Getting a Grant is Just the First Step: Administrative Capacity and Successful Grant Implementation

Abstract

This study examines the link between the pace of utilizing the awarded intergovernmental grants and the administrative capacity of recipient government organizations. Past research focused on the relationship between higher administrative capacity and obtaining grants. However, there is a lack of attention to how capacity affects grant funds utilization, which is critical for achieving societal impact. To address this issue, the study analyzes the Coronavirus Relief Fund (CRF) established by the CARES Act to aid state and local governments with COVID-19-related expenses. The study justifies and performs multiple regression analyses using data from various sources, including the U.S. Department of the Treasury, the U.S. Census Annual Survey of Public Employment and Payroll, and the Government Finance Officers Association. The study discovered that financial administrative capacity was positively linked to the proportion of funds spent early in the CRF program rollout, a finding that withstood scrutiny when employing various measures of administrative capacity. However, the connection between capacity and spending tapered off toward the end of the program rollout, potentially due to workload stabilization, increased program clarity from the federal government, capacity-building by recipients, and the use of external experts. The findings of this study carry significant implications for both research and practice, underlining the necessity of studying the implementation stage of government grant programs and investing in building administrative capacity within recipient organizations.

Keywords

intergovernmental grant administrative capacity funds absorption

A community can obtain a grant to perform a planning function or build a facility but still lack the time, staff, skills, and instrumentalities to effectively use the funds. (Honadle, 1981, p. 577)

Introduction

Intergovernmental grants play a critical role in federal systems like the United States (Oates, 1999). However, grants can also impose a significant burden on recipients, and recipients’ varying levels of preparedness to meet these costs can hinder some governments’ ability to participate in the intergovernmental playing field (ACIR, 1978). One key factor affecting a government's ability to handle intergovernmental grants is its administrative capacity (Ingraham & Donahue, 2000). While several studies have demonstrated that higher administrative capacity is associated with a higher likelihood of pursuing and obtaining grants (Bickers & Stein, 2004; Collins & Gerber, 2006; Hall, 2008; Lowe et al., 2016; Manna & Ryan, 2011), little attention has been given to how administrative capacity impacts the use of grant funds after receipt. To maximize the impact of grant funds and avoid penalties, it is critical to utilize them fully and follow established regulations (GAO, 2016). Therefore, this study examines the relationship between administrative capacity and the percentage of grant funds used.

The investigation centers around the utilization of the Coronavirus Relief Fund (CRF), a federal grant totaling $150 billion, established through the CARES Act in March 2020 to aid state and local governments in covering expenses related to the COVID-19 pandemic. The bulk of the grant was awarded to 204 primary recipients, including 50 state governments, 116 large counties, and 38 large cities (CRS, 2021). The federal government imposed strict rules on the use of funds and reporting but provided poor guidance to recipients. The funds were utilized for important purposes, such as public payroll, business programs, medical supplies, and distance learning technology. However, many recipients experienced a slow absorption of funds (Leachman, 2020; NAPA, 2021), which may be attributed to the burdens inherent in the CRF program. Therefore, this study aims to explore the relationship between recipients’ administrative capacity and the proportion of the award used, to determine if higher administrative capacity leads to greater utilization of grant funds.

Specifically, the study examines the relationship between recipients’ capacity and the proportion of the award spent during three cumulative periods of 2020 (2nd quarter, 2nd–3rd quarters, and 2nd–4th quarters). To measure administrative capacity, the study utilizes two novel methods: the number of financial administration employees per 100,000 residents and the receipt of the Certificate of Achievement for Excellence in Financial Reporting from the Government Finance Officers Association (GFOA). The study also includes numerous control variables to minimize bias, such as variables characterizing the severity of the COVID-19 pandemic, political forces, and preparedness to withstand fiscal shocks. The study draws data from the U.S. Department of the Treasury, the U.S. Census Annual Survey of Public Employment and Payroll, GFOA, and other sources.

Despite facing a massive health and economic crisis, the average recipient spent only a fraction of their award, with 19.7% spent by the end of June 2020, 30.4% by the end of September 2020, and 69.1% by the end of the year, which was the original deadline to use the funds. The study found that financial administrative capacity was positively associated with the proportion of funds spent early in the program rollout. This finding remained robust when using either measure of administrative capacity. For instance, a 10% increase in employees was associated with up to a 0.52 percentage point increase in spending in the first 3 months. Moreover, GFOA winners spent up to 10.4 percentage points more than other recipients during the same period. However, the relationship between capacity and spending diminished late in the program rollout, potentially due to workload stabilization, increased program clarity from the federal government, capacity-building by recipients, and the use of external experts from higher-level and peer governments, professional associations, or consulting firms.

The study's findings have significant implications for both research and practice. From a research perspective, the study highlights the importance of studying the implementation phase of government grant programs, in addition to the application and receipt phases. This shift in focus can lead to a more comprehensive understanding of the challenges in intergovernmental relations and improve grant effectiveness. Moreover, the study contributes to the ongoing discourse on administrative capacity by proposing innovative measures to assess capacity and demonstrating the link between capacity and the success of grant implementation. By showing that capacity matters in grant implementation, the study emphasizes the importance of investing in building internal administrative capacity within recipient organizations and expanding access to external experts who can help recipients overcome capacity constraints. Additionally, the study's findings suggest that grant programs need to be designed to reduce the burden on grant recipients.

The Significance of Administrative Capacity in Federal Systems

Intergovernmental Grants

Intergovernmental grants are commonly used to finance subnational spending. For example, in 2017, state governments in the United States received 32% of their general revenue from the federal government, while local governments received 32% of their general revenue from state governments and 4% from the federal government (according to the Annual Survey of State and Local Government Finances). Throughout the 20th century, federal–local interaction was more extensive than the current 4%, driven by initiatives such as the New Deal, Urban Renewal, general revenue sharing system, and post-9/11 policies. However, over time, this interaction level has diminished, partly due to the capacity constraints experienced by local governments (Davidson, 2007; Griffin, 2007; Inman, 1987). These grants serve several important functions (Oates, 1999). First, they support activities with spillover effects. Second, they can redistribute funds from wealthier to poorer areas. Third, they allow combining more localized program administration while keeping tax collection at the national level. Additionally, due to institutional and other constraints, state and local governments must rely on the federal government to respond to economic, natural, and health-related disasters (Green & Loualiche, 2021; Miao et al., 2018).

However, one concern about the federal aid system is that it can impose high costs on recipients, such as application and implementation expenses (ACIR, 1978; Stein, 1981). Therefore, recipients must be willing and capable of managing grant programs (Gargan, 1981; Handley, 2008). In addition, there may be other conditions that recipients must meet, such as having enough financial resources to fulfill matching requirements (Hall, 2008). Consequently, grants are more likely to go to governments with the ability to absorb a range of associated costs (Collins & Gerber, 2006; Stein, 1981). However, this ability may be inversely related to need, based on size, wealth, and demographics, which raises equity concerns (Collins & Gerber, 2006; Gargan, 1981; Stein, 1981).

Administrative Capacity

The government's ability to effectively utilize its resources to carry out its policy objectives (Ingraham & Donahue, 2000, p. 294)—or simply its ability “to do what it wants to do” (Gargan, 1981, p. 656)—is referred to as administrative (bureaucratic, management) capacity.¹ While capacity is widely accepted as consisting of multiple dimensions, there is little agreement on what those dimensions specifically entail. For instance, Ingraham and Donahue (2000) define capacity as financial, human resource, information technology, and capital management systems and their interrelations. Meanwhile, Bowman and Kearney (1988) include responding effectively to change, making efficient and effective decisions, and managing conflicts as part of their capacity definition. Gargan (1981) highlights policy management, resource management, and program management as key areas of capacity, while Honadle (1981) emphasizes the identification of needs, priority setting, resource allocation and management, and impact evaluation as critical aspects of capacity.

The factors contributing to differences in administrative capacity among governments are poorly understood. According to Ingraham and Donahue (2000), capacity may be influenced by internal decisions, external mandates, and environmental conditions. Effective leaders invest in various dimensions of capacity and ensure coherence among them. Weiland (1998) found that regulations can improve institutional capacity by promoting intergovernmental interactions and training. Local conditions, such as the size, qualifications of the labor pool, own-source revenue, and even weather, also play a role (Hall, 2008; Ingraham & Donahue, 2000). Previous research has suggested that smaller communities may have lower capacity due to a lack of resources for problem analysis and strategic planning (Gargan, 1981; Honadle, 1981).

Administrative capacity is critical to an organization's ability to achieve its desired outcomes. Research has consistently shown that governments with more administrative capacity tend to perform better than those with less capacity (Bowman & Kearney, 1988; Ingraham & Donahue, 2000). As Hou et al. (2003) point out, “management matters” (p. 295). Studies have demonstrated that administrative capacity plays a role in promoting the maintenance of state rainy day funds (Hou et al., 2003), ensuring the financial sustainability of nonprofit nursing homes (Park & Matkin, 2021), improving state credit quality (Krueger & Walker, 2010), lowering municipal bond interest rates (Simonsen et al., 2001), managing the opioid crisis (McCrea, 2020), integrating sustainability into municipal strategic plans (Hawkins et al., 2021), and managing cash flow (Lofton & Ivonchyk, 2022). Ali and Altaf (2021) and Bell and Smith (2022) have found that low administrative capacity increases the administrative burdens on clients.

Capacity and Grants

In particular, numerous studies have shown that having greater administrative capacity is associated with a higher likelihood of securing competitive grants. For example, Bickers and Stein (2004) found that interjurisdictional collaboration among governmental jurisdictions within metropolitan areas positively impacted local actors’ ability to secure new federal grant awards. The authors suggest that such collaboration generates institutional infrastructure that subsequently helps secure federal assistance. Similarly, Collins and Gerber (2006) observed a positive relationship between administrative capacity (measured by the total number of local government employees) and access to the federal state-administered Community Development Block Grant (CDBG) program in four states. They suggest that governments that have invested in capacity can bear higher transaction costs and that funders use capacity as a heuristic to award grants. Hall (2008) also found that federal awards at the county-level increase with higher administrative capacity, measured by the total number of employees at the local and regional levels, as means to prepare, submit, and manage grants increase. Additionally, Lowe et al. (2016) found that civic capacity helps attract federal transportation grants due to assistance from nongovernmental stakeholders. Moreover, Manna and Ryan (2011) noted that states with higher capacity (measured as population density) are more likely to apply for and win federal education grants due to administrative talents and resources.

While much research has been conducted on the link between administrative capacity and grant awards, there has been limited focus on the connection between capacity and successful implementation of grants. However, successful implementation is crucial as it guarantees the proper and complete use of funds, preventing the possibility of having to repay them, often with additional fees. Ultimately, delivering results hinges on the full and appropriate use of funds. However, not all governments are equally equipped to comply with rules and manage programs, so variation is expected in the successful implementation of grants. For example, in the supranational European Union context, state administrative capacity has been known to improve funds absorption—that is, the capacity to use allocated amounts (Incaltarau et al., 2020; Van Wolleghem, 2022). The primary objective of this article is to test the following hypothesis:

Hypothesis 1: Governments with higher administrative capacity can take advantage of a higher share of grant funds.

By examining the relationship between administrative capacity and successful grant implementation, this study aims to contribute to the existing literature on grant-making and shed light on the importance of administrative capacity for public sector effectiveness.

The CRF

To investigate the connection between administrative capacity and grant implementation, this article focuses on the CRF. The CRF is a $150 billion federal grant designed to assist state and local governments in responding to the COVID-19 pandemic. The CRF was created on March 27, 2020, as part of the Coronavirus Aid, Relief, and Economic Security (CARES) Act. The CRF allocated funds to states based on their population, with each state guaranteed to receive at least $1.25 billion. Funds were split between the state and local governments in states with cities/counties serving over 500,000 people, while the remaining states received the full allocation. The amount of local grants was determined by multiplying the total allocation by the percentage of the state population attributed to the local government and 45%. If two local governments serving a population of at least 500,000 overlapped, both were eligible for assistance, but the payment to the larger locality was based only on its unique population (CRS, 2021). Overall, the CRF funds were distributed to 50 states, 116 counties, and 38 cities,² with the per capita amount varying somewhat due to the allocation rules (see Appendix A, Figure A1).

The strict rules imposed by the CARES Act on the permissible use of funds and reporting,³ along with inconsistent guidelines, led to high transaction costs for recipients of the CRF. Recipients were burdened with quarterly reporting and strict limits, such as using funds for necessary COVID-19 expenses not previously accounted for in the budget, incurred from March 1 to December 31, 2020, with an unexpected deadline extension to December 31, 2021, announced on December 21, 2020. Failure to comply with these rules would result in returning inappropriately spent or unspent funds. The Treasury provided no initial guidance; and the delayed and contradictory guidance documents, such as “guidance,” “frequently asked questions,” “reporting and record retention requirements,” and “reporting and recordkeeping frequently asked questions,” added to the confusion (see Appendix A, Table A1 for more details).

Based on the best available data, CRF funds were primarily allocated to economic support, public health and safety payroll, public health, small business, and distance learning categories (see Appendix A, Figure A2 for more details). Economic support and public health and safety payroll were for supporting public health and nonhealth payroll, while small businesses and economic support were for reimbursing business interruption costs. Public health expenses included communication, enforcement, and supplies, while distance learning expenses were for technological improvements. For example, Green and Loualiche (2021) found that CRF helped decrease cuts to public employment. Primary recipients of CRF funds were allowed to transfer some of their funds to another government for expenditure, as long as the same rules were followed. For instance, a county could transfer funds to be spent by a city, town, or school district within the county (CRS, 2021).

Although the rapid depletion of CRF funds was widely anticipated given the magnitude of the economic and health crisis caused by the pandemic, the actual spending rate of the funds by recipient governments was unexpectedly slow (Leachman, 2020). While some of the delays in spending may have been intentional, such as caution in depleting the funds too quickly or seeking stakeholder input before proceeding, arguably, the primary reason for the slow spending was the unpreparedness of recipient governments in dealing with the novel program, the complexity of the requirements, and the confusing guidelines (Leachman, 2020). As a result, the inability to absorb the funds promptly slowed the response to the pandemic. In addition, it may have discouraged federal policymakers from allocating additional funds to state and local governments in 2020. Therefore, this article delves into the crucial role that administrative capacity of recipient governments played in determining the speed of spending CRF funds.

Empirical Methods and Data

Empirical Methods

The following equation is estimated to investigate the relationship between the speed of spending CRF funds and administrative capacity:

\frac{A m o u n t s p e n t_{i t}}{T o t a l a w a r d_{i}} = α + δ C a p a c i t y_{i} + X_{i t} β + γ_{i} + θ_{i} + ε_{i t},

(1)

where Amount spent is the amount of CRF funds used by the recipient (state, county, or city) i by the end of period t. Three different regressions are estimated for the three cumulative periods of (1) 2nd quarter of 2020, (2) 2nd–3rd quarters of 2020, and (3) 2nd–4th quarters of 2020. The Total award is the CRF funds allocated to each recipient i. Capacity is recipients’ administrative capacity pre-COVID-19. X represents a vector of period-variant and pre-COVID-19 period-invariant control variables measured at the recipient level. Thus, for state governments, these variables are measured at the state level, for county governments at the county level, and for city governments at the city level. γ is a dummy variable that takes a value of one if the recipient is a county government and zero otherwise. Similarly, θ is a dummy variable that equals one if the recipient is a city government and zero otherwise. State governments serve as the base category. By incorporating county and city dummy variables, roughly, the effects are estimated separately for each recipient type, that is, state, county, and city, and then averaged.⁴ Finally,

ε

is an idiosyncratic error. Robust standard errors are clustered at the state level to allow for mutual dependence among governments within each state. Equation (1) is estimated using the standard OLS estimator, but given that the outcome is between zero and 100%, the robustness of results to using the fractional probit model is verified.

Equation (1) combines recipients of all levels, including state, county, and city governments, into a single model with county and city dummies. This approach works best if the benefits of administrative capacity are relatively homogenous across diverse government levels. This assumption appears reasonable based on prior studies that have demonstrated positive effects of capacity for both state governments (e.g., Manna & Ryan, 2011) and local governments (e.g., Lofton & Ivonchyk, 2022). An alternative is separate models for state, county, and city recipients. However, the limited number of observations in the data set makes this option unfeasible as it would result in insufficient statistical power. Hence, pooling the recipients and estimating the effect for an average recipient in the pooled sample are a pragmatic solution given the available data constraints.⁵

Obtaining an unbiased estimate of the effect of capacity on the speed of spending funds is a challenge due to the omitted variable bias. However, two factors reduce this issue in this particular setting. First, the amount of CRF award was determined exogenously by the CARES Act. Second, capacity is not only influenced by internal decisions but also by external forces such as regulations. To address any potential confounding factors, a range of control variables were incorporated into the analysis. The subsequent section will provide evidence of the robustness of the effect estimates, even after taking these control variables into consideration. This indicates that the effects observed are likely due to capacity rather than other factors.

Spending Speed

The U.S. Department of the Treasury provided the Amount spent and the Total award used in this study. For the 2nd quarter of 2020, the Treasury only reported aggregated spending amounts by the recipient. However, for the other two periods (2nd—3rd quarters and 2nd—4th quarters of 2020), the Treasury published a detailed, line-item budget that was used to aggregate spending amounts by recipient.⁶ Recipients self-reported spending. Spending funds in period t indicates that goods or services were purchased during period t, and payment was either made during the same period or will occur soon.⁷ State FIPS⁸ were assigned to each state. County and state FIPS were assigned to each county. For cities, a crosswalk provided by the Missouri Census Data Center was used to assign place, county, and state FIPS⁹ based on the county housing the largest share of the city's population.¹⁰

Administrative Capacity

Measuring administrative capacity is a complex task that has been approached in various ways in previous studies. Direct measures include capacity grades based on institutional arrangements and other factors (Bowman & Kearney, 1988; Hou et al., 2003; Krueger & Walker, 2010), the total number of government employees per capita (Collins & Gerber, 2006; Hall, 2008), full-time (rather than part-time) employment status (Lofton & Ivonchyk, 2022), and administrative spending (Park & Matkin, 2021). Alternatively, some studies use factors likely associated with administrative capacity as proxy measures, such as population (Simonsen et al., 2001), population density (Manna & Ryan, 2011), poverty (Bell & Smith, 2022), interjurisdictional collaboration (Bickers & Stein, 2004), and civic capital (Lowe et al., 2016). Hall (2008, p. 464) emphasizes that capacity should be defined “in relation to its application,” “as the capacity to do something in particular.”

In this study, the number of finance government employees is the primary measure of administrative capacity. According to interviews and surveys conducted by the National Academy of Public Administration (NAPA) in 2021, the burden of complying with the CRF requirements fell mainly on budget and financial personnel. Specifically, recipients reported that this personnel spent significant time and effort seeking clarification on allowable expenses, keeping up with changing rules, maintaining records of expenditures, and verifying subrecipients and contractors. The 2017 U.S. Census Annual Survey of Public Employment and Payroll provides data on the number of employees, which is measured in full-time equivalent (FTE) employees and normalized by 100,000 residents.

In addition to the number of finance government employees, an alternative measure of administrative capacity is considered in this study. A dummy variable is coded as one if the recipient received the Certificate of Achievement for Excellence in Financial Reporting from the GFOA in fiscal year 2019. This certificate is awarded to state and local governments for achieving high transparency and disclosure in their annual comprehensive financial reports, indicating strong financial administrative capacity. Governments must apply and submit reports that go beyond standard financial reporting requirements to be considered for the certificate. Using this alternative measure demonstrates that the study's results are not confined to a single conception of administrative capacity. Additionally, this measure may be more exogenously determined than the number of employees.

Other capacity measures may also be relevant. One such measure is the per capita total count of government employees, which encompasses various aspects of capacity, including general administration and health administration, in addition to budgeting and finance. Furthermore, when considering local governments, it is conceivable that state governments could assume leadership in grant implementation, thus making state capacity a pertinent variable. However, due to the National Academy of Public Administration's (2021) findings, which highlight the significance of own financial administrative capacity, the analysis focuses solely on this aspect for the sake of parsimony.¹¹

Control Variables

The number of COVID-19 cases and the unemployment rate represent the severity of the pandemic. These two variables vary across the three periods. The daily number of new COVID-19 cases provided by the Centers for Disease Control and Prevention (CDC) has been aggregated by calculating the average within each period and standardized by 100,000 population.¹² Similarly, the monthly unadjusted unemployment rate from the U.S. Bureau of Labor Statistics has been aggregated by calculating the average within each period. In the case of cities, county COVID-19 cases and unemployment are used, as city-level data are not obtainable.¹³

The decision to use federal money may be influenced by political factors, as noted by Nicholson-Crotty (2012), such as differences in spending decisions between Democrats and Republicans and partisan conflict on spending. So, two political variables, ideology and political divide, are included. The share of votes that went to the Democratic candidate in the 2020 presidential elections is used to measure ideology, based on data from the MIT Election Science Lab. The political divide is measured by the vote margin, where 0 indicates the highest divide (a split vote), and 100 represents the lowest divide (all votes go to one person). To ensure that an increase in the divide implies an increase in the variable, the margin variable is multiplied by (−1). For cities, the county-level values of these variables are used.

Higher financial resiliency may lead to a reduced need or a greater ability to co-finance projects. Moreover, higher resiliency may result from higher capacity or generate more resources to invest in capacity.¹⁴ Based on previous studies (Green & Loualiche, 2021; Seegert, 2016), higher resiliency is controlled for using a more diversified tax portfolio, a lower sales tax exposure, and a larger ending balance. The 2017 Annual Survey of State and Local Government Finances provides data to calculate diversification and sales tax exposure. Diversification is measured using a Herfindahl–Hirschman Index (HHI)¹⁵ subtracted from 100% so that an increase in the index signifies an increase in diversification. Sales tax exposure is the ratio between sales tax and general revenue. The ending balances for governmental activities¹⁶ in fiscal year 2019 were collected from annual comprehensive financial reports sourced from the GFOA, Federal Audit Clearinghouse, and individual government websites.¹⁷

Finally, an array of socioeconomic variables from the 5-year American Community Survey 2015–2019 is included, such as population, population density, median age, the share of the Black and Hispanic population, median household income, and the share of the population with Bachelor's degree or above. In addition, dummy variables account for whether the recipient is located in the country's Midwest, South, or West regions (the base group is the Northeast region).

Descriptive Statistics

Figure 1 and Table 1 present summary statistics for 204 CRF recipients, separately for states, counties, and cities. In the first 3 months of the CRF rollout, states spent 18.4% of their award, counties spent 17.1%, and cities spent 29.3%. In the first 6 months, the share went up to 33.1% for states, 26.9% for counties, and 37.5% for cities. So, cities were the fastest and counties were the slowest to start spending money. By the end of 2020, an average recipient used only 69.1% of their award, with states and counties catching up to cities. Financial administrative capacity, as measured by the number of employees, was the highest among states (62.5 FTE employees per 100,000 residents) and cities (51.5 employees) and the lowest among counties (32.1 employees). Ninety-five percent of cities received GFOA award; for states and counties, this proportion was 86%. Consistent with the evolution of the pandemic, COVID-19 cases started highest in cities and later advanced everywhere. Compared to states and counties, cities look more Democratic, less politically divided, denser, younger, and more diverse. States had more diversified revenues and higher ending balances but relied more on sales taxes than localities.

Figure 1.

The speed of spending Coronavirus Relief Fund money. Panel A, state recipients. Panel B, county recipients. Panel C, city recipients.

Table 1.

Summary Statistics.

	State recipients		County recipients		City recipients
Award spent, % Q2 2020	18.38	(19.87)	17.11	(17.81)	29.29	(26.50)
Q2–3 2020	33.11	(16.33)	26.93	(20.64)	37.53	(27.48)
Q2–4 2020	67.60	(18.04)	68.76	(25.12)	71.90	(19.79)
Award per capita, $	575.66	(474.98)	159.54	(59.98)	185.81	(37.85)
Finance staff, FTE/100K	62.52	(24.29)	32.12	(19.13)	51.51	(34.61)
GFOA winner dummy	0.86	(0.35)	0.86	(0.35)	0.95	(0.23)
Unemployment, % Q2 2020	11.81	(3.32)	13.39	(3.33)	13.91	(3.58)
Q2–3 2020	9.86	(2.76)	11.30	(2.64)	12.05	(3.05)
Q2–4 2020	8.52	(2.37)	9.73	(2.30)	10.54	(2.67)
Daily COVID-19 cases/100K Q2 2020	7.42	(4.22)	8.81	(6.85)	9.00	(6.31)
Q2–3 2020	10.87	(4.57)	11.78	(4.62)	12.83	(4.28)
Q2–4 2020	23.29	(8.29)	20.72	(6.23)	21.51	(7.05)
Democrats, %	47.69	(10.36)	57.97	(11.32)	65.44	(10.63)
Political divide, p. points	−17.38	(11.20)	−23.23	(16.89)	−32.83	(21.21)
Revenue diversification, %	66.86	(6.45)	54.59	(11.15)	63.15	(7.37)
Sales tax exposure, %	14.33	(7.37)	7.26	(11.15)	9.49	(8.85)
Balance, % of revenue	5.92	(7.07)	5.25	(8.42)	2.78	(13.52)
Population, mil.	6.48	(7.30)	1.17	(1.17)	1.20	(1.39)
Density, per sq. mile	201.35	(265.64)	1,658.87	(1,686.74)	5,613.47	(5,308.64)
Median age	38.48	(2.36)	37.68	(3.56)	34.79	(2.06)
Black, %	10.60	(9.52)	13.97	(11.82)	21.19	(18.76)
Hispanic, %	11.90	(10.38)	21.11	(16.93)	26.07	(18.16)
Median household income, $000	62.63	(10.29)	72.46	(18.05)	62.31	(18.77)
Bachelors, %	21.24	(3.81)	25.14	(6.85)	24.70	(8.61)
Midwest dummy	0.24	(0.43)	0.20	(0.40)	0.13	(0.34)
Northeast dummy	0.18	(0.39)	0.18	(0.39)	0.11	(0.31)
South dummy	0.32	(0.47)	0.35	(0.48)	0.37	(0.49)
West dummy	0.26	(0.44)	0.27	(0.44)	0.39	(0.50)
N	50		116		38

Note: The table presents means and standard deviations. Higher values signify an increase in the variable for revenue diversification and political divide.

Results

Main Results

Table 2 presents the main findings on the relationship between the speed of spending CRF funds and administrative capacity measured as the number of financial administration employees. Results are presented by period: the first 3 months, the first 6 months, and the first 9 months of the CRF rollout. Columns (1), (3), and (5) include no controls, and columns (2), (4), and (6) include all controls discussed earlier in the study. The proportion of the variance explained (adjusted R²) increases for multiple regressions as compared to single regressions: from 0.013 to 0.165 for the first period, from 0.003 to 0.054 for the second period, and from 0.000 to 0.055 for the last period.

Table 2.

Main Results.

	Spent, %, Q2 2020		Spent, %, Q2–3 2020		Spent, %, Q2–4 2020
	(1)	(2)	(3)	(4)	(5)	(6)
Fin. staff (log)	3.926**	5.185**	2.659	0.399	1.412	1.994
	(1.775)	(2.241)	(1.678)	(2.355)	(2.207)	(3.522)
Award per capita (log)		−4.944*		−3.601		−3.750
		(2.843)		(3.718)		(3.234)
Unemployment		0.582		−0.387		−0.767
		(0.352)		(0.807)		(0.964)
COVID-19 cases		−0.290		0.143		0.295
		(0.280)		(0.561)		(0.277)
Democrats		−0.253		−0.493		0.024
		(0.270)		(0.328)		(0.305)
Political divide		−0.058		−0.029		0.349***
		(0.121)		(0.118)		(0.107)
Revenue diversification		0.205		0.174		0.256
		(0.236)		(0.227)		(0.213)
Sales tax exposure		0.058		0.096		0.060
		(0.264)		(0.296)		(0.229)
Balance		−0.032		0.288*		0.016
		(0.137)		(0.160)		(0.165)
Population (log)		3.335		2.665		0.536
		(3.183)		(2.112)		(1.903)
Density		0.001**		0.000		−0.000
		(0.001)		(0.001)		(0.001)
Median age		0.586*		1.522***		1.377**
		(0.332)		(0.486)		(0.539)
Black		0.388		0.497		0.285
		(0.312)		(0.319)		(0.214)
Hispanic		0.122		0.254		0.080
		(0.188)		(0.208)		(0.141)
Income (log)		33.977***		3.799		4.227
		(8.387)		(10.393)		(12.035)
Bachelors		−0.602		0.428		0.459
		(0.447)		(0.481)		(0.580)
County		0.813		−2.620		3.897
		(7.947)		(8.955)		(7.960)
City		9.017		10.767		10.308
		(7.680)		(7.293)		(9.658)
Midwest		−6.445		−3.561		11.158
		(6.385)		(7.733)		(7.460)
South		−10.302		−13.154		−2.468
		(8.341)		(8.097)		(6.241)
West		−1.295		2.868		9.505
		(7.093)		(8.764)		(7.045)
N	204	204	204	204	204	204
R ²	0.018	0.251	0.008	0.152	0.002	0.152
Adjusted R ²	0.013	0.165	0.003	0.054	0.000	0.055

Note: Constant is not reported. Standard errors are presented in parentheses and clustered at the state level. * 0.10, ** 0.05, and *** 0.01.

In the first 3 months, the coefficients on capacity are positive and statistically significant at the 5% level in both simple and multiple regressions. The magnitude of capacity coefficients is consistent across columns (1) and (2)—3.9 and 5.2, respectively—increasing confidence that results are not due to confounding. Thus, a 10% change in capacity increases the share of funds spent by 0.39–0.52 percentage points. So, in the short term (3 months), financial administrative capacity played a significant role in the absorption of CRF funds, as expected. However, in the longer term—for the first 6 months and the first 9 months of the CRF rollout—the financial administrative capacity coefficients remain positive but became smaller and statistically insignificant in all models. Thus, the magnitude is 0.4–2.7 for the first 6 months and 1.4–2.0 for the first 9 months.

The described results are robust to using the fractional probit model that accounts for the outcome being between zero and 100%.¹⁸ Thus, when all controls are included in the fractional probit model, the marginal effect of capacity is 4.6 percentage points for the first 3 months (statistically significant at the 5% level), 0.3 percentage points for the first 6 months (not statistically significant), and 2.2 percentage points for the first 9 months (not statistically significant). The similarity between OLS and fractional probit results is likely because, as can be seen from Figure 1, the dependent variable is distributed between zero and 100%.

When it comes to control variables, there is not much evidence that the decision to use federal money was strongly influenced by the severity of the pandemic, political factors, or financial resiliency in either period. However, there is some confirmation that, as could be expected, higher award per capita is associated with slower absorption in the short term. Also in the short term, the share of funds spent is positively related to several socioeconomic characteristics, including density and income, previously known to facilitate high government performance (Bell & Smith, 2022; Manna & Ryan, 2011).

Robustness Checks

In this section, instead of using the number of financial administration employees to measure capacity, the Certificate of Achievement for Excellence in Financial Reporting from the GFOA dummy is used. While these two measures offer distinct insights into governments’ financial administrative capacity, the number of employees and the dummy are positively related. Thus, GFOA winners have more employees per capita than governments that did not receive this award (44.6 versus 32.9 employees). Based on a simple t-test, this difference is statistically significant at the below 5% level.

The results of this robustness check are presented in Table 3, which is organized similarly to Table 2. The coefficients are positive and statistically significant in all specifications for the program rollout's first 3 and 6 months. Based on specifications with all controls, GFOA winners spent 10.4 percentage points more than the rest of the recipients in the first 3 months and 9.2 percentage points more in the first 6 months. For the first 9 months, the coefficient on the dummy in the specification with all controls is positive but smaller (6.1) and not statistically significant.¹⁹ So, results are not confined to a single conception of administrative capacity. However, when GFOA award is used, the effect of capacity on spending persists for two quarters rather than one quarter.

Table 3.

Results Using an Alternative Measure of Financial Administrative Capacity.

	Spent, %, Q2 2020		Spent, %, Q2–3 2020		Spent, %, Q2–4 2020
GFOA winner	8.967***	10.371***	6.612**	9.220**	8.997**	6.148
	(2.738)	(3.529)	(3.236)	(4.541)	(3.678)	(4.635)
Controls	No	Yes	No	Yes	No	Yes
N	204	204	204	204	204	204
R ²	0.020	0.254	0.010	0.166	0.017	0.156
Adjusted R ²	0.016	0.168	0.005	0.070	0.012	0.059

Note: Control variables include award per capita (log), unemployment, COVID-19 cases, Democrats, political divide, revenue diversification, sales tax exposure, balance, population (log), density, median age, Black, Hispanic, income (log), Bachelors, type of government dummies, and region dummies. Coefficients on control variables and constant are not reported. Standard errors are presented in parentheses and clustered at the state level. * 0.10, ** 0.05, and *** 0.01.

Discussion

Grant implementation is just as important as the receipt of grants, yet past literature has primarily focused on applying for and receiving grants (Bickers & Stein, 2004; Collins & Gerber, 2006; Hall, 2008; Lowe et al., 2016; Manna & Ryan, 2011). This article uses the CRF as an example to illustrate that fund absorption is a crucial problem that needs attention. The study documents a significant variation in how quickly CRF recipients spent their awards. Thus, by the end of June 2020, the average share spent was only 19.7% of the award (with a standard deviation of 20.6%). By the end of September 2020, it increased to 30.4% (with a standard deviation of 21.5%), and by the end of the year, it rose to 69.1% (with a standard deviation of 22.6%). Unspent federal aid represents a missed opportunity to provide services and resources, and it is essential to study the factors that affect the usage of grant money in intergovernmental practice.

The analysis reveals the critical role of recipients’ administrative capacity in determining the speed at which they absorb funds from the CRF. Two new measures of capacity are used: the number of financial administration employees per capita and the receipt of the Certificate of Achievement for Excellence in Financial Reporting from the GFOA. Thus, in the program's first 3 months, a 10% change in the number of financial administration employees per capita increased the share of funds spent by 0.39–0.52 percentage points. In the same period, GFOA winners spent 9.0–10.4 percentage points more funds than the rest of the recipients. The findings, in line with Incaltarau et al. (2020) and Van Wolleghem (2022), support the notion that governments lacking administrative sophistication not only encounter challenges in securing grants, as previous studies have indicated (Bickers & Stein, 2004; Collins & Gerber, 2006; Hall, 2008; Lowe et al., 2016; Manna & Ryan, 2011), but also face difficulties in effectively utilizing the funds even after the acquisition.

One notable finding from this article is the significant role that capacity plays in the initial stages of the program rollout, compared to its diminished impact in later stages. This effect is observed within the first 3 months when capacity is measured by the number of employees and within the first 6 months when measured by the GFOA award. Several factors may contribute to this result.

Firstly, the initial phase of program rollout is typically the most demanding and challenging. During this period, organizations need to establish essential processes, train staff, and overcome various complexities. A high level of administrative capacity, including a sufficiently large and professional workforce, enables effective and timely actions, preventing delays in program implementation. For instance, the National Academy of Public Administration (2021) notes that counties invested significant time and effort in seeking clarification on allowable expenses during the first 6 months of implementing the CRF. As the program matures, the workload stabilizes, reducing the need for high capacity.

Secondly, the quantity and quality of federal guidelines evolve over time. Previous studies have highlighted that grant outcomes are influenced not only by recipients’ capacity but also by the design of the grant program itself (Collins & Gerber, 2006; Gargan, 1981; Stein, 1981). As mentioned earlier, the Treasury initially provided limited guidance to CRF recipients. However, as time progressed, the Treasury addressed gaps and provided additional clarity. For example, between April and June 2020, the Treasury enhanced its directions on eligible expenditures and transfers to other governments. In July and August 2020, they clarified issues related to recordkeeping and reporting. These improvements in guidance may reduce the reliance on administrative capacity (see Appendix A, Table A1).

Lastly, recipients may be able to develop their capacity over time. Capacity is not a static factor; it can be built and refined (Hall, 2008). Recipients may pursue internal and external pathways to enhance their capacity (Honadle, 1981). Internally, they can increase their staffing levels to strengthen their capacity. Externally, they might seek assistance from higher-level and peer governments, professional associations, or consulting firms. The ability to build and adapt capacity throughout the program's life cycle makes the initial capacity level less significant in the long run.

Conclusion

In this article, the focus of the investigation is the role that administrative capacity plays in the implementation of grants, specifically its effect on the utilization of grant funds. The study is carried out within the context of the CRF federal grant provided to state and local governments due to COVID-19. Two variables are used to measure administrative capacity: the number of financial administration employees per capita and the dummy variable for receiving the GFOA award. By controlling for various potential confounding factors, the study reveals that administrative capacity positively correlates with the initial spending of funds during the program rollout, specifically within the first 3–6 months. However, this relationship becomes weaker and dissipates after that period. Overall, the study highlights the importance of solid administrative capacity for effective grant implementation, particularly during the early stages of the program.

The study's findings should be approached with appropriate caution, taking into account several limitations. These limitations include the small sample size and the possible interference from confounding variables. It is also important to recognize that measures of administrative capacity might not capture the entirety of the concept. Moreover, the study does not delve into the precise mechanisms, such as employee reassignment or expertise utilization, through which recipients leverage their capacity for grant implementation. Exploring these avenues and examining the moderating effects of factors like unionization and organizational culture present promising directions for future research. Furthermore, the study's focus on a specific grant with a high administrative burden involving state and large local governments may limit its external generalizability. Future research could cover smaller government organizations and grants with varying levels of burden on recipients.

This study contributes to the existing literature on intergovernmental grants and administrative capacity. The study indicates that by emphasizing the initial stages of the grant cycle, such as identifying funding sources and receiving grants, researchers have overlooked issues that grant recipients encounter postaward, such as their capacity to absorb funds fully. Addressing this gap in research can enhance the effectiveness of grants. Additionally, the study brings attention back to the critical role of administrative capacity, which has been overshadowed by the focus on policy design, as noted by Moynihan (2022). This study proposes innovative measures of administrative capacity that can be used in other studies, and it expands the understanding of the contexts in which administrative capacity is essential.

The study's results have significant implications for policymakers and practitioners designing and implementing grant programs. They highlight the importance of administrative capacity in ensuring the effective delivery of services and resources to the intended recipients through grants. According to Hall (2008), administrative capacity is buildable. Honadle (1981) emphasizes that internal capacity-building should be a priority, although building capacity may also involve nurturing external experts in higher-level and peer governments, professional associations, and consulting firms. While the study primarily focuses on administrative capacity, it also emphasizes the need to design grants that minimize costs for recipients, as highlighted by Collins and Gerber (2006), Gargan (1981), and Stein (1981). This approach would ensure that even less administratively sophisticated governments can benefit from grants. Conlan and Reagan (2020) propose that the federal government could reduce emergency aid burdens by utilizing existing programs, such as Medicaid or state aid to schools, rather than creating new ones like the CRF.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Iuliia Shybalkina

Notes

Author Biography

Iuliia Shybalkina is an Assistant Professor at the Martin School, University of Kentucky, specializing in public budgeting, financial management, and public administration. Her research covers various aspects, such as public participation in local finance, local revenue and expenditures, economic development, intergovernmental relations, and state and local finance challenges.

Appendix A. Coronavirus Relief Fund Details

Table A1.

The Evolution of Directions for Recipients of Coronavirus Relief Fund Money.

No.	Date	Direction type	Direction description
1	Mar 27, 2020	CARSE Act	Listed covered expenses, including “necessary due to the public health emergency,” “not accounted for in the budget as of Act,” and “incurred Mar-Dec 2020.”
2	Apr 22, 2020	Guidance	Provided definitions and examples of eligible and ineligible expenditures.
3	Apr 22, 2020	FAQ	Answered questions related to the administration of fund payments, including returning unspent funds, transfers to other governments, retaining purchased assets, and recordkeeping.
4	May 4, 2020	FAQ Update	Answered questions related to eligible expenditures.
5	May 28, 2020	FAQ Update	Multiple additions and revisions, including updates on transfers to other governments contradicting the previous direction.
6	Jun 24, 2020	FAQ Update	Multiple additions and revisions.
7	Jun 30, 2020	Guidance Update	Updates on the covered period.
8	Jul 2, 2020	Reporting and Record Retention Requirements	Outlined interim reporting for the period Mar–Jun 2020.
9	Jul 8, 2020	FAQ Update	Multiple additions and revisions
10	Aug 10, 2020	FAQ Update	Multiple additions and revisions.
11	Aug 28, 2020	Reporting and Recordkeeping FAQ	Answered questions related to prime recipients. System for Award Management, terminology, reporting, GrantSolutions Portal, and record retention and audit.
12	Sep 2, 2020	Guidance Update	Updates on covering public payroll and benefits and administrative costs.
13	Sep 2, 2020	FAQ Update	Multiple additions and revisions.
14	Sep 21, 2020	Reporting and Recordkeeping FAQ Update	Multiple additions and revisions.
15	Oct 19, 2020	FAQ Update	Multiple additions and revisions.
16	Nov 25, 2020	Reporting and Recordkeeping FAQ Update	Multiple additions and revisions, including on recoupment in the event of compliance failure.
17	Jan 15, 2021	Guidance Update	The covered period extended to Dec 2021.
18	Mar 2, 2021	Reporting and Recordkeeping FAQ Update	Multiple additions and revisions.

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