Does Revenue Concentration Really Bring Organizational Efficiency? Evidence From Habitat for Humanity

Abstract

Past empirical studies demonstrate a positive connection between revenue concentration and organizational efficiency. This supports the idea that concentrating revenue helps minimize transaction costs of nonprofit organizations, resulting in greater efficiency. However, this finding contradicts the belief that revenue concentration increases the risk of revenue volatility, leading to service delivery disruptions and reduced efficiency in nonprofits. Moreover, these studies have used an efficiency measure that might not be suitable. To address this, our study examines the relationship between revenue concentration and organizational efficiency using a more appropriate measure. Analyzing data from Habitat for Humanity, we discover a U-shaped relationship: nonprofits are most efficient when fully diverse or fully concentrated in revenue. These findings contribute to the ongoing debate on nonprofit revenue diversification, with significant implications for nonprofits. They also highlight the importance of using more appropriate efficiency measures in future scholarly research.

Keywords

revenue concentration revenue diversification organizational efficiency organizational performance transaction cost modern portfolio theory

Introduction

Revenue concentration and its inverse, revenue diversification, are of interest to scholars and nonprofit managers because they can impact a nonprofit organization’s financial health and mission outputs (Berrett & Holliday, 2018; Chen, 2022; Chikoto-Schultz & Sakolvittayanon, 2020; Hung & Hager, 2019; Mitchell & Calabrese, 2023). Revenue diversification is traditionally seen as providing a greater benefit to organizations. However, in more recent years, scholars have found that revenue diversification can have adverse effects, suggesting that revenue concentration may be more beneficial to organizations (Chikoto & Neely, 2014; Frumkin & Keating, 2011; Mendoza-Abarca & Gras, 2019; Mitchell & Calabrese, 2023; Sacristán López de los Mozos et al., 2016; von Schnurbein & Fritz, 2017). Hung and Hager (2019) highlight flexibility, autonomy, income and growth potential, and community connections as the key advantages of revenue diversification. On the other hand, they cite increased complexity and administration costs, crowding out of private donations, and mission drift as possible disadvantages associated with revenue diversification. By extension, we can reverse the logic to reveal the advantages of revenue concentration: reduced complexity, lower administrative costs, and increased private donations and mission focus. In sum, over the past three decades, scholars have given serious attention to the advantages and disadvantages of both revenue concentration and diversification. Liu and Kim (2022, p. 806) emphasize this point by noting, “Managing diverse revenue streams provides both valuable opportunities and distinct challenges for nonprofits.”

Even though previous studies have extensively examined the positives and negatives of revenue concentration (diversification), there is a dearth of literature exploring the effect of revenue concentration (diversification) on organizational efficiency. Organizational efficiency is important to nonprofit organizations for at least three reasons. First, potential donors care about how nonprofit organizations allocate resources. For example, organizations perceived as efficient might receive more contributions (Charles et al., 2020). Second, efficient nonprofit organizations are more productive since they minimize inputs needed to produce the most outputs (Coupet & Berrett, 2018). Nonprofit organizations are more likely to fulfill their missions when they are productive. Finally, efficiency makes organizations more resilient (Henshaw, 2021) because efficient organizations know how to better use resources, connections, and networks to quickly bounce back from unexpected events.

To the best of our knowledge, only a few studies have empirically examined the relationship between revenue concentration (diversification) and organizational efficiency (e.g., Frumkin & Keating, 2011; Mendoza-Abarca & Gras, 2019; Sacristán López de los Mozos et al., 2016). These studies have found that revenue concentration leads to efficiency in nonprofit organizations. However, caution must be applied to these findings as they use expense ratios, which may be appropriate for evaluating nonprofit overhead but are unsuitable for determining efficiency (Coupet & Berrett, 2018). The main concern is that these studies might have measurement errors, and the results might be biased. Therefore, using Data Envelopment Analysis (DEA) as a more appropriate efficiency measure, this study examines the relationship between revenue concentration (diversification) and nonprofit efficiency.

Moreover, theoretical perspectives in the literature are at odds with one another. For example, the transaction costs perspective argues that revenue concentration leads to organizational efficiency primarily because it reduces nonprofit organizations’ costs of exchange (Chikoto-Schultz & Sakolvittayanon, 2020; Williamson, 1979), whereas, the modern portfolio perspective contends that revenue diversification leads to organizational efficiency mainly because it reduces nonprofit organizations’ risk of revenue volatility (Carroll & Stater, 2008; Markowitz, 1952). Therefore, this study tests these competing perspectives to understand whether the relationship between revenue concentration and nonprofit efficiency is positive, negative, or curvilinear. Using a sample of U.S.-based Habitat for Humanity affiliates from 2010 to 2016, this study finds a curvilinear U-shaped relationship between revenue concentration and efficiency. Nonprofit organizations are most efficient when they are fully diverse or fully concentrated. This finding challenges the previous studies’ linear findings that revenue concentration leads to efficiency in nonprofit organizations (Frumkin & Keating, 2011; Mendoza-Abarca & Gras, 2019; Sacristán López de los Mozos et al., 2016).

This study contributes to the literature in several ways. First, this study uses a more appropriate measure of organizational efficiency. Using the input-to-output ratio can reduce measurement errors, more accurately examining the relationship between revenue concentration (diversification) and nonprofit efficiency (Coupet & Berrett, 2018). Second, this study uses multiple theories to test competing hypotheses on the relationship (Carroll & Stater, 2008; Chikoto-Schultz & Sakolvittayanon, 2020; Markowitz, 1952; Williamson, 1979). Our finding suggests that it is difficult, or even impossible, to understand the relationship using any single theory. The relationship is more complicated than previously thought or expected (e.g., Frumkin & Keating, 2011; Mendoza-Abarca & Gras, 2019; Sacristán López de los Mozos et al., 2016). Third, this study adds to the heated discussion about the advantages and disadvantages of nonprofit revenue diversification in the literature (Berrett & Holliday, 2018; Chikoto & Neely, 2014; Chikoto-Schultz & Sakolvittayanon, 2020; Frumkin & Keating, 2011; Hung & Hager, 2019; Mendoza-Abarca & Gras, 2019; Mitchell & Calabrese, 2023; Sacristán López de los Mozos et al., 2016; von Schnurbein & Fritz, 2017). Specifically, we contribute to the literature by answering the question of whether and how revenue concentration (diversification) leads to organizational efficiency. Finally, based on the finding, this study has important implications for nonprofit organizations considering or already using revenue concentration (diversification) as a strategy for organizational efficiency.

The rest of the article is organized as follows. The next section reviews the literature on revenue concentration and nonprofit efficiency, and introduces transaction cost theory and modern portfolio theory (MPT) to frame the proposed hypotheses. This section is followed by the data, methods, and results. Finally, the article concludes by discussing the theoretical and practical contributions, directions for future research, and limitations.

Literature Review and Hypothesis Development

Revenue Concentration and Nonprofit Efficiency

Revenue concentration (diversification) has received no shortage of interest from scholars, as indicated by a recent meta-analysis that synthesized 40 quantitative studies that examine how revenue diversification affects the financial health of nonprofit organizations (Hung & Hager, 2019). Over the past three decades, scholars predominantly studied nonprofit financial health through the lens of financial vulnerability (Allen et al., 2014; Cordery et al., 2013; de Andres-Alonso et al., 2016; Despard et al., 2017; Greenlee & Trussel, 2000; Hager, 2001; Tevel et al., 2015; Trussel, 2002) and financial volatility (Carroll & Stater, 2008; Chikoto et al., 2016; Mayer et al., 2014; Wicker et al., 2015). However, these studies show mixed findings. For example, some studies show that revenue diversification supports nonprofit financial health (Carroll & Stater, 2008; Chang & Tuckman, 1994; Hager, 2001; Tevel et al., 2015). In contrast, other studies show that revenue concentration supports financial health (Chikoto et al., 2016; Frumkin & Keating, 2011; Sacristán López de los Mozos et al., 2016; von Schnurbein & Fritz, 2017). As nonprofit scholars and practitioners’ interest in the effect of revenue concentration (diversification) is on the rise, some scholars have started to examine efficiency as another dimension of financial health (Frumkin & Keating, 2011; Sacristán López de los Mozos et al., 2016).

Frumkin and Keating (2011) investigate the effect of revenue concentration on organizational efficiency. They operationalize efficiency with three measures. First, they find that revenue concentration positively affects fundraising efficiency. They also find that revenue concentration is associated with lower administrative and fundraising expense ratios. Some donors see the lower ratios as indicating efficient use of their donations. Similarly, Sacristán López de los Mozos et al. (2016) were also interested in knowing whether revenue diversification makes it easier or harder for organizations to fundraise. Consistent with Frumkin and Keating’s (2011) findings, they find that revenue diversification is negatively associated with the fundraising efficiency ratio. This finding suggests that organizations that concentrate their revenue have lower fundraising costs and thus appear more attractive to donors (Sacristán López de los Mozos et al., 2016). In another study, Mendoza-Abarca and Gras (2019) find that revenue diversification negatively affects efficiency and thus moderates the negative relationship between efficiency and product diversification (Mendoza-Abarca & Gras, 2019). However, they use the program expense ratio as a proxy for efficiency.

While Frumkin and Keating (2011) and Sacristán López de los Mozos et al. (2016) use the fundraising efficiency ratio, which is a valid measure of efficiency because it is a ratio of inputs to outputs, Frumkin and Keating (2011) and Mendoza-Abarca and Gras (2019) also use the expense ratios as a proxy for efficiency. The expense ratios are considered normative efficiency measures (Mitchell, 2018). Mitchell and Calabrese (2019) argue that overhead minimization is an institutionalized practice in nonprofit management, and as a result, expense ratios get conflated with efficiency. As efficiency involves measuring how inputs turn into outputs (Grandy, 2009), nonprofit scholars and practitioners might best measure the efficiency with input/output-based techniques like DEA (Coupet & Berrett, 2018).

Therefore, this study addresses these measurement errors while acknowledging that two competing perspectives should be taken regarding the relationship between revenue concentration and nonprofit efficiency. On one side, scholars posit that revenue concentration will lead to efficiency (Froelich, 1999; Frumkin & Keating, 2011; Mendoza-Abarca & Gras, 2019; Sacristán López de los Mozos et al., 2016), while on the other side, scholars posit that revenue concentration will lead to inefficiency (Bowman, 2006; Markowitz, 1952). Readers of this article can best understand these two perspectives through the lenses of transaction cost theory and MPT.

Transaction Cost Theory Perspective

Transaction cost theory posits that the ideal organizational structure reaches efficiency by minimizing the costs of exchange, which are simply the costs of running an economic system of companies (Williamson, 1979). Applying this to nonprofits, Krashinsky (1986) separates transaction costs into those between producers and consumers, and among consumers, such as donors. While this theory is traditionally applied to the decision to make or buy, or in other words, to do something in-house vs. contract out, this can also apply to the decision to concentrate or diversify revenue sources (Chikoto-Schultz & Sakolvittayanon, 2020). According to this theory and the literature, revenue diversification’s complexities incur various transaction costs (Chikoto-Schultz & Sakolvittayanon, 2020). The first costs are associated with the time, attention, and money necessary to search for new funders (Chikoto-Schultz & Neely, 2016; Frumkin & Keating, 2011; Hager & Hung, 2020). For example, pursuing a new income stream requires market research to identify potential funders, which incurs costs of time and money. Then, there are costs related to negotiating a settlement agreeable to nonprofit organizations and their funders in drawing up a contract. These are the direct costs of bargaining and documenting. Moreover, there are also costs associated with ensuring the parties involved do not deviate from the contract. These include, but are not limited to, managing diverse motivations, preferences, and objectives of multiple funders (Gronbjerg, 1993; Pfeffer & Salancik, 2003; Young, 2007) and meeting different accountability requirements (Chikoto, 2015; Ebrahim, 2003). As efficiency is associated with using the least amount of resources (Coupet & Berrett, 2018), concentrating revenue streams may avoid incurring these different types of transaction costs and save on expenses (Hung & Hager, 2019), which in turn may make nonprofits more efficient. Put differently, expenses may go down and efficiency may go up when nonprofits concentrate their revenue streams.

While empirical studies have linked transaction costs to revenue concentration, most of these focus on the relationship between revenue concentration and growth, finding that revenue concentration leads to greater growth in terms of annual revenues (Chikoto & Neely, 2014; Foster & Fine, 2007; Lin & Wang, 2016; von Schnurbein & Fritz, 2017). However, several studies have focused on the relationship between revenue concentration and efficiency. For example, Frumkin and Keating (2011) find that that when an organization concentrates its revenue, it will have fewer transaction costs in terms of administrative and fundraising expenses. In another study, Sacristán López de los Mozos et al. (2016) find that nonprofits that increase their revenue diversification experience lower fundraising efficiency, and argue that having more donors leads to extra efforts and more fundraising expenses. Thus, we propose the following hypothesis.

H1: Revenue concentration is associated with greater nonprofit efficiency.

Modern Portfolio Theory Perspective

Applied to businesses and individual investment portfolios, MPT focuses on building a portfolio of investments that maximize return and minimize risks (Markowitz, 1952). However, according to Markowitz (1959), it is not simply about diversification but a careful portfolio selection based on the risk of different revenue sources, the correlation between different revenue sources, and the organization’s objectives. In the case of nonprofits, portfolio diversification refers to pursuing diverse sources of revenue (Chikoto-Schultz & Sakolvittayanon, 2020).¹ The idea is that an increase in one revenue source could offset a decrease in another revenue source, thus reducing the risk of revenue volatility (Carroll & Stater, 2008; Mayer et al., 2014; Wicker et al., 2015), which in turn maximizes total revenue of the nonprofit organization. Furthermore, as efficiency is associated with producing the maximum number of goods and services (Coupet & Berrett, 2018), diversifying revenue streams may grow revenue and thereby help nonprofit organizations fulfill their missions (Hung & Hager, 2019), resulting in more outputs and efficient organizations. That is, outputs and efficiency may go up when nonprofits diversify their revenue streams.

While empirical studies have linked MPT to revenue diversification, most focus on financial predictability and volatility rather than efficiency. Kingma (1993) was the first to apply MPT to nonprofits and focused on financial predictability. He found that an increased dependency on the government for financial resources helps organizations achieve financial predictability because government funding is a relatively stable source of revenue (Kingma, 1993). At the same time, other nonprofit studies have found that revenue diversification leads to decreased revenue volatility. For example, applying MPT, using a broad sample of nonprofits across the United States from 1991 to 2003, Carroll and Stater (2008) find that revenue diversification is associated with decreased volatility and increased organizational longevity. Using a similar sample, Mayer et al. (2014) also find that revenue diversification is associated with reduced volatility but that this effect depends on the portfolio’s revenue composition. While these latter studies focus on financial volatility, it can be argued that an organization that has reduced financial volatility could offer its services continuously (Kingma, 1993), enabling it to deliver services more efficiently. Bowman (2017) also applies portfolio theory to the revenue composition of nonprofits by studying nonprofit membership associations’ portfolios. He asserts that organizations that minimize risk or maximize their return with revenue selection are considered efficient. Thus, we propose the following hypothesis.

H2: Revenue diversification is associated with greater nonprofit efficiency.

Combined Perspective

There appear to be two competing theoretical perspectives on the relationship between revenue concentration (diversification) and organizational efficiency. One is that revenue concentration will lead to greater efficiency. The other is that revenue diversification will lead to greater efficiency. According to transaction cost theory, organizations that rely on a single source of revenue are most efficient because of various reduced costs (Chikoto-Schultz & Sakolvittayanon, 2020; Frumkin & Keating, 2011; Williamson, 1979), while according to MPT, organizations that have a balanced portfolio of diverse revenue streams can minimize risk, generate more revenue, and maximize outputs, thereby increasing efficiency (Bowman, 2017; Kingma, 1993; Markowitz, 1952). However, organizations in the middle, which neither fully diversify nor fully concentrate their revenue, become less efficient due to increased costs and risk. For example, on one hand, it is costly for a nonprofit organization with a single dominant source of revenue and a few other less prevalent sources of income to find an additional source of income that can bring in an amount of revenue equal to the dominant one. It incurs various costs mentioned above, especially when the skills needed to generate the additional source of income are very different from those required for obtaining the dominant source of revenue. Efficiency may go down when costs go up. On the other hand, it is risky for a nonprofit organization to operate with a single dominant source of revenue. This makes the organization less likely to generate sufficient revenue and maximize outputs, especially when an unexpected event happens, and the dominant source of revenue is gone. Efficiency may go down when outputs go down. This suggests that neither transaction cost nor MPT can completely explain the relationship between revenue concentration (diversification) and nonprofit efficiency. Therefore, combining the two theories might offer a better understanding of the relationship. Considering the two theories, we argue that a nonprofit might reach its highest efficiency when its revenue is fully diverse or concentrated. In other words, the nonprofit might be less efficient when its revenue structure is neither diverse nor concentrated. To this end, we propose the following hypothesis:

H3: Efficiency is curvilinearly (taking a U shape) associated with revenue concentration.

Data and Methods

Data

This study utilizes a sample of U.S.-based Habitat for Humanity affiliates. The organization’s stated mission is “Seeking to put God’s love into action, Habitat for Humanity brings people together to build homes, communities, and hope” (Habitat for Humanity, 2022). We selected Habitat for Humanity for this study as the similarity in operations and production functions among affiliates allows for a comparable efficiency calculation. Furthermore, given financial cutbacks and increased demand for services (Ridder et al., 2012), especially with the affordable housing crisis in the United States (Rohe, 2017), there is increasing pressure for Habitat affiliates to become more efficient.

Data for this study come from three sources: (a) Habitat for Humanity provided the production and geographic service area (GSA) data; (b) Candid provided the financial data from Form 990; and (3) Zillow provided home value data. The initial data set provided by Habitat for Humanity included 1,505 organizations and 9,134 observations from 2010 to 2016. When merging the Habitat production reports with financial data from Form 990, some organizations and observations were dropped as they did not submit Form 990, used the 990-EZ, or could not be identified in Candid. Furthermore, some observations were removed because DEA is sensitive to outliers. In particular, affiliates that did not produce any houses, or reported producing one or more houses but reported U.S.$0 program or management expenses, were dropped from the sample. In addition, observations with an efficiency score higher than 1 were evaluated as, in some cases, this might indicate an outlier. However, we investigated each case and determined none were outliers to be removed. We also imputed missing values for all values with zero to maintain the sample size.² After the merging and data cleaning, the final sample included an unbalanced panel of 708 organizations and 2,751 observations. It was reduced to the time range of 2011 to 2016 due to lagged variables.

Variables

Dependent Variable

The dependent variable in this study is organizational efficiency. DEA, a mathematical programming model that determines the efficiency of a decision-making unit by maximizing the sum of each organization’s input-to-output ratio (Charnes et al., 1978), is used to measure the efficiency score of each affiliate. DEA produces an efficiency score from 0 to 1. An efficiency score of 1 indicates the organizations that utilize the least amount of inputs to produce the maximum number of outputs. These organizations create what is called the efficiency frontier. The rest of the organizations are then rated against the most efficient, receiving a score that indicates their distance from their peers on the efficiency frontier. Following the input and output selection of Coupet and Berrett (2018), the inputs include management and program expenses, whereas the outputs include the number of new, recycled, repaired, and rehabilitated houses. In this context, new is the number of newly built homes. Repaired is the number of house repairs, anything from fresh paint to a new roof. Recycled is the number of houses foreclosed on by a Habitat homeowner and turned around for a new Habitat homebuyer. Rehabilitated is the number of houses donated to Habitat and rehabbed for a new Habitat homebuyer.

As DEA can handle multiple inputs and outputs, weighted ratios are required. However, as a function of the programming, DEA assigns weights automatically to inputs and outputs that maximize an organization’s efficiency. One example would be a Habitat affiliate that did not build any homes but repaired several; DEA will heavily weight its outputs of repaired houses relative to other outputs. Thus, weights are unconstrained to maximize affiliates’ efficiency, so that, no affiliate is punished for focusing on one output over the other (Coupet & Berrett, 2018).

Independent Variable

The independent variable in this study is revenue concentration. The Herfindahl–Hirschman Index (HHI), applied to nonprofits by Chang and Tuckman (1994), measures revenue concentration. A score of 1 indicates highly concentrated, whereas, a score close to 0 indicates diversification. Due to the sensitivity of the measurement, “researchers should opt to use more disaggregated revenue streams, whenever possible, to avoid information loss in the aggregation process” (Chikoto et al., 2016 as cited in the work of Chikoto-Schultz & Sakolvittayanon, 2020, p. 102). Therefore, this study examines the HHI with five, six, and seven revenue sources. The five revenue sources include contributions, government grants, net income from special events, earned revenue, and investments. Contributions include revenue from federated campaigns, membership dues, fundraising events, and other gifts. Earned income includes program service revenue, royalties, gross profit, and other revenue. Investments include income from investments, bonds, net rental, net sales, and net gaming. Six revenue sources include contributions, government grants, net income from special events, earned income minus other revenue, other revenue, and investments. Seven revenue sources include contributions, government grants, net income from special events, earned income minus other revenue, other revenue, investments minus net sales, and net sales. Any revenue sources with a negative value were assigned U.S.$0 before calculating the HHI (Chikoto & Neely, 2014; Hager, 2001; Levine Daniel & Kim, 2018). A 1-year lag of the HHI scores is used in this study.

Controls

A 1-year lag of organizational age, size, leverage, margin, GSA, and Home Value Index are used as controls. In a study by Ayayi and Wijesiri (2018), organizational age affects efficiency. Therefore, the year of the data minus the formation year is used to determine the organization’s age. Organization size has been found to affect efficiency (Coupet, 2018; Frumkin & Keating, 2011). This study uses the log of end-of-year assets as a proxy for organizational size. Leverage has also been found to affect efficiency (Frumkin & Keating, 2011). Leverage is determined by dividing total end-of-year liabilities by total end-of-year assets. Another financial flexibility variable used is margin, calculated by taking the total revenue minus total expenses, all divided by the total revenue. Two community-level variables are included. The GSA indicates the population size of the community in which the Habitat affiliate serves (1 = Small Less than 50,000, 2 = Intermediate 50,000-99,999, 3 = Medium 100,000-249,000, 4 = Large 250,000-749,000, and 5 = Very Large 750,000 or greater). The log of the Zillow Home Value Index (ZHVI), which is the typical home value for the region (Zillow, 2021), is included to control for the different levels of spending that may be required to buy land or houses in various parts of the country (Berrett, 2022). To mitigate the impact of outliers in the data set, assets, leverage, margin, and the Home Value Index were winsorized at the bottom and top 1%. This method involves replacing values in the extreme ends of a distribution with the next highest value in the lower tail and the lowest value in the upper tail for a specified percentage of cases (Blaine, 2018).

Method

This study utilizes a two-stage approach. The first stage, DEA, is described above in the dependent variable section. The second stage applies a regression model to evaluate the relationship between revenue concentration and efficiency. Nonprofit studies using a two-stage approach with DEA have used Tobit regression (Ayayi & Wijesiri, 2018) and dynamic panel models, such as Arellano–Bond estimators (Coupet, 2017). However, others have argued that Tobit and linear models are inappropriate (McDonald, 2009; Nissi et al., 2019; Ramalho et al., 2010). In particular, Nissi et al. (2019, p. 400) argue,

The standard linear model is not appropriate for such analysis, because the predicted values of efficiency scores may lie outside of the range of the efficiency scores. Moreover, the standard approach of using censored normal regression techniques such as Tobit model, with limits at zero and unity, is also questionable.

This is further supported by Papke and Wooldridge (1996, 2008) and McDonald (2009), who suggest using the fractional response regression model to handle a dependent variable greater than or equal to zero or less than or equal to one. Fractional response regression models can use “a probit, logit, or heteroskedastic probit model for the conditional mean” (Texas A&M University, n.d., p. 720). In this case, a fractional logit model is used, as the conditional mean of the outcome is interpretable as a probability (Texas A&M University, n.d.). In addition, we control for years to consider time because fractional logit regression is not easily applied to longitudinal studies in Stata.

Results

Descriptive Statistics

Table 1 provides descriptive statistics of all the variables, while Table 2 provides a correlation matrix. An organization in this sample has an average efficiency score of .19, indicating that the organizations are not very efficient. Their HHI averages 0.53 when using five revenue sources, suggesting they lean slightly more toward having concentrated revenue sources. On average, organizations in the sample are 22 years in age, have U.S.$3,947,230 in assets, a leverage ratio of 0.22, and a margin ratio of 0.25. The average GSA is 2.85, and the average Home Value Index is U.S.$172,516. While the correlation matrix shows that the HHI scores are highly correlated, it is important to note that they get tested in separate models. For the rest of the variables, the correlation matrices show no issues with multicollinearity. In addition, an assessment of the variance inflation factor (VIF) scores shows that none are higher than 5 and that they have a mean VIF of 1.68, 1.67, and 1.67 when using HHI5, HHI6, and HHI7, respectively.

Table 1.

Descriptive Statistics.

Variable	M	SD	Minimum	Maximum
Efficiency	0.19	0.25	0	1
Herfindahl–Hirschman Index (five sources)_t-1	0.53	0.14	0.24	1
Herfindahl–Hirschman Index (six sources)_t-1	0.51	0.14	0.23	1
Herfindahl–Hirschman Index (seven sources) _t-1	0.51	0.15	0.23	1
Assets _t-1	U.S.$3,947,230	U.S.$5,206,415	U.S.$262,756	U.S.$29,900,000
Age _t-1	22.13	5.40	1	40
Leverage _t-1	0.22	0.20	0	0.83
Margin _t-1	0.25	0.26	−0.78	0.96
Geographic service area	2.85	1.28	1	5
Zillow Home Value Index _t-1	U.S.$172,516	U.S.$107,082	U.S.$36,679	U.S.$615,376

Note. N = 2,751.

Table 2.

Correlation Matrix.

Variables	1	2	3	4	5	6	7	8	9
Herfindahl–Hirschman Index (five sources) _t-1	1.00
Herfindahl–Hirschman Index (six sources) _t-1	0.92***	1.00
Herfindahl–Hirschman Index (7 sources) _t-1	0.92***	1.00***	1.00
Assets _t-1	−0.28***	−0.25***	−0.25***	1.00
Age _t-1	−0.09***	−0.09***	−0.10***	0.46***	1.00
Leverage _t-1	−0.11***	−0.11***	−0.11***	0.34***	0.07***	1.00
Margin _t-1	0.02	0.03	0.03	−0.09***	−0.06***	−0.19***	1.00
Geographic service area	−0.23***	−0.20***	−0.21***	0.62***	0.34***	0.18***	−0.11***	1.00
Zillow Home Value Index _t-1	0.01	0.04*	0.04*	0.10***	−0.05***	0.09***	0.00	0.07***	1.00

Note. N = 2,751.

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

Analytic Findings

Table 3 estimates the fractional regression model where organizational efficiency is the dependent variable and the independent variable is revenue concentration, using three different indexes examining both the linear and nonlinear relationships.

Table 3.

Fractional Response Model Results.

Variables	Model 1Five revenue sources (linear)	Model 2Five revenue sources (curvilinear)	Model 3Six revenue sources (linear)	Model 4Six revenue sources (curvilinear)	Model 5Seven revenue sources (linear)	Model 6Seven revenue sources (curvilinear)
Herfindahl–Hirschman Index _t-1	0.68*** (0.26)	−3.11** (1.33)	0.99*** (0.25)	−2.02 (1.23)	1.00*** (0.25)	−1.89 (1.23)
Herfindahl–Hirschman Index² _t-1		3.09*** (1.11)		2.53** (1.05)		2.43** (1.05)
Assets _t-1	0.66*** (0.05)	0.66*** (0.05)	0.67*** (0.05)	0.67*** (0.05)	0.67*** (0.05)	0.67*** (0.05)
Age _t-1	−0.02*** (0.01)	−0.02*** (0.01)	−0.02*** (0.01)	−0.02*** (0.01)	−0.02*** (0.01)	−0.02*** (0.01)
Leverage _t-1	−0.90*** (0.18)	−0.90*** (0.018)	−0.89*** (0.18)	−0.88*** (0.18)	−0.89*** (0.18)	−0.88*** (0.18)
Margin _t-1	0.26 (0.17)	0.22 (0.17)	0.26 (0.17)	0.23 (0.17)	0.26 (0.17)	0.23 (0.17)
Geographic service area	−0.10** (0.03)	−0.11*** (0.03)	−0.10*** (0.03)	−0.11*** (0.03)	−0.10*** (0.03)	−0.11*** (0.03)
Zillow Home Value Index _t-1	−0.56*** (0.05)	−0.56*** (0.05)	−0.57*** (0.05)	−0.57*** (0.05)	−0.57*** (0.05)	−0.57*** (0.05)
Year	Yes	Yes	Yes	Yes	Yes	Yes
Constant	−4.56*** (0.82)	−3.54*** (0.90)	−4.70*** (0.81)	−3.94*** (0.87)	−4.70*** (0.81)	−3.97*** (0.87)
Prob. > chi²	0.00	0.00	0.00	0.00	0.00	0.00
R-squared	0.06	0.06	0.06	0.06	0.06	0.06
Obs.	2,751

Note. Robust standard errors in parentheses.

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

Table 3 provides evidence for Hypothesis 1 that revenue concentration is positively associated with efficiency. This finding means that being more dependent on a single revenue source makes the organization more efficient. Although models 1, 3, and 5 in Table 3 reflect the HHI with different numbers of revenue sources, the positive relationships remain consistent across all three models, validating the findings. As a result of these findings, we reject Hypothesis 2, which suggests that revenue concentration is negatively associated with efficiency. However, the results in models 2, 4, and 6 in Table 3 also show that a curvilinear U-shaped relationship is present when assessing the relationship between revenue concentration and efficiency. This finding suggests that organizations are most efficient when they are fully diverse or fully concentrated, which supports Hypothesis 3 (see Figures 1 –3).

Figure 1.

Predicted Efficiency Values for Herfindahl–Hirschman Index With Five Revenue Sources.

Figure 2.

Predicted Efficiency Values for Herfindahl–Hirschman Index With Six Revenue Sources.

Figure 3.

Predicted Values for Herfindahl–Hirschman Index With Seven Revenue Sources.

Finally, each of the control variables, except for margin, has significant relationships with efficiency. Organization size is positively associated with efficiency, perhaps because the economies of scale come into play for larger organizations (Sacristán López de los Mozos et al., 2016). However, organizational age is negatively associated with efficiency. This may be because older organizations are less able to adapt to their changing environment due to learning challenges, which may increase the risk of failure (Autio et al., 2000; Ayayi & Wijesiri, 2018; Barron et al., 1994). Leverage is also negatively associated with organizational efficiency. This finding is surprising, considering that Frumkin and Keating (2011) found a positive correlation between the revenue concentration index and leverage. However, as the margin ratio goes up, this indicates that an organization has more liabilities, which may result in less financial flexibility and a decrease in efficiency. The GSA, or in other words, population size, is negatively associated with efficiency. Locations with larger populations, such as urban settings, often have higher expenses, which may decrease organizational efficiency. Similarly, the Home Value Index is negatively associated with efficiency, likely due to higher costs of building a house.

Discussion

This study tests the competing perspectives of revenue concentration and organizational efficiency relationship, using an input-to-output ratio for the efficiency measure. Using a sample of U.S.-based Habitat for Humanity affiliates, we find a curvilinear U-shaped relationship between revenue concentration and organizational efficiency. The finding has important implications for theory and practice.

Our finding challenges the previous studies that use a single theory to understand the relationship between revenue concentration and organizational efficiency (Bowman, 2017; Frumkin & Keating, 2011). It suggests that multiple theories are needed to understand the relationship. In the same vein, the statistical results of our analysis show that the relationship between revenue concentration and organizational efficiency is not linear as previous studies suggested (Frumkin & Keating, 2011; Mendoza-Abarca & Gras, 2019; Sacristán López de los Mozos et al., 2016). Instead, our results indicate that nonprofit organizations are most efficient when they have a fully concentrated or fully diverse revenue structure. Moreover, nonprofit organizations are least efficient when they are in the middle of this range. Specifically, those nonprofit organizations that have a fully concentrated revenue structure are those that heavily rely on a dominant source of revenue. According to transaction cost theory, they are more likely to avoid various transaction costs (Chikoto-Schultz & Sakolvittayanon, 2020), such as search costs (Chikoto-Schultz & Neely, 2016; Hung & Hager, 2019), negotiation costs, and/or governance costs (Chikoto, 2015; Ebrahim, 2003; Gronbjerg, 1993; Pfeffer & Salancik, 2003; Young, 2007), incurred when diversifying revenue structures. Second, the nonprofit organizations that have a fully diverse revenue structure are those that generate equal amounts of revenues from different sources. According to MPT, they are more likely to be able to reduce the risk of volatility of revenue (Carroll & Stater, 2008; Mayer et al., 2014; Wicker et al., 2015), especially when unexpected events occur. The last question, then, is: Who are those nonprofit organizations in the middle? They are organizations with one or two dominant sources of revenue, with a few other less prevalent sources of income. The combination of the two theories provides a richer explanation of why they are least efficient. For those organizations, it is relatively costly to maintain less prevalent sources of income (Frumkin & Keating, 2011). For example, a nonprofit organization with dominant government funding plus less prevalent sources of income, such as private donations and earned income, needs to hire extra employees with fundraising and business skills to maintain its current revenue structure. Moreover, it is relatively risky for those organizations to continue delivering services, especially when their dominant source(s) of revenue is cut and only less prevalent sources of income are left (Hager & Hung, 2020). In sum, both transaction cost and modern portfolio theories can help us better understand this curvilinear U-shaped relationship.

This study broadly contributes to the ongoing debate over revenue diversification’s advantages and disadvantages (Berrett & Holliday, 2018; Chikoto & Neely, 2014; Chikoto-Schultz & Sakolvittayanon, 2020; Frumkin & Keating, 2011; Hung & Hager, 2019; Mendoza-Abarca & Gras, 2019; Mitchell & Calabrese, 2023; Sacristán López de los Mozos et al., 2016; von Schnurbein & Fritz, 2017). Scholars have contributed substantially to our understanding of whether and how revenue concentration (diversification) affects nonprofit financial health over the past three decades (Hager & Hung, 2020). Indeed, nonprofit financial health has something to do with nonprofits’ revenue diversification or concentration strategies, and it deserves our attention to analyze it. However, financial health is not an end but rather a means to fulfill the missions of nonprofit organizations, and, unfortunately, the question of whether and how revenue concentration (diversification) affects nonprofit outputs, outcomes, impact, or, more generally, performance has not been fully examined and needs further attention. To the best of our knowledge, only two studies have examined this relationship (Berrett & Holliday, 2018; Kim, 2017). This line of research is lacking because nonprofit performance is difficult to measure (Lecy et al., 2012), and performance data are often unavailable (Mitchell & Calabrese, 2023). This study’s focus on organizational efficiency moves this line of research further using both financial and performance data to understand the effect of concentration (diversification) on an important and understudied dimension of organizational performance. Most importantly, our study of organizational efficiency allows us to join the heated debate in the literature and among practitioners on whether revenue diversification benefits nonprofit organizations. Specifically, a benefit on what and how?

This study has implications for nonprofit organizations considering or using revenue concentration (diversification) strategy for organizational efficiency. Suppose a nonprofit organization’s goal is to pursue efficiency. According to the findings from this study, a nonprofit organization can either heavily rely on a dominant source of revenue or generate equal amounts of revenue from different sources. First, nonprofit organizations that depend on or rely on a dominant source of revenue can reduce various transaction costs (Chikoto-Schultz & Sakolvittayanon, 2020). However, they also increase the risk of volatility of revenue (Carroll & Stater, 2008; Mayer et al., 2014; Wicker et al., 2015). The dominant revenue source may be lost when unexpected events occur. Therefore, organizations need to ensure that their dominant source of revenue is stable, so that, even when unexpected events occur, their dominant source of revenue is less likely to decrease to a level that affects organizational operation. Furthermore, over-dependence on a dominant source of revenue may also jeopardize nonprofit organizations’ autonomy due primarily to the potentially powerful influence of a single funder. The excessive influence of a certain funder, coupled with the volatility of a dominant revenue source, can pose significant threats to the operational independence and financial stability of the organization. Second, nonprofit organizations that cultivate or decide to bring in equal amounts of revenue from various sources can minimize the risk of the volatility of the revenue (Carroll & Stater, 2008; Mayer et al., 2014; Wicker et al., 2015). However, they also increase various transaction costs (Chikoto-Schultz & Sakolvittayanon, 2020). The transaction costs will substantially increase if the skills required for the revenues are quite different. Therefore, one strategy nonprofit organizations can consider is related diversification (Rumelt, 1982). Related diversification is a strategy that diversifies revenue sources requiring similar skills and/or strengths. For example, a government-supported nonprofit organization can consider diversifying its revenue to include foundation grants to reduce transaction costs since pursuing both requires common skills and/or strengths, such as grant writing and relationship building.

In recent years, there has been a growing emphasis on nonprofit efficiency (Hung & Berrett, 2023a, 2023b), prompting this study to propose avenues for future research. By continuing to employ expense ratios, not only will measurement errors persist, but it will also perpetuate the misguided notion that these ratios reflect organizational efficiency (Gregory & Howard, 2009; Lecy & Searing, 2015; Tian et al., 2020; Wing et al., 2004a, 2004b; Wing, Hager, et al., 2004). In reality, these ratios merely indicate how nonprofit organizations allocate their financial resources. Therefore, this study strongly advocates for future research to use input-to-output ratios, leveraging methods like DEA or stochastic frontier analysis, to accurately gauge nonprofit efficiency. Moreover, to gain a deeper understanding of the choices made by nonprofits in terms of revenue diversification or concentration, future studies may benefit from drawing on insights from the public finance literature and integrating public choice theory into their theoretical explorations.

As with all research, there are some limitations worth noting. First, this study focuses on Habitat for Humanity affiliates; therefore, generalizability to the broader nonprofit sector must be taken with caution. Furthermore, affiliates cannot report a house until it has been completed. This means that during the first year of working on the house, affiliates are investing time, money, and effort into the proeject, but since they cannot report any progress until the house is finished, it may appear as if they not achieving anything in that initial year. This lack of reported outputs might make them seem inefficient or unproductive in the short term, even though they are actually working towards completing the house. The use of lagged HHI and control variables should address any methodological issues related to this, but it is still important to note. In addition, Form 990 data can have limitations, including an abundance of missing data for revenue sources. However, we take careful consideration with imputing the missing data with zero (please see footnote 2 for details). Finally, according to Coupet et al. (2021), DEA has limitations. First, the selection of appropriate inputs and outputs is critical in DEA. Second, DEA is nonparametric, meaning that it is not statistical. Third, DEA can be highly sensitive to outliers, which can inadvertently affect the efficiency scores of other organizations in the sample. We do our best to address the limitations of DEA by following the input and output selection of Coupet and Berrett (2018), pairing DEA with other statistical procedures, and dropping outliers before determining the DEA scores.

Conclusion

It has long been widely accepted by scholars and practitioners alike that revenue diversification provides multiple benefits to nonprofit organizations (Berrett & Holliday, 2018; Hung & Hager, 2019). However, recent studies have offered compelling evidence that there are advantages to concentrating nonprofits’ revenue structures as well (Mendoza-Abarca & Gras, 2019; Sacristán López de los Mozos et al., 2016). As extensive research has been conducted on the effect of revenue concentration (diversification) on the financial health of nonprofits, this study suggests that more research effort should be devoted to nonprofit outputs, outcomes, impact, and/or performance. Moreover, as nonprofit revenue strategies are quite complex, we call on future studies to employ multiple theories to understand how the strategies affect nonprofit behaviors. We also conclude that it is important for researchers to use an appropriate measure of efficiency to clearly understand nonprofit performance. Finally, we conclude that if a nonprofit seeks to rely on a dominant source of revenue for efficiency, it needs to ensure the source is stable. Likewise, if a nonprofit seeks to generate equal amounts of revenue from different sources for efficiency, it needs to consider a related revenue diversification strategy requiring similar skills and/or strengths.

Footnotes

Acknowledgements

The authors thank an anonymous reviewer for this suggestion.

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 iDs

Jessica L. Berrett

ChiaKo Hung

Notes

Author Biographies

Jessica L. Berrett is an assistant professor in the College of Public Service at the University of Colorado Colorado Springs. Her specialty is nonprofit management with a research focus on financial and performance management. Her research has been published in Nonprofit Management & Leadership, VOLUNTAS, The American Review of Public Administration, and Public Performance & Management Review.

ChiaKo Hung is an assistant professor of nonprofit management and public administration at the University of Hawai‘i at Mānoa. His areas of interest are nonprofit finance, fundraising, social entrepreneurship, and meta-analysis. His research has been published in Public Performance & Management Review, Nonprofit Management & Leadership, VOLUNTAS, and the Journal of Public Administration Research and Theory.

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