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

ii

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

iii

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.

iv

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

v

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

1

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.

4

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

5

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.

14

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


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


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


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

Ui2 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


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

Ui2 948216

Rho 0.5154

N 7810

F 69.84727


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


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

Ui2 1241560

Rho 0.5260

N 7810

F 63.67444


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


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


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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417

impact of family ownership on capital structure decisions – an indian study

Documents

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non family firms

founding family ownership

capital structure decisions

value creation of family

project work

project report

firm performance