IMPACT OF FAMILY OWNERSHIP ON CAPITAL STRUCTURE DECISIONS – AN INDIAN STUDY

IMPACT OF FAMILY OWNERSHIP ON CAPITAL STRUCTURE
DECISIONS – AN INDIAN STUDY
AMITENDRA SINGH THENUA
A project report submitted
In partial fulfilment of the requirement for award of the degree of
MASTER OF SCIENCE
IN
FINANCIAL ECONOMICS
MADRAS SCHOOL OF ECONOMICS
and
CENTRAL UNIVERSITY OF TAMIL NADU
May 2014
MADRAS SCHOOL OF ECONOMICS
CHENNAI - 600025

i
Degree and Branch : MASTER OF SCIENCE
(FINANCIAL ECONOMICS)
Month and Year of Submission : MAY 2014
Title of the Project Work : IMPACT OF FAMILY OWNERSHIP ON
CAPITAL STRUCTURE DECISIONS -
AN INDIAN STUDY
Name of Student : AMITENDRA SINGH THENUA
Roll Number : P120807
Name and Designation : Dr. SAUMITRA BHADURI
Of Supervisor Professor,
Madras School of Economics
Chennai- 600025

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BONAFIDE CERTIFICATE
This is to certify that this project report titled “Impact of family ownership on capital
structure decisions – An Indian study” is the bonafide work of Mr Amitendra
Singh Thenua who carried out the project under my supervision. Certified further,
that to the best of my knowledge the work reported herein does not form part of any
other Project Report of the basis of which a degree or award was conferred on an earlier
occasion on this or any other candidate.
Dr. K.R, SHANMUGAM Dr. SAUMITRA BHADURI
Director, Professor,
Madras School of Economics Madras School of Economics
Chennai -600025 Chennai - 600025

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Abstract
This study analyses the impact of family ownership characteristics of a firm on its
capital structure decisions, focusing on Indian economy. The aim of this study is to
understand whether the motivation to have control over the firm or the motivation to
reduce risk, is dominant among Indian family firms. The motivation of maintaining
control over the firm is achieved through issue of debt rather than equity so as to avoid
dilution of voting rights and the motivation of reducing firm specific risk is achieved
by issuing less debt. This study shows how family ownership characteristics affect the
leverage of a firm. The study has been conducted for the sample period 2003-2012
with annual data. The results indicate that family firms have higher leverage as
compared to non- family firms and the founder member acting as CEO, Chairman or
Managing director has a positive impact on leverage.

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Acknowledgement
I express my sincere gratitude to my supervisor Dr. Saumitra Bhaduri, Professor,
Madras School of Economics, Chennai, for his constant support, encouragement and
guidance throughout the period of this project.
I would also like to express my gratitude to Dr. Madhuri Malhotra, Assistant Professor,
Madras School of Economics, Chennai, who as my panel member, provided useful
insight which helped in broadening my perspective of the topic.
Amitendra Singh Thenua

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Table of Contents
CHAPTER 1: Introduction 1
1.1 Introduction 1
1.2 Control motivation of family firms 3
1.3 Risk reduction motivation of family firms 3
CHAPTER 2: Literature review 4
2.1 Control motivations and capital structure decisions 4
2.2 Founding family ownership and firm performance 6
2.3 Capital structure decisions in family firms 8
2.4 CEO and value creation of family firms 11
CHAPTER 3: Methodology 12
3.1 Hypotheses 12
3.2 Definition of variables 12
3.3 Model 16
3.4 Data 17
CHAPTER 4: Results 20
4.1 Results 20
4.2 Interpretation 33
CHAPTER5: Conclusion 35
REFERNCES 36

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List of Tables
Table 1: Descriptive statistics (2003-2012) 18
Table 2: Pooled OLS regression of Market leverage 20
Table 3: Pooled OLS regression of Long term market leverage 21
Table 4: Pooled OLS regression of financial market leverage 22
Table 5: Random effects model for market leverage 23
Table 6: Random effects model for Long term market leverage 24
Table 7: Random effects model for financial market leverage 25
Table 8: Fama -Macbeth model for market leverage 26
Table 9: Fama - Macbeth model for long term market leverage 27
Table 10: Fama- Macbeth model for financial market leverage 28
Table 11: Pooled OLS, Random effects and FM model for market 29
Leverage
Table 12: Pooled OLS, Random effects and FM model for long 29
Term market leverage
Table 13: Pooled OLS, Random effects and FM model for 31
Financial market leverage

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CHAPTER 1
Introduction
1.1 Introduction
A lot of papers have been published since the famous paper of Modigliani and Miller
in 1958, but still after 56 years we are not able to develop a complete understanding
about the capital structure choice. Past literature on this area of study has provided
very little empirical evidence on the different theories of capital structure choice. Also,
most of the work done so far has been based on firms in the United States and it is not
clear how these facts relate to different theoretical models of capital structure choice.
This study aims to provide a fresh perspective to all the past literature by analysing the
behaviour of firms in India, which respond very differently as compared to firms in
other market economies such as that of United States and Europe.
One of the objectives of this study is to see whether capital structure decisions in other
countries respond the same way to factors affecting them as in the case of firms in
United States.
Myers (2003) suggested that any future research in the field of capital structure
decisions should be channelized towards understanding how capital structure decisions
vary due to differences in incentives among managers and shareholders. Holderness
and Sheehan (1988) and Gugler (2001) propose that variation in capital structure
decisions occurs due to different incentives and motivations which are directly related
to risk and control of each type of large shareholder.
It has been generally accepted that family controlled businesses differ significantly
from professionally managed businesses but a limited work is available for
understanding and validating this premise. Also, there is a mixed opinion on the
impacts that family controlled businesses have as compared to professionally managed
businesses. For example, Fama and Jensen (1983) suggest that family controlled
businesses should be more efficient than professionally managed businesses because

2
the costs of monitoring are less in the case of family controlled firms. Holderness and
Sheehan (1988) and Gugler (2001) propose that differing capital structure decisions
are due to different incentives and motivations which are directly related to risk and
control of each type of large shareholder.
Daily and Dollinger (1991) demonstrated that there were differences between family
controlled businesses and professionally managed businesses with respect to firm size,
firm age, firm strategy and internal control systems. They found that family controlled
businesses tend to be smaller, to have high mortality rates and rely less on formal
control systems than professionally managed firms.
Masulis (1988) suggests that managers prefer having less leverage than shareholders
so as to reduce the risk of their undiversified investment in the firm. Mishra and
McConaughy (1999) found that family controlled businesses use less debt as compared
to professionally managed businesses.
This study aims to find how family controlled businesses in India respond as compared
to professionally managed businesses, the study aims to find whether family firms use
more leverage than non- family firms or not, whether family firms are more profitable
than non- family firms or not and whether the founder of the firm acting as the CEO,
Managing director or CEO has a positive or negative impact on leverage.
Family businesses have always been the backbone of the Indian economy and have
dominated India’s economic landscape. Family run businesses account for more than
85 % of all the Indian companies and hence their understanding is important, so as to
have a clear understanding about India’s economy.
The aim of this study is to analyse the impact that family ownership characteristics
have on a firm’s capital structure decisions. There has been a mixed opinion by past
studies on whether family run businesses tend to be more debt friendly or more equity
friendly, but so far there has been no clear mandate on how firms respond to different
ownership characteristics.

3
There are two school of thoughts on the effects of family ownership characteristics on
capital structure decisions, one is the motivation to have control over the firm and other
is the motivation to reduce firm specific risk.
1.2 Control motivation of family firms
From a blockholders point of view such as that of a family owner, new equity financing
is not one of the best or optimal paths to solve the trade-off between getting external
funds to finance the firm’s investment and possibly losing or diluting their control, or
keeping their control over the firm and, in case of insufficient internal funds, passing
on valuable investments. This trade – off is solved by debt as it does not pose any
threat to their control and also takes care of the financing required for new investments.
Thus, this school of thought believes that family run businesses are driven by their
motivation to have control over their firm and hence prefer debt over equity because
the issue of equity leads to a dilution of their voting rights and hence so as to avoid
this dilution they prefer the issue of debt. This study aims to empirically investigate
whether capital structure mechanisms are used by Indian blockholders (family
businesses) to maintain control over the firm.
1.3 Risk reduction motivation of family firms
The same blockholders which have high control motivations also hold undiversified
portfolios and hence face high firm specific risk. For family blockholders, this high
firm specific risk is highly important because they want to ensure their firm’s survival.
Institutional blockholders tend to have high portfolio diversification as established by
Tufano (1996) whereas family blockholders tend to have low portfolio diversification
as established by Anderson and Reeb (2003). Thus, family blockholders would want
to reduce their debt or leverage, which would reduce their firm specific risk and hence
reduce the risk of their highly undiversified portfolios.
Thus, this other school of thought believes that family run businesses are driven by
their motivation to reduce the firm specific risk and hence will prefer less debt.

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CHAPTER 2
Literature review
The optimal capital structure choice is one of the most popular and unresolved issues
in financial economics literature. Modigliani and Miller (1958) provided foundations
for the capital structure research. One of the major determinants of capital structure
decisions is agency cost and in this study we are comparing two distinct groups of
firms that do not have equal agency costs because one group consists of family firms
and the other group consists of non- family firms.
Due to studies in the past, it has been widely assumed that agency costs are lower in
family firms as compared to non-family firms and thus the need for disciplining the
role of debt in family firms is expected to be less and hence they are expected to have
lower leverage ratios. But, the empirical evidence regarding this issue is largely
inconclusive and generally focused on market based economies like United States.
This study aims to give a fresh perspective on the role of debt in case of family firms
by analysis the behaviour of Indian business groups.
2.1 Control motivations and capital structure decisions
In March 2006, Andrew Ellul published his paper “Control motivations and Capital
structure decisions” in which he describes how blockholders use leverage to secure
their control over the firm. Blockholders with high control motivations over their firms
tend to face a trade-off between raising external finance and losing the control over
their firm because a blockholder with control motivations prefers to raise money
through debt rather than equity because equity will dilute his control over the firm and
this results in higher debt-equity ratios.
The counter motivation that blockholders face in response to the control motivation is
the need for risk reduction because undiversified blockholders want to reduce leverage

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so as to reduce the firm specific risk they face. Ellul in his study finds that among
family blockholders the motivation for control defeats the motivation for risk reduction
and hence the capital structure decisions are influenced by the need to have control
over the firm.
The dataset used by Ellul comprised of 5975 firms from 38 countries and the period
of study was 1992-2006. He also found that family blockholders do not use leverage
unnecessarily to regain control over the firm. They exercise the use of leverage only
when their control is contestable.
An interesting argument presented in this paper is the comparison drawn between
family blockholders and institutional blockholders both have control motivations and
face the counter motivation of risk reduction yet both behave very differently. Past
studies show that family blockholders tend to have undiversified portfolios whereas
Institutional blockholders tend to have diversified portfolios and hence in case of
family blockholders the control motivation is high but the risk of bankruptcy is also
high thus they do not use leverage unnecessarily and use this mechanism to exercise
control only when there voting power is insufficient to keep control. Institutional
blockholders who have diversified portfolios face low firm specific risk and hence can
use leverage as a mechanism to maintain control as compared to family blockholders.
Ellul has used a dataset in which there are no financial firms and the firms have to be
publicly traded and financial and accounting data for the last five years has to be
available for each firm.
Ellul in his paper describes a family firm as one where the founder, or descendants of
his/her family (either by blood or through marriage), is the largest blockholder (either
individually or as a Group) and has an ownership stake of at least 10% of cash flow
rights.

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2.2 Founding family ownership and firm performance
In June 2003, Anderson and Reeb published their paper “Founding family ownership
and firm performance: evidence from the S&P-500” in which they find that family
firms perform better than non-family firms, the relationship between family holdings
and firm performance is non-linear and firm performance is better when family
members serve as CEO’s rather than having outside CEO’s. Anderson and Reeb
successfully showed that minority shareholders are not adversely affected by family
ownership as it is an effective organizational structure.
Past literature shows that continued family ownership in U.S. corporations leads to
poor firm performance but there have been other works also which show that
combining ownership and control can be advantageous, families have longer
investment horizon and hence greater investment efficiency. Thus, there is no clear
consensus on whether founding family presence hinders or facilitates firm
performance.
In this paper, Anderson & Reeb use the data of 500 firms in the S&P -500 during the
period 1992-1999. Due to substantial ownership of cash flow rights, family firms have
both the means and the incentive to take actions that benefit them at the expense of
firm performance. Fama & Jensen in their 1985 paper show that large concentrated
shareholders like family firms derive greater benefits from pursuing objectives such as
firm survival or technological innovation rather than enhancing shareholder’s value.
Large ownership stakes also reduce the probability of bidding by other agents and
hence reduce the value of the firm. One of the greatest costs imposed by large
shareholders is to remain active in the management of the firm even if they are not
qualified to do so. Families may also expropriate wealth from the firm through
excessive compensation, related party transactions or special dividends which can
impact the firm’s capital expansion plans and lead to poor operating and stock price
performance.
But,

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Concentrated investors have substantial economic incentives to diminish agency
conflicts and maximize firm value. Family’s wealth is closely linked to firm welfare
hence families have strong incentive to monitor managers and minimize the free rider
problem inherent with small shareholders. Families have longer horizons than other
shareholders hence have a willingness to invest in long term projects relative to shorter
managerial horizons. Family firms invest more efficiently than non-family firms
because they intend to pass the firm to succeeding generations and view their firms as
an asset to pass on to their descendants rather than wealth to consume during their
lifetimes. One consequence of families maintaining a long term presence is that the
firm will enjoy a lower cost of debt financing as compared to non-family firms.
Family firms generally have family members serving as firm’s CEO. Some negatives
of having a family CEO is that the family can better meet their consumption goals
through the firm rather than through their wealth. Also, a family member may get the
top position at the cost of excluding more talented and capable outside professional
managers. Family CEO’s are potentially less accountable to shareholders and directors
than outside CEO’s. Placing family members as CEO can also lead to resentment on
the part of senior nonfamily executives because tenure, merit, and talent are not
necessarily requisite skills for top management positions.
The focus of this paper is to find the relation between family ownership and firm
performance. The paper addresses this issue by looking into four different aspects:
Are family firms less profitable or less valuable than non- family firms?
Does the relationship between family ownership and firm performance differ between
younger and older family firms?
Is the performance – ownership relationship linear over all ranges of family holdings?
Does family members acting as CEO negatively impacts firm performance?

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In this paper, Anderson & Reeb use S&P-500 firms from 1992 to 1999 as their sample,
they exclude bank and public utilities because it is difficult to calculate Tobin’s q for
banks and government regulations potentially affect firm performance.
This paper finally concludes that family firms perform at least as well as non- family
firms and if we use profitability based measures of firm performance such as ROA
then we find that family firms are significantly better performers than non- family
firms. Greater profitability in family firms relative to non – family firms relative to
non- family firms stems from those firms in which family members serve as CEO’s.
2.3 Capital structure decisions in family firms
In March 2009, Ampenberger, Schmid, Achleitner and Kaserer wrote a paper “Capital
structure decisions in family firms- Evidence from bank-based economy” in which
they try to examine how three different characteristics of a family firm: ownership,
supervisory and management board activities impact capital structure decisions. They
have used Germany as their country of study.
They were able to conclude from their study that family firms have significantly lower
leverage ratios than non- family firms, management board involvement by the
founding family has a consistently negative influence on leverage across all their
models, the influence of ownership and supervisory board representation is
insignificant in almost all their models, leverage level is the lowest if founding family
is simultaneously a large shareholder with monitoring incentives and involved in firm
management with convergence of interest effects and the presence of founder CEO in
firm management has a significant negative effect on firm’s leverage ratio.
Past literature analyzing the relationship between managerial ownership and capital
structure to test how agency costs affect debt levels, show that there is a significant
negative relationship between managerial ownership and leverage ratios. Thus, there
is a prevalent view that entrenched managers prefer less than optimal debt levels so as
to reduce human capital risk, to avoid performance pressure induced by fixed interest

9
payments and for reasons of job retention if other candidates are better qualified. But,
a recent study by John and Litov in 2008 shows that firms with entrenched managers
rely more on debt than well governed firms.
Mishra and McConaughy in 1999 applied a matching methodology to isolate the effect
of founding family control from managerial ownership effects. They took a sample of
large U.S family firms where the CEO is still either the founder or a relative of the
founder and matched those family firms with non- family firms with similar firm
characteristics in terms of managerial ownership, firm size and industry. They found
that family firms use a significantly lower level of debt and this difference is not driven
by the level of managerial ownership but rather by founding family peculiarities.
Family firms are concerned about two negative effects of debt, first is the increasing
cost of financial distress and other is the risk of losing control over their firms.
In this paper the following hypotheses are made:
Family firms have lower leverage ratios than their non- family counterparts, Family
ownership leads to lower levels of leverage due to convergence of interest effects and
lower agency costs, Firms in which the founder still acts as the CEO show lower levels
of leverage, The impact of agency costs on leverage will be the strongest if both the
family firm aspects: ownership and management occur simultaneously
The rationale for the hypotheses being made in this paper are that debt reduces the
agency costs of free cash flow by reducing the cash flow available for spending at the
discretion of managers. As founding families usually remain large long-term
shareholders they are able to overcome the free-rider problem commonly associated
with atomistic shareholder structures.
Effective monitoring due to family ownership is one rationale for lower agency costs
in family firms. Monitoring activities might be even more effective if the founding
family is institutionally involved in firm’s oversight. Hence, supervisory board
involvement of the founding family reduces agency costs as well. Whenever a member

10
of the founding family is present in the management or board, interests of shareholders
and management are aligned. This convergence-of-interest effect further reduces (or
even eliminates) agency costs within family firms.
Other family firm characteristics also affect capital structure decisions such as family
firms show long term commitment, spanning more than one family generation because
family reputation is tied to the image and economic success of the family firm. Thus,
founding families are concerned about the loss of control over the firm. Family firms
can react in two ways to this situation:
They may either prefer debt over equity so as to avoid dilution of voting rights or they
may not prefer debt so as to avoid active credit monitoring.
Founding families are usually large and undiversified investors and hence face a high
risk exposure to one single asset which is the family firm. Thus, they have increased
risk aversion and hence lower leverage ratios.
In this paper, we have an unbalanced dataset of 660 industrial companies in Germany
between 1995 and 2006. The sample selection rule require that:
The common stock of the firm should be listed in the CDAX for at least one year of
the sampling period.
Only industrial firms are included in the sample and financial firms were excluded.
Only listed firms were included.
In this paper, a firm is classified as a family firm if it satisfies at least one of the
following conditions:
The founding family has voting rights of at least 25% (family ownership) or at least
one member of the founding family is represented in the supervisory board
(supervisory board participation) or at least one member of the founding family is
involved in top management (management board participation).

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2.4 CEO and value creation of family firms
In 2006, Villalonga and Amit published the paper “How do family ownership, control
and management affect firm value”. In this paper they used data on all Fortune-500
firms during the period 1994 and 2000.
They found that family ownership creates value only when the founder serves as CEO
of the family firm or as Chairman with a hired CEO. When descendants serve as CEO’s
then firm value is destroyed. Owner – manager conflict is more costly in non- family
firms than the conflict between family and non- family shareholders in founder CEO
firms but if it is a descendant CEO firm then the conflict between family and non-
family shareholders is more costly than the owner- manager conflict in case of non-
family firms.

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CHAPTER 3
Methodology
3.1 Hypotheses
This study aims to test the following hypotheses:
Family firms are expected to have higher leverage than non - family firms because
control motivations play a far more dominant role than risk reduction motivations in
the Indian economy as the objective of the owner is to maintain control over the firm
and pass the firm as an asset to its future generations.
Family firms are less profitable as compared to non – family firms because large
concentrated shareholders like family firms derive greater benefits from pursuing
objectives such as firm survival or technological innovation rather than enhancing
shareholder’s value. . Families may also expropriate wealth from the firm through
excessive compensation, related party transactions or special dividends which can
impact the firm’s capital expansion plans and lead to poor operating and stock price
performance.
If the founder of the firm is still the CEO, Managing director or Chairman of the firm
then it will lead to higher leverage as the founder will have higher control motivations
as compared to his/her relatives or descendants.
3.2 Definition of variables
The following variables have been used as independent variables in the study:
Firm age: It is the natural logarithm of the difference between the year of study and
the year of incorporation of the firm.
Firm’s age = LN (year of study – year of incorporation)

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This variable is being used because as the age of the firm increases then the entity
slowly moves from a concentrated shareholding to a divided shareholding and hence
the use of leverage decreases, another argument is that as the firm’s age increases, it
learns to employ its resources more efficiently and relies more on internal funds than
on debt.
But, there is also a counter argument that as the age of the firm increases so does its
creditworthiness and assets thus for an older firm it becomes more easier to borrow
than a younger firm as they have more tangible assets and hence greater borrowing
capacity and profitability.
Thus, this study aims to find the impact that Firm’s age has on leverage in case of
Indian firms.
Firm’s size: It is the natural logarithm of the firm’s total assets
Firm’s size = LN (Total Assets)
This variable is being used because it is believed that informational asymmetries
between insiders in a firm and the capital markets are lower for large firms. So, large
firms should be more capable of issuing informationally sensitive securities like equity
and thus should have lower debt. Thus, a negative relationship is expected between
firm size and leverage.
But, there is a counter argument that as the size of the firm increases so does its
borrowing capacity as the size of its tangible assets also increases, hence a positive
relation is also possible between firm size and Total assets.
This study aims to find the relationship between leverage and firm’s size in case of
Indian firms.

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Profitability: It is the ratio of EBITDA over Total assets.
Profitability = EBITDA / Total assets
This variable is being used because according to Pecking order theory, firms prefer to
finance new investment projects with retained earnings followed by new debt while
issuing external equity is only the last resort of financing.
And, as the profitability of the firm increases, internal funds or retained earnings also
increases thus reducing the reliance on debt.
Hence, a negative relationship is expected between profitability and leverage.
Tangibility: It is the ratio of Tangible assets to total assets.
Tangibility = Tangible Assets / Total Assets
Where, tangible assets is the sum of the net of Land & buildings, Plant & machinery,
Transport, communication, equipment & infrastructure and Furniture, social amenities
& other fixed assets.
This variable is being used because tangible assets play a very important role in the
borrowing capacity of the firm as tangible assets are relatively easier to collateralize
and hence reduce the cost of debt thus causing an increase in the amount of leverage.
Thus, a positive relation is expected between tangibility and leverage.
Family firm dummy: It is 1 if the firm is a family firm and it is 0 if the firm is not a
family firm.
In this study, a firm is defined to be a family firm if it satisfies at least one of the
following conditions:

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Condition 1:
At least one member of the founding family is represented in the Board of directors
Condition 2:
The founder, or descendants of his/her family (either by blood or through marriage)
have an ownership stake of at least 20% or the CEO
Condition 3:
Chairman or Managing director of the firm is a member of the founding family.
If any one of these conditions is satisfied then the firm is categorized as a family firm,
otherwise not.
Founder dummy: It is 1 if the Chairman, Managing director or CEO of the firm is the
founder of the firm and 0, otherwise.
This study uses three definitions of leverage as a dependent variable.
The following have been used as dependent variables in the study:
Market leverage: It is the ratio of total liabilities to the market value of equity plus
total liabilities.
Market leverage = Total liabilities / (Total liabilities+ Market value of equity)
This definition of leverage is being used because Rajan and Zingales (1995), Fama and
French (2002), Baker and Wurgler (2002) or Kayan and Titman (2007) have used
similar definition of leverage in their studies.

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Long term market leverage: It is the ratio of the difference between Total liabilities
and Current liabilities over the sum of Total liabilities and market value of equity. It is
a long term measure of leverage.
Long term market leverage =
(Total liabilities – Current liabilities) / (Total liabilities + market equity)
Financial market leverage: It is the ratio of the difference between Total liabilities
and the sum of provisions, deferred tax and accounts payable over the difference of
the sum of total liabilities and market equity and the sum of provisions, deferred tax
and accounts payable.
Financial market leverage =
(Total liabilities – provisions – deferred tax – accounts payable) / (Total liabilities +
market equity – provisions – deferred tax – accounts payable)
When we calculate financial leverage then we are taking into account interest bearing
debt components such as accounts payable.
3.3 Model
In this study the data structure is organised as a balanced panel of 781 firms that are
tracked over the period 2003 to 2012.
The panel structure of our data allows us to present regression estimates of either a
pooled OLS model, a fixed effects model or a random effects model.
But, as in the dataset we have two time invariant regressors: Family firm dummy and
Founder dummy thus fixed effects model is not a good option because when a fixed
effect (FE) model is assumed in panel data, the FE or FD (First Difference) methods
provide consistent estimates only for time-varying regressors, not for time-invariant
regressors. In particular, coefﬁcients corresponding to time-invariant regressors are not

17
estimable in a ﬁxed-effects framework due to collinearity with the vector of individual
dummies.
Thus, the most suitable model for estimation would be Pooled OLS regression and
Random effects model.
The three equation for estimation are:
Market leverage = α0 + α1 Family firm dummy + α2 Founder dummy + α3
Profitability + α4 Firm age + α5 Firm size + α6 Tangibility
Long term market leverage = α0 + α1 Family firm dummy + α2 Founder dummy
+ α3 Profitability + α4 Firm age + α5 Firm size + α6 Tangibility
Financial market leverage = α0 + α1 Family firm dummy + α2 Founder dummy +
α3 Profitability + α4 Firm age + α5 Firm size + α6 Tangibility
3.4: Data
This study uses a balanced panel dataset of 781 firms, which are listed and permitted
companies. Only non-financial firms, which comprises of manufacturing firms,
mining firms and services firms excluding electricity generation & distribution
companies, transport services and Telecommunication services are included because
financial and utility firms are affected by government regulations.
The sample period of study is 10 years annual data from 2003 to 2012.
Thus, this study makes its empirical investigations with 7810 observations.
All the data has been collected from CMIE Prowess database and the qualitative
variables are collected with the help of www.moneycontrol.com

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The independent variables for the estimation of model are Firm Age, Firm Size,
Profitability, Tangibility, Family Firm dummy and Founder Dummy, whereas three
different definitions of leverage are used as dependent variables for estimation.
Table 1: Descriptive statistics (2003-2012)
Variables1
All Firms Family
Firms
Non-family
firms
Number of firms 781 662 119
Percentage ( % ) 100 84.76 15.24
Mean Firm Age: LN(yt – yi) 3.305910 3.264735 3.534964
Mean Firm Size: LN (TA) 7.591089 7.420776 8.538547
Mean Profitability (%) 13.9649 13.3684 17.2829
Mean Tangibility (%) 32.6966 33.6016 27.6618
Mean Market leverage: TL/(TL+ME) 65.41838 67.80373 52.14862
Mean Long term market leverage:
(TL-CL) / (TL + ME)
53.03186 55.30722 40.37397
Mean Financial market leverage:
(TL – P –D –A ) / (TL + ME – D- A –P)
63.12877 65.76926 48.43967
Our data set comprises of 781 firms, of which 84.76 % that is 662 firms are family
firms and the remaining 15.24 % that is 119 firms are non – family firms. The mean
firm age of non – family firms is greater than that of family firms by 8.28 %. Also, the
mean firm size of non – family firms is greater than that of family firms by 15.06%,
this may be due to the fact that non- family firms are mostly either government
enterprises or foreign firms and thus have a greater pool of assets as compared to Indian

19
family firms. Also, the lower firm age of Indian family firms’ maybe due to the fact
that most of them were incorporated after Independence of India, whereas foreign
firms were incorporated much earlier.
1
LN: Natural logarithm, yt: Current year, yi: Incorporation year, TA: Total assets, TL: Total liabilities, ME: Market value of equity, CL:
Current liabilities, P: Provisions, D: Deferred taxes and A: Accounts payable
The mean profitability of Non – family firms is higher than that of family firms by
29.28 % and the mean tangibility of family firms is greater than that of non – family
firms by 21.47 %.
In terms of all the definitions of leverage, the mean value of different leverages is
significantly lower for non – family firms as compared to family firms. In terms of
mean market leverage, non – family firms have 30.02 % lower mean market leverage
than non- family firms, in case of mean long term market leverage non-family firms
reported 36.99 % lower mean long term market leverage than family firms and finally
in the case of mean financial market leverage, non- family firms reported 35.77 %
lower mean financial market leverage than family firms.

20
CHAPTER 4
Results
4.1 Results
First, we regress market leverage on Family firm dummy, Founder dummy,
Profitability, Tangibility, Firm size and Firm age using Pooled OLS regression and
then a similar exercise is carried out with Long term market leverage and financial
market leverage as dependent variables. We get the results as in Table 2, Table 3 and
Table 4 respectively.
Table 2: Pooled OLS regression of Market leverage
Variables Coefficient Standard error t – statistic Probability
Profitability -32.99026 1.539478 -21.42951
0.0000
Tangibility 20.52721 1.245447
16.48181 0.0000
Firm size -1.212197 0.115095
-10.53219 0.0000
Firm Age -1.329933 0.397071
-3.349359 0.0008
Family firm dummy 7.904049 1.152005
6.861124 0.0000
Founder dummy 3.705158 1.055048
3.511838 0.0004
Intercept 67.22375 1.599890
42.01774 0.0000
R- square 0.185576
F 296.3335
N 7810
Durbin-watson 1.868448

21
In the Pooled OLS regression of market leverage, all the variables are coming out to
be statistically significant at 5 % level of significance. Profitability, firm size and firm
age have negative coefficients and hence are negatively related to market leverage
whereas Tangibility, Family firm dummy and Founder dummy have positive
coefficients and hence are positively related to market leverage.
Table 3: Pooled OLS regression of Long term market leverage
Variables Coefficient Standard error t - statistic Probability
Profitability
-25.78215 1.371471 -18.79890 0.0000
Tangibility
31.48159 1.109528 28.37385 0.0000
Firm size
-0.776082 0.102534 -7.569024 0.0000
Firm Age
-2.520180 0.353738 -7.124432 0.0000
Family firm dummy
6.913186 1.026284 6.736133 0.0000
Founder dummy
3.774857 0.939908 4.016197 0.0001
Intercept
51.65685 1.425290 36.24304 0.0000
R- square 0.232191
F 393.2808
N 7810
In the Pooled OLS regression of long term market leverage, all the variables are
coming out to be statistically significant at 5 % level of significance. Profitability, firm
size and firm age have negative coefficients and hence are negatively related to long
term market leverage whereas Tangibility, Family firm dummy and Founder dummy
have positive coefficients and hence are positively related to long term market
leverage.

22
Table 4: Pooled OLS regression of financial market leverage
Profitability
-35.33156 1.593237 -22.17596 0.0000
Tangibility
21.34593 1.288938 16.56087 0.0000
Firm size
-1.282989 0.119114 -10.77114 0.0000
Firm Age
-1.914124 0.410937 -4.657954 0.0000
Family firm dummy
8.873786 1.192233 7.442996 0.0000
Founder dummy
4.049250 1.091890 3.708477 0.0002
Intercept
66.36254 1.655758 40.07985 0.0000
R- square 0.201121
F 327.4066
N 7810
In the Pooled OLS regression of financial market leverage, all the variables are coming
out to be statistically significant at 5 % level of significance. Profitability, firm size
and firm age have negative coefficients and hence are negatively related to financial
market leverage whereas Tangibility, Family firm dummy and Founder dummy have
positive coefficients and hence are positively related to financial market leverage.
Thus, for all the definitions of leverage Pooled OLS regression gives a positive
relationship with Family firm dummy, Founder dummy and Tangibility and a negative
relationship with Profitability, Firm size and Firm age.
Now, we regress market leverage on Family firm dummy, Founder dummy,
Profitability, Tangibility, Firm size and Firm age using Random effects model and then
a similar exercise is carried out with Long term market leverage and financial market
leverage as dependent variables. We get the results as in Table 5, Table 6 and Table 7
respectively

23
Table 5: Random effects model for market leverage
Profitability
-13.80917 1.159212 -11.91255 0.0000
Tangibility
11.90395 1.514961 7.857591 0.0000
Firm size
0.394942 0.208244 1.896532 0.0579
Firm Age
1.188399 0.747071 1.590744 0.1117
Family firm dummy
9.114239 2.608748 3.493722 0.0005
Founder dummy
6.363443 2.385040 2.668066 0.0076
Intercept
43.66921 2.769780 15.76631 0.0000
R- square 0.122688
Ui
1085.2764
Ui
2
1177825
Rho 0.5202
N 7810
F 58.03305
Durbin- watson
1.278378
In the Random effects model regression of market leverage; Profitability, Tangibility,
Family firm dummy and Founder dummy are coming out to be statistically significant
at 5% level of significance, whereas Firm size is statistically significant at 10% level
of significance. Profitability has a negative coefficient and hence is negatively related
to market leverage whereas Firm size, Firm age, Family firm dummy, Founder dummy
and Tangibility have positive coefficients and hence are positively related to market
leverage.

24
Table 6: Random effects model for Long term market leverage
Profitability
-10.05737 1.036111 -9.706845 0.0000
Tangibility
16.90134 1.351742 12.50338 0.0000
Firm size
0.792697 0.185151 4.281353 0.0000
Firm Age
-0.590285 0.663951 -0.889049 0.3740
Family firm dummy
8.439441 2.311788 3.650612 0.0003
Founder dummy
6.118518 2.113623 2.894801 0.0038
Intercept
32.75511 2.463163 13.29798 0.0000
R- square 0.157640
Ui 973.763
Ui
2
948216
Rho 0.5154
N 7810
F 69.84727
Durbin- watson 1.806081
In the Random effects model regression of long term market leverage; Profitability,
Firm size, Tangibility, Family firm dummy and Founder dummy are coming out to be
statistically significant at 5% level of significance. Profitability and Firm age have a
negative coefficient and hence are negatively related to long term market leverage
whereas Firm size, Family firm dummy, Founder dummy and Tangibility have
positive coefficients and hence are positively related to long term market leverage.

25
Table 7: Random effects model for financial market leverage
Variables Coefficient Standard error t - statistic Probability
Profitability
-14.73753 1.188634 -12.39872 0.0000
Tangibility
12.61929 1.556600 8.106960 0.0000
Firm size
0.464387 0.214880 2.161142 0.0307
Firm Age
0.891443 0.771258 1.155829 0.2478
Family firm dummy
10.13141 2.702731 3.748583 0.0002
Founder dummy
6.968535 2.470853 2.820295 0.0048
Intercept
40.37961 2.857324 14.13196 0.0000
R- square 0.134007
Ui 1114.253
Ui
2
1241560
Rho 0.5260
N 7810
F 63.67444
Durbin- watson 1.865114
In the Random effects model regression of financial market leverage; Profitability,
Firm size, Tangibility, Family firm dummy and Founder dummy are coming out to be
statistically significant at 5% level of significance. Profitability has a negative
coefficient and hence is negatively related to financial market leverage whereas Firm
size, Family firm dummy, Founder dummy, Firm age and Tangibility have positive
coefficients and hence are positively related to financial market leverage.

26
Table 8: Fama – Macbeth model for market leverage
Profitability
-48.5316 1.875098 -6.16 0.000
Tangibility
21.03041 1.347015 15.61 0.000
Firm size
-1.373095 .2469419 -5.56 0.000
Firm Age
-2.167572 .6169686 -3.51 0.007
Family firm dummy
7.488387 .5635563 13.29 0.000
Founder dummy
3.302914 .5746036 5.75 0.000
Intercept
73.9061 2.831666 26.10 0.000
R- square 0.2523
N 7810
F 334.32
In the Fama macbeth regression model of market leverage; Profitability, Firm size,
Firm age, Tangibility, Family firm dummy and Founder dummy are coming out to be
statistically significant at 5% level of significance. Profitability, Firm size and Firm
age have negative coefficients and hence are negatively related to market leverage
whereas Family firm dummy, Founder dummy and Tangibility have positive
coefficients and hence are positively related to market leverage.

27
Table 9: Fama - Macbeth model for long term market leverage
Profitability -37.64526 1.961355 -6.31 0.000
Tangibility 31.92614 1.087562 29.36 0.000
Firm size -.9383395 .2067565 -4.54 0.001
Firm Age -3.246189 .4450645 -7.29 0.000
Family firm dummy 6.524419 .377841 17.27 0.000
Founder dummy 3.438107 .6782386 5.07 0.001
Intercept 57.39328 2.317092 24.77 0.000
R- square 0.2877
N 7810
F 816.40
In the Fama macbeth regression model of long term market leverage; Profitability,
Firm size, Firm age, Tangibility, Family firm dummy and Founder dummy are coming
out to be statistically significant at 5% level of significance. Profitability, Firm size
and Firm age have negative coefficients and hence are negatively related to market
leverage whereas Family firm dummy, Founder dummy and Tangibility have positive
coefficients and hence are positively related to long term market leverage.

28
Table 10: Fama- Macbeth model for financial market leverage
Profitability -51.7248 1.185267 -6.32 0.000
Tangibility 21.90625 1.367689 16.02 0.000
Firm size -1.457767 .2609573 -5.59 0.000
Firm Age -2.806753 .6402859 -4.38 0.002
Family firm dummy 8.423532 .5350889 15.74 0.000
Founder dummy 3.622334 .6050136 5.99 0.000
Intercept 73.47826 2.881459 25.50 0.000
R- square 0.2692
N 7810
F 379.46
In the Fama macbeth regression model of financial market leverage; Profitability, Firm
size, Firm age, Tangibility, Family firm dummy and Founder dummy are coming out
to be statistically significant at 5% level of significance. Profitability, Firm size and
Firm age have negative coefficients and hence are negatively related to market
leverage whereas Family firm dummy, Founder dummy and Tangibility have positive
coefficients and hence are positively related to financial market leverage.

29
Table 11: Pooled OLS, Random effects and FM model for market leverage
Variables Coefficien
t (OLS)
Coefficien
t (RE)
Coefficien
t (FM)
Standar
d error
(OLS)
Standar
d error
(RE)
Standar
d error
(FM)
t –
statistic
(OLS)
t –
statistic
(RE)
t –
statisti
c (FM)
Probabilit
y (OLS)
Probabilit
y (RE)
Probabilit
y (FM)
Profitabilit
y
-32.99026
-13.80917 -48.5316
1.539478
1.15921
2
1.87509
8
-
21.4295
1
-
11.9125
5 -6.16 0.0000 0.0000 0.000
Tangibility 20.52721
11.90395 21.03041
1.245447
1.51496
1
1.34701
5
16.4818
1
7.85759
1 15.61 0.0000 0.0000 0.000
Firm size -1.212197
0.394942 -1.373095
0.115095
0.20824
4
.246941
9
-
10.5321
9
1.89653
2 -5.56 0.0000 0.0579 0.000
Firm Age -1.329933
1.188399 -2.167572
0.397071
0.74707
1
.616968
6
-
3.34935
9
1.59074
4 -3.51 0.0008 0.1117 0.007
Family
firm
dummy
7.904049
9.114239 7.488387
1.152005
2.60874
8
.563556
3
6.86112
4
3.49372
2 13.29 0.0000 0.0005 0.000
Founder
dummy
3.705158
6.363443 3.302914
1.055048
2.38504
0
.574603
6
3.51183
8
2.66806
6 5.75 0.0004 0.0076 0.000
Intercept 67.22375
43.66921 73.9061
1.599890
2.76978
0
2.83166
6
42.0177
4
15.7663
1 26.10 0.0000 0.0000 0.000
R- square 0.185576 0.122688 0.2523
N 7810 7810 7810
F 296.3335 58.03305 334.32
The following results were obtained for market leverage:
The coefficient of profitability is statistically significant and negative in case of all
three (OLS, RE & FM) estimation procedures, thus profitability is negatively related
to market leverage. The coefficient of tangibility is statistically significant and positive
in case of all three (OLS, RE & FM) estimation procedures, thus tangibility is
positively related to market leverage. The coefficient of Firm size is statistically
significant and negatively related to market leverage in case of OLS & FM estimation
procedures whereas it is statistically insignificant and positively related to market
leverage in case of RE estimation procedure. The coefficient of Firm age is statistically
significant and negatively related to market leverage in case of OLS & FM estimation
procedures whereas it is statistically insignificant and positively related to market
leverage in case of RE estimation procedure.

30
The coefficients of Family firm dummy and Founder dummy are statistically
significant and positive in in case of all three (OLS, RE & FM) estimation procedures,
thus Family firm dummy and Founder dummy are positively related to market leverage
Table 12: Pooled OLS, Random effects and FM model for long term market
leverage
t (OLS)
Coefficien
t (RE)
Coefficien
t (FM)
Standar
d error
(OLS)
Standar
d error
(RE)
Standar
d error
(FM)
t –
statistic
(OLS)
t –
statistic
(RE)
t –
statisti
c (FM)
Probabilit
y (OLS)
Probabilit
y (RE)
Probabilit
y (FM)
Profitabilit
y
-25.78215 -10.05737
-37.64526
1.37147
1
1.03611
1
1.961355 -
18.7989
0
-
9.70684
5
-6.31
0.0000 0.0000
0.000
Tangibility
31.48159 16.90134
31.92614
1.10952
8
1.35174
2
1.087562
28.3738
5
12.5033
8
29.36
0.0000 0.0000
0.000
Firm size
-0.776082 0.792697
-.9383395
0.10253
4
0.18515
1
.2067565 -
7.56902
4
4.28135
3
-4.54
0.0000 0.0000
0.001
Firm Age
-2.520180 -0.590285
-3.246189
0.35373
8
0.66395
1
.4450645 -
7.12443
2
-
0.88904
9
-7.29
0.0000 0.3740
0.000
Family
firm
dummy
6.913186 8.439441
6.524419
1.02628
4
2.31178
8
.377841
6.73613
3
3.65061
2
17.27
0.0000 0.0003
0.000
Founder
dummy
3.774857 6.118518
3.438107
0.93990
8
2.11362
3
.6782386
4.01619
7
2.89480
1
5.07
0.0001 0.0038
0.001
Intercept
51.65685 32.75511
57.39328
1.42529
0
2.46316
3
2.317092
36.2430
4
13.2979
8
24.77
0.0000 0.0000
0.000
R- square
0.232191 0.157640
0.2877
N 7810 7810 7810
F 393.2808 69.84727 816.40
The following results were obtained for long term market leverage:
to long term market leverage.

31
The coefficient of tangibility is statistically significant and positive in case of all three
(OLS, RE & FM) estimation procedures, thus tangibility is positively related to long
term market leverage.
The coefficient of Firm size is statistically significant and negatively related to long
term market leverage in case of OLS & FM estimation procedures whereas it is
statistically significant and positively related to long term market leverage in case of
RE estimation procedure.
The coefficient of Firm age is statistically significant and negatively related to long
term market leverage in case of OLS & FM estimation procedures whereas it is
statistically insignificant and negatively related to long term market leverage in case
of RE estimation procedure.
thus Family firm dummy and Founder dummy are positively related to market
leverage.

32
Table 13: Pooled OLS, Random effects and FM model for financial market
leverage
t (OLS)
Coefficien
t (RE)
Coefficien
t (FM)
Standar
d error
(OLS)
Standar
d error
(RE)
Standar
d error
(FM)
t –
statistic
(OLS)
t –
statistic
(RE)
t –
statisti
c (FM)
Probabilit
y (OLS)
Probabilit
y (RE)
Probabilit
y (FM)
Profitabilit
y
-35.33156 -14.73753
-51.7248
1.59323
7
1.18863
4
1.185267 -
22.1759
6
-
12.3987
2
-6.32
0.0000 0.0000
0.000
Tangibility
21.34593 12.61929
21.90625
1.28893
8
1.55660
0
1.367689
16.5608
7
8.10696
0
16.02
0.0000 0.0000
0.000
Firm size
-1.282989 0.464387
-1.457767
0.11911
4
0.21488
0
.2609573 -
10.7711
4
2.16114
2
-5.59
0.0000 0.0307
0.000
Firm Age
-1.914124 0.891443
-2.806753
0.41093
7
0.77125
8
.6402859 -
4.65795
4
1.15582
9
-4.38
0.0000 0.2478
0.002
Family
firm
dummy
8.873786 10.13141
8.423532
1.19223
3
2.70273
1
.5350889
7.44299
6
3.74858
3
15.74
0.0000 0.0002
0.000
Founder
dummy
4.049250 6.968535
3.622334
1.09189
0
2.47085
3
.6050136
3.70847
7
2.82029
5
5.99
0.0002 0.0048
0.000
Intercept
66.36254 40.37961
73.47826
1.65575
8
2.85732
4
2.881459
40.0798
5
14.1319
6
25.50
0.0000 0.0000
0.000
R- square 0.201121
0.134007
0.2692
N 7810 7810 7810
F 327.4066 63.67444 379.46
to financial market leverage.
The coefficient of tangibility is statistically significant and positive in case of all three
(OLS, RE & FM) estimation procedures, thus tangibility is positively related to
financial market leverage.
The coefficient of Firm size is statistically significant and negatively related to
financial market leverage in case of OLS & FM estimation procedures whereas it is
statistically significant and positively related to financial market leverage in case of
The coefficient of Firm age is statistically significant and negatively related to
financial market leverage in case of OLS & FM estimation procedures whereas it is

33
statistically insignificant and negatively related to financial market leverage in case of
thus Family firm dummy and Founder dummy are positively related to financial
market leverage.
4.2 Interpretation
In the light of the results obtained in this study, we can clearly state that Profitability
has a negative relationship with all the stated definitions of leverage and this result is
in accordance with the Pecking order theory, that is as the profitability of a firm
increases then its retained earnings also increase and it has to rely less on leverage to
finance new investments therefore causing a reduction in the amount of leverage used.
Tangibility has a positive relationship with all the stated definitions of leverage and
this is because tangible assets are relatively easier to collateralize as compared to
intangible assets and hence a growth in tangibility of the firm results in an increased
borrowing capacity and a reduced borrowing, both factors contributing to an increased
incentive to issue more debt and hence increased leverage.
Firm Size has a negative relationship with all definitions of leverage in case of Pooled
OLS regression whereas a positive relationship with all definitions of leverage in case
of Random effects model, we choose the results of Random effects model because the
random effects model is consistent even if the true model is the pooled estimator. Thus,
firm size has a positive relationship with leverage and this result is consistent with the
previous result of tangibility because as firm size increases then there is a growth of
Total assets and hence a growth of Tangible assets, which reflect a greater borrowing
capacity due to an increase in the capacity of collateralization and therefore an increase
in leverage.

34
Firm age is statistically insignificant in case of Random effects model but statistically
significant in case of Pooled OLS regression and has a negative relationship with
leverage, which may be due to the fact that as firm grows, it shifts from large
concentrated shareholding towards divided shareholding and the control motivation
declines and it may also be because of the efficient utilization of resources by the firm
as it grows with time. But, as Firm age turns out to be statistically insignificant, this
study does not provide a clear view about its impact on leverage.
Family firm dummy and Founder dummy both are statistically significant in case of
both Pooled OLS regression model and Random effects model, both the dummy
variables have a positive relationship with all the stated definitions of leverage.
Thus, we can state that in case of the Indian economy family firms are more dominated
by the motivation to have control over their firms rather than the motivation to reduce
firm specific risk and hence in order to maintain control over their firms they have
higher leverage so as to finance new investments without diluting their stake in the
firm, which happens in the case where financing is done through equity.
The presence of founder as Chairman, Managing director or CEO of the firm has a
positive relationship with leverage or an increased leverage because Founder member
will have the highest motivation to have control over his firm and will never want to
get his/ her stake in the company to get diluted, thus preferring more debt to equity.
Founder members are also very keen on fast growth of their firms and hence they
rapidly invest in new technologies and projects, which increases investment and this
investment is financed through leverage so as to avoid dilution in their stake.

35
CHAPTER 5
Conclusion
The purpose of this study was to see whether in case of Indian family firms, the
motivation to have control over the firm dominates the motivation to reduce firm
specific risk or not and we have concluded through our findings that family firms have
higher leverage as compared to non- family firms and this leverage is used as a tool
for financing new investments without diluting their stake in the firm.
It is also evident that if the founder is the Chairman, Managing director or CEO of the
firm then the motivation for control would be even stronger and this will result in a
higher leverage.
We also observe that mean profitability of non- family firms is higher than the mean
profitability of family firms by 29.28 %, which indicates that large concentrated
shareholders like family firms derive greater benefits from pursuing objectives such as
firm survival or technological innovation rather than enhancing shareholder’s value.
Families may also expropriate wealth from the firm through excessive compensation,
related party transactions or special dividends which can impact the firm’s capital
expansion plans and lead to poor operating and stock price performance.

36
References
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Christoph, Kaserer ; Thomas , Schmid. (March 2009) Capital structure
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Working paper no.2009-5 CEFS
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 Flannery, Mark J. & Rangan, Kasturi P.(March 2006). Partial adjustment
toward target capital structures. Journal of Financial Economics. vol. 79(3),
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 Raghuram G. Rajan & Luigi Zingales. (December 1995). What Do We
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37
 Andrew Ellul. (March 2006). Control motivations and capital structure
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417

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