ARE SOCIAL ENTERPRISES MORE EFFICIENT THAN TRADITIONAL

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1 ARE SOCIAL ENTERPRISES MORE EFFICIENT THAN TRADITIONAL NONPROFITS? A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in the Georgetown Public Policy Institute By Mark C. Magro, B.A. Washington, DC April 09, 2007

2 ARE SOCIAL ENTERPRISES MORE EFFICIENT THAN TRADITIONAL NONPROFITS? Mark C. Magro, B.A. Thesis Advisor: Sencer Ecer, Ph.D. ABSTRACT A relatively new organization has emerged from the third sector of modern economies in the recent decade, the social enterprise. The term social enterprise describes an organization or venture that advances its social mission through entrepreneurial, earned income strategies encompassing both the nonprofit and private sector. This study focuses on social enterprises in the nonprofit sector and examines whether social enterprises are more efficient in their operations compared to traditional nonprofits. Using both Tobit and an ordinary least squares (OLS) regression models, I conclude that social enterprises are more efficient in managing their overhead, administrative and fundraising expenses compared to traditional philanthropic nonprofits. Policymakers and nonprofit managers should support the continued development of the social enterprise sector as it could lead to greater efficiency and more resources being devoted to addressing social goals, and thus become an important factor in the continued advancement of US society. ii

3 I would like to thank my thesis advisor, Dr. Sencer Ecer, for his guidance in this endeavor. I also want to thank my parents and family for their constant support and love. Finally, special thanks to Vidisha for making anything possible and everything worth it. iii

4 TABLE OF CONTENTS Chapter 1. Introduction...1 Chapter 2. Literature Review...5 Chapter 3. Research Hypotheses...8 Chapter 4. Database...9 Chapter 5. Variable Measures...12 Dependent Variables...12 Explanatory Variables...13 Revenue Variables...14 Organizational Control Variables...16 Chapter 6. Descriptive Statistics...18 Chapter 7. Model Specification...22 Heteroskedasticity...24 Multicollinearity...24 Chapter 8. Results...26 Overhead Ratio...26 Fundraising Efficiency Ratio...28 Administrative Efficiency Ratio...30 Comparison of Results across Models...32 Chapter 9. Discussion and Policy Implications...37 Chapter 10. Conclusion...40 References 43 Appendix A: Regression Results...46 Appendix B: Descriptive Statistics...52 iv

5 Chapter 1. Introduction A relatively new organization has emerged from the third sector of modern economies in the recent decade, the social enterprise. The term social enterprise describes an organization or venture that advances its social mission through entrepreneurial, earned income strategies encompassing both the nonprofit and private sector. This study focuses on social enterprises in the nonprofit sector as an overwhelming majority of social enterprises are formed as nonprofit organizations. The purpose of this study is to examine whether social enterprises are more efficient in their operations compared to traditional nonprofits. There is a debate over the mix of characteristics that constitute a social enterprise, as the term has no exact definition or consistent usage. Most researchers refer to a broad definition describing an organization creating social value using innovative methods while still financially motivated in a manner described as double bottom line (Emerson and Twersky, 1996). A social enterprise can either be a for-profit, such as The Body Shop, which blends social responsibility and profit goals, or a nonprofit organization, such as the Girls Scouts, which has a social mission to help girls build skills and become leaders and partly finances this mission with sales of cookies. Social enterprises that are nonprofit organizations tend to be more business-like or market driven as part or all of their operations take place in a marketplace. This has led to part of the dispute over the definition as many such as Borshee (2001) associate a social enterprise with their ability 1

6 to generate income to partially support their social mission, while Dees subscribes to a broader definition of a social enterprise encompassing all organizations with a social mission (Dees, 1998). For the purposes of this study, the key distinguishing factor of a social enterprise from a traditional nonprofit is the ability to generate revenue from within the organization, as opposed to relying on grants and public support. Dees (1996) describes these organizations as Hybrid enterprises, integrating both philanthropic and commercial methods into their core operations. From the perspective as a nonprofit, a social enterprise can be defined follows: A social enterprise is any nonprofit-owned revenue-generating venture created for the purpose of contributing to a social cause while operating with the discipline, innovation and determination of a for-profit business. (virtventures.com) The level of revenue generation from commercial activities is also not fully agreed upon. A common method to segment social enterprises within the nonprofit sector requires that at least half of funding is derived from trading activities or the exchange of goods and services. 1 While this characteristic is not agreed upon by all academics, it is the best method to identify nonprofits that rely on earned income strategies and is used to segment social enterprises in the nonprofit sector in this study. The roots of the term social enterprise comes from 19 th century businessmen concerned with the welfare of the employees who became known as social 1 Small Business Service: UK Department of Trade and Industry (DTI). A Survey of Social Enterprises Across the UK,

7 entrepreneurs (Banks, 1972). Today the term has become a buzz word in the nonprofit sector seeking a means to finance its activities in a competitive fundraising environment and in an increasingly socially conscious business sector. However, the nonprofit sector has traditionally generated income through its activities, usually through small fees and government contracted services. In that sense many believe the term social enterprise is no more than a new name for certain nonprofits. The notion of nonprofits generating significant revenues via commercial ventures was discussed by academics in the 1980s (Skloot, 1987), although the degree to which nonprofits rely on revenues from goods and services and the more business oriented approach has been a recent phenomenon. The fiscal constraint of the 1980s led to large decreases in the budgets of many nonprofit organizations traditionally relying on government grants. Additionally the devolution of social programs throughout the 1980s and 1990s led many nonprofits to focus on service provision and fees (Salamon, 1993). The commercialization of the nonprofit sector began in the 1980s in response to increased competition for limited revenue sources. These changes have led to a dividing of the nonprofit sector between organizations that can provide a good or service for a fee, and those that rely more on traditional philanthropic funding (Toepler, 2004). Social enterprises, which are more reliant on revenue generation through commercial ventures such as selling goods and services, are thought to be more market driven. Furthermore, the focus on generating revenues requires a business approach to managing a nonprofit and a more professional staff. For these reasons, social enterprises may be more financially efficient in there operations. This study attempts to prove social enterprises are more efficient by 3

8 measuring their efficiency using common financial ratios. A review of past studies of financial efficiency in the nonprofit sector provides a basis for my analysis. 4

9 Chapter 2. Literature Review In terms of a nonprofit organization, efficiency can be defined as fulfilling its mission at the lowest cost. However, measuring mission fulfillment is inherently subjective and difficult, so most reviews of nonprofit organizations focus on financial efficiency based on information from the IRS Form 990. The Form 990 is required by the IRS for tax-exempt organizations and is made public for most nonprofit organizations. There are many organizations that rate nonprofits, the largest and most respected are ratings from the Better Business Bureau Wise Giving Alliance (Give.org), the American Institute of Philanthropy (charitywatch.org) and Charity Navigator (charitynavigator.org). These organizations use the financial data from Form 990s to calculate financial efficiency ratios. The most commonly used ratios to evaluate nonprofits measure overhead costs, administrative efficiency, and fundraising efficiency. The lack of transparency in the nonprofit sector, as well as consumer demand for information on charitable organizations, has led to heavy reliance on financial data from the Form 990 to rate nonprofits. A 2001 public opinion poll by the Princeton Survey Research Associates found that the way an organization spends its money is the most important factor to those contemplating a donation. Academic studies on the importance of these financial ratios in donor decisions have had mixed results. Tinkelman (1999) found that audited nonprofits which report higher program efficiency ratios have higher donation growth rates. However, Frumkin and Kim (2001) found that administrative efficiency ratios were not a factor in private donation growth for nonprofits that reported 5

10 them. While there are significant studies focusing on the effects of these financial ratios on funding, there is relatively little analysis of the factors that affect measures of efficiency. The largest source of literature on the subject comes from the Nonprofit Overhead Cost Study a joint research project between the Urban Institute and Indiana University. There study by Hager, Pollak and Rooney (2001) concluded that age, size and subsector have a statistically significant effect on overhead ratios however they do not explain much of the variation. The study found larger and younger nonprofits tend to exhibit greater efficiencies in expending dollars on program-related expenses, and spend less to raise additional contributions. There is also evidence that government contributions and grants reduce administrative efficiency (Frumkin and Kim, 2002), the theory being that increased administrative hurdles and oversight by government agencies leads to greater costs. Frumkin and Kim also find receipts of program service revenue are correlated with higher efficiencies as measured by the administrative efficiency ratio. The findings from this study support this claim however the magnitude of the effect is much smaller. The past studies of efficiency in the nonprofit sector only focus on one measure of efficiency and often leave out important explanatory variables. To expand the scope of research on nonprofit, I focus on three different measures of nonprofit efficiency and include all available relevant explanatory variables. I attempt to answer whether social enterprises are more efficient than traditional nonprofit organizations and provide an analysis of this sub-sector of nonprofits. Additionally, I provide evidence to either 6

11 support or refute existing research on the factors that contribute to organizational efficiency within the nonprofit sector. 7

12 Chapter 3. Research Hypotheses All hypotheses for this study are based on the presumption that the financial ratios are an effective and appropriate measure of efficiency in the nonprofit sector. The main hypothesis stems from the commercialization of the nonprofit sector and presumes that nonprofits with higher proportions of program service revenue are more market driven. Furthermore, these organizations respond to market conditions similarly to for-profit organizations in a competitive environment and become more efficient or are forced to leave the sub-sector of social enterprises. Therefore the first hypothesis is: H1: As program service revenue as a proportion of total revenue increases, the overhead (OH) ratio and administrative efficiency (AE) ratio decrease. (In other words, social enterprises within the nonprofit sector are more efficient as measured by the OH and AE ratio.) Henry Hansmann (1989) predicted an eventual split within the nonprofit sector leading to a philanthropic nonprofit sector composed of traditional nonprofits and a commercial nonprofit sector focusing on services with fees. This divide creates nonprofits with differing organizational expertise based on capabilities essential to the nonprofit. Since traditional nonprofits rely heavily on contributions they should become more efficient at attaining contributions, hence the second hypothesis is: H2: Social enterprises are less efficient than the traditional nonprofits in fundraising efficiency. This study provides empirical evidence to support or refute these hypotheses. 8

13 Chapter 4. Database The data for this analysis comes from the National Center for Charitable Statistics (NCCS) GuideStar database. The data is from 2003, the most recent year that is made available by NCCS. GuideStar is comprised of data from annual IRS filings of Form 990 by 501(c)(3) organizations. Most 501(c)(3) nonprofit organizations with $25,000 in gross receipts are required by law to file with the IRS and are included in the GuideStar database. The GuideStar database is the most current and comprehensive source of financial data on nonprofit organizations and is used by many charity watchdog groups to rate nonprofits. The ratings are supposed to show which nonprofits are the most efficient at spending donations by relying on such measures as overhead ratios and fundraising efficiency. However, there are problems and limitations with the data as found by a number of authors (Abramson, 1995; Froelich et al., 2000). The major concern is over the completeness and accuracy of the data from the Form 990, as it is not based on audited financial statements. A study by the Center of Nonprofits and Philanthropy at the Urban Institute found that most nonprofits misclassify expenses (Hager et al., 2001), especially smaller nonprofits. This misclassification problem is attributed to limited budgets of nonprofits with inadequate accounting systems, an overemphasis on efficiency ratios by donors and poor auditing by accountants. Despite these problems, a study by Froelich, Knoepfle and Pollack (2000) concluded that the data from the Form 990 can be 9

14 considered an adequate and reliable source of financial information for many types of investigations, especially those that focus on basic financials from the income statement and balance sheet. Even other important variables such as program service revenues and expenses and fundraising expenses have only somewhat lower [reliability] but reasonable consistency with audited financial statements, (Froelich et al., ). In order to minimize any potential problems, I omitted data that contain errors. This includes restricting the data to nonprofits that had non-negative revenues and expense variables as well as variables that were calculated improperly. These specifications reduce the database by approximately one third resulting in 150,972 nonprofit organizations for Nonprofit organizations with non-positive total revenues or expenses only composed about 1 percent of the total database. The majority of the reduction in the database (33% jointly) stems from organizations with non-positive program expenses (23%) and administrative expenses (31%). A high positive correlation (r =.73) was found between organizations with non-positive program expenses and non-positive administrative expenses, indicating that accounting or reporting problems may be a factor. The excluded organizations were surprisingly only very weakly correlated (r =-.04) with the size of an organization (total revenue) and the age of an organization, (r =-.017), suggesting that small and relatively young nonprofits are not the source of these reporting inconsistencies. Additionally, the excluded organizations were found to have little to no correlation (all less than r =.1) with any variable of interest in my model. For these reasons there is less concern over sample selection bias in the reduced database and 10

15 the database can be considered a random sample. 11

16 Chapter 5. Variable Measures Dependent Variables The dependent variables in the models tested are common measures of efficiency in the nonprofit sector: Overhead (OH) Ratio = Administrative Expenses + Fundraising Exp. + Special Event Exp. Total Revenue The overhead ratio represents the percentage of revenues that are used to raise contributions and administer the nonprofit. A smaller overhead ratio is an indication of organizational efficiency. This variable is positive but is not bound between [0:1] since expenses can be greater than revenues, however, this problem is addressed by a Tobit model so that coefficient estimates are not biased due to outliers. Administrative Efficiency (AE) Ratio = Administrative Expenses Total Expenses The AE ratio is a measure of level of administrative expenses to total expenses. A smaller AE ratio indicates efficiency in managing a nonprofit. This variable by definition is bound between [0:1]. Fundraising Efficiency (FE) Ratio = Fundraising Expenses + Special Event Expenses Total Contributions + Gross Special Event Income The FE ratio is a measure of how efficiently the nonprofit raises revenues through contributions and a smaller ratio is more efficient. This variable is also positive but not bound between [0:1] since expenses can be greater than contributions and 12

17 income, however a Tobit model is also used to address this problem. Explanatory Variables The explanatory variable of interest is a continuous variable (PCTSE) measuring program service revenue as a percentage share of total revenue, indicating the degree to which the organization relies on generating revenue through earned income strategies (selling goods and services). Program service revenue is comprised of revenues from trading activities or the exchange of goods and services including government fees and contracts (excludes government grants). These revenues come from direct payments from clients or customers as well as government payments for services, for example, a hospital would report on this line all of its charges for medical services (either from the client or government reimbursed), hospital parking lot fees, laboratory fees etc. (IRS 1996, 11) PCTSE = Program Service Revenue Total Revenue Further, I segment the nonprofit sector into organizations reliant on charity or philanthropy and social enterprise (SE) organizations. A dummy variable for social enterprises (SE) is created and set equal to 1 when program service revenue divided by total revenue is greater than 50 percent, otherwise SE equals 0. The social enterprise (SE) variable is an indicator for nonprofit organizations that are fundamentally different from traditional charity driven organizations as they rely on earned income and operate 13

18 more in a market setting. Since PCTSE and SE are almost an identical measure, both variables cannot be included in the model because of multi-collinearity (r =.93). Since the percentage of total revenues from earned income is not universally agreed upon for social enterprises, I use the PCTSE in the regression models as it measures the effect on efficiency of operating more like a social enterprise. Revenue Variables Government Contributions (Gov t Grants): An indicator variable for nonprofit organizations receiving any government grants. The predicted sign is positive, reducing efficiency, as increased oversight and procedures by government agencies lead to higher costs. Direct Public Support (Dir Pub Sup): A continuous variable which includes individual, corporate and foundation contributions or grants (includes donated items), measured as a percentage of total revenues (scaled by 100,000 in the regression model). The predicted sign is positive, reducing efficiency, as nonprofits with higher levels of contributions and grants may have a less business-like approach. Indirect Public Support (Ind Pub Sup): Indicator variable for nonprofit organizations receiving contributions through federated fundraising campaigns such as the United Way or from affiliated organizations, measured as a percentage of total revenues. The predicted sign is negative as organizations receiving revenue from federal fundraising 14

19 campaigns may indicate they are high performing nonprofits and are more efficient in their operations. Membership Dues (Dues): An indicator variable for nonprofit organizations receiving membership dues and assessments which are payments that have a corresponding nominal benefit attached to them. Examples include: subscriptions to newsletters or discounts on events. The predicted sign is negative as nonprofits have members which may act as shareholders and monitor the organization. Further the nonprofit is providing a benefit to members, a business like approach. Other Revenue (OthRev): Dummy variable for nonprofits organizations receiving revenue from unconventional sources such as interest on loans to employees or trustees, or interest on notes receivable not held as investments. Nonprofits with other revenue sources may have more sophisticated financing schemes and a more professional staff; hence the predicted sign is negative (higher efficiency). Investment Income (Invst Inc): Continuous variable measuring the level of revenues from investments, such as interest or dividends on savings and investments (scaled by 10,000 in regression model). The predicted sign is undetermined. 15

20 Organizational Control Variables Size of Organization (Log Tot Rev): Continuous Variable measured by the natural logarithm of total revenues. The predicted sign is negative as larger organizations are expected to have lower levels of administrative and fundraising expenses relative to revenues and total expenses. Age of Organization (Age): The variable is calculated by the difference between the year the IRS granted 501(c)(3) statuses and the year (The data is from 2003 and includes organizations granted 501(c)(3) in 2003) Subsector: Indicator variables for subsector the nonprofit operates in as classified by the National Taxonomy of Exempt Entities (NTEE) category: Higher Education, Arts Culture and Humanities, Education, Health, Human Services, Religion, International, Public and Societal benefit, Mutual benefit, Environment, and Hospitals. These categories are mutually exclusive and collectively exhaustive. Location (Urban): An indicator variable for nonprofit organizations located in an urban location or Metropolitan Statistical Area (MSA). The predicted sign is negative as urban nonprofits may have access to more professional staff and may be able to generate revenues more efficiently due to proximity to wealth. DC: Dummy variable for nonprofit organizations located in Washington DC. The 16

21 predicted sign is negative since these organizations are centrally located to policy makers and likely perform higher levels of advocacy work making them highly visible. For these reasons nonprofits in DC may be able to raise contributions with less expense. 17

22 Chapter 6. Descriptive Statistics The descriptive statistics of the three dependent variable ratios are analyzed for robustness in this model. The fundraising efficiency (FE) ratio has 17,539 or 11.7 percent missing data compared to the other two ratios due to nonprofits with zero total contributions and gross special event income. Additionally, of nonprofits with positive total contributions or gross special event income, 55,081 nonprofits report no fundraising or special event expenses leading to 41 percent of nonprofits having an FE ratio equal to zero. The large number of FE ratios equal to zero or significantly greater than 1 causes a large standard deviation (18.95) which must be addressed in my model. The overhead (OH) ratio also has a similar problem with many data points significantly greater than 1 due to expenses that are greater than revenues. The administrative efficiency (AE) ratio does not have this problem as administrative expenses cannot exceed total expenses by definition. The descriptive statistics from Table 1 show that 33 percent of the nonprofit organizations in the dataset are defined as social enterprises. The average nonprofit relies on approximately 1/3 of revenues from program services or exchange of goods and services, and the average total revenue is approximately $5 million. The average age of nonprofit organizations in the dataset is 20 years, and the largest percentage is located in California with 11 percent followed by New York with 8 percent (Appendix B: Chart2). 18

23 Table 1. Summary Statistics for all Nonprofits N=150,972 Variable Mean Standard Deviation Minimum Maximum Missing Dependent OH Ratio E Administrative Efficiency E Fundraising Efficiency ,539 Independent Social Enterprise (D) Program Service Revenue (% of TR) Revenue Streams - Govt Grants (D) Direct Public Support 532,851 5,772, ,905,005 - Indirect PS (D) Membership Dues (D) Other Revenue (D) Investment Income 119,367 1,908, ,691,126 - Organizational Characteristics Size- Log Total Revenue Size- Total Revenue 5,299,088 70,745, E+10 - Age Urban (D) Social enterprises are similar to the entire nonprofit sector in age and location, however, they are vastly different in the sources of revenue and size. The average social enterprise is nearly double the size of the average nonprofit with over $12 million in revenues versus $5 million (see Table 2). Further, social enterprises on average rely on close to 85 percent of their revenues from selling goods and services and have half the average direct public support of the average nonprofit. Social enterprises have significantly lower overhead ratios (.20 <.32) warranting further econometric analysis to 19

24 determine if they are more efficient relative to the entire nonprofit sector. Social enterprises also have lower administrative efficiency ratios (.158<.169) on average compared to nonprofit sector, however, the difference is marginal. Social enterprises do have close to 100 percent higher fundraising efficiency ratios (.611 >.326), meaning they are relatively inefficient at raising contributions. This finding seems to follow logic as social enterprises are more focused on generating revenue through goods and services as opposed to donations; hence they are at a competitive disadvantage. The distribution of social enterprises across subsectors is also very similar to the entire nonprofit sector (see Appendix B: Table 16, Chart 1). However, social enterprises have much larger presences in the Hospital and Higher Education subsectors relative to the nonprofit sector as a whole. In fact, social enterprises have a positive and significant correlation with the Hospital subsector of r =0.15 and the Higher Education sector with an r =0.08. These subsectors are where service revenue generation is most likely to occur so these results are expected. Conversely social enterprises are negatively correlated with the Public Societal Benefit subsector having an r = (see Appendix B: Table 19). 20

25 Table 2. Summary Statistics for Social Enterprises N=50,399 Variable Mean Standard Deviation Minimum Maximum Missing Dependent OH Ratio E Administrative Efficiency E Fundraising Efficiency ,199 Independent Program Service Revenue (% of TR) Revenue Streams - Govt Grants (D) Direct Public Support 297,952 3,962, ,999,854 - Indirect PS (D) Membership Dues (D) Other Revenue (D) Investment Income 192,618 1,969, ,113,000 - Organizational Characteristics Size- Log Total Revenue Size- Total Revenue 12,215, ,005, E+10 - Age Urban (D) Further segmenting the nonprofit sector into social enterprises and non social enterprises shows a large difference in program service revenue as a share of total revenue. Nonprofits that are not considered social enterprises on average only receive 8.2 percent (see Appendix B: Table 17) of revenues from program services while social enterprises receive 84.7 percent on average, a difference of 76.5 percent. The difference in the overhead ratio also increases when segmenting the nonprofit sector, as social enterprises are approximately 18 percent more efficient than non social enterprises. 21

26 Chapter 7. Model Specification An empirical model of efficiency in the nonprofit sector, estimated using both a Tobit and an ordinary least squares (OLS) regression: EFF i = ά + β(pctse i )+ λ R i + γ X i + ε I ; where EFF is efficiency as measured by expense ratios (three previously mentioned ratios) for each nonprofit i. ά is a constant term and PCTSE is a continuous variable measuring program service revenue as a proportion of total revenue. R is a vector for revenue generation sources and X is a vector of organizational aspects for which I control for and ε is an error term. The coefficient β represents the marginal effect on efficiency of an increase in the proportion of total revenues from program services. If β<0, then social enterprises are more efficient, while a β>0 means they are less efficient. OH Ratio: In order to properly restrict the OH ratio between 0 and 1, a censored regression model is needed since the OH ratio has 4,755 observations greater than 1. (see Table 3) The Tobit model is used to right-censor the OH ratio so values greater than 1 (OH>1) are recorded as OH=1 and can be estimated with maximum likelihood estimation, a general method for obtaining parameter estimates and performing statistical inference on estimates. The model is estimated using the QLIM (Qualitative and limited dependent variable Model) procedure in SAS which can be used to analyze a multivariate limited 22

27 dependent variable model where the dependent variable is observed only in a limited range of values. 2 The QLIM procedure is used in this case to estimate a right censored Tobit model with heteroskedasticity using maximum likelihood estimation. Table 3. Variable Summary Statistics of Continuous Responses (N=150,972) Mean Standard Error Type Lower Bound Upper Bound N Obs Lower Bound N Obs Upper Bound OHratio Censored FE Ratio: Due to the high number of nonprofits with an FE ratio equal to zero (55,081 or 41 percent), the FE ratio is a corner solution response. This type of dependent variable takes on the value of zero with some regularity but is roughly continuously distributed over positive values. A two-way (left- and right-censored) Tobit model is used to estimate a corner solution response as 55,081 nonprofits report no fundraising or special event expenses leading to 41 percent of nonprofits having an FE ratio equal to zero. Additionally the FE ratio is right-censored at FE=1 as there 1,488 observation where the FE ratio is greater than 1 (see Table 4). Table 4. Summary Statistics of Continuous Responses (N=150,972) Variable Mean Standard Error Type Lower Bound Upper Bound # Obs Lower Bound # Obs Upper Bound FE Ratio Censored The QLIM procedure, SAS Support website: 23

28 AE Ratio: The AE ratio is neither a corner solution response nor needs to be censored as it is already bound between [0:1]. For this reason, OLS regression is used to estimate the parameters and robust standard errors are calculated. Heteroskedasticity In order to determine if heteroskedasticity is present in any of my three models, I regress the square of the residuals from an OLS regression on the independent variables in my model. The results in Table 5 indicate the presence of heteroskedasticity in the OH and AE ratio models. Table 5. F-test for Heteroskedasticity OH Ratio (N= 150,972) FE Ratio (N= 150,972) AE Ratio (N= 133,433) F value 3.63*** *** *** Significant at the 1% level Robust standard errors are calculated in the OH ratio and AE ratio model producing valid standard errors due to the large size of the dataset. Multicollinearity To test for multicollinearity, correlations were run between the different 24

29 independent variables in the model. Including only the PCTSE variable while excluding the SE variable negates any significant effects on the standard errors due to problem of multicollinearity. The most significant correlations occur between the revenue source variables (see Appendix B: Table 18), however the largest coefficient is approximately r = Due to the low correlation of explanatory variables and the large sample size, multicollinearity is not an issue in this model. 25

30 Chapter 8. Results Overhead Ratio Overhead (OH)Ratio = Administrative Exp + Fundraising Exp + Special Event Expenses Total Revenue OH ratio= B 0 + B 1 PctSE + B 2 Govt Grant + B 3 Ind Pub Sup + B 4 Mem.Dues+ B 5 Other Rev + B 6 Dir Pub Sup + B 7 Invst Inc + B 8 Log Total Rev + B 9 Age + B 10 Age-sq + B 11 Urban + B 12 DC + B 13 Higher Education + B 14 Art Cult Humanities + B 15 Education + B 16 Health + B 17 Human Service + B 18 Religion + B 19 International + B 20 Public Social Benefit + B 21 Mutual Benefit + B 22 Environment + B 23 Hospital + ε Table 6 shows partial results from the right-censored Tobit model estimating the relationship between revenue generation sources and overhead efficiency (see Appendix A: Table 13 for the full results). 26

31 Table 6. Tobit Regressions Results for the Overhead Ratio Parameter (Right Censored) (N=150,972) Estimate Robust Standard Error t Value Approx Pr > t Intercept PctSE Govt Grant (D) Ind Pub Sup (D) Mem. Dues (D) Other Rev (D) Dir Pub Sup (100,000's) Invst Income (10,000's) Log Total Revenue Age (D) indicates a Dummy variable The variable PCTSE is both negative and statistically significant at the.01 level indicating that social enterprises are more efficient in operations. The coefficient estimate for PCTSE suggests that a 1 percentage point increase in revenue from program services corresponds to a.071 percent decrease in the overhead ratio or a.071 percent increase in operational efficiency. The dummy variables for government grants and indirect public support are also negative and statistically significant at the.01 level indicating that nonprofits that receive grants or contributions through the government or federated funders (i.e. United Way) are more efficient. The coefficient estimate of for government grants suggests that 27

32 nonprofits receiving government grants are 3.46 percent more efficient on average. This result provides evidence contrary to the findings of Frumkin and Kim, The dummy variables from membership dues and other revenue are positive and significant indicating that nonprofits with these sources of revenue are less efficient. The coefficient estimate suggests a large efficiency decrease of 5.9 percent for nonprofits with member dues and a 2.5 percent decrease in nonprofit efficiency for other revenue sources. The size of an organization (log Total Revenue) is also negative and strongly significant suggesting that larger organizations are more efficient; however the effect of age is positive and significant. The coefficient estimate for size is interpreted as a 1 percent increase in total revenue corresponds with a 3.3 percent increase in efficiency. This marginal effect is relatively large for size however the coefficient for age indicates an insignificant effect on efficiency. Fundraising Efficiency Ratio Fundraising Efficiency (FE) Ratio = Fundraising Expenses + Special Event Expenses Total Contributions + Gross Special Event Income FE ratio= B 0 + B 1 PctSE + B 2 Govt Grant + B 3 Ind. Pub Sup + B 4 Mem.Dues+ B 5 Other Rev + B 6 Dir Pub Sup + B 7 Invst. Inc + B 8 Log Total Rev + B 9 Age + B 10 Age-sq + B 11 Urban + B 12 DC + B 13 Higher Education + B 14 Art Cult Humanities + B 15 Education + B 16 Health + B 17 Human Service + B 18 Religion + B 19 International + B 20 Public Social Benefit + B 21 Mutual Benefit + B 22 Environment + B 23 Hospital + ε Table 7 shows partial results from the right- and left-censored Tobit model estimating the relationship between revenue generation sources and fundraising 28

33 efficiency (see Appendix A: Table 14 for the full results). Table 7. 2-Way Tobit Regressions Results for the Fundraising Efficiency Ratio (Right- and Left-Censored) (N=133,433) Robust Standard Approx Parameter Estimate Error t Value Pr > t Intercept PctSE Govt Grant (D) Ind. Pub Sup (D) Mem. Dues (D) Other Rev (D) Dir Pub Sup (100,000's) Invst Income (10,000's) Log Total Revenue (D) Indicates a Dummy variable The variable PCTSE is both negative and statistically significant at the.01 level indicating that social enterprises are more efficient in operations. The coefficient estimate for PCTSE suggests that a 1 percentage point increase in revenue from program services corresponds to a percent decrease in fundraising efficiency. These results provide evidence to refute hypothesis 2, that social enterprises are relatively less efficient than traditional nonprofits at fundraising. This is surprising since social enterprises on average have higher FE ratios and traditional nonprofits rely heavily on contributions. Another interesting result from this model is the magnitude of the estimates for the dummy variables on government grants and membership dues. The large and 29

34 significant coefficient estimate of for government grants suggests that nonprofits receiving government grants are 11.9 percent more efficient on average, while nonprofits with membership dues are close to 10 percent less efficient. The remaining results can be interpreted in the same manner as the overhead ratio results. 3 Administrative Efficiency Ratio Administrative Efficiency (AE) Ratio = Administrative Expenses Total Expenses AE ratio= B 0 + B 1 PctSE + B 2 Govt Grant + B 3 Ind. Pub Sup + B 4 Mem.Dues+ B 5 Other Rev + B 6 Dir Pub Sup + B 7 Invst Inc + B 8 Log Total Rev + B 9 Age + B 10 Age-sq + B 11 Urban + B 12 DC + B 13 Higher Education + B 14 Art Cult Humanities + B 15 Education + B 16 Health + B 17 Human Service + B 18 Religion + B 19 International + B 20 Public Social Benefit + B 21 Mutual Benefit + B 22 Environment + B 23 Hospital + ε Table 8 shows partial results from the OLS regression estimating the relationship between revenue generation sources and administrative efficiency (see Appendix A: Table 15 for the full results). 3 See the comparison of results section for further discussion. 30

35 Table 8. OLS Regression Results for the Overhead Ratio (N=150,972), Adj.R-Sq =.034 Variable DF Parameter Estimate Robust Standard Error t Value Intercept *** PctSE *** Govt Grant (D) *** Ind. Pub Sup (D) *** Mem. Dues (D) *** Other Rev (D) *** Dir Pub Sup (100,000's) *** Invst Income (10,000's) *** Log Total Revenue *** Age *** *** Significant at the 1% level, (D) indicates a dummy variable All coefficients on revenue source variables are statistically significant at the.01 level and the model explains 3.4 percent of the variation in the AE ratio, however, the magnitude of the effect for all variables is unsubstantial. The variable PCTSE is both negative and statistically significant at the.01 level indicating that social enterprises are more efficient, although, the coefficient estimate for PCTSE suggests that a 1 percent increase in percentage of revenue from program services corresponds to only a percent decrease in administrative efficiency. 31

36 Comparison of Results across Models The results from the three regressions provide empirical evidence in support of hypothesis 1 that as program service revenue as a proportion of total revenue increases, financial efficiency ratios decrease. Social enterprises are more efficient as measured by the common financial ratios (OH, FE and AE ratio). The sign on PCTSE is negative across all three models indicating the robustness of evidence supporting hypothesis 1. A comparison of the coefficient estimates across all three models shows that the effect of earned income on financial efficiency is greatest as measured by the OH ratio and relatively insignificant as measured by the AE ratio (see Table 9). Table 9. Comparison of Estimated Effects Across Dependent Variables OH Ratio FE Ratio AE Ratio (N= 150,972) (N= 150,972) (N= 133,433) Intercept *** *** *** Revenue Streams PctSE *** *** *** Govt Grant (D) *** *** *** Ind. Pub Sup (D) *** *** Mem. Dues(D) *** *** *** Other Rev (D) *** *** *** Dir Pub Sup (100,000's) 4.1E E-04*** -5.9E-05*** Invst Income (10,000's) 1.2E-05*** -5.8E E-06*** Organizational Characteristics Log Total Rev *** *** *** Age *** *** *** Age-sq 3.0E E-05*** 9.6E-06*** Urban *** *** *** 32

37 DC (D) *** *** Subsector- (D) Higher Education *** *** *** Art Cult Humanities *** *** *** Education *** *** Health * Human Service * Religion ** *** *** International ** Public Social Benefit Mutual Benefit ** Environment ** Hospital *** ** *** (D) indicates a Dummy variable * Significant at the 10% level, ** Significant at the 5% level, *** Significant at the 1% level While the marginal effects of a 1 percent increase of PCTSE on efficiency is not substantively large, calculating the effects based on the average difference between social enterprises and the nonprofit sector leads to substantive efficiency losses of 3.6 percent for the OH ratio. This effect is larger when segmenting the nonprofit sector into traditional nonprofits only (non social enterprises) and social enterprises leading to a 5.4 percent increasing in overhead efficiency and a 1.2 percent increase in fundraising efficiency (see Tables 10 & 11). 33

38 Table 10. Average Estimated change in Efficiency vs. Nonprofit Sector Social Enterprises Nonprofit Sector Difference (% Points) OH model FE model AE model ProgramService Revenue (% of TR) Marginal Effect Avg. Estimated Effect (%) Table 11. Average Estimated change in Efficiency vs. Non Social Enterprises Social Enterprises Non Social Enterprises Difference (% Points) OH model FE model AE model ProgramService Revenue (% of TR) Marginal Effect Avg. Estimated Effect (%) Table 12 below shows the direction of the effect of the independent variables across all three dependent variables. An interesting result is that only six out of the twenty-three independent variables have a consistent sign and are statistically significant across all three dependent variables. This suggests that certain independent variables may be indicative of relative efficiencies on some financial ratios but not across all measures of efficiency. PCTSE and size (LogTotRev) are the two independent variables that have a negative and statistically significant effect across all three measures of efficiency, which indicates the robustness of the results. This connection is not surprising given the two 34

39 variables are the most highly correlated (r =.31, ) and conveys that as organizations grow in terms of revenue they became more efficient, confirming results by Hager et al, (2001). The result is in agreement with the notion of economies of scale, whereby firms exhibit decreasing average costs as the size of the firm increases. 4 The four other variables that exhibited consistent but positive effects across all three dependent variables are the following dummy variables: Membership Dues, Other Revenue, Higher Education and Art Culture Humanities. These similar results across three different models indicate the robustness of the negative effect of membership dues and other revenue on nonprofit efficiency as well as the lower efficiency in the subsectors of Higher Education and Art Culture Humanities. 4 Coase, R.H. (1937), The Nature of the Firm, Economica 4, pp

40 Table 12. Direction of Effect on Dependent Variables OH Ratio (N= 150,972) FE Ratio (N= 150,972) AE Ratio (N= 133,433) Revenue Streams PctSE _*** _*** _*** Govt Grant (D) _*** _*** +*** Ind. Pub Sup (D) _*** + _*** Mem. Dues(D) +*** +*** +*** Other Rev (D) +*** +*** +*** Dir Pub Sup (100,000's) + +*** _*** Invst Income (10,000's) +*** _ +*** Organizational Characteristics Log Total Rev _*** _*** _*** Age +*** +*** _*** Age-sq + _*** +*** Urban +*** +*** _*** DC (D) _*** _*** + Subsector- (D) Higher Education +*** +*** +*** Art Cult Humanities +*** +*** +*** Education +*** +*** _ Health +* _ + Human Service _ +* _ Religion _*** _*** +*** International _** Public Social Benefit + + _ Mutual Benefit ** + Environment _** + _ Hospital +*** _** +*** (D) indicates a Dummy variable * Significant at the 10% level, ** Significant at the 5% level, *** Significant at the 1% level 36

41 Chapter 9. Discussion and Policy Implications The substantial size of the nonprofit sector with annual revenues over $1.2 trillion conveys the importance of small operational efficiency gains of 3-5 percent, which equate to $3.6-6 billion annually in savings. Further, the largest transfer in wealth in human history, of the magnitude of $41 trillion in the US, is expected to take place over the next 50 years, resulting in huge social capital potential. The nonprofit sector is growing substantially in terms of the number of nonprofits and there is a limited pool of resources in the form of grants for nonprofits to pursue. Intense competition for those funds is leading many nonprofits to turn to earned income strategies. Further, there has been a shift in foundation giving the past decade to focus more on program grants at the expense of general support (organizational) grants, which can be used for organization building (Letts et al, 1997). The decline of organizational grants has been detrimental to nonprofit capacity building. As a result, nonprofits have been unable to achieve scale and operate efficiently as they chase program grants to sustain themselves, often straying from their mission to ensure survival (Foster and Bradach, 2005). Earned income revenues can supply vital financing for organizational support and investment in capacity to bring about efficiency gains. The revenues provide a constant stream of income as opposed to grants which are usually made for short time periods of one to three years. This stable revenue source allows for longer-term investment 37

42 decisions to be made which are necessary to improve the capacity and efficiency of organizations. A changing government role in society is also providing a greater opportunity for nonprofits to pursue increased earned revenue. The devolution of government programs has meant that nonprofits are increasingly being relied upon by the government to fulfill its social contract. The government is a substantial source of fee for service based revenue, and this situation is leading to changes within the nonprofit sector. The method the government uses to allocate service contracts must be analyzed by policymakers as to not promote inefficiencies. A shift in the US economy to an increased focus on knowledge and reputational goods also provides ample opportunities for the social enterprise to earn income. Many nonprofits are cashing in on their reputation by partnering or cobranding with the private sector to sell goods and services or just their brand. In 2002, the Interbrand Corporation, a global branding consultancy, calculated the net present value of Habitat for Humanity s brand to be $1.8 billion, on par with the brand value of Starbucks. 5 These partnerships, however, also require policy decisions on how to provide tax breaks or subsidies for private sector actors engaging in social purposes. For example, should corporations receive tax breaks for providing donations to nonprofit organizations that cobrand products and services, or should customers of nonprofit goods and services pay sales tax? Policy decisions have important implications to the revenues of social enterprises 5 Quelch, John and Laidler, Nathalie. Habitat for Humanity International: Brand Valuation. Case study Harvard Business School,

43 and thus the efficiency of the nonprofit sector. Policymakers may want to encourage increased earned income strategies through the creation of a new legal form that falls in between traditional philanthropy based nonprofits and the private sector. This social enterprise sector could allow for limited profit distribution much like a public utility company regulated by a public utility commission (Young, 2001). This partial profit motive may provide new sources of capital for social enterprises to expand by tapping private capital markets, and creating greater social goods in a more efficient manner. The lure of earned income strategies, however, must be taken with caution, as many commercial ventures turn out to be unprofitable. There are few reliable statistics to gauge the profitability of social enterprises, but according to the National Federation of Independent Business s Education Foundation, only 39 percent of small businesses (for profit) are profitable and half fail in the first five years. Nonprofit organizations must consider these facts when deciding to invest in a commercial venture as there management and staff are not specialized in business ventures. Further, a commercial business can use up valuable management time and become a distraction from the mission of the nonprofit even doing harm to its reputation. For these reasons, nonprofit organizations should not venture too far from their mission and model when seeking additional revenue through earned income strategies, as they are not the panacea for the entire sector. 39

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