REVENUE DIVERSIFICATION IN

DUTCH CHARITY ORGANIZATIONS:

DOES IT LEAD TO GROWTH?

by Femke de Jong

s856006

Master Thesis

Tilburg University

School of Economics and Management

Finance Department

Supervisor: dr. D.A. Hollanders

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Revenue Diversification in Dutch Charity Organizations: Does it lead

to Growth?

Name: F. (Femke) de Jong

Student number: s856006

Supervisor: dr. D.A. Hollanders

Date of Defense: October 7th, 2014

Session Chair: Dr. A. Manconi

Faculty: Tilburg School of Economics and Management

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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.

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Table of Contents

Introduction ..................................................................................................................................... 5

I. Literature review ..................................................................................................................... 7

i. Diversification..................................................................................................................... 7

Corporate diversification ........................................................................................................ 7

Financial diversification........................................................................................................ 11

ii. Diversification in relation to Non-profit firms and Growth.............................................. 13

Diversification in the non-profit sector ................................................................................. 13

Diversification and growth ................................................................................................... 16

II. Research Methodology ......................................................................................................... 17

III. Data ....................................................................................................................................... 20

IV. Descriptive statistics ............................................................................................................. 22

V. Results ................................................................................................................................... 25

VI. Conclusions ........................................................................................................................... 32

VII. Discussion and recommendations ......................................................................................... 34

REFERENCES ............................................................................................................................. 36

APPENDICES .............................................................................................................................. 38

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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 non-

profit 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.

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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: “Non-

profit 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

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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 . D IVERSIFICATIO 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

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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 self-

interest, 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,

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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 20th

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 Q1 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)

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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

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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).

F INANCIAL 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

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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

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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 . D IVERSIFICATION 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

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(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

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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

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 non-

profit 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

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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 long-

term 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, non-

profit 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

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):

𝐺𝑖𝑡 = 𝛼𝑖 + 𝛽𝐷𝐼𝑉𝐷𝐼𝑉𝐸𝑅𝑖𝑡−1 + 𝛽𝐷𝐼𝑉2𝐷𝐼𝑉𝐸𝑅𝑖𝑡−12 + 𝛽𝑂𝐸𝑂𝐸𝑖𝑡−1 + 𝛽𝐹𝐹𝐹𝐹𝑖𝑡−1

+ 𝛽𝑆𝑖𝑧𝑒𝑆𝐼𝑍𝐸𝑖,𝑡−1 + 𝛽𝑠𝑒𝑐𝑆𝐸𝐶𝑇𝑂𝑅𝑖𝑡 + 𝛽𝑦𝑌𝐸𝐴𝑅𝑖𝑡 + 𝑢𝑖𝑡 (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

19

not hold, since there may be differences across sectors2 (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 − ∑ 𝑝𝑠2𝑛

𝑠=1 (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 (DIVER2). 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

βDIV’s; the β’s differ from -8.34 to 7.89. 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.

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 2012. 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.

21

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 2005-2012. On average, the

total income per year was equal to € 3.506.473.648

Table 1

Distribution of organization in several sectors

Sector Mission description # firms % of total

International aid Development aid 533 41.58%

Victim 17 1.33%

Refugee aid 7 0.55%

Health care Health care 130 10.14%

Disabled 70 5.46%

Blind and visually impaired, hard of hearing 13 1.01%

Welfare Community and social goals 219 17.08%

Human rights 20 1.56%

Art and Culture 46 3.59%

Sports and recreation 15 1.17%

Education and Science 41 3.20%

Religion 47 3.67%

Nature and

environment

Environmental interests 27 2.11%

Nature Protection 43 3.35%

Animals 54 4.21%

1282 100%

The CBF collects the financial data of the charity organization since the year 2005. 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 2008. 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%

22

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.

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 2007. 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 0.3391 (Appendix A). Logically, removing the 95 negative observations has also

impact on the standard deviation of the Diversification Index: the standard deviation decreases

from 3.8682 (table 3) to 0.2408 (Appendix A). The negative values also explain the maximum of

the extreme high quadratic term of the diversification index (DIVER2). When the negative values

are removed, the mean and standard deviation of DIVER2 are 0.1730 and 0.1702 respectively

(Appendix A). The maximum will lie on 0.6571 (Appendix A).

Table 3

Summery statistics for the full sample (2005-2012)

Variable N Mean

Standard

Deviation Min. Max.

Growth Total Revenue (TR) 5833 0.4026 4.2145 -0.9865 185.0411

Growth Program Expenses (PE) 5765 0.5340 6.5703 -0.9988 302.4643

Growth Fixed Assets (FA) 1813 2.4082 32.1507 -1.0000 815.5970

DIVER 7269 0.2370 3.8682 -210.8668 0.8106

DIVER2 7269 15.0174 724.0323 0 44,464.8000

Organizational Efficiency (OE) 4921 0.1057 0.1555 0.0001 1

Debt Margin (DM) 2773 0.4179 1.7426 0.0000 70.2558

Total Margin (TM) 4707 2.2582 10.6417 0.0021 583.4288

Size 4747 12.4945 2.5016 4.5468 19.5310

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

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 0 528 7.26% 7.26% 0.6442 0.3083 -0.0342

0 to 0.25 2,436 33.51% 40.78% 0.7471 0.6140 2.0198

0.25 to 0.5 2,067 28.44% 69.21% 0.6551 0.4138 1.3201

0.5 to 0.75 2,095 28.82% 98.03% 0.2074 0.1930 3.5170

0.75 to 1 143 1.97% 100% 0.0248 0.1283 2.6084

Total 7,269 100% 0.5354 0.4026 2.4191

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).

Table 5

Growth in Total Revenue

Growth in Total

Revenue Freq. % of total

Cum. Rel.

freq. Mean SD Diver Diver2 OE

Debt

Margin

Total

Margin Size

Less then - 0.5% 379 6.5 6.5 -0.6809 0.1270 -1.5507 284.6278 0.1654 0.2894 9.1800 11.2700

-0.5% to 0 2,248 38.54 45.04 -0.1825 0.1344 0.3349 0.1766 0.0943 0.4583 1.9673 12.6060

0 to +0.5% 2,277 39.04 84.07 0.1726 0.1322 0.3721 0.1991 0.0839 0.4376 1.3989 13.2125

+0.5% to +1% 433 7.42 91.5 0.7013 0.1455 0.3428 0.1783 0.0985 0.2678 1.5683 12.1629

+1% to +5% 424 7.27 98.77 2.0439 1.0332 0.2881 0.3173 0.1043 0.3208 1.7944 11.6403

+5% or higher 72 1.23 100 20.1874 31.9198 0.2198 0.0950 0.0750 0.3561 1.3755 11.4083

Total 5,833 100 0.4026 4.2145 0.2227 18.6770 0.0958 0.4226 2.2033 12.6429

Table 6

Growth in Program Expenses

Growth in

Program Expenses Freq. % of total

Cum. Rel.

freq. Mean SD Diver Diver2 OE

Debt

Margin

Total

Margin Size

Less then - 0.5% 363 6.3 6.3 -0.6817 0.1321 -0.3580 123.1511 0.1887 0.3193 3.3951 11.1935

-0.5% to 0 2,148 37.26 43.56 -0.1759 0.1349 0.1530 29.4276 0.0894 0.3799 2.0706 12.7454

0 to +0.5% 2,350 40.76 84.32 0.1679 0.1269 0.3522 0.5566 0.0760 0.4892 1.6058 13.2804

+0.5% to +1% 421 7.3 91.62 0.7044 0.1506 0.3134 0.1777 0.0701 0.3375 1.9630 12.1486

+1% to +5% 392 6.8 98.42 2.0498 0.9537 0.2764 0.1302 0.0723 0.2550 2.3512 11.5086

+5% or higher 91 1.58 100 24.2797 46.4539 0.2321 0.1020 0.0806 0.2762 1.2462 10.5336

Total 5,765 100 0.5340 6.5703 0.2233 18.9748 0.0875 0.4195 1.9606 12.7136

Table 7

Growth in Fixed Assets

Growth in Fixed

Assets Freq. % of total

Cum. Rel.

freq. Mean SD Diver Diver2 OE

Debt

Margin

Total

Margin Size

Less then - 0.5% 125 6.89 6.89 -0.6678 0.1473 0.4247 0.2289 0.1031 0.4928 1.9884 13.0726

-0.5% to 0 1,011 55.76 62.66 -0.1722 0.1385 0.4029 0.3698 0.1035 0.3713 2.7213 13.9047

0 to +0.5% 426 23.5 86.16 0.1445 0.1286 0.4597 0.2729 0.0890 0.4332 2.2653 15.0585

+0.5% to +1% 75 4.14 90.29 0.7111 0.1498 0.4287 0.2352 0.1048 0.2684 3.0138 13.9582

+1% to +5% 116 6.4 96.69 2.2165 1.0038 0.2758 2.5181 0.1022 0.3257 1.3006 14.0450

+5% or higher 60 3.31 100 70.8615 163.7166 0.4827 0.2787 0.0972 0.2122 1.8127 14.3861

Total 1,813 100 2.4082 32.1507 0.4134 0.4657 0.0997 0.3820 2.4543 14.1456

In each of these tables, especially in table 5 (Total Revenue) and table 6 (Program Expenses) the

same effect can be observed. For small levels of growth, the Diversification Index (DIVER) is

small or even negative, for higher values of growth the Diversification Index increases, but for

the highest values the index decreases.

Appendix C shows the correlation matrix of each of the variables. This matrix in does not

show a significant relation between the diversification Index and the Growth variables.

Appendix D shows the correlation matrix from the variables as included in equation (1), with a

1-year lag for the dependent variables. This matrix shows some significant correlations, for

example between the Diversification Index and the Growth in Total Revenue. But, correlation

and causality are two very different things and therefore there cannot anything be concluded

from these correlation matrices. The next sector gives the results of running the regressions,

which, in contrast, can tell about causality.

V. Results

To determine whether fixed effect panel data model or a random effect panel data model

is preferred, a Hausman test should be executed. This test tests whether the unique errors are

correlated with the regressors. The null hypothesis (H0) of this test states they are not and that the

preferred model is the random effect model (Torres-Reyna, n.d.). For both Growth in Program

Expenses and Growth in Total Revenue as dependent variable, the P-value of the Hausman test

turns out to be 0.000 and so the most efficient model turns out to be the fixed effects model. For

Growth in Fixed Assets the P-value is equal to 0.7737, which suggest that the Random Effects

model should be more efficient. The fixed effect model will be applied to all of the three

different dependent variables. Since the null hypothesis cannot be rejected for Growth in Fixed

Assets, the Random Effects model will also be investigated for this dependent variable.

Tables 8, 9 and 10 present the fixed effects regression results from equation (1) for each

of the different dependent variables; the growth in Total Revenue, Program Expenses and Fixed

Assets respectively. In these tables, various specifications of the equation are estimated. Each of

the specifications are explained in the notes beneath the tables. The results from the panel data

model with random effects are shown in table 11.

26

Note. This table provides the fixed effects regression results of equation (1) with growth in Total Revenue as

dependent variable. Specification (a) regresses only the diversification index (DIVER) on the dependent variable,

controlled for size and year. Specification (b) includes also the quadratic term of DIVER. In specification (c) and

(d) also the variables Organizational Efficiency (OE) and a proxy for Financial Flexibility (FF) are included (Debt

Margin or Total Margin respectively). In specification (a)-(d) the negative values of DIVER are included.

Specification (e) contains the same variables as specification (c), but before running this regression, the negative

values of DIVER are removed. Obs refers to the firm-year observations for each regression, the total observations

are equal to 10,217 (i = 1,283; t = 8); the F statistic is used to test whether all coefficients are different from zero

(H0: β1 = … = βi = 0); R square refers to the part of variation in the dependent variable explained by the model. The

standard error is shown in parenthesis under the coefficients. The ***, ** and * denote statistical significance at

the 1%, 5% and 10% level, respectively.

Table 8

Regression results Growth in Total Revenue

Dependent variable:

Growth Total Revenue

Variable (a) (b) (c) (d) (e)

DIVER -0.03665***

(0.0130)

-0.3454***

(0.0718)

-0.3589***

(0.1228)

-0.7662***

(0.1913)

-0.3745***

(0.1331)

DIVER2 -0.0018***

(0.0004)

-0.0020***

(0.0007)

-0.0184**

(0.0080)

-0.0021***

(0.0008)

Organizational Efficiency (OE) 2.4124***

(0.7904)

4.8597***

(0.6332)

2.3759***

(0.7866)

Total Margin (TM) 0.0378***

(0.0078)

0.0375***

(0.0077)

Debt Margin (DM) -0.0423

(0.0380)

Size -2.3293***

(0.1426)

-2.3266***

(0.1421)

-2.5344***

(0.1668)

-1.2642***

(0.1298)

-2.4339***

(0.1682)

Year 2010 0.1360

(0.1463)

0.1763

(0.1460)

0.1801

(0.1720)

-0.1370

(0.0962)

0.2192

(0.1715)

2011 -0.0544

(0.1480)

-0.0139

(0.1477)

-0.0052

(0.1740)

-0.1857*

(0.0978)

0.0266

(0.1733)

2012 0.0511

(0.1509)

0.0901

(0.1505)

0.0953

(0.1770)

-0.1075

(0.1095)

0.1215

(0.1765)

Constant 29.7159***

(1.7989)

29.7508***

(1.7838)

32.2206***

(2.1117)

17.5818***

(1.7883)

30.9367***

(2.1316)

Obs 3413 3413 2912 1967 2892

F stat 57.1317*** 51.1756*** 36.5353*** 27.0517*** 33.5534***

R square 0.1127 0.1202 0.1350 0.1464 0.1262

27

Note. This table provides the fixed effects regression results of equation (1) with growth in Program Expenses

as dependent variable. Specification (a) regresses only the diversification index (DIVER) on the dependent

variable, controlled for size and year. Specification (b) includes also the quadratic term of DIVER. In

specification (c) and (d) also the variables Organizational Efficiency (OE) and a proxy for Financial Flexibility

(FF) are included (Debt Margin or Total Margin respectively). In specification (a)-(d) the negative values of

DIVER are included. Specification (e) contains the same variables as specification (c), but before running this

regression, the negative values of DIVER are removed. Obs refers to the firm-year observations for each

regression, the total observations are equal to 10,217 (i = 1,283; t = 8); the F statistic is used to test whether all

coefficients are different from zero (H0: β1 = ... = βi = 0); R square refers to the part of variation in the dependent

variable explained by the model. The standard error is shown in parenthesis under the coefficients. The ***, **

and * denote statistical significance at the 1%, 5% and 10% level, respectively.

Table 9

Regression results Growth Program Expenses

Dependent variable:

Growth Program Expenses

Variable (a) (b) (c) (d) (e)

DIVER -0.0082

(0.0309)

-0.0832

(0.1718)

0.0020

(0.2629)

0.0397

(0.3350)

-0.0809

(0.2919)

DIVER2 -0.0005

(0.0010)

-0.0003

(0.0015)

0.0022

(0.0139)

0.0010

(0.0018)

Organizational Efficiency (OE) 53.4422***

(2.3040)

25.6913***

(1.2800)

53.6592***

(2.3189)

Total Margin (TM) 0.1178*

(0.0658)

0.1067

(0.0679)

Debt Margin (DM) -0.0062

(0.0630)

Size -1.6833***

(0.3512)

-1.6825***

(0.3454)

-1.8674***

(0.3572)

0.1272

(0.2158)

-2.4339***

(0.1682)

Year 2010 0.1795

(0.3511)

0.1913

(0.3519)

0.1042

(0.3681)

0.1644

(0.1606)

0.2192

(0.1715)

2011 -0.2638

(0.3547)

-0.2520

(0.3555)

-0.3717

(0.3712)

-0.0855

(0.1819)

0.0267

(0.1733)

2012 -0.4266

(0.3617)

-0.4152

(0.3623)

-0.3139

(0.3784)

-0.0855

(0.1819)

0.1215

(0.1765)

Constant 21.9628***

(4.3533)

21.87389***

(4.3540)

19.6380***

(4.5340)

-3.8102

(2.9763)

30.9367***

(2.1316)

N 3353 3353 2859 1852 2839

F stat 5.8988*** 4.9628*** 73.0794*** 50.8665*** 72.5131***

R square 0.0131 0.0133 0.2410 0.2457 0.2411

28

Note. This table provides the fixed effects regression results of equation (1) with growth in Fixed Assets as

dependent variable. Specification (a) regresses only the diversification index (DIVER) on the dependent

variable, controlled for size and year. Specification (b) includes also the quadratic term of DIVER. In

specification (c) and (d) also the variables Organizational Efficiency (OE) and a proxy for Financial Flexibility

(FF) are included (Debt Margin or Total Margin respectively). In specification (a)-(d) the negative values of

DIVER are included. Specification (e) contains the same variables as specification (c), but before running this

regression, the negative values of DIVER are removed. Obs refers to the firm-year observations for each

regression, the total observations are equal to 10,217 (i = 1,283; t = 8); the F statistic is used to test whether all

coefficients are different from zero (H0: β1 = … = βi = 0); R square refers to the part of variation in the

dependent variable explained by the model. The standard error is shown in parenthesis under the coefficients.

The ***, ** and * denote statistical significance at the 1%, 5% and 10% level, respectively.

Table 10

Regression results Growth Fixed Assets (Fixed Effects)

Dependent variable:

Growth Fixed Assets

Variable (a) (b) (c) (d) (e)

DIVER -0.5315

(0.7045)

-3.5027

(2.3465)

-5.5548

(3.6659)

-6.0583

(3.9989)

-5.7587

(3.7435)

DIVER2 -0.0825

(0.0621)

-0.1515

(0.1529)

-0.1720

(0.1661)

-0.1596

(0.1559)

Organizational Efficiency (OE) -2.3522

(15.8039)

-1.9224

(19.6608)

-2.5769

(15.9673)

Total Margin (TM) -0.0156

(0.0756)

-0.0155

(0.0760)

Debt Margin (DM) -1.2552

(2.4901)

Size -3.2037

(2.9214)

-3.0919

(2.9217)

-3.2792

(2.6206)

-5.3517

(3.6057)

-3.6540

(2.7618)

Year 2010 -4.1115*

(2.3456)

-3.9257*

(2.3490)

-2.2211

(2.1718)

-2.3457

(2.3063)

-2.2395

(2.1924)

2011 -4.0485*

(2.3945)

-3.8603

(2.3980)

-2.0278

(2.2032)

-1.8155

(2.3541)

-2.0403

(2.2237)

2012 -2.1158

(2.4590)

-2.0220

(2.4593)

0.1611

(2.2588)

0.3433

(2.4118)

0.2076

(2.2817)

Constant 50.4255

(40.9939)

49.9747

(40.9823)

52.4409

(37.2581)

83.9867

(52.5188)

57.9678

(39.3285)

N 1796 1796 1566 1390 1554

F stat 1.2989 1.3743 1.1439 .2258 1.1744

R square 0.0054 0.0068 0.0088 0.0102 0.0091

29

Note. This table provides the random effects regression results of equation (1) with growth in Program Fixed

Assets as dependent variable. Specification (a) regresses only the diversification index (DIVER) on the

dependent variable, controlled for size and year. Specification (b) includes also the quadratic term of DIVER. In

specification (c) and (d) also the variables Organizational Efficiency (OE) and a proxy for Financial Flexibility

(FF) are included (Debt Margin or Total Margin respectively). In specification (a)-(d) the negative values of

DIVER are included. Specification (e) contains the same variables as specification (c), but before running this

regression, the negative values of DIVER are removed. Obs refers to the firm-year observations for each

regression, the total observations are equal to 10,217 (i = 1,283; t = 8); the Chi2 statistic is used to test whether

all coefficients are different from zero (H0: β1 = ... = βi = 0); R square refers to the part of variation in the

dependent variable explained by the model. The standard error is shown in parenthesis under the coefficients.

The ***, ** and * denote statistical significance at the 1%, 5% and 10% level, respectively.

Table 11

Regression results Growth Fixed Assets (Random Effects)

Dependent variable:

Growth Fixed Assets

Variable (a) (b) (c) (d) (e)

DIVER -0.3819

(0.5952)

-1.8639

(1.7677)

-2.7194

(24680)

-1.4177

(2.4680)

-2.7555

(2.4646)

DIVER2 -0.0422

(0.0473)

-0.0402

(0.1046)

0.0108

(0.1052)

-0.0417

(0.1052)

Organizational Efficiency (OE) 2.7484

(7.1079)

1.1674

(8.1410)

2.7904

(7.1294)

Total Margin (TM) 0.0055

(0.0489)

0.0056

(0.0491)

Debt Margin (DM) -0.4981

(1.2900)

Size 0.0372

(0.3582)

0.0842

(0.3621)

0.0384

(0.3754)

0.0522

(0.3903)

0.0267

(0.3774)

Year 2010 -3.9304*

(2.2210)

-3.8602*

(2.2226)

-2.3004

(2.0338)

-2.5642

(2.1687)

-2.3218

(2.0534)

2011 -3.4410

(2.1868)

-3.3769

(2.1881)

-1.9509

(2.0025)

-2.8052

(2.1266)

-1.9778

(2.0211)

2012 -2.6598

(2.2002)

-2.6521

(2.2003)

-0.9049

(2.0089)

-0.5572

(2.1967)

-0.9221

(2.0277)

Constant 4.6302

(5.3045)

4.5544

(5.3055)

4.3742

(5.6568)

3.5401

(6.0379)

4.6000

(5.7049)

Obs 1796 1796 1566 1390 1554

Chi2 4.30 5.09 6.14 6.27 6.14

R square 0.0042 0.0054 0.0064 0.0062 0.0065

30

The Diversification Index turns out to have a statistically significant negative relation

with Growth in Total Revenue (table 8). This negative relationship is observable in each of the

specifications. Compared to the simple regression in specification (a), where only SIZE and

YEAR are included in the model, this effect is stronger when the quadratic term of DIVER is

included (DIVER2, specification (b)) and becomes even more stronger when the other

independent variables Organizational Efficiency (OE) and Financial Flexibility (FF) are included

in the model (specification (c)). Since an increase of 1 in DIVER is not realistic, it is necessary to

interpret the estimator of βdiv correctly. If one increases his diversification level with 0.10 (ceteris

paribus), he will experience on average, a negative growth – a shrink – of almost 3.6%

(specification (c)), according to this model. Thereby, it has to be noticed that the value of DIVER,

is in principle always between 0 and 1. The relationship between the Diversification Index and

Growth in Total Revenue turns out to be a quadratic, mountain shaped relation. This quadratic

relation is statistically significant, but minimal: for specification (b) and (c) the effect is very

small and barely observable since the accompanying estimator is close to zero (-0.0018 and

-0.0020 for specification (b) and (c) respectively). Since DIVER – and therefor DIVER2 – can, in

principle, only lie between 0 and 1, the influence of this quadratic term is negligible. The

economic significance of this quadratic term can therefore be rejected. The other independent

variables (OE and FF) turn out to have also an effect on growth. Organizational Efficiency turns

out to have a positive significant effect, with a statistically significant coefficient estimator.

Notwithstanding that this estimator has to be placed in perspective since the values of

Organizational Efficiency will always lie between 0 and 1, the economic significance is quite

large: if one achieves a 10% higher level of Organizational Efficiency (ceteris paribus), he will

experience, on average, a 24% growth of Total Revenue. With respect to Financial Flexibility,

the different proxies show different results. When Total Margin (TM) is used as a proxy

(specification (c)), there is a statistically significant positive effect. The economic significance of

this estimator is quite large, since an increase of one standard deviation (10.64) of the Total

Margin Ratio will lead, on average and ceteris paribus, to an increase of the growth in Total

Revenue of 40%. However, when Debt Margin (DM) is included in the model instead of Total

Margin (specification (d)), there is no statistical significant effect. This might be due to the

amount of observations, which is in specification (d) much lower than in the other specifications

as a result of the lack of balance sheet data, which is necessary to calculate the debt margin. This

31

lower amount of observations might also be the reason for the differences between the other

estimators of specification (d) and the further specifications. Specification (e) is estimated from a

database where the negative values of DIVER are excluded. In this specification are the same

independent variables used as in specification (c), since this latter show the most useful and

complete outcomes (comparing specifications (a) to (d)). The inclusion or the exclusion of the

negative values do not have that much effect; the differences between the specifications (c) and

(e) are minimal. The control variable SIZE does have a statistical significant effect in all

specifications, unlike YEAR, which does not have any significant effect on growth in Total

Revenue. For all the specifications, the included variables turn out to be statistically jointly

significant since the F-statistics are significant and therefor the null hypothesis can be rejected

(H0: all β’s are equal to zero). The specification explain all about 13% of the overall variation of

in the Growth of Total Revenue (R-square, specification (c)).

When the regressions are ran with Growth in Program Expenses as dependent variable

(table 9), the results are not that clear as for Total Revenue (table 8). The estimation coefficients

of DIVER and DIVER2 do not show any statistically significant values, not in any of the

specifications. There cannot be drawn any conclusion about the relation between the

Diversification Index and the Growth in Program Expenses, based on this model and database.

Organizational Efficiency does have, again, a strong positive relationship with the dependent

variable: this effect is even much stronger for the growth in Program Expenses with respect to

growth in Total Revenue, given the size of the estimator. The results suggest that when

estimating the relationship of Financial Flexibility and Growth in Program Expenses with Total

Margin, there is a significant positive effect (specification (c)). This is actually not confirmed

when the negative values of DIVER are removed from the database (specification (e)). The

control variable SIZE has again a significant positive effect, and YEAR does not. Again, the

included variables of each of the specifications turn out to be jointly significant and useful, since

the F statistics, for each specification, is large enough to reject the null hypothesis (H0: all β’s are

equal to zero). For specification (c), the model explains about 24% of the variation in the growth

of the Program Expenses (R-square).

The fixed effects model with Growth in Fixed Assets as dependent variable, turns out to

have not any significant results (table 10). Not for the individual estimators, but also the included

32

variables are not jointly significant (due to the small F statistic). This might be due to the lack of

observations; since the Growth in Fixed Assets is calculated by balance sheet items, there is

limited data about this variable available. Unfortunately, there cannot be drawn any conclusion

about the relation of the diversification index with the Growth in Fixed Assets. Also when the

same model is estimated with random effects (table 11), there are no significant results.

VI. Conclusions

The results presented in the previous section suggest, like expected, a relation between

revenue diversification and growth. This supports the hypothesis stated in section II (βDIV and/or

βDIV2 ≠ 0). Nevertheless, it was expected that there was a positive relationship, but these results

suggest a negative one: the data shows that when an organization is more diversified, he will, on

average, experience a negative growth. The argument – stated in section IV – that a team large

enough, with human resources and knowledge about every different revenue stream, can develop

skills to be even more effective at fundraising does not hold. Even when the costs of

diversification are ignored and only the growth in Total Revenue is measured, diversification has

a negative impact. Focusing on several particular revenue streams, seems to be the right strategy

to achieve growth.

These results confirm the expectation of Frumkin and Keating (2011), which suggested a

positive relation between revenue concentration and growth, i.e. a negative relation between

diversification and growth. Although they did not find this relationship confirmed, the results

presented here suggest that they were right. Their statement seems to be valid: non-profits can

create long-term marketing relationships and a reputation, by developing specialized skills that

enable effective fundraising. The results are also in line with the first wave of research about the

question whether (corporate) diversification leads to the creation or destruction of firm value

(Martin and Sayrak, 2003): the results suggest that diversification leads to value destruction since

the Growth in Total Revenue decreases when one diversifies. The statement that the

diversification discount disappears (or drops) when one controls for several firm factors (Campa

and Kedia, 2002) is not confirmed by the results: when controlled for other factors like Financial

Flexibility and Organizational Efficiency, the results become more significant and show a larger

negative effect. Kingma (1993) pointed out that non-profit organizations bear the additional risk

33

of not-receiving any addition expected funding at all. This might indicate the different

conclusions between the literature which confirms the diversification premium and the

conclusion presented here: diversification may lead to a diversification premium for for-profit

firms, however not for non-profit organizations. The factors Organizational Efficiency and

Financial Flexibility have, in line with the hypotheses (βOE > 0 and βFF > 0), a significant effect

on Growth. Organizational Efficiency turns out to have a positive effect on Growth, which is in

line with the expectations; being organized well has a good impact on Growth. Financial

Flexibility, with Total Margin as proxy, has also a positive impact on Growth, which confirms

the expectation.

The results are different from the results of Andrés et al. (2014), who do suggest a

U-shaped quadratic relation between diversification and growth. The estimator of the quadratic

term is converted (U-shaped vs. mountain shaped), but meanwhile, the estimator of the

Diversification Index shows the same negative significant effect. Andrés et al. (2014) interpret

the U-shaped effect by suggesting that in early stages of diversification, diversification hinders

growth, but in later stages, it becomes a source of further growth options. It seems that this last

stage is different for non-profit firms (Andrés et al. (2014) investigate the relationship for

for-profit organizations), which is in line with the suggestion about the diversification discount

above. The differences between the work of Andrés et al. (2014) and this master thesis may also

be (partly) caused by the different proxies used; Andrés et al. (2014) use the market to book asset

ratio, Tobin’s Q and the ratio of R&D expenses to total sales as a proxies for Growth. All these

proxies are not applicable to non-profit firms since there is no market data available, and the

R&D expenses for non-profit firms are of a different nature. The difference in growth

measurement that are used may cause the different results.

Concluding, the main question stated in the title and introduction of this master thesis –

Revenue Diversification in Dutch Charity Organizations: Does it lead to Growth? – should get a

negative answer. The results indicate a negative relation which means that organizations who

increase their level of diversification, experience – on average and ceteris paribus – a shrinkage

of their total revenues. Revenue diversification does not lead to growth.

34

VII. Discussion and recommendations

The study presented here can be improved. This section provides some suggestions which

one can keep in mind by investigating the same or a related topic.

First, the results might be improved by redoing this study with a broader database. Due to

a lack in balance sheet data, the number of observations for some variables were small, which

might lead to less or in-significant results. When a database is constructed with more years of

observations, those results might get more significant. Second, these database only includes

Charity organizations from the Netherlands. Although there are enough organizations to

investigate, a broader study might be interesting to also investigate the situation in other

countries.

Third, Santalo & Becerra (2008) state that the conflicting results and interpretation

between the scholars can be explained since they estimate the effect of diversification over a

large variety of industries. Since this study investigates only one industry (Dutch Charity

organizations), this problem is not applicable. It can be discussed whether it justified to assume

that the average effect across the several sectors is equal. It seems that this assumption does not

hold, like stated in the footnote of section II. With the dataset used, the regressions per sector did

deliver only significant results for a few sectors with a larger amount of organizations. A lot of

sectors contain only a few organizations in this dataset (table 1), and therefore no significant

results can be obtained. A larger dataset can overcome this problem. In addition, Santalo and

Beccera (2008) state that diversified organizations experience a diversification discount only,

when they compete in industries where focused firms hold a considerable market share. It is hard

to argue whether the Dutch non-profit market is a concentrated market or not. Further

investigation of the Dutch charity market in this perspective, might be interesting: to investigate

whether the non-profit market differs from for-profit markets in concepts of soft information and

vertically integration – two concepts named by Santalo and Becerra (2008) to explain the

heterogeneity across sectors – and whether there are differences between the several sectors

within the non-profit sector.

Fourth, it might be that the relationship between Organizational Efficiency and Growth is

also quadratic, since an organization spending too much on the organization itself, might lose the

35

trust of donators, which might lead to less donations (Carroll & Stater, 2009). This effect is not

investigated and might be interesting for further research. Next to that, in response to similar

studies, this master thesis controls for size, measured by the natural logarithm of the book value

of assets. It might be interesting to control also for the size of total revenue, since this might

influence the possibility to grow.

Finally, this master thesis does not investigate the relationship between the growth of the

net result of the charity organization and diversification. Diversification brings several costs, like

personnel costs, administrative costs and fundraising expenses (Kingma, 1993). Since

diversification does not lead to a growth in total revenue, it is expected that when these costs are

also taken into account, the result becomes even more significant. Nevertheless, this might be an

interesting topic for future research.

36

REFERENCES

Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of financial economics, 37(1), 39-

65.

Berger, P. G., & Ofek, E. (1996). Bustup Takeovers of Value‐Destroying Diversified Firms. The Journal of Finance,

51(4), 1175-1200.

Bodie, Z., Kane, A., & Marcus, A. J. (2011). Investments and portfolio management. New York, NY. McGraw-

Hill/Irwin.

Bowman, W. (2002). The uniqueness of nonprofit finance and the decision to borrow. Nonprofit Management and

Leadership, 12(3), 293-311.

Campa, J. M., & Kedia, S. (2002). Explaining the diversification discount. The Journal of Finance, 57(4), 1731-

1762.

Carroll, D. A., & Stater, K. J. (2009). Revenue Diversification in nonprofit organizations: does it lead to financial

stability?. Journal of Public Administration Research and Theory, 19(4), 947-966.

Centraal Bureau voor de Statistiek [CBS] (2014). Nederland in 2013, een economisch overzicht. Retrieved from

http://www.cbs.nl/NR/rdonlyres/911988A7-1223-49D7-BEEC-1C3927924FD1/0/2014Nederlandin2013pub.pdf

Centraal Bureau Fondsenwerving [CBF] (2014). Financiële overzichten. Retrieved from

http://www.cbf.nl/cijfers/baten-en-lasten/

Chevalier, J. (2000). Why do firms undertake diversifying mergers? An analysis of the investment policies of

merging firms. Unpublished working paper. University of Chicago.

Corbin, J. J. (1999). A study of factors influencing the growth of nonprofits in social services. Nonprofit and

Voluntary Sector Quarterly, 28(3), 296-314.

De Andrés, P., de la Fuente, G., & Velasco, P. (2014). Growth opportunities and the effect of corporate

diversification on value. Article in press, The Spanish Review of Financial Economics.

Elton, E. J., & Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of Banking & Finance, 21(11),

1743-1759.

Erdorf, S., Hartmann-Wendels, T., Heinrichs, N., & Matz, M. (2013). Corporate diversification and firm value: a

survey of recent literature. Financial Markets and Portfolio Management, 27(2), 187-215.

Ferris, S. P., Sen, N., Lim, C. Y., & Yeo, G. H. (2002). Corporate focus versus diversification: the role of growth

opportunities and cashflow. Journal of International Financial Markets, Institutions and Money, 12(3), 231-252.

Friend, I., & Blume, M. (1970). Measurement of portfolio performance under uncertainty. The American Economic

Review, 561-575.

Frumkin, P., & Keating, E. K. (2011). Diversification reconsidered: the risks and rewards of revenue

concentration. Journal of social entrepreneurship, 2(2), 151-164.

Graham, J. R., Lemmon, M. L., & Wolf, J. G. (2002). Does corporate diversification destroy value?. The Journal of

Finance, 57(2), 695-720.

Grossman, D. A. (1992). Paying nonprofits: Streamlining the new york state system. Nonprofit Management and

Leadership, 3(1), 81-91.

Hadlock, C. J., Ryngaert, M., & Thomas, S. (2001). Corporate Structure and Equity Offerings: Are There Benefits to

Diversification?. The journal of business, 74(4), 613-635.

Harris, M., Kriebel, C. H., & Raviv, A. (1982). Asymmetric information, incentives and intrafirm resource

allocation. Management Science, 28(6), 604-620.

Hirschman, A. O. (1964). The paternity of an index. The American Economic Review, 761-762.

Jegers, M. (1997). Portfolio theory and nonprofit financial stability: A comment and extension. Nonprofit and

voluntary sector quarterly, 26(1), 65-72.

37

Jegers, M., & Verschueren, I. (2006). On the capital structure of non‐profit organisations: an empirical study for

californian organisations. Financial Accountability & Management, 22(4), 309-329.

Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American economic

review, 323-329.

Jensen, M. C., & Murphy, K. J. (1990). Performance pay and top-management incentives. Journal of political

economy, 225-264.

John, K., & Ofek, E. (1995). Asset sales and increase in focus. Journal of Financial Economics, 37(1), 105-126.

Jong, de, F. (2012). Models Based on Panel Data. Retrieved from https://edubb.uvt.nl/bbcswebdav/pid-785197-dt-

content-rid-1880431_1/courses/323063-2013-2014/Paneldata%282%29.pdf

Kingma, B. R. (1993). Portfolio theory and nonprofit financial stability. Nonprofit and Voluntary Sector

Quarterly, 22(2), 105-119.

Lang, L.H.P., Stulz, R.M., (1994). Tobin’s q, corporate diversification and firm performance. Journal of political

Economy 102, 1248-1280

Lee, C. F. (1977). Functional form, skewness effect, and the risk-return relationship. Journal of financial and

quantitative analysis, 12(01), 55-72.

Lewellen, W. G. (1971). A pure financial rationale for the conglomerate merger.The Journal of Finance, 26(2), 521-

537.

Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.

Martin, J. D., & Sayrak, A. (2003). Corporate diversification and shareholder value: a survey of recent literature.

Journal of Corporate Finance, 9(1), 37-57.

Ramanujam, V., & Varadarajan, P. (1989). Research on corporate diversification: A synthesis. Strategic

management journal, 10(6), 523-551.

Rijksoverheid (2014). Financiering ontwikkelingssamenwerking. Retrieved from

http://www.rijksoverheid.nl/onderwerpen/ontwikkelingssamenwerking/financiering-ontwikkelingssamenwerking

Santalo, J., & Becerra, M. (2008). Competition from specialized firms and the diversification–performance

linkage. The Journal of Finance, 63(2), 851-883.

Sharpe, W. F. (1966). Mutual fund performance. Journal of business, 119-138.

Shleifer, A., & Vishny, R. W. (1989). Management entrenchment: The case of manager-specific

investments. Journal of financial economics, 25(1), 123-139.

Sorensen, E. H., Qian, E., Schoen, R., & Hua, R. (2004). Multiple alpha sources and active management. The

Journal of Portfolio Management, 30(2), 39-45.

Stein, J. C. (1997). Internal capital markets and the competition for corporate resources. The Journal of

Finance, 52(1), 111-133.

Stowe, J. D., & Xing, X. (2006). Can growth opportunities explain the diversification discount?. Journal of

Corporate Finance, 12(4), 783-796.

Tobin, J. (1958). Liquidity preference as behavior towards risk. The review of economic studies, 65-86.

Torres-Reyna, P. (n.d.). Panel Data Analysis, Fixed and Random Effects. Retrieved from

http://dss.princeton.edu/training/Panel101.pdf

Villalonga, B. (2000). Matching BITS to COMPUSTAT: Towards richer data for large sample research within

firms. Unpublished manuscript, Anderson Graduate School of Management, University of California, Los Angeles.

Villalonga, B. (2004). Diversification discount or premium? New evidence from the business information tracking

series. The Journal of Finance, 59(2), 479-506.

38

APPENDICES

Appendix A

Summery statistics for the full sample (2005-2012) when DIVER > 0

Variable N Mean

Standard

Deviation Min. Max.

Growth Fixed Assets (FA) 1798 2.4280 32.2838 -0.1000 815.5971

Growth Total Revenue (TR) 5761 0.4024 4.2213 -0.9865 185.0411

Growth Program Expenses (PE) 5694 0.5356 6.6076 -0.9931 302.4643

DIVER 7174 0.3391 0.2408 0 0.8106

DIVER2 7174 0.1730 0.1702 0 0.6571

Organizational Efficiency (OE) 4862 0.1049 0.1546 8.13E-05 1

Debt Margin (DM) 2737 0.4191 1.7534 4.82E-07 70.2558

Total Margin (TM) 4655 2.1369 10.2835 0.0021 583.4288

Size 4695 12.493 2.5014 4.5468 19.5310

Note. N refers to the total firm-year observations and is equal to 10,217 (i = 1,282; t = 8)

Appendix B

Summery statistics for the full sample, by Sector

Sector

Code

Growth

PE

Growth

TR

Growth

FA DIVER DIVER2 OE

Debt

Margin

Total

Margin Size

OW 0.5649 0.4094 1.7367 0.1870 10.0365 0.0806 0.3338 1.7651 11.5502

SH 0.0950 0.0542 2.3820 0.2633 0.1315 0.0772 0.3133 0.9949 13.0359

VU 0.1290 0.1123 0.1272 0.4593 0.2629 0.0352 0.3552 0.6616 14.8919

VO 0.3790 0.5508 3.9115 0.3951 0.2208 0.1244 0.3346 3.1043 13.3252

GE 1.5289 0.4362 1.3923 0.3220 0.1758 0.0969 0.3326 3.0822 12.2528

BL 0.4267 4.2549 3.6243 -0.0265 11.86865 0.1273 0.1273 4.7271 13.6474

MA 0.6986 0.2223 5.0650 0.1986 29.8043 0.1591 0.4077 3.0347 12.8241

ME 0.3700 0.1825 0.1902 0.3191 0.1603 0.0941 0.3594 0.8597 13.3072

CU 0.2244 0.6643 1.4014 -0.3571 166.7682 0.1210 0.4417 3.8623 14.5650

SR 0.2973 0.3240 0.2503 0.4674 0.2650 0.1274 0.4138 1.1319 13.7343

OO 1.2922 1.4590 13.0692 0.2704 0.1641 0.1048 0.6355 2.4280 11.3577

KL 0.0805 0.0600 0.4272 0.2920 0.1560 0.1153 0.2931 1.7718 13.3797

MI 0.0760 0.1464 0.3582 0.4407 0.2227 0.0959 0.3804 1.0541 13.5457

NA 0.1582 0.1074 0.1990 0.4603 0.2693 0.1065 0.3787 1.8771 14.9863

DI 0.1532 0.1421 0.4831 0.3635 1.1250 0.1037 1.3533 2.2001 12.7754

Total 0.5343 0.4026 2.4095 0.2372 15.0463 0.1054 0.4179 2.2635 12.5041

39

Appendix C

Correlation matrix

Growth

PE

Growth

TR

Growth

FA

Diver Diver2 OE Debt

Margin

Total

Margin

Size

Growth PE 1

Growth TR 0.0722* 1

Growth FA -0.0059 0.0007 1

Diver 0.0023 0.0060 0.0098 1

Diver2 -0.0037 -0.0073 -0.0004 0.9826* 1

OE -0.0239 -0.0121 0.0097 -0.0204 0.0139 1

Debt Margin -0.0042 -0.0063 -0.0144 0.0073 -0.0045 -0.0096 1

Total Margin -0.0100 -0.0196 0.0012 -0.1540* 0.1458* 0.1964* -0.0160 1

Size -0.0550* -0.0404* 0.0035 0.0228 -0.0014 -0.1260 -0.0600* 0.0494* 1

Note. The * denote statistical significance at the 5% level.

Appendix D

Correlation matrix

Growth

PE

Growth

TR

Growth

FA

Diver Diver2 OE Debt

Margin

Total

Margin

Size

Growth PE 1

Growth TR 0.0722* 1

Growth FA -0.0059 0.0007 1

Diver -0.0019 -0.0344* -0.0183 1

Diver2 -0.0032 -0.0226 0.0097 -0.9826* 1

OE -0.2710* 0.1257* -0.0070 -0.0204 0.0139 1

Debt Margin -0.0060 0.0110 -0.0085 0.0073 -0.0045 -0.0096 1

Total Margin 0.0282 0.0867* 0.0025 -0.1540* 0.1458* 0.1964* -0.0160 1

Size -0.0678 -0.1065* 0.0016 0.0227 -0.0014 0.1258* -0.0605* 0.0494* 1

Note. The * denote statistical significance at the 5% level.