Can Nonprofit Enterprises Lead to Better Revenue Conditions in Local Government?

Abstract

Nonprofit enterprises may play an important role in revenue diversification from a government perspective, especially when local governments suffer from revenue shortages. This study attempts to examine whether an increasing number of nonprofit enterprises influence revenue diversification, as measured by the Herfindahl–Hirschman Index (HHI), as well as volatility using a panel data set from 2007 to 2012. The results indicate that local governments can secure more diversified and increased income sources as more nonprofit enterprises are created throughout the county. Moreover, nonprofit enterprises with stable business categories contribute more and therefore actively improve revenue conditions of local governments.

Keywords

nonprofit enterprise revenue diversification volatility of tax revenues

Introduction

The number of nonprofit enterprises has increased rapidly in recent history. The total number of nonprofit enterprises was 21,745 in 2007, as shown in Table 1, but this number increased to 23,749 in 2012 (representing an increase of 9.2%). Nonprofit organizations have grown even during economic recession because the needs of the service have been increased as well. In addition, some nonprofit organizations are engaged in creating enterprises rather than sticking to a purely nonprofit format for several reasons, such as financial instability and decreased availability of grants. Thus, several studies (Anheier & Ben-Ner, 2003; Hansmann, 1980, 1996) use the term nonprofit enterprise rather than nonprofit organization.

Table 1.

Number of Nonprofits by Type in Maryland From 2007 to 2012.

Type of nonprofits	2007	2008	2009	2010	2011	2012
Arts, culture, and humanity	1,614	1,593	1,701	1,957	1,727	1,739
Education	2,588	2,572	2,797	3,294	2,930	2,433
Environment	951	939	936	959	890	1,084
Health	1,544	1,500	1,602	1,782	1,597	1,971
Human service	5,712	5,934	4,965	7,993	7,231	3,914
Public and social benefit	7,393	7,437	8,125	8,182	7,682	1,440
Other/unknown	1,943	2,511	3,968	1,653	1,344	11,158
Total	21,745	22,486	24,094	25,820	23,401	23,739

These nonprofit enterprises create positive results because they can offer social goods or services relatively cheaply and provide working opportunities for disadvantaged groups by cooperating with the regional community (Young, 2006). In addition, activities from nonprofit enterprises can create income that is not exempt from Unrelated Business Income Taxes (UBIT). The effects of nonprofit enterprises on society have primarily been examined, and evaluative studies of nonprofits have been conducted with increasing frequency, because nonprofit organizations receive government funding as well as other donations from foundations. However, the impact of nonprofit enterprises on local government has not yet been investigated, especially with regard to revenue management, including the tax base.

The emergence of nonprofit enterprises may play an important role as a revenue diversification strategy from a government perspective, because nonprofit enterprises create income from job creation, the sale of goods and services, and economic investments. Thus, a government may receive revenues from the market activities of nonprofit enterprises, and it is therefore meaningful to investigate. In particular, when local governments are experiencing revenue shortages, and especially during an economic downturn, they should strive to search for different revenue sources and income from other entities, such as nonprofit enterprises, rather than relying solely on property taxes. Table 2 shows a current trend in the proportion of tax revenues.

Table 2.

Proportion of Taxes by Type in Maryland From 2007 to 2012.

Year	Property tax	Income tax	Other tax	Total
2007	45.88	36.96	17.16	100.00
2008	47.65	37.45	14.90	100.00
2009	54.35	33.67	11.98	100.00
2010	57.63	31.00	11.38	100.00
2011	55.58	32.70	11.72	100.00
2012	54.68	33.74	11.58	100.00

Note. Amount expressed in percentage.

The expansion of nonprofit enterprises has provided potential revenue-seeking strategies for local government during periods of revenue shortage. These sources can positively influence revenue diversification as well as independence from the financial perspective of local governments. However, increasing numbers of nonprofits are considered potential threats to the revenue base of local governments. This is because nonprofits are exempt from income taxation on revenues related to noncommercial activities, such as the attainment of social benefits (Worth, 2013).

The purpose of this study is to investigate whether the emergence of nonprofit enterprises improves revenue conditions, especially revenue diversification, as well as the stability of local government. Using a panel data set from 2007 to 2012 for counties in the state of Maryland, this article examines how an increasing number of nonprofit enterprises may influence revenue diversification, as measured by the Herfindahl–Hirschman Index (HHI), as well as the revenue stability of local government, which is determined according to the volatility of nonproperty tax revenues. The full theoretical framework is shown in Figure 1.

Figure 1.

Theoretical framework.

Literature Review

Nonprofit Enterprise as Nonprofit Organization

Recently, nonprofit enterprises have been examined in the context of commercialization, in that they increase their own revenue sources from commercial activities rather than relying primarily on donations from private foundations and grants (Salamon, 1999; Weisbrod, 2004). Nonprofit enterprises have also increased commercial sources of revenue by selling their services and products. Thus, income from these activities has been growing at a faster rate (Guo, 2006; Salamon, 1999). Through various processes, these nonprofit enterprises establish new businesses (starting at a small size) and hire people in their regional communities. The commercialization behaviors demonstrate “nonprofits’ aggressive profit-seeking behavior,” even though the nonprofit sector appears not to pursue profits and focus more on social goals (Guo, 2006, p.124).

The commercialization stream among nonprofit enterprises stems from the market environment, with decreases in private funding or government grants and increased competition for available funds. In addition, nonprofit enterprises are no longer located in a protected environment that is relatively free from financial concerns because of their nonprofit characteristics (Young, 2001). They are required to demonstrate their performance in society and their effectiveness in terms of cost savings to donors to receive continuous funding (Light, 2000). Shore (1999) argues that under this environment, nonprofit enterprises must initiate their own commercial businesses to generate income that is independent of donations and government grants. This commercialization is employed by building an enterprise entity. A nonprofit enterprise is an entity that uses business plans or tools to improve social conditions rather than exclusively pursuing economic profits. Such a commitment would be applied by means of the entity’s financial value.

Furthermore, regarding revenues, nonprofit enterprises are exempt from income tax on revenues for activities, with the purpose of achieving the enterprise’s social goals. However, income from activities that are not employed for social goals represents a potential base for UBIT (Worth, 2013). Overall, nonprofit enterprises have concerns that unrelated business activities could become the largest portion of an organization’s activities, because they spend their resources and time on unrelated business activities to conduct business activities related to social goals. Under these circumstances, nonprofits may have concerns about their tax exemption being disallowed.

These nonprofit enterprises also have indirect impacts on economic outcomes in local government in terms of causing money to circulate locally through job growth and the creation of small local businesses (Haugh, 2006; Young, 2006). Thus, they may become potential revenue sources for government in that they support local jobless residents in securing jobs and starting new businesses, after which these businesses then pay income and business taxes. Moreover, according to previous research, small businesses, rather than large businesses, are the most critical source of job creation in the United States. Nonprofit enterprises have primarily initiated small businesses because in the early stages, they do not have sufficient funding to conduct their business on a large scale (Birch, 1981; Haltiwanger, Jarmin, & Miranda, 2013; Neumark, Wall, & Zhang, 2011). In particular, nonprofit enterprises achieve economic development in the community through the revitalization of communities and support of disadvantaged groups, such as the homeless, the disabled, and at-risk youth (Grogan & Proscio, 2000; Vidal, 2002).

Revenue Diversification and Volatility

Revenue diversification is the process of diversifying revenue structures by relying on diverse revenue sources rather than one source. Buchanan (1949) argues that it is necessary to consider the revenue side for service delivery of government, claiming, “The real problems in fiscal theory are limited to the tax side” (p. 501). Revenue diversification initially focused on subnational governments to weather fiscal crises. Recently, the housing crisis and the Great Recession have caused larger cities to experience fiscal stress. In addition, local governments have suffered from reduced fiscal aid from state government, as well as decreased property tax revenues. Local governments have experienced severe revenue shortages as property values have fallen, because property taxes represent a large proportion of the general revenue source. Under these conditions, it is difficult for local governments to maintain their current service level (Chernick, Langley, & Reschovsky, 2011). Thus, local governments must search for alternative revenue sources. Revenue diversification has been considered one such tool for alleviating fiscal crises and economic downturns and for maintaining current service levels (Carroll, 2005; Shannon, 1987).

The purpose of revenue diversification is the pursuit of a mixed revenue structure “through the selection of a combination of taxes that will generate a given level of revenue and minimize the level of instability” (White, 1983, p. 105). Likewise, Akpadock (1996) argues that communities should diversify their economic base, thus enabling them to endure future structural changes when national economic conditions worsen.

As a result, local governments have established a higher reliance on sales and income taxes to create a more diversified local tax base and reduce the risk of revenue shortages, and this could deliver a range of services to residents (Chernick et al., 2011). In addition, the U.S. Advisory Commission on Intergovernmental Relations (ACIR) produced a document (1974) regarding the use of “income and sales taxes for local governments to promote more balanced revenue structures” (Carroll, 2009, p. 29). The commission justified tax diversification under this strategy through (a) less preference for property tax and increased popularity of sales and income taxes, and (b) the belief that revenue diversification made local government more independent and less burdensome for state governments that were otherwise required to increase tax to support local governments (Carroll, 2009).

Revenue diversification is also related to the regional economic base in local areas, which contributes to the diversification away from property tax (Bartle, Ebdon, & Krane, 2003). Regarding the tax base, an economy with various industries is considered a “well-defined” tax structure for the government (Yan, 2011). The tax structure (revenue patterns) reflects the condition of the local economy, and local governments are able to provide services based on their revenue sources. Thus, a stable revenue stream from diversified industries is important, in that the government would be better maintained in a sound financial position to endure fiscal difficulties through better financial management (Yan, 2011).

The emergence of nonprofit organizations could contribute to diversified tax bases and stable revenue streams. This assumes that some industries can create stable income sources better than others because they are less sensitive to economic conditions. If corporations from different industries are located in a given region, they create different effects on the economy depending on the volatility of the business cycle (Siegel, Johnson, & Alwang, 1995). In addition, variations in the regional sensitivity of business cycles are determined by the proportion of stable and unstable industries in the region. The unstable sectors represent those external sectors that create additional revenue or employment beyond the base necessity. Consequently, if a region is occupied by stable sectors, its economic base fluctuates less with changing business cycles (Yan, 2011).

Thus, based on previous research and extant literature, we have established six hypotheses. These hypotheses are listed as follows:

Hypothesis 1A: A county with a larger number of nonprofit enterprises will have a more diversified tax revenue structure.

Hypothesis 1B: A county with a larger number of nonprofit enterprises from a stable business type will have a more diversified tax revenue structure.

Hypothesis 2A: A county with a larger number of nonprofit enterprises will have less volatility in nonproperty tax revenues.

Hypothesis 2B: A county with a larger number of nonprofit enterprises from a stable business type will have less volatility in nonproperty tax revenues.

Hypothesis 3A: A county with a larger number of nonprofit enterprises will have more nonproperty tax revenues.

Hypothesis 3B: A county with a larger number of nonprofit enterprises from a stable business type will have more nonproperty tax revenues.

Data and Method

This article tests the three sets of hypotheses—tax revenue diversity, volatility of nonproperty taxes, and nonproperty tax revenues—by using empirical models, and each set of hypotheses is divided into two, investigating the impact by using the total number of nonprofit enterprises and the number of nonprofit enterprises based on their business type. To prove the effects of diffusion through the emergence of nonprofit enterprises, the number of nonprofit enterprises, rather than their size or total revenue, is used in our analysis.

Twenty-four counties in Maryland are used as sample units, and the data set is paneled from 2007 to 2012. We choose counties as our unit of analysis because nonprofit enterprises can carry out a variety of services at the county government level comparing with city government in Maryland. County governments work with nonprofit organizations to attain public purposes and meet social needs while they also support nonprofit programs. As for our models, we use a series of ordinary least squares (OLS) regressions with all quantitative dependent and independent variables. In addition, this study uses a fixed-effect regression model to control unobserved factors (time trend and other differences within county). In each regression model, two different sets of independent variables are tested based on whether the nonprofit enterprises are summed up totally or divided by their business types.

As Yan (2011) argues, a stable revenue stream is one of the most important factors in sustaining better financial management, so we separate the types of nonprofit enterprises into two groups: the stable business types, such as education, health, and human services; and the unstable business types, such as the arts, culture, and humanity, the environment, and public and social benefits. This division is based on the stability of occupation in nonprofit areas. Based on the definition of Siegel et al. (1995), we divide the businesses of nonprofit enterprises based on the proportion of additional revenues. Nonprofit enterprises from stable business types comprise a higher percentage of revenues from main sources, such as contributions and government grants (Wing, Pollak, & Blackwood, 2008).

Diversification of Tax Revenues

This research seeks to determine whether an increase in the total number of nonprofit enterprises or the number of nonprofit enterprises in each business type leads to more or less diversified tax revenues for local governments. The diversification of tax revenues is measured on the basis of the HHI. The origin of this index was to measure the level of competition or trust, but today it is widely used to calculate the level of various diversifications (Carroll, 2009; Herfindahl, 1950; Turnbull, 1998). This method calculates the diversification value, ranging from 0 to 1, and demonstrates that tax revenues become more diversified as the values of HHI increase. An increasing value of HHI in terms of tax revenues means that a local government secures the ground for consistent tax revenues, because it has various sources of tax revenues, such as property taxes, income taxes, and sales taxes. In our analysis, HHI values are calculated according to three types of tax revenues: property tax, income tax, and other taxes. The formula to obtain HHI is found in the following equation:

HHI = \frac{1 - \sum_{i = 1}^{n} R_{i}^{2}}{1 - (1 / n)},

where R_i is the proportion of total tax revenue from each tax category and n denotes the total number of revenue bases. In our analysis, the number of categories (n) is equal to 3 (property tax, income tax, and other taxes).

Volatility of Nonproperty Tax Revenues

Revenue volatility of nonproperty taxes is defined by how significantly actual nonproperty taxes (property tax revenues subtracted from total tax revenues) deviate from expected nonproperty tax revenues. In our analysis, this approach, which can be applied to examine variations based on both the counties and the time periods, is used (Carroll, 2009; White, 1983). To compare actual nonproperty tax revenues, a trend line to estimate the expected nonproperty tax revenues is necessary, and this is calculated by using the following regression model:

{NPT}_{i, t} = \exp (α + β_{1} t + β_{2} i),

where NPT_i,t represents the expected nonproperty tax revenues, i represents the dichotomy variables indicating each county, and t represents a year variable. Therefore, the number of county dichotomy variables included in the model is n − 1 (in our analysis, we have 23 dummy variables, because Maryland is comprised of 24 counties). From this regression model, the expected nonproperty tax revenues contain the trends of both counties and years. Following calculations regarding the expected nonproperty tax revenues in county i at the time t, the volatility of the nonproperty tax revenues can be obtained by computing the standard deviation between the actual and the expected nonproperty tax revenues.

Nonproperty Tax Revenues

In addition to the effects of nonprofit enterprises on the diversification of tax revenues and the volatility of nonproperty tax revenues, it is necessary to investigate whether nonprofit enterprises are worth increasing and whether local governments benefit from supporting them in terms of the nonproperty revenues themselves. The third model tests whether nonproperty tax revenues have statistically positive relationships with the number of nonprofit enterprises.

Model Specification

In our analysis, three dependent variables are used: revenue diversification of tax revenues, volatility of nonproperty tax revenues, and nonproperty tax revenues. Based on the basic model from Carroll (2009), the OLS model offered for our analysis measures how governmental, economic, and social factors affect the revenue structures of local governments. We consider county and year impacts by using a fixed-effect model, but the results are not displayed in the table. In this model, the three dependent variables are estimated as shown in Equation 3:

{Dependent}_{i, t} = α + Nonprofit + \sum (Governmental {Variables}_{i, t}) + \sum (Economic {Variables}_{i, t}) + \sum (Social {Variables}_{i, t}) + ε_{i, t} .

In Equation 3, the variables used to control for the influence of local government’s fiscal capacity are debt per capita, tax leverage, grant per capita, and investment earnings per capita. These variables assess the extent to which local government has the capacity to produce sufficient resources for an unexpected deficit. The unemployment rate variable measures the annual unemployment rate for each county, and variables measuring the natural log of a county’s population and land size in square miles are included to control for the size of the jurisdiction. Finally, to erase other possible impacts on tax revenue patterns of local government, variables measuring personal income changes and total expenditure per capita are included in our analysis. The specific explanations of the variables are listed in Table 3, and descriptive statistics for all variables are provided in Table 4.

Table 3.

Variable Specification: Variables, Descriptions, and Data Sources.

Variable	Description and data source
Dependent variable
HHI tax	HHI measure of three tax revenue categories: property tax, income tax, and other tax; Source: CAFR in each county
Volatility of nonproperty tax	Standard deviation value of residuals from general nonproperty tax revenue growth trend regression model; Source (measured in US$1 million): CAFR in each county
Nonproperty tax	Total nonproperty tax (property tax revenue subtracted from the total tax revenues) by using natural log; Source: CAFR in each county
Independent variable
Total number of nonprofits	Total number of nonprofit enterprises; Source: Maryland Nonprofits (www.marylandnonprofits.org)
Number of nonprofits by type	Number of nonprofit enterprises in six categories: (a) Arts, culture, and humanity, (b) education, (c) environment, (d) health, (e) human services, and (f) public and social benefit; Source: Maryland Nonprofits
Debt per capita	Total debt service fund expenditures divided by total population; Source: CAFR in each county and U.S. Census Bureau
Tax leverage	Total general operation expenditures of the local government divided by total tax revenues; Source: CAFR in each county
Unemployment rate	Unemployment rate in each county from 2007 to 2012; Source: U.S. Bureau of Labor Statistics
Land size	Land size of each county measured in square miles; Source: U.S. Census Bureau
Personal income changes	Percentage of total income change from preceding year in each county from 2007 to 2012; Source: U.S. Bureau of Economic Analysis
Investment earnings per capita	Total investment earnings of the local government divided by total population; Source: CAFR in each county
Total expenditure per capita	Total general expenditures divided by total population; Source: CAFR in each county
Grant per capita	Total operation grant from upper level government divided by total population; Source: CAFR in each county
Population	Municipal population transformed by using natural log; Source: U.S. Census Bureau

Note. HHI = Herfindahl–Hirschman Index; CAFR = Comprehensive Annual Financial Reports.

Table 4.

Descriptive Statistics (n = 144).

Variable	M	SD	Minimum	Maximum
Dependent variable
HHI tax	0.78	0.10	0.54	0.98
Volatility of nonproperty tax	156.08	314.41	0.03	1,765.43
Nonproperty tax	18.41	1.42	15.71	21.28
Independent variable
Total number of nonprofits	974.85	1,343.52	61	5,570
Debt per capita	61.18	136.27	0	1,428.77
Tax leverage	1.25	0.17	0.11	1.63
Unemployment rate	6.68	2.30	2.60	12.20
Land size	402.29	153.09	80.90	660.20
Personal income change	2.87	2.42	−6.60	9.12
Investment earnings per capita	26.73	42.27	0	320.78
Total expenditure per capita	2,248.34	4,496.86	862.58	48,191.50
Grant per capita	221.86	407.59	38.35	4,189.02
Population	11.72	1.17	9.90	13.81

Note. HHI = Herfindahl–Hirschman Index.

Results

Table 5 provides the regression results for the HHI values of the total tax revenue model. The model, using the total number of nonprofit enterprises as an independent variable, indicates that the number of nonprofit enterprises has a positive effect on the diversification of tax revenues. It provides evidence that nonprofit enterprises help to sustain local governments with more diversified tax revenue sources by providing more income tax or other types of taxes. In addition, tax leverage and the unemployment rate have negative effects on the diversification of tax revenues at the 10% and 1% levels of significance, respectively. If the number of nonprofit enterprises by type is used in the model, some unstable types of nonprofit enterprises, such as those in the arts, culture, and the humanities, and the environment, have a negative effect on the diversification of tax revenues. The unemployment rate also negatively affects diversification.

Table 5.

Regression Model Results (HHI of Total Tax as a Dependent Variable).

Variable	Coefficient	SE	Coefficient	SE
Total nonprofits	0.0001*	0.000
Arts, culture, and humanity			−0.0014***	0.001
Education			0.0000	0.001
Environment			−0.0016***	0.001
Health			0.0005	0.001
Human service			0.0001	0.000
Public and social benefit			0.0004	0.000
Debt per capita	−0.0005	0.000	−0.0005	0.000
Tax leverage	−0.0700*	0.037	−0.0623*	0.031
Unemployment rate	−0.0290***	0.004	−0.0285***	0.004
Land size	0.0000	0.000	0.0001**	0.000
Personal income change	−0.0006	0.002	0.0001	0.002
Investment earnings per capita	−0.0000	0.000	0.0000	0.000
Total expenditure per capita	0.0000	0.000	0.0000	0.000

Note. Coefficients and standard errors for regional and year dummy variables are not reported. HHI = Herfindahl–Hirschman Index.

p < .1. **p < .05. ***p < .01.

Turning to the analysis of revenue volatility, Table 6 reveals that the volatility of nonproperty tax revenues is not statistically significant in the model using the total number of nonprofit enterprises. However, nonprofit enterprises in unstable business categories, such as the arts, culture, and the humanities, and the environment, increase the volatility of nonproperty tax revenues. Nonprofit enterprises in stable business categories, such as human services, however, decrease the volatility of nonproperty tax revenues. In addition, nonprofit enterprises from business types such as education decrease the volatility of nonproperty tax revenues. Thus, the types of nonprofit enterprises affect the volatility of nonproperty tax revenues.

Table 6.

Regression Model Results (Volatility of Nonproperty Tax as a Dependent Variable).

Variable	Coefficient	SE	Coefficient	SE
Total nonprofits	−0.12	0.28
Arts, culture, and humanity			17,300***	1.85
Education			−7.92**	3.02
Environment			12.70***	1.92
Health			0.32	2.96
Human service			−0.85**	0.40
Public and social benefit			1.39	1.31
Debt per capita	−3.87	3.84	−2.48	1.59
Tax leverage	−199	371.00	−69.90	159.00
Unemployment rate	−8.39	33.70	−15.30	15.80
Land size	0.19	0.23	0.04	0.10
Personal income change	−8.66	15.70	3.29	7.35
Investment earnings per capita	−0.07	2.13	0.34	0.91
Total expenditure per capita	0.12	0.10	52,120	0.04
Grant per capita	−0.28	0.73	0.14	0.31
Population	−62,200.00**	2,320.00	−251.00	1,060.00

Note. Coefficients and standard errors for regional and year dummy variables are not reported.

p < .1. **p < .05. ***p < .01.

Table 7 provides the regression results for total nonproperty tax revenues. The first regression model, by using the total number of nonprofit enterprises, proves that such enterprises increase nonproperty tax revenues. Debt per capita, tax leverage, unemployment rate, personal income change, and operating grants from federal or state governments have negative effects on nonproperty tax revenues. Some variables, such as total expenditures per capita and population, however, help to increase nonproperty tax revenues. However, nonproperty tax revenues are not affected by the type of nonprofit enterprise, and no significant variables can be found from the number of nonprofit enterprises by type.

Table 7.

Regression Model Results (Nonproperty Tax as a Dependent Variable).

Variable	Coefficient	SE	Coefficient	SE
Total nonprofits	0.0002***	0.000
Arts, culture, and humanity			−0.0004	0.001
Education			0.0031	0.002
Environment			0.0020	0.001
Health			−0.0043	0.003
Human service			−0.0001	0.000
Public and social benefit			0.0002	0.000
Debt per capita	−0.0018*	0.001	−0.0020**	0.001
Tax leverage	−0.7392***	0.112	−0.7140***	0.120
Unemployment rate	−0.0557***	0.010	−0.0600***	0.012
Land size	−0.0000	0.000	−0.0001	0.000
Personal income change	−0.0135**	0.005	−0.0020**	0.009
Investment earnings per capita	0.0006	0.001	0.0007	0.001
Total expenditure per capita	0.0001	0.000	0.0001***	0.000
Grant per capita	−0.0004**	0.000	−0.0003	0.000
Population	0.9566***	0.046	0.9365***	0.045

Note. Coefficients and standard errors for regional and year dummy variables are not reported.

p < .1. **p < .05. ***p < .01.

Conclusion

This study concludes that local governments can secure more varied income sources and collect more nonproperty tax income as more nonprofit enterprises have emerged in the county. As demonstrated in the results, if local governments support the emergence of nonprofit enterprises, these nonprofit enterprises will contribute positively to tax revenue diversification and the nonproperty tax revenues of local government. However, the influence of nonprofit enterprises on the volatility of nonproperty tax structures is not statistically significant. In addition, regarding the type of nonprofit enterprise, unstable types of nonprofit enterprises are negatively related to revenue diversification and increase revenue volatility. Thus, different types of nonprofit enterprises have different impacts on tax revenue diversification and the volatility of nonproperty tax revenues.

In our analysis, nonprofit enterprises are helpful in advancing strategies for revenue diversification of local governments, as well as in obtaining increased revenues from nonproperty tax income. However, this study indicates that nonprofit enterprises in unstable business categories do not demonstrate any unique characteristics of “public” or “social” entrepreneurship. They are similar to enterprises in the private sector in that they increase the volatility of revenue and decrease revenue diversification. Nonprofit enterprises in stable business categories have a more “public” characteristic in society. This is related more to social welfare services that meet needs for the vulnerability of disadvantaged groups. This result provides a potential future research topic because it is meaningful to examine why nonprofit enterprises from unstable business types show different characteristics compared with those from stable business types. For future research, we could investigate socioeconomic factors to find the difference. For example, people with high incomes may be more likely to provide private donations to nonprofit enterprises from unstable business types, such as the arts, culture, and humanity.

These findings suggest policy options for financial sustainability and stability during economic downturns; they will be especially helpful for local governments where more nonprofit enterprises exist to buffer economic stress. They have indirect positive impacts on economic conditions in local government through invigorating economic development, providing jobs for the jobless, and investing in small businesses. In conclusion, this study sheds light on the symbiotic relationship between local governments and nonprofit enterprises. The nonprofit enterprises create potential revenue sources for local governments and contribute to their financial independence; they also create more revenue diversification and reduce volatility of tax revenues. Local governments can also contribute to revenue stability of nonprofit enterprises by providing grants to them. Even though the increasing number of nonprofit enterprises is considered a potential threat to revenue base of local governments owing to the tax exempt on revenues, their commercialization trends can also exert a positive influence on securing revenue sources in local government.

The study has some limitations as well. First of all, nonprofit enterprises are sometimes defined ambiguously. This study uses numbers from 501(c)(3) of the Internal Revenue Service (IRS) revenue code as the data for nonprofits; the definitions are somewhat overlapping in research. Second, it is questionable as to whether this research can be generalized to other areas in the United States. Third, the state of Maryland has extremely varied counties in terms of overall structure and other characteristics, making it difficult to contain all possible differences in the model.

However, there is no previous study about the effects of nonprofit enterprises on the revenue condition of local governments. Most research in this area deals with topics related to the effects of nonprofit enterprises on overall society and performance for obtaining funds. Governments have continuously focused on nonprofit enterprises and their impact on society. Local governments, however, seem to focus less on the impacts of nonprofit enterprises on their revenues. For these reasons, this study about the influence of increasing numbers of nonprofit enterprises on revenue conditions in local government is novel. Thus, this study plays a role in the creation of a bridge to connect local governments and nonprofit enterprises for the benefit of identifying the financial benefits of such enterprises and relaying them to the community as a whole.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Author Biographies

Soyoung Park is a doctoral candidate in the School of Public and International Affairs, University of Baltimore. Her research interests are public budgeting and financial management at the state and local level.

Sungchan Kim is a doctoral candidate in the Department of Political Science and Public Administration at Mississippi State University. His research interests include public budgeting and financial management practices of state and local governments.

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