REVENUE DIVERSIFICATION IN DUTCH CHARITY ORGANIZATIONS: DOES IT LEAD TO GROWTH?

Transcription

1 REVENUE DIVERSIFICATION IN DUTCH CHARITY ORGANIZATIONS: DOES IT LEAD TO GROWTH? by Femke de Jong s Master Thesis Tilburg University School of Economics and Management Finance Department Supervisor: dr. D.A. Hollanders

2 Revenue Diversification in Dutch Charity Organizations: Does it lead to Growth? Name: F. (Femke) de Jong Student number: s Supervisor: dr. D.A. Hollanders Date of Defense: October 7 th, 2014 Session Chair: Dr. A. Manconi Faculty: Tilburg School of Economics and Management 2

3 Abstract This master thesis investigates the relationship between revenue diversification and growth. To investigate this, a dataset composed out of data provided by the Central Bureau of Fundraising (CBF) is used. This dataset consists of financial data of 1,282 Dutch charity organizations. A panel data model with fixed effects is used to test the hypothesis whether there is a relation between diversification and growth. The results suggest that revenue diversification turns out to have a negative impact on growth, even when controlled for several other factors which might influence this. Reason for this negative relationship, in contrast to a positive relationship that other scholars find, might be the differences between the for- and the non-profit world. Preface I would like to thank David Hollanders for supervising me during the process of writing this master thesis, his critical notes and useful help. I would also like to thank Jos Grazell, for helping me at the start of this process and discussing the subject with me. Furthermore, I would like to thank Ad Graaman and Fred de Jong of the CBF, for providing me all the data I needed. Next to that, I would like to thank my fellow students and my friends, who made my student life incredibly wonderful, in every kind of way. I will definitely miss it. I want to thank Henk, for all his support and critical questions. And last but not least, I would like to thank my parents, Anne & Jikky, who made it possible for me to study, to develop myself during these last six years and gave me the opportunity to get the most out of this period. 3

4 Table of Contents Introduction... 5 I. Literature review... 7 i. Diversification... 7 Corporate diversification... 7 Financial diversification ii. Diversification in relation to Non-profit firms and Growth Diversification in the non-profit sector Diversification and growth II. Research Methodology III. Data IV. Descriptive statistics V. Results VI. Conclusions VII. Discussion and recommendations REFERENCES APPENDICES

5 Introduction The financial crisis that hit the world in 2008 had not only impact on the world economy, but also on the economic climate of the Netherlands. As a result, among many other consequences, the number of bankruptcies increased, unemployment rates grew and government expenditures were cut. In 2013, the real disposable income of the average Dutch household decreased for the sixth year in a row (Centraal Bureau voor de Statistiek [CBS], 2014). Among the first expenses, households intend to cut their donations to charities. This does not only occur on micro level at households, also governments cut their expenses on development aid at large scale; from 0.8 percent of the Gross National Product (GNP) in 2008 to 0.7 in 2013 (Rijksoverheid, 2014). As the GNP has been decreasing since the start of the economic crisis as well, the budget for development aid has decreased even more. These economic and social trends cause a more competitive and complex environment for funding of charity projects. Therefore, a sound financial base is more and more important to charity organizations. One of the broadly accepted theories about how non-profits can manage their financials in a sustainable way is the concept of revenue diversification (Frumkin and Keating, 2011). The Dutch charity organizations do not only rely on donations from individuals and government subsidies. The Central Bureau of Fundraising (CBF), a Dutch independent foundation that collects and provides information about fundraising organizations in the Netherlands, identifies 10 different revenue streams, which include next to subsidies and donations for example mailings, legacies and investment income (CBF, 2014). Spreading your revenue income on several different revenue streams is called revenue diversification. When one does not diversify, one bears the risk to have zero income when that particular source of income disappears. By diversifying, one can spread this risk of running out of income. This concept of diversification is an old concept that is not only applicable for revenue sources or for the nonprofit world. The concept is widely applied within the financial world, where diversifying can be defined as a management technique where one chooses a wide range of different investments within a portfolio. By doing this, such portfolio will, on average, yield higher returns and pose a lower risk than any individual investment found within the portfolio (Bodie, Kane, & Marcus, 2011). Many scholars investigated this concept and its implications; the consequences for costs and benefits of the firm, the stability of the portfolio and shareholder and firm value. 5

6 Diversifying is also an applied concept at corporate firms. Many firms choose to diversify in terms of merging and acquiring, going international or enter different product markets (corporate diversification). Since the 90s, the concept of diversification is also applied in the non-profit sector. Scholars used modern financial concepts, like diversification, to model the financial situation of non-profit organizations. One needs to realize the duality of non-profit organizations when applying these general financial models to the non-profit world: Nonprofit organizations often face the dual task of achieving mission-related goals while maintaining a healthy financial condition that ensures organizational survival. (Carroll and Stater, 2008, p. 947). Much research has been executed to review the relation between revenue diversification and the stability of the firm. It is concluded that by spreading their risk among multiple revenue sources, a non-profit reduces its revenue volatility and strengthens its financial position. Whether such multiple revenue sources lead also to an increase of the total revenue, is an underestimated subject. When an organization employs an additional revenue source, and this revenue source brings a lot of additional income, the total revenue will grow and thereby the organization may also grow. From that perspective, diversification may lead to growth. Whether it is ethically right to pursue growth as a charity organization is a justified question. Therefore, a distinction has to be made between growing of the charity organization itself (the overhead) and the mission fulfillment of the organization. Charity organizations that grow (only) in overhead costs should look into ethical aspects of their future concerning their mission and social responsibility. Nevertheless, to fulfill its mission, a charity organization will have to raise enough funds. Growth in revenue is therefor, in principle, not a negative thing, but one needs to spend their funds wisely in respect to a sustainable future position. To accomplish sustainable growth, one cannot simply utilize more income sources and therefor gain more income. Managing more different sources involves, for example, fundraising and administrative costs, which may exceed the benefits. The relation is perhaps not as easy as it sounds. Therefore, this thesis focusses on the relation between revenue diversification and growth and investigates whether non-profit organizations that diversify more experience more 6

7 growth. Hence, the main question which will be answered in this master thesis is: Revenue Diversification in Dutch Charity Organizations: Does is lead to Growth? This master thesis is organized as follows: The thesis first contains a brief exploration of the several disciplinary perspectives of diversification in the current literature (section I). Afterwards, the research methodology (section II) and the data are described (section III), the descriptives and results are given (section IV and section V). Finally, the results are compared to the existing literature and the final conclusion is provided (section VI) and discussed with recommendations for further research (section VII). I. Literature review Diversification is a well investigated subject in the economic literature. Much research has been done about the motives to diversify and the impact it has on the firm. Diversification knows many forms in many different fields. In this review, first the main concepts about diversification in these different fields will be explained. Secondly, the diversification concept will be applied to the non-profit world and linked to growth. i. DIVERSIFICATIO N The concept of diversification can be split into two different broad concepts: Corporate and Financial diversification. Corporate diversification is defined by Ramanujam and Varadarajan (1989) as follows: The entry of a firm of business unit into new lines of activity, either by processes of internal business development or acquisition, which entail changes in its administrative structure, systems, and other management processes. (p. 525). Financial diversification, on the other hand, is defined as a risk management technique that mixes a wide variety of assets in a portfolio, so that the exposure to the risk of any particular asset is limited (Bodie et al, 2011). Therefore, a certain company can choose to diversify on both corporate and financial level. Both concepts and its implications will be explained and analyzed in this section. CORPORATE DIVERSIFICATION Corporate diversification and its effects on a firm s value is a long-standing controversy in the literature (de Andrés, de la Fuente, & Valesco, 2014). On the one hand, a large body of literature states that there is a so-called diversification discount, but on the other hand, some 7

8 scholars suggest a diversification premium. Based on the literature investigation done by Martin & Sayrak (2003), this contradiction will be studied and explained. To start, diversification brings, naturally, costs and benefits. One potential cost arising from diversification results from the agency theory: managers choose to diversify out of selfinterest, ignoring the interests of the stockholders. They expect the pure pleasure of empire building; namely, that it will increase their compensation (Jensen and Murphy, 1990), power and prestige (Jensen, 1986). Next to that, managers want to make themselves indispensable by making investments that require their particular skills (Shleifer and Vishny, 1989). This self-enriching choices are not optimal for an organization and therefor a cost. Thereby, managers tend to over invest, especially when they have access to an internal capital market. When an internal capital market is created, a firm s cash flows generated by one segment can be used to finance another segment (cross-subsidization) and, likewise, a segment s assets can be used as collateral for obtaining funding for other segments (Erdorf, Hartmann-Wendels, Heinrichs, & Matz, 2013). Cross-subsidization is a positive thing in itself, but when a firm has excess or free cash flow, it gives a greater opportunity to over invest (Martin and Sayrak, 2003). Next to these agency problems, an inefficiency problem is recognized. Diversified firms may not have more free cash flow, but simply do a worse job of allocating their resources than focused firms. This problem can also be identified as a cost of cross-subsidization (Erdorf et al., 2013). It is possible that this problem arises due to information asymmetry problems between the firm s central management and the management of the operating divisions (Harris, Kriebel & Raviv, 1982). When the agency theory and the inefficiency problem are linked, it might imply that managers decide to transfer and invest the money from internal capital markets in a value destroying way. As mentioned, an internal capital market is a positive thing and can, next to the costs, bring benefits to the organization. An internal capital market offers a number of possible sources of value to the firm s owners (Martin and Sayrak, 2003): internally raised equity capital is less costly than funds raised in the external capital market, due to transaction and information costs. Thereby it gives managers decision control instead of the investors. Managers can do a better job of project selection ( winner picking ) and thus enhance film value (Stein, 1997). Having internal capital markets gives also the opportunity to transfer money from operating divisions with limited opportunities to others that are more promising to create shareholder value. Thereby, 8

9 cross-subsidization can be efficient if it helps the firms eliminate some of the costs of financial constraints (Erdorf et al, 2013). Yet another benefit of diversification is formed by the possibility to reduce the variance of future cash flows by diversifying activities at firm level. This diversification serves to increase the firm s debt capacity, since creditors may rely on the combined fortunes of all the diversified firm s units, which adds value to the firm (Lewellen, 1971). But to what extent do these costs and benefits create or destroy shareholder value? When diversification destroys shareholder value, the shares of a diversified firm sell at a discount; when diversification creates shareholder value, the shares sell at a premium. Over the 20 th century, the existence of a diversification discount or premium is examined a lot. Martin and Sayrak (2003) review the literature and structure this in three rounds of waves of research. The first round forms the basis for the present consensus among most financial economists: corporate diversification destroys value. The evidence that supports this conclusion comes from a variety of sources: to start, diversified firms tend to have a lower Tobin s Q 1 then specialized firms do (Lang and Stulz, 1994). This difference occurs after controlling for firm size, research & development and access to financial markets (Lang & Stulz, 1994). Secondly, diversified firms trade at discounts of up to 15% when compared to the value of a portfolio of comparable stand-alone firms. This effect is even larger for firms who diversify in unrelated markets. Inefficient cross-subsidization might account for a part of this discount (Berger & Ofek, 1995). Third, diversified firms face an increased likelihood of being broken up through reorganization (Berger & Ofek, 1996) and finally, firms who increase their focus by selling part of their assets (which can be seen as the opposite of diversification), experience an increase in stock price returns after the announcement (John & Ofek, 1995). The reasons of these mentioned value destructions are divided in two possibilities and in line with the costs of diversification stated before: either diversified firms take inefficient action in their allocation of internal generated funds, or poor allocation are made on purpose due to agency problems. In both cases, these problems result in non-optimal cross-subsidization, where the firm s weaker divisions are supported with investments from cash flows from stronger divisions (Martin and Sayrak, 2003). 1 Tobin s Q is defined as the market value of the firm divided by the book value of assets (Lang and Stulz, 1994) 9

10 In the second round, a number of studies begun to argue that the discount is attributable to factors other than diversification. The notion that diversified firms sell at a discount is not contested, but it is argued that the discount does not arise due to diversification, but is a result of the acquired or acquiring firm selling at a discount prior to merging (Martin and Sayrak, 2003). They doubt the endogeneity of the diversification decision: the discount is caused by the systematic difference between the firms that choose to diversify and the typical focused firms and the diversified firms tend to trade at a discount prior to diversifying (among others, Graham, Lemmon, & Wolk, 1999; Chevalier, 2000). Next to that, when one controls for fundamental differences between conglomerate firms and single segment firms in terms of size, capital expenditures/sales, EBIT/sales, industry growth rate and R&D/sales, the diversification discount drops or disappears entirely (Campa and Kedia, 2002). During the third and last round it is argued that there is no diversification discount, and in fact, diversified firms trade at a significant premium. Differences compared to these and previous results are attributed to the possibility of measurement errors in prior research (Martin & Sayrak, 2003). Villalonga (2000, 2004) retests the diversification discount hypothesis by correcting for thee data limitations, since it is believed that the previous attempts to assess the diversification discount are flawed by these limitations: (1) the extent of disaggregation in segment financial reporting is less than the true extent of firm diversification such that firms are actually more diversified than is indicated in segment financial reporting. Thereby, (2) the definition of a business segment is so flexible that it allows firms to combine two or more activities that are vertically related into a single segment. Finally, (3), some industries are fundamentally composed of segments of diversified firms (Villalonga, 2000). When controlling for these problems, diversified firms trade at a significant premium, not at a discount, when compared to non-diversified firms from the same industry (Villalonga, 2000, 2004). Reason for this premium is formed by the argument that that diversified firms may have better access to capital markets than focused firms, due to valuation problems faced by investors in the presence of asymmetric information (Hadlock, Ryngaert and Thomas, 2001) All the studies described above, show a lot of conflicting results and interpretations. This can be explained by the fact that these studies estimate the effect of diversification on performance on a large variety of industries (Santalo and Beccera, 2008). These studies do not 10

11 take into account the possibility that the diversification could confer a competitive advantage on some industries but not in others. Since they do all measure the average effect of diversification on performance, this could lead to different conclusions. Santalo and Becerra (2008) show that the effect of diversification on performance is not heterogeneous across industries. They report clear evidence that diversified firms observe a diversification discount if, and only if, they compete in industries where focused firms hold a considerable market share. The rationale behind this is that the focused firms will have a competitive advantage over diversified firms driven by a process affiliated to natural selection. There are two reasons for this heterogeneity: first, in industries in which soft information is important, diversified firms might gain a better financial performance because of better excess to financial resources. As reason they point out that the soft information of a company might be easier to transmit inside rather than outside of the company, and therefore, corporate headquarters of a conglomerate could have access to valuable information unavailable to external capital markets. Second, they state that the market structure of vertically connected industries will influence the diversification advantage. Vertically integrated firms might enjoy a larger competitive advantage over specialized companies in more concentrated industries because of their lower transaction costs in dealing with industries with only a few players (Santalo and Beccera, 2008). In their paper, Santalo and Becerra (2008) just show that the competitive advantage is not homogenous across industries; they do not identify the key industry characteristics that determine the competitive advantage of diversification. These characteristics are still not identified present-day and are still an interesting topic for future research (Erdorf et al., 2013). FINANCIAL DIVERSIFICATION The first formal portfolio selection model including diversification is from 1952 (Markowitz). This model describes the first step of the process of investment portfolio selection: the efficient frontier of risky assets. This frontier is based on the traditional risk-return relationship combined with the law of large numbers and shows the portfolio with the highest possible return given a certain level of risk, or, alternatively, the lowest possible level of risk (the variance) given a certain return (Bodie, Kane, & Marcus, 2011). By keeping a diversified portfolio with many assets with a lowest mutual correlation, one can derive a portfolio that keeps actual returns close to the amount of anticipated returns (Markowitz, 1952). To derive this 11

12 optimal portfolio, one has to keep in mind that not only the characteristics of the individual securities have to be considered, but that an investor should consider how each security co-moved with all other securities (Markowitz, 1952). The most striking conclusion after Markowitz work is that each rational portfolio manager will face the same optimal risky portfolio, since this portfolio maximizes the reward-to-risk ratio, which is represented by the Sharpe ratio (Sharpe, 1966). This ratio is for each (rational) portfolio manager the same, since the risk aversion level does not matter in this model (Bodie, Kane, & Marcus, 2011). Tobin (1958) came up with the separation theory, which describes the two-step procedure of the portfolio choice problem: the first one leads indeed to the same portfolio, but in the second step, the portfolio manager has to choose the optimal combination between risky assets and a risk free investment, which depends on his risk aversion level. This level is represented by the utility function (indifference curve) and causes different optimal portfolios for each different level of risk aversion. Next to considering the risk and return of a portfolio, later research provided other measurements to describe the distribution of the portfolio; like the systematic risk (Friend and Blume, 1970) and skewness (Lee, 1977). The theory above describes a solution for a single-period period, but what if the true problem an investor faces is a multi-period problem? Many scholars analyzed this problem and found that the multi-period problem can be solved as a sequence of single-period problems, under several sets of reasonable assumptions (Elton & Gruber, 1997). The optimal portfolio in the multiple-period case will be different from the single-period case, since the utility function changes when the investment period changes. Another problem which arises with investigating multi-period investing is the question whether the returns and variances are correlated. To derive the optimal portfolio, one needs to estimate the financial data, like covariances, which is used as input for the model. Estimating this data was an enormous work before the development of factor models. These models explain the relationship between the rate of return of a security with a certain factor. The earliest factor model was the single index model, which was developed and popularized by Sharpe (1967). This model regresses the expected return of a security on the excess return of a market index. The slope of this regression is the Beta, which measures the security s sensitivity to the index. This sensitivity describes the systematic risk; the 12

13 risk inherent to the entire market or an entire market segment, which cannot be diversified away. The other part of the total risk a security occurs, non-systematic or non-market risk, can be diversified away if one increases the number of securities in his portfolio. The financial diversification theory is a difficult theory with many implications. But understanding the importance of diversification, can help financial managers to achieve superior performing portfolio s (Sorensen et al., 2004). Not only financial managers in the for-profit world, but also financial managers of non-profit organizations may benefit from applying these theories wisely. ii. DIVERSIFICATION IN RE LATION TO NON-PROFIT FIRMS AND GROWTH In this section, the features and characteristics of both corporate and financial diversification will be applied to the non-profit world and linked to growth. DIVERSIFICATION IN THE NON-PROFIT SECTOR As described above, many economists throughout the years, have modeled how one can derive an optimal investment portfolio and thereby maximize return while minimizing financial risk. These methods are particularly applicable for for-profit organizations. To bridge this gap between the traditional portfolio theories and the non-profit world, one has to take in mind the differences and similarities between non-profit and for-profit managers (Kingma, 1993). Just like for-profit managers, non-profit managers choose optimal combinations of risk a return by selecting different streams of financing. In the non-profit world, managers want to provide a certain level of services, comparable to a certain level of return, while minimize unpredictable changes in these services (risk). The risks a non-profit manager bears, are different for the different revenue streams (Kingma, 1993); for example, for donations, a non-profit organization risks fundraising expenses; for subsidies, a non-profit organization risks the expenses which one will occur when complying with the government standards. Besides these risks, the non-profit manager may run an additional risk in terms of future funding: a non-profit manager may, by requesting funding from a particular source, not only risk the expense of a promising service but also the risk of not receiving the additional expected funding at all (Kingma, 1993). Opposed to for-profit, non-profit organizations differ in their capacity to absorb unexpected changes in revenues (Kingma, 1993); both in delay of certain revenue streams, like government subsidies 13

14 (Grossman, 1992), and also the finding that the revenue streams may be lower than expected. But notwithstanding this last difference, the decision that the non-profit manager needs to make on his combination of revenue sources, can be seen as a typical risk-return problem, coming from the risk-return trade-off, which is defined by Kingma (1993) as: Any increase in expected revenues, whether from an increase in the time and effort devoted to fundraising, grant applications, or requesting additional government revenues, is subject to an increase in risk (p. 109). Although one has to be careful about applying traditional portfolio theory to the non-profit setting, the complications are conquerable (Jegers, 1997). The managerial preferences that Tobin (1958) suggested by the separation theory (introduced earlier in this section), can also be included in Kingma s portfolio model. (Jegers, 1997). By introducing this concept, Jegers (1997) differs from Kingma (1993), since the latter assumes that the expected returns of the different revenue streams are equal. With this equality assumption, managerial risk aversion becomes irrelevant (Jegers, 1997). Therefore, the equality assumption is left behind, and the interaction between managerial risk preferences and the optimal risk-return combination is included (Jegers, 1997). Next to this assumption, some other differences between the non-profit world and traditional portfolio theory have to be considered (Jegers, 1997). First, in contrast to a typical investor who is allowed to lend or borrow with a certain risk-return relationship, the revenue (or service level) of a non-profit manager is uncertain. Secondly, an investor has only uncertainty about the return that he receives on his investment, but the non-profit manager knows uncertainty about the whole revenue. Third, the investor can choose his optimal portfolio; he can determine which part of his investment he wants to invest in the particular assets. The non-profit manager, in contrast, can only hope that, the share that he expects from a certain source is equal to the optimal combination that he desires. Finally, the non-profit manager does not face an unconstrained optimization problem as the investor in the traditional finance theory does, since he may be restricted for legal or financial reasons (Jegers, 1997). The financial concept of diversification appears to be very applicable to the non-profit world. Also the concepts of corporate diversification can be applied to the non-profit situation. Just as for-profit firms, non-profit firms may have to deal with self-enriching managers: managers who do not have the mission of the organization in mind, but differ in terms of empire 14

15 building. The typical agency problems can also play a role for non-profit organizations. The most non-profit organizations will also have an internal capital market: a part of the revenues retrieved will be unrestricted and can be used wherever in the organization (this in contrast to (semi-) restricted funds which restrict the organization to inject the funds in to several projects of parts of the organization). As for for-profit organizations, cross-subsidization brings several costs and benefits to the non-profit organization: the inefficiency problem may play a role here; comparing the services of a non-profit organization with the return of a for-profit organization, a firm can allocate the funds to a project which may not give the highest return in terms of achieving the mission of the organization. But a large internal capital market gives the organization chances to fulfil several projects. As described before, the effects of diversification among for-profit organizations are investigated widely. Carroll & Stater (2009) examine the effects of revenue diversification among non-profit organizations, and in particular, if diversification leads to greater revenue stability over time. Stable, healthy non-profit organizations will be more capable of continuing to work toward their missions and financial stability over time will lead to greater ability to provide programs, compensate staff and promote mission awareness (Carroll & Stater, 2009). When measuring the impact of diversification on revenue volatility (or equally, revenue stability), it is suggested that organizations with a higher diversified revenue portfolio, experience lower revenue volatility, which implies a stable organization. Revenue diversification, or in other words, equalizing the reliance on the several different revenue streams, is a viable strategy for a stable organization (Carroll and Stater, 2009). Carroll and Stater (2008) estimate also the effect of organizational efficiency and financial flexibility on revenue volatility. About the effect of organizational efficiency exist several conflicting beliefs: some argue that having lower efficiency, measured by relative higher non-programmatic expenses to total expenses, leads to less trust and therefor discourage gifts. Others argue that having less non-programmatic costs reduces organizational capacity (a.o. Bowman, 2006) and experience fewer program and funding disruptions (Keating et al. 2005). Carroll and Stater (2008) take over the first argument: Organizations that have less administration and fundraising costs, are able to spend more resources into mission fulfillment, which increases their perceived effectiveness and consequently their income potential (p. 954). Concerning financial flexibility, Carroll and Stater (2008) state that an organization having greater financial flexibility, detectable by greater 15

16 equity balances and higher operating margins (Chang and Tuckman, 1994), has better opportunities to engage in future financial planning and to reduce uncertainty during the annual budget process. DIVERSIFICATION AND GROWTH Although there has been a wide spread research about diversification and its impact on firm value, the relationship between diversification and a firm s growth opportunities is a less exposed subject (Andrés et. al, 2014). The growth opportunities of a firm are one of the factors that may influence the effect of diversification on firm value, and may cause the conflicting opinions about diversification creating value or not, like the factor as industry named before. The factor growth opportunities in relation to diversification is yet underexposed. Some scholars investigated this subject (Andrés et al., 2014) but their contributions are contradictory. Bernando and Chowdhry (2002) explain the diversification discount by stating that single segment firms have a lot of growth opportunities, but firms who already diversified, already exploit some of these. Ferris, Sen, Lim and Yeo (2002) state that diversification destroys value for firms with a weak cash flow position and low growth opportunities available. In contrast, Stowe and Xing (2006) show that the diversification discount still remains after controlling for growth opportunities. Andrés et al. (2014) then show that the effect of diversification on firm value is contingent on growth opportunities; growth opportunities has a mediating effect. Their results provide evidence for a quadratic relationship between diversification and growth. This relationship can be interpreted as that when a firm chooses to diversify in early stages, this involves replacing growth opportunities by assets in place. However, in later stages, diversification becomes a net source of further growth options (Andrés et al., 2014). Just like an underexposed relationship between diversification and growth in the literature in the for-profit world, there is also done little research about the growth of the nonprofit firm. Scholars investigate factors that influence the growth of the non-profit sector, like Corbin (1999), but there is less to find specific for a non-profit firm. Frumkin & Keating (2011) investigate the risks and rewards that revenue concentration (as the opposite of diversification) can bring. They state that at the time their paper was published, one did not explore weather revenue concentration had any positive effects, or in other words, what the positive effects of risk-seeking behavior are in terms of growth and efficiency. By diversifying revenue streams 16

17 across many sources of funding, non-profits may let slip some opportunities that come from capitalizing on a particular segment or funding market, which may result in growth or limited overhead costs, which arise from multiple funding streams (Frumkin & Keating, 2011). They derive this link with growth from the thought that by concentrating, non-profits are in a position to develop specialized skills that will enable these managers to be more effective at fundraising or obtaining government contracts, which will lead to the development of a reputation and longterm marketing relationships. The link with efficiency is derived from the thought that revenue concentrators may experience lower overhead and administrative costs, since they need to manage less revenue streams and there accompanying (complex) contracts and contributions. After testing the implications of revenue diversification on efficiency gains (i.e. relative limited costs) they confirm their hypothesis that firms with a high level of concentration may achieve higher levels of efficiency. However, the hypothesis that revenue diversification has a negative impact on growth is not satisfied: they do not find a significant relationship between revenue. II. Research Methodology Although Frumkin & Keating (2011) suggest that revenue concentration has no systematic effect on growth, this master thesis investigates the relationship between diversification and growth again. Frumkin and Keating (2011) state that by concentrating, nonprofit managers have a position to develop specialized skills, but this can be countered by the argument that this has nothing to do with revenue concentration, but with the human resources and their capacity and knowledge concerning each revenue stream. A team, large enough, may develop better skills on several funding streams then a single person may on his a single stream. With the result that, these revenue streams together may lead to growth of the total revenue stream, the organization and the program expenses. Therefore, the relationship between diversification and growth for non-profit organizations will be tested. Besides, Frumkin & Keating (2011) base their results on the differences between means and medians respectively, of the most concentrated and most diversified firms of their database. Here, the relationship will be tested by way of a regression model including several control variables. The regression model results from two theoretical notions: the concept of Andrés et al. (2014) will be combined with the concept developed by Carroll and Stater (2008). The first one 17

18 derived empirical evidence about the relation between diversification and growth in the for-profit world, the second one investigates whether revenue diversification leads to financial stability for non-profit firms. By combining both empirical models, the relation between diversification and growth in the non-profit world can be tested. This brings the following equation (1): 2 G it = α i + β DIV DIVER it 1 + β DIV2 DIVER it 1 + β OE OE it 1 + β FF FF it 1 + β Size SIZE i,t 1 + β sec SECTOR it + β y YEAR it + u it (1) where i indicates firm i, t indicates the year of observation, αi and βx are the coefficients to be estimated and uit is the error-term. To control for the endogenous simultaneity problem, 1-year lag for each independent variable is included. This model is a panel data model, which is estimated with a fixed effect. Using this panel data model, it is possible to observe the same units (organizations) collected over a number of periods (years) and thereby it controls for individual heterogeneity (Torres-Reyna, n.d.). A linear regression model, indexed for both units and time (i and t) imposes that the intercept term α and the slope coefficients in β are identical for all units and time periods (de Jong, 2012). The error term varies over units and time and captures all unobservable factors that affect the dependent variable. Since the same units are repeatedly observed, it is not representative to assume that the error terms from different periods are uncorrelated. Therefor a fixed effect is included in the model, which takes care of the dependence of the error terms and thereby it can capture unobserved individual effects (de Jong, 2012). The fixed effect model removes the effect of the time-invariant characteristics from the predictor variable, so the predictors net effect can be estimated. Thereby, it is assumed that these time invariant characteristics are unique to the individual and are not correlated with other individual characteristics (Torres-Reyna, n.d.). Since it is not assumed that the variation across entities is random and uncorrelated with the predictor, I chose to use the fixed effect model instead of the random effect model (Torres-Reyna, n.d.). The assumptions that are made to verify the described model, are mostly innocuous assumptions, i.e. they are plausible. However, in the fixed effect model, it is assumed that the β s are identical across groups, and so the regression estimator reports the average effect. If this assumption does not hold, this is more problematic. It has to be noticed that this assumption may 18

19 not hold, since there may be differences across sectors 2 (the several sectors are described in Section III). The dependent variable, Growth (G) is predicted by three different measurements, the annual growth in total revenue (TR), program expenses (PE) and fixed assets (FA), (Frumkin & Keating, 2011). The degree of revenue diversification (DIVER) is measured by a diversification index which is based on the Herfindahl-Hirschman Index (HERF) (Hirschman, 1964). The Diversification Index is calculated with the following equation (2): DIVER = 1 n 2 s=1 p s (2) where n is the number of a firm s revenue sources, and Ps the proportion of the firm s revenue from source s. This index is positively related to diversification and will, in principle, always be between 0 (concentration) and 1 (diversification). Since Andrés et al. (2014) find a quadratic relation between diversification and the growth opportunities, the quadratic term of DIVER is also included in the model (DIVER 2 ). In line with the conclusion of Andrés et al. (2014), it is expected that diversification has an effect on the growth of the organization (βdiv and/or βdiv2 0). In spite of the expected costs that come with diversification, it is expected that the net result of the costs and the benefits is positive and therefor leads to growth. In line with Carroll and Stater (2008), the variables Organizational Efficiency (OE) and Financial Flexibility (FF) are included in the model. Organizational Efficiency will be measured by the ratio of administrative and fundraising expenses to total expenses. It is believed that well organized organizations will receive more revenue in the end and therefor Organizational Efficiency has a positive relationship with growth (βoe > 0). Financial Flexibility will be measured with two different proxies which are typically used to measure the financial condition of a non-profit organization (Jegers and Verschueren, 2006): (1) Debt margin, which provides information about the extent to which an organization is capable to meet its financial obligations and is calculated as by dividing the organization s year-end liabilities by the year-end assets, and 2 The results show that there are, indeed, differences across sectors. When different regressions are ran (not included in this paper) for each of the different sectors, there are quit some differences observable between the different β DIV s; the β s differ from to Thereby it has to be noticed that not all the sectors contain enough firm to build a large enough dataset per sector to get significant results. Therefore, the decision is made to stay with the fixed effect model as described. 19

20 (2) Total margin, which gives information about the profitability or increasing value of an organization, calculated as the proportion of net assets to total revenue. Greater values of Debt margin indicate less financial flexibility, while greater values of Total margin indicate greater financial flexibility (Carroll and Stater, 2008). Since financial flexibility contributes to a healthier organization, it is expected that this has a positive effect on growth (βff > 0). Finally, in line with common literature, several control variables are included. Similar to Andrés et al. (2014) and Carroll and Stater (2008), I control for firm size (SIZE), estimated by the natural logarithm of the book value of total assets, and for several charity sectors (like health care, international aid etc.), by including dummies for each mission (SECTOR). Finally, there is controlled for time-effects by including year dummies (YEAR). III. Data In order to examine the relationship between diversification and growth and to investigate the influence from the other variables, a dataset with financial data of non-profit firms is collected. This dataset is based on financial data of the Central Bureau on Fundraising (CBF). The CBF is a Dutch independent foundation who collects and provides information about Dutch fundraising organizations in the Netherlands. The information includes data from the financial statements of the organizations. From this financial data, all the variables are derived. The data consist of annual financial information for each individual non-profit during the 8-year time period between 2005 and The dataset includes 1,282 different organizations. Although the majority of organizations (548 or 45.27%) are observed in every year the analysis, the number of organizations observed each year varies. 132 organizations (10.23%) are observed for only one year during the time period. The CBF categorizes the Dutch Charity Organizations in different sectors, related their (main) mission. There are 4 main sectors and 15 sub sectors. The distribution of the organizations is shown in table 1. 20

21 Table 1 Distribution of organization in several sectors Sector Mission description # firms % of total International aid Development aid % Victim % Refugee aid % Health care Health care % Disabled % Blind and visually impaired, hard of hearing % Welfare Community and social goals % Human rights % Art and Culture % Sports and recreation % Education and Science % Religion % Nature and environment Environmental interests % Nature Protection % Animals % % The CBF collects the financial data of the charity organization since the year In the first years, only the information about income and expenses were collected. As from 2008, also the balance sheet data is gathered. Therefore, some data required to calculate certain variables, is only available as from The diversification index, calculated by equation (2), is derived from the revenue sources each organization uses. The CBF marks 10 different revenue sources, which are displayed in table 2. Table 2 Revenue Sources Revenue source Percentage of total revenue Collections 1,75% Mailings 2,02% Legacies 6,71% Gifts, grants, donations and contributions 25,16% Sales, own lotteries contests 1,98% Revenue from Joint actions 2,10% Revenue from actions from third parties 12,13% Government subsidies 37,51% Interest income and income from investments 2,71% Other revenues 7,92% Note. The Percentage of total revenue refers to the average percentage that the organizations in the dataset receive from each revenue source over the time period On average, the total income per year was equal to

22 IV. Descriptive statistics Table 3 provides the descriptive statistics of all variables, including the control variables, included in the model. The number of observations (N) differs strongly as a result of the absence of the balance sheet data from 2005 to The diversification Index (DIVER) should, in principle, always lie between 0 and 1. This differs in the dataset since it occurs that Dutch charity organizations have a negative income from the revenue source Interest income and income from investments. This negative income may lead to a negative diversification index, which is the case in 95 observations. This negative indexes cause a lower mean value of DIVER (0.2370). When the negative outcomes are excluded from the database, the mean value of DIVER increases to (Appendix A). Logically, removing the 95 negative observations has also impact on the standard deviation of the Diversification Index: the standard deviation decreases from (table 3) to (Appendix A). The negative values also explain the maximum of the extreme high quadratic term of the diversification index (DIVER 2 ). When the negative values are removed, the mean and standard deviation of DIVER 2 are and respectively (Appendix A). The maximum will lie on (Appendix A). Table 3 Summery statistics for the full sample ( ) Variable N Mean Standard Deviation Min. Max. Growth Total Revenue (TR) Growth Program Expenses (PE) Growth Fixed Assets (FA) DIVER DIVER , Organizational Efficiency (OE) Debt Margin (DM) Total Margin (TM) Size Note. N refers to the total firm-year observations and is equal to 10,217 (i = 1,282; t = 8). Negative values of DIVER are allowed. When running the regression, the different sectors that the CBF identifies (table 1), will be used as a control variable. In Appendix B, the mean value of each variable is reported per sector. Due to heterogeneity, there are quite some differences between the sectors, both in the 22

23 growth variables, the diversification index and in the other variables. From the mean values reported in Appendix B, it does not follow directly that the sector with the higher diversification index is also the sector who experiences the highest growth. Table 3 shows the diversity of the growth variables - total revenues, program expenses and fixed assets. All three know small negative values and big positive values. The mean value of Growth in Fixed Assets lies higher than the mean value of Growth in Total Revenue and Growth in Program Expenses, but also the standard deviation is higher for this latter. Tables 5 to 7 report the statistics of each dependent and independent variable per level of growth. For each one of them, the 5% or higher growth level knows high variation: the standard deviations are high and also the mean values lie at an extreme higher value, while the N for each of this level is much lower compared to the other levels. In table 4, an overview of the mean growth per level of diversification is given. It is expected that for higher levels of diversification, the organization will experience higher growth. The numbers reported in table 4 suggest that this expectation cannot be confirmed. For each of the three growth measurements, a parabolic trend is present: when one increases his level of diversification from zero or less to the first level (0 to 0.25), the growth of the firm increases. When a firm reaches higher levels of diversification, this effect turns around and one experience less growth. This suggestion confirms the addition of the quadratic term of DIVER in the model. Table 4 Diversification Index Diversification Index Freq. % of total Cum. Rel. freq Growth PE Growth TR Growth FA Less than % 7.26% to , % 40.78% to 0.5 2, % 69.21% to , % 98.03% to % 100% Total 7, % This effect can also be shown the other way around. For each of the dependent variables, growth in Program Expenses, Total Revenue and Fixed Assets, a table is created to oversee the mean values of each independent variable for each level of growth (table 5 to 7). 23