociological relevance regarding the state's role in shaping the geography of inequality.

Despite this, sociologists have devoted limited empirical attention to the development and distribution of place-based policies or how they intersect with underlying patterns of inequality. Existing studies often examine a single program, location, or time period (Busso, Gregory, and Kline 2013; Rosenblatt and DeLuca 2017; Tach and Emory 2017), limiting insight into the broader development and geographic contours of place-based policymaking as a whole. To address this, we develop a novel longitudinal dataset of federal place-based policies linked to specific counties and neighborhoods from 1990 to 2019. This allows us to assess the scope and unequal geographic reach of such policies across the entire United States. Ultimately, our findings suggest that although federal place-based policies have directed funding to economically disadvantaged areas, they also have exacerbated organizational and financial inequalities among neighborhoods. We conclude that in the absence of a robust welfare state (Esping-Andersen 1990), such piecemeal, targeted approaches to addressing poverty will inevitably contribute to the unequal distribution of resources across places.

1. Place-based policies in theory and practice

Scholars and policymakers have long debated the relative merits of people- and place-based approaches to improving opportunities in disadvantaged communities. People-based interventions subsidize individuals directly, while place-based policies recognize that individual disadvantages are spatially clustered and that neighborhood conditions shape opportunities and quality of life (Sharkey and Faber 2014). Advocates of place-based investment frequently justify targeting disadvantaged communities on equity grounds. The federal government has been criticized for neglecting low-income communities, or for actively extracting resources from them, particularly those with large Black populations (Fullilove 2004; Massey and Denton 1993; Rothstein 2017; Squires 2011). Despite these rationales, the equity and efficiency of place-based policies remains contested (Brenner, Marcuse, and Mayer 2012; Dawkins 2013; Freeman 2005; Glaeser 2012; Glaeser and Gottlieb 2008; Marcuse 1985; Neumark and Simpson 2015; Robinson 2019).

Place-based investments have been part of federal policymaking since the 1950s urban renewal programs, when the federal government partnered with nonprofits and local governments to redevelop urban neighborhoods (Dunning 2022). Following backlash against urban renewal initiatives, federal policies that provided formula funding based on identifiable community need came into fashion. State and local governments channeled federal funding to nonprofit Community Development Corporations within neighborhoods (Dommel and Rich 1987) via block grant programs such as the Community Development Block Grant Program (CDBG) and the HOME Investment Partnership Program, which provide automatic funding to state and local governments on a formula basis to support economic development and housing. The rationale for geographic eligibility has historically been one of equity, to minimize political influence, favoritism, and grantsmanship. Faced with scarce resources, federal policymakers sought to appear equitable in their distribution of aid through the use of transparent formulas (Smith and Lipsky 1995) based on a "fair share" principle relative to population size or according to community need (Rich 1989). Yet, these formulas also reflect underlying political priorities (Dunning 2022). Because federal block grants are allocated to state and local governments, local officials have wide discretion over how and where funds are disbursed (Rohe and Galster 2014).

Since the 1980s, federal–local intergovernmental transfers experienced widespread retrenchment, with block grants replaced by competitive grants and, more recently, by tax credit programs (Biles 2011; Pacewicz 2015; Smith and Lipsky 1995). In the 1990s, the Clinton administration used public resources to leverage private investment in so-called "untapped" markets of high-poverty neighborhoods (Sanger 1999), facilitating programs such as Low-Income Housing Tax Credits (LIHTC), Empowerment Zones, and HOPE VI. This federal shift to competitive grant and tax credit programs empowered coalitions of local elites—government agencies, nonprofits, developers, and business leaders—to compete for federal and state place-based funding while negotiating locally for their share of dwindling block grant funds (Levine 2021; Dunning 2022; Marwell 2009; Tuckman 1998).

The market-based approaches expanded further in the 2000s amid broader trends toward financialization and neoliberalism. The expansion and deregulation of global financial markets facilitated central city revitalization (Hyra 2008; Pacewicz 2013; Sassen 2000; Wyly and Hammel 1999), fueled by growing access to capital, financial innovation, securitization, and subprime lending (Hackworth 2007; Massey et al. 2016). The use of public funds to leverage private investment in housing and economic development through tax credits proliferated, aligning with broader bipartisan support for fiscalization—using tax credits for social policy purposes (McCabe 2018; O'Brien 2017).

Federal funding for place-based efforts expanded under both Democratic and Republican administrations since the 2000s: New Markets Tax Credits and Federally Qualified Health Centers in the Bush era; Choice Neighborhoods and Promise Neighborhoods under Obama; and Trump-era programs such as Opportunity Zones. Although these programs target different dimensions of neighborhood environments, they share common features through competitive federal tax credits and grants delivered via local public–private partnerships (Lees et al. 2013). Nonprofits continued to play an influential role, given that many competitive grant and tax credit programs use nonprofit community development organizations as intermediaries (Levine 2021). Given the reliance on local entities, these competitive application processes have the potential to exacerbate inequality among economically disadvantaged places based on their local capacity.

2. Policy innovation and shifting foundations of inequality

As Charles Tilly famously observed, inequality results from the "conjunction of socially organized categories" and is maintained by "an extensive institutional infrastructure" (Tilly 2003: 31). Public policy has long been viewed as a central component of the institutional infrastructure that creates, maintains, and disrupts societal inequalities. On the one hand, policy can serve an important compensatory role, using public regulations and resources to mitigate disparities that emerge from society or the economy. During the War on Poverty, for example, the Johnson administration's Office of Economic Opportunity intentionally distributed more funding to areas with higher shares of nonwhite and poor residents to redress discrimination and alleviate poverty (Bailey and Duquette 2014). On the other hand, policy also has been used routinely to reinforce or exacerbate societal inequalities by directing resources to already-advantaged groups. Federal policies have restricted access to rights and benefits based on social categories such as race, employment status, nativity, parental status, or sexual orientation (Brown 2013; Powell et al. 2010; Soss, Fording, and Schram 2011). At the extreme, state actions that favor already-advantaged groups can set in motion processes of cumulative advantage, with early advantages facilitating access to more resources, compounding inequality over time (DiPrete and Eirich 2006).

Because public policy has used social categories to enable or constrain access to person-based resources, place-based policies—which instead rely upon community-level characteristics—may also enable or constrain access to resources on the basis of community features. From the theoretical discussion above, we identify three scenarios for how resources may be distributed to places—fair share, compensatory, or compounding. In a fair share scenario, communities receive similar amounts of funding conditional on population size, with other community characteristics not influencing funding levels. This is in line with the stated goals of many block grant and formula funding programs such as CDBG and HOME (Dommel and Rich 1987; Rohe and Galster 2014). In a compensatory scenario, communities with greater disadvantages receive more investment. Compensatory public intervention may be justified if the extent of disadvantage is deemed so great that private markets are unlikely to intervene. Indeed, many place-based policies prioritize—or even restrict—funding to neighborhoods with high levels of economic disadvantage. Finally, in the third scenario of compounding inequality, more funding goes to more advantaged communities, as more disadvantaged neighborhoods are passed over in favor of eligible neighborhoods with less dire circumstances and greater community capacity to both apply for and implement initiatives (Levine 2021).

Although sociologists and public officials frequently define neighborhood disadvantage by economic conditions, other community-level characteristics may also affect the allocation of public funding. This is especially true for competitive grant programs that require community organizational capacity to apply for and deliver on the funded neighborhood activities. It is even more true for competitive tax credit programs that require both organizational capacity and interest from private investors and businesses. Therefore, beyond economic disadvantage, we consider three community characteristics that may be linked to both federal place-based funding and neighborhood inequality: racial-ethnic composition, organizational capacity, and housing markets.

Although contemporary place-based policies do not explicitly award funding based on racial-ethnic composition, race and ethnicity have long shaped neighborhood-level investments in the United States. Historically, African-American communities were excluded from federal investment opportunities (Faber 2020; Katznelson 2005), and when included the support was sometimes predatory and extractive, as in the examples of urban renewal and subprime lending (Fullilove 2004; Goetz 2013; Rugh and Massey 2010; Squires 2011). War on Poverty era programs sought to remedy racial discrimination in federal policymaking by funding areas with predominantly nonwhite populations (Bailey and Duquette 2014). Whether modern federal place-based policies continue this compensatory logic, compound historical racial exclusion, or distribute resources evenly remains an open question.

Community organizational capacity is also a sociologically significant dimension of neighborhood inequality with implications for federal place-based funding. Classic urban sociological theories portrayed disadvantaged neighborhoods as devoid of organizational resources (Wilson 1987), but contemporary scholarship highlights wide variation in their organizational density and capacity (Allard 2008; Small and McDermott 2006). Local nonprofit and voluntary organizations are central to neighborhood collective action, civic engagement, and protest (Sampson et al. 2005). They are also important agents of social control and community improvement (Levine 2021; Sharkey 2018; Sharkey, Torrats-Espinosa, and Takyar 2017), and they provide social and legal services in low-income communities (Immergluck 2016; Marwell 2009; Massey 2015; Squires 2011). Nonprofit organizations are key beneficiaries of federal funding, adapting their activities in response to shifting government priorities (Dunning 2022; Smith and Lipsky 1995). As intermediaries, community-based organizations have become influential actors in local politics and governance, shaping the distribution of resources among and within communities (Dunning 2022; Levine 2021). Following from this research, an organizational perspective on place-based initiatives suggests that local nonprofit density might enhance a community's ability to apply for place-based funding and to deliver interventions.

Place-based policies are also connected to financialization in the US housing market (Davis and Kim 2015; Krippner 2005), resulting in uneven investor demand for place-based funding across housing markets. Historically, public officials in programs like CDBG directed funding to areas with deteriorating housing infrastructure (Dommel and Rich 1987), indicating an attempt to compensate for disparities in physical distress and development capabilities across communities. More recently, scholars have shown how deregulation of financial markets fueled central city revitalization, with public investments an enabling force behind the flow of capital to disadvantaged neighborhoods that spurred gentrification (Hyra 2008; Pacewicz 2013; Wyly and Hammel 1999). To the extent that federal place-based funding subsidizes local business elites or financial investors who seek to profit from investments in undervalued city land, a greater density of local businesses and a stronger local housing market may garner more demand for place-based funding.

3. The present study

In this paper, we develop a sociological account of the place-based turn in federal policymaking that links the unequal geographic distribution of federal resources to the institutional and socioeconomic characteristics of local communities. We create a novel longitudinal data source that allows us to analyze geographic variation in federal place-based investment for over a quarter century (1990–2019). We assess investment collectively across distinct "silos" of policymaking and research—housing, economic development, education, health, crime prevention, and other multi-dimensional initiatives—to reveal the full scale of such investment. Funding allocations reflect multiple processes including federal agencies' definitions of eligibility and evaluation criteria; communities' decisions and capacity to apply and the strength of their applications; as well as political influence across multiple tiers of federal, state, and local governance. What we empirically observe in this study is the total amount of funding each community received as a result of these political and institutional processes.

Drawing on these data, we ask to what extent federal place-based funding has been distributed equally across counties and neighborhoods in the United States. We test three distinct selection hypotheses based on our theoretical account above: (1) fair share hypothesis: communities received roughly equal amounts of funding conditional on size, with community characteristics not influencing funding levels; (2) compensatory hypothesis: more disadvantaged communities received more funding; and (3) compounding hypothesis: more advantaged communities received more funding. Because most federal place-based policies have eligibility criteria regarding economic disadvantage, we expect to find empirical support for the compensatory selection hypothesis. However, we also consider additional metrics of community disadvantage on the basis of racial-ethnic composition, organizational capacity, and housing market characteristics across multiple spatial levels. For these dimensions, we expect to find evidence of compounding advantage given the roles of organizational capacity and financial markets theorized above. We also consider whether investment patterns are the same across distinct funding mechanisms including block grants, competitive grants, and tax credits. Because neighborhoods are spatially embedded within broader markets and governance structures, we consider these characteristics at both the neighborhood level and the broader county level through multi-level modeling and interactions. Finally, we ask whether initial funding disparities in the 1990s grew over time, consistent with processes of cumulative (dis)advantage. Our findings inform broader theories of neighborhood inequality and neighborhood change by showing how durable systems of spatial inequality intersect with, and are redefined by, government intervention and policy innovation.

4. Methodology

4.1. Data sources and definitions

We obtained data on the sources of federal place-based funding from public records of Congress and federal agencies. We developed an initial list of candidate programs by consulting Notices of Funding Availability, Congressional appropriations and bills, annual budgets of federal agencies, existing federal data sources, primary documents from presidential archives, and inquiries with key executive and agency personnel and other policy experts. Because we examined annual federal appropriations, budgets, and authorizing legislation, we captured all programs that received Congressional approval or agency funding in each year, even if the program is no longer in existence.

From the initial list of programs, we developed criteria to identify the final set of federal programs to include in this analysis. We defined an initiative as place-based if eligibility was determined, and implementation occurred, for a specific bounded geographic area (Kline and Moretti 2014; Neumark and Simpson 2015). We excluded policies that targeted people based on individual or familial characteristics (e.g., family poverty status) and retained policies that targeted geographic units based on community-level characteristics (e.g., neighborhood poverty rate). Second, we included funding from federal sources in the form of grants, loans, or tax expenditures. These were awarded on both formula and competitive bases, and some allocation decisions were made by state or local governments. Third, we focus on the period 1990–2019. There was relatively little federal place-based investment prior to 1990, and 2019 was the most recent year for which data were available for all initiatives and that predates COVID-era interventions. Table 1 lists the names and key details for all initiatives that met the above criteria. We identified 25 distinct programs, totaling over $471 billion in federal place-based funding over the 30-year time period.

Table 1.

Data Sources and Funding Information for Federal Place-Based Programs, 1990-2015

PLACE-BASED PROGRAM	FUNDING AGENCY	TOTAL FUNDING	YEARS	ELIGIBILITY	DOMAIN/ TYPE	ALLOCATION
Low Income Housing Tax Credit (LIHTC)	HUD	$214,361,150,826	1990-2019	No geographic restrictions for where units are developed, but qualified census tracts (QTCs) and difficult development areas (DDA) receive ‘bonus’ tax credits. QCTs have high poverty rates or low median household incomes, and DDAs have high land, construction, and utility costs relative to area median incomes.	Housing/Tax	Federal tax credits are allocated to state housing finance agencies that solicit and review applications and award credits.
HOME Investments Partnership Program (HOME)	HUD	$30,007,168,543	1990-2019	States and local jurisdictions with inadequate housing supply, high resident poverty rates, and fiscal distress.	Housing/Formula	Federal funds allocated to state and local governments that determine specific affordable housing activities to receive funding.
Neighborhood Stabilization Program (NSP)	HUD	$7,266,664,544	2008-2010	Neighborhoods with the greatest need for stabilization based on a high concentration of foreclosed and/or vacant properties, delinquent loans, and subprime loans.	Housing/Formula	Federal funds allocated to state and local governments. Two rounds of funding were on a formula basis, one round was competitive.
HOPE VI	HUD	$8,722,294,788	1993-2010	Distressed public housing based on population density, rates of vandalism and criminal activity, availability of supportive services, and occupied by residents dependent on public assistance who are low-income or unemployed.	Housing/Grant	Local public housing authorities applied for the federal grant and use it to leverage additional public and private funding.
Choice Neighborhoods	HUD	$1,076,357,579	2010-2019	Neighborhoods with distressed public or HUD-assisted housing occupied by residents meeting poverty thresholds that experience distress related to high crime or vacancy rates.	Housing/Grant	Grantees apply for federal funds. Grantees include local governments, public housing authorities, and community organizations.
Rural Innovation Fund (RIF)	HUD	$4,382,182	2010	Rural areas, Indian tribal entities, Colonias, Appalachian distressed counties, and communities in the Lower Mississippi Delta region.	Housing/Grant	Grantees apply for federal funds. Grantees include state and local government agencies and community organizations.
Federally Qualified Health Centers	HHS	$64,018,442,484	1990-2019	Medically underserved areas (MUAs) at various geographic scales are calculated using four criteria: provider to population ratio, infant mortality rate, percent of the population 65+, and percent of the population below the federal poverty level.	Health/Grant	Grantees are community-based health care providers that apply for FQHC designation.
Healthy Food Financing Initiative (HFFI)	USDA, Treasury, and HHS	$102,271,924	2011-2019	Neighborhoods in urban and rural communities with food deserts, defined as low-income areas in which residents do not live in close proximity to affordable and healthy food retailers.	Health/Grant	Grantees are food retailers in eligible underserved areas.
Drug Free Communities	HHS	$618,231,040	1997-2019	Communities with high rates of youth substance use. Grantees must specify specific target geographic area (from neighborhood to county) and document actions that will lead to change in community-level substance use outcomes.	Health/Grant	Grantees are community coalitions that include various public agencies and nonprofit organizations.
Promise Neighborhoods	Dept of Education	$493,411,648	2010-2019	Distressed neighborhoods with an education need (defined as a neighborhood within the attendance zone of a low-performing school) and a family and community support need (based on neighborhood health indicators, crime rates, and housing and poverty thresholds).	Education/Grant	Grantees are nonprofit organizations and higher education institutions in partnership with local school districts.
New Markets Tax Credits (NMTC)	Dept of Treasury	$66,116,648,670	2001-2019	Rural or urban census tracts meeting individual poverty or median family income thresholds. Rural census tracts that are either located in an empowerment zone contiguous to one other low-income community or that experience net out-migration.	Economic Development/Tax	Individual and corporate investors receive federal tax credits in exchange for investing in Community Development Entities located in low-income communities. CDEs apply for federal tax credits and allocate them to individual and corporate investors.
Community Development Financial Institution (CDFI) Fund	Dept of Treasury	$4,168,925,247	1994-2019	Economically distressed communities with financial institutions (CDFIs and CDEs) recognized for their support of underserved populations in low-income areas.	Economic Development/Grant	Grantees are federally recognized CDFIs.
Federal Empowerment Zones, Enterprise Communities, Renewal Communities (EZ/EC/RC)	HUD	$13,825,710,799	1995-2019	Economically distressed census tracts with high levels of poverty and unemployment.	Economic Development/Tax	Zone designations are made on competitive basis to state and local governments. Businesses operating within designated zones receive federal tax credits.
Economic Development Administration (EDA)	Dept of Commerce	$5,221,582,360	2001-2019	Economically distressed communities, including those negatively impacted by changes to the coal economy, determined by average per capita income, the unemployment rate of the region, or deemed a “special need” by the EDA.	Economic Development/Grant	Eligible grantees are state and local governments, higher education institutions, and nonprofits.
Brownfields Economic Development Initiative (BEDI)	HUD	$81,944,391	2005-2010	Redevelopment of brownfields, defined as vacant or underused industrial and commercial sites with real or potential environmental contamination	Economic Development/Grant	Eligible grantees were state and local governments.
StrikeForce for Rural Growth and Opportunity	USDA	$51,425,664	2010-2015	Rural counties meeting persistent poverty thresholds.	Economic Development/Grant	Eligible grantees are local governments and nonprofit and for-profit organizations.
Appalachia Economic Development Initiative (AEDI)	HUD	$588,500	2015	Counties in the Appalachia Region that are chronically underserved, undercapitalized, and lack capacity to support business development.	Economic Development/Grant	Grantees are partnerships between local governments and nonprofit organizations.
Jobs Plus	HUD	$106,307,765	2014-2019	Public housing developments with high levels of unemployment	Economic Development/Grant	Grantees are public housing authorities
Project Safe Neighborhoods (PSN)	DOJ	$310,607,000	2003-2019	Cities with gun related violence and gang violence, especially elevated youth gang-related incidences.	Crime/Grant	Grantees are U.S. district attorney’s offices working with federal, state, and local law enforcement and other community representatives.
Innovations in Community Based Crime Reduction (CBCR/BCJI)	DOJ	$79,519,893	2012-2019	High-poverty neighborhoods with crime hot spots, defined as micro-places in communities that have persistent crime problems.	Crime/Grant	Grantees are community organizations.
Strategic Approaches to Community Safety Initiative (SACSI)	DOJ	$3,431,629	1998, 2000	Cities diverse in size, region of the country, and severity of crime that targeted homicide, youth violence, firearms, or sexual assault.	Crime/Grant	Grantees were U.S. district attorney’s offices working with local public and private organizations.
Community Development Block Grants (CDBG)	HUD	$53,063,802,777	1990-2019	Counties and cities awarded funding on a formula basis according to poverty rates, population, age of housing stock, and housing overcrowding. Local jurisdictions determine specific neighborhoods to receive funding.	Multi-dimensional/Formula	Federal funds are allocated on a formula basis to local and state governments who then determine specific projects to fund that benefit low-income households and distressed housing.
Rural Housing and Economic Development (RHED)	HUD	$198,022,910	2002-2005; 2007-2009	Rural areas.	Multi-dimensional/Grant	Grantees include state and local government agencies and community organizations.
Building Neighborhood Capacity Program (BNCP)	Depts of Treasury, Education, DOJ, HHS	$2,960,094	2012-2014	Distressed neighborhoods based on measures of public safety, education, housing, human services, and health.	Multi-dimensional/Grant	Grantees include cross-sector coalitions of public agencies and community organizations.
Youth Opportunity Grants	Depts of Labor and Education	$1,272,342,285	1999-2005	Economically distressed census tracts with high levels of poverty and unemployment.	Multi-dimensional/Grant	Grantees were local workforce agencies.

The primary geographic unit for place-based programs differed in metropolitan and non-metropolitan areas. Most place-based initiatives in non-metropolitan areas used the county as the geographic target, so for non-metropolitan areas we used the county as the sole unit of analysis. For metropolitan areas, most place-based programs targeted neighborhoods, although the way neighborhoods were defined varied across programs. For consistency over time and across interventions, we allocated all metropolitan funding into 2010 vintage Census tracts. For geographic units that did not correspond with 2010 Census tract boundaries, we considered all tracts that were fully or partially within the geographic target area as "treated" by the intervention and allocated the funding equally to all tracts within the target area. We were able to determine the specific Census tracts for almost all (98.6%) of metropolitan place-based funding. We adjusted all funding for inflation using the Personal Consumption Expenditures index and present amounts in 2019 dollars.

4.2. Community-level covariates

Our national analyses use counties to provide complete coverage and consistent geography across the entire United States. In our multivariate analyses that predict place-based funding as a function of county characteristics, we include predictors at the county level using 1990, 2000, and 2010 decennial Census data unless noted otherwise (Logan, Xu, and Stults 2014; Manson et al. 2018). We include county controls for total population size and population density to adjust for the fact that places with larger populations may receive more funding purely as a function of their size.

We calculate a county-level economic disadvantage index (Sampson, Raudenbush, and Earls 1997) that includes rates of poverty, unemployment, public assistance receipt, single-parent households, and population under age 18. We measure racial composition as the share of county residents who are non-Hispanic Black. We include county nonprofit density as a measure of organizational capacity. This measure was calculated from the National Center for Charitable Statistics, and we include a measure of all 501c3 organizations per capita. We capture local business density from the Census Bureau's County Business Patterns data, which measures the number of for-profit establishments per capita in the county. To measure the strength of the county-level housing market, we construct a standardized scale that includes measures of the vacancy rate, the share of housing units constructed within the last 5 years, and appreciation as measured by change in the median home value in the preceding decade. These three metrics capture different yet interrelated aspects of the housing market that institutions and individuals use for investment decisions (Joint Center for Housing Studies 2020; Quigley and Raphael 2005).

4.3. Neighborhood-level covariates

In metropolitan areas, place-based funding ultimately targeted specific neighborhoods, so we also conduct a multi-level neighborhood analysis for metropolitan areas. We construct census tract–level measures identical to the county-level variables described above. However, for nonprofit and business densities we use zip code measures rather than tract-level measures and rely upon a zip code crosswalk to align geographic boundaries over time (Bailey and Helmuth 2024). Likewise, we use the Census Bureau's series on Zip Code Business Patterns API to compile data on neighborhood business density. We also include measures of the tract's spatial position within the broader metropolitan area: distance in miles to the central business district (Lee and Lin 2018) and location in a principal city using designations from the Census. We exclude tracts with fewer than twenty-five residents.

4.4. Analyses

In the analyses that follow, we first provide descriptive evidence of the expansion of federal place-based policymaking since 1990 and its uneven reach across the United States. We then use OLS to regress logged county-level place-based funding on our predictor variables, testing hypotheses regarding how economic disadvantage, racial composition, organizational capacity, and strength of the housing market are associated with the intensity of subsequent federal place-based investment. Specifically, we model how these county characteristics at the start of the decade (1990, 2000, or 2010) were associated with the amount of place-based funding received in the ensuing decade (1990–1999, 2000–2009, or 2010–2019). If measures of community disadvantage are positively associated with greater funding, this supports the compensatory hypothesis. By contrast, positive associations between community advantage and greater funding would support the compounding hypothesis. If community characteristics are not associated with place-based funding, this would be consistent with the fair share hypothesis. Because associations were substantively similar across the three time periods (results available in Appendix D), we pool the data in a single model and include a decadal dummy variable as well as county-clustered standard errors.

After estimating these regressions at the county level to understand the uneven distribution of funding across these larger geographic units for the entire nation, we then estimate regressions at the neighborhood level for metropolitan areas only. Because neighborhoods are spatially embedded within broader markets and governance structures, we estimate a multi-level model that nests tracts and zip codes within metropolitan counties over time, which allows us to understand both how neighborhood-level disadvantage is associated with federal place-based funding, as well as how broader county-level characteristics shape funding above and beyond neighborhoods' own characteristics. To further examine the spatial embeddedness of place-based funding, we present cross-level interactions between neighborhood and county-level predictors. For both sets of analyses, we also report regression results separately for formula, grant, or tax expenditure funding to test whether funding amounts differed by federal funding mechanism.

5. Results

5.1. The place-based turn in federal policymaking

We identified over $471 billion in federal place-based investment between 1990 and 2019. Figure 1 charts the growth of this funding, which began at under $5 billion annually in 1990. During the 1990s, annual funding more than doubled to about $10 billion by decade's end, driven primarily by creation of competitive grant and tax credit programs—HOPE VI to redevelop distressed public housing and Empowerment Zones (EZ) to stimulate economic activity and employment in high-poverty, high-unemployment areas.

Figure 1.

Annual place-based funding from federal sources, 1990–2019. Source: Data compiled by authors from public records of Congress and federal agencies. Annual funding amounts adjusted for inflation to 2019 dollars.

Annual funding continued to increase substantially during the 2000s, largely due to new economic development programs like the New Markets Tax Credit (NMTC) program, which used federal tax credits to stimulate local economic activity in a broader range of poor communities than EZ programs. Federal place-based funding peaked during the Great Recession of 2007–2009 at about $23 billion annually, fueled by temporary relief funding and new programs such as the Neighborhood Stabilization Program (NSP) for neighborhoods with high foreclosure rates.

After the 2007–2009 recession, federal place-based funding allocations declined slightly but remained above pre-recession levels through 2019, averaging around $21 billion per year. To put this in perspective, post-2010 funding levels far surpassed annual budgets of federal means-tested programs like Temporary Assistance for Needy Families ($16.5 billion) or Head Start ($10 billion), though they remained modest compared to large social programs like Medicaid and Social Security. In 1990, federal place-based funding represented 0.215% of federal outlays; by 2015, it had more than doubled to 0.524%. Nationally, this translated to about $1,685 per person, or $12,794 per person in poverty, from 1990 to 2019.

Federal place-based funding mechanisms evolved considerably over time, with most growth coming from tax expenditures (Fig. 2). Competitive grant programs constituted a smaller share of growth, while formula funding declined. These shifts reflect a strong trend toward fiscalization: place-based programs increasingly used tax credits to incentivize private developers, employers, and investors to build housing or provide economic opportunities in disadvantaged neighborhoods. Since the 1990s, tax expenditure funding expanded from roughly $4 billion to over $15 billion annually, constituting over two-thirds of all place-based funding since 2010. This increase was driven by expansion of the LIHTC housing program and the creation and growth of the NMTC economic development program.

Figure 2.

Annual place-based funding from federal tax expenditures, competitive grants, and formula funding, 1990–2019. Source: Data compiled by authors from public records of Congress and federal agencies. Annual funding amounts adjusted for inf lation to 2019 dollars.

5.2. The uneven geography of federal place-based investment

We traced federal place-based funding to specific geographies and found it to be national in reach, touching nearly every US county between 1990 and 2019. Just 23 counties received no funding during this 30-year period. Figure 3 maps total per capita funding by county.

Figure 3.

Per capita place-based funding by county, 1990–2019. Source: Data compiled by authors from public records of Congress and federal agencies. Annual funding amounts adjusted for inflation to 2019 dollars.

Despite broad coverage, variation in place-based funding was primarily local. Fully 94.6% of the variation occurred within states, not across state lines; in metropolitan areas, 91% of variation occurred among neighborhoods within metros rather than between metros. As Table 2 shows, the average tract received $6.3 million between 1990 and 2019, but that distribution was heavily skewed. Nationally, roughly 10% of tracts received virtually no funding (<$5 per person), whereas about 10% received at least $4,540 per person (with tract totals ranging from $10 million to $100 million). At the top, about 1% of tracts received over $100 million in federal place-based funding.

Table 2.

Total and Per Capita Funding on Place-Based Policies, 1990-2019

National
	Total	$471,174,195,553
	Total per capita	$1,685
	Total per person in poverty	$12,794
	Total Tax Expenditures	$294,303,510,295
	Total Competitive Grants	$86,533,049,394
	Total Formula Funding	$90,337,635,864
County
	Mean funding	$149,912,994
	Mean per capita funding	$1,531
	Mean per person-in-poverty funding	$10,472
	Percentiles of county per capita funding
	10th	$262
	25th	$577
	50th	$1,055
	75th	$1,779
	90th	$2,986
Tract
	Mean funding	$6,338,890
	Mean per capita funding	$2,096
	Mean per person-in-poverty funding	$14,985
	Percentiles of tract per capita funding
	10th	$5
	25th	$45
	50th	$333
	75th	$1,556
	90th	$4,540
	Share of tracts with zero funding	3.20%
	Share of high poverty (>20%) tracts with zero funding	1.18%

Over time, these disparities reflected a strong pattern of cumulative advantage, as shown in Figure 4. Panel A sorts high-poverty counties (poverty rates >20 percent) into funding percentiles based on 1990 allocations. In the 1990s, the top 5 percent (95th percentile) of high-poverty counties received $500.8 million annually—$410 million more than the middle 50 percent, which received $90.6 million. These initial disparities compounded over the next two decades in a pattern consistent with cumulative advantage, as tracts that received the most funding in the 1990s garnered the lion's share of additional funding from subsequent expansions and new programs. By 2019, the "early winners" (top 5 percent) received $2.6 billion annually, compared to $742.3 million for the middle 50 percent—a nearly $2 billion annual gap.

Figure 4.

Total annual funding amounts and mean number of programs for high-poverty counties 1990–2019, by 1990 funding percentile. Source: Data compiled by authors from public records of Congress and federal agencies. Annual funding amounts adjusted for inflation to 2019 dollars. Funding percentiles calculated based on 1990 funding levels. Sample restricted to counties with poverty rates above 20 percent in 1990.

Panel B shows that counties with early funding advantages also accessed a broader array of place-based programs. These "early winners" averaged over seven separate funding streams during the Great Recession and around six programs from 2012 onward. In comparison, the middle 50th percentile stayed below four programs on average; they received less money and from fewer sources. Some of this stems from policy design, such as categorical eligibility across programs. For example, communities with Empowerment Zone (EZ) designations in the 1990s were categorically eligible for later programs, like New Markets Tax Credits (NMTC). This illustrates one way that federal policy design enabled cumulative advantage for place-based funding. But policy design is not the whole story, as many early winners received future funding unrelated to categorical eligibility.

Examining early winner communities reveals how the process of cumulative advantage unfolded. In the early 1990s, they received substantial funding from four programs—CDBG, FQHC, LIHTC, and HOME. As new initiatives launched, these communities were well positioned to receive more. Take Orleans Parish, Louisiana (New Orleans), which received $23.6 million from those four initial programs in 1990. In the mid-1990s, this was followed by $4.7 million in HOPE VI funds and smaller grants from EZ and CDFI, which grew substantially in the early 2000s. In the 2000s, it secured EDA and PSN grants and $114–$340 million annually in NMTC investments. During the Great Recession, it received large NSP allocations and later JOBS Plus and Choice Neighborhoods funding. Most of these programs were renewed for multiple years. In total, Orleans Parish received nearly $3.5 billion in place-based funding, with 74% coming from tax credit programs. Other early winners—that sustained funding over time and attracted more place-based investment as the government rolled out new initiatives—included major urban counties like the Bronx, Brooklyn, Baltimore, Philadelphia, Detroit, and St Louis, but also smaller metros like Albany, Georgia; Lawrence, Kansas; Mobile, Alabama; and Yakima, Washington. Early winners even included micropolitan counties in Texas, Arizona, Louisiana, and Michigan, as well as rural Monroe County, Mississippi.

5.3. County disadvantage and federal place-based investment

What community characteristics explain the unequal distribution of funding across counties? To cover different scales and geographies, we begin with a national county-level analysis and then turn to a multi-level neighborhood analysis of metropolitan areas. First, we estimate a pooled model that regresses decadal logged county funding (1990–1999, 2000–2009, 2010–2019) on county characteristics at the start of the corresponding decade (1990, 2000, 2010). We include total population and population density as controls, so exponentiated coefficients can be interpreted as percent changes in decadal funding adjusted for county size. Model 1 of Table 3 reports coefficients, with the first column displaying raw coefficients and the second column displaying coefficients from standardized independent variables. We discuss the standardized coefficients below, as they allow comparisons across predictors with different units of measurement—we exponentiate them to interpret the logged dependent variable in percentage terms (Manning 1998).

Table 3.

OLS regressions of county-level logged place-based funding on county characteristics, 1990–2019 (pooled by decade).

	Total Funding (Coefficients)	Total Funding (Betas)	Tax Expenditures (Coefficients)	Tax Expenditures (Betas)	Competitive Grants (Coefficients)	Competitive Grants (Betas)	Formula Funding (Coefficients)	Formula Funding (Betas)
Disadvantage Index	0.616*** (0.083)	0.616*** (0.083)	1.340*** (0.144)	1.340*** (0.144)	1.414*** (0.121)	1.414*** (0.121)	0.228 (0.138)	0.228 (0.138)
Percent Non-Hispanic Black	0.012** (0.004)	0.169** (0.052)	0.029*** (0.008)	0.418*** (0.118)	0.028*** (0.007)	0.407*** (0.104)	0.011 (0.006)	0.159 (0.091)
Housing Market Scale	0.955*** (0.080)	0.955*** (0.080)	2.724*** (0.151)	2.724*** (0.151)	1.019*** (0.134)	1.019*** (0.134)	0.853*** (0.100)	0.853*** (0.100)
Nonprofit Density (per 1,000 residents)	0.223* (0.110)	0.187* (0.092)	0.438* (0.180)	0.367* (0.151)	0.486** (0.151)	0.407** (0.126)	−0.041 (0.135)	−0.034 (0.113)
Business Density (per 1,000 residents)	0.002 (0.002)	0.080 (0.108)	0.005 (0.003)	0.252 (0.179)	0.000 (0.003)	0.012 (0.136)	0.005* (0.003)	0.275* (0.137)
County Population (10,000s)	0.021*** (0.006)	0.606*** (0.180)	0.033** (0.010)	0.963** (0.304)	0.028** (0.009)	0.822** (0.270)	0.025*** (0.007)	0.736*** (0.211)
Population Density (10,000s per sq. mile)	−0.063 (0.375)	−0.010 (0.058)	−0.907 (0.619)	−0.141 (0.096)	−0.294 (0.566)	−0.046 (0.088)	−0.159 (0.469)	−0.025 (0.073)
Region (ref = Northeast)
South	−1.651*** (0.142)	−1.651*** (0.142)	−4.000*** (0.304)	−4.000*** (0.304)	−2.875*** (0.308)	−2.875*** (0.308)	−1.823*** (0.171)	−1.823*** (0.171)
Midwest	−1.287*** (0.115)	−1.287*** (0.115)	−2.341*** (0.279)	−2.341*** (0.279)	−3.501*** (0.287)	−3.501*** (0.287)	−1.493*** (0.144)	−1.493*** (0.144)
West	−1.415*** (0.161)	−1.415*** (0.161)	−3.889*** (0.352)	−3.889*** (0.352)	−0.930** (0.318)	−0.930** (0.318)	−2.351*** (0.241)	−2.351*** (0.241)
Metropolitan Status	1.274*** (0.112)	1.274*** (0.112)	2.481*** (0.230)	2.481*** (0.230)	1.771*** (0.211)	1.771*** (0.211)	1.596*** (0.146)	1.596*** (0.146)
Decade (ref = 1990)
2000	0.375*** (0.077)	0.375*** (0.077)	0.666*** (0.163)	0.666*** (0.163)	4.489*** (0.146)	4.489*** (0.146)	−0.619*** (0.095)	−0.619*** (0.095)
2010	0.304*** (0.087)	0.304*** (0.087)	−0.137 (0.196)	−0.137 (0.196)	4.655*** (0.160)	4.655*** (0.160)	−1.102*** (0.100)	−1.102*** (0.100)
Constant	16.289*** (0.106)	16.289*** (0.106)	14.278*** (0.274)	14.278*** (0.274)	9.855*** (0.289)	9.856*** (0.289)	15.167*** (0.137)	15.167*** (0.137)
Observations	9,405	9,405	9,405	9,405	9,405	9,405	9,405	9,405
R-squared	0.304	0.304	0.214	0.214	0.317	0.317	0.225	0.225

Robust standard errors in parentheses

^***p<0.001

^**p<0.01

^*p<0.05

We find strong support for the compensatory hypothesis regarding economic disadvantage. A 1 SD increase in the economic disadvantage index is associated with 85% (e^.62) more federal place-based funding. This aligns with stated funding priorities, as many programs explicitly prioritized economically disadvantaged areas. We also find support for the compensatory hypothesis for the share of non-Hispanic Black residents—a 1 SD increase is associated with 18% (e^.169) more funding, net of the other predictors in the model. Of course, economic and racial composition are interrelated due to historical patterns of racial discrimination and disinvestment (Faber 2020). Nevertheless, the relationship between funding and racial composition implies that communities with more non-Hispanic Black residents received more funding, even when controlling for contemporary economic disadvantage.

For a key measure of county advantage—strength of the housing market—we find evidence in the opposite direction, supporting the compounding inequality hypothesis. Counties with stronger housing markets—measured by lower vacancy rates, more new housing construction, and greater appreciation in home values over the preceding decade—received significantly more place-based funding than places with slacker housing markets. A 1 SD increase on this measure was associated with 160% (e^.955) more place-based funding during the decade. We find a positive association between the county-level density of nonprofit organizations (number of nonprofits per 1,000 residents) and subsequent receipt of federal place-based funding. A one standard deviation increase in nonprofit density was associated with 21 percent (e^.187) more place-based funding. The measure of local business density was neither statistically nor substantively significant, however.

Next, we examine predictors of tax, competitive grant, and formula funding separately in Table 3 and graph them in Fig. 5. The overall pattern described above holds most strongly for tax and competitive grant funding, with weaker, mostly insignificant associations for formula funding. Federal place-based tax credit funding went disproportionately to areas with greater economic disadvantage and a larger share of non-Hispanic Black residents, in line with the compensatory hypothesis. But tax credit funding also went disproportionately to areas with higher nonprofit density and stronger housing markets, consistent with the compounding hypothesis. We find similar results for competitive grants. Federal formula funding followed a different selection pattern: associations between economic disadvantage, percent Black, and nonprofit density were substantively small and statistically insignificant, consistent with the fair share hypothesis. Local housing market strength remains positive and significant for formula funding, though the effect is smaller than for the other funding types. Appendix D presents additional regressions by metropolitan status, decade, and lagged funding. These analyses suggest that the results described here are strongest in metropolitan areas and, consistent with cumulative advantage, prior funding strongly predicts future funding, even controlling for community characteristics.

Figure 5.

Fully adjusted predicted place-based funding (logged) by funding mechanism. Notes: Coefficients for slopes are from coefficients in Table 3. The range from low to high for each predictor is defined as the minimum and maximum for each variable (excluding outliers). All other variables set at sample means and include controls for population size, population density, region, and metropolitan status.

5.4. Neighborhood disadvantage and spatial embeddedness in metropolitan areas

We next trace federal place-based funding to specific census tracts within metropolitan areas. We use these data in a multi-level regression model, in which neighborhoods (defined as census tracts and zip codes, depending on the measure) are nested within metropolitan counties. Like the previous analysis, we estimate a pooled model that regresses decadal logged tract funding (1990–1999, 2000–2009, or 2010–2019) on tract, zip code, and county characteristics at the start of the corresponding decade (1990, 2000, or 2010); these results are presented in Table 4. We find associations between neighborhood disadvantage and funding similar to what we found at the county level. There is strong evidence to support the compensatory hypothesis for economically disadvantaged tracts, as expected given stated funding priorities: a 1 SD increase in the neighborhood economic disadvantage index is associated with 212% (e^1.138) more funding. Net of economic disadvantage and the other measures in the model, the share of neighborhood residents who are Black is also positively associated with the amount of place-based funding the tract received, in line with the compensatory hypothesis. Zip code business density also is associated with significantly less funding, in line with the compensatory hypothesis. We find evidence for the compounding inequality hypothesis with regard to tract housing characteristics and density of nonprofit organizations: tracts with stronger housing markets received significantly more investment, as did zip codes with greater density of nonprofit organizations.

Table 4.

Multi-level regressions of decadal tract-level logged place-based funding on tract, zip code, and county characteristics, 1990–2019.

	Model 1		Model 2		Model 3		Model 4
	Total Funding (Coefficients )	Total Funding (Standardized )	Tax Expenditures (Coefficients )	Tax Expenditures (Standardized )	Competitive Grants (Coefficients )	Competitive Grants (Standardized )	Formula Funding (Coefficients )	Formula Funding (Standardized )
Tract-Level Characteristics
Disadvantage Index	1.138*** (0.014)	1.138*** (0.014)	2.011*** (0.021)	2.011*** (0.021)	0.691*** (0.015)	0.691*** (0.015)	1.141*** (0.016)	1.141*** (0.016)
Percent Non-Hispanic Black	0.013*** (0.001)	0.297*** (0.017)	0.013*** (0.001)	0.293*** (0.023)	0.016*** (0.001)	0.359*** (0.018)	0.017*** (0.001)	0.383*** (0.019)
Housing Market Scale	0.324*** (0.020)	0.324*** (0.020)	0.551*** (0.029)	0.551*** (0.029)	0.198*** (0.022)	0.198*** (0.022)	0.173*** (0.023)	0.173*** (0.023)
Distance to Central Business District (miles)	−0.006*** (0.001)	−0.093*** (0.021)	0.006*** (0.001)	0.104*** (0.024)	−0.015*** (0.002)	−0.243*** (0.028)	−0.012*** (0.001)	−0.202*** (0.025)
Principal City	0.637*** (0.028)	0.637*** (0.028)	0.426*** (0.037)	0.426*** (0.037)	0.524*** (0.033)	0.524*** (0.033)	0.810*** (0.032)	0.810*** (0.032)
Zip Code-Level Characteristics
Nonprofit Density	0.201*** (0.029)	0.069*** (0.010)	0.624*** (0.041)	0.214*** (0.014)	0.374*** (0.034)	0.128*** (0.012)	0.112*** (0.033)	0.038*** (0.011)
Business Density	−0.004*** (0.001)	−0.036*** (0.009)	0.001 (0.002)	0.005 (0.013)	0.002* (0.001)	0.021* (0.011)	−0.006*** (0.001)	−0.057*** (0.011)
County-Level Characteristics
Disadvantage Index	0.218*** (0.060)	0.218*** (0.060)	−0.294*** (0.053)	−0.294*** (0.053)	0.755*** (0.112)	0.755*** (0.112)	−0.132 (0.069)	−0.132 (0.069)
Percent Non-Hispanic Black	−0.013*** (0.004)	−0.170*** (0.046)	−0.012*** (0.003)	−0.157*** (0.042)	−0.003 (0.007)	−0.039 (0.084)	−0.011** (0.004)	−0.145** (0.053)
Housing Scale	−0.219*** (0.066)	−0.219*** (0.066)	−0.055 (0.060)	−0.055 (0.060)	0.120 (0.124)	0.120 (0.124)	−0.219** (0.077)	−0.219** (0.077)
Nonprofit Density (per 1,000 residents)	0.369*** (0.076)	0.301*** (0.062)	0.546*** (0.063)	0.446*** (0.052)	0.736*** (0.146)	0.601*** (0.119)	0.227** (0.088)	0.186** (0.072)
Business Density (per 1,000 residents)	−0.002 (0.001)	−0.081 (0.053)	−0.003* (0.001)	−0.122* (0.048)	−0.004 (0.002)	−0.162 (0.098)	0.001 (0.001)	0.032 (0.061)
Constant	10.799*** (0.119)	10.886*** (0.116)	2.028*** (0.097)	2.035*** (0.094)	3.104*** (0.238)	3.096*** (0.233)	10.255*** (0.137)	10.326*** (0.134)
Observations (tracts)	176,439	176,439	176,439	176,439	176,439	176,439	176,439	176,439
Number of zip codes	42,415	42,415	42,415	42,415	42,415	42,415	42,415	42,415
Number of counties	3,294	3,294	3,294	3,294	3,294	3,294	3,294	3,294
Number of decades	3	3	3	3	3	3	3	3

Notes. Standard errors in parentheses. All continuous variables centered at national means. All models include controls for: tract-level population and population density, county-level population and population density, and region

^***p<0.001

^**p<0.01

^*p<0.05

The neighborhood-level results also varied across the different tax, grant, and formula funding mechanisms, highlighting the divergent consequences of these funding mechanisms for resource inequality: All are compensatory on the basis of neighborhood economic disadvantage and race, and compounding on the basis of neighborhood housing market and nonprofit density, but these patterns are strongest for tax funding. To further explore heterogeneity, we present these results over time in Appendix E. The findings are largely consistent across the three decades, with two notable exceptions. First, during the 2000–2009 period, we find that tracts with stronger housing markets received less investment, which we interpret as a response to the Great Recession. Second, business density is not related to place-based funding in the 1990s, but this association becomes appreciably stronger over time, consistent with trends toward greater fiscalization and financialization over this period. Similar to the county-level cumulative advantage results, previous tract-level funding was strongly predictive of future place-based investment net of other characteristics.

Neighborhoods are embedded within broader economic, political, and institutional contexts, so we also included county-level covariates in the multi-level models to determine whether the broader county context influenced neighborhood funding, above and beyond conditions within the neighborhood itself. At the county level, we find that tracts received significantly more place-based tax funding if they were located in more economically disadvantaged counties, and significantly less funding if they were located in counties with a higher share of non-Hispanic Black residents. The positive association with county nonprofit density remains strongly significant for tracts across all funding mechanisms. Interestingly, and in line with formula funding priorities, net of the other covariates in the model, the strength of the county housing market was associated with significantly less funding to a tract, a result driven by federal formula funding.

To further examine the spatial embeddedness of place-based funding, we present two cross-level interactions. First, Figure 6a tests whether resources targeted to economically disadvantaged tracts depend not only on the tract's own characteristics, but also on the extent of economic disadvantage within the surrounding county. We plot the margins from this cross-level interaction, showing separate regression lines for tracts between the 5th and 95th percentiles of economic disadvantage. The regression line for the most economically disadvantaged tracts (95th percentile) is negative, meaning disadvantaged tracts received more place-based funding if they were located in more economically advantaged counties. The opposite was true for more economically advantaged tracts (5th, 25th, and 50th percentiles)—for them, being in a more economically disadvantaged county was positively associated with significantly more place-based funding. These divergent associations by county context have important implications for our compensatory and compounding hypotheses. In economically disadvantaged counties, there was little spatial targeting and low- and high-income tracts received similar amounts of place-based funding regardless of their own economic disadvantage. In economically advantaged counties, however, place-based funding was considerably more targeted and compensatory toward tracts with more economic disadvantage.

Figure 6.

Fully adjusted tract–county interactions predicting place-based funding (logged). Notes: Margins obtained from model in column 1 of Table 4, plus a cross-level interaction between county and tract measures.

Second, Figure 6b shows a cross-level interaction between county nonprofit density and tract economic disadvantage, which plots separate regression lines for tracts at the 5th, 50th, and 95th percentiles of economic disadvantage. This interaction tests whether the association between nonprofit density and place-based funding differs for more and less economically advantaged tracts. We found that this association was significantly stronger for more economically advantaged tracts, which can be seen in the steeper positive slopes for the tracts at the 5th and 50th percentiles than for tracts at the 95th percentile of economic disadvantage. This significant cross-level interaction means that funding differences among tracts are smaller—and less compensatory—in counties with greater nonprofit density. In other words, economically advantaged tracts benefit more from a greater density of nonprofits in the surrounding county than do economically disadvantaged tracts, which receive substantial funding regardless of the density of nonprofit organizations in the county.

6. Discussion

In this paper, we documented a substantial and sustained place-based turn in federal policymaking that has occurred over nearly three decades. What began as nominal investments of about $5 billion per year in the 1990s across a small number of programs within a few federal agencies has grown to $22 billion per year since 2010 across twenty-five programs and many agencies. We traced federal place-based funding to specific counties and neighborhoods and found that federal place-based program funding has had national reach, with almost all counties (98.5%) receiving some funding during this period. Despite its national scope, federal place-based investment was uneven across counties and especially among neighborhoods, with the top 1% of tracts receiving over $100 million each over this period. The funding trends reveal a pattern of cumulative advantage, as poor communities with initial funding advantages in the 1990s went on to receive the vast majority of place-based funding in the subsequent decades, leading to growing divergence among high-poverty communities in access to federal place-based resources over time.

The findings also show how the growth in funding for federal place-based policies was connected to broader trends of social policy fiscalization in the United States (McCabe 2018; O'Brien 2017). Federal tax expenditure programs constitute a growing share of federal investment in disadvantaged communities. Just three place-based tax expenditure programs—LIHTC, NMTC, and the CDFI Fund—accounted for two-thirds of all federal place-based funding. This shift is consequential because, at least at the federal level, tax expenditures are often less politically contested than other forms of federal place-based funding as they do not show up on the spending side of the ledger. Instead, they are considered taxes foregone and reduce total tax revenues available (Howard 1999), and thus are not always subject to the same annual political battles over appropriations. Americans are also less likely to consider tax expenditures "government programs," and therefore both progressive and regressive redistribution via tax expenditures are less subject to political polarization than direct expenditures (Mettler 2011). Despite the political appeal of fiscalization, scholars have raised concerns about its implications for democratic institutions and public redistribution, as tax policies make it easier for politicians and investors to conceal the full costs of programs as well as who profits from them (McCabe 2018; Mettler 2011; O'Brien 2017)—as has been the case for the Trump-era Opportunity Zones (Corinth and Feldman 2024). It also allows other forms of public redistribution to whither, as tax credits reduce revenue available to fund other public initiatives.

Furthermore, financialization—the increasing importance of financial markets and financial institutions (Davis and Kim 2015; Krippner 2005)—has enabled the ascent of place-based approaches to policymaking. Place-based programs like LIHTC and NMTC have spurred the creation of vast syndicates of secondary investors—mostly large national and international financial corporations—who make investments and lend credit to local development projects in exchange for access to federal tax credits; many of these secondary investors also own equity stakes in the developments. Urban scholars have linked the integration and deregulation of financial markets to gentrification and central city revitalization efforts since the 1990s (Hyra 2008; Sassen 2000; Wyly and Hammel 1999; Pacewicz 2013). Consistent with this perspective, we found that the association between local business density and greater place-based tax expenditures grew stronger over time. Our measure of business density likely understates the extent of financialization, however, as the density of local businesses does not capture the secondary financial markets and investors who take advantage of the federal tax incentives offered by many of the largest federal place-based policies. This also highlights that some beneficiaries of place-based tax credit relief are investors not part of the disadvantaged community that qualifies for the funding (Robinson 2020).

Local context also influenced the distribution of federal place-based funding, generating patterns of both compensatory and compounding inequality. We found that federal place-based policies were compensatory with respect to economic and racial disadvantage. This result is consistent with expectations for economic disadvantage, given that the stated funding guidelines for many programs gave priority to areas with high rates of poverty or unemployment. Net of economic disadvantage, we also found that racial composition was associated with compensatory funding patterns, with greater funding going to areas with larger shares of Black residents. This association was much smaller than the association for economic disadvantage, but it is notable given the absence of explicit race-based targeting in contemporary social policy in the United States. The implications of greater place-based federal investment are unclear, however, particularly for minority communities that have experienced harmful forms of federal intervention before (Hyra et al. 2013; Immergluck and Smith 2005; Rugh and Massey 2010) and are at disproportionate risk of escalating gentrification and displacement (Hackworth 2007; Lees et al. 2013).

Although federal funding was compensatory on the basis of community economic and racial disadvantage, other funding patterns exacerbated existing forms of spatial inequality. In particular, federal place-based funding was linked to the organizational capacity of local communities, measured by the density of nonprofit organizations. The importance of nonprofit organizations in our analyses resonates with case study research showing that, as the state has increasingly relied on private actors to deliver public services, nonprofit organizations are key beneficiaries of federal funding and are endowed with political, social, and economic power in their local communities (Dunning 2022; Levine 2021). Such organizations also are embedded within larger networks spanning across the city and beyond, shaping access to resources and political influence (Allard and Small 2013; Sampson et al. 2005; Sharkey, Torrats-Espinosa, and Takyar 2017). Nonprofits may be particularly important for place-based initiatives given their historical and continuing importance to local community development, crime prevention, and housing services; their roles as intermediaries for financial institutions; and their leadership in fair housing enforcement and litigation (Biles 2011; Levine 2021; Smith and Lipsky 1995; Marwell 2009). At the same time, however, the importance of a robust nonprofit sector reveals another dimension of inequality that has been exacerbated by the design of federal place-based programs, as communities that were organizationally dense have benefited more from federal place-based funding than communities with less organizational capacity. Nonprofit density is not a passive characteristic of localities, but rather one that some local governments have worked actively to bolster as part of neoliberal economic development efforts focused on attracting competitive grant funding (Arena 2012; Levine 2021). These community-based organizational inequalities can compound over time, as organizations that receive funding develop capacity and influence, becoming more competitive for future funding opportunities (Tuckman 1998).

Competitive place-based funding models have further compounded resource inequality by relying increasingly on tax credits, which depend on interest from the financial and private sectors and—as we show—privilege areas that already have strong and improving housing markets (Brazil and Portier 2022). In the United States, neoliberalism has been characterized by increased state reliance on privatized, market-oriented entities to address social problems such as poverty (Hyra 2012; Soss, Fording, and Schram 2011). As the case of place-based policies reveals, the federal government has supported and bolstered the private sector through its use of tax incentives to subsidize development. In many cases, these federal efforts follow similar approaches pioneered by state and local governments to leverage private investment and securitize community resources (Pacewicz 2013, 2015). The private sector, in turn, often includes both nonprofit organizations as grantees and larger financial institutions as secondary investors. As a result, organizational capacity and connections to housing and financial markets have become central factors in determining which places benefit from the place-based turn in federal policymaking.

Although the federal government has made a significant investment in place-based initiatives, there are many other sources of community development funding that may or may not be compensatory. We found that federal agencies increasingly allocate funding using specific geographic criteria, but we note that this type of funding is only one component of a vast patchwork of public and private resources that may ultimately benefit some communities more than others. Future research should consider how federal place-based funding is integrated with these other funding sources and the collective implications for spatial inequality. We also acknowledge that the funding allocations studied here are the outcome of a multi-stage process involving multiple local and federal actors; in some cases, this begins with decisions by local actors who compete for funding and continues with federal actors who determine eligibility and scoring criteria and make the awards. Future research should investigate the stages of this process in more detail to understand where precisely the compensatory and compounding dynamics emerge. Moreover, although we were able to trace federal investments to specific census tracts or counties, future case study research should investigate in greater detail the specific, tangible actions that occurred within and across neighborhoods as a result of the funding. Finally, this analysis has considered which communities received place-based funding, but ultimately, future research should investigate how this funding impacts the wellbeing and prosperity of communities and their residents.

More broadly, the resource inequality produced by place-based policies reflects tensions long debated by scholars and policymakers regarding targeting within public policy (Skocpol 1995; Wilson 1987). These decisions reveal and institutionalize public priorities to support some groups of people or places more so than others. The social processes behind these allocations not only speak to values embedded within state institutions, but also have real material consequences for the life chances of individuals residing in areas of concentrated disadvantage. Further work will be needed to address the political and economic interests underlying government distribution via place-based policies in the context of fiscalization and financialization, as targeted government interventions continue to respond to, and potentially reconfigure, durable patterns of spatial inequality in the United States. While place-based policies have been and continue to be politically palatable tools for the federal government to invest in disadvantaged communities, we conclude that this piecemeal, competitive approach to policymaking will inevitably fall short in equitably distributing resources within the US welfare state.

Supplementary Material

Supplementary material is available at Social Forces online.

Biographies

Laura Tach is Professor of Public Policy and Sociology in the Brooks School of Public Policy at Cornell University. Her research studies poverty and anti-poverty policies for families and communities. Her work has been published in a range of sociology, demography, and policy outlets including American Journal of Sociology, Demography, Social Problems, and City & Community.

Alexandra Cooperstock is a postdoctoral research associate at Brown University affiliated with the Population Studies and Training Center and the Annenberg Institute. Her research explores how racial/ethnic and class inequalities are produced and perpetuated in neighborhood and school environments, and how policy contexts influence the neighborhood–school nexus. This work contributes to research on social stratification, the sociology of education, community and urban sociology, public policy, and the field of social and spatial demography.

Sam Dodini is a Senior Research Economist at the Federal Reserve Bank of Dallas. His wide-ranging research topics include labor market institutions and firms, labor force skill development and human capital investments, income inequality, occupational licensing, community and regional development, and interactions with public tax and transfer systems.

Emily Parker is an Assistant Professor in the Edward J. Bloustein School of Planning and Public Policy at Rutgers University–New Brunswick. She received her PhD from Cornell University and postdoctoral training from the University of Michigan. She researches how public policy and community context matter to the link between health and poverty. Her work has been published in outlets including Social Forces, Social Problems, and Social Science & Medicine.

Data availability

All data in this paper come from public sources. The data underlying this article will be shared on reasonable request to the corresponding author.