Further Evidence on Competition in Nonprofit Donor Markets

Abstract

This article introduces a novel empirical approach to the nonprofit literature that can measure competition between nonprofit organizations. Our approach provides a framework to determine how the number of organizations may be incorporated into empirical competitive analysis. We then systematically estimate the average population needed to support a given number of nonprofits in a market. We find that, for the 10 nonprofit industries examined, markets reach competitive levels once four or more nonprofits have entered. The results suggest that a relatively small number of nonprofits are needed to ensure robust competition. Our findings demonstrate that donor market competition is both predictive in nonprofit entry decisions and remarkably similar to competitive behavior among for-profit firms. We discuss several implications of these findings, in terms of both policy and future empirical research.

Keywords

competition entry threshold nonprofit

Introduction and Motivation

Measures of market structure are widely used in the nonprofit literature as a proxy for the intensity of competition. We introduce an alternative empirical approach, derived from a recently developed theoretical framework that estimates nonprofit competitive behavior. We treat the choice of entry as endogenous, thus mitigating a potentially large source of bias that is common in the existing nonprofit literature examining competitive behavior. Our article reports on three primary findings, which have not been previously identified. First, we demonstrate a large and consistent impact of donor market competition among a wide range of nonprofit sectors. Second, we show that competitive levels of entry can be achieved with relatively few (around four) organizations. Finally, the effects of competition on organizational behavior are highly nonlinear, with the main impact of competition generated by the first three incumbents.

Why is competition among nonprofit organizations important to consider? In the case of for-profit firms, competition permits consumers to shift purchases from one organization to another. Thus, competition is the primary mechanism for organizations to determine which goods or services their consumers value. Competition further ensures that consumers may choose an organization that provides products with the highest quality or at the lowest cost. There is an analogous case in the nonprofit sector, where consumers may be represented by donors, foundations, government agencies, or clients. Competitive markets allow these constituents to choose the nonprofit that provides the best service, whereas funders can match their scarce resources with the most efficient or effective nonprofit. Existing empirical evidence supports the claim that nonprofit constituencies benefit from the choice provided by competitive donor markets (Aldashev & Verdier, 2010; Castaneda et al., 2008; Feigenbaum, 1987; Keisler & Buehring, 2005; Lynk, 1995; Melnick et al., 1999; Simpson & Shin, 1998). Thus, robust competition among nonprofit providers is one important market mechanism to ensure that constituents receive the best value from nonprofit providers.

There are, however, concerns about the adverse impacts of competition in the nonprofit sector. First, researchers have noted that it is possible for competition to be too intense, resulting in an arms race of wasteful fundraising expenditures that reduce the net resources available to serve constituents (Kessler & McClellan, 2000; Rose-Ackerman, 1996; Thornton, 2006). Furthermore, competition may degrade investments in management capacity, thereby reducing the ability to provide services over the long term (Calabrese, 2013; Lecy & Searing, 2014; Mitchell, 2017). Finally, sector-specific evidence suggests that competition may lower costs, but play less of a role in ensuring service quality (Lamothe, 2015).

Measuring competition in the nonprofit sector remains uniquely challenging. Competition among arts, health care, and education nonprofits operates through relatively familiar processes, because consumers typically pay an observable price for an identifiable service (Marginson, 2006; Seaman, 2004; Shortell & Hughes, 1988; Town & Vistnes, 2001). Yet, competition will also play a role when prices and output are not observable—such as when nonprofits compete for donations or grants (Harrison & Irvin, 2018). This article offers an empirically estimable model of nonprofit competition that is broadly applicable across all nonprofit sectors, and—importantly—does not require observing prices or output directly. Intuitively, our approach maps a recently developed theoretical framework to an empirical model that measures competition indirectly by estimating entry thresholds, or the population necessary to support a given number of nonprofit organizations. We explain how population thresholds will change with entry and provide corresponding empirical results.

There are two primary implications for our findings, one policy oriented and one methodological. First, funding agencies (government and private) rely on nonprofit organizations to deliver public services (Witesman, 2007). Government agencies often allocate awards to nonprofits via a competitive process (Amirkhanyan, 2010; Bennett et al., 2003; Bennett & Iossa, 2009; Chau & Huysentruyt, 2006; Y. Kim & Brown, 2012; Witesman & Fernandez, 2012). Although competition has been extensively examined among for-profits (Baron, 1972; Keisler & Buehring, 2005; McAfee & McMillan, 1986; Mccall, 1970), less is known about the impact of competition in a nonprofit setting. A 2009 Government Accountability Office (GAO) report estimated that, in 2006, federal agencies channeled more than 235 billion through the nonprofit sector (Government Accountability Office, 2009). Our results demonstrate that competitive forces are similarly effective across a broad spectrum of nonprofit industries. We show that markets with as few as four organizations will result in markets that fully exhaust competitive pressures.

Second, we demonstrate that the number of organizations, even if normalized for population (i.e., density), may not be a reliable measure of market competitiveness. The reduction in market power is highly nonlinear. Our results indicate that most of the reduction in market power is significantly dissipated once the second or third nonprofit enters a market. Linear measures of market structure may systematically underestimate (for low N) or overestimate (for high N) the impact of competition on organizational performance. Furthermore, typical measures of market structure are likely endogenous, either because of unobserved heterogeneity or reverse causality, which we discuss in the next section. Either form of endogeneity will result in biased parameter estimates (Evans et al., 1993). Our results, therefore, inform researchers considering standard measures of market structure of an alternative method to infer competitive pressures and the implications of those pressures in nonprofit markets. We discuss these implications in more detail in the “Results” section and provide suggested solutions in the “Discussion” section.

Review of Nonprofit Competition and Estimation

Many recent empirical studies in the nonprofit literature have applied measures of competition in their analysis. Most commonly, competition is parameterized using a measure of market structure such as organization count (N), organization density $(N / P o p u l a t i o n)$ , or nonlinear parameters such as the Herfindahl–Hirschman index (HHI), Blau, or Simpson’s index. The latter have the advantage of including information on both the number of nonprofits and the relative distribution of revenue or assets.

Some nonprofit studies use market structure as an independent (right-hand side) variable for the competitiveness of nonprofit organizations (or nonprofit firms, depending on the primary discipline and literature). Recent examples include Paarlberg et al. (2018), Paarlberg and Hwang (2017), and Prentice (2016), each using county nonprofit density (N/Population) on the right-hand side of the regression to predict the impact of competition on the financial performance of nonprofit firms. Paarlberg et al. (2018), Paarlberg and Hwang (2017), and Faulk et al. (2016) include nonlinear measures of concentration (e.g., the Blau index or HHI), which contain information about the distribution of resources among nonprofits. These papers suggest that increased density (their analog for competition) will generally reduce nonprofit financial performance.

Other papers have used N, or the number of nonprofits within a specified market, as a right-hand side predictor variable. Barbetta et al. (2018) use the total number of nonprofits (N) in Italy as a predictor of future market entry. Andersson and Ford (2016) use the number of voucher schools (N) to predict participation in a Milwaukee school choice program. Leroux and Wright (2010) test an adjacent claim, where an increase in competition, measured by nonprofit count (N) within a metropolitan statistical area (MSA), will erode the quality of nonprofit decision making.

Nonprofit researchers have also applied various theories (e.g., government failure, interdependence, or resource dependence) to explain the number and distribution of organizations in the community environment. These empirical studies demonstrate the potential reverse causality, in that, they typically estimate nonprofit market structure (N or density) as a dependent (or left-hand side) variable, which is then regressed on some set of community characteristics. For example, Polson (2017) estimates the number of nonprofits (N) in a U.S. county as a function of community religiosity. Grønbjerg and Paarlberg (2001), Jeong and Shicun Cui (2020), and M. Kim (2015) each estimate nonprofit density (N/Population) as a function of community demographic characteristics. Lecy and Van Slyke (2013) estimate nonprofit density as a function of both size and concentration of various revenue sources within U.S. MSAs.

Nonprofit studies that apply market structure parameters (N, density, HHI) as independent variables commonly invoke a population ecology framework. Population ecology was developed within sociology to explain competitive pressures among organizations, and has been widely applied in the nonprofit research literature (Aldrich & Pfeffer, 1976; Hannan & Freeman, 1987, 1989). Population ecology starts with an environment (or ecology), which then determines organizational behavior (Amburgey & Rao, 1996; Baum & Shipilov, 2006). Drawing an analogy from biological processes, population ecology describes how demographic resources determine the carrying capacity of a community, imposing a maximum density of organizations along with entry and exit pressures.

Similar to population ecology, the structure–conduct–performance (SCP) paradigm developed as the dominant theory for market competition in economics and industrial organization (Cowling & Waterson, 1976; Perloff et al., 2007). SCP dates to original work in the field of industrial organization by Mason (1939) and Bain (1951). The core idea of SCP is that market structure, defined by the number and concentration of organizations in a market, will predict organization conduct. Conduct includes the organization’s pricing, investment, and output decisions. Conduct then predicts organizational performance, which includes profitability, product variety, and technological efficiency (Cowling & Waterson, 1976; Perloff et al., 2007).

The application of SCP has been historically widespread in the economics literature (Schmalensee, 1989), but has fallen out of favor among industrial organization researchers (Einav & Levin, 2010) because it describes a process where the market environment determines firm conduct, but ignores the reverse process where organization actions may determine the competitive environment (Perloff et al., 2007; Tirole, 2001). This potential endogeneity has also been acknowledged in the nonprofit theoretical literature (Paarlberg & Hwang, 2017; Paarlberg & Varda, 2009) but not generally addressed empirically. Our article builds on this key point, where nonprofit entry is a choice (i.e., endogenous) and introduces a competitive framework, recently adapted to nonprofit competition, that can be empirically estimated.

To illustrate the endogeneity issue, it has been demonstrated that large investments (e.g., advertising, production capacity, or R&D) can suppress for-profit competition (Delorme et al., 2002; Resende, 2007). Similarly, in the nonprofit sector, it is plausible that existing structural barriers to entry (such as entrenched relationships with institutional funders) may cause low-density markets (Faulk et al., 2017). It is equally plausible that incumbents in low-density markets are more capable of deploying strategies (such as fundraising campaigns or accreditation standards) that resist new entry (Horwitz & Nichols, 2009). Similar examples exist for other performance measures such as profits/net-income or other financial measures. Thus, the direction of the causal relationship between market structure, conduct, and organization performance is ambiguous.

Similarly, an unobserved parameter (such as legal environment or manager skill) may simultaneously influence both organizational performance and market structure (Harrison & Laincz, 2008; Lakdawalla & Philipson, 2006; Thornton et al., 2012). These factors are inherently embedded in both organizational performance and market structure parameters. Therefore, the market structure parameter does not cleanly identify the impact of nonprofit competition. Empirical studies that exhibit endogeneity bias are well documented, but are often overlooked in the nonprofit literature (Bresnahan, 1989; Evans et al., 1993).

In summary, a central problem with SCP/population ecology–style studies are that entry into a market is treated as exogenous, rather than as a choice variable by the nonprofit organization. In the next section, we offer an intuitive description of an alternative theoretical approach formally derived in Gayle et al. (2017), now noted as GHT. We then translate the theoretical model into a theoretically grounded metric of market competitiveness, where the market entry is a choice variable by the organization (Bresnahan & Reiss, 1991; Clarke & Davies, 1982). We discuss the model implications for empirical measurement with the goal of demonstrating how the approach can be applied more broadly in the nonprofit competition literature. We refer readers seeking more technical detail about the theoretical model to the original paper.

Intuitive Description of the Theoretical Model

The following is an intuitive summarization of the formal mathematical model discussed in GHT (Gayle et al., 2017). Competition in the nonprofit sector may occur along many dimensions such as government contracts or service quality. We choose to not only focus on competition for donations to motivate the model but also discuss how other dimensions may also be considered. In the model, nonprofits compete for donations via solicitations, which are costly to produce. For a nonprofit to enter a donor market, it must expect that total donations will be sufficient to pay for both its charitable output and the cost of solicitation. More important, the model can be easily generalized to other sources of revenues solicited by a nonprofit (e.g., government grants or foundation awards or to program service/fee-for-service markets). For example, universities will recruit students as a function of their probability of attending, whereas hospitals will introduce new units based on their likelihood to attract patients. The key issue for the theoretical model is that nonprofits compete strategically for scarce resources (i.e., grants, donations, government awards, or paying customers). Our empirical application will also focus on the market for donations, while statistically controlling for alternative revenue streams. We discuss possible extensions to this approach in the final section of the article.

When a nonprofit organization is alone in the market, it can solicit the most valuable donors first and earn the highest possible total dollar value for each solicitation. A second organization will enter the market only if they are able to cover both their costs of charitable production and their cost of solicitation. This implies that the donor market must be of sufficient size to support two organizations. Why? Because with two organizations, the donor market is now split, and nonprofits must expect lower donations from their average donor. To see this, we note that the limiting factor on entry is the number of donors available to support the organization. For each donor solicited, either they will choose to give to only one nonprofit or choose to split the donation. It is analytically convenient to think of the market split evenly, but—in practice—any ratio will offer identical results. The key idea is that each organization (both the new entrant and the incumbent) is soliciting from an—on average—less generous pool of donors. Therefore, the number of donors required to support an additional entrant must increase with each new entrant. The primary insight of the model is that new organizations will enter the market only if they can, at least, breakeven (i.e., total revenues ≥ total costs). A benefit of this theoretical framework and corresponding empirical model is that it does not require us to observe financial data.

From this framework, we can use the observed size of the population relative to the number of firms in a market to infer changes in competition without requiring information on prices or output. This is easiest to see with a numerical example. Assume that we observe a monopolist nonprofit in a market with 20,000 potential donors (define this as S₁). Then, assume that we do not observe a duopoly until the market expands with an additional 50,000 potential donors, increasing the total market size to S₂ = 70,000. This results in an average market size of 35,000 donors for the two firms, that is, 70,000/2 (define this as s₂). From the model above, we infer that the increase from S₁ = 20,000 to s₂ = 35,000 occurs because each organization will need to solicit more donors to achieve breakeven revenue. Suppose then that the market must increase to 150,000 donors before we observe three firms in the market. The ratio of donors to firms (i.e., 150,000/3) is now s₃ = 50,000. Note that, as with the second entrant, the third entrant requires an even larger donor population to be viable. This would imply that the marginal donation relative to the marginal cost is falling for the third nonprofit. In a for-profit setting, this would identify a scenario of declining variable profits—for nonprofit purposes, we describe this as declining variable net donative revenues (VNDR). The larger the increase in the ratio of donor population to number of firms, the larger the decline in VNDR.

What would a competitive equilibrium look like in this framework? As the market converges to a competitive equilibrium, the rivalry induced by the next new entrant will stabilize. This circumstance would imply that the VNDR will also stabilize. Why must this happen? As entry continues, the market will reach a point where VNDR cannot fall further, because the nonprofit could not finance its cost of production (i.e., the nonprofit is not breaking even). At this point, the required number of incremental donors necessary to induce entry becomes constant. Nonprofits in such a market will have achieved an efficient outcome in the sense that donor market competition ensures nonprofit organizations solicit just enough to cover their true cost of production. This information is valuable to donors, funders, and contractors (Bajari et al., 2014; Bennett & Iossa, 2009; Saussier et al., 2009; Thornton & Lecy, 2019). In the “Empirical Model” section, we discuss how we use this framework to build an empirical model for identifying competitive behavior.

Data

To test the model, we examine 10 nonprofit subsectors to determine whether market competition generates similar entry thresholds for a general population of nonprofits, where donations are a significant portion of revenues. Our sample is drawn from the National Center for Charitable Statistics (NCCS) core files of nonprofit organizations, which includes organizations operating under section 501(c)(3) of the U.S. Federal tax code. We construct our sample based on “donative nonprofits,” which are commonly characterized by being financed primarily through charitable donations (Hansmann, 1980). We focus on donative nonprofits because charitable organizations play a significant role in the U.S. economy by encouraging private donor markets to produce collective goods and services that may be substitutable for government provision. This market construction facilitates deliberate comparison with much of the previously cited work in measuring nonprofit competition. However, the framework is also generalizable to government grants or fee-based income, which may suggest a different market definition—such studies should refer to the original GHT paper for the detailed criteria of defining relevant markets.

The sample is constructed by determining the relevant market for donations. We use the Core-Based Statistical Area (CBSA) as a reasonable approximation for the size of the donor market for competing nonprofits. The Office of Management and Budget defines the CBSA as having at least one urbanized population of 10,000 or greater. We use CBSAs to construct a cross section of nonprofit markets in sectors (or industries) drawn from year 2005 Form 990 data (NCCS Core files). CBSAs have been used by the Census since 2003. CBSAs include both Metropolitan and Micropolitan statistical areas. Previous research concludes that familiarity with the nonprofit, demand for services, and social conformity are determinants of charitable giving (Dellavigna et al., 2012; Glückler & Ries, 2012), which lends credibility to our choice of contiguous geographic markets. We infer the existence of a nonprofit from their Form 990 tax filing for a particular year. We use the National Taxonomy of Exempt Entities (NTEE) taxonomy to identify two-digit subsectors that satisfy the following criteria:

Nonprofit organizations contained within the two-digit subsector are primarily headquartered within a CBSA.

Nonprofit organizations contained within the two-digit subsector compete in relatively distinct markets.

Nonprofit organizations contained within the two-digit subsector use inputs (donations and labor) that are derived locally and outputs that are consumed locally.

Nonprofit organizations contained within the two-digit subsector are reasonably homogeneous in their outputs. Consequently, donors would likely perceive them as substitutes.

Nonprofit organizations contained within the subsector produce output that is not substitutable with for-profit output.

Nonprofit organizations contained within the subsector receive a nontrivial fraction of their revenues as private donations.

From these criteria, we examine 10 nonprofit industries not previously analyzed in GHT. Some CBSAs contain a very large number of organizations for any particular nonprofit industry. This creates a long right tail in the distribution of the number of organizations per market that could skew our results. To mitigate this problem, we trim the largest five CBSAs from the sample. Note also that this deletion includes Washington, D.C., which contains a disproportionate number of nonprofits relative to its population size. Furthermore, we trim CBSAs with less than 20,000 in population, reducing the sample by 258 CBSAs. A market is identified by a CBSA–subsector pair. The sample includes 652 CBSAs over 10 nonprofit sectors, for a total of 6,520 distinct markets. Table 1 presents summary statistics for each nonprofit subsector.

Table 1.

Summary of the Number of Nonprofit Firms by Sector and CBSA for 2005.

Sector name	NTEE codes	Sum (N)	M (N)	Max (N)	Number of CBSA
1. Abuse prevention	I70–I73	917	1.4	26	652
2. Employment and vocational training	J20–J22, J30, J32–J33	2,582	4.0	79	652
3. Substance abuse treatment	F20–F22	2,221	3.4	72	652
4. Hot lines and crisis prevention	F40, F42	337	0.5	8	652
5. Crime prevention and rehabilitation	I20, I21, I23, I30, I31, I40, I43, I44	1,297	2.0	37	652
6. Food pantries and programs	K30, K31, K34–K36	1,474	2.3	39	652
7. Homeless shelters	L40–L41, P85	1,328	2.0	57	652
8. Community centers	P28	1,035	1.6	42	652
9. Family counseling	P46	588	0.9	20	652
10. Residential care	P73, P70, P72	2,362	3.6	102	652

Note. Markets are identified by a sector–CBSA pair; 652 CBSAs were included in the sample for a total of 6,520 distinct markets. Does not include five larges CBSAs, by nonprofit firms: Washington, D.C.; Chicago; Dallas-Ft. Worth; Los Angeles; New York. Does not include micropolitan areas less than 20,000 in population. NTEE Code details can be found at https://nccs.urban.org/classification/national-taxonomy-exempt-entities. NTEE = National Taxonomy of Exempt Entities; CBSA = Core-Based Statistical Area.

For example, “Employment and Vocational Centers” is the largest nonprofit sector in the sample, with 2,582 total organizations in 2005. This industry averaged four organizations per CBSA. For every sector, there are some markets with zero organizations of that type within a particular CBSA. The lowest concentration of organizations is in the “Hot Lines & Crisis Prevention” sector, with an average 0.5 organizations per CBSA. There are 171 CBSAs that contain only one (monopolist) homeless shelter. We top-code the markets containing more than 10 organizations to mitigate the influence of CBSA outliers with a very large number of organizations in a particular subsector.

Table 2 presents a description of the distribution of organizations across markets with the top coding implemented. For example, there are 311 CBSA–sector pairs that contain no abuse prevention center, 174 monopolist, and 68 duopolistic markets.

Table 2.

The Count of Markets With N Number of Firms by Sector.

cbsa_N	Abuse prevention	Employment and vocational training	Substance abuse treatment	Hot lines and crisis prevention	Crime prevention and rehabilitation	Food pantries and programs	Homeless shelters	Community centers	Family counseling	Residential care
0	311	177	206	451	322	218	292	345	424	202
1	174	161	180	126	149	173	171	146	124	157
2	68	94	89	49	60	103	62	56	43	95
3	35	60	41	14	28	62	33	32	16	53
4	14	42	24	4	23	24	24	22	11	29
5	12	14	16	2	8	14	13	11	4	20
6	9	21	13	1	7	9	11	3	10	16
7	2	11	13	1	9	11	7	3	3	8
8	8	9	7	4	5	0	6	2	6	9
9	4	4	8	0	4	5	4	2	1	8
10	15	59	55	0	37	33	29	30	10	55
Total	652	652	652	652	652	652	652	652	652	652

Note. For this table and subsequent regressions, the total number of firms per CBSA has been top coded at 10. CBSA = Core-Based Statistical Area.

We merge these markets with demographic data derived from the Bureau of Economic Analysis (BEA) Regional Economic Accounts. Table 3 offers summary statistics for these covariates. The average population of a CBSA within the sample is 223,997. The average per-capita income was US$18,555. We also summarize Medicaid transfer payments, which averaged US$1,535,592 per CBSA. Finally, we included a set of demographic variables, which include poverty rate, college graduation rates, percent single, percent with children, and percent Black.

Table 3.

Summary of Demographic Statistics for Sample CBSAs in 2005.

Variable	M	SD	Minimum	Maximum
CBSA population	223,997	516,397	20,048	4,623,252
Per capita income (US$)	18,555	3,144	7,070	34,237
Medicaid transfers (US$)	1,535,592	5,716,868	0	96,700,000
Poverty rate (%)	11.2	4.4	3.6	35.1
College graduation rate (%)	19.1	8.4	0.0	57.0
Percent single	25.6	3.5	11.1	35.3
Percent with children	81.2	7.2	48.6	98.8
Percent Black	9.7	12.9	0.1	70.9
Median rent (US$)	553	163	288	1,333
Land area (sq mi)	586	701	1	8,357

Source. BEA Regional Economic Accounts.

Note. CBSA = Core-Based Statistical Area.

Empirical Model

Our empirical strategy identifies the donor market population necessary for n nonprofits to enter a geographic donor market (see GHT for full details). To make our discussion more concrete, denote S_n as the total population of potential donors in the CBSA. This value corresponds to the numerical example in our theoretical model section, S₁ = 20,000, S₂ = 70,000, S₃ = 150,000. Then, $s_{n}$ is the ratio of the donor population to the number of firms, or per-organization donor population. Again, recall in our example, $s_{1}$ = 20,000, $s_{2}$ = 35,000, $s_{3}$ = 50,000, and so forth. To calculate $S_{n}$ and $s_{n}$ , let n represent the total number of nonprofits observed in a market (CBSA–sector pair). Recall also from the theoretical model that nonprofits enter a market when they expect that total revenues (TR) equal or exceed total costs (TC).

We empirically estimate the total net value of the nonprofit in market m with n firms as $v_{m} (n) = T R_{m} (n) - T C_{m} (n) = \cdot X_{m} β + S_{m, n} γ + α + ε_{m}$ , where $X_{m}$ is a vector of market covariates and $S_{m n}$ is the observed population of potential donors. Nonprofits will enter a market when $v_{m} (n) \geq 0$ . We therefore infer that when we observe n nonprofits in the market, it must be the case that $v_{m} (n) \geq 0$ in equilibrium, whereas n + 1 nonprofits would not viable, $v_{m} (n + 1) < 0$ . Bresnahan and Reiss (1991) demonstrate that such conditions can be estimated as an ordered probit. We refer the interested reader to GHT for further details on the exact construction of the ordered probit regression.

We include a vector of covariates ( $X_{m}$ ), which captures demand-side characteristics of donors in the market that potentially influence the decision to enter the market m (Barman, 2008; Harrison & Thornton, 2014). Recall, $S_{m n}$ is the observed potential donor population for a given CBSA. We observe significant cross-sectional variation in donor market population and a corresponding variation in number of nonprofits. This variation allows us to estimate the average relationship between $S_{m}$ and n. We can then estimate the breakeven donor population necessary to support n firms. It is crucial to note that this regression model explicitly incorporates the nonprofit’s choice to enter the market. That is, n, the probability of observing n in a market, per the ordered probit model, is the dependent (not an independent variable); it inherently addresses the endogeneity bias inherent in the SCP paradigm (Schmalensee, 1989).

Homogeneity among organizations is a common assumption for this type of model, which allows us to focus on the impact of competition on the average nonprofit. So, our model focuses on the number of nonprofits in a market, rather than the particular identity of the nonprofit competitor. Our approach could be extended to include variation in the costs of production, or the entry order of firms. However, such empirical analysis is likely premature, until we evaluate aggregate competitive trends. Previous models using n, a competition index such as the HHI, or density also make a homogeneity assumption, making our current analysis comparable with existing studies. A contribution of our analysis is that these assumptions are now more explicit and aid in our understanding of the implicit assumptions in prior empirical work.

$S_{n}$ is derived from the coefficients of the ordered probit model. (For the precise calculation of $S_{n}$ , see Bresnahan & Reiss, 1991, or GHT). Recall entry thresholds are interpreted as the population required to support n organizations. These values are presented in bold for each sector in Table 5. We then calculate $s_{n} = \frac{S_{n}}{n}$ , which is the per-organization population required to support N firms in the market. The change in $s_{n}$ provides information on competitive rivalry with an additional organization. We use the entry threshold ratio, $s_{n + 1} / s_{n}$ to infer whether a market is approaching a competitive equilibrium.

For example, an entry threshold ratio greater than one $(e .g ., \frac{s_{2}}{s_{1}} = \frac{35, 000}{20, 000} > 1)$ indicates that a larger than proportional number of donors are needed to sustain two entrants than needed for one entrant, implying a decrease in the VNDR. From the theoretical model, we would infer that the intensity of competition has increased as long as the entry threshold ratio maintains a value greater than one. Once the entry threshold falls to one, each new entrant requires the same number of donors to support the additional organization as the previous incumbent nonprofit. The relative size of a donor base required to support a new entrant $(s_{n + 1})$ will stabilize, such that $s_{n + 1} = s_{n}$ . This process implies that the ratio, $s_{n + 1} / s_{n}$ will converge to one. Thus, a competitive equilibrium has been reached. Formally,

{\begin{matrix} \frac{s_{n + 1}}{s_{n}} > 1 \Rightarrow market rivalry increasing \\ \frac{s_{n + 1}}{s_{n}} = 1 \Rightarrow market rivalry equlibrium \\ \frac{s_{n + 1}}{s_{n}} < 1 \Rightarrow market rivalry decreasing \end{matrix}} .

Results

Table 4 presents the ordered probit parameter estimates for the covariates (X). As an example, for interpretation, we find that increases in market population and income are strongly associated with nonprofit entry across all industries. Similarly, higher Medicaid expenditures are statistically associated with entry in Sectors 2, 6, 7, and 10. Increased poverty rates are associated with entry only for family counseling. Median rental rates are positively associated with entry in Sectors 4, 5, and 8. An increase in the percent of Black members of the community reduces entry for employment and vocational training. These mixed results are generally consistent with prior estimates and illustrate a tension between the demand side for nonprofit services—where lower incomes increase demand for social services—and the supply side—where entry is supported by higher incomes.

Table 4.

Ordered Probit Covariate Estimates.

	1		2		3		4		5		6		7		8		9		10
Nonprofit Sub-Sectors	Abuse prevention		Employment and vocational training		Substance abuse treatment		Hot lines and crisis prevention		Crime prevention and rehabilitation		Food pantries and programs		Homeless shelters		Community centers		Family counseling		Residential care
CBSA population	1.89E–06	**	6.93E–06	**	6.38E–06	**	9.27E–07	**	3.44E–06	**	3.03E–06	**	3.03E–06	**	2.61E–06	**	1.43E–06	**	4.02E–06	**
SE	0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000
Per-capita income	9.77E–05	**	5.04E–05	*	6.64E–05	**	0.0000906	**	8.47E–05	**	0.0000641	**	6.19E–05	**	0.0000929		1.67E–04	**	0.000084	**
SE	0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000
Medicaid transfers	6.61E–09		4.31E–08	*	3.06E–08		1.61E–08		1.81E–08		1.13E–07	**	6.75E–08	**	7.02E–08		−9.34E–09		8.55E–08	**
SE	0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000
Poverty rate	0.0139126		3.83E–04		0.0092039		0.0234123		0.0108855		0.0047822		0.0044693		0.0314066		−9.81E–04	**	0.026638
SE	0.018		0.018		0.018		0.023		0.019		0.018		0.019		0.019		0.023		0.017
Median rent	0.0001157		0.0001463		0.0013052	**	0.0010063	**	0.0001419		0.0002531		0.0010359	**	0.0001864	**	−1.04E–04		0.0001196
SE	0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000
Land area (sq mi)^a	0.0002348	**	−0.0000914	*	0.0002286	**	0.000056		0.0000721		0.0000184		−0.0000301		0.0000492	**	1.44E–04	*	0.0001331	**
SE	0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000		0.000
% w/ bachelor’s degree^a	1.461996	**	0.271282		−0.3969584		0.9484542		1.795469	**	1.532348	**	1.144843	*	0.7823218		1.44E+00	**	1.558714	**
SE	0.590		0.575		0.590		0.634		0.607		0.574		0.586		0.604		0.641		0.573
% single^a	1.895712		8.419667	**	4.173259	**	6.075781	**	1.823882		3.143339	**	3.321771	**	4.442474	**	9.16E–01		2.932798	**
SE	1.571		1.523		1.542		1.856		1.625		1.492		1.564		1.615		1.821		1.486
% with children^a	0.4944849		1.187837	*	0.6718324		1.066175		0.9274385		0.7252263		−0.3496558		0.8643738		−0.1639984		1.626675	**
SE	0.772		0.739		0.738		0.940		0.798		0.721		0.767		0.818		0.911		0.735
% Black^a	0.3277302		−1.127787	**	−0.1281075		−0.0902715		0.7994773	*	−0.3811609		0.754465		−0.7527146		1.79E+00	**	−0.1124849
SE	0.483		0.469		0.466		0.587		0.487		0.462		0.480		0.504		0.562		0.452

Note. The dependent variable is the number of nonprofit firms in the CBSA/subsector pair. CBSA = Core-Based Statistical Area.

Population weighted.

p < 0.10. **p < 0.05. ***p < 0.01.

We use the estimates from the regression to calculate the average market size required to induce the n organizations to enter the market (S_n). The values for (S_n) are interpreted as the average population necessary to support n organizations in a subsector where they breakeven, or earn just enough revenues to cover their full costs. These values are presented in bold for each sector in Table 5. As markets grow larger, new organizations enter the market. We calculate $s_{n} = \frac{S_{n}}{n},$ or per-organization population required to support n firms. Taking the ratio $s_{n + 1} / s_{n}$ allows us to calculate the marginal entrant population requirement as a ratio of the previous entrant requirement.

Table 5.

Entry Thresholds for All Nonprofit Sectors.

Nonprofit Sub-Sectors	Abuse prevention			Employment and vocational training			Substance abuse prevention			Crisis prevention			Crime prevention and rehab
Nonprofit Sub-Sectors	S_n	s_n	Ratio	S_n	s_n	Ratio	S_n	s_n	Ratio	S_n	s_n	Ratio	S_n	s_n	Ratio
N_1	81,183	81,183	3.53	–18,231	−18,231	−2.98	–11,203	−11,203	−6.58	794,965	794,965	1.14	101851.2	101,851	1.71
N_2	573,808	286,904	1.02	108,591	54,295	1.20	147,346	73,673	1.15	1,807,059	903,529	1.06	347,515	173,757	0.96
N_3	878,294	292,765	0.97	195,175	65,058	1.03	253,454	84,484	0.96	2,865,571	955,190	0.96	499,258	166,419	0.91
N_4	1,131,431	282,858	0.92	269,069	67,267	1.02	324,943	81,236	0.94	3,657,508	914,377	0.88	603,743	150,936	0.97
N_5	1,301,773	260,355	0.98	343,338	68,668	0.92	382,190	76,438	0.94	4,039,521	807,904	0.90	731,198	146,240	0.90
N_6	1,534,190	255,698	0.99	378,333	63,056	1.03	431,237	71,873	0.95	4,372,126	728,688	0.90	792,068	132,011	0.93
N_7	1,777,802	253,972	0.91	453,971	64,853	0.98	477,980	68,283	0.99	4,572,287	653,184	0.92	859,476	122,782	0.99
N_8	1,844,925	230,616	1.06	508,570	63,571	0.98	540,058	67,507	0.98	4,799,290	599,911		972,197	121,525	0.97
N_9	2,201,462	244,607	0.98	560,844	62,316	0.94	593,699	65,967	1.03				1,060,167	117,796	0.98
N_10	2,403,357	240,336		587,051	58,705		680,593	68,059					1,149,428	114,943
	Food pantries			Homeless shelters			Community shelters			Family counseling			Residential care
	S_n	s_n	Ratio	S_n	s_n	Ratio	S_n	s_n	Ratio	S_n	s_n	Ratio	S_n	s_n	Ratio
N_1	–88,715	−88,715	−1.13	34,190	34,190	5.16	135,762	135,762	1.67	467,563	467,563	1.18	–222,303	−222,303	−0.34
N_2	200,268	100,134	1.40	352,670	176,335	1.01	454,596	227,298	0.97	1,098,979	549,490	0.93	153,245	76,622	1.72
N_3	421,242	140,414	1.14	535,050	178,350	0.95	659,610	219,870	0.97	1,527,297	509,099	0.90	394,338	131,446	1.04
N_4	638,267	159,567	0.98	678,130	169,533	0.98	849,260	212,315	0.99	1,829,467	457,367	0.93	545,656	136,414	1.01
N_5	783,518	156,704	0.98	833,436	166,687	0.95	1,053,933	210,787	0.97	2,128,452	425,690	0.89	685,594	137,119	0.96
N_6	922,988	153,831	0.99	950,684	158,447	0.98	1,227,528	204,588	0.90	2,266,313	377,719	1.01	787,898	131,316	0.91
N_7	1,064,187	152,027	1.08	1,086,714	155,245	1.00	1,295,629	185,090	0.93	2,661,237	380,177	0.91	839,444	119,921	0.99
N_8	1,311,477	163,935	0.97	1,237,434	154,679	1.08	1,374,058	171,757	0.92	2,777,257	347,157	1.00	952,524	119,066	1.02
N_9	1,433,402	159,267		1,502,855	166,984	0.99	1,427,850	158,650	0.93	3,109,264	345,474	0.92	1,093,019	121,447	0.97
N_10				1,649,176	164,918		1,482,298	148,230		3,190,596	319,060		1,181,021	118,102

For example, a monopolist (n = 1) “Abuse Prevention” center requires 81,183 people in a market at the average of all control variables to break even. We emphasize this last point; this estimate does not imply that all markets will observe at least 81,183 with one nonprofit. As an example, markets with income above the average (holding everything else constant) will likely require a lower population given the positive relation between income and number of firms. With that point in mind, a duopolist (n = 2) abuse prevention market is not observed until there is a total population of at least S₂ = 573,808 (or s₂ = 286,904 per organization), much more than twice the monopolist threshold. It is the change in threshold ratio that contains the relevant information about competition. The second abuse prevention organization requires $\frac{s_{2}}{s_{1}} =$ 3.53, or more than 3 times the population to enter as the monopolist. Thus, it takes a greater than proportional increase in population to support an additional entrant. The duopolist organizations solicited more donors relative to the monopolist. Consequently, per-firm revenues of the duopoly organizations are lower than the monopolist, compressing their VNDR. The entry threshold ratio falls sharply to near one for all subsequent entry, where roughly an additional ≈ 250,000 in population is required to support each additional organization.

As an additional example, an “Employment and Vocational Training” monopolist requires a −18,231 population to support entry. The counterintuitive negative value occurs because the regression model predicts population for the market after it is normalized for the covariates (e.g., average income, demographics). In practical terms, for a CBSA with the average income, Medicaid transfers, and so forth, in this sector, we would expect to always see at least one employment and vocational training organization. For our analysis, this does not impose any serious difficulty because we are interested in the incremental change in population required to induce entry. For employment and vocational training, a duopoly market requires a total population $S_{2}$ = 108,591 ( $s_{2}$ = 54,295 per organization), which is more than 70,000 more donors per organization (54,295 + 18,231) than needed in the statistically adjusted monopoly market. The third organization requires a total population S₃ = 195,175 (s₃ = 65,058 per organization), or an entry ratio of 1.20. By the fourth organization, the entry ratio is close to one, and stabilizes thereafter. This indicates that each additional entrant requires (roughly) an additional 65,000 in population.

The model interprets the nonlinearity in the second organization’s entry as evidence that the monopolist nonprofit can earn variable donative net revenues (VDNR) in excess of the competitive equilibrium. Because the monopolist had exclusive access to donors (in their market), it can operate with a smaller population base (with higher per capita donation revenues) than is required for subsequent organizations. The increase in population requirements $(s_{n})$ for subsequent entrants is the result of a fall in the VDNR, represented by the average expected gift, net of acquisition costs. Increases in the per-organization entry thresholds for the subsequent organizations suggest that the monopolist abuse prevention center maintained some market power.

Differences in the magnitudes of entry thresholds across nonprofit sectors are likely driven by the differences in the fixed cost of production for those services relative to demand. Thus, comparatively little intuition can be gained from comparing entry thresholds across sectors because of these differences in costs. What is important for this study is comparing the change in entry thresholds (i.e., ratios $s_{n + 1} / s_{n}$ ) within subsectors. A key finding from our analysis is that entry ratios converge to 1 within three or four organizations (shaded in Table 5). Our results demonstrate entry decisions consistent with competitive equilibrium for a relatively small number of organizations. This finding is remarkably consistent across subsectors, despite the wide variety of output and revenue structures represented across the 10 subsectors tested in this sample.

These results are demonstrated visually in Figures 1 and 2. Figure 1 plots the threshold ratios for the first five sectors. In the figure, one can observe the trend in the threshold ratios for each additional entrant. For example, “Abuse Prevention” centers have an initial ratio of 3.53 but falls and stabilizes around 1 by the second entrant. The consistency across all 10 subsectors is remarkable. Every subsector in our sample has threshold ratios that stabilize around one by the fourth entrant.

Figure 1.

Threshold ratios for Sectors 1 to 5.

Figure 2.

Threshold ratios for Sectors 6 to 10.

Limitations and Future Research Avenues

There are several known limitations in our approach, which provide opportunities for extension. First, we rely on NTEE subsectors as a method for nonprofit market classification, which makes the study both internally consistent and comparable with previous studies. Yet, we acknowledge that the NTEE classification may undercount the total number of nonprofits participating in an activity, particularly for organizations that engage in multiple activities. If the undercount of nonprofits is not systematically correlated across geographic markets, the parameter estimates are not biased, only attenuated toward zero. This is an important consideration for interpreting our findings. For example, we demonstrate that markets with four or more NTEE-designated nonprofits will exhibit competitive behavior. However, this does not imply that all markets with four or more nonprofits are saturated (i.e., should force consolidation), especially given that additional non–NTEE-designated organizations may be active in social service provision. Previous studies have used a variety of methods to define the relevant competitors but suggest additional research in market definitions, particularly as electronic filing and activity codes become more robust. A meta-analysis would be another method to examine the impact of market definition on competition measurement.

We also acknowledge our empirical findings assume homogeneity (of production technologies) across firms. This assumption is identical to any empirical work that includes N or density as a regressor. A primary contribution of the article is to create a more transparent linkage between theoretical assumptions and empirical estimates than has been common in most nonprofit literature. That said, refinements and extensions in the model can be made. Where there are obvious differences in cost or technologies (i.e., national chains of food pantries vs. independent operators), the model could control for such variables and bifurcate markets into more granular submarkets. We leave this avenue of research to future scholars.

Every project that parameterizes competition must define the relevant geographic market in which those organizations compete. This article has taken the approach of first choosing a geographic market (the CBSA), then constructing the sample using firms that are most likely to be competing locally within that geography for donations. One might instead use county or other geographic constructs or define the competitive market based on other revenue streams. The for-profit competition literature has emphasized the importance of identifying markets to mitigate any competitive overlap between markets. The hospital literature also demonstrates a potentially useful methodology, assuming data availability, based on patient flow data (Kessler & McClellan, 2000).

Finally, our use of population threshold ratios and corresponding results suggest the use of first-differenced models (i.e., the change rather the level) and dynamic modeling to allow for focus on the change in competitive pressure with additional nonprofits rather than the level of competition. This method is also advantageous in accounting for organization-level fixed effects and providing well-documented instruments for dynamic panels (e.g., Arrellano-Bond–type estimators).

Discussion and Conclusion

Our work provides a consistent theoretical and empirical framework to examine nonprofit competition. Our approach is tractable as it employs an ordered probit, a widely available technique in current software packages, that can be adapted to analyze competition in other nonprofit industries and potentially with a focus on other revenue streams. We estimate a model of nonprofit entry, which allows us to infer changes in competition intensity for many different types of nonprofit sectors without observing changes in prices or quantities. We make this inference by comparing the differences in the number of nonprofits that have entered into geographically distinct markets for 10 nonprofit industries that, while generating donation revenue, vary considerably in industry maturity, size, and revenue mix. We highlight the benefits of breakeven thresholds, which represent the population required to support a given number of nonprofit organizations. Remarkably, for all 10 subsectors, we find results consistent with competitive donor markets once four or more nonprofits are in the market.

Several implications from this finding emerge. First, our results indicate that market competition is effective in creating nonprofit rivalry. This finding is important because market competition is commonly used as a mechanism to enforce low-cost and high-quality outcomes in nonprofit revenue markets (i.e., donations, grants, etc.). Our results should give confidence to individual and institutional donors and grant makers that competitive pressures can be relied upon to drive out nonessential expenditures and organizational slack within markets. We should emphasize that our findings do not imply that all markets are currently in competitive equilibrium, but that competitive donor markets can be expected with as few as four nonprofits.

Second, this model provides guidance for researchers who wish to include measures of nonprofit market structure, while emphasizing potential for endogeneity issues due to entry as a choice variable. The degree of nonlinearity we find suggests further investigation of the complex impact of competition in nonprofit markets. Beyond squared terms, our findings point to fruitful possibilities such as higher order polynomials or dummy variables when markets cross beyond four or five firms. Splines or other nonparametric approaches may be insightful in detecting key changes in competitive behavior.

We do however urge caution in treating organization market size/density as an independent variable. Our article reinforces both theoretically and empirically the idea that nonprofits treat entry as a strategic choice, thus the endogeneity of market structure is an important consideration for researchers. Researchers now commonly apply an instrument, such as predicted HHI, to control for the endogeneity of the number of organizations (see Dafny et al., 2012; Duggan, 2002; Kessler & McClellan, 2000, for further examples). Clear identification becomes more important when researchers want to observe how changes in the competitive environment affect important market outcomes. Our study suggests why nonprofit research should move in that direction.

Our findings demonstrate that the impact of incremental entry in organization performance is highly nonlinear, with the greatest impact generated by the second and third entrants into the market. Interestingly, this finding is nearly identical to the results found among for-profit industries in Bresnahan and Reiss (1990, 1991). This correspondence suggests that competitive pressures operate similarly in nonprofit organizations as for-profit organizations, despite significant differences in ownership structure, organizational objectives, and management incentives. Our article documents the depth of this result across nonprofit industries.

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.

ORCID iD

Jeremy Thornton

Author Biographies

Teresa Harrison is a Professor of Economics and Director of the Center for Nonprofit Governance at the LeBow College of Business at Drexel University. Her research emphasizes nonprofit competition, market entry and merger behaviors.

Jeremy Thornton is the Dwight Moody Beeson Chair of Business and Professor of Economics at the Brock School of Business at Samford University. His research focuses on nonprofit competition for charitable donations.

References

Aldashev

Verdier

(2010). Goodwill bazaar: NGO competition and giving to development. Journal of Development Economics, 91(1), 48–63.

Aldrich

Pfeffer

(1976). Environments of organizations. Annual Review of Sociology, 2(1), 79–105.

Amburgey

Rao

(1996). Organizational ecology: Past, present, and future directions. The Academy of Management Journal, 39(5), 1265–1286.

Amirkhanyan

(2010). Monitoring across sectors: Examining the effect of nonprofit and for-profit contractor ownership on performance monitoring in state and local contracts. Public Administration Review, 70(5), 742–755.

Andersson

Ford

(2016). Social entrepreneurship through an organizational ecology lens: Examining the emergence and evolution of the voucher school population in Milwaukee. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(4), 1760–1780.

Bain

(1951). Relation of profit rate to industry concentration: American manufacturing. The Quarterly Journal of Economics, 65(3), 295–324.

Bajari

Houghton

Tadelis

(2014). Bidding for incomplete contracts: An empirical analysis of adaptation costs. American Economic Review, 104(4), 1288–1319.

Barbetta

Canino

Cima

Verrecchia

(2018). Entry and exit of nonprofit organizations. Nonprofit Policy Forum, 9(2), 1–12.

Barman

(2008). Organizational genesis in the nonprofit sector: An analysis of demand, supply, and community characteristics. International Journal of Organization Theory & Behavior, 11(1), 40–63.

10.

Baron

D. P.

(1972). Incentive contracts and competitive bidding. American Economic Review, 62(3), 384–394.

11.

Baum

Shipilov

(2006). Ecological approaches to organizations. In Hardy

Lawrence

Nord

(Eds.), The SAGE handbook of organization studies (pp. 55–110). SAGE.

12.

Bennett

Iossa

(2009). Contracting out public service provision to not-for-profit firms. Oxford Economic Papers, 62(4), 784–802.

13.

Bennett

Iossa

Legrenzi

(2003). The role of commercial non-profit organizations in the provision of public services. Oxford Review of Economic Policy, 19(2), 335–347.

14.

Bresnahan

T. F.

(1989). Empirical studies of industries with market power. In Schmalensee

Willig

R. D.

(Eds.), Handbook of industrial organization (pp. 1011–1057). Elsevier.

15.

Bresnahan

T. F.

Reiss

(1990). Entry in monopoly markets. Review of Economic Studies, 57(4), 531–553.

16.

Bresnahan

T. F.

Reiss

(1991). Entry and competition in concentrated markets. Journal of Political Economy, 99(5), 977–1009.

17.

Calabrese

(2013). Running on empty: The operating reserves of U.S. nonprofit organizations. Nonprofit Management and Leadership, 23(3), 281–302.

18.

Castaneda

Garen

Thornton

(2008). Competition, contractibility, and the market for donors to nonprofits. Journal of Law, Economics, and Organization, 24(1), 215–246.

19.

Chau

Huysentruyt

(2006). Nonprofits and public good provision: A contest based on compromises. European Economic Review, 50(8), 1909–1935.

20.

Clarke

Davies

(1982). Market structure and price-cost margins. Economica, 49(195), 277–287.

21.

Cowling

Waterson

(1976). Price-cost margins and market structure. Economica, 43(171), 267–274.

22.

Dafny

Duggan

Ramanarayanan

(2012). Paying a premium on your premium? Consolidation in the US health insurance industry. American Economic Review, 102(2), 1161–1185.

23.

Dellavigna

List

Malmendier

(2012). Testing for altruism and social pressure in charitable giving. Quarterly Journal of Economics, 127(1), 1–56.

24.

Delorme

Kamerschen

Klein

Voeks

Kamerschen

Voeks

(2002). Structure, conduct and performance: A simultaneous equations approach. Applied Economics, 34(17), 2135–2141.

25.

Duggan

(2002). Hospital market structure and the behavior of not-for-profit hospitals. The RAND Journal of Economics, 33(3), 433–446.

26.

Einav

Levin

(2010). Empirical industrial organization: A progress report. Journal of Economic Perspectives, 24(2), 145–162.

27.

Evans

Froeb

Werden

(1993). Endogeneity in the concentration—Price relationship: Causes, consequences, and cures. The Journal of Industrial Economics, 41(4), 431–438.

28.

Faulk

McGinnis Johnson

Lecy

(2017). Competitive advantage in nonprofit grant markets: Implications of network embeddedness and status. International Public Management Journal, 20(2), 261–293.

29.

Faulk

Willems

McGinnis Johnson

Stewart

(2016). Network connections and competitively awarded funding: The impacts of board network structures and status interlocks on nonprofit organizations’ foundation grant acquisition. Public Management Review, 18(10), 1425–1455.

30.

Feigenbaum

(1987). Competition and performance in the nonprofit sector: The case of US medical research charities. Journal of Industrial Economics, 35(3), 241–253.

31.

Gayle

P. G.

Harrison

T. D.

Thornton

J. P.

(2017). Entry, donor market size, and competitive conduct among nonprofit firms. International Journal of Industrial Organization, 50, 294–318.

32.

Glückler

Ries

(2012). Why being there is not enough: Organized proximity in place-based philanthropy. Service Industries Journal, 32(4), 515–529.

33.

Government Accountability Office. (2009). Nonprofit sector: Significant federal funds reach the sector through various mechanisms but more complete and reliable funding data are needed (GAO-09-193).

34.

Grønbjerg

Paarlberg

(2001). Community variations in the size and scope of the nonprofit sector: Theory and preliminary findings. Nonprofit and Voluntary Sector Quarterly, 30(4), 684–706.

35.

Hannan

Freeman

(1987). The ecology of organizational founding: American labor unions, 1836-1985. American Journal of Sociology, 92(4), 910–943.

36.

Hannan

Freeman

(1989). Organizational ecology. Harvard University Press.

37.

Hansmann

(1980). The role of nonprofit enterprise. The Yale Law Journal, 89(5), 835–901.

38.

Harrison

Irvin

(2018). Competition and collaboration: When are they good for the nonprofit sector? In Seaman

Young

(Eds.), Handbook of research on nonprofit economics and management (pp. 117–131). Edward Elgar.

39.

Harrison

Laincz

(2008). Nonprofits, crowd-out, and credit constraints (Working Paper No. 2013–5). LeBow College of Business.

40.

Harrison

Thornton

(2014). Too many nonprofits? An empirical approach to estimating trends in nonprofit demand. Nonprofit Policy Forum, 5(2), 213–229.

41.

Horwitz

Nichols

(2009). Hospital ownership and medical services: Market mix, spillover effects, and nonprofit objectives. Journal of Health Economics, 28(5), 924–937.

42.

Jeong

Shicun Cui

(2020). The density of nonprofit organizations: Beyond community diversity and resource availability. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 31(1), 213–226.

43.

Keisler

Buehring

(2005). How many vendors does it take to screw down a price? A primer on competition. Journal of Public Procurement, 5(3), 291–317.

44.

Kessler

McClellan

(2000). Is hospital competition socially wasteful? Quarterly Journal of Economics, 115(2), 577–615.

45.

Kim

(2015). Socioeconomic diversity, political engagement, and the density of nonprofit organizations in U.S. counties. American Review of Public Administration, 45(4), 402–416.

46.

Kim

Brown

(2012). The importance of contract design. Public Administration Review, 72(5), 687–696.

47.

Lakdawalla

Philipson

(2006). The nonprofit sector and industry performance. Journal of Public Economics, 90(8–9), 1681–1698.

48.

Lamothe

(2015). How competitive is “competitive” procurement in the social services? The American Review of Public Administration, 45(5), 584–606.

49.

Lecy

J. D.

Searing

E. A. M.

(2014). Anatomy of the nonprofit starvation cycle: An analysis of falling overhead ratios in the nonprofit sector. Nonprofit and Voluntary Sector Quarterly, 44(3), 539–563.

50.

Lecy

J. D.

Van Slyke

(2013). Nonprofit sector growth and density: Testing theories of government support. Journal of Public Administration Research and Theory, 23(1), 189–214.

51.

Leroux

Wright

(2010). Does performance measurement improve strategic decision making? Findings from a National Survey of nonprofit social service agencies. Nonprofit and Voluntary Sector Quarterly, 39(4), 571–587.

52.

Lynk

(1995). Nonprofit hospital mergers and the exercise of market power. The Journal of Law and Economics, 38(2), 437–461.

53.

Marginson

(2006). Dynamics of national and global competition in higher education. Higher Education, 52(1), 1–39.

54.

Mason

(1939). Price and production policies of large scale enterprises. American Economic Review, 29(1), 61–74.

55.

McAfee

McMillan

(1986). Bidding for contracts: A principal-agent analysis. The RAND Journal of Economics, 17(3), 326.

56.

Mccall

(1970). The simple economics of incentive contracting. American Economic Review, 60(5), 837–846.

57.

Melnick

Keeler

Zwanziger

(1999). Market power and hospital pricing: Are nonprofits different? Health Affairs, 18(3), 167–173.

58.

Mitchell

G. E.

(2017). Fiscal leanness and fiscal responsiveness: Exploring the normative limits of strategic nonprofit financial management. Administration & Society, 49(9), 1272–1296.

59.

Paarlberg

Nesbit

Christensen

Bullock

(2018). A field too crowded? How measures of market structure shape nonprofit fiscal health. Nonprofit and Voluntary Sector Quarterly, 47(3), 453–473.

60.

Paarlberg

Hwang

(2017). The heterogeneity of competitive forces: The impact of competition for resources on united way fundraising. Nonprofit and Voluntary Sector Quarterly, 46(5), 897–921.

61.

Paarlberg

Varda

(2009). Community carrying capacity: A network perspective. Nonprofit and Voluntary Sector Quarterly, 38, 597–613.

62.

Perloff

Karp

Golan

(2007). Estimating market power and strategies. Estimating market power and strategies. Cambridge University Press.

63.

Polson

(2017). Religious environments and the distribution of anti-poverty nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 46(1), 156–174.

64.

Prentice

(2016). Understanding nonprofit financial health: Exploring the effects of organizational and environmental variables. Nonprofit and Voluntary Sector Quarterly, 45(5), 888–909.

65.

Resende

(2007). Structure, conduct and performance: A simultaneous equations investigation for the Brazilian manufacturing industry. Applied Economics, 39(7), 937–942.

66.

Rose-Ackerman

(1996). Altruism, non-profits and economic theory. Journal of Economic Literature, 44, 701–728.

67.

Saussier

Staropoli

Yvrande-Billon

(2009). Public-private agreements, institutions, and competition: When economic theory meets facts. Review of Industrial Organization, 35(1–2), 1–18.

68.

Schmalensee

(1989). Inter-industry studies of structure and performance. In Schmalensee

Willig

(Eds.), Handbook of industrial organization (Vol. 2, pp. 951–1009). Elsevier.

69.

Seaman

(2004). Competition and the non-profit arts: The lost industrial organization agenda. Journal of Cultural Economics, 28(3), 167–193.

70.

Shortell

Hughes

(1988). The effects of regulation, competition, and ownership on mortality rates among hospital inpatients. New England Journal of Medicine, 318(17), 1100–1107.

71.

Simpson

Shin

(1998). Do nonprofit hospitals exercise market power? International Journal of the Economics of Business, 5(2), 141–157.

72.

Thornton

(2006). Nonprofit fund-raising in competitive donor markets. Nonprofit and Voluntary Sector Quarterly, 35(2), 204–224.

73.

Thornton

Lecy

(2019). Good enough for government work? An incomplete contracts approach to the use of nonprofits in U.S. federal procurement. Nonprofit Policy Forum, 10(3), 1–18.

74.

Thornton

Lohrke

Gonas

(2012). The social entrepreneur as trailblazer: A non-normative role for social enterprise in a market economy. ACRN Oxford Journal of Finance and Risk Perspectives, 4(4), 145–167.

75.

Tirole

(2001). The theory of industrial organization. MIT Press.

76.

Town

Vistnes

(2001). Hospital competition in HMO networks. Journal of Health Economics, 20(5), 733–753.

77.

Witesman

(2007). Players in the public policy process: Nonprofits as social capital and agents. Journal of Policy Analysis and Management, 26(1), 204–207.

78.

Witesman

Fernandez

(2012). Government contracts with private organizations: Are there differences between nonprofits and for-profits? Nonprofit and Voluntary Sector Quarterly, 42(4), 689–715.