a study on the determinants of capital...

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A STUDY ON THE DETERMINANTS OF CAPITAL

STRUCTURE AND PROFITABILITY

(With Special Reference to Food Industry in India)

Thesis submitted to Pondicherry University in partial fulfillment of

the requirements for the award of the degree of

DOCTOR OF PHILOSOPHY

IN COMMERCE

By R. DEEPA

Under the guidance and supervision of

Dr. R. AZHAGAIAH Associate Professor of Commerce

Kanchi Mamunivar Centre for Post-Graduate Studies (Autonomous) (Centre with Potential for Excellence by UGC)

(Government of Puducherry) Puducherry-605 008, India

DEPARTMENT OF COMMERCE KANCHI MAMUNIVAR CENTRE FOR POST GRADUATE STUDIES

(AUTONOMOUS) (CENTRE WITH POTENTIAL FOR EXCELLENCE BY UGC)

PUDUCHERRY – 605 008, INIDA

AUGUST 2011

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Place : Puducherry

Date:

CERTIFICATE

This is to certify that the thesis entitled “A STUDY ON THE

DETERMINANTS OF CAPITAL STRUCTURE AND PROFITABILITY (With

Special Reference to Food Industry in India)” submitted for the award of the Degree

of Doctor of Philosophy in Commerce by R. DEEPA is the bonofide research work

carried out by her independently under my guidance and supervision. I also certify

that this has not been previously submitted for the award of any degree or diploma or

associateship to any other university or institution.

Dr.R.AZHAGAIAH

Research Guide & Supervisor

HOD DEPARTMENT OF COMMERCE

DIRECTOR KANCHI MAMUNIVAR CENTRE FOR POST GRADUATE STUDIES

PUDUCHERRY

Dr. R. AZHAGAIAH Associate Professor of Commerce Kanchi Mamunivar Centre for Post-Graduate Studies (Autonomous) (Centre with Potential for Excellence by UGC) (Government of Puducherry) Puducherry – 605 008, India.

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R. DEEPA Ph. D., Research Scholar Department of Commerce Kanchi Mamunivar Centre for Post-Graduate Studies (Autonomous) (Centre with Potential for Excellence by UGC) (Government of Puducherry) Puducherry-605 008

DECLARATION

I hereby declare that the thesis entitled “A STUDY ON THE

DETERMINANTS OF CAPITAL STRUCTURE AND PROFITABILITY

(With Special Reference to Food Industry in India)” submitted to the Pondicherry

University in partial fulfillment of the requirements for the award of the degree of

DOCTOR OF PHILOSOPHY IN COMMERCE is my original work and it has not

been previously submitted either in part or whole to this or any other University for

the award of any Degree / Diploma.

Place : Puducherry

Date: (R. DEEPA)

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ACKNOWLEDGEMENTS I have great pleasure in expressing my gratitude to the people who enabled me

for the successful completion of the work. I take this opportunity to express my deep

sense of gratitude to my research guide and supervisor, Dr. R. Azhagaiah, Associate

Professor, Department of Commerce, Kanchi Mamunivar Centre for Post-Graduate

Studies, (Autonomous), (Centre with Potential for Excellence by UGC), Puducherry

for his keen interest, valuable guidance, useful suggestions and kind advice for the

successful completion of my thesis work.

I record my hearty thanks to Dr. O. P. Shyma, Director, Kanchi Mamunivar

Centre for Post Graduate Studies, (Autonomous), (Centre with Potential for

Excellence by UGC), Puducherry for giving me an opportunity to do the Ph.D. course

and for her total encouragement throughout the period of the study.

It is my privilege to express my sincere thanks to Dr. Chinta Venkateswara

Rao, Head of the Department of Commerce, Kanchi Mamunivar Centre for Post

Graduate Studies, (Autonomous), Puducherry for all the help that he has extended and

encouragement given to me. I also express my gratitude and respect to other members

of the Faculty of Commerce, Kanchi Mamunivar Centre for Post Graduate Studies,

(Autonomous), Puducherry for their word of advice and encouragement.

I express gratefulness to my Doctoral Committee members Dr. D.Aravazhi

Irissappane, Associate Professor, Department of Commerce, Kanchi Mamunivar

Centre for Post Graduate Studies, (Autonomous), Puducherry and

Dr.B.Charumathi, Associate Professor, Department of Management Studies, School

of Management, Pondicherry University, Puducherry for their critical comments and

suggestions in shaping the research work in every stage.

I also wish to express my sincere thanks to all my family members, fellow-

scholars and my friends for rendering timely help and encouragement for the

successful completion of this thesis.

Last but not the least, I am thankful to the lord almighty for having best-owed

upon me his grace, without which I would not have competed my thesis work

successfully.

(R. DEEPA)

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CONTENTS CERTIFICATE

DECLARATION ACKNOWLEDGEMENTS LIST OF TABLES LIST OF CHARTS, GRAPHS, AND DIAGRAMS LIST OF ACRONYMS

CHAPTER TITLE PAGE NO. I INTRODUCTION AND DESIGN OF THE STUDY

I.1 Introduction I.2 Capital Structure Theories 1.3 Profitability Theories I.4 Food Industry in India I.5 Food Management in India I.6 Classification of Food Industry in India I.7 Advantages and Challenges Faced by Food Industry in India I.8 Current Status of Food Industry in India I.9 Origin of the Research Problem I.10 Objectives of the Study I.11 Hypotheses Development I.12 Methodology of the Study I.13 Plan of Analysis I.14 Limitations of the Study I.15 Chapter Design I.16 Conclusion

1-38

II REVIEW OF LITERATURE II.1 Introduction II.2 Previous Studies II.3 Research Gap and Concluding Remarks

39-66

III CONCEPTS AND THEORIES OF CAPITAL STRUCTURE AND PROFITABILITY: A REVIEW

III.1 Introduction III.2 Leverage III.3 Capital Structure Theories III.4 Profitability Theories

III.5 Conclusion

67-86

IV DETERMINANTS OF PROFITABILITY AND CAPITAL STRUCTURE: ANALYSIS BY FIRM SIZE-WISE, INCOME SIZE-WISE AND SECTOR-WISE APPROACHES

PART I Analysis of Capital Structure of Selected Firms: Firm Size-Wise, Income Size-Wise, and Sector-Wise Approach

IV.1 Introduction IV.2 Objectives of Part I

`IV.3 Hypotheses Development

87-219

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IV.4 Research Methods of the Study IV.5 Overall Analysis of Relation between PBITD and

Various Constituents of CS IV.6 Sales Size- wise Analysis of Relation between

PBITD and Various Constituents of CS IV.7 Income Size- wise Analysis of Relation between

PBITD and Various Constituents of CS IV.8 Sector-wise Analysis of Relation between PBITD

and Various Constituents of CS IV.9 Conclusion PART II Determinants of Capital Structure and

Profitability: Firm Size- wise, Income Size-wise, and Sector-wise Approaches

IV.10 Introduction IV.11 Objectives of Part II IV.12 Hypotheses Development IV.13 Research Methods IV.14 Regression on Determinants of LEV (Equation 1)

IV.15 Overall Analysis of Determinants of LEV IV.16 Sales Size-wise Analysis of Determinants of LEV IV.17Income-wise Analysis of Determinants of LEV IV.18 Sector-wise Analysis of Determinants of LEV IV.19 Conclusion IV.20 Regression on Determinants of P (Equation II) IV.21 Hypotheses Development IV.22 Overall Analysis of Determinants of P IV.23 Sales Size-wise Analysis of Determinants of P IV.24 Income Size-wise Analysis of Determinants of P IV.25 Sector-wise Analysis of Determinants of P

IV.26 Conclusion IV.27 Conclusive Remarks

V SUMMARY OF FINDINGS, CONCLUSION, SUGGESTIONS, AND SCOPE FOR FURTHER STUDIES

V.1 Origin of the Research Problem V.2 Objectives of the Study V.3 Hypotheses of the Study V.4 Methodology V.5 Regression Analysis V.6 Summary of Findings of the Analysis V.6.1 Preliminary Analysis of the Relation

between PBITD and Different Constituents of CS

V.6.2 Results of Analysis of Determinants of LEV V.6.3 Results of Analysis of Determinants of P V.7 Conclusion V.8 Suggestions for Improvement of the Food Industry V.9 Limitations of the Study V.10 Scope for Further Studies

220-237

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BIBLIOGRAPHY

APPENDICES

APPENDIX-A List of Firms of Food Industry in India Selected For the Study

APPENDIX-B Sales Size-Wise Grouping of Firms APPENDIX-C Income Size-Wise Grouping of Firms APPENDIX-D Sector-Wise Grouping of Firms

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LIST OF TABLES

TABLE NO.

TITLE PAGE NO.

I.1 Production and Procurement of Rice and Wheat in India 7

I.2 Agriculture Related FDI Inflows in India 12

I.3 Food Product Firms under various Categories 22

I.4 Description of Ratios Used 24

II .1 Summary of Important Research Works 40

IV.1 Overall Correlation Matrix of Food Industry in India 90

IV.2 Regressions on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Overall) 92

IV.3 Correlation Matrix of Small Size Firms of Food Industry in India 94

IV.4 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Small Size Firms)

95

IV.5 Correlation Matrix of Medium Size Firms of Food Industry in India 96

IV.6 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Medium Size Firms)

97

IV.7 Correlation Matrix of Large Size Firms of Food Industry in India 98

IV.8 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Large Size Firms)

99

IV.9 Correlation Matrix of Low Income Size Firms of Food Industry in India 101

IV.10 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Low Income Size Firms)

102

IV.11 Correlation Matrix of Medium Income Size Firms of Food Industry in India 103

IV.12 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Medium Income Size Firms)

104

IV.13 Correlation Matrix of High Income Size Firms of Food Industry in India 105

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IV.14 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (High Income Size Firms)

106

IV.15 Correlation Matrix of Sector-wise Firms of Food Industry in India (Sector I) 109

IV.16 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Sector I) 110

IV.17 Correlation Matrix of Sector-wise Firms of Food Industry in India (Sector II) 111

IV.18 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Sector II) 112

IV.19 Correlation Matrix of Sector-wise Firms of Food Industry in India (Sector III) 113

IV.20 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in India (Sector III) 114

IV.21 Summary of Overall Results of the Relation between PBITD and the Constituents of CS 115

IV.22 Summary of Sales Size-wise Analysis of the Relation between PBITD and the Constituents of CS 116

IV.23 Summary of Income Size-wise Analysis of the Relation between PBITD and the Constituents of CS 117

IV.24 Summary of Sector-wise Analysis of the Relation between PBITD and the Constituents of CS 118

IV.25 Ratios of Independent Variables Determining LEV 123

IV. 26 Overall Descriptive Statistics of Determinants of LEV of Food Industry in India 124

IV. 27 Overall Correlation Matrix of Determinants of LEV of Food Industry in India 125

IV. 28 Overall Results of Regression on Determinants of LEV of Food Industry in India 127

IV. 29 KMO and Bartlett's Test of Determinants of LEV of Food Industry in India 127

IV. 30 Overall Factor Analysis of Determinants of LEV of Food Industry in India 128

IV. 31 Descriptive Statistics of Determinants of LEV of Small Size Firms of Food Industry in India 130

IV. 32 Correlation Matrix of Determinants of LEV of Small Size Firms of Food Industry in India 131

IV. 33 Results of Regression on Determinants of LEV of Small Size Firms of Food Industry in India 132

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IV. 34 KMO and Bartlett's Test of Determinants of LEV of Small Size Firms of Food Industry in India 133

IV. 35 Factor Analysis of Determinants of LEV of Small Size Firms of Food Industry in India 133

IV. 36 Descriptive Statistics of Determinants of LEV of Medium Size Firms of Food Industry in India 135

IV. 37 Correlation Matrix of Determinants of LEV of Medium Size Firms of Food Industry in India 136

IV. 38 Results of Regression on Determinants of LEV of Medium Size Firms of Food Industry in India 137

IV. 39 KMO and Bartlett's Test of Determinants of LEV of Medium Size Firms of Food Industry in India 137

IV. 40 Factor Analysis of Determinants of LEV of Medium Size Firms of Food Industry in India 138

IV. 41 Descriptive Statistics of Determinants of LEV of Large Size Firms of Food Industry in India 139

IV. 42 Correlation Matrix of Determinants of LEV of Large Size Firms of Food Industry in India 140

IV. 43 Results of Regression on Determinants of LEV of Large Size Firms of Food Industry in India 141

IV. 44 Descriptive Statistics of Determinants of LEV of Low Income Size Firms of Food Industry in India 143

IV. 45 Correlation Matrix of Determinants of LEV of Low Income Size Firms of Food Industry in India 144

IV. 46 Results of Regression on Determinants of LEV of Low Income Size Firms of Food Industry in India 145

IV. 47 KMO and Bartlett's Test of Determinants of LEV of Low Income Size Firms of Food Industry in India 146

IV. 48 Factor Analysis of Determinants of LEV of Low Income Size Firms of Food Industry in India 146

IV. 49 Descriptive Statistics of Determinants of LEV of Medium Income Size Firms of Food Industry in India 148

IV. 50 Correlation Matrix of Determinants of LEV of Medium Income Size Firms of Food Industry in India 149

IV. 51 Results of Regression on Determinants of LEV of Medium Income Size Firms of Food Industry in India 150

IV. 52 KMO and Bartlett's Test of Determinants of LEV of Medium Income Size Firms of Food Industry in India 150

IV. 53 Factor Analysis of Determinants of LEV of Medium Income Size Firms of Food Industry in India 151

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IV. 54 Descriptive Statistics of Determinants of LEV of High Income Size Firms of Food Industry in India 152

IV. 55 Correlation Matrix of Determinants of LEV of High Income Size Firms of Food Industry in India 154

IV. 56 Results of Regression on Determinants of LEV of High Income Size Firms of Food Industry in India 155

IV. 57 Descriptive Statistics of Determinants of LEV of Firms of Sector I of Food Industry in India 157

IV. 58 Correlation Matrix of Determinants of LEV of Firms of Sector I of Food Industry in India 158

IV. 59 Results of Regression on Determinants of LEV of Firms of Sector I of Food Industry in India 159

IV. 60 Descriptive Statistics of Determinants of LEV of Firms of Sector II of Food Industry in India 160

IV. 61 Correlation Matrix of Determinants of LEV of Firms of Sector II of Food Industry in India 161

IV. 62 Results of Regression of Determinants of LEV of Firms of Sector II of Food Industry in India 162

IV. 63 KMO and Bartlett's Test of Determinants of LEV of Firms of Sector II of Food Industry in India 163

IV. 64 Factor Analysis of Determinants of LEV of Firms of Sector II of Food Industry in India 163

IV. 65 Descriptive Statistics of Determinants of LEV of Firms of Sector III of Food Industry in India 165

IV. 66 Correlation Matrix of Determinants of LEV of Firms of Sector III Food Industry in India 166

IV. 67 Results of Regression on Determinants of LEV of Firms of Sector III of Food Industry in India 167

IV. 68 KMO and Bartlett's Test of Determinants of LEV of Firms of Sector III of Food Industry in India 167

IV. 69 Factor Analysis of Determinants of LEV of Firms of Sector III of Food Industry in India 168

IV.70 Summary of Overall Results of the Determinants of LEV 169

IV.71 Summary of Sales Size-wise Analysis of the Determinants of LEV 171

IV.72 Summary of Income Size-wise Analysis of the Determinants of LEV 173

IV.73 Summary of Sector-wise Analysis of the Determinants of LEV 174

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IV.74 Ratios of Independent Variables Determining P 176

IV. 75 Overall Descriptive Statistics of Determinants of P of Food Industry in India 177

IV. 76 Overall Correlation Matrix of Determinants of P of Food Industry in India 178

IV. 77 Overall Results of Regression on Determinants of P of Food Industry in India 179

IV. 78 KMO and Bartlett's Test of Determinants of P of Food Industry in India 179

IV. 79 Overall Factor Analysis of Determinants of P of Food Industry in India 180

IV. 80 Descriptive Statistics of Determinants of P of Small Size Firms of Food Industry in India 182

IV. 81 Correlation Matrix of Determinants of P of Small Size Firms of Food Industry in India 183

IV. 82 Results of Regression on Determinants of P of Small Size Firms of Food Industry in India 184

IV. 83 KMO and Bartlett's Test of Determinants of P of Small Size Firms of Food Industry in India 185

IV. 84 Factor Analysis of Determinants of P of Small Size Firms of Food Industry in India 185

IV. 85 Descriptive Statistics of Determinants of P of Medium Size Firms of Food Industry in India 186

IV. 86 Correlation Matrix of Determinants of P of Medium Size Firms of Food Industry in India 187

IV. 87 Results of Regression on Determinants of P of Medium Size Firms of Food Industry in India 188

IV. 88 KMO and Bartlett's Test of Determinants of P of Medium Size Firms of Food Industry in India 188

IV. 89 Factor Analysis of Determinants of P of Medium Size Firms of Food Industry in India 189

IV. 90 Descriptive Statistics of Determinants of P of Large Size Firms of Food Industry in India 189

IV. 91 Correlation Matrix of Determinants of P of Large Size Firms of Food Industry in India 190

IV. 92 Results of Regression on Determinants of P of Large Size Firms of Food Industry in India 191

IV. 93 KMO and Bartlett's Test of Determinants of P of Large Size Firms of Food Industry in India 191

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IV. 94 Factor Analysis of Determinants of P of Large Size Firms of Food Industry in India 192

IV. 95 Descriptive Statistics of Determinants of P of Low Income Size Firms of Food Industry in India 194

IV. 96 Correlation Matrix of Determinants of P of Low Income Size Firms of Food Industry in India 195

IV. 97 Results of Regression on Determinants of P of Low Income Size Firms of Food Industry in India 196

IV. 98 KMO and Bartlett's Test of Determinants of P of Low Income Size Firms of Food Industry in India 196

IV. 99 Factor Analysis of Determinants of P of Low Income Size Firms of Food Industry in India 197

IV. 100 Descriptive Statistics of Determinants of P of Medium Income Size Firms of Food Industry in India 197

IV. 101 Correlation Matrix of Determinants of P of Medium Income Size Firms of Food Industry in India 198

IV. 102 Results of Regression on Determinants of P of Medium Income Size Firms of Food Industry in India 199

IV. 103 KMO and Bartlett's Test of Determinants of P of Medium Income Size Firms of Food Industry in India 200

IV. 104 Factor Analysis of Determinants of P of Medium Income Size Firms of Food Industry in India 200

IV. 105 Descriptive Statistics of Determinants of P of High Income Size Firms of Food Industry in India 201

IV. 106 Correlation Matrix of Determinants of P of High Income Size Firms of Food Industry in India 202

IV. 107 Results of Regression of Determinants of P of High Income Size Firms of Food Industry in India 203

IV. 108 KMO and Bartlett's Test of Determinants of P of High Income Size Firms of Food Industry in India 203

IV. 109 Factor Analysis of Determinants of P of High Income Size Firms of Food Industry in India 204

IV. 110 Descriptive Statistics of Determinants of P of Firms of Sector I of Food Industry in India 206

IV. 111 Correlation Matrix of Determinants of P of Firms of Sector I of Food Industry in India 207

IV. 112 Results of Regression on Determinants of P of Firms of Sector I of Food Industry in India 208

IV. 113 KMO and Bartlett's Test of Determinants of P of Firms of Sector I of Food Industry in India 208

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IV. 114 Factor Analysis of Determinants of P of Firms of Sector I of Food Industry in India 209

IV. 115 Descriptive Statistics of Determinants of P of Firms of Sector II of Food Industry in India 209

IV. 116 Correlation Matrix of Determinants of P of Firms of Sector II Food Industry in India 210

IV. 117 Results of Regression on Determinants of P of Firms of Sector II of Food Industry in India 211

IV. 118 KMO and Bartlett's Test of Determinants of P of Firms of Sector II of Food Industry in India 211

IV. 119 Factor Analysis of Determinants of P of Firms of Sector II of Food Industry in India 212

IV. 120 Descriptive Statistics of Determinants of P of Firms of Sector III of Food Industry in India 212

IV. 121 Correlation Matrix of Determinants of P of Firms of Sector III Food Industry in India 213

IV. 122 Results of Regression of Determinants of P of Firms of Sector III of Food Industry in India 214

IV. 123 KMO and Bartlett's Test of Determinants of P of Firms of Sector III of Food Industry in India 214

IV. 124 Factor Analysis of Determinants of P of Firms of Sector III of Food Industry in India 215

IV.125 Summary of Overall Results of the Determinants of P 216

IV.126 Summary of Sales Size-wise Analysis of the Determinants of P 217

IV.127 Summary of Income Size-wise Analysis of the Determinants of P 218

IV.128 Summary of Sector-wise Analysis of the Determinants of P 219

V.1

Summary of Findings - Regression Results of Preliminary Analysis 229

V.2 Summary of Findings - Regression Results of Determinants of LEV 231

V.3 Summary of Findings - Regression Results of Determinants of P 232

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LIST OF CHARTS, GRAPHS, AND DIAGRAMS CHART NO. TITLE PAGE NO.

IV.A Trend Line Showing Relation between PBITD and Various Constituents of CS 90

IV.B Comparison of Trend line showing PBITD of Small Size, Medium Size, and Large Size Firms 93

IV.C Comparison of Trend Line Showing Total Debt of Small Size, Medium Size Firms, and Large Size Firms

93

IV.D Comparison of Trend line showing PBITD of Low Income Size, Medium Income Size, and High Income Size Firms

100

IV.E Comparison of Trend line showing Total Debt of Low Income Size, Medium Income Size, and High Income Size Firms

100

IV.F Comparison of Trend Line showing PBITD of Firms of Sector I, Sector II and Sector III 107

IV.G Comparison of Trend Line showing Total Debt of Firms of Sector I, Sector II and Sector III 108

IV.H Overall Trend Line Showing Relation between P and LEV_TD 124

IV.I Trend Line Showing Relation between P and LEV_TD of Small Size Firms 129

IV.J Trend Line Showing Relation between P and LEV_TD of Medium Size Firms 134

IV.K Trend Line Showing Relation between P and LEV_TD of Large Size Firms 139

IV.L Trend Line Showing Relation between P and LEV_TD of Low Income Size Firms 142

IV.M Trend Line Showing Relation between P and LEV_TD of Medium Income Size Firms 147

IV.N Trend Line Showing Relation between P and LEV_TD of High Income Size Firms 152

IV.O Trend Line Showing Relation between P and LEV_TD of Firms of Sector I 156

IV.P Trend Line Showing Relation between P and LEV_TD of Firms of Sector II 160

IV.Q Trend Line Showing Relation between P and LEV_TD of Firms of Sector III 164

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IV.R Overall Trend Line Showing Relation between P_SAL and P_TASSET 177

IV.S Trend Line Showing Comparison between P_TASSET of Small Size Firms, Medium Size Firms and Large Size Firms

181

IV.T Trend Line Showing Comparison between P_SAL of Small Size Firms, Medium Size Firms and Large Size Firms

182

IV.U Trend Line Showing Relation between P_TASSET of Low Income Size Firms, Medium Income Size Firms and High Income Size Firms

193

IV.V Trend Line Showing Relation between P_SAL of Low Income Size Firms, Medium Income Size Firms and High Income Size Firms

193

IV.W Trend Line Showing Comparison between P_TASSET of Firms of Sector I, Sector II and Sector III

205

IV.X Trend Line Showing Relation between P_SAL of Firms of Sector I, Sector II and Sector III 205

GRAPH NO. TITLE PAGE NO.

III.A

Behaviour of rA , rD and rE as per the Net Income Approach 70

III.B Behaviour of rA , rD and rE as per the Net Operating Income Approach 71

DIAGRAM NO. TITLE PAGE NO.

I.A Classification of Food Industry 9

III. A Factors Determining Capital Structure 68

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LIST OF ACRONYMS

AG : Age

AIP : Aggressive Investing Policy

APMC : Agriculture Produce Marketing Act

BSE : Bombay Stock Exchange

CAPINS : Capital Intensity

CMIE : Centre for Monitoring Indian Economy

COLASS : Collateral Asset

CS : Capital Structure

EPS : Earnings Per Share

FCI : Food Corporation of India

FDI : Foreign Direct Investment

FICCI : Federation of Indian Chambers of Commerce and Industry

FL : Financial Leverage

GDP : Gross Domestic Product

GROW : Growth

ICDS : Integrated Child Development Services

LEV : Leverage

LEV_LTD : Leverage to Long Term Debt

LEV_STD : Leverage to Short Term Debt

LEV_TD : Leverage to Total Debt

LIQ : Liquidity

LTD : Long Term Debt

MFPs : Mega Food Parks

MMA : Modigliani-Miller Approach

MSP : Minimum Support Price

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NAC : National Advisory Council

NDTXSH : Non Debt Tax Shield

NFSA : National Food Security Act

NIA : Net Income Approach

NOIA : Net Operating Income Approach

NSE : National Stock Exchange

P : Profitability

P_SAL : Profitability to Sales

P_TASSET : Profitability to Total Asset

PBITD : Profit before Interest, Tax and Depreciation

PDS : Public Distribution System

PPP : Public Private Partnerships

ROA : Return on Assets

ROI : Return on Investment

SIZ : Size

STD : Short Term Debt

TA : Traditional Approach

TD : Total Debt

VGFS : Viability Gap Funding Scheme

VOL : Volatility

Chapter I INTRODUCTION AND DESIGN OF THE STUDY

A STUDY ON THE DETERMINANTS OF CAPITAL STRUCTURE AND PROFITABILITY 0



CHAPTER I

INTRODUCTION AND DESIGN OF THE STUDY

I.1 Introduction

The capital structure (CS) problem is, perhaps, one of the prime areas of

attraction for many researchers in the area of finance. It deals with the firm’s

choice of the types of securities to issue. Myers (1984)1 has rightly mentioned CS

as “The capital structure puzzle”. The determinants of optimal CS and its

influence on the CS decision are still an unsolved problem, giving ample scope

for further research. Pioneered by the work of Modigliani and Miller, (1958)2

who studied the impact of corporate tax in use of debt capital, many researchers

viz., Jensen and Meckling (1976)3, Myers (1984)4, Kester (1986)5, Titman and

Wessel (1988)6, Rajan and Zingales (1995)7, Jonson (1998)8, Booth Collins et al.

(2001)9, Dogra and Gupta (2009)10 have analyzed the factors that determine the

CS of a firm and while there exist varied views about the relation between

profitability (P) and CS. The necessity for such researches, in this area, has

gained importance as globalization and stiff competition have forced today’s

corporate firms to determine that level of debt, which offers increased P to the

firm’s owners without unduly increasing the risk of insolvency and at the same

time make the firm a less attractive target for corporate restructuring viz., merger

or takeover.

This chapter gives a precise introduction to the food industry in India, the

problems faced by the industry, governmental measures and the significance of

the study in untangling the problem. The CS theories and the P theories are also

briefly discussed to identify the gap in the previous works and also to set the

objectives of the present study. It also presents the research design of the study,

wherein the problems of the study, the significance, scope, objectives, hypotheses,

methodology, research methods for analysis, sampling design, period of the

study, limitations, and chapter design are stated.



I.2 Capital Structure Theories

CS has been defined as “that combination of debt and equity that attains the stated managerial goals (i.e.) the maximization of the firm’s market value”. The optimal CS is also defined as that combination of debt and equity that minimizes the firm’s overall cost of capital11.

I.2.1 Net Income Approach According to this approach, the cost of debt and the cost of equity remain unchanged when the debt – equity ratio varies. Debt is treated as the cheaper source of finance. So when debt increases the average cost of capital decreases. This approach suggests that the cost of capital decreases continuously with leverage so the firm should use as much leverage as possible12.

I.2.2 Net Operating Income Approach This approach states that an increase in the use of debt funds, which are apparently cheaper, is offset by an increase in the equity capitalization rate. So there is no advantage in using debt financing. David Durand13 advocated eloquently in support of this approach and he argued that the market value of a firm depends on its net operating income and business risk. Leverage merely changes the distribution of income and risk between debt and equity without affecting the total income and risk which influence the market value of the firm.

I.2.3 Traditional Approach

The Traditional Approach is compounded between net income approach and net operating income approach. It assumes the cost of debt capital remains more or less constant up to a certain degree of leverage (LEV) but rises thereafter at an increasing rate. The cost of equity capital remains more or less constant up to a certain degree of LEV and rises only gradually up to a certain degree of leverage and rises sharply thereafter. The average cost of capital as consequences of the above behavior of cost of debt and equity, decrease up to a certain point, remains more or less unchanged for moderate increase in LEV, thereafter and rises beyond a certain point14.



I.2.4 Modigliani and Miller Proposition

Modern theory of CS began with the valuable contribution of Modigliani and Miller (1958)15 who framed the basis upon which the other researchers have made improvements. They identified the lack of an adequate theory of the effect of financial structure on market valuations and have formulated a theory that can answer the cost-of-capital question. This theory also permitted the development of the theory of investment of a firm under conditions of uncertainty.

The theory assumed a perfect capital market where there is no problem of asymmetric information: there are no transaction costs; no bankruptcy cost and the securities are infinitely divisible. Managers act in the interest of shareholders and the firms can be grouped into equivalent risk classes on the basis of their business risk. They assumed that there is no tax. However, Modigliani and Miller (1963)16 made a correction to bring out the tax advantages of debt financing in the work “Corporate Income Taxes and the Cost of Capital: A Correction”. In this work they viewed the value of the firm as a function of leverage and the tax rate.

In their proposition I they considered the value of the firm to be independent of its CS. This proposition was more or less similar to that of the net operating income approach. They viewed the value of a firm as a function of expected operating income divided by the discount rate appropriate to its risk class. They proved that the average cost of capital within a given class is independent of the degree of LEV17.

The proposition II held that financial leverage (FL) increases the expected earnings per share (EPS) while the share price remains constant. This is because the change in the expected earnings is offset by a corresponding change in the return required by the shareholders18.

Their proposition III made an attempt to develop the Theory of Investment. They concluded that an investment financed by common stock is advantageous to the current stockholders if and only if its yield exceeds the capitalization rate. When a corporate income tax, under which interest is a



deductible expense, is considered, gain can accrue to stockholders from having debt in the CS, even when capital markets are perfect19.

I.2.5 Trade-off Theory Trade-off theory, implies that company’s CS decision involves a trade-off

between the tax benefits of debt financing and the costs of financial distress.

I.2.6 Static Trade-off Theory In a Static Trade–off framework the firm is viewed as setting a target debt to value ratio and gradually moving towards it (Myers 1984)20. The theory says that every firm has an optimal debt–equity ratio that maximizes its value. This optimal debt-equity ratio is determined by a trade off of the cost and benefits of borrowing, holding the firm’s assets and investment plans constant. The benefits derived from interest tax shields are offset against various costs of financial distress and agency cost.

I.2.7 Dynamic Trade-off Theory

Implementing the role of time is very significant in identifying the optimal CS. The first dynamic models to consider the tax savings versus bankruptcy cost trade-off are Kane et al. (1984)21 and Brennan and Schwartz (1984)22. Their models took into consideration: uncertainty, taxes, and bankruptcy costs, but no transaction costs.

I.2.8 Effects of Bankruptcy Cost

Another important imperfection affecting CS decision is the presence of bankruptcy cost. When a firm is unable to meet its obligations it results in financial distress that can lead to bankruptcy because a major contributor to financial distress is debt. Expected bankruptcy cost is higher for firms with more volatile earnings, which should drive smaller, less-diversified firms toward fewer targets LEV.

I.2.9 Agency Costs

Jensen and Meckling (1976)23 put forward the concept of agency costs. Agency theory recognizes that the interests of managers and shareholders may conflict. Debt financing is a crucial factor that limits the free cash flow available



to managers and thereby helps to control this agency problem (Jensen and Meckling 1976)24.

I.2.10 Signalling Theory

Myers and Majluf (1984)25 proposed a new theory, called the signalling, or asymmetric information theory of CS. They demonstrated that with asymmetric information, equity issues are rationally interpreted on average as bad news, since managers are motivated to make issues when the stock is overpriced.

I.2.11 Market Timing Theory

Baker and Wurgler (2002)26 suggested a new theory of CS: the “market timing theory of CS”, which states that the current CS is the cumulative outcome of past attempts to time the equity market.

I.3 Profitability Theories

P consists of two words profit and ability. It is necessary to differentiate between Profit and P at this point. Profit, from accounting point of view, is arrived at by deducting from total revenue of an enterprise all amount expended in earning that income. Profitability (P) is defined as the ability of a given investment to earn a return from its use27.

P can be measured as profit shown as a percentage of sales known as profit margin. It can also be expressed as Return on Investment (ROI)28. Since this study concentrates on the relationship between CS & P, the ROI may be apt. The theories mentioned below focus on the P of the firm.

I.3.1 Pecking Order Theory

In contrast to Static Trade-off theory, which states that every company has a target optimal debt-equity ratio, the pecking order stresses a financing order rather than a target debt-equity ratio. This theory states that highly profitable firms prefer internal funds and when external funds are required the firm will borrow, rather than issuing equity. The pecking order theory explains why the bulk of external financing comes from debt. It also explains why more profitable firms borrow less: not because their target debt ratio is low – in the pecking order they don’t have a target-but because profitable firms have more internal



financing available. Less profitable firms require external financing, and consequently accumulate debt29.

I.3.2 Free Cash Flow Theory

This theory is also framed for matured firms that are prone to over invest. It says that high debt levels will increase value, despite the threat of financial distress, when a firm’s operating cash flow significantly exceeds its profitable investment opportunities. Thus, the profit earning capacity increases the value of the firm despite the threat of financial distress30.

I.4 Food Industry in India

India is the world's second largest producer of food next to China, and has the potential of being the biggest with the food and agricultural sector. India is the third largest producer of food grain and the second largest producer of fruits and vegetables31. Agriculture sector provides employment to 52% of country’s work force and is the single largest private sector occupation, while, the contributory share of agriculture in gross domestic product (GDP) has decreased over years. The share in GDP was 55.4% in 1950-51, 52% in 1960-61, reduced to18.5% in 2006-07 and agriculture and allied sectors account only for 15.7% of the GDP for the year 2009–1032. This is a crucial area to be considered to enhance the Indian economy.

I.5 Food Management in India

Food management in India has three basic objectives:

Procurement of food grains from farmers at remunerative prices.

Distribution of food grains to consumers particularly the vulnerable sections of the society at affordable prices.

Maintenance of food buffers for food security and price stability.

The instruments of food management are the minimum support price (MSP) and central issue price (CIP). The nodal agency which undertakes the procurement and distribution and storage of food grains is the Food Corporation of India (FCI)33. Table I.1 shows the procurement of rice and wheat over the years.



Table I.1 Production and Procurement of Rice and Wheat in India

Year Production (in mn MT)

Procurement of rice and wheat (in mn MT)

Procurement as % of production

Rice Wheat Total

2003-2004 88.5 72.2 160.7 39.6 25

2004-2005 83.1 68.6 151.7 39.5 26

2005-2006 91.8 59.4 151.2 36.9 24

2006-2007 93.4 75.8 169.2 36.2 21

2007-2008 96.7 78.6 175.3 51.4 29

2008-2009 99.2 80.7 179.9 59.1 33

2009-2010 89.1 80.7 169.8 54.0 32

Source: Economic Survey 2010 and Monthly Food Bulletin October 2010, Department of Food and Public Distribution, Ministry of Agriculture and Cooperation

Driven by the recent rise in food prices and food scarcity faced by various countries, food security has begun to gain importance, with various governments taking an active role in agricultural development. To overcome the rising price and solve problems such as under-nutritioned children and women, growing hunger and inequality, the government has involved in the process of enacting National Food Security Act (NFSA). NFSA is envisaged as a path-breaking legislation, aimed at protecting all children, women and men in India from hunger and food deprivation34. The motivation for the proposed NFSA to provide a guarantee of adequate nutrition is derived from the right to food as an aspect of the right to life under Article 21 (interpreted by the Supreme Court as a right to life with dignity), which is a fundamental right of all citizens. Even though the NFSA focuses mainly on food entitlements, the National Advisory Council (NAC) recommends that it should take a broad view and not restrict itself only to the Public Distribution System (PDS). NFSA supplements (i) the NAC recommendations on food security released on 23 October 2010; and (ii) the NAC Framework Note on the Draft National Food Security Bill released on 21 January 2011. Major issues covered under the act are as follows:



35 kgs per household per month at Rs 3/2/1 for rice/wheat/millets for priority category; 20 kgs at (at most) half of MSP for General category.

Universalization of Integrated Child Development Services (ICDS).

Counselling and support for optimal Infant and Young Child Feeding.

Nutrition take-home rations for children under 3 and pregnant/lactating women.

I.5.1 Important Recommendations of NAC with regard to NFSA

The NAC recommends an expansion of decentralised procurement as the path to higher procurement. More and more states should be encouraged to procure locally. This is far superior to FCI procuring food grains from a few states and distributing them across the country35.

The NAC strongly recommends inclusion of other nutritious cereals (such as bajra, jowar, ragi, and maize) as part of the food security basket using millets in several nutrition-related schemes, including ICDS, mid-day meals, community canteens and destitute feeding programmes36.

All ICDS services (supplementary nutrition, growth monitoring, nutrition and health education, immunization, referral and pre-school education) should be extended to every child under the age of 6 years, all pregnant women and lactating mothers and all adolescent girls37.

It also recommends that the Ministry for Consumer Affairs, Food & Public Distribution should serve as the nodal Ministry for the implementation of the NFSA38.

India and the US also launched a joint initiative for an ‘Evergreen Revolution’ in agriculture to promote food security across the world39. As part of the food security initiative, India’s expertise in agriculture sector will be shared with farmers in Africa. G-20 Finance Ministers have reached a compromise deal to correct global economic imbalances and expressed concern over excessive commodity price volatility impacting the world food security, an issue pressed by India40.



I.6 Classification of Food Industry in India The Food Industry in India is grouped into the following categories:

Diagram I.A Classification of Food Industry

I.7 Advantages and Challenges Faced by Food Industry in India

India has diverse agro-climatic conditions and has a large and diverse raw material base suitable for food processing companies. India is becoming the eastern hub of the food industry. Not only does it have leading productions of various materials like milk, fruits and vegetables, grains and animal products but the food processing sector is also growing at a rapid rate to cater to domestic needs and the export market (JS Pai, executive director, Protein Foods and Nutrition Development Association of India (PFDAI))41.

I. Food industry

Dairy

Tea

Sugar

Vegetable oils &

products

Coffee

Other products

II. Beverages and tobacco

Tobacco products

Beer and Alcohol

Other products include: Cocoa products& confectionery Bakery products Processed /packaged foods Starches Marine food Poultry & meat product Floriculture Milling products



With its growing population, India is not only one of the largest producers of food materials but also a large consumer of food. This has brought about an imbalance between demand and supply leading to rise in food prices. The economic slump had an adverse impact on most industries including the food & beverage (F&B) industry. However, the Indian economy managed to sustain from the collapse of the global growth and trade due the global meltdown that took place in the year 2007-09 followed by the year 2008-09 which saw negative growth in agriculture and allied sector due to erratic monsoon and late rain in the year 2009-2010, which reflected in decreased food crop production leading to rise in food inflation. The major problems faced by the industry are rising food prices, increasing transportation costs due to a rise in oil prices, and decline in consumer spending. Nevertheless, the F&B industry has been relatively less affected when compared to other industries. This is mainly attributed to the fact that food products continue to be essential to consumers in spite of the slowdown. The rising demand for food item and relatively slower supply response from the agricultural sector has led to frequent spikes in food inflation.

Another major problem faced by food industry is the lack of proper infrastructure facility and proper storage facility. Although India is one of the world’s major food producers it accounts for less than 1.5 per cent of international food trade due to lack of proper infrastructure facility42. High food prices, resulting from the combined effects of the weak 2009 monsoon and inefficiencies in the government's food distribution system have shook Indian economy to the core. Lack of proper infrastructural facilities has led to a storage loss as high as 30% (The Food Corporation of India), which has added to the food inflation caused due to rising demand and adverse weather, tightening food supplies. Urgent efforts are needed to expand, improve and modernize storage of food grains in the country in order to arrest the wastage of food grains. However, the NAC has been informed that the government has already finalized a plan for food grain storage that will extend storage capacities to 58 million metric tonnes in the near future43.

Development of food industry would be the right alternate for overcoming these setbacks. This development should necessarily be backed by good analysis



and study of the financial structure of food industry to help it to grow faster and direct the growth of the industry in the right path.

I.8 Current Status of Food Industry in India

The Food Industry in India is growing at over 9% per annum (Pai further indicated)44. The size of the food industry is as large as Rs. 4 lakh crore and growing fast. It is one-fifth of the US food industry, which is $550 billion (Rs. 22 lakh crore). The total food production in India is likely to become double in the next ten years and there is an opportunity for large investments in food and food processing technologies, skills and equipment, especially in areas of canning, dairy and food processing, specialty processing, packaging, frozen food/refrigeration and thermo Processing. The “food industry is expected to reach $ 258 billion by fiscal year 2015 and $318 billion by fiscal year 2020 from the current level of $181 billion” (Federation of Indian Chambers of Commerce and Industry (FICCI) Food Processing Committee Chairman, Shrijeet Mishra). Foreign direct investment (FDI) in agriculture has increased six-fold, rising from $96.4 million in 2004 to $656 million in 200845. The US is the largest source country followed by the Germany and the UK. The agriculture related FDI flows are shown in table I.2.

Table I.2 Agriculture Related FDI Inflows in India

FDI Inflows in India in $ million % to total

Sugar 5.0 0.76

Vegetable oil 44.1 6.72

Tea and coffee 52.4 7.99

Hybrid seeds and plantation 1.2 0.18

Horticulture 4.1 0.62

Food processing 150 22.86

Fermentation industries 388.7 59.23

TOTAL 656.2 100 Source: “FDI in agriculture”, The Financial Express, January 21, 2010: 9.



In the budget 2011-12, the Government of India has announced to set up 15 more mega food parks (MFPs) and also urged that the states should reform the Agriculture Produce Marketing Act (APMC) to improve the supply chain. In the 11th Five year plan, the number of food parks will be increased to a total of 30. The budget also allocated US$ 135 million to the Food Processing Ministry from the existing US$ 90 million. As a measure to boost investment in agriculture the minister extended the Viability Gap Funding Scheme (VGFS) for public private partnerships (PPP) for setting up modern storage capacity besides giving infrastructure status to cold chains46.

The Vision 2015 of the Government of India also provides for enhancing the level of processing of perishable to 20 per cent, enhancing value addition to 35 per cent47. All these facts indicate a wider scope for development of food industry in India. Studies and researches facilitate the advancement of the industry; hence the study is a step ahead of one such attempt. So analysis of the CS of food products manufacturing firms and analysis of the P of such developing industry becomes significant.

I.9 Origin of the Research Problem

Review of past studies in precise brings out different findings about between CS and P. Modigliani and Miller (1958)48 finding states that “when a corporate income tax is considered, gain can accrue to stakeholders from having debt in the CS even when capital markets are perfect”. Pecking Order Theory and Signaling theory have pointed out the importance of P in deciding CS. In contrast to Static Trade off Theory, the Pecking Order Theory states that when external finance is required, firms issue the safest security first (Myers 1984)49. Major US industrial firms follow a financing hierarchy (pecking order) and the managers consider the projected cash flow from asset to be financed as the main criteria in governing financing decisions (Pinegar and Wilbricht 1989)50. The works of Titman and Wessel (1988)51, Kester (1986)52, Chang (2003)53 and many others have considered P as one of the determinants of CS. Wald (1999)54found that P was “the single largest determinant of debt/asset ratios” in cross-sectional tests for the US, the UK, Germany, France and Japan (Myers 2001)55, which shows how important P is in determining the CS of the firms. The ability of the



firm to earn consistent profit is the deciding factor of a firm’s CS. “Debt capacity” depends on the future P and value of the firm; it may be able to increase borrowing if it does well, or be forced to pay down debt if it does poorly (Myers 2001)56. The works of Myers (1984)57, Kester (1986)58, Friend Hasbrouch (1988)59, Friend & Lang (1988)60, Titman and Wessel (1988)61, Rajan and Zingales (1995)62, Jonson (1998)63, Booth Collins et al. (2001)64, and Dogra and Gupta (2009)65 give empirical evidences in support of the negative relation between P & LEV. Long & Malitz (1985)66 pointed out that LEV increases with increases in P but their result was insignificant. Though there are varied views regarding the type of relation, the works give strong evidence that there is a binding link between P& CS.

To throw light into these forbidden areas the following questions are considered relevant to be raised and need to be answered:

What type of relation prevails between profitability and leverage?

How important is profitability in determining the debt ratio / leverage of a firm?

What is the impact of size and sector-wise difference on the relation between profitability and leverage?

What are the other variables that determine leverage of the firms?

What are the variables that determine the profitability of the firms?

Does size and sector-wise difference influence the relation between the predictor variables and profitability?

In order to seek answer to the stated questions the following objectives are set.

I.10 Objectives of the Study I.10.1 General Objectives

To analyze the determinants of capital structure (leverage) with particular focus on the impact of profitability on capital structure.

To analyze the determinants of profitability.



I.10.2 Specific Objectives

To study the relationship between profitability and leverage in general.

To analyze the impact of profitability in determining capital structure / leverage of a firm when compared to that of the other variables.

To analyze the impact of non debt tax shield, collateral asset, profitability, growth rate, size, age and volatility on leverage.

To analyze the impact of aggressive investment policy, capital intensity, growth rate, size and volatility on profitability; and

To analyze the impact of sales size, income size and sector-wise differences of firms in deviating the relationship between profitability and leverage.

I.11 Hypotheses Development Hypotheses for the Preliminary Analysis

Myers (1984)7 developed pecking order theory, which gave a new frame to the role played by the profit earned in CS choice. It assumes that firms give more preference to retained earnings when deciding about financing a project. Titman and Wessel (1988)8, Pinegar and Wilbricht (1989)9 backed the pecking order theory with supporting findings.

Titman and Wessel (1988)10, in their work “the determinants of capital structure choice”, analyzed LEV in three measures viz., short-term, long-term, and convertible debt rather than an aggregate measure of TD. Hence, the study also attempts to sub-divided CS to make a closer and detailed study about the relation between PBITD and the different constituents. The hypotheses are thus: Ho

1 = “There is no significant relationship between profit earned and the size of long term debt of the firms”. Ho

2 = “There is no significant relationship between profit earned and the size of short term debt of the firms”. Ho

3 = “There is no significant relationship between profit earned and the size of total debt borrowed by the firms”. Ho

4 = “There is no significant relationship between profit earned and the size of equity capital of the firms”.



Rajan and Zingales (1995)11 put forth to light the impact of size on the relation between P and LEV stating that the negative influence of P on LEV should become stronger as firm size increases. The impact of size is studied in two ways- average turnover size and average income size. Hence the hypotheses are: Ho

5 = “There is no significant influence of size of sales on the relation between profit earned and the various constituents of capital structure of the firms”. Ho

6 = “There is no significant influence of size of income on the relation between profit earned and the various constituents of capital structure of the firms”.

Barton, Hill, and Sundaram (1989)12 studied relativity of business as a determinant of LEV along with the other variables, hence the sector differences are also taken into consideration to study the impact of profit earned on level of various constituents of CS.

Hence, the hypothesis is:

Ho7 = “There is no significant influence of sectoral differences on the relation

between profit earned and the various constituents of capital structure of the firms”.

Hypotheses for the Core Analysis

Since we have two main objectives i.e., “to analyze the determinants of capital structure” and “to analyze the determinants of profitability”, the hypotheses developed are classified under two heads as stated below:

I.11.1 Hypotheses for Analyzing the Determinants of Capital Structure

Myers (1984)67 introduced pecking order which the firms use for financing their investment. His theory suggested that there is a negative relationship between LEV and P. The works of Titman and Wessel (1988)68 also supported this view. Barton & Gordon (1988)69, Johnson (1998)70 and other works also supported for a negative relation between CS and P. Hence, large size firms with



consistent P depend more on internal funds and when they are not sufficient they raise funds from a safer source i.e. debt capital.

In contrast to this, there are some views that the firms optimally recapitalize at the end of each period and this leads to a positive relationship between LEV and P. To illustrate, Pandey (2004)71 predicted a positive relation between CS and P. Less profitable firm will employ more internal funds as external financing is costlier, thereby P creates the necessity of the firm to shield its excess profit from taxes.

Leland (1994)72 found that LEV ratio is invariant to changes in P. Kane, Marcus and MacDonald (1984)73 and Wiggins (1990)74 also retain the Modigliani-Miller proposition that the firm’s cash flows are invariant to debt choice. These variations in the views of various experts create an interest in testing the existence of relationship between P and CS. Thus, the hypothesis is:

Ho8 = “There is no significant relationship between profitability and leverage of

the firms”.

Modigliani and Miller (1958)75, pointed out that the size of non-debt corporate tax shields like deductions for depreciation and investment tax credits may affect leverage, and Givoly Collins et al. (1992)76 indicated that there exists a substitution effect between debt and non debt tax shields, and that both corporate and personal tax rates affect leverage decisions which makes it necessary to analyze whether there exists relationship between NDTXSH and CS. Hence, the hypothesis is:

Ho9 = “There is no significant relationship between non debt tax shield and

leverage of the firms”.

Myers (1984)77 argued that collateral asset (COLASS) will help the firms to easily access debt capital and therefore there exists a positive relationship between COLASS and debt level, while Harris and Raviv (1991)78 argued that small firms with low levels of fixed assets would have more problems of asymmetric information, making them issue more debt, since under priced equity issues only is possible. Titman and Wessel (1988)79 identified opposite relation



between collateralizable capital and debt level. Hutchinson and Michaelas (1998)80 used collateral as one of the determinants of CS. Therefore, the hypothesis is:

Ho10 = “There is no significant relationship between collateral assets and leverage

of the firms”.

Titman and Wessel (1988)81 established that growth (GROW) rates were negatively related to long-term debt, accepting the pecking order theory. Smith and Watts (1992)82, Balakrishnan and Fox (1993)83, and Fama and French (2002)84 indicated that GROW was found to have negative relation with LEV as the dependant variable, while Barton and Gordon (1988)85 provided evidence in the contrary stating that GROW rate is positively correlated with debt. Hence, the hypothesis is:

Ho11= “There is no significant relationship between growth and leverage of the

firms”.

Volatility (VOL) in profit increases the risk associated with the debt capital. Bradley, Jarrel, and Kim (1984)86, Kester (1986)87, and Titman and Wessels (1988)88 proved that leverage decreases with VOL. Johnson (1997)89 also determined the impact of VOL on LEV as one of the determinants. Hence, the hypothesis is:

Ho12 = “There is no significant relationship between volatility and leverage of the

firms”.

Small enterprises are characterized by variability in profits and growth. Hutchinson and Michaelas (1998)90 showed that P did not affect the CS of small size firms, which fact shows some kind of influence of size (SIZ) on the relation of P with CS. Titman and Wessels (1988)91 indicated that larger firms have diversified business and therefore have lower possibility of experiencing financial distress, which leads to positive relationship between firm SIZ and debt level. Frank and Goyal (2003)92, and Rajan and Zingales (1995)93 argued that larger firms have lesser problem of asymmetrical information reducing the chances of undervaluation of the new equity issue, which encourage large firms to use



equity financing. Therefore, there exists a negative relation between SIZ and LEV.

Booth Collins et al. (2001)94, Panday (2002)95, and Chen and Zhao (2004)96 also suggested that there exists a negative relation between P and SIZ in developing countries, therefore, the impact of SIZ of the firms on the relationship between P and CS should be analyzed. Hence, the hypothesis is:

Ho13= “There is no significant influence of size in deviating the relationship

between profitability and capital structure of the firms”.

Barton, Hill, and Sundaram (1989)97 suggested that relativity of business influences LEV. Miao (2005)98 also found that LEV ratio varies across industries. Thus, the hypothesis is:

Ho14= “There is no significant influence of sectoral differences of firms in

deviating the relationship between profitability and capital structure”.

I.11.2 Hypotheses for Analyzing the Determinants of Profitability

Liquidity affects both the firm’s P as well as the operating risk (Papaioannou, Strock, and Travlos 1992)99. Aggressive investing policy (AIP) on the other hand, though risk involved, increases the profit of the firms, hence it becomes important to study the relation between AIP and P. Hence, the hypothesis is:

Ho15 = “There is no significant relationship between aggressive investment policy

and profitability of the firms”.

Capital intensity (CAPINS) imposes a greater degree of risk because assets are frozen in long lived forms that may not be easy to sell, hence the difference in CAPINS may be associated with difference in P (Bettis 1981)100. CAPINS can affect P because, in uncontestable markets it offers the opportunity to make binding commitments of resources. It does so by tilting the cost structure of production from ongoing towards sunk cost: firms that compete in CAPINS industries typically have to shoulder large, unrecoverable outlays of capital in advance of production decision.



CAPINS can affect P because cut-throat competition might eliminate all future profits depressing each firm’s net security level. Thus, P declines with capital intensity (Ghemawat and Caves 1986)101. Based on this view, the following hypothesis is formulated:

Ho16 = “There is no significant relationship between capital intensity and

profitability of the firms”.

Fluctuation in the profit earned by firms makes debt capital costlier. Consistent profit earning capacity is also looked into as a determinant of P. Competitive market creates much of such risk. In more competitive markets where price cut out were sort for, P gets reduced due to higher cost of debt. The chances of financial distress and bankruptcy also increase (Pandey 2002)102. Volatility (VOL) in earning should be studied when considering consistent profit earning capacity; hence the following hypothesis is formulated:

Ho17 = “There is no significant relationship between volatility and profitability of

the firms”.

More profitable firms tend to issue more debt as debt capital may be available at a cheaper rate. The negative relation between P and LEV ratio arises from firm’s preference of internal funds over external funds and the availability of internal funds (Chen and Zhao 2004) 103. It is recommendable that more profitable firms should hold less debt since higher profit generates more internal funds (Bevan and Danbolt 2002)104. However, growing firms may have greater fund requirement to grab new opportunities, which may exceed their retained earnings, hence they act according to pecking order and choose debt rather than equity. Therefore, to study the relation between growth (GROW) and P the following hypothesis is formulated:

Ho18= “There is no significant relationship between growth and profitability of

the firms”.

Small enterprises are characterized by variability in profits and growth. Increase in P along with increase in size and age may aid them to grow at a faster rate (Storey Collins et al. 1987)105. The influence of sectoral difference on the relation between the predictor and P is also to be analyzed and hence to study



the impact of SIZ and sectoral differences on P the following hypothesis is formulated:

Ho19 = “There is no significant influence of size in deviating the relationship

between the predictor variables and profitability of the firms”.

Ho20= “There is no significant influence of sectoral differences in deviating the

relationship between predictor variables and profitability of the firms”.

I.11.3 Summary of Hypotheses of the Study

The aggregate of hypotheses developed to achieve the stated objectives are as follows:

“There is no significant relationship between profit and different constituents of capital structure”.

“There is no significant impact of size on the relation between profit and the various constituents of capital structure”.

“There is no significant impact of sectoral differences on the relation between profit and the various constituents of capital structure”.

“There is no significant relationship between profitability and leverage in general”.

“There is no significant impact of size (sales-wise and income-wise) in influencing the relationship between profitability and leverage”.

“There is no significant impact of sectoral difference in deviating the relationship between profitability and leverage”.

“There is no significant relationship between non debt tax shield, collateral assets, growth as well as volatility and leverage”.

“There is no significant relationship between capital intensity, aggressive investment policy, volatility, growth as well as size and profitability”.

I.12 Methodology of the Study

I.12.1 Sources of Data and Period of the Study

The study is based on secondary data, which are collected from Centre for Monitoring Indian Economy (CMIE) Prowess package for a period of 10 years



on year to year basis ranging from 1999-2000 to 2008-2009. The data for the food products manufacturing firms collected for the period are subject to limitations such as continuous listing for 10 years, availability of data for the years under study, and hence the final sample is restricted to 86 firms as shown in table I.3.

Table I.3 Food Product Firms Under Various Categories

Category Number of

Firms Total

I. Food Products Dairy products Tea Sugar Vegetable oils & product Coffee Other products

• Cocoa products & confectionery

• Bakery products

• Processed / packaged foods

• Starches

• Marine food

• Poultry & meat product

• Floriculture

• Milling products

• Other agricultural products TOTAL

12 37 167 14 102 42 62 78 252

72 213 150 350 21

766 1572

II. Beverages & Tobacco Tobacco Products Beer & Alcohol

35 140

TOTAL 175

TOTAL FIRMS IN FOOD INDUSTRY 1747 Source: CMIE (Centre for Monitoring Indian Economy) Prowess package as on 30th January, 2010.



I.12.2 Sampling Design Multi-stage sampling technique is used for the study and the different

stages followed are mentioned below:

Stage 1: The total of 1747 food products manufacturing firms are reduced to 1572 since firms coming under Beverages & Tobacco categories are ignored as they occupy a negligible share (10%) of the total firms in food industry.

Stage 2: Out of 1572 food products manufacturing firms, 1314 firms are found to have details of incorporated year as on 30th January, 2010.

Stage 3: Among the incorporated firms, 309 are identified to have Bombay Stock Exchange (BSE) listing flag and 62 are having National Stock Exchange (NSE) listing flag. The NSE listed firms, being few in numbers are ignored, and BSE listed firms are taken into consideration for further stage.

Stage 4: Out of the 309 BSE listed firms, 99 firms are found to have continuously listed, based on BSE trading data availability, over the period of study.

Stage 5: Final sample constitutes 86 actively traded firms in BSE listing flag with availability of complete data required for the study for the study period.

I.12.3 Research Methods for Analysis Descriptive statistics such as mean, median and standard deviation are used to neutralize the fluctuation in the value of explained as well as explaining variables. Correlation co-efficient is extensively used to study one-to-one relationship between the variables. Multiple regression is also used to study various variables that influence the debt ratio / leverage in a firm. Factor analysis is also used to determine the factors influencing P and CS. Appropriate ratios as stated below are used to calculate individual relative properties of the selected variables.



Table I.4 Description of Ratios Used

Variables Description Inference LEV_STD Short term debt/Book value of

equity A high value denotes high leverage in terms of short term debt and vice versa

LEV_LTD Long term debt/Book value of equity

A high value denotes high leverage in terms of long term debt and vice versa

LEV_TD Total debt/Book value of equity

A high value denotes high leverage in terms of total debt and vice versa

VOL Standard deviation of earnings before interest, taxes and depreciation (EBITD) / Total Assets

A high value denotes greater volatility in earnings from the assets invested and vice versa

COLASS Ratio of Property, Plant and Equipment / Total Assets

A high value denotes higher share of fixed asset to total asset, which implies greater share of assets is invested for increasing earning and vice versa

NDTXSH Ratio of the sum of depreciation and amortization / Total Assets

A high value denotes a higher non debt tax shield and vice versa

P PBITD/Fixed Assets A high value denotes higher profitability in terms of fixed assets

SIZ Logarithm of Sales over Years Turnover adjusted for fluctuation over years

AG Total number of years from the date of incorporation

The number of years the firm has been carrying out business

GROW Compounded annual growth rate (CAGR) of total assets

The growth of total asset over years

P_TASSET PBITD/ Total Assets It indicates the return on assets invested. High value denotes large return on asset and vice versa

P_SAL PBITD/Sales It indicates the profit margin earned on turnover of firm. A high value implies a great profit margin and vice versa

AIP Current Assets /Total Assets It indicates the proportion of current assets to total assets. A low value indicates more aggressive use of assets for increasing earnings and vice versa

CAPINS Total Assets / Sales It indicates how intensively the assets are used to increase turnover. A low value indicates large turnover for the investment in assets and vice versa

Source: Compiled from secondary sources



I.13 Plan of Analysis

The analysis part is divided into two parts namely, preliminary analysis

and core analysis. The preliminary analysis portrays the relation between profit

before interest taxes and depreciation (PBITD) and the various constituents of

CS, while the core analysis put to light the determinants of CS and P.

I.13.1 Preliminary Analysis

The preliminary study analyses the relation between PBITD and the

various constituents of CS viz., short term debt (STD), long term debt (LTD),

total debt (TD) and equity. The analysis aims at exploring the nature of impact

of PBITD on various constituents of CS in food industry in India. The impact of

size, income and sector-wise differences are also portrayed in this part, which is

considered to be the base for the core analysis, focusing the nature of relation

between P and LEV, hence the equation is:

CS = α +PBITD+ Є

I.13.2 Core Analysis

The core analysis is again subdivided into two parts namely (i)

determinants of CS and (ii) determinants of P. Regression equation I is

formulated to study the determinants of CS, and equation II is designed to study

the determinants of P. The impact of size and sector-wise differences on CS and

P is also brought to light.

I.13.3 Regression Equation 1

Equation I attempts to study the determinants of CS. The dependent

variable LEV is studied under three heads viz., short term debt (LEV_STD), long

term debt (LEV_LTD) and total debt (LEV_TD). Hutchinson and Michaelas

(1998) 106 analyzed CS in terms of STD, LTD and TD. Titman and Wessel (1988)



107 also analyzed the implication with regard to different types of debt

instruments viz. short-term, long-term and convertible debt rather than an

aggregate measure of total debt, hence the equation is:

LEV = α + β1 VOL + β2 COL ASS+ β3 NDT XSH + β4 P + β5 SIZ + β6 AG + β7

GROW + Є

I.13.4 Regression Equation II

Equation II is formulated to study the determinants of P. The term P has

been defined by Lowe, Naughton, and Taylor (1994) 108 as the average rate of

return on assets (ROA).

P_TASSET = PBITD/ Total Assets

Profit margin is also used as dependent variable to measure profitability.

P_SAL = PBITD/Sales, hence, the equation is:

P = α + β1 AIP + β2 CAPINS+ β3 SIZ + β4 GOW + β5 VOL + Є

I.13.5 Controlling Variables

Negative influence of P on LEV increases with the size of the firm (Rajan and Zingales 1995)109. Profitable large size firms have relatively less debt when compared to that of the smaller and riskier firms. Smaller firms tend to use significantly more short term debt than that of the larger firms (Titman and Wessel 1988) 110. Debt capital decreases with higher P and SIZ in developing countries (Booth Collins et al. 2001 111, Panday 2002 112, and Chen and Zhao 2004113). To study the impact of size, the sample firms are grouped based on sales size and income size as detailed below:

Barton, Hill, and Sundaram (1989) 114 proved the relativity of business as a determinant of LEV. Bhattacharjee (2010) 115 concluded that leverage varied across industries and between firms belonging to the same industrial sector. The sector wise impact is studied by grouping the firms into three sectors.



I.14 Limitations of the Study Analysis of the study is based on financing data collected from CMIE

Prowess Package. The quality of the study depends purely upon the accuracy, reliability and quality of secondary data.

The firms chosen are restricted to 86 due to limitations such as lack of continuous listing, non-availability of data pertaining to those firms in the data source.

The sector-wise classification has grouped the firms into three sectors out of which the second and third sectors constitute related firms since there is more number of sectors with less number of firms.

The firms of Beverages & Tobacco category are left out as they occupy a negligible share (only 175 out of 1747 of food manufacturing firms,

Sales size-wise

analysis

Average Sales turnover of < Rs.100 crore Average Sales turnover of > Rs.100 crore but < Rs.500 crore Average Sales turnover of >Rs.500 crore

Small Size

Medium Size

Large Size

Sector-wise analysis

32 vegetable oil firms 30 firms comprising 9 firms of tea sector, 11 firms of dairy sector, and 10 firms of sugar sector

24 firms comprising of miscellaneous sectors which include coffee (1), cocoa products & confectionery (1), processed /packaged foods (1), starches (2), marine food (3), poultry & meat product (1), floriculture (2), milling products ( 3), and other agricultural products (10).

Sector I

Sector II

Sector III

Income size-wise analysis

Average Profit (PBITD) < Rs.10 crore Average Profit > Rs.10 crore but < Rs.50 crore

Average Profit >Rs.50 crore

Low Income

Medium Income

High Income



recording a share of 10%) and the nature of social concern of these firms also differs, hence firms categorized under food products alone are included in the study.

I.15 Chapter Design The study consists of five chapters.

Chapter 1 gives a brief introduction of CS and P which brings to light the status of Food Industry in India. It also presents the research design of the study, wherein the problems of the study, the significance, scope, objectives, hypotheses, methodology, research methods for analysis, sampling design, period of the study, limitations, and chapter design are elaborated.

Chapter 2 presents review of literature related to the study. Chapter 3 gives review of the concepts and theories of CS and P. Chapter 4 is analysis chapter. It is divided into two parts (Part I and Part

II). While Part I is the preliminary analysis, which attempts to study the relation between PBITD and various constituents of CS which forms the basis for the core analysis, Part II analyses the determinants of CS and P in terms of size and sectors.

Chapter 5 presents summary of findings and conclusion of the study. It also proposes appropriate suggestions and scope for future research.

I.16 Conclusion Many researchers have tried to identify the variables that predict the debt

level of corporate firms. The present study attempts to bring out the variables that determine the CS of food industry in India since agriculture is the livelihood of large portion of people in India. Moreover, India is the world's second largest producer of food next to China, and has the potential of being the biggest with the food and agricultural sector. India is not only one of the largest producers of food but also a large consumer which has led to difference in demand and supply resulting in food inflation that shook Indian economy to the core. Lack of proper infrastructural facilities also supplemented to the problem. The government has now felt the necessity for enacting National Food Security Act (NFSC) in order to provide a guarantee of adequate nutrition to all people. This Act mainly



focuses on food entitlements. This alone cannot efficiently solve the problem. Development of food industry would largely help in overcoming the problem. Growth should be supported by good researches and study about the financial aspects of that industry. Globalization and stiff competition have forced today’s corporate firms to determine that level of debt, which offers increased P to the firm’s owners without unduly increasing the risk of insolvency. This is also an empirical work aiming at bring the food industry to light and to direct their growth in the right path. Review of past studies will be more helpful in identifying the gaps and to determine the objectives to be attained through this empirical work. The empirical and theoretical works are argued in comprehensively in the second chapter.



References: 1 Myers, S. C. 1984. The capital structure puzzle. The Journal of Finance 39(3), (December): 575-92. 2 Modigliani, F., and M. H. Miller. 1958. The cost of capital, corporation finance and the theory of investment. The American Economics Review 48 (3), (June): 261-97. 3 Jensen, M. C. and W. H. Meckling. 1976. Theory of firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3, (October): 305-60. 4 Myers, S. C. 1984. loc cit. 5 Kester, C. W. 1986. Capital and ownership structure: A comparison of United States and Japanese manufacturing corporations. Financial Management 15(1): 5-16. 6 Titman, S., and R. Wessels. 1988. The determinants of capital structure choice. The Journal of Finance 43(1), (March): 1-19. 7Rajan, R. G., and L. Zingales. 1995. What do we know about capital structure? Some evidence from international data. The Journal of Finance 50(5), (December): 1421-60. 8 Johnson, S. A. 1998. The effect of bank debt on optimal capital structure. Financial Management 27(1) (Spring): 47-56. 9 Booth, L., V. Aivazian, A. Demirguc-Kunt, and V. Maksimovic. 2001. Capital structure in developing countries. The Journal of Finance 27 (4), (December): 539 -60. 10 Dogra, B., and S. Gupta. 2009. An empirical study on capital structure of SMEs in Punjab. The Icfai Journal of Applied Finance 15(3), (March): 60-80. 11 Bhalla, V. K. 1997. Financial Management and Policy. Anmol Publications Pvt. Ltd., New Delhi: 832. 12Prasanna Chandra. 2008. Financial Management: Theory and Practice. Tata McGraw Hill, New Delhi: 480. 13Ibid: 480-1.



14Ibid. 15 Modigliani, F., and M. H. Miller. 1958. loc. cit. 16Modigliani, F., and M. H. Miller. 1963. Corporate income taxes and the cost of capital: A correction. The American Economic Review 53(3), (June): 433-43. 17 Modigliani, F., and M. H. Miller. 1958. loc. cit. 18Modigliani, F., and M. H. Miller. 1958. loc. cit. 19 Modigliani, F., and M. H. Miller. 1958. loc. cit. 20 Myers, S. C. 1984. The capital structure puzzle. The Journal of Finance 39(3), (December): 575-92. 21 Kane, A., Marcus, A., and R. MacDonald. 1984. How big is the tax advantage to debt? Journal of Finance 39: 841–52.

22Brennan, Michael, and E. Schwartz. 1984. Optimal financial policy and firm valuation. Journal of Finance 39: 593-607. 23 Jensen, M. C. and W. H. Meckling. 1976. loc. cit. 24 Ibid. 25 Myers, S. C., and N. S. Majluf. 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13: 187-221. 26 Baker, M., and J. Wurgler. 2002. Market timing and capital structure. The Journal of Finance 57(1), (February):1-32. 27 Sharma, S. 2000. Financial Management for 21st Century. ABD Publishers, Jaipur: 295.

28 Ibid.

29 Myers, S. C. 1984. loc. cit.

30 Myers, S. C. 2001. Capital structure. The Journal of Economic Perspective 15 (2), (Spring): 81.

31www.indianfoodindustry.net/ 32Pratiyogita Darpan, Indian Economy 2008: 85.

http://www.indianfoodindustry.net/



33 Ibid: 87.

34http://nac.nic.in/foodsecurity/nfsb.pdf

35http://nac.nic.in/images/recommendations_oct.pdf

36 Ibid.

37 Ibid.

38 Ibid.

39www.civilscurrentaffairs.blogspot.com/ 40www.competitionmaster.com 41www.indianfoodindustry.net 42www.indiainbusiness.nic.in/.../Food_Processing_and_Agribusiness.pdf 43 http://nac.nic.in/foodsecurity/nfsb.pdf

44www.indianfoodindustry.net 45 “FDI in agriculture”, The Financial Express, January 21, 2010: 9. 46www.ibef.org/artdispview.aspx?in=22&art_id=28463 47http://smetimes.tradeindia.com/smetimes/news/industry/2011/Mar/08/inadequate-infrastructure-cripples-food-processing-sector54021.html 48 Modigliani, F., and M. H. Miller. 1958. loc. cit. 49 Myers, S. C. 1984. loc. cit. 50 Pinegar, J. M., and L. Wilbricht. 1989. What managers think of capital structure theory: A survey. Financial Management 18(4), (Winter): 82-91. 51 Titman, S., and R. Wessels. 1988. loc. cit. 52 Kester, C. W. 1986. loc. cit. 53Chang, S. J. 2003. Ownership structure, expropriation and performance of group- affiliated companies in Korea. The Academy of Management Journal 46(2), (April): 238-53. 54 Wald, J. K. 1999. How firm characteristics affect capital structure: An international comparison. Journal of Financial Research 22: 161-87.

http://www.civilscurrentaffairs.blogspot.com/

http://www.competitionmaster.com/


http://www.indiainbusiness.nic.in/.../Food_Processing_and_Agribusiness.pdf


http://www.ibef.org/artdispview.aspx?in=22&art_id=28463

http://smetimes.tradeindia.com/smetimes/news/industry/2011/Mar/08/inadequate-infrastructure-cripples-food-processing-sector54021.html





55 Myers, S. C. 2001. loc. cit. 56 Ibid. 57 Myers, S. C. 1984. loc. cit. 58 Kester, C. W. 1986. loc. cit. 59 Friend, I., and J. Hasbrouck. 1988. Determinants of capital structure, Research in Finance 7(2): 1-19. 60 Friend, I., and L. Lang. 1988. An empirical test of the impact of managerial self-interest on corporate capital structure. Journal of Finance, 43(2): 271-81. 61 Titman, S., and R. Wessels. 1988. loc. cit. 62Rajan, R. G., and L. Zingales. 1995. loc. cit. 63Johnson, S. A. 1998. loc. cit. 64 Booth, L., V. Aivazian, A. Demirguc-Kunt, and V. Maksimovic. 2001. loc. cit. 65 Dogra, B., and S. Gupta. 2009. loc. cit.

66Long, M., and I. Malitz. 1985. The investment-financing nexus: Some empirical evidence. Midland Corporate Finance Journal 3(1): 53-9. 67 Myers, S. C. 1984. loc. cit. 68 Titman, S., and R. Wessels. 1988. loc. cit. 69Barton, S. L., and P. J. Gordon. 1988. Corporate strategy and capital structure. Strategic Management Journal 9 (6), (November – December): 623-32. 70 Johnson, S. A. 1998. loc. cit. 71 Pandey, I. M. 2004. Financial Management, Vikas Publishing House Pvt. Ltd., New Delhi: 770. 72 Leland, H. E. 1994. Corporate debt value, bond covenants, and optimal capital structure. The Journal of Finance 49 (4), (September): 1213-52. 73 Kane, A., A. Marcus, and R. MacDonald. 1984. loc. cit. 74Wiggins, J. 1990. The relation between risk and optimal debt maturity and the value of leverage. Journal of Financial and Quantitative Analysis 25(4): 377-86.



75 Modigliani, F., and M. H. Miller. 1958. loc. cit. 76 Givoly, D., C. Hayn, A. R. Ofer, and O. Sarig. 1992. Taxes and capital structure: Evidence from firms' response to the Tax Reform Act of 1986. The Review of Financial Studies 5(2): 331-55. 77 Myers, S. C. 1984. loc. cit. 78 Harris, M., and A. Raviv. 1991. The theory of capital structure. The Journal of Finance 46(1), (March): 297-355. 79 Titman, S., and R. Wessels. 1988. loc. cit. 80 Hutchinson, P., and N. Michaelas. 1998. The determinants of capital structure of micro, small and medium-sized enterprises, (Working Paper):1-10. 81 Titman, S., and R. Wessels. 1988. loc. cit. 82 Smith, C. W., and R. L. Watts. 1992. The investment opportunity set and corporate

financing, dividend, and compensation policies. Journal of Financial Economics 32: 263-92. 83Balakrishnan, S., and I. Fox. 1993. Asset specificity, firm heterogeneity and capital structure. Strategic Management Journal 14(1), (January): 3-16. 84 Fama, E. F., and K. R. French. 2002. Testing trade-off and pecking order predictions about dividends and debt. The Review of Financial Studies 15(1), (Spring): 1-33. 85Barton, S. L., and P. J. Gordon. 1988. loc. cit. 86 Bradley, M., G. A. Jarrel, and E. H. Kim. 1984. On the existence of an optimal capital structure: Theory and evidence. Journal of Finance 39: 857-78. 87 Kester, C. W. 1986. loc. cit. 88 Titman, S., and R. Wessels. 1988. loc. cit. 89 Johnson, S. A. 1997. An empirical analysis of the determinants of corporate debt ownership structure. The Journal of Financial and Quantitative Analysis 32(1), (March): 47-69. 90 Hutchinson, P., and N. Michaelas. 1998. loc. cit.



91 Titman, S., and R. Wessels. 1988. loc. cit. 92Frank, M. Z., and V. K. Goyal. 2003. Testing the pecking order theory of capital structure. Journal of Financial Economics 67: 217-48. 93 Rajan, R. G., and L. Zingales. 1995. loc. cit. 94 Booth, L., V. Aivazian, A. Demirguc-Kunt, and V. Maksimovic. 2001. loc. cit. 95Pandey, I. M. 2002. Capital structure and market power interaction: Evidence from Malaysia. Asia Pacific Journal of Economics and Business 8(2), (December): 78-91. 96Chen, L., and X. S. Zhao. 2004. Profitability, means reversion of leverage ratios, and capital structure choice. (Working Paper), (September): 1-42. 97 Barton, S. L., N. C. Hill, and S. Sundaram. 1989. An empirical test of stakeholder theory predictions of capital structure. Financial Management 18(1), (Spring): 36-44. 98 Miao, J. 2005. Optimal capital structure and industry dynamics. The Journal of Finance 60( 6), (December): 2621-59. 99 Papaioannou, G. J., E. Strock, and N. G. Travlos. 1992. Ownership structure and corporate liquidity policy. Managerial and Decision Economics 13(4), (July - August): 315-22. 100 Bettis, R. A. 1981. Performance difference in related and unrelated diversified firms. Strategic Mnagement Journal 2(4), October-December: 379-93. 101Ghemawat, P., and R. E. Caves. 1986. Capital commitment and profitability: An empirical investigation. Oxford Economic Papers, New Series 38(1): 94-110. 102 Pandey, I. M. 2004. loc. cit. 103 Chen, L., and X. S. Zhao. 2004. loc. cit. 104 Bevan, A., and J. Danbolt. 2002. Capital structure and its determinants in the United Kingdom: A decomposition analysis. Applied Financial Economics 12(2): 159-70.



105 Storey, D., K. Keasey, R. Watson and P. Wynarczyk. 1987. The Performance of Small Firms. Profits, Jobs and Failures, Croom Helm Ltd., New Hampshire, USA. 106Hutchinson, P., and N. Michaelas. 1998. loc. cit. 107 Titman, S., and R. Wessels. 1988. loc. cit. 108 Lowe, J., T. Naughton, and P. Taylor. 1994. The impact of corporate strategy on the capital structure of Australian companies. Managerial and Decision Economics 15(3), (May - June): 245-57. 109 Rajan, R. G., and L. Zingales. 1995. loc. cit. 110 Titman, S., and R. Wessels. 1988. loc. cit. 111 Booth, L., V. Aivazian, A. Demirguc-Kunt, and V. Maksimovic. 2001. 112 Pandey, I. M. 2002. loc. cit. 113 Chen, L., and X. S. Zhao. 2004. loc. cit. 114Barton, S. L., and P. J. Gordon. 1988. loc. cit. 115 Bhattacharjee, B. J. 2010. Determinants of capital structure of Indian industries. The Indian Journal of Commerce 63(3), (July-September): 14-25.

Chapter II REVIEW OF LITERATURE




CHAPTER II

REVIEW OF LITERATURE II.1 Introduction

Capital structure (CS) is an area which has been studied over a long period in different angles. The works contemplates on finding out the optimal CS that enhances the performance or value of the firm and the factors that determine the CS of the firm. Modigliani and Miller (1958)1, Schnabel (1984)2, and Givoly Collins et al. (1992)3 have brought to light the impact of corporate tax on CS choice. Castanias (1983)4, Fischer (1989)5, and Leland (1994)6 have studied the influence of bankruptcy cost on the corporate debt level. Harris and Raviv (1990)7, Jonson (1998)8, and Koch and Shenoy (1999)9 have proved empirically the informational role of debt in the capital market. The relation between CS and profitability (P) has been explicated in the works of Myers (1984)10, Kester (1986)11, Rajan and Zingales (1995)12, Jonson (1998)13, Booth Collins et al. (2001)14, Dogra and Gupta (2009)15. The review of the past literature regarding this area will give us a detailed knowledge of different aspects of research that has been carried out and how important will this study be in this concern.

II.2 Previous Studies Modigliani and Miller (1958)16, in their work “The cost of capital,

corporation finance and the theory of investment” have formulated a theory that can answer the cost of capital question and this theory permitted the development of theory of investment of a firm under the conditions of uncertainty and found that correlation between cost of capital and leverage (LEV) was significantly equal to zero. The expected yield on common stock in any given class should increase with leverage. They concluded that an investment financed by common stock is advantageous to the current stock holders if and only if its yield exceeds the capitalization rate. When a corporate income tax under which interest is a deductible expense is considered, gain can accrue to stakeholders from having debt in the CS even when capital markets are perfect.

Castanias (1983)17, in a study “Bankruptcy risk and optimal capital structure” examined the relationship between failure rates and LEV ratios for 36



lines of business. The results were inconsistent with the irrelevance hypothesis firms in lines of business that tend to have "high" failure rates also tend to have less debt in their CS. Based upon the cross-sectional implications of the tax shelter-bankruptcy cost hypothesis, an alternative test of the irrelevance hypothesis was performed. The empirical results were not consistent with the CS irrelevance model of Miller. The results were consistent with the thesis that ex ante default costs are large enough to induce the typical firm to hold an optimum mix of debt and equity.

Lee et al. (1983)18, in a paper “Screening, market signaling, and capital structure theory” developed an equilibrium model in which informational asymmetries about the qualities of products offered for sale were resolved through a mechanism which combines the signaling and costly screening approaches. The paper concluded that the firm's optimal choices of debt-equity ratio and debt maturity structure subsequently signaled to prospective shareholders the relevant parameters of the firm's earnings distribution.

Myers (1984)19, in a work “The capital structure puzzle” tried to push research in some new direction by introducing new way of viewing static trade off framework and the old fashioned pecking order framework. In contrast to static trade off theory, the pecking order theory states that when external finance is required, firms issue the safest security first. They start with debt, then possibly by securities such as convertible bonds, then perhaps equity as a last resort. The works on asymmetric information also gives production roughly in line with pecking order theory. The mangers follow the general rule “issue safe securities before risky ones”. Risk has its impact on target debt ratio. Risky firms tend to borrow less. So there are other factors which influence a company’s financing behavior and their target debt ratios. Therefore, static trade off theory works only to a certain extent. The modified pecking order theory recognizes both asymmetric information as well as costs of financial distress.

Schnabel (1984)20, in a paper “Bankruptcy, interest tax shields and 'optimal' capital structure: A cash flow formulation” presented a cash flow formulation of the CS problem in the presence of corporate taxes. In contrast to



the classic result of Modigliani and Miller, it has been shown that an optimal CS does not involve exclusive reliance on debt financing.

Table II .1 Summary of Important Research Works

Sl. No.

Year Author/s Name of the study

Findings

1. 1958 Modigliani and Miller

The cost of capital, corporation finance and the theory of investment

Correlation between cost of capital and leverage (LEV) was significantly equal to zero. When a corporate income tax under which interest is a deductible expense is considered, gain can accrue to stakeholders from having debt in the CS even when capital markets are perfect.

2. 1984 Schnabel Bankruptcy, interest tax shields and 'optimal' capital structure: A cash flow formulation

In contrast to the classic result of Modigliani and Miller, it has been shown that an optimal CS does not involve exclusive reliance on debt financing.

3. 1984 Myers The capital structure puzzle

In contrast to Static Trade off theory, the pecking order theory states that when external finance is required, firms issue the safest security first.

4. 1986 Ghemawat and Caves

Capital commitment and profitability: An empirical investigation

CAPINS can affect P because cut-throat competition might eliminate all future profits, depressing each firm’s security level, proving that profits decline with CAPINS.

5. 1988 Harris Capital intensity and the firm's cost of capital

The result was consistent with theoretical prediction that both higher predicted profitability and higher capital requirements raise CAPINS. Higher firm-specific cost of capital reduced CAPINS.

6. 1988 Titman and Wessels

The determinants of capital structure choice

GROW rates were negatively related to long-term debt, accepting the pecking order theory.



7. 1989 Pinegar and Wilbricht

What managers think of capital structure theory: A survey

Corporate managers are more likely to follow a financing hierarchy than to maintain a target debt- equity ratio.

8. 1990 Harris and Raviv

Capital structure and the informational role of debt

Debt plays an important role in allowing investors to generate information useful for monitoring management and implementing efficient operating decisions.

9. 1992 Givoly Collins et al.

Taxes and capital structure: Evidence from firms' response to the Tax Reform Act of 1986

There exists a substitution effect between debt and non debt tax shields, and that both corporate and personal tax rates affect leverage decisions.

10. 1994 Harries Asset specificity, capital intensity and capital structure: An empirical test

Predicted CAPINS increased long term debt in the firm’s CS and predicted P decreases it, rejecting the transaction cost theory of CS.

11. 1995 Rajan and Zingales

What do we know about capital structure? Some evidence from international data

The negative influence of P on LEV should become stronger as firm size increases.

12. 1996 Berkovitch and Israel

The design of internal control and capital structure

The theory predicted that firm value and debt level were positively correlated when shareholders have absolute control, and were negatively correlated when debt holders have veto power.

13. 1996 Leland and Toft

Optimal capital structure, endogenous bankruptcy, and the term structure of credit spreads

The study pointed out that the tax advantage of debt must be balanced against bankruptcy and agency costs in determining the optimal maturity of the CS.

14. 1997 Berger, Ofek and Yermack

Managerial entrenchment and capital structure decisions

Entrenched managers seek to avoid debt; LEV is lower when the CEO strong monitoring from the board of directors or major stockholders.



15. 1998 Leland Agency cost, risk management and capital structure

Hedging benefits are greater when agency costs are low; hedging permits greater LEV.

16. 1998 Jonson The effect of bank debt on optimal capital structure

Banks debt reduced information asymmetries; LEV was significantly positively related to the fixed-asset ratio and significantly negatively related to the market –to book ratio, firm size, non-debt tax shields and P.

17. 2000 Simerly and Li

Environmental dynamism, capital structure and performance: A theoretical integration and an empirical test

The results from both models indicated a statistically significant negative impact of the dynamism and leverage interactive term on firm performance (as measured by both average Return on Asset (ROA) and average Return on Investment (ROI)).

18. 2001 Booth Collins et al.

Capital structures in developing countries

More profitable the firm, the lower the debt ratio, regardless of how the debt ratio is defined, which is consistent with the Pecking-Order Hypothesis.

19. 2003 Chang Ownership structure, expropriation and performance of group-affiliated companies in Korea

P is positively associated with inside ownership and family portions of inside ownership.

20. 2009 Dogra and Gupta

An empirical study on capital structure of SMEs in Punjab

Optimum CS enhances the P and the value of the firm.

21. 2010 Bhattacharjee Determinants of capital structure of Indian industries

Sustainable growth along with credit worthiness of the firm influences debt-equity ratio.

22.

2011 Dawood, Moustafa, and El-Hennawi

The Determinants of Capital Structure in Listed Egyptian Corporations

SIZ, P, LIQ, and business risk are the key determinants of CS though they differed across the different industries in Egypt.



Ghemawat and Caves (1986)21, in a work “Capital commitment and profitability: An empirical investigation” examined how the overall scope for commitment opportunities measured by the fixed capital intensity (CAPINS) of production processes, will influence P. They suggested that CAPINS can affect P because cut-throat competition might eliminate all future profits, depressing each firm’s security level. The sample was restricted to 274 observations of manufacturing business operating principally in North America. Their study proved that profits decline with CAPINS.

Kester (1986)22, in a study “Capital and ownership structure: A comparison of United States and Japanese manufacturing corporations” tested the hypothesis that Japanese manufacturing firms were more highly leveraged than the U.S. manufacturing firms. The determinants of CS taken into consideration were growth (GROW), P, risk, and size (SIZ) and industry classification. Sample included 344 Japanese firms and 452 U.S. firms in 27 different industries. The LEV was measured on market value and book value bases. Regression result showed a negative relation between LEV and P under both the bases. He concluded that on a market value basis there were no significant country differences in LEV between U.S. and Japanese manufacturing firms after controlling for characteristics such as GROW, P, risk, SIZ and industry classification, however there existed a significant country difference when LEV was measured on book value basis and this result was concentrated among the mature, capital –intensive industries.

Litzenberger (1986)23, in a paper “Some observations on capital structure and the impact of recent recapitalizations on share prices” showed that a value-maximizing CS may be inconsistent with shareholder utility maximization and that the Miller’s debt and taxes equilibrium may be inconsistent with a complete capital market.

John (1987)24, in a paper “Risk-Shifting incentives and signaling through corporate capital structure” examined optimal corporate financing arrangements under asymmetric information for different patterns of temporal resolution of uncertainty in the underlying technology. Agency signaling equilibrium states that private information of corporate insiders, at the time of financing, is signaled



through CS choices which deviate optimally from agency-cost minimizing financing arrangements, which, in turn, induce risk-shifting incentives in the investment policy. In the agency-signaling scenario outsiders have less information than insiders not only about the private actions of insiders, but also about the exogenous characteristics of the firms. The information equilibrium obtained involved CS choices by insiders which not only minimize the costs of conflict among the various claimants, but also reveal their private information credibly to the market.

Barton and Gordon (1988)25, in a work entitled “Corporate strategy and capital structure” investigated that corporate strategy perspective complemented the traditional financial paradigm in explaining CS in large U.S. corporations. The result showed reasonable support for the hypothesized positive relationship between sales growth and debt, while earnings risk appeared to be strongly related to debt. The negative relationship between CAPINS and debt was not supported and P was negatively related to debt. The financial variables were P, SIZ, GROW rate, CAPINS and earning risk. The final sample size was 279 firms. The analysis strongly supported the hypothesis that P is inversely related to debt, projecting that the hypothesis that SIZ & CAPINS are inversely related to debt was insignificant. The hypothesis that GROW rate is positively correlated with debt was supported to a certain extent by the findings.

Harris (1988)26, in a study entitled “Capital intensity and the firm's cost of capital” explored whether specification errors rather than measurement errors explained empirical result. Reports showed negative CAPINS coefficients in structure-performance equations support allegations of gross measurement error in accounting-based measures of economic profitability. The result was consistent with theoretical prediction that both higher predicted profitability and higher capital requirements increase CAPINS. Higher firm-specific cost of capital reduced CAPINS.

Lee and Kwok (1988)27 carried out an analysis entitled “Multinational corporations vs. domestic corporations: International environmental factors and determinants of capital structure”. The study focused on the multinational corporations’ (MNCs) CS, discussing whether MNCs have different CS than



domestic corporations (DCs), and if so, what causes such differences. A list of international environmental variables viz. political risk, international marketing imperfections, complexity of operations, opportunities for international diversification, foreign exchange risk and local factors of host countries were considered. CS determinants, such as agency costs and bankruptcy costs were used. MNCs tended to have higher agency costs of debt than DCs. MNCs appeared to have lower bankruptcy costs than DCs, the difference largely disappeared when the size effect was controlled.

Titman and Wessels (1988)28, in a work “The determinants of capital structure choice” analyzed the explanatory power of some of the recent theories of optimal CS. They identified opposite relation between collateralizable capital and debt level. This they stated based on the manager’s tendency to avoid secured debt financing as they increase the level of monitoring and reduce the level of perquisites. GROW rates were negatively related to long-term debt, accepting the pecking order theory which assumes that firms give more preference to retained earnings when deciding about financing a project.

Barton, Hill, and Sundaram (1989)29, in their study “An empirical test of stakeholder theory predictions of capital structure” tested a sample of 179 firms in the Fortune 500 categorized into 2 different strategy groups: related and unrelated. Cross-sectional regressions showed that CS is significantly related to the strategy variable; closely related products, markets, and technologies tend to have lower debt ratios than the firms with unrelated businesses.

Fischer, Heinkel, and Zechner (1989)30, in a study titled “Dynamic capital structure choice: Theory and tests” developed a model of dynamic CS choice in the presence of recapitalization costs. They found that even small recapitalization costs led to wide swings in a firm’s debt ratio over time. They used debt ratio range of a firm instead of static leverage measures and pointed out that increasing corporate tax rate or risk less interest rate or decreasing personal tax rate increases the tax advantage of debt, caused a decrease in the optimal debt ratio range and a higher initial debt ratio. Increasing bankruptcy cost also made it optimal to allow debt ratio to vary over a wider range. The return to LEV also decreased with bankruptcy cost. Thus, in their model where bankruptcy costs



and recapitalization costs drive optimal CS decisions, the advantage of LEV increases with variance.

Pinegar and Wilbricht (1989)31, in a study entitled “What managers think of capital structure theory: A survey” analyzed based on 176 responses received from 176 firms chosen out of the list of fortune 500 firms for 1986, out of which, 121firms indicated that they follow a financing hierarchy, while 47 indicated that they seek to maintain a target CS. The financing hierarchy showed that the managers first prefer internal equity (retained earnings) for financing new projects. The next priority goes to straight debt, convertible debt, external common equity, straight preferred stock and convertible preferred stock in a sequence. So the projected cash flow from the asset is the major determinant of the choice of the managers among various sources of capital, leading to conclude that corporate managers are more likely to follow a financing hierarchy than to maintain a target debt- equity ratio.

Harris and Raviv (1990)32, in their paper titled “Capital structure and the informational role of debt” analyzed the theory of CS based on the effect of debt on investors’ information about the firm and in their ability to oversee management. The paper was based on the idea that debt allows investors to discipline management and provides information useful for this purpose. The results were the debt level, market value of debt, firm value, debt-to value ratio, and promised bond yield all increased with increases in liquidation value and decreased with increases in default costs. More highly leveraged firms after larger promised yields had lower debt coverage ratios and had lower P of reorganization after default. The model they developed predicted that firms with higher liquidation value had more debt, and higher yield debt, and were more likely to default, but had higher market value than similar firms with lower liquidation value. They concluded that debt plays an important role in allowing investors to generate information useful for monitoring management and implementing efficient operating decisions.

Dybvig and Zender (1991)33, in a study “Capital structure and dividend irrelevance with asymmetric information” proved that the Modigliani and Miller propositions on the irrelevancy of CS and dividends were valid in a large class of



models with asymmetric information; corporations should move toward contracts with better incentives, and that new models should be built that recognize the limitations to optimal contracting.

Harris and Raviv (1991)34, in their paper “The theory of capital structure” analyzed the CS theories based on agency costs, asymmetric information, product/ input market interactions and corporate control considerations (but excluding tax-based theories) and found that agency model predicted that LEV is positively associated with firm’s value and is negatively related to the extent of growth opportunities, interest coverage, and the cost of investigating firm prospects. They pointed out that LEV increased with the extent of informational asymmetry. They also gave some empirical evidences in support of the theoretical results in support of works of Kester (1986), Fried & Hasbrouch (1988), Friend & Lang (1988), Gonedes et al. (1988), and Titman and Wessels (1988) who projected to support the theoretical result that LEV increased with decrease in P and the work of Long & Malitz (1985) showed result against the previous results.

Raymar (1991)35, in a work “A model of capital structure when earnings are mean-reverting” developed a multi period model of optimal CS under the assumption that earnings follow an autoregressive process. The reversion parameter of the earnings series was shown to be positively related to various measures of variability and negatively related to leverage. The study stated that if earnings processes are not homogeneous across firms, then standard earnings risk measures in CS studies do not adequately represent cross-sectional differences in variability in firm value.

Givoly Collins et al. (1992)36, in their work “Taxes and Capital Structure: Evidence from Firms' Response to the Tax Reform Act of 1986” studied the interaction between taxes and leverage decisions in a controlled environment in the years surrounding the enactment of the Tax Reform Act. The results supported the tax-based theories of CS and indicated that there exists a substitution effect between debt and non debt tax shields, and that both corporate and personal tax rates affect leverage decisions.



Kracaw, Lewellen, and Woo (1992)37, in a study “Corporate growth, corporate strategy and the choice of capital structure” made a theoretical study to understand the manner in which firms should position themselves to prevent the corporate strategies from being impeded by inflation. They stated that an appropriate choice of CS can assist in accomplishing this objective. They also stated that the impact of inflation on the firm’s growth rate and the ultimate changes in the firm’s strategy can be nullified by adopting an appropriate financing policy, and concluded that an appropriate CS policy allows the corporate strategy (build, maintain and harvest) that maximizes shareholder’s value to continue to do so regardless of how rapidly or irregularly prices rise.

Mehran (1992)38, in their work “Executive incentive plans, corporate control, and capital structure” investigated the relationship between the firm's CS and executive incentive plans, managerial equity investment, and monitoring by the board of directors and major shareholders. The paper found a positive relationship between the firm's leverage ratio and percentage of executives' total compensation in incentive plans, percentage of equity owned by managers, percentage of investment bankers on the board of directors, and percentage of equity owned by large individual investors. Regression results indicated a positive relation between the percentage of ownership by individual investors and the firm's leverage ratio, supporting the argument that major shareholders were also effective monitors. These findings were consistent with the view that the firm's CS is related to agency costs between managers and shareholders.

Papaionnou, Strock, and Travlos (1992)39, in their work “Ownership structure and corporate liquidity policy” attempted to study the relationship between corporate liquidity and managerial ownership in the firm’s stock. They pointed out that liquidity of the firm affected both P and operating risk. The sample contained 225 firms from fortune 500 companies in 1980. Tobin q Ratio was found out for 194 firms, and they concluded that the firm’s liquidity declines with increase in cash cycle and debt ratio; liquidity ratio is directly related to firm’s commitment to intangible resources like Research & Development (R&D) and advertising.



Bagwell and Zechner (1993)40, in a paper “Influence costs and capital structure” analyzed the role of CS in the presence of intra firm influence activities. They identified several key factors that determine the optimal CS: the top management's prior assessment of the likelihood that it will be optimal to divest a specific division; the costs of influence activities to the firm and to the divisional managers; and the difference in the valuation of the division's assets in the current firm and under alternative uses.

Balakrishnan and Fox (1993)41, in a paper “Asset specificity, firm heterogeneity and capital structure” conducted an empirical investigation of the importance of specialized assets and other unique characteristics of a firm to explain the variance in CS across firms. They took a sample of 295 firms with a minimum of four firms per industry. Independent variables viz. risk, depreciation, R & D, advertising expenses, and growth were used to determine the LEV of the firm. GROW was found to be negative and insignificant when using LEV as the dependant variable. There existed a negative relation between R&D and LEV while, the relation between advertising and LEV was significantly positive. They concluded that unique firm specific assets and skills were the most important determinants of CS. The firm-specific effects contributed most to the variance in LEV, suggesting a strong link between strategy and CS.

Berglöf and Thadden (1994)42, in a work “Short-Term versus long-term interests: Capital structure with multiple investors” studied the problem of financial contracting and renegotiation between a firm and outside investors when the firm cannot commit to future payouts, but assets can be contracted upon. The study showed that CS with multiple investors specializing in short-term as well as long-term claims is superior to a structure with only one type of claim, because this hardens the incentives for the entrepreneur to renegotiate the contract ex post.

Harries (1994)43, in a work “Asset specificity, capital intensity and capital structure: An empirical test” made an attempt to resolve the controversies in investment – LEV – GROW relationships. An empirical mode of profit margin, CAPINS, LEV and risk were developed. The relationship between debt financing and capital investment across 73 fortune 500 firms were tested. Measures of both



higher post earnings growth and higher future perfect growth opportunities were taken into consideration in the leverage equation. It was pointed out that a higher firm-specific predicted cost of capital (CoC) lowers CAPINS. Predicted CAPINS increased long term debt in the firm’s CS and predicted P decreases it. It was pointed out that increased debt financing increases the firm’s systematic risk. The analysis rejects the transaction cost theory of CS.

Leland (1994)44, in an article “Corporate debt value, bond covenants, and optimal capital structure” examined corporate debt values and CS in a unified analytical framework. It derived closed-form results for the value of long-term risky debt and yield spreads, and for optimal CS, when firm’s asset value follows a diffusion process with constant volatility (VOL). Debt values and optimal LEV were found to be explicitly linked to firm’s risk, taxes, bankruptcy costs, risk-free interest rates, payout rates, and bond covenants. The result showed that a rise in the risk-free interest rate (increasing the cost of debt financing) led to a greater optimal debt level. Higher interest rates generated greater tax benefits, which, in turn, dictated more debt despite its higher cost. The firms choose significantly lower optimal LEV when bankruptcy costs were substantial, thereby making debt less risky. But in case of protected debt, higher bankruptcy costs implied higher interest rates at the optimal LEV. Optimal LEV was high (and/or yield spreads seemed low) for unprotected debt.

Lowe, Naughton, and Taylor (1994)45, in an analysis “The impact of corporate strategy on the capital structure of Australian companies” suggested that corporate strategy influences CS, particularly for the most diversified firms. Profit, cash flow, the rate of growth and the level of earnings’ risk were considered as important additional internal influences on CS. In this study they found a positive relationship between CAPINS and debt ratios, and they concluded that firms with a great deal of unrelated diversification had different debt/equity ratios and associated financial parameters concerning growth, risk and cash flow relative to firms with less diversified portfolios.

Spiegel and Spulber (1994)46, in a study “The capital structure of a regulated firm” examined the equilibrium price, investment, and CS of a regulated firm using a sequential model of regulation. Their three-stage model of



the regulatory process showed that CS can play a role in the strategic interaction between regulators and firms. The firm choose its equity and debt strategically that affected the outcome of the regulatory process. In equilibrium, the firm issued a positive amount of debt and the likelihood of bankruptcy was positive. Debt raised the regulated price, thus mitigating regulatory opportunism. However, underinvestment due to lack of regulatory commitment to prices persists in equilibrium.

Berens and Cuny (1995)47, in their article “The capital structure puzzle revisited” recognized that firm’s value typically reflects a growing stream of earnings, while current debt reflects a non growing stream of interest payments. They found that the debt to value was a distorted measure of corporate tax shielding; high cross-sectional variation of debt ratios with very small change in debt-related costs. This variation was found to be independent of tax shielding, and so they concluded that debt ratios provide an inappropriate framework for empirically examining the trade-off theory of CS.

Rajan and Zingales (1995)48, in a paper titled “What do we know about capital structure? Some evidences from international data” investigated the determinants of CS choice by analyzing the financing decisions of public firms in the major industrialized countries. At an aggregate level, firm LEV was found to be fairly similar across the G-7 countries. P was found to be negatively correlated with LEV. They stated that in the short run, dividends and investments were fixed, and if debt financing was the dominant mode of external financing, then changes in P will be negatively correlated with changes in LEV. Large firms tend to issue less equity. They also emphasized that the negative influence of P on LEV should become stronger as firm size increases.

Roden and Lewellen (1995)49, in a work “Corporate capital structure decisions: Evidence from leveraged buyouts” focused mainly on three CS determinants viz., agency cost, bankruptcy risk, and tax considerations, which were found to have an impact, both on the degree of leverage employed in the transactions as well as on the attributes of the borrowings undertaken. They found evidence that the financing package were designed systematically to respond to differences across firms in their growth prospects, in the variability of



their earnings, in their liquidity characteristics, in their plans to sell assets, and in opportunities to achieve tax savings from the deductibility of interest costs. The prospective cash flow profile of the target firm was also a matter of concern for the financing decision.

Staking and Babbel (1995)50, in a study “The relation between capital structure, interest rate sensitivity, and market value in the property-liability insurance industry” gave specific attention to traditional theories regarding CS, including the tradeoff between the tax benefits and increasing P of incurring the cost of financial distress associated with LEV, and the tradeoff between protecting franchise or charter value and expropriating value through increasing exposure to interest rate risk. They concluded that the market value of equity at first grows but then later declined as LEV increases. Interest rate risk had the opposite effect. Equity value first declined with interest rate risk, but then rose at high levels of interest rate risk. The results were consistent with the prediction that financial institutions will expend scarce resources to control risk in order to protect franchise value.

Berkovitch and Israel (1996)51, in a paper “The design of internal control and capital structure” studied the impact of internal control on CS in two sense viz., when the company control was allocated only to shareholders and when it was allocated to other stakeholders, such as debt holders or the management team. Their theory predicted that firm value and debt level were positively correlated when shareholders have absolute control, and were negatively correlated when debt holders have veto power. These predictions highlighted the importance of incorporating internal control when studying financial policies of firms.

Leland and Toft (1996)52, in an article titled “Optimal capital structure, endogenous bankruptcy, and the term structure of credit spreads” examined the optimal CS of a firm that can choose both the amount and maturity of its debt. Bankruptcy was found to be endogenously determined rather than by the imposition of a positive net worth condition or by a cash flow constraint. The result showed that short term debt did not exploit tax benefits as completely as long term debt, however it reduced or eliminated agency costs. They pointed out



that the tax advantage of debt must be balanced against bankruptcy and agency costs in determining the optimal maturity of the CS.

Berger, Ofek, and Yermack (1997)53, in a work “Managerial entrenchment and capital structure decisions” studied the associations between managerial entrenchment and firms' CS. The result suggested that entrenched chief executive officers (CEOs) seek to avoid debt. The cross-sectional analysis showed that LEV levels are lower when CEOs do not face pressure from either ownership and compensation incentives or active monitoring. The managers do not issue the optimal amount of debt without pressure from a disciplining force. The results indicate that the LEV is lower when CEO has a long tenure in office, has weak stock and compensation incentives, and does not face strong monitoring from the board of directors or major stockholders.

Chauvin and Hirschey (1997)54, in a paper “Market structure and the value of growth” found statistically significant positive effects of growth on the current market value of the firm over 1974-90 periods. The study investigated market share, advertising and research & development expenditures as attributes of market structure with the potential to influence the effects of growth on the current market value of the firm. The cross-sectional relation between the market value of the firm and company characteristics was found to be dependent upon market conditions.

Johnson (1997)55, in a research work “An empirical analysis of the determinants of corporate debt ownership structure” examined the relation between corporate debt ownership structure and several firm’s characteristics such as age (AG), SIZ, VOL, market-to-book ratio, collateral value of assets (COLASS), fixed asset ratio, and firm’s LEV. The sample size of 847 firms was taken for analysis. It was concluded that firms used more public debt if they face lower information and monitoring cost, have a lower likelihood and costs of inefficient liquidation and have fewer incentives to take actions harmful to lenders. Bank debt use and private non-bank debt use were both statistically related to leverage, the fixed asset ratio and the market-to-book ratio, but the signs of relationships were opposite across the sources. The only similarity found



between the determinants of the two sources was that both were negatively related to age.

Spiegel and Spulber (1997)56, in a work “Capital structure with countervailing incentives” showed that the regulated firm’s choice of CS is affected by countervailing incentives; the firm wishes to signal high value to capital markets to boost its market value while also signaling high cost to regulators to induce rate increases.

Jonson (1998)57, in a work “The effect of bank debt on optimal capital structure” examined the relation between LEV and bank debt use to analyze the effect of bank screening and monitoring on CS. A sample of 716 firms were taken for analysis, focusing on the companies with bank debt rather than the amount of bank debt as, the main consideration was on presence of bank monitoring. Wilcoxon two- sample test was used to analyze difference in medians across the samples and multiple regression was used to analyze the relation between LEV and bank debt use and find out the controlling factors. It was concluded that LEV is significantly greater (at 0.01% level) for firms with bank debt than for firms with only non-bank private debt. The firms borrowing from bank led to increased bank monitoring and this subsequently induced the companies to choose safe project which reduced defaulting and increases the reputation of the company. Banks also reduced information asymmetries. It was also found that LEV was significantly positively related to the fixed-asset ratio and significantly negatively related to the market –to book ratio, firm size, non-debt tax shields and P.

Leland (1998)58, in a study “Agency cost, risk management and capital structure” made an attempt to analyze CS and investment risk. Asset substitution and risk management were analyzed closely. Agency cost and benefits to hedging were held to be inversely related in many cases. Thus, hedging benefits are greater when agency costs are low. The study concluded that hedging permits greater LEV.

Sengupta P. (1998)59, in a paper “Corporate disclosure quality and cost of debt” made an attempt to prove that firms with high disclosure quality rating



from financial analysts enjoy a lower effective interest cost of issuing debt. This paper thus investigated the link between a firm’s overall disclosure quality and its cost of debt financing. Sample of 114 firms were taken in total and 103 firms with total interest cost were taken for regression. It was concluded that there exist a significant negative association between a measure of a firm over all disclosure quality and two alternative measures of firms incremental borrowing cost.

Koch and Shenoy (1999)60, in their work “The information content of dividend and capital structure policies” analyzed a subsample of firms with no significant increase in cash flow during the sample period. The empirical evidence was consistent with the free-cash-flow hypothesis, and it suggested that dividends and CS policies provide more predictive information for over and under investing firms than for value-maximizing firms.

Ang et al. (2000)61, in the study “Agency costs and ownership structure” provided measures of absolute and relative equity agency costs for corporations under different ownership and management structures. They tested a sample of 1,708 small corporations from the Federal Reserve Board (FRB)/National Survey of Small Business Finances (NSSBF) database and concluded that agency costs are higher when an outsider manages the firm. It varied inversely with the manager's ownership share; monitoring of banks reduces agency cost.

Morek, Nakamura, and Shivdasani (2000)62, in a study “Banks, ownership structure and firms value in Japan” analyzed the role of bank ownership on the firm’s value. They pointed out that in Japan and Germany, the banks also hold moderate level of equity stakes. Japanese firms have a main bank’ which is its largest provider of debt financing. They concluded that higher levels of bank ownership are associated with increased interest cost for firms that are dependent on banks.

Simerly and Li (2000)63, in their study “Environmental dynamism, capital structure and performance: A theoretical integration and an empirical test” used a sample of 700 large U.S. firms in varieties of industries. Financial information was collected from COMPUSTAT. They concluded that firms experiencing stable



environments (lower dynamism), leverage was positively linked to performance, and for firms experiencing relatively to very dynamic environments (medium to higher dynamism), and leverage was negatively related to performance. The results from both models indicated a statistically significant negative impact of the dynamism and leverage interactive term on firm performance (as measured by both average Return on Asset (ROA) and average Return on Investment (ROI)).

Thomsen and Pedersen (2000)64, in their work “Ownership structure and economic performance in the largest European companies” examined the impact of ownership structure on company economic performance in 435 of the largest European firms. They distinguished between five ownership categories- banks, institutional investors, other non financial companies, personal/ family, and government. They found that non financial investors (family, non financial company & government) hold large shares on average and the financial investors (banks & institutional investors) hold lesser share; compared to other owner identities, financial investor ownership is found to be associated with higher shareholder value and P but lower sales growth; and effect of ownership concentration is dependent on owner identity.

Booth Collins et al. (2001)65, in their work “Capital structures in developing countries” analyzed the CS determinants of developed and developing countries. They found that the variables that are relevant for explaining CS in the U.S. and European countries are also relevant in developing countries. Result in both the country and pooled data results showed that the more profitable the firm, the lower the debt ratio, regardless of how the debt ratio is defined which is consistent with the Pecking-Order Hypothesis. The debt ratios in developing countries seem to be affected in the same way and by the same types of variables that are significant in developed countries.

Goldstein, Ju, and Leland (2001)66, in a paper titled “An EBIT-based model of dynamic capital structure” proposed a model of dynamic CS. They stated that when a firm has the option to increase future debt levels, tax advantages to debt increase significantly, and both the optimal leverage ratio



range and predicted credit spreads are more in line with what is observed in practice.

Chui, Lloyd, and Kwok (2002)67, in a study “The determination of capital structure: Is national culture a missing piece to the puzzle?” suggested that national culture affects corporate CS. The empirical hypotheses were tested against a sample of 5591 firms across 22 countries. Results showed that countries with high scores on the cultural dimensions of "conservatism" and "mastery" tend to have lower corporate debt ratios. The effects are strong and remain significant even after accounting for differences in economic performance, legal systems, financial institutions, and some other well-known determinants of debt ratios.

Chang (2003)68, in a research work “Ownership structure, expropriation and performance of group-affiliated companies in Korea” studied a sample of 419 group-affiliated public firms in Korea to examine simultaneous causality between ownership structure and firm performance in business groups. The findings showed that P is positively associated with inside ownership and family portions of inside ownership, which suggests that inside ownership and family portion are higher in more profitable firms; there was a positive relationship between ownership concentration and performance; performance determines ownership structure but not vice versa.

Leary and Roberts (2005)69, in a work “Do firms rebalance their capital structures?” examined empirically whether firms engage in a dynamic rebalancing of their CS while allowing for costly adjustment. They found that firms actively rebalance their leverage to stay within an optimal range. Their evidence suggested that the persistent effect of shocks on leverage observed in previous studies were more likely due to adjustment costs than indifference toward CS. Interestingly, their evidence was consistent with the predictions of the modified pecking order. Firms were less likely to utilize external capital markets when they had sufficient internal funds, but more likely when they have large investment needs. Thus, while firms follow a dynamic rebalancing strategy, adverse selection costs may be an important determinant in their financing decision.



Miao (2005)70, in a paper “Optimal capital structure and industry dynamics” provided a competitive equilibrium model of CS and industry dynamics. He indicated that firms make financing, investment, entry, and exit decisions subject to idiosyncratic technology shocks. The CS choice reflects the tradeoffs between the tax benefits of debt and the associated bankruptcy and agency costs. More efficient firms are less likely to exit and have lower agency costs. It was concluded that interaction between financing and production decisions is important in an industry equilibrium after analyzing the changes in technology growth, technology risk, entry distribution, entry cost, fixed cost, bankruptcy cost, and tax policy.

Dogra and Gupta (2009)71, in a research paper entitled “An empirical

study on capital structure of SMEs in Punjab” analyzed various factors influencing CS and their impact on the decision-making ability of the SMEs. A sample of 50 manufacturing units was taken for the purpose of analysis. They pointed out that optimum CS enhances the P and the value of the firm; SMEs relied more on their own funds and comparatively less on borrowed funds. They ranked flexibility, management control, liquidity and cost of capital with ranks from 1-4 respectively as the prime determinants of CS.

Malabika Deo and Jackline (2009)72, in a study entitled “The determinants of debt ownership structure:Some empirical evidence" across industries found that firms do not have a specific norm or preference for debt choices. They concluded that lesser P made firms go for long term borrowings while increasing costs (both agency costs and bankruptcy costs) associated with raising funds induced the firms to shift to short term borrowings. The total debt (TD) increased with increase in size for smaller sized and large sized firms whereas it increases with decrease in size of medium sized firms. TD decreased with increase in P and increased with increase in COLASS.

Bhattacharjee (2010)73, in a paper entitled “Determinants of capital structure of Indian industries” conducted an empirical study of the determinants of CS of 151 selected firms across 13 industrial sectors. The major finding was that the variables like liquidity (LIQ) and GROW in terms of performance of the firms have significant influence on debt-equity ratio. In other words, sustainable



growth along with credit worthiness of the firm influences debt-equity ratio i.e., degree of financial leverage. The study concluded that leverage varied across industries and between firms belonging to the same industrial sector.

Vinayek and Gupta (2010)74, in their work “Determinants of capital structure in drugs and pharmaceutical industry in India: A comparative study of pre and post liberalization period” examined the determinants of CS of firms in drugs and pharmaceutical industry in pre-liberalization period and post liberalization period. They found a significant difference in the determinants of CS in pre-liberalization as well as on post-liberalization periods. The variables like P, CAPINS, and COLASS, which were insignificant in the pre-liberalization period were significant to the market value debt equity ratios in the post-liberalization period, while SIZ which was found to be the significant determinant of CS in the pre-liberalization period. They concluded that the difference was due to the changes in business environment and capital market.

Azhagaiah and Deepa (2011)75 in a study “Impact of firm size on the relationship between profitability and capital structure” analyzed the impact of sales size on the relationship between P and LEV, considering the size as the control variable. The findings proved that there exists a positive correlation between P and LEV in case of small size firms while, the showed a negative relation between P and LEV providing evidence that debt capital decreases with increase in SIZ of the firm.

Azhagaiah and Deepa (2011)76, in an empirical work entitled “Determinants of profitability: A study with reference to income size-wise analysis of selected firms” analyzed the impact of income on determinants of P by grouping the firms of tea, dairy and vegetable oil sector into three size categories viz., “low income”, “medium income”, and “high income” firms based on their profit before interest tax and depreciation (PBITD). The results indicated that GROW and VOL determined the P of medium and high income firm, while CAPINS was the significant major determinant variable of P in case of low income firms.



Azhagaiah and Deepa (2011)77 in an empirical work “Choice of capital structure model: An empirical analysis with reference to static trade-off Vs pecking order theories in beverage and alcohol industry in India” attempted to determine the predictors of CS in the beverage and alcohol industry in India and also to find out the approach followed by these firms to decide their CS. The findings proved that pecking order hierarchy is followed in beverage and alcohol industry in India. COLASS and P are found to be the major determinant of CS.

Dawood, Moustafa, and El-Hennawi (2011)78, in the empirical work “The Determinants of Capital Structure in Listed Egyptian Corporations” investigated the financing decisions of listed Egyptian corporations and the key factors that affect their choice of CS. The results indicated that the overall significant determinants were mainly: SIZ, P, LIQ, and business risk though they differed across the different industries in Egypt. Egypt tends to follow a certain hierarchy of finance consistent with the modified Pecking Order theory of capital structure.

Panigrahi (2011)79, in the work “Location as a Determinant of Capital Structure: A Study of Indian Private Sector Firms” analyzed whether the location of a firm affects its CS decisions of Indian companies. The analysis was conducted on a sample of 300 Indian private sector companies, comprising of 20 different sectors for the period 1999-2000 to 2007-2008, duly grouping them on the basis of their regions as western, eastern, southern and northern region. Findings showed that the region or location of a company strongly influences the quantum of inflow of funds.

Sheikh and Wang (2011)80, in a study entitled “Determinants of capital structure: An empirical study of firms in manufacturing industry of Pakistan" explored the factors that affect the CS of manufacturing firms. The results suggested that P, LIQ, VOL, and COLASS are related negatively to the debt ratio, whereas SIZ is positively linked to the debt ratio. The findings of the study are consistent with the predictions of the trade-off theory, pecking order theory, and agency theory.



II.3 Research Gap and Concluding Remarks The studies revolve around emphasizing the significance of CS in

maximizing the value of the firm which is considered as the basic objective of financial management. Factors influencing the CS choice are the next most important aspect that has been taken into consideration. Tax advantage on using debt finance has been analyzed by many experts and they have put forth different views. Modigliani and Miller (1958)81 have pointed out that debt finance is beneficial if corporate income tax under which interest is a deductible expense is considered. In contrast Schnabel (1984)82 has shown that an optimal CS does not involve exclusive reliance on debt financing. However, the result of study of Givoly Collins et al. (1992)83 and many others supported the tax-based theories of CS and indicated that there exists a substitution effect between debt and non debt tax shields, and that both corporate and personal tax rates affect leverage decisions. Another major area analyzed was the effect of bankruptcy cost on LEV and signaling effect on debt finance. Leland and Toft (1996)84 and Miao (2005)85 emphasized that the CS choice reflects the tradeoffs between the tax benefits of debt and the associated bankruptcy and agency costs.

P is another important factor which is considered crucial to be linked with CS choice. This is because determining the optimal CS is aimed at increasing the profit earning capacity of the firm. Many experts have expressed views differently about the relation between CS and P. Myers (1984)86 introduced pecking order theory, which states firms prefer internal funds and when it gets exhausted they look in for debt finance rather than equity finance because it is a better signal to the market. Many other studies have supported the pecking order hypothesis. Kester (1986)87, Titman and Wessels (1988)88, Barton and Gordon (1988)89, Pinegar and Wilbricht (1989)90, Harris and Raviv (1991)91, Harries (1994)92, Jonson (1998)93, Simerly and Li (2000)94, and Booth Collins et al. (2001)95 have found a negative relation between P and CS in support of pecking order theory. On the other hand, Rajan and Zingales (1995)96 have emphasized on size of the firm stating that large firms tend to issue less equity and that the negative influence of P on LEV should become stronger as firm size increases. Dogra and Gupta (2009)97 have studied the relation the other way stating that optimum CS enhances the P and the value of the firm. These varied views and results of many studies persuade one to study in detail the relation between P and CS. Hence, this study is maiden attempt to study the relation between P and CS.



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Chapter III CONCEPTS AND THEORIES OF CAPITAL STRUCTURE AND PROFITABILITY: A REVIEW




CHAPTER III

CONCEPTS AND THEORIES OF CAPITAL STRUCTURE AND PROFITABILITY: A REVIEW

III.1 Introduction The review of past works portrays an apparent picture about the empirical

analysis carried out and the varied arguments of the researchers. A review of concepts and theories will give a vivid idea about the basis on which these studies were carried out. It will also help us to understand how crucial the study is in improving their industry.

A firm funds its operation with capital raised from varied sources. A mix of these various sources is generally referred to as capital structure (CS). The CS has been defined as “that combination of debt and equity that attains the stated managerial goals (i.e.) the maximization of the firm’s market value”. The optimal CS is also defined as that “combination of debt and equity that minimizes the firm’s overall cost of capital”1. The firm’s balance sheet constitutes different proposition of debt instruments, preferred and common stock, which represents the CS of the firm. The CS is an unsolved problem, which has attracted both academics and practitioners as the objective of financial management is to maximise shareholder’s wealth. The key issue here is the relationship between CS and firm’s value. The firm’s value is maximised when cost of capital is minimised. Therefore, they are inversely related.

There are different views on how CS influences value of the firm. The optimal CS is a question which the managers themselves cannot answer. There are varied factors that influence the debt level in a firm. Among the key factors the first is the benefits and cost associated with various financing choices. The trade-off between the benefits and cost leads to well-defined target debt ratio. The second is the existence of shocks that cause firms to deviate, at least temporarily, from their targets. The third is the presence of factors that prevent firms from immediately making CS changes that offset the effect of the shocks or financial distress that move them away from their targets. Profit, cash flow, the rate of growth and the level of earning’s risk are important additional internal factors which influence on CS2.The various factors that influence the CS of a firm are illustrated in diagram III. A.



III.2 Leverage Leverage (LEV) generally mean “the increased ability of accomplishing

some purpose. It is the employment of an asset/ source of finance for which firms pay fixed cost/ fixed return3”. Hence, it is the firm’s ability to use fixed cost assets or funds in lieu of variable costs assets or funds to increase the returns to its owners. Such LEV magnifies profits and losses. The LEV is of two types:

Operating leverage, and

Financial leverage

Both the types of LEV have the same effect on shareholders but are accomplished in very different ways, for very different purposes strategically. Operating leverage is determined by the relationship between the firm’s sales revenue and its earnings before interest and taxes (EBIT), on the other hand, financial leverage represents the relationship between EBIT and the earnings available for ordinary shareholders. Thus, EBIT is used as the pivotal point in defining operating and financial leverages4.

Diagram III. A Factors Determining Capital Structure

Capital Structure

Personal Tax

Bankruptcy

Agency Costs

Corporate Governance

Signalling Ownership Structure

Macro Economic Variables

Floatation and other

Direct Costs

Government and other regulations

Corporate Tax



Source: Compiled from secondary sources.

III. 3 Capital Structure Theories A number of theories explain the relationship between cost of capital, CS

and value of the firm. (see diagram III.B). They are:

Net income approach (NIA)

Net operating income approach (NOIA)

Traditional approach (TA)

Modigliani-Miller approach (MMA)

The two extreme boundaries of valuation of the earnings of a firm are the net income approach and the net operating income approach. According to the net income approach, the firm is able to increase its total valuation and lower its cost of capital, as it increases the degree of LEV. The net operating income approach implies that the total valuation of the firm is unaffected by its CS as this approach is purely definitional, however, behavioural or economic meaning is attached to them. Modigliani and Miller (1958) 5 offered behavioural support for the independence of the total valuation and the cost of capital of the firm from its CS. The traditional approach assumes that there is an optimal CS and that the firm can increase its total value through the judicious use of LEV.

To find out what happens to the total valuation of the firm and to its cost of capital when the ratio of debt to equity, or degree of LEV is varied the assumptions such as (i) no income tax, corporate or personal and no transaction cost, (ii) 100 per cent dividend payout ratio, (iii) operating income is not expected to grow or decline over time.

Given the above assumptions, the analysis focuses on the following rates:

rD = 𝐼𝐼𝐷𝐷

= 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑖𝑖𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑐𝑐ℎ𝐴𝐴𝑖𝑖𝑎𝑎𝑖𝑖𝑖𝑖𝑀𝑀𝐴𝐴𝑖𝑖𝑀𝑀𝑖𝑖𝑖𝑖 𝑣𝑣𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 𝑜𝑜𝑜𝑜 𝑑𝑑𝑖𝑖𝑑𝑑𝑖𝑖

Assuming that the debt is perpetual, rD represents the cost of debt.

rE = 𝑃𝑃𝐸𝐸

= 𝐸𝐸𝐸𝐸𝐴𝐴𝑖𝑖𝑖𝑖𝐸𝐸 𝑖𝑖𝐴𝐴𝑖𝑖𝐴𝐴𝑖𝑖𝐴𝐴𝑎𝑎𝑖𝑖𝑀𝑀𝐴𝐴𝑖𝑖𝑀𝑀𝑖𝑖𝑖𝑖 𝑣𝑣𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 𝑜𝑜𝑜𝑜 𝑖𝑖𝐸𝐸𝐴𝐴𝑖𝑖𝑖𝑖𝐸𝐸



When the dividend payout ratio is 100 per cent and earnings are constant,

rE, as defined here, represents the cost of equity.

rA = 𝑂𝑂𝑉𝑉

= 𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖𝐴𝐴𝑖𝑖𝑖𝑖𝐴𝐴𝑎𝑎 𝑖𝑖𝐴𝐴𝑐𝑐𝑜𝑜𝑖𝑖𝑖𝑖𝑀𝑀𝐴𝐴𝑖𝑖𝑀𝑀𝑖𝑖𝑖𝑖 𝑣𝑣𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 𝑜𝑜𝑜𝑜 𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖

Where V= D + E. rA is the overall capitalisation rate of the firm. Since it is

the weighted average cost of capital, it may be expressed as:

rA = rD �𝐷𝐷

𝐷𝐷+𝐸𝐸� + rE�

𝐸𝐸𝐷𝐷+𝐸𝐸

�

The changes in rD, rE and rA corresponding to changes in financial LEV

(Debt/Equity) are discussed in the following sections6.

III. 3.1 Net Income Approach

According to this approach, the cost of debt, rD, and the cost of equity, rE,

remain unchanged when D/E varies7. The constancy of rD and rE with respect to

D/E means that rA, the average cost of capital, measured as

rA = rD �𝐷𝐷



�

declines as D/E increases. This happens because when D/E increases, rD, which is

lower than rE, receives a higher weight in the calculation of rA.

The net income approach is graphically shown in graph III. A. D/E is

plotted on the abscissa; rE, rD and rA are plotted on the ordinate.



Graph III.A Behaviour of rA , rD and rE as per the Net Income Approach

From graph III.A it is clear that as D/E increases, rA decreases because the proportion of debt, the cheaper source of finance, increases in the CS.

III. 3.2 Net Operating Income Approach

According to the net operating income approach, the overall capitalisation rate and the cost or debt remain constant for all degrees of LEV. In the equation

rA = rD �𝐷𝐷



�

rA and rD are constant for all degrees of LEV. Given this, the cost of equity can be expressed as:

rE = rA + (rA - rD) (D/E)

The above behaviour of rD, rE, and rA in response to changes in (D/E) is shown in graph III. B.

Rate of Return

rE

rD

D/E

rA



Graph III.B Behaviour of rA , rD and rE as per the Net Operating Income Approach

The critical premise of this approach is that the market capitalises the firm

as a whole at a discount rate which is independent of the firm’s debt-equity ratio.

As a consequence, the division between debt and equity is irrelevant. An

increase in the use of debt funds which are ‘apparently cheaper’ is offset by an

increase in the equity capitalization rate. This happens because equity investors

seek higher compensation as they are exposed to greater risk arising from

increase in the degree of LEV. They increase the capitalisation rate rE (lower the

price- earnings ratio, P/E), as the degree of LEV increases8.

The net operating income position has been advocated eloquently by

David Durand, who argued that the market value of a firm depends on its net

operating income and business risk. The change in the degree of LEV employed

by a firm cannot change these underlying factors. It merely changes the

distribution of income and risk between debt and equity without affecting the

total income and risk which influence the market value of the firm.

Rate of Return rE

rA

rD

D/E



Diagram III. B THEORIES OF CAPITAL STRUCTURE AND PROFITABILITY


Capital Structure Theories Profitability Theories

Traditional Theories

Tax Benefit Theories

Trade-off Theories

Cost Based Theories

Market Timing Theory

Net Income Approach

Net Operating Income Approach

Traditional Approach

Modigliani-Miller Approach

Merton Miller Argument

Static Trade-off Theory

Dynamic Trade-off Theory

Bankruptcy Cost Theory

Agency Costs Theory

Signaling Theory

Pecking Order Theory

Free Cash Flow Theory



Modigliani and Miller, in a seminal contribution made in 1958, forcefully advanced the proposition that the cost of capital of a firm is independent of its CS9.

It assumes that rA is constant, regardless of the degree of LEV. The use of supposedly “cheaper” debt funds is offset exactly by the increase in the required equity return, rE, thereby, it implies that there is no one optimal CS.

III. 3.3 Traditional Approach The main propositions of the traditional approach are:

The cost of debt capital, rD, remains more or less constant up to a certain degree of LEV but rises thereafter at an increasing rate.

The cost of equity capital, rE, remains more or less constant or rises only gradually up to a certain degree of LEV and rise sharply thereafter.

The average cost of capital, rA, as a consequence of the above behaviour of rE and rD, (i) decreases up to a certain point; (ii) remains more of less unchanged for moderate increases in LEV thereafter; and (iii) rises beyond a certain point.

The principal implication of the traditional position is that the cost of capital is dependent on the CS and there is an optimal CS which minimises the cost of capital10. Staking and Babbel (1955) 11 findings also supports this approach as their result show that the market value of equity at first grows but then later declines as LEV increases.

III 3.4 Modigliani-Miller Proposition Modigliani and Miller (MM)12, in their original proposition advocated

that the relationship between LEV and the cost of capital is explained by the net operating income approach. They make a formidable attack on the traditional position by offering behavioural justification for having the cost of capital, rA, remain constant throughout all degrees of LEV.

The theory assumed a perfect capital market where there is no problem of asymmetric information: there are no transaction costs; no bankruptcy cost and



the securities are infinitely divisible. Managers act in the interest of shareholders and the firms can be grouped into equivalent risk classes on the basis of their business risk; and they assumed that there is no tax13.

In their proposition I they considered the value of the firm to be independent of its CS. This proposition was more or less similar to that of the net operating income approach. They viewed the value of a firm as a function of expected operating income divided by the discount rate appropriate to its risk class, and proved that the average cost of capital within a given class is independent of the degree of LEV14.

The proposition II held that financial leverage increases to expected earnings per share (EPS) while the share price remains constant. This is because the change in the expected earnings is offset by a corresponding change in the return required by the shareholders15.

Their proposition III made an attempt to develop the Theory of Investment, wherein they concluded that an investment financed by common stock is advantageous to the current stockholders if and only if its yield exceeds the capitalization rate. When a corporate income tax, under which interest is a deductible expense, is considered, gain can accrue to stockholders from having debt in the CS, even when capital markets are perfect16.

III 3.4.1 Criticisms of MM Theory The LEV irrelevance theorem of MM is valid if the perfect market

assumptions underlying their analysis are true. The real world, however, is characterised by various imperfections such as existence of tax, bankruptcy costs, agency costs, and informational asymmetries, hence these imperfections led to the development of further studies in the area.

III 3.5 Taxes and Capital Structure Since the publication of Modigliani and Miller's (M&M) path-breaking

article in 1958, the issue of whether an optimal capital structure exists has generated considerable interest within academic circles, hence the irrelevance of CS rests on an absence of market imperfections. One of the most important



imperfections is the presence of taxes. When taxes are very much applicable to corporate income, debt financing is advantageous. Modigliani and Miller (1963)17 in the work “Corporate Income Taxes and the Cost of Capital: A Correction” have made a correction to bring out the tax advantages of debt financing. In this work they viewed the value of the firm as a function of LEV and the tax rate. While dividends and retained earnings are not deductible for tax purposes, interest on debt is a tax- deductible expense. As a result, the total income available for both the shareholders and debt holders is greater when debt capital is used. The tax deductibility of corporate interest payments favours the use of debt. This simple effect however, can be complicated by the existence of personal taxes (Miller 1977)18 and non-debt tax shields (DeAngelo and Masulis 1980) 19. Castanias (1983)20 cross-sectional test of CS irrelevance hypothesis and the tax shelter-bankruptcy cost hypotheses showed results inconsistent with the CS irrelevance hypothesis but consistent with the tax shelter-bankruptcy cost hypotheses. Stulz (1990)21 argued that debt can have both a positive as well as negative effect on the value of the firm (even in the absence of corporate taxes and bankruptcy cost). Stulz (1990) assumed that managers have no equity ownership in the firm and receive utility by managing a larger firm. The “power of manager” may motivate the self-interested managers to undertake negative present value project. To solve this problem, shareholders force firms to issue debt however, if firms are forced to pay out funds, they may have to forgo positive present value projects. Therefore, the optimal debt structure is determined by balancing the optimal agency cost of debt and the agency cost of managerial discretion.

But there are few controversial findings. Schnabel (1984)22 showed that an optimal CS does not involve exclusive reliance on debt financing in contrast to the classic result of Modigliani and Miller. Berens and Cuny (1995)23 have revisited the CS puzzle in the perspective of growth. Nominal firm growth, due to inflation or real growth, distorts the debt ratio as a measure of tax shielding. Firms typically issue debt characterized by fixed interest payments, even when they expect positive growth in earnings. To totally shield itself from corporate tax, a firm should not set debt equal to firm value. Instead, it should set its current interest payments equal to current earnings.



III 3.6 Merton Miller Argument

Merton Miller argued that the original MM proposition, which says that financial LEV does not matter in a tax free world, is valid in a world where both corporate and personal taxes exist. He stated that changes in CS have no effect on the firm’s total valuation. This position is the same as Modigliani-miller’s original proposition in a world of no taxes, but it contrasts sharply with their 1959 corporate tax adjustment article, in which they found that debt had substantial advantage, companies will issue debt till the tax rate for the marginal bondholder, tpd , is the same as the corporate tax rate, tc. Beyond this point, there is no tax advantage to companies from issuing debt. Miller’s equilibrium has the personal tax effect entirely offsetting the corporate tax advantage. Accordingly, his model implies that at the margin, the personal tax rate on debt income, tpd, must equal the corporate tax rate, tc. When tpd = tc changes in the proportion of debt in the CS do not change the total after-tax income to investor. As a result, CS decisions by the corporation would be irrelevant24.

III 3.7 Trade-off Theory The term trade-off theory is used by different authors to describe a family

of related theories. Management running a firm evaluates the various costs and benefits of alternative LEV plans and strives to bring a trade-off between them. Often it is assumed that an interior solution is obtained so that marginal costs and marginal benefits are balanced. Thus, trade-off theory, implies that company’s CS decision involves a trade-off between the tax benefits of debt financing and the costs of financial distress. When firms adjust their CS, they tend to move toward a target debt ratio that is consistent with theories based on tradeoffs between the costs and benefits of debt. Hovakimian, Opler, and Titman (2001)25 empirical work, explicitly account for the fact that firms may face impediments to movements toward their target ratio, and that the target ratio may change over time as the firm's profitability (P) and stock price change.

III 3.7.1 Static Trade-off Theory In a static trade-off framework the firm is viewed as setting a target debt

to value ratio and gradually moving towards it (Myers 1984)26. The theory says



that every firm has an optimal debt–equity ratio that maximizes its value. The theory affirms that firms have optimal CSs, which they determine by trading off the costs against the benefits of the use of debt and equity. The benefits from debt tax shield are thus adjusted against cost of financial distress. Agency cost, informational asymmetry and transaction cost are some of the other costs to be mitigated. The theory predicts that an optimal target financial debt ratio exists, which maximizes the value of the firm. The optimal point can be attained when the marginal value of the benefits associated with debt issues exactly offsets the increase in the present value of the costs associated with issuing more debt (Myers 2001)27.

III 3.7.2 Dynamic Trade-off Theory Implementing the role of time is very significant in identifying the

optimal CS. In a dynamic model, the correct financing decision typically depends on the financing margin that the firm anticipates in the next period. Some firms expect to pay out funds in the next period, while others expect to raise funds. Stiglitz (1972)28 took the drastic step of assuming away uncertainty. The first dynamic models to consider the tax savings versus bankruptcy cost trade-off are Kane, Marcus, and MacDonald (1984)29 and Brennan and Schwartz (1984)30. Their models took into consideration: uncertainty, taxes, and bankruptcy costs, but no transaction costs. These firms maintain high levels of debt to take advantage of the tax savings and to adjust to shocks without any cost as there is no transaction cost. Strebulaev (2007)31 analyzed a model quite similar to that of Fischer, Heinkel, and Zechner (1989)32 and Goldstein, Ju, and Leland (2001)33. Again, if firms optimally finance only periodically because of transaction costs, then the debt ratios of most firms will deviate from the optimum most of the time. In the model, the firm's LEV responds less to short-run equity fluctuations and more to long-run value changes.

III 3.8 Effects of Bankruptcy Cost Another important imperfection affecting CS decision is the presence of

bankruptcy cost. When a firm is unable to meet its obligations it results in financial distress that can lead to bankruptcy because a major contributor to financial distress is debt. The greater the level of debt, the larger the debt



servicing burden associated with it, the higher the probability of financial distress. If there is a possibility of bankruptcy, and if administrative and other costs associated with bankruptcy are significant, the levered firm may be less attractive to investors than that of the unlevered one. As a result, the investors are likely to penalize the price of the stock as LEV increases34.

Expected bankruptcy cost rise when P declines, and the threat of this cost pushes less profitable firms toward lower LEV targets. Similarly, expected bankruptcy cost is higher for firms with more volatile earnings, which should drive smaller, less-diversified firms toward fewer targets LEV. Taxes have two offsetting effects on optimal CS. The deductibility of corporate interest payments pushes firms toward more target LEV, while the higher personal tax rate on debt, relative to equity, pushes them toward less LEV. Baxter (1967)35 used the concept of bankruptcy costs to argue for the existence of an optimal capital structure. Expected bankruptcy cost depends on the cost of bankruptcy (eg., legal fees, loss of sales, employees and suppliers) and the probability of occurrence. Increased debt financing will increase the probability of bankruptcy and will in turn increase expected bankruptcy costs. The optimal debt ratio is reached when the marginal tax savings from debt financing is equal to the marginal loss from expected bankruptcy costs.

III 3.9 Agency Costs Jensen and Meckling (1976)36 put forward the concept of agency costs.

There is an agency relationship between the shareholders and creditors of firms that have substantial amounts of debt. In such firms shareholders have little incentive to limit losses in the event of a bankruptcy. Agency theory recognizes that the interests of managers and shareholders may conflict and that, left on their own, managers may make major financial policy decisions, such as the choice of a CS, that are suboptimal from the shareholders' standpoint. The theory also suggests, however, that compensation contracts, managerial equity investment, and monitoring by the board of directors and major shareholders can reduce conflicts of interest between managers and shareholders Mehran (1992)37. It is also suggested that CS models that ignore agency costs are incomplete. Debt financing is another crucial factor that limits the free cash flow available to



managers and thereby helps to control this agency problem Jensen and Meckling (1976)38.

Myers (1977)39 put forth another type of agency cost of debt which arises from the underinvestment problem. When a firm has debt which matures after an investment option expires, shareholder save the incentive to reject projects that have positive net present values because the benefits from accepting the projects accrues to the bondholders without increasing the shareholders' wealth. The issuance of debt therefore leads to suboptimal investment for the firm, requiring this type of agency cost to be traded off against the tax savings of debt financing to determine the optimal CS. Ang, Cole, and Lin (2000)40 on the other hand, stated that agency costs are significantly higher when an outsider rather than an insider manages the firm and lower with greater monitoring by banks.

III 3.10 Signalling Theory The pioneering study of Donaldson (1961)41 examined how companies

actually establish their CS.

Firms prefer to rely on internal accruals, that is, on retained earnings and depreciation cash flow.

Expected future investment opportunities and expected future cash flows influence target dividend payout ratios. Firms set the target payout ratios at such a level that capital expenditures, under normal circumstances are covered by internal accruals.

Dividends tend to be sticky in the short run. Dividends are raised only when the firm is confident that the higher dividend can be maintained; dividends are not lowered unless things are very bad.

If a firm’s internal accruals exceed its capital expenditure requirements, it will invest in marketable securities, retire debt, raise dividend, and resort to acquisitions or buyback its shares.

If a firm’s internal accruals are less than its non-postponable capital expenditures, it will first draw down its marketable securities portfolio and then seek external finance. When it resorts to external finance, it will first



issue debt, then convertible debt, and finally equity stock, thus, there is a pecking order of financing. Noting the inconsistency between trade-off theory and the observed

pecking order of financing, Myers and Majluf (1984)42 proposed a new theory,

called the signalling, or asymmetric information theory of CS. They

demonstrated that with asymmetric information, equity issues are rationally

interpreted on average as bad news, since managers are motivated to make issues

when the stock is overpriced. Ross’s (1977)43 model suggests that the value of

firms will rise with LEV, since increasing LEV increases the market’s perception

of value. Asquith and Mullins (1983)44, Masulis and Korwar (1986)45, and

Mikkelson and Partch (1986)46 also empirically observed that announcements of

new equity issues are greeted by sharp declines in stock prices. This is a major

reason why equity issues are comparatively rare among large established

corporations. Debt also plays an important role in allowing investors to generate

information useful for monitoring management and implementing efficient

operating decisions Harris and Raviv (1990)47.

III 3.11 Market Timing Theory of Capital Structure Baker and Wurgler (2002)48 have suggested a new theory of CS: the

“market timing theory of CS”. This theory states that the current CS is the

cumulative outcome of past attempts to time the equity market. Market timing

implies that firms issue new shares when they perceive they are overvalued and

that firms repurchase their own shares when they consider these to be

undervalued. As a consequence, current CS is strongly related to historical

market values. The results suggest the theory that CS is the cumulative outcome

of past attempts to time the equity market.

III.4 Profitability Theories P consists of two words profit and ability. It is necessary to differentiate

between the term Profit and Profitability at this point. The term Profit, from



accounting point of view, is arrived at by deducting from total revenue of an

enterprise all amount expended in earning that income while the term

Profitability is defined as the ability of a given investment to earn a return from

its use49. The predictions on P are ambiguous. The trade-off theory predicts that

profitable firms should be more highly levered to offset corporate taxes Ross

(1977)50. Titman and Wessels (1988)51 and Fama and French (2002)52 on the other

hand, found profits and LEV to be negatively correlated. The theories discussed

below will explain the relation between P and CS.

III 4.1 Pecking Order Theory Donaldson (1961)53 followed by Myers (1984)54 suggests that management

followed a preference ordering when it comes to financing. His work suggests

that the costs of issuing risky debt or equity overwhelm the forces that determine

optimal LEV in the trade-off model, the result is the pecking order. He also

argued that the trade-off theory fails to predict the wide degree of cross-sectional

and time variation of observed debt ratios. The pecking order theory is mainly a

behavioural explanation of why certain companies finance the way they do. It is

consistent with some rationale arguments, such as asymmetric information and

signalling, as well as with flotation costs. Moreover, it is consistent with the

observation that the most profitable companies within an industry tend to have

the least amount of LEV.

The pecking order theory explains why the bulk of external financing comes from debt; why more profitable firms borrow less: not because their target debt ratio is low.

The order followed is as follows:-

Firms prefer internal finance

If external finance is required, firms issued the safest security first. They start with debt, then possible hybrid securities such as convertible bonds then perhaps equity as a last resort.



This Pecking Order Theory suits large firms with high P and which has

enough internal funds in the form of retained earnings and depreciation. These

firms follow a stringent dividend policy and a target dividend payout ratio. Thus,

this theory states that highly profitable firms prefer internal funds and when

external funds are required the firm will borrow, rather than issuing equity. The

pecking order theory predicts that high-growth firms, typically with large

financing needs, will end up with high debt ratios because of a manager’s

reluctance to issue equity. Smith and Watts (1992)55 and Fama and French

(2002)56 also suggested that high-growth firms consistently use less debt in their

CS. Firms that choose to fund with equity today will leave less expensive sources

of funding for future needs. If they choose debt funding now, then they will tend

to have only more expensive funding available in the future. This reasoning made

Cornell and Shapiro (1987)57 to hypothesize that, firms with higher levels of net

organizational capital, the firms should be predominantly equity financed and

hold relatively large cash balances. Corporate managers are more likely to follow

a financing hierarchy than to maintain a target debt- equity ratio Pinegar and

Wilbricht (1989)58.

III 4.2 Free Cash Flow Theory This theory is also framed for matured firms that are prone to over invest.

It says that high debt levels will increase value, despite the threat of financial

distress, when a firm’s operating cash flow significantly exceeds its profitable

investment opportunities Myers (2001)59. Thus, the profit earning capacity

increases the value of the firm despite the threat of financial distress. Firms with

a positive free cash flow use this cash flow to lower their debt ratio. Firms with a

negative free cash flow increase their debt ratio to respond to the lack of internal

funds. The percentage adjustment is smaller for firms with relatively more debt

than for firms with relatively low debt.



III.5 Conclusion CS is an area that is unresolved with scope to be looked into, though

there are many theoretical and empirical works. The works of Modigliani and

Miller (1958)60 & (1963)61 analyzed in detail the impact of tax benefit on

determining the CS of a firm. The trade-off theory focused on the impact of

other external factors on neutralising the benefits of use of debt and suggested an

optimal CS to trade-off between benefits and cost involved in using debt capital.

Jensen and Meckling (1976)62 pointed out the agency cost involved in conflict of

interest between the managers and the shareholders which leads to finance

investment opportunities through outside fund. Myers (1984)63 suggested a

hierarchy for funding the CS. His pecking order theory suits to large size firms

with considerably high P. The signaling theory pioneered by Gordon Donaldson

(1961)64 and further developed by Myers and Majluf (1984)65 and others

portrayed the bad signal that the firm would confer if they issue equity capital

instead of debt capital which forces the firm to issue debt capital. All these works

analysed in detail the role played by debt capital in determining the optimal CS

to enable the firm to increase their P and thereby improve the value of the firm

however, still determinants of optimal CS remains an unresolved puzzle.



References: 1 Bhalla, V. K. 1997. Financial Management and Policy. Anmol Publications Pvt. Ltd., New Delhi: 832. 2 Lowe, J., T. Naughton, and P. Taylor. 1994. The impact of corporate strategy on the capital structure of Australian companies. Managerial and Decision Economics 15(3), (May - June): 245-57. 3 Khan, M. Y., and P. K. Jain. 2004. Financial management. Tata McGraw-Hill Publishing Company Ltd., New Delhi: 14.3. 4 Ibid. 5 Modigliani, F., and M. H. Miller. 1958. The cost of capital, corporation finance and the theory of investment. The American Economic Review 48 (3), (June): 261-97. 6Prasanna Chandra. 2008. Financial Management: Theory and Practice. Tata McGraw Hill, New Delhi: 479. 7Ibid: 480. 8Ibid: 480-1. 9Ibid: 481. 10Ibid: 482-3. 11 Staking, K. B., and D. F. Babbel. 1995. The relation between capital structure, interest rate sensitivity, and market value in the property-liability insurance industry. The Journal of Risk and Insurance 62 (4), (December): 690-718. 12 Modigliani, F. and M. H. Miller. 1958. op. cit. 261-97. 13Ibid. 14Ibid. 15Ibid. 16Ibid. 17 Modigliani, F., and M. H. Miller. 1963. Corporate income taxes and the cost of capital: A correction. The American Economic Review 53(3), (June): 433-43.



18 Miller, M. H. 1977. Debt and taxes. Journal of Finance. (32): 261–76. 19 DeAngelo, H. and R. Masulis. 1980. Optimal capital structure under corporate and personal taxation. Journal of Financial Economics 8: 3-29. 20Castanias, R. 1983. Bankruptcy risk and optimal capital strucutre. The Journal of Finance 38 (5), (December): 1617-35. 21 Rene, S. 1990. Managerial discretion and optimal financing policies. Journal of Financial Economics 26: 3-27. 22Schnabel, J. A. 1984. Bankruptcy, interest tax shields and 'optimal' capital structure: A cash flow formulation. Managerial and Decision Economics 5(2), (June): 116-19. 23 Berens, J. L. and C. J. Cuny. 1995. The capital structure puzzle revisited. The Review of Financial Studies 8 (4), (Winter): 1185-208. 24 Van Horne, J. C. 2007. Financial Management and Policy. Prentice Hall of India, New Delhi: 253-308. 25 Hovakimian, A., T. Opler, and S. Titman. 2001. The debt-equity choice. The Journal of Financial and Quantitative Analysis 36(1), (March): 1-24. 26 Myers, S. C. 1984. The capital structure puzzle. The Journal of Finance 39(3), (December): 575-92. 27 ______________. 2001. Capital structure. The Journal of Economic Perspective 15 (2), (Spring): 81-102. 28 Stiglitz, J.E. 1972. A re-examination of the Modigliani-Miller Theorem. American Economic Review 59, (December): 784-93. 29 Kane, A., A. Marcus, and R. MacDonald. 1984. How big is the tax advantage to debt? Journal of Finance 39:841–52.

30 Brennan, M., and E. Schwartz. 1984. Optimal financial policy and firm valuation.Journal of Finance 39: 593-607. 31 Strebulaev, I. A. 2007. Do tests of capital structure theory mean what they say? Journal of Finance 62(4): 1747–87.



32Fischer, E.O., R. Heinkel, and J. Zechner. 1989. Dynamic capital structure choice: Theory and tests. The Journal of Finance 44(1), (March): 19-40. 33 Goldstein, R., N. Ju, and H. Leland. 2001. An EBIT-based model of dynamic capital structure. Journal of Business 74: 483-512. 34 Van Horne, J. C. 2007. loc. cit. 35 Baxter, N. 1967. Leverage risk of ruin and the cost of capital. Journal of Finance. August: 663-81. 36 Jensen, M .C., and W.H. Meckling. 1976. Theory of firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3, (October): 305-60. 37 Mehran, H. 1992. Executive incentive plans, corporate control, and capital structure. The Journal of Financial and Quantitative Analysis 27(4), (December): 539-60. 38 Jensen, M .C., and W.H. Meckling. 1976. op.cit: 305-60. 39 Myers, S .C. 1977. Determinants of corporate borrowing. Journal of Financial Economics, (November): 147-75. 40 Ang, J. S., R. A. Cole, and J. W. Lin. 2000. Agency costs and ownership structure.

The Journal of Finance 55(1), (February): 81-106. 41 Donaldson, G. 1961. Corporate debt capacity: A study of corporate debt policy and the determination of corporate debt capacity. Boston: Division of Research, Harvard School of Business Administration. 42 Myers, S. C., and N. S. Majluf. 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13: 187-221. 43 Ross, S. A. 1977. The determination of financial structure: The incentive signaling approach. Bell Journal of Economics, (Spring): 23-40. 44 Asquith, P., and D .W. Mullins. 1983. The impact of initiating dividend payments on shareholders’ wealth. Journal of Business (January): 77-96.



45 Masulis, R.W., and A. N. Korwar. 1986. Seasoned equity offerings. Journal of Financial Economics (January): 91-118. 46 Mikkelson, W.H., and M. M. Partch. 1986. Valuation effects of security offerings and the issuance process. Journal of Financial Economics ( January): 31-60. 47 Harris, M., and A. Raviv. 1990. Capital structure and the informational role of debt. The Journal of Finance 45 (2), (June): 321-49. 48 Baker, M., and J. Wurgler. 2002. Market timing and capital structure. The Journal of Finance 57(1), (February):1-32. 49 Sharma, S. 2000. Financial Management for 21st Century. ABD Publishers, Jaipur: 295. 50 Ross, S. A. 1977. op.cit: 23-40. 51 Titman, S., and R. Wessels. 1988. The determinants of capital structure choice. The Journal of Finance 43(1), (March): 1-19. 52 Fama, E. F., and K. R. French. 2002. Testing trade-off and pecking order predictions about dividends and debt. The Review of Financial Studies 15(1), (Spring): 1-33. 53 Donaldson, G. 1961. loc. cit. 54Myers, S. C. 1984. loc. cit. 55 Smith, Clifford W., and R. L. Watts. 1992. The investment opportunity set and corporate

financing, dividend, and compensation policies. Journal of Financial Economics 32: 263-92. 56 Fama, E. F., and K. R. French. 2002. op.cit. 1-33. 57 Cornell, B., and A. C. Shapiro. 1987. Corporate stakeholders and corporate finance. Financial Management (Spring): 5-14. 58 Michael, P. J., and L. Wilbricht. 1989. What managers think of capital strucutre theory: A survey. Financial Management 18(4), (Winter): 82-91. 59 Myers, S.C. 2001. loc.cit.



60 Modigliani, F., and M. H. Miller. 1958. loc.cit. 61 Modigliani, F., and M. H. Miller. 1963. loc.cit. 62 Jensen, M .C., and W.H. Meckling. 1976. loc.cit. 63 Myers, S. C. 1984. loc.cit. 64 Donaldson, G. 1961. loc.cit. 65 Myers, S. C., and N. S. Majluf. 1984. loc.cit.

Chapter IV DETERMINANTS OF PROFITABILITY AND CAPITAL STRUCTURE: ANALYSIS




CHAPTER IV

DETERMINANTS OF PROFITABILITY AND CAPITAL STRUCTURE: ANALYSIS BY FIRM SIZE-WISE, INCOME SIZE-WISE

AND SECTOR-WISE APPROACHES

The relation between profitability (P) and capital structure (CS) has been

a major concern in many of the studies done earlier. P has been considered as

one of the crucial factors determining the CS of firms, however, there are

different views regarding the nature of relation between P and CS. The works of

Myers (1984)1, Kester (1986) 2, Rajan and Zingales (1995) 3, Jonson (1998) 4,

Booth Collins et al. (2001) 5, Dogra and Gupta (2009) 6 and many others have

put forth different views about the relation between CS and P.

Food industry is one of the developing industries in India, which is in the

developing phase. Researches about the various financial variables are crucial for

its development. The prosperity of food industry would mean to enlighten the

life of many people in India as India basically has an agriculture based economy.

Hence, this study is a step forward hoping to reach such a goal.

This chapter is divided into two parts viz., part I and part II. Part I is a

preliminary analysis, which gives an outline of the relation between profit earned

by the firms and the mixture of debt capital and equity capital in the firm’s

capital structure. It also attempts to bring to light the impact of size, income and

sector differences on the relation that profit has on various constituents of CS.

Part II of the chapter deals with the analysis in identifying the variables that

influence the CS decision of the firms and in identifying the variables influencing

the P of the firms. The determinants are analysed by use of control variables viz.,

the size, income and sector differences to accentuate their impact on leverage

(LEV) and P.



PART I ANALYSIS OF CAPITAL STRUCTURE OF SELECTED FIRMS: FIRM SIZE-

WISE, INCOME SIZE-WISE, AND SECTOR-WISE APPROACHES

IV.1 Introduction The analysis aims at exploring the nature of impact of profit before

interest taxes and depreciation (PBITD) on various constituents of CS in food industry in India. The CS constitutes internal funds (equity capital), and external funds (debt capital). The external funds have been classified as short term debt (STD), long term debt (LTD), and total debt (TD) which is the sum of STD and LTD. This part of the study is considered to be the base for the main analysis, which focuses at revealing the nature of relation between P and LEV.

IV.2 Objectives of Part I

To study the nature of relation between PBITD and different constituents of CS.

To analyse if size measured in terms of average sales and average income (PBITD) influences the relation between PBITD and CS.

To analyse the inter-sector influence on the relation between PBITD and CS.

IV.3 Hypotheses Development A preliminary analysis is carried out to study the influence of profit

earned on different constituent of CS (internal funds viz., equity capital and external funds viz., short term debt (STD), long term debt (LTD), and total debt (TD) which is the sum of STD and LTD), which is considered as the base for the core analysis, which focuses at revealing the nature of relation between P and LEV. The impact of sales size, income size and sectoral differences are also studied. Ho

1 = “There is no significant relationship between profit earned and the size of long term debt of the firms”. Ho

2 = “There is no significant relationship between profit earned and the size of short term debt of the firms”. Ho

3 = “There is no significant relationship between profit earned and the size of total debt borrowed of the firms”.



Ho4 = “There is no significant relationship between profit earned and the size of

equity capital of the firms”. Ho

5 = “There is no significant impact of size of sales on the relation between profit earned and the various constituents of capital structure of the firms”. Ho

6 = “There is no significant influence of size of income on the relation between profit earned and the various constituents of capital structure of the firms”. Ho

7 = “There is no significant influence of sector differences on the relation between profit earned and the various constituents of capital structure of the firms”.

IV.4 Research Methods Sources of Data and Period of the Study

The study is based on secondary data which are collected from Centre for Monitoring Indian Economy Pvt. Ltd. (CMIE) Prowess package. The required data are collected for a period of 10 years on year to year basis ranging from 1999-2000 to 2008-2009. The data for the food products manufacturing firms collected for this period are subject to limitations such as availability of incorporation dates, continuous listing for 10 years and the sources of completion of data for the years under study. Research Methods Used for Analysis

Correlation co-efficient is extensively used to determine the one-to-one relationship between PBITD and different constituents of CS. Simple ordinary least square regression (OLS) is used to find if PBITD determines the proportion of various constituents on CS in food industry in India. Linear trend line is also used to give an overall picture about the relation between PBITD and CS in food industry over the period under study.

IV.5 Overall Analysis of Relation between PBITD and Various Constituents of CS

The overall trend analysis of selected 86 sample firms in food industry, over a period of 10 years, shows (see chart IV. A) that there has been a gradual rise in PBITD of firms of food industry after March 2005. The level of LTD and STD and eventually TD has grown along with it. However, the equity capital



remains more or less same without significant fluctuations, hence it reveals to be unaffected by changes in PBITD of the firms.

Chart IV.A Trend Line Showing Relation between PBITD and Various Constituents of CS

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd.

The overall correlation matrix (see table IV.1) gives a better picture about the relationship between PBITD and the various constituents of CS. There exists a significant positive correlation (at 1% level) between PBITD and various constituents of CS (i.e., equity, STD, LTD, and TD).

To test whether PBITD is a good predictor of the various constituents of CS simple regression (see table IV.2) has been run.

Table IV.1

Overall Correlation Matrix of Food Industry in India

Variables EQUITY STD LTD TD PBITD EQUITY 1

STD .578 1

(.00) LTD .304 .723 1

(.00) (.00) TD .495 .947 .906 1

(.00) (.00) (.00) PBITD .813 .776 .490 .702 1

(.00) (.00) (.00) (.00)


0

50

100

150

200

250

300

Mar

-00

Sep-

00

Mar

-01

Sep-

01

Mar

-02

Sep-

02

Mar

-03

Sep-

03

Mar

-04

Sep-

04

Mar

-05

Sep-

05

Mar

-06

Sep-

06

Mar

-07

Sep-

07

Mar

-08

Sep-

08

Mar

-09

PBITD

Equity shares

Short term debt

Long term debt

Total debt



**. Correlation is significant at 0.01 level (2-tailed). Figures in parentheses denote p value.

PBITD has significant positive coefficients (at 1% level) with the various

constituents of CS (viz., equity, STD, LTD, and TD), which shows that the profit

earned by the firms has significant impact on determining the size of insiders as

well as outsiders funds in their CS. The Adj-R2 value is above 50% for STD and

equity, indicating that the model fit is good and PBITD is a better predictor of

STD and equity than that of the LTD and TD. The F-stat value is also significant

(at 1% level) for TD, LTD, STD, and equity indicating that the model fit is

significant. However, a closer view into the issue will give a better idea about the

impact of PBITD on insider’s fund and outsider’s fund. For the purpose, the

selected firms are classified using three control variables viz., the sales size-wise,

income size-wise and sector-wise.

IV.6 Sales Size- wise Analysis of Relation between PBITD and Various Constituents of CS

Kester (1986)13 stated that there are no significant country differences in

LEV between U.S. and Japanese manufacturing firms after controlling for

characteristics such as growth, P, risk, size and industry classification. Rajan and

Zingales (1995)14 emphasized that the negative influence of P on LEV should

become stronger as firm size increases. Booth Collins et al. (2001)15, Panday

(2002)16, and Chen and Zhao (2004) 17also suggested that debt capital decreases

with higher P and SIZ in developing countries.

To study the impact of size the selected sample firms are grouped into

three size categories based on the quantum of sales. The firms with sales upto

Rs.100 crore are grouped as ‘small size firms’; the firms with sales above Rs.100

crore but upto Rs.500 crore are grouped as ‘medium size firms’; and firms with



above Rs.500 crore are taken as ‘large size firms’, considering the average sales

over a period of 10 years as base for this purpose.



Table IV.2 Regression on Total Debt, Long Term Debt, and Short Term Debt of Food Industry in

India (Overall)

Variables

Unstandardized Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

TOTAL DEBT (Constant) 59.371 22.264 2.667 .00 PBITD 2.687 .297 .702 9.038 .00 F value 81.688** (.00) R2 0.493 Adjusted R2 0.487 LONG TERM DEBT (Constant) 49.242 12.674 3.885 .00 PBITD .871 .169 .490 5.148 .00 F value 26.505** (.00) R2 0.24 Adjusted R2 0.23 SHORT TERM DEBT (Constant) 10.265 12.042 .852 .39 PBITD 1.816 .161 .776 11.292 .00 F value 127.498** (.00) R2 0.603 Adjusted R2 0.598 EQUITY (Constant) 8.316 .933 8.909 .00 PBITD .160 .012 .813 12.802 .00 F value 168.893** (.00) R2 0.661 Adjusted R2 0.657


**.Significant at 0.01 level;*.Significant at 0.05 level

The trend line of small size firms, medium size and large size firms shows (see chart IV. B) that the small size firms maintain relatively the same level of PBITD over the years of study. There is not much of fluctuation in the PBITD of small size firms, whereas, the trend line of medium size firms shows that there is



a slight fall and rise in their PBITD, despite the fact that they could maintain PBITD without much of flux. On the other hand, the large size firms’ PBITD shows a precipitous rise after the year 2004-05.

Chart IV.B Comparison of Trend line showing PBITD of Small Size, Medium Size, and

Large Size Firms


The trend line showing TD of small size, medium size, and large size

firms exemplify (see chart IV. C) that the TD of small size firms has ascended

during the last few years of the period under study although their PBITD does

not show any such rise, while the medium size firms show that they have

endeavoured to increase their external borrowing over the years. In contrast, the

large size firms, which have steep rise in PBITD, also show steep rise upto the

year 2007-08 and a slight decrease in their external borrowing in the year 2007-

08 although there is no fall in their PBITD in the year 2008-09.

0

50

100

150

200

250

300

350

SMALL SIZE FIRMS

MEDIUM SIZE FIRMS

LARGE SIZE FIRMS



Chart IV.C Comparison of Trend Line Showing Total Debt of Small Size, Medium Size

Firms, and Large Size Firms

Source: Computed results based on compiled data collected from CMIE prowess

Pvt. Ltd.

IV.6.1 Correlation Co-efficient and Regression Results of Constituents of CS of Small Size Firms

Table IV.3 Correlation Matrix of Constituents of CS of Small Size Firms of Food

Industry in India Variables EQUITY STD LTD TD PBITD EQUITY 1

STD .427** 1

(.00) LTD .137 .780** 1

(.37) (.00) TD .218 .864** .988** 1

(.16) (.00) (.00) PBITD .293 .495** .344* .374* 1

(.06) (.00) (.02) (.014) Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at 0.01 level (2-tailed). *. Correlation is significant at 0.05 level (2-tailed). Figures in parentheses denote p value.

0

200

400

600

800

1000

1200

1400

SMALL SIZE FIRMS

MEDIUM SIZE FIRMS

LARGE SIZE FIRMS



The correlation matrix of small size firms (see table IV.3) shows that PBITD has significant positive correlation (at 1% level) with STD (0.50) and (at 5% level) with LTD (0.34) and TD (0.37). There is no significant correlation between PBITD and equity in case of small size firms. The regression result (see table IV.4) shows that PBITD has significant positive coefficients with STD (at 1% level) and TD and LTD (at 5 % level). However, the model fit is not good in all the cases, indicating that there are other variables predicting the constituents of CS in small size firms.

Table IV.4 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of Food

Industry in India (Small Size Firms)

Variables

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta TOTAL DEBT (Constant) 20.320 9.805 2.073 .04 PBITD 4.764 1.847 .374 2.579 .014 F value 6.654* (.014) R2 0.140 Adjusted R2 0.119 LONG TERM DEBT (Constant) 13.522 8.044 1.681 .10 PBITD 3.551 1.515 .344 2.343 .02 F value 5.491* (.02) R2 0.118 Adjusted R2 0.097 SHORT TERM DEBT (Constant) 6.224 2.216 2.808 .00 PBITD 1.522 .417 .495 3.645 .00 F value 13.283** (.00) R2 0.245 Adjusted R2 0.226 EQUITY (Constant) 7.553 1.310 5.767 .00 PBITD .483 .247 .293 1.959 .057 F value 3.838 (.057) R2 0.086 Adjusted R2 0.063

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **.Significant at 0.01 level;*.Significant at 0.05 level



IV.6.2 Correlation Co-efficient and Regression Results of Constituents of CS of Medium Size Firms

The correlation matrix of medium size firms (see table IV.5) shows that PBITD has significant correlation with STD (0.67), LTD (0.73), and TD (0.78) (at 1% level). The regression result (see table IV.6) shows that PBITD also has a significant coefficient with STD, LTD, and TD (at 1% level). The Adj- R2 value, indicating the model fit is also above 50% in case of TD (0.60) and LTD (0.51) shows that PBITD is a good predictor of external funds for medium sized firms in food industry in India. The medium sized firms’ external borrowings are thus dependent to a greater extent on the profit earned by them.

Table IV.5 Correlation Matrix of Constituents of CS of Medium Size Firms of Food

Industry in India Variables EQUITY STD LTD TD PBITD

EQUITY 1

STD .467 1 (.00)

LTD .551 .513 1 (.00) (.00)

TD .584 .681 .978 1 (.00) (.00) (.00)

PBITD .349 .670 .726 .782 1 (.05) (.00) (.00) (.00)


**. Correlation is significant at 0.01 level (2-tailed). Figures in parentheses denote p value.



Table IV.6 Regression Results on Total Debt, Long Term Debt, and Short Term Debt of

Food Industry in India (Medium Size Firms)

Variables

Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

TOTAL DEBT

(Constant) 43.095 16.684 2.583 .015

PBITD 3.832 .567 .782 6.760 .00

F value 45.702** (.00)

R2 0.612

Adjusted R2 0.598

LONG TERM DEBT

(Constant) 24.688 15.712 1.571 .12

PBITD 3.032 .534 .726 5.680 .00

F value 32.266** (.00)

R2 0.527

Adjusted R2 0.510

SHORT TERM DEBT

(Constant) 18.407 4.841 3.802 .00

PBITD .800 .164 .670 4.862 .00

F value 23.644** (.00)

R2 0.449

Adjusted R2 0.430

EQUITY

(Constant) 9.083 1.696 5.355 .00

PBITD .116 .058 .349 2.005 .054

F value 4.020 (.054)

R2 0.122

Adjusted R2 0.091





IV.6.3 Correlation Co-efficient and Regression Results of Constituents of CS of Large Size Firms

The correlation matrix of large size firms (see table IV.7) shows highly

significant correlation between PBITD and equity (0.87) and significant

correlation between PBITD and STD (0.60). The regression result shows that

(see table IV.8) PBITD also has highly significant coefficient with equity (at 1%

level) and the Adj-R2 value is also above 70% (0.73), indicating that in large size

firms PBITD is the major predictor of the level of ownership capital. Thus, large

size firms use retained earnings for grabbing the investing opportunities.

Table IV.7 Correlation Matrix of Constituents of CS of Large Size Firms of Food Industry

in India

Variables EQUITY STD LTD TD PBITD

EQUITY 1

STD .384 1

(.22)

LTD -.152 .706* 1

(.64) (.01)

TD .179 .950** .891** 1

(.58) (.00) (.00)

PBITD .868** .601* .154 .452 1

(.00) (.04) (.63) (.14)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at 0.01 level (2-tailed). *. Correlation is significant at 0.05 level (2-tailed).

Figures in parentheses denote p value.




Food Industry in India (Large Size Firms)

Variables


t Sig. B Std. Error Beta

TOTAL DEBT

(Constant) 294.277 212.355 1.386 .19

PBITD 1.750 1.092 .452 1.603 .14

F value 2.571 (.14)

R2 0.205

Adjusted R2 0.125

LONG TERM DEBT

(Constant) 185.129 103.292 1.792 .10

PBITD .262 .531 .154 .493 .63

F value .243 (.63)

R2 0.024

Adjusted R2 -0.074

SHORT TERM DEBT

(Constant) 109.148 121.890 .895 .39

PBITD 1.488 .627 .601 2.375 .03

F value 5.643* (.03)

R2 0.361

Adjusted R2 0.297

EQUITY

(Constant) 8.011 5.674 1.412 .18

PBITD .162 .029 .868 5.540 .00

F value 30.695** (.00)

R2 0.754

Adjusted R2 0.730


**Significant at 0.01 level;*Significant at 0.05 level



IV.7 Income Size- wise Analysis of Relation between PBITD and Various Constituents of CS

Income earned is used as another control variable to get a better picture about the impact of PBITD on the different constituents of CS. The firms within the same income range are grouped together to analyse the impact of PBITD. The firms are grouped into three sub-categories viz. ‘low income size firms’ with profit (PBITD) < Rs.10 crore; ‘medium income size firms’ with profit > Rs.10 crore but < Rs.50 crore; ‘high income size firms’ with income >Rs.50 crore. The average income (PBITD) for the period of 10 years is considered as base for this purpose.

The trend line of low income size firms shows (see chart IV. D) no extraordinary rise or fall in their PBITD over the study period. While, the medium income size firms show a gradual rise in PBITD the large income size firms could put forth a steep rise in their PBITD. Thus, the results closely correspond with that of the trend line of small size, medium size, and large size firms based on its sales size.

Chart IV.D Comparison of Trend line showing PBITD of Low Income Size, Medium Income

Size, and High Income Size Firms


Pvt. Ltd.

0

50

100

150

200

250

300

350

400

450

LOW INCOME SIZE FIRMS

MEDIUM INCOME SIZE FIRMS

HIGH INCOME SIZE FIRMS



Chart IV.E Comparison of Trend line showing Total Debt of Low Income Size, Medium

Income Size, and High Income Size Firms


Pvt. Ltd.

The trend line showing TD of low income size firms shows (see chart IV. E) that they maintain the same level of external borrowings over the study period since, they sustain their PBITD at the same level without much enhancement. The medium income size firms, however, maintain their borrowing level closer to that of low income size firms although their PBITD shows an increase from the year 2004-05. The TD of high income size firms shows that, as their PBITD rises, they have also endeavoured to increase their TD to exploit opportunities although there is a slight fall in TD in the year 2008-09. IV.7.1 Correlation Co-efficient and Regression Results of Constituents of CS of Low Income Size Firms

The correlation matrix of low income size firms (see table IV.9) shows that there exists a highly significant correlation between PBITD and equity (0.52) as well as between PBITD and STD (0.45) (at 1% level). The regression results (see table IV.10) show that PBITD of low income size firms has significant coefficient with STD, as well as with equity (at 1% level). However, the model fit with regard to all constituents of CS viz., STD, LTD, TD and equity is not good as the Adj- R2 value remains below 50% (i.e., 0.18, -0.01, 0.02, and 0.26 respectively), which fact shows that the PBITD has a very little role in determining the level of STD, LTD, TD and equity in case of low income size firms.

0100020003000400050006000700080009000

10000

LOW INCOME SIZE FIRMS

MEDIUM INCOME SIZE FIRMS

HIGH INCOME SIZE FIRMS



Table IV.9 Correlation Matrix of Constituents of CS of Low Income Size Firms of Food

Industry in India Variables EQUITY STD LTD TD PBITD

EQUITY 1

STD .384 1 (.00)

LTD .173 .717 1 (.21) (.00)

TD .236 .829 .984 1 (.09) (.00) (.00)

PBITD .522 .447 .095 .190 1 (.00) (.00) (.49) (.17)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at 0.01 level (2-tailed). Figures in parentheses denote p value.


Food Industry in India (Low Income Size Firms)

Variables


t Sig. B Std. Error Beta TOTAL DEBT (Constant) 26.204 9.476 2.765 .00 PBITD 3.545 2.542 .190 1.394 .16 F value 1.944 (.16) R2 0.063 Adjusted R2 0.018 LONG TERM DEBT (Constant) 19.570 7.706 2.540 .014 PBITD 1.426 2.067 .095 .690 .49 F value .476 (.49) R2 0.009 Adjusted R2 -0.010 SHORT TERM DEBT (Constant) 6.633 2.194 3.023 .00 PBITD 2.119 .589 .447 3.600 .00 F value 12.957** (.00) R2 0.199 Adjusted R2 0.184 EQUITY (Constant) 5.542 1.101 5.032 .00 PBITD 1.302 .295 .522 4.408 .00 F value 19.429** (.00) R2 0.272 Adjusted R2 0.258 Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd.




IV.7.2 Correlation Co-efficient and Regression Results of Constituents of CS of Medium Income Size Firms

The correlation matrix of medium income size firms (see table IV.11) shows that there exists a highly significant correlation between PBITD and LTD (0.83) as well as between PBITD and TD (0.77). The regression results (see table IV.12) show that the PBITD has highly significant coefficient with LTD (6.73) as well as with TD (7.05). The model fit is also good with LTD as well as with TD, recording Adj-R2 value 0.669 and 0.58 respectively, indicating that the profit earned by medium income size firms has significant role in determining the external borrowings of the firms.

Table IV.11 Correlation Matrix of Constituents of CS of Medium Income of Size Firms Food

Industry in India

Variables EQUITY STD LTD TD PBITD EQUITY 1

STD .280 1

(.20) LTD .390 .045 1

(.07) (.84) TD .484 .438 .916 1

(.02) (.04) (.00) PBITD .319 .072 .828 .773 1

(.14) (.75) (.00) (.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. *. Correlation is significant at 0.05 level (2-tailed). **. Correlation is significant at 0.01 level (2-tailed). Figures in parentheses denote p value.

IV.7.3 Correlation Co-efficient and Regression Results of Constituents of CS of High Income Size Firms The PBITD of high income size firms (see table IV.13) shows a highly significant correlation with internal funds (equity capital). The regression result (see table IV.14) also shows that PBITD has highly significant coefficient with equity (0.20). The Adj- R2 value is also high (0.77), indicating that the PBITD is a very good predictor variable of internal funds in case of high income size firms.




Food Industry in India (Medium Income Size Firms)

Variables


t Sig. B Std. Error Beta TOTAL DEBT (Constant) -10.656 36.363 -.293 .77 PBITD 7.054 1.296 .773 5.442 .00 F value 29.620**(.00) R2 0.59 Adjusted R2 0.57 LONG TERM DEBT (Constant) -58.555 28.616 -2.046 .05 PBITD 6.725 1.020 .828 6.593 .00 F value 43.469** (.00) R2 0.685 Adjusted R2 0.669 SHORT TERM DEBT (Constant) 49.006 23.197 2.113 .04 PBITD .268 .827 .072 .324 .74 F value .105 (.74) R2 0.005 Adjusted R2 -0.045 EQUITY (Constant) 7.774 4.428 1.756 .09 PBITD .237 .158 .319 1.504 .14 F value 2.261 (.14) R2 0.102 Adjusted R2 0.057 Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **.Significant at 0.01 level;*.Significant at 0.05 level

Table IV.13 Correlation Matrix of Constituents of CS of High Income Size Firms of Food

Industry in India Variables EQUITY STD LTD TD PBITD EQUITY 1

STD .376 1

(.28) LTD -.257 .667 1

(.47) (.04) TD .137 .947 .871 1

(.71) (.00) (.00) PBITD .893 .557 -.037 .351 1

(.00) (.10) (.92) (.32) Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at 0.01 level (2-tailed). *. Correlation is significant at 0.05 level (2-tailed). Figures in parentheses denote p value.




Food Industry in India (High Income Size Firms)

Variables


t Sig. B Std. Error Beta TOTAL DEBT (Constant) 332.989 316.978 1.051 .32 PBITD 1.560 1.471 .351 1.060 .32 F value 1.124(.32) R2 0.123 Adjusted R2 0.014 LONG TERM DEBT (Constant) 272.486 146.054 1.866 .09 PBITD -.070 .678 -.037 -.103 .92 F value .011 (.92) R2 0.001 Adjusted R2 -0.124 SHORT TERM DEBT (Constant) 60.503 185.308 .327 .75 PBITD 1.630 .860 .557 1.895 .09 F value 3.592 (.09) R2 0.310 Adjusted R2 0.224 EQUITY (Constant) -1.551 7.477 -.207 .84 PBITD .195 .035 .893 5.604 .00 F value 31.410** (.00) R2 0.797 Adjusted R2 0.772 Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd.


IV.8 Sector-wise Analysis of Relation between PBITD and Various Constituents of CS

Each sector within an industry may have different business environment based on their nature of business (Lee and Kwok 1988)18. The sectoral classification will certainly show the impact of these business environment and nature of business on the relation between PBITD and different constituents of CS. To study the impact of nature of business on the CS decision the firms are further grouped into three categories based on sector.



Since there is more number of sectors with firms of few in numbers, the firms are combined and grouped into three sectors constituting related firms; thereby the firms are classified into three sectors viz., Sector I, Sector II and Sector III. Sector I constitutes 32 vegetable oil firms; Sector II constitutes 30 firms which include 9 firms of tea sector, 11 firms of dairy sector, and 10 firms of sugar sector; Sector III constitutes 24 firms comprising of miscellaneous sectors, which include coffee (1), cocoa products & confectionery (1), processed /packaged foods (1), starches (2), marine food (3), poultry & meat product (1), floriculture (2), milling products ( 3), and other agricultural products (10).

The trend line shows (see chart IV. F) that firms of sector III earn lowest PBITD and of sector II earn highest PBITD, while PBITD of firms of sector I lies between these two sectors. The PBITD of sector III shows a gradual rise after a slight fall in the year 2001-02 without much of flux. The trend line of sector I firms follow closely the line of sector III firms until 2005-06 and experienced a steep increase thereafter, although there is a small fall in the year 2008-09, while on the contrary, the PBITD of sector II has grown gradually upto the year 2006-07 followed by a slight fall in the year 2007-08 and a very steep increase in the year 2008-09. However, all the sectors show a gradual increase in their PBITD without much of fluctuations.

Chart IV.F Comparison of Trend Line showing PBITD of Firms of Sector I, Sector II and

Sector III


0

20

40

60

80

100

120

SECTOR I

SECTOR II

SECTOR III



The TD of sector III (see chart IV. G) grow gradually corresponding to the trend line of their PBITD, while the TD of sector I firms, which has a PBITD closer to that of the sector III, maintain TD level closer to that of sector II firms, which has comparatively higher level of PBITD, which in turn reveals the fact that sector I firms borrow more external capital for slight increase in their PBITD. Firms of sector II, although have higher PBITD, maintain comparatively lesser TD.

Chart IV.G Comparison of Trend Line showing Total Debt of Firms of Sector I,

Sector II and Sector III


IV.8.1 Correlation Co-efficient and Regression Results of Constituents of CS of Sector I

Table IV.15 Correlation Matrix of Constituents of CS of Sector-wise Firms of Food Industry

in India (Sector I) Variables EQUITY STD LTD TD PBITD EQUITY 1

STD .467 1 (.01)

LTD .448 .952 1 (.010) (.00)

TD .464 .992 .983 1 (.01) (.00) (.00)

PBITD .669 .872 .893 .891 1 (.00) (.00) (.00) (.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at 0.01 level (2-tailed). *. Correlation is significant at 0.05 level (2-tailed). Figures in parentheses denote p value.

050

100150200250300350400450

SECTOR I

SECTOR II

SECTOR III



The correlation matrix of sector I (see table IV.15) shows that there exists a highly significant correlation between PBITD and LTD (0.89), STD (0.87), TD (0.89), and equity (0.67). The regression results (see table IV.16) also show that the PBITD of firms under sector I has significant coefficient with LTD (3.19), STD (4.53), TD (7.72), and equity (0.13) (at 1% level). The Adj-R2 value is above 70% for the models with dependent variables LTD (0.79), STD (0.75), and TD (0.79), which indicates that PBITD is a very good predictor of the size of external funds of firms belonging to the sector I.


Food Industry in India (Sector I)

Variables


t Sig. B Std. Error Beta TOTAL DEBT

(Constant) -7.784 32.736 -.238 .81 PBITD 7.720 .719 .891 10.736 .00 F value 115.254**(.00)

R2 0.793 Adjusted R2 0.787

LONG TERM DEBT (Constant) 7.118 13.407 .531 .59

PBITD 3.194 .294 .893 10.844 .00 F value 117.596** (.00)

R2 0.797 Adjusted R2 0.790

SHORT TERM DEBT (Constant) -14.903 21.106 -.706 .48

PBITD 4.526 .464 .872 9.763 .00 F value 95.317** (.00)

R2 0.761 Adjusted R2 0.753

EQUITY (Constant) 6.751 1.236 5.461 .00

PBITD .134 .027 .669 4.934 .00 F value 24.349** (.00)

R2 0.448 Adjusted R2 0.430

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **Significant at 0.01 level;*Significant at 0.05 level



IV.8.2 Correlation Co-efficient and Regression Results of Constituents of CS of Sector II The correlation matrix of firms under sector II (see table IV.17) shows that PBITD has highly significant correlation with TD (0.85), STD (0.97) and equity (0.84). The regression result (see table IV.18) shows that PBITD has a significant coefficient with TD (1.78), STD (1.41) and equity (0.16) (at 1% level). The Adj-R2 value is above 50%, indicating that PBITD is crucial in determining the level of TD (0.71), STD (0.95) and equity (0.70) for firms under sector II.

Table IV.17 Correlation Matrix of Constituents of CS of Sector-wise Firms of Food Industry

in India (Sector II)

Variables EQUITY STD LTD TD PBITD

EQUITY 1

STD .841 1

(.00)

LTD .179 .367 1

(.34) (.05)

TD .674 .880 .765 1

(.00) (.00) (.00)

PBITD .842 .974 .339 .847 1

(.00) (.00) (.07) (.00)


IV.8.3 Correlation Co-efficient and Regression Results of Constituents of CS of Sector III The correlation matrix (see table IV.19) shows that there exists a highly significant correlation between PBITD and STD (0.83), LTD (0.84), and TD (0.85). There also exists a significant correlation between PBITD and equity capital (0.50). The regression result (see table IV.20) shows that PBITD has a highly significant coefficient with STD (0.68), LTD (3.89), and TD (4.58). PBITD has significant coefficient with equity (at 5% level). The Adj- R2 value is



high with regard to STD (0.68), LTD (0.69), TD (0.71), indicating the fitness of the model in these cases. Thus, PBITD is a good predictor of external funds for firms belonging to sector III.


Food Industry in India (Sector II)

Variables


t Sig. B Std. Error Beta

TOTAL DEBT (Constant) 100.772 24.412 4.128 .00

PBITD 1.775 .210 .847 8.435 .00

F value 71.153**(.00)

R2 0.718

Adjusted R2 0.708

LONG TERM DEBT

(Constant) 81.670 22.123 3.692 .00

PBITD .363 .191 .339 1.904 .06

F value 3.626 (.06)

R2 0.115

Adjusted R2 0.083

SHORT TERM DEBT

(Constant) 19.539 7.195 2.716 .01

PBITD 1.411 .062 .974 22.750 .00

F value 517.552** (.00)

R2 0.949

Adjusted R2 0.947

EQUITY

(Constant) 11.245 2.181 5.157 .00

PBITD .155 .019 .842 8.256 .00

F value 68.154** (.00)

R2 0.709

Adjusted R2 0.698 Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **Significant at 0.01 level;*Significant at 0.05 level



Table IV.19 Correlation Matrix of Constituents of CS of Sector-wise Firms of Food Industry in India

(Sector III) Variables EQUITY STD LTD TD PBITD EQUITY 1

STD .625 1 (.00)

LTD .543 .889 1 (.01) (.00)

TD .563 .919 .998 1 (.00) (.00) (.00)

PBITD .498 .834 .838 .850 1 (.013) (.00) (.00) (.00)



Food Industry in India (Sector III)

Variables


t Sig. B Std. Error Beta TOTAL DEBT (Constant) 7.600 13.711 .554 .58 PBITD 4.577 .606 .850 7.554 .00 F value 57.064**(.00) R2 0.722 Adjusted R2 0.709 LONG TERM DEBT (Constant) 4.574 12.225 .374 .71 PBITD 3.893 .540 .838 7.206 .00 F value 51.932** (.00) R2 0.702 Adjusted R2 0.689 SHORT TERM DEBT (Constant) 3.026 2.187 1.384 .18 PBITD .684 .097 .834 7.077 .00 F value 50.084** (.00) R2 0.695 Adjusted R2 0.681 EQUITY (Constant) 7.840 1.234 6.355 .00 PBITD .147 .055 .498 2.690 .01 F value 7.236* (.01) R2 0.248 Adjusted R2 0.213 Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **Significant at 0.01 level;*Significant at 0.05 level



IV.9 Conclusion The preliminary study about the relation between PBITD and the various

constituents of CS has put forth some important facts about the Food Industry in India. The overall analysis shows that PBITD has highly significant positive correlation with STD, LTD, TD and equity (see table IV.21). However the regression result suggests that PBITD is a good determinant of STD and equity where the Adj-R2 value is greater (0.60 for STD and 0.66 for equity). The peculiar feature about the industry is that as their PBITD increases they rely more on STD and equity capital rather than availing LTD, which would be easily available as their profit increases. These firms, thus tend to reduce the risk involved in fixed interest bearing commitments if they rely on outside capital. Thus the hypothesis Ho

1, Ho2, Ho

3 and Ho4 are rejected in case of overall result of

food industry. A closer picture about the relation between PBITD and the various constituents of CS can be got through categorized study and so the industry is classified based on the sale size and income size. A sector wise analysis is also conducted.

Table IV.21 Summary of Overall Results of the Relation between PBITD and the

Constituents of CS Hypotheses Overall Results Supporting Works

Ho1 = “There is no

significant relationship between profit earned and long term debt”

+ve** Rejected

Myers (1984), Kester (1986),Titman and Wessel (1988), Pinegar and Wilbricht (1989), Barton and Gorden (1988), ,

Harris and Raviv (1991), Harries (1994), Rajan and Zingales (1995), Jonson

(1998), Simerly and Li (2000), Booth Collins et al. (2001), Fama and French (2002) support relation between profit

earned and CS.


significant relationship between profit earned and short term debt”

+ve** Rejected


significant relationship between profit earned and total debt”

+ve** Rejected


significant relationship between profit earned and equity”

+ve** Rejected




IV.9.1 Sales Size-wise Analysis In case of small size firms, PBITD has significant correlation with STD,

LTD, and TD. However, the Adj-R2 value is very low revealing that PBITD is not a major determinant of STD, LTD and TD, there are other variables that determine the size of outside borrowing in case of small size firms thereby it leads to reject Ho

1, Ho2, Ho

3 in case of small size firms whereas, Ho4 is accepted

and there is no significant relation between equity and PBITD of small size firms (see table IV.22).

The correlation result of medium size firms shows highly significant correlation between PBITD and STD, LTD, TD. The regression result also supports this (0.43 for STD, 0.51 for LTD, and 0.60 for TD). Thus, PBITD determines the size of outside borrowings of medium size firms. Similar to small size firms, Ho

1, Ho2, Ho

3 are rejected in case of medium size firms also whereas, Ho

4 is accepted and there is no relation between equity and PBITD in medium size firms.

The regression result of large size firms show that PBITD is a very important determinant of equity capital (Adj-R2 value is 0.73). The firms with higher sales volume tend to maintain a higher equity size as their PBITD increases. Hence, Ho

2, Ho4 are rejected in case of large size firms and Ho

1, Ho3 are

accepted. Table IV.22

Summary of Sales Size-wise Analysis of the Relation between PBITD and the Constituents of CS

Hypotheses Relation between Predictors and Dependent Variables Small Size Firms Medium Size Firms Large Size Firms

Ho1 = “no significant

relationship between profit earned and long term debt”

+ve* Rejected

+ve** Rejected

Accepted


relationship between profit earned and short term debt”

+ve** Rejected

+ve** Rejected

+ve* Rejected


relationship between profit earned and total debt”

+ve* Rejected

+ve** Rejected

Accepted


relationship between profit earned and equity”

Accepted Accepted +ve**

Rejected




Therefore, the hypothesis Ho5 that “there is no significant impact of size of sales

on the relation between profit earned and the various constituents of capital structure” is rejected.

IV.9.2 Income Size-wise Analysis

Income size-wise analysis is a perfect alternative for analyzing the impact

of profit earned on different constituents of CS. The low income firms show that

PBITD has highly significant positive correlation with STD and equity. Thus the

hypotheses Ho2, Ho

4 are rejected and the hypotheses Ho1, Ho

3 are accepted.

However, the Adj-R2 value is too low to support the regression model that

PBITD is a significant determinant of the various constituents of CS. Thus,

similar to the case of small size firms, low income size firms also have other

factors influencing STD, LTD, TD and equity (see table IV.23).

PBITD is a significant predictor of LTD and TD in case of medium

income firms. There is a highly significant correlation between PBITD and LTD,

TD. The Adj-R2 value (0.67 for LTD and 0.58 for TD) also shows that PBITD

determines the level of LTD and TD over 50%. Thus the hypotheses Ho1, Ho

3 are

rejected and the hypotheses Ho2, Ho

4 are accepted, which fact result is just

contrary to the result of low income firms.

PBITD has highly significant correlation with equity capital in case of

high income firms. The regression result also supports the findings (Adj-R2 value

is 0.77) and PBITD is a major determinant of level of equity in high income

firms. Thus as their income increases the firms try to maintain higher size of

equity capital rather that outside debt. Ho4 is rejected and the Ho

1, Ho2, Ho

3 are

accepted in case as of high income firms.

Therefore, the hypothesis Ho6 that “there is no significant impact of size

of income on the relation between profit earned and the various constituents of

capital structure” is rejected.



Table IV.23 Summary of Income Size-wise Analysis of the Relation between PBITD and the

Constituents of CS Hypotheses Relation between Predictors and Dependent Variables

Low Income Size Firms

Medium Income Size Firms

High Income Size Firms



Accepted +ve**

Rejected Accepted



+ve** Rejected

Accepted Accepted



Accepted +ve**

Rejected Accepted



+ve** Rejected

Accepted +ve**

Rejected


IV.9.3 Sector-wise Analysis The sector wise result show different results. PBITD of sector I has highly

significant positive correlation with all the constituents of CS. Thus the hypothesis Ho

1, Ho2, Ho

3 and Ho4 are rejected. The result matches with that of the

overall result. The regression result also shows that the PBITD is a major determinant of STD (Adj-R2 value is 0.75), LTD (Adj-R2 value is 0.79), TD (Adj-R2 value is 0.79) and equity (Adj-R2 value is 0.43) (see table IV.24).

On the other hand, the correlation result of sector II shows that PBITD of the firms have highly significant positive correlation with STD, TD and equity. The regression result also supports for the same result, recording Adj-R2 value as 0.95, which fact shows that PBITD determines STD over 90% where as the Adj-R2 value is 0.71 for TD and 0.70 for equity. Hence, the hypotheses Ho

2, Ho3 and

Ho4 are rejected and the hypothesis Ho

1 is accepted.

PBITD of sector III has significant positive correlation with all the constituents of CS. Therefore, Ho

1, Ho2, Ho

3 and Ho4 are rejected. The Adj-R2

value shows the model fitness in case of STD (0.68), LTD (0.69), and TD (0.71), which fact reveals that PBITD is one of the major determinants of external borrowing in sector III.



Table IV.24 Summary of Sector-wise Analysis of the Relation between PBITD and the

Constituents of CS

Hypotheses Relation between Predictors and Dependent Variables

Sector I Sector II Sector III



+ve** Rejected

Accepted +ve**

Rejected



+ve** Rejected

+ve** Rejected

+ve** Rejected



+ve** Rejected

+ve** Rejected

+ve** Rejected



+ve** Rejected

+ve** Rejected

+ve* Rejected


Therefore, the hypothesis Ho7 that “there is no significant influence of

sector differences on the relation between profit earned and the various constituents of capital structure” is accepted.

The overall result, thus, shows that PBITD is a major determinant of STD and equity capital while there are other determinants which also predict the size of LTD and TD. While PBITD is not a good determinant of any of the constituents of the CS for small size firms, it explains above 50% of variation in LTD and TD of medium size firms. PBITD is a major determinant of equity capital in case of large size firms. The result of small size firms, medium size firms and large size firms also matches with the results of low income size, medium income size, and high income size firms respectively although there are very little variations. Thus, SIZ has significant impact on the relation between P and LEV as proved by Rajan and Zingales (1995)19, Booth Collins et al. (2001)20. Hence, Ho

5: “there is no significant influence of size of sales on the relation between profit earned and the various constituents of capital structure” is rejected. The hypothesis Ho

6: “there is no significant influence of size of income



on the relation between profit earned and the various constituents of capital structure” is also rejected. Sector I shows that PBITD is the major determinant of all the constituents of CS (viz., STD, LTD, TD and equity) with a high Adj. R2 value. However, Sector II depends more on STD, TD, and equity as their profit (PBITD) increases rather than relying on LTD, while PBITD is the major determinants of external borrowings of firms of Sector III. Therefore, it leads to infer that sectoral classification has least impact on the relation between profit earned and the various constituents of capital structure. Hence, Ho

7 is accepted for the reason that, as indicated by the findings of Barton, Hill, and Sundaram (1989)21, sectoral differences does not have impact on the relation between PBITD and CS.

The findings show that there is impact of profit earned on various constituents of CS. The main study profoundly analyses the determinants of LEV and P in Food Industry in India.

PART II DETERMINANTS OF CAPITAL STRUCTURE AND PROFITABILITY: FIRM

SIZE- WISE, INCOME SIZE-WISE, AND SECTOR WISE APPROACHES IV.10 Introduction

India has a large and diverse agriculture background, and is one of the world’s leading producers of food articles. It is also a major consumer, with an expanding population to feed. Agriculture and allied sectors accounted for 15.7% of the GDP in 2009–1022. High food prices, resulting from the combined effects of the weak 2009 monsoon and inefficiencies in the government's food distribution system have shook Indian economy to the core. The expansion of Food Industry in India would be the right alternate for this. Studies and researches facilitate the advancement of the industry; hence the study is one step ahead of all such attempts.

The preliminary analysis of food industry concentrated on finding if the profit earned has any impact on the constituents of CS. As the results are positive, an in depth study about the relation between P and LEV is carried out in part II, which tries to analyze the determinants of LEV and P and also to study if controlling variables such as sales size, income size and sectoral classification influence the relation between the determinants and the dependent



variables LEV and P. Many theoretical and empirical works have been carried out to bring out the relation between P and CS. To illustrate a few, Myers (1984)23 introduced pecking order theory, which states an order which large firms follow to escape the problem of informational asymmetry. Firms prefer internal funds and when it gets exhausted they look in for debt finance rather than equity finance. Major US industrial firms follow a financing hierarchy (pecking order) and the managers consider the projected cash flow from asset to be financed as the main criteria in governing financing decisions (Pinegar and Wilbricht 1989)24. The works of Titman& Wessels (1988)25, Kester (1986)26, Chang (2003)27and many others have considered P as one of the determinants of CS. Wald (1999)28 found that P was “the single largest determinant of debt/asset ratios” in cross-sectional tests for the US, UK, Germany, France and Japan (Myers 2001) 29, which shows how important P is in determining the CS of the firms. The ability of the firm to earn consistent profit is the deciding factor of a firm’s CS. “Debt capacity” depends on the future P and value of the firm; it may be able to increase borrowing if it does well, or be forced to pay down debt if it does poorly (Myers 2001) 30. The works of Myers (1984) 31, Kester (1986) 32, Friend and Hasbrouch (1988)33, Friend and Lang (1988)34, Titman and Wessels (1988)35, and Chen and Zhao (2004)36 give empirical evidences in support of the negative relation between P & LEV. Long and Malitz (1985) 37 revealed that LEV increases with increases in P but their result was insignificant. Though there are varied views regarding the type of relation, the works give strong evidence that there is a binding link between P& CS. Hence, the study is carried out to achieve the objectives stated in chapter I.

IV.11 Objectives of Part II

The objectives of the study are to analyse the relationship between P and LEV in general and to analyze the impact of non debt tax shield (NDTXSH), collateral asset (COLASS), growth rate (GROW), size (SIZ), age (AG) and volatility (VOL) on LEV in determining the CS of the firm.

The influence of sales size, income size and sectoral differences of firms on the relationship between P and CS is also intended to be analyzed.



IV.12 Hypotheses Development

There are different views about the relation between P and LEV. A few worth mentioning are Myers (1984)38, Titman and Wessels (1988)39, Barton and Gordon (1988)40, Johnson (1998)41, Booth Collins et al. (2001)42, and Fama and French (2002)43 and they argued that there is a negative relationship between P and LEV. In contrast to this, Pandey (2004)44 predicted a positive relation between CS and P while Leland (1994)45, Kane, Marcus, and MacDonald (1984)46, and Wiggins (1990)47 found that LEV ratio is invariant to changes in P. Hence, it becomes essential to analyze the relation between P and LEV.

Other firm-level characteristics identified to influence LEV are size of the firm (SIZ), asset structure (COLASS), growth (GROW), volatility (VOL), non-debt tax shield (NDTXSH) and age (AG). Modigliani and Miller (1958)48, and Givoly Collins et al. (1992)49, in their study pointed out that the size of non-debt corporate tax shields like deductions for depreciation and investment tax credits may affect leverage, while Fisher, Heinkel, and Zechner (1989)50 study provided evidences that tax benefits to debt are mostly negligible, which makes it necessary to analyze whether there exists relationship between NDTXSH and CS.

Titman and Wessels (1988)51, Balakrishnan and Fox (1993)52, studies established that GROW rates were negatively related to long-term debt; while Barton and Gordon (1988)53, work provided evidence in the contrary stating that, GROW rate is positively correlated with debt and hence, the relation between GROW and LEV is to be analysed. VOL in profit earned increases the risk associated with the debt capital. Johnson (1997)54, Titman and Wessels, (1988)55 determined the impact of VOL on LEV as one of the determinants, while Hutchinson and Michaelas (1998)56 and Titman and Wessels (1988)57, identified opposite relation between collateralizable capital and debt level of the relation of these variables with LEV. Hence, the hypotheses are: Ho

8 = “There is no significant relationship between profitability and leverage of the firms”. Ho

9 = “There is no significant relationship between non-debt tax shield and leverage of the firms”.



Ho10 = “There is no significant relationship between collateral assets and leverage

of the firms”. Ho

11= “There is no significant relationship between growth and leverage of the firms”. Ho

12 = “There is no significant relationship between volatility and leverage of the firms”.

Size influences the earning capacity and eventually the borrowing power of the firms. The impact of size is studied in the works of Hutchinson and Michaelas (1998)58, Booth Collins et al. (2001)59, Panday (2002)60, and Chen and Zhao (2004)61. Therefore, the impact of SIZ of the firms on the relationship between P and CS should be analyzed. Barton, Hill, and Sundaram (1989)62, Baker (1973)63, and Lee and Kwok (1988)64 findings suggested that relativity of business influences LEV. Thus, the hypotheses are: Ho

13= “There is no significant influence of size in deviating the relationship between profitability and capital structure of the firms”. Ho

14= “There is no significant influence of sectoral differences of firms in deviating the relationship between profitability and capital structure”.

IV.13 Research Methods IV.13.1 Sources of Data

The study is based on secondary data, which are collected from CMIE (Centre for Monitoring Indian Economy) Prowess package for a period of 10 years on year to year basis ranging from 1999-2000 to 2008-2009.

IV.13.1 Research Methods for Analysis Descriptive statistics such as mean, median and standard deviation are used to neutralize the fluctuation in the value of explained as well as explaining variables. Correlation co-efficient is extensively used to study one-to-one relationship between variables. Multiple regression is also used to determine the various variables that influence the debt ratio / leverage in a firm. Besides, factor analysis is also used to determine the factors influencing P. Different appropriate ratios as stated in chapter I are also used to calculate individual relative properties.



IV.14 Regression on Determinants of LEV (Equation 1) Regression equation I attempt to identify the determinants of LEV in food

industry in India. The dependent variable LEV is studied under three heads viz., short term debt (LEV_STD), long term debt (LEV_LTD) and total debt (LEV_TD). Hutchinson and Michaelas (1998)65, in their study analyzed CS in terms of short term debt, long term debt and total debt. Titman and Wessels (1988)66 also analyzed the implications with regard to different types of debt instruments viz. short-term, long-term and convertible debt rather than an aggregate measure of total debt, (the descriptions of various ratios of independent nature are given in table IV.25) hence the equation is:

LEV = α + β1 VOL + β2 COL ASS+ β3 NDTXSH + β4 P + β5 SIZ + β6 AG + β7

GROW + Є Table IV.25

Ratios of Independent Variables Determining LEV Variables Description Inference

LEV_STD Short term debt / Book value of equity A high value denotes high leverage in terms of short term debt and vice versa

LEV_LTD Long term debt / Book value of equity A high value denotes high leverage in terms of long term debt and vice versa

LEV_TD Total debt / Book value of equity A high value denotes high leverage in terms of total debt and vice versa



COLASS Ratio of Property, Plant and Equipment / Total Assets

A high value denotes higher share of fixed asset to total asset, which implies greater share of assets is invested for increasing earning and vice versa

NDTXSH Ratio of the sum of depreciation and amortization / Total Assets

A high value denotes a higher non debt tax shield and vice versa

P PBITD / Fixed Assets A high value denotes higher profitability in terms of fixed assets

SIZ Logarithm of Sales over Years Turnover adjusted for fluctuation over years AG Total number of years from the date of

incorporation The number of years the firm has been carrying out business

GROW Compounded annual growth rate (CAGR) of total assets

The growth of total asset over years




IV.15 Overall Analysis of Determinants of LEV The overall analysis constitutes analysis of 86 firms to show an overall picture about the industry. The trend analysis over a period of 10 years (see chart IV.H) shows that there is a steep rise in LEV_TD with slight rise in P from the year 2005-06 to 2007-08, thereafter there is a fall in P as well as a fall in LEV_TD.

The overall descriptive statistics (see table IV. 26) shows that LEV_TD has the highest mean value. The deviation from the mean value is also high. The other dependent variables, LEV_LTD and LEV_STD also have higher standard deviation, indicating that the size of leverage on total borrowings vary highly within the industry. NDTXSH also has high standard deviation, however P and GROW have low standard deviation, indicating that there is no much of variation in the industry’s P and GROW although there is a high deviation in CS. The correlation matrix (see table IV. 27) also shows a clear picture of the relation between P and LEV.

Chart IV. H Overall Trend Line Showing Relation between P and LEV_TD


0.228 0.24 0.203 0.23 0.272 0.232 0.284 0.343 0.45 0.395

5.836.354

6.787.452

9.028

9.3489.716

11.984

17.019

16.3

0

2

4

6

8

10

12

14

16

18

P

LEV_TD



Table IV. 26 Overall Descriptive Statistics of Determinants of LEV of Food Industry in India

Variables N Minimum Maximum Mean Std. Deviation

LEV_STD 86 .036 41.859 3.899 6.081

LEV_LTD 86 .089 48.860 6.101 8.289

LEV_TD 86 .186 69.506 9.981 12.790

VOL 86 .009 .759 .085 .115

COL ASS 86 .034 .836 .435 .185

NDTXSH 86 -4.890 16.005 .284 2.030

P 86 -.349 1.389 .290 .288

SIZ 86 -.905 3.677 1.819 .903

AG 86 13 53 24.12 10.010

GROW 86 -.174 .463 .080 .126


The correlation matrix shows (see table IV. 27) that there exists a highly significant (at 1% level) positive correlation between GROW and LEV_STD (0.343), LEV_LTD (0.54), and LEV_TD (0.51). SIZ also has highly significant positive correlation with LEV_STD (0.50), LEV_LTD (0.43), and LEV_TD (0.51). The AG of the industry has highly significant positive correlation with LEV_STD (0.30), LEV_TD (0.29) and significant correlation (at 5% level) with LEV_LTD (0.22).

P has significant positive correlation with LEV_STD (0.50), and LEV_TD (0.51), which fact indicates that the industry is a growing industry and does not have sufficient retained earnings to backup their investment opportunities and still they rely on external borrowings and the pecking order hypothesis (Myers 1984) is not very much relevant to the industry. However, the size-wise analysis shows a contradictory result, which fact shows that VOL as well as COLASS have significant negative correlation with LEV_TD (-0.23) and with LEV_STD (-0.23) respectively.



Table IV. 27 Overall Correlation Matrix of Determinants of LEV of Food Industry in India

Variables LEV _STD

LEV _LTD

LEV _TD VOL COLASS NDTXSH P SIZ AG GROW

LEV_STD 1

(.00)

LEV_LTD .577 1

(.00)

LEV_TD .849 .921 1

(.00) (.00)

VOL -.227 1

(.04)

COLASS -.226 .218 1

(.04) (.04)

NDTXSH 1

P .249 .241 -.478 1

(.02) (.03) (.00)

SIZ .503 .427 .514 -.299 -.455 .480 1

(.00) (.00) (.00) (.01) (.00) (.00)

AG .302 .218 .285 .237 1

(.01) (.04) (.01) (.03)

GROW .343 .537 .509 -.348 -.246 .357 .558 1

(.00) (.00) (.00) (.00) (.02) (.00) (.00)


The overall regression results show that (see table IV. 28) SIZ (2.65) and AG (0.13) have highly significant positive coefficients with LEV_STD. The Adj-R2 is 0.24, indicating that the predictor variables determine changes in LEV_STD only to the extent of 20%. However, the F-stat value (4.86) is highly significant, revealing that the variability in the predictor variables explains a statistically significant portion of variability in the dependent variable, LEV_STD. GROW has highly significant positive coefficient with LEV_LTD (30.20) and LEV_TD (model 1 is 35.21 & model 2 is 35.23). SIZ has significant



positive coefficient (4.59) with LEV_TD in model 1 and highly significant coefficient (4.48) with LEV_TD in model 2 after removing predictor variables viz., VOL, COLASS, NDTXSH. AG has significant positive coefficient with LEV_TD in models 1 (0.27) & 2 (0.27). The Adj-R2 value is, however, low (30%) which indicates that the model fit is not quite good for the overall industry.

The Kaiser–Meyer-Olkin measure (see table IV. 29) is nearly 0.6, indicating that factor analysis shows a fair result and the Barlett’s test of sphericity is significant (at 1% level), indicating that the correlation matrix is not an identity matrix, thus, the result of factor analysis would be fair and reliable.

Table IV. 28 Overall Results of Regression on Determinants of LEV of Food Industry in India

Variables

Un-standardized Coefficients Beta Value LEV in terms of short term debt

LEV in terms of long term debt

LEV in terms of total debt

Model 1 Model 2 (Constant) -3.405

(0.24) -4.128 (0.28)

-7.400 (0.20)

-6.430 (0.06)

VOL -1.137 (0.83)

1.429 (0.84)

0.327 (0.97)

-

COLASS -1.583 (0.68)

3.702 (0.46)

1.881 (0.80)

-

NDTXS -0.050 (0.86)

-.064 (0.86)

-0.117 (0.84)

-

P -1.211 (0.63)

-2.123 (0.52)

-3.334 (0.50)

-3.767 (0.40)

SIZ 2.648** (0.00)

1.966 (0.09)

4.590* (0.01)

4.477** (0.00)

AG 0.134* (0.03)

.131 (0.11)

.265* (0.03)

0.272* (0.02)

GROW 5.233 (0.36)

30.201** (0.00)

35.211** (0.00)

35.234** (0.00)

R2 0.304 0.349 0.380 0.379 Adj-R2 0.241 0.290 0.325 0.349 F Stat 4.857**

(.00) 5.964** (.00)

6.838** (.00)

12.373** (.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: Figures in parentheses are ‘p’ values;**Significant at 0.01 level;*Significant at 0.05 level



Table IV. 29

KMO and Bartlett's Test of Determinants of LEV of Food Industry in India

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .585

Bartlett's Test of Sphericity

Approx. Chi-Square 894.208

df 45

Sig. .000


Table IV. 30 Overall Factor Analysis of Determinants of LEV of Food Industry in India

Factor Eigen value Variable convergence

Factor loadings

Factor 1

3.893

LEV_TD .969

LEV_LTD .904

LEV_STD .810

GROW .558

AG .369

Factor 2

1.469

P .816

COLASS -.810

SIZ .667

Factor 3

1.146

NDTXSH -.809

VOL .566


For factor analysis (see table IV. 30), the variables are grouped into three factors, showing high level of correlation among the variables within the factor. Factor 1 constitutes LEV_TD, LEV_LTD, LEV_STD, GROW, and AG. Factor 2 constitutes P, COLASS, SIZ, and factor 3 constitutes NDTXSH and VOL.

IV.16 Sales Size-wise Analysis of Determinants of LEV

Many empirical works were attempted to study the impact of SIZ on CS

and P. Rajan and Zingales (1995) 67 pointed out that the negative influence of P

on LEV increases with the size of the firm. Profitable large size firms have

relatively less debt when compared to that of the smaller and riskier firms. Small

size firms tend to use significantly more short term debt than that of the larger



firms (Titman and Wessels 1988) 68. A size-wise analysis is carried out to study

the impact of size on the various predictor variables on the dependent variables.

In order to test the same the firms with sales turnover of < Rs.100 crore are

grouped as ‘small size firms’, the firms with sales turnover of > Rs.100 crore but

< Rs.500 crore are considered as ‘medium size firms’, and firms with sales

turnover of >Rs.500 crore are considered as ‘large size firms’.

IV.16.1 Analysis of Small Size Firms

The trend analysis of small size firms shows (see chart IV.I) that there is a

steep rise in LEV_TD after the years 2005-06 till 2007-08, thereafter, there has

been a sudden fall, which matches with the overall trend line due to the impact

of global meltdown in 2007.

Chart IV.I Trend Line Showing Relation between P and LEV_TD of Small Size Firms


Small size firms include 43 firms with average sales turnover < Rs.100

crore. The descriptive statistics shows (see table IV. 31) that the standard

deviation is high among the small size firms with respect to LEV_LTD when

0.1041

0.135

0.124

0.148

0.156

0.133

0.142

0.257

0.3680.314

3.395

3.608

3.589

4.135

4.367

4.3014.307

5.689

10.309

7.945

0

2

4

6

8

10

12

P

LEV_TD



compared to LEV_STD as well as LEV_TD. However, the P and GROW have

lesser deviations, indicating that the small size firms have more or less same

profit earning capacity and the GROW is also closely related irrespective of the

level of deviation in LEV.

Table IV. 31 Descriptive Statistics of Determinants of LEV of Small Size Firms of Food

Industry in India


LEV_STD 43 .036 10.217 1.526 2.382

LEV_LTD 43 .089 48.860 3.635 8.612

LEV_TD 43 .186 57.282 5.164 1.049

VOL 43 .009 .759 .113 .150

COLASS 43 .034 .836 .478 .199

NDTXSH 43 -4.890 16.005 .420 2.851

P 43 -.278 1.008 .194 .258

SIZ 43 -.905 1.945 1.154 .768

AG 43 15 53 22.21 9.339

GROW 43 -.174 .463 .0137 .109


The correlation results (see table IV. 32) show the relation between the

predictor variables and the dependent variables. GROW has highly significant

correlation with the dependent variables LEV_LTD (0.64) and LEV_TD (0.61)

and has significant correlation with LEV_STD (0.38). AG has highly significant

positive correlation with LEV_STD (0.52), LEV_LTD (0.40) and LEV_TD

(0.45). SIZ has significant positive correlation with LEV_STD (0.36), LEV_LTD

(0.32) and LEV_TD (0.34), hence GROW, AG, SIZ influence the size of LEV of

small size firms. P has an insignificant positive relation with LEV while VOL,

COLASS, and NDTXSH have insignificant negative correlation with LEV.



Table IV. 32 Correlation Matrix of Determinants of LEV of Small Size Firms of Food Industry

in India

Variables LEV

_STD

LEV

_LTD

LEV

_TD VOL COLASS NDTXSH P SIZ AG GROW

LEV_STD 1

LEV_LTD .738** 1

(.00)

LEV_TD .832** .988** 1

(.00) (.00)

VOL 1

COLASS 1

NDTXSH 1

P -.399** 1

(.00)

SIZ .361* .320* .344* -.509** .380* 1

(.017) (.03) (.02) (.00) (.01)

AG .517** .400** .446** 1

(.00) (.00) (.00)

GROW .375* .642** .612** -.370* .417** 1

(.01) (.00) (.00) .015 (.00)




Table IV. 33 Results of Regression on Determinants of LEV of Small Size Firms of Food

Industry in India

Variables

Un-standardized Coefficients Beta Value

LEV in terms of short term debt



Model 1 Model 2

(Constant) -1.941 (0.25)

-6.759 (0.22)

-8.716 (0.20)

-7.224 (0.08)

VOL .166 (0.94)

8.864 (0.25)

9.038 (0.33)

9.560 (0.28)

COLASS -.405 (0.83)

2.740 (0.67)

2.365 (0.76)

-

NDTXS -.027 (0.81)

.033 (0.93)

.007 (0.98)

-

P .814 (0.55)

3.734 (0.41)

4.554 (0.41)

4.079 (0.43)

SIZ .710 (0.18)

.882 (0.61)

1.596 (0.45)

1.324 (0.47)

AG .119** (0.00)

.256* (0.03)

.375* (0.01)

.374** (0.00)

GROW 3.149 (0.37)

46.258** (0.00)

49.405** (0.00)

49.991** (0.00)

R2 0.406 0.513 0.507 0.506

Adj-R2 0.287 0.416 0.409 0.439

F Stat 3.420** (0.00)

5.277** (.00)

5.147** (.00)

7.578** (.00)


The regression result reveals whether the explaining variables, GROW, AG, and SIZ are good predictors of LEV. AG and GROW have highly significant positive coefficient (see table IV. 33) with LEV_STD (0.12) and with LEV_LTD (46.25) respectively, and LEV_TD (model 1 (49.41) model 2 (49.99)). AG has significant positive coefficient with LEV_TD in models 1(0.38*) & 2 (0.37**)



after removing variables COLASS and NDTXSH. The Adj-R2 value is low for LEV_STD (0.29) and above 40% for LEV_LTD (0.42), and LEV_TD (0.45). Thus, the regression model fit is above 40% in case of small size firms. The F-stat is highly significant in all the models revealing that the variability in LEV is significant with the variability in the predictor variables.

Kaiser-Meyer-Olkin measure reveals (see table IV. 34) about 60% (0.59), indicating that the factor analysis for the data would be appropriate for small size

firms. The Barlett’s test also shows a highly significant χ2 value (583.73), affirming that the correlation matrix is not an identity matrix and factor analysis can be conducted on it.

Table IV. 34 KMO and Bartlett's Test of Determinants of LEV of Small Size Firms of Food

Industry in India




df 45

Sig. .000


The results of factor analysis (see table IV. 35) shows that the predictor variables can be reduced to 3 factors; factor 1 constituting of LEV_TD, LEV_LTD, LEV_STD, AG and GROW; factor 2 constituting COLASS, SIZ and P; and factor 3 constituting VOL and NDTXSH.

Table IV. 35 Factor Analysis of Determinants of LEV of Small Size Firms of Food Industry in

India Factor Eigen value Variable convergence Factor loadings

Factor 1

3.868

LEV_TD .943 LEV_LTD .917 LEV_STD .838 AG .657 GROW .631

Factor 2

1.610 COLASS -.794 SIZ .776 P .742

Factor 3

1.265

VOL .766 NDTXSH -.732




IV.16.2 Analysis of Medium Size Firms The trend line of medium size firms shows (see chart IV.J) a gradual rise

in LEV_TD over the years with the rise in P unlike the trend in the case of small size firms, which fact shows that they are quite stable. The fall from the year 2007-08 to 2008-09 is also gradual, explaining their position in handling situations.

Chart IV.J Trend Line Showing Relation between P and LEV_TD of Medium Size Firms


Medium size firms include 31 firms whose average turnover is > Rs.100 crore but < Rs. 500 crore. The firms show (see table IV. 36) higher standard deviation with regard to LEV. AG also shows a higher standard deviation, indicating that the firms belonging to different age groups have average turnover falling in the same range, which in turn, shows that the AG of the firm is not related to their prosperity. P and GROW show a low deviation, which indicates that the firms with different sizes of LEV have more or less the same level of P and GROW.

The correlation matrix (see table IV. 37) of medium size firms shows that SIZ has highly significant positive correlation with LEV_LTD (0.55**) and significant positive correlation (0.45*) with LEV_TD, while VOL has significant negative correlation (-0.37*) with LEV_TD. AG has significant positive correlation (0.39*) with LEV_STD.

0.295

0.298

0.205

0.262

0.352

0.281

0.319

0.304

0.3390.336

6.471 7.243 8.155 8.88810.792

11.943 12.772

15.691

19.62

19.399

0

5

10

15

20

25

P

LEV_TD



Table IV. 36 Descriptive Statistics of Determinants of LEV of Medium Size Firms of Food

Industry in India


LEV_STD 31 .640 25.727 4.121 4.639

LEV_LTD 31 .804 26.226 8.032 6.513

LEV_TD 31 1.638 42.223 12.097 9.501

VOL 31 .009 .234 .062 .058

COLASS 31 .099 .711 .434 .159

NDTXSH 31 .009 2.406 .191 .491

P 31 -.349 .826 .299 .217

SIZ 31 1.603 2.686 2.292 .217

AG 31 13 48 24.26 9.143

GROW 31 -.054 .422 .131 .105


The regression run to analyze if the predictors have significant coefficients with the dependent variables shows (see table IV. 38) that the model is fit to the extent of 40% (Adj-R2 value for model 3 is 0.41) in case of LEV_LTD, LEV_STD (Adj-R2 value for model 2 is 0.17) and LEV_TD (0.245) the model fit is only about 20%. AG has a significant coefficient (0.21) with LEV_STD in model 2 after removing COLASS, NDTXSH and GROW from the predictor variables. SIZ has highly significant positive coefficient with LEV_LTD (18.32) in model 2 after removing the predictor variables viz., VOL, NDTXSH and P. It also has a highly significant positive coefficient (18.99) with LEV_LTD in model 3 after removing the predictor variable COLASS in addition to VOL, NDTXSH and P. GROW has a significant positive coefficient with LEV_LTD in models 2 (21.32) & 3 (22.74). AG also has a significant coefficient with LEV_LTD in model 3 (0.23) while SIZ alone has significant positive coefficient (19.37) with LEV_TD, however, the other predictor variables have insignificant coefficients with LEV_TD. The F-stat value is significant in all the three models of LEV_LTD, indicating that the variability of the predictor variables is significantly related to the variability in LEV.



Table IV. 37 Correlation Matrix of Determinants of LEV of Medium Size Firms of Food

Industry in India

Variables

LEV

_STD

LEV

_LTD

LEV

_TD VOL COLASS NDTXSH P SIZ AG GROW

LEV_STD 1

LEV_LTD .442* 1

(.013)

LEV_TD .790** .898** 1

(.00) (.00)

VOL -.366* 1

(.04)

COLASS 1

NDTXSH 1

P -.519** 1

(.00)

SIZ .553** .451* -.406* 1

(.00) (.011) (.02)

AG .385* 1

(.03)

GROW .379* 1

(.03)




Table IV. 38 Results of Regression on Determinants of LEV of Medium Size Firms of Food

Industry in India Variables



LEV in terms of long term debt LEV in terms of total debt

Model 1 Model 2 Model 1 Model 2 Model 3

(Constant) -6.349 (0.58)

-7.742 (0.46)

-42.955 (0.00)

-43.570 (0.00)

-44.124 (0.00)

-48.203 (0.03)

VOL -17.555 (0.28)

-18.724 (0.22)

-5.205 (0.78)

-

-

-22.799 (0.44)

COLASS 2.564 (0.74)

- 10.143 (0.28)

4.602 (0.47)

-

12.095 (0.40)

NDTXS -.648 (0.72)

- .504 (0.81)

-

-

-.208 (0.95)

P 5.580 (0.32)

3.170 (0.39)

5.331 (0.42)

-

-

10.844 (0.30)

SIZ 2.415 (0.58)

3.058 (0.45)

17.282 (0.00)

18.316** (0.00)

18.989** (0.00)

19.368* (0.02)

AG .174 (0.13)

.209* (0.02)

.148 (0.28)

0.199 (0.10)

0.233* (0.04)

.322 (0.13)

GROW -6.546 (0.53)

- 15.342 (0.22)

21.324* (0.03)

22.740* (0.02)

8.125 (0.67)

R2 0.295 0.278 0.495 0.475 0.464 0.421

Adj-R2 0.080 0.167 0.342 0.394 0.405 0.245

F Stat 1.374 (0.26)

2.503 (0.06)

3.226* (0.01)

5.871** (0.00)

7.795** (0.00)

2.394 (0.054)


The Kaiser-Meyer-Olkin measure (see table IV. 39) is only 0.42, which is quite lower than the minimum level 0.60, hence the factor analysis results may be only illusionary results. However, the Bartlett’s Test of sphericity is highly significant (258.81), proving that the correlation matrix is not an identity matrix encouraging performing factor analysis on the variables in case of medium size firms.



Table IV. 39

KMO and Bartlett's Test of Determinants of LEV of Medium Size Firms of Food Industry in India




df 45

Sig. .000


Hence, factor analysis has been carried out and the variables are grouped into 4 factors. Factor 1 (see table IV. 40) constitutes LEV_STD, LEV_TD and AG; factor 2 constitutes SIZ, VOL, LEV_LTD; factor 3 constitutes COLASS, P, and NDTXSH, and factor 4 constitutes only one variable, viz., GROW as it is not inter related with the other variables so as to constitute a factor.

Table IV. 40 Factor Analysis of Determinants of LEV of Medium Size Firms of Food Industry

in India Factor Eigen value Variable convergence Factor loadings

Factor 1

3.126

LEV_STD .813

LEV_TD .765

AG .756

Factor 2

1.855

SIZ .870

VOL -.686

LEV_LTD .588

Factor 3

1.494

COLASS .842

P -.722

NDTXSH -.647

Factor 4 1.155 GROW .935


IV.16.3 Analysis of Large Size Firms The trend line of large size firms shows (see chart IV.K) that they are in a

better position than that of the medium size and small size firms. The LEV_TD shows a steady rise without any sudden fall, hence the external variables do not influence the size of external borrowing of large size firms as they are able to raise capital at all times.



Chart IV.K Trend Line Showing Relation between P and LEV_TD of Large Size Firms


Table IV. 41 Descriptive Statistics of Determinants of LEV of Large Size Firms of Food

Industry in India


LEV_STD 12 3.277 41.859 11.830 1.071

LEV_LTD 12 .354 27.648 9.944 9.034

LEV_TD 12 4.242 69.506 21.774 1.850

VOL 12 .012 .111 .0426 .029

COLASS 12 .165 .482 .283 .104

NDTXSH 12 .009 .158 .034 .042

P 12 .274 1.389 .615 .330

SIZ 12 2.684 3.677 2.984 .284

AG 12 16 51 30.58 12.384

GROW 12 .057 .381 .182 .108


The descriptive statistics of large size firms (see table IV. 41) show that the standard deviation is very high in case of LEV_LTD as well as in AG, thus the firms grouped under large size firms have varied level of LTD and the AG of the firms also varies, which prove that the firms of different AG are grouped as large size firms. However, P and GROW have comparatively low standard deviation.

0.502

0.471

0.478

0.44

0.479

0.458

0.705

0.75

1.0280.836

12.899

13.901

14.6615.627

21.176

20.72521.205

24.97

34.34638.232

05

1015202530354045

P

LEV_TD



The correlation matrix (see table IV. 42) of the large size firms shows that SIZ has significant positive correlation (0.60) with LEV_STD while the other predictor variables do not have significant correlation with the dependent variable, LEV. P shows an insignificant negative correlation with LEV unlike the case of small size and medium size firms. The regression result (see table IV. 43) of large size firms show that SIZ has significant positive coefficient (34.45) with LEV_STD. The Adj-R2 value (0.40) also supports that the regression model is 40.4% fit in case of LEV_STD, although the Adj-R2 value is insignificant for the other cases.

Table IV. 42 Correlation Matrix of Determinants of LEV of Large Size Firms of Food Industry

in India Variables LEV

_STD LEV

_LTD LEV _TD VOL COLASS NDTXSH P SIZ AG GROW

LEV_STD 1

LEV_LTD .756 1 (.00)

LEV_TD .947 .925 1 (.00) (.00)

VOL 1

COLASS .594 1 (.04)

NDTXSH .626 1 (.02)

P 1

SIZ .597 1 (.04)

AG .593 1 (.04)

GROW 1




Table IV. 43 Results of Regression on Determinants of LEV of Large Size Firms of Food

Industry in India

Variables





(Constant) -88.497 (0.06)

-49.500 (0.27)

-137.998 (0.10)

VOL 140.687 (0.45)

248.704 (0.27)

389.391 (0.30)

COLASS -73.727 (0.29)

-92.454 (0.25)

-166.181 (0.23)

NDTXS 132.407 (0.33)

192.739 (0.23)

325.146 (0.24)

P -11.683 (0.32)

-2.411 (0.85)

-14.094 (0.52)

SIZ 34.445* (0.03)

22.701 (0.14)

57.146 (0.05)

AG 34.445 (0.59)

-0.155 (0.68)

0.026 (0.96)

GROW 52.127 (0.16)

37.547 (0.34)

89.675 (0.20)

R2 0.783 0.604 0.724

Adj-R2 0.404 -0.089 0.241

F Stat 2.064 (0.25)

0.872 (0.59)

1.498 (0.36)


The KMO result shows that the coefficient of correlation is not positive definite and so factor analysis is not carried out.

IV.17 Income-wise Analysis of Determinants of LEV There are different views about the impact of income earned on CS. To

illustrate, more profitable firms tend to issue more debt as debt capital may be available to them at a cheaper rate (Chen and Zhao 2004)69, however it is



recommendable that more profitable firms should hold less debt because higher profit generates more internal funds (Bevan and Dabnolt 2002)70. The firms within the same income size range are grouped together to analyze the impact of PBITD. The firms are grouped into three sub-categories viz., ‘low income size firms’ with profit (PBITD) < Rs.10 crore; ‘medium income size firms’ with profit > Rs.10 crore but < Rs.50 crore; ‘high income size firms’ with income >Rs.50 crore. The average income (PBITD) for the period of 10 years under study is considered for this purpose.

IV.17.1 Analysis of Low Income Size Firms The low income size firms’ trend line shows (see chart IV.L) that there is

a steep rise in the level of external borrowings with a slight rise in P. However, there is a steep fall in LEV_TD after the year 2007-08. The trend line is similar to that of the small size firms, which fact shows the instability in case of low income size firms.

Chart IV.L Trend Line Showing Relation between P and LEV_TD of Low Income Size

Firms


The descriptive statistics of 54 low income size firms show (see table IV. 44) that the standard deviation for LEV_STD, LEV_LTD, and LEV_TD is very high and AG also shows a high level of deviation. The firms of different incorporation years fall under low Income size firms. The maximum AG is 53 and the minimum AG is 15, which could clearly explain that mere existence for a

0.1110.133

0.119

0.174

0.211

0.151

0.137

0.238

0.380.3

3.64

4.047

4.2654.437

4.904

4.7825.304

6.948

9.679

7.465

0

2

4

6

8

10

12

P

LEV_TD



longer period has not done any good things to improve their income level. The size of borrowing also varies among the firms listed under low income size firms though the P does not show relatively high standard deviation.

Table IV. 44

Descriptive Statistics of Determinants of LEV of Low Income Size Firms of Food Industry in India


LEV_STD 54 .036 25.727 2.055 3.861

LEV_LTD 54 .089 48.860 3.492 7.105

LEV_TD 54 .186 57.282 5.547 9.699

VOL 54 .009 .759 .1044 .139

COLASS 54 .034 .836 .456 .193

NDTXSH 54 -4.890 16.005 .422 2.560

P 54 -.349 1.008 .200 .248

SIZ 54 -.905 2.457 1.367 .812

AG 54 15 53 22.52 9.668

GROW 54 -.174 .463 .036 .120


The correlation coefficient (see table IV. 45) of the low income size firms show that AG (0.53) with LEV_STD, and SIZ have significant positive correlation (0.33) with LEV_STD. With regard to LEV_LTD, GROW (0.50) and AG (0.44) have highly significant positive correlation with LEV_LTD while SIZ has significant positive correlation (0.30) with LEV_LTD. GROW (0.43), AG (0.54), and SIZ (0.35) have highly significant positive correlation with LEV_TD. In low income size firms, GROW, AG, and SIZ play a significant role in determining the size of LEV as that of in small size firms.

The regression result (see table IV. 46) of the low income size firms show that AG has highly significant positive coefficient with LEV_STD (0.20), LEV_LTD (0.30) and LEV_TD (0.50). GROW has highly significant positive coefficient with LEV_LTD (29.55) and LEV_TD (28.83) while SIZ has significant positive coefficient with LEV_STD. The Adj-R2 value is 0.42 for



LEV_TD, indicating that the model is 40% fit in case of LEV_TD. The Adj-R2 value is comparatively lesser in case of LEV_STD and LEV_LTD. The F-stat is, however, highly significant in case of LEV_STD (4.02), LEV_LTD (5.66), and LEV_TD (6.37), indicating that variance of the dependent variable is significantly related to the variance of the predictor variables.

Table IV. 45 Correlation Matrix of Determinants of LEV of Low Income Size Firms of Food

Industry in India

Variables LEV _STD

LEV _LTD

LEV _TD VOL COLASS

NDTXSH P SIZ AG GROW

LEV_STD 1

LEV_LTD .523 1

(.00)

LEV_TD .781 .941 1

(.00) (.00)

VOL 1

COLASS 1

NDTXSH 1

P -.404 1

(.00)

SIZ .328 .301 .351 -.510 .303 1

(.015) (.02) (.00) (.00) (.02)

AG .529 .444 .535 1

(.00) (.00) (.00)

GROW .499 .434 -.327 -.273 .323 .437 1

(.00) (.00) (.016) (.04) (.017) (.00)




Table IV. 46 Results of Regression on Determinants of LEV of Low Income Size Firms of

Food Industry in India

Variables





(Constant) -5.762 (-.71)

-9.365 (0.02)

-15.132 (0.00)

VOL -1.711 (0.63)

6.001 (0.33)

4.299 (0.59)

COLASS 2.582 (0.38)

5.838 (0.25)

8.446 (0.21)

NDTXS 0.018 (0.92)

0.077 (0.80)

0.095 (0.82)

P 1.849 (0.37)

-2.164 (0.54)

-0.302 (0.94)

SIZ 1.501* (0.03)

1.579 (0.19)

3.080 (0.05)

AG 0.196** (0.00)

0.300** (0.00)

0.496** (0.00)

GROW -0.711 (0.87)

29.552** (0.00)

28.832** (0.00)

R2 0.380 0.463 0.492

Adj-R2 0.285 0.381 0.415

F Stat 4.021** (0.00)

5.660** (0.00)

6.374** (0.00)


The Kaiser-Meyer-Olkin measure (see table IV. 47) is not quite

satisfactory as it is below 0.60 (0.54). However, the Barlett’s test χ2 value (694.20) is highly significant, indicating that the correlation matrix is not an identity matrix and encouraging factor analysis. The factor analysis of the low income size firms shows (see table IV. 48) that the variable converges into three factors. Factor 1 constitutes LEV_TD, LEV_LTD, LEV_STD and AG. Factor 2



comprises of COLASS, SIZ, P and GROW, and factor 3 constitutes NDTXSH and VOL. The predictor variable, AG alone converges with the dependent variable, indicating a high level of inter correlation between them.

Table IV. 47 KMO and Bartlett's Test of Determinants of LEV of Low Income Size Firms of





df 45

Sig. .000


Table IV. 48 Factor Analysis of Determinants of LEV of Low Income Size Firms of Food

Industry in India Factor Eigen value Variable convergence Factor loadings

Factor 1 3.471

LEV_TD 0.954

LEV_LTD 0.867

LEV_STD 0.800

AG 0.719

Factor 2 1.877

COLASS -0.776

SIZ 0.747

P 0.725

GROW 0.595

Factor 3 1.172 NDTXSH 0.792

VOL -0.715


IV.17.2 Analysis of Medium Income Size Firms The trend line of medium income size firms shows (see chart IV.M) that

there is a steep rise after the year 2005-06 in the size of borrowings and a steady rise after the year 2007-08, the period during which the low income size firms as well as small size firms showed a sudden fall in the size of external borrowings. The P of medium income size firms also follows a steady line without much fluctuation.



Chart IV.M Trend Line Showing Relation between P and LEV_TD of Medium Income Size

Firms


The descriptive statistics of medium income size firms (see table IV. 49)

show that LEV_STD, LEV_LTD, and LEV_TD have comparatively higher

standard deviation, indicating that the firms grouped as medium income size

firms have different sizes of borrowings, and the AG also has higher standard

deviation, indicating a wider range of firms with varied AG falling under the

same category. The P and GROW, however, have comparatively lesser

deviations.

The correlation matrix of medium income size firms (see table IV, 50)

shows that P has significant positive correlation with LEV_LTD (0.44), and

LEV_TD (0.44), while it showed an insignificant positive correlation with LEV.

GROW also has significant positive correlation with LEV_TD in case of medium

income size firms, while AG has an insignificant negative correlation with

LEV_STD, LEV_LTD, and LEV_TD.

0.321

0.352

0.316

0.2681

0.321

0.32

0.434

0.413

0.3550.405

7.133

7.493

8.322

10.33

13.653

14.72513.728

17.881

26.51328.142

0

5

10

15

20

25

30

LEV_TD

P



Table IV. 49 Descriptive Statistics of Determinants of LEV of Medium Income Size Firms of



LEV_STD 22 1.111 16.199 5.005 3.967

LEV_LTD 22 .965 30.349 9.861 7.541

LEV_TD 22 4.242 40.566 14.792 9.485

VOL 22 .010 .120 .047 .031

COLASS 22 .099 .711 .424 .175

NDTXSH 22 .007 .340 .058 .092

P 22 .079 .919 .350 .199

SIZ 22 1.945 2.947 2.408 .259

AG 22 13 48 24.77 7.904

GROW 22 .018 .422 .148 .104


The regression result of the medium income size firms (see table IV. 51) shows that GROW has significant positive coefficient (15.41) with LEV_STD in model 2 after removing the predictor variables NDTXSH, P, SIZ and AG. The Adj- R2 value of model 2 is increased from 0.07 in model 1 for LEV_STD to 0.26. The F-stat value is significant at 5% level (3.45) in model 2, proving the predictability of variance of dependent variables by the predictor variable.

The predictor variable, VOL has significant negative coefficient in models 1 (-105.77) and 2 (-98.70) with LEV_LTD. P also has highly significant positive coefficient with LEV_LTD in models 1 (31.96) and 2 (35.96) after removing predictors SIZ, AG, and GROW, which proves that medium income size firms do not have sufficient retained earnings and depend on external borrowing, hence their LEV increases with P as they borrow more to utilize the growth opportunities. COLASS has highly significant positive coefficient with LEV_LTD (27.75) in model 2 as the predictor variable VOL has significant negative coefficient in models 1 (-105.77) and 2 (-98.70) with LEV_LTD. P also has highly significant positive coefficient with LEV_LTD in models 1 (31.96) and 2 (35.96) after removing SIZ, AG and GROW, which reveals that medium income size firms do not have sufficient retained earnings and depend on external borrowing, hence their LEV increases with P as they borrow more to



utilize the growth opportunities. COLASS has highly significant positive coefficient with LEV_LTD (27.75) in model 2 as their external borrowings have to be supported by higher level of COLASS. NDTXSH also has significant positive coefficient with LEV_LTD (26.68) in case of medium income size firms. P has significant positive coefficient with LEV_TD in model 2 (21.25) after removing the predictor variables COLASS, NDTXSH and AG. GROW also has significant positive coefficient in model 2 with LEV_LTD (from 0.54 to 0.60) and with LEV_TD (from 0.41 to 0.47), thus the model fit is about 50% with both LEV_LTD and LEV_TD. The F-stat value is highly significant in model 2 with LEV_LTD (8.80) and LEV_TD (5.68), thereby it leads to infer that GROW and P have been the major predictors of LEV_LTD and LEV_TD of medium income size firms.

Table IV. 50 Correlation Matrix of Determinants of LEV of Medium Income Size Firms of

Food Industry in India Variables LEV

_STD LEV

_LTD LEV _TD VOL COLASS NDTXSH P SIZ AG GROW

LEV_STD 1

LEV_LTD 1

LEV_TD .651 .915 1 (.00) (.00)

VOL 1

COLASS 1

NDTXSH 1

P .438 .443 -.662 1 (.04) (.03) (.00)

SIZ -.429 1 (.04)

AG 1

GROW .445 .480 1 (.03) (.02)




Table IV. 51 Results of Regression on Determinants of LEV of Medium Income Size Firms of


Variables


LEV in terms of short

term debt

LEV in terms of long

term debt


Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

(Constant) 15.341

(0.38)

7.913

(0.00)

4.147

(0.85)

-11.403

(0.05)

22.509

(0.49)

32.454

(0.07)

VOL -34.521

(0.27)

-30.959

(0.21)

-105.765*

(0.02)

-98.695*

(0.013)

-139.565*

(0.02)

-150.949*

(0.012)

COLASS -12.788

(0.18)

-8.769

(0.05)

22.168

(0.09)

27.748**

(0.00)

7.289

(0.68)

-

NDTXSH -2.250

(0.84)

-

22.212

(0.15)

26.677*

(0.03)

20.337

(0.34)

-

P -3.534

(0.62)

-

31.957**

(0.00)

35.962**

(0.00)

27.331

(0.06)

21.249*

(0.013)

SIZ -2.099

(0.68)

-

-4.636

(0.50)

-

-7.524

(0.44)

-10.115

(0.15)

AG 0.011

(0.92)

-

-.062

(0.69)

-

-.051

(0.82)

-

GROW 19.406

(0.10)

15.409*

(0.04)

10.252

(0.50)

-

29.844

(0.17)

43.259*

(0.011)

R2 0.380 0.365 0.690 0.674 0.610 0.572

Adj-R2 0.070 0.259 0.535 0.598 0.414 0.471

F Stat 1.227

(0.35)

3.452*

(0.03)

4.449**

(0.00)

8.799**

(0.00)

3.122*

(0.03)

5.677**

(0.00)




Table IV. 52 KMO and Bartlett's Test of Determinants of LEV of Medium Income Size Firms

of Food Industry in India




df 45

Sig. .000


The KMO (see table IV. 52) measure (0.36) is not satisfactory to conduct a factor analysis, however, the Bartlett’s test is highly significant (188.22), encouraging to conduct factor analysis. Factors of medium income size firms (see table IV. 53) comprises of LEV_TD, LEV_LTD, GROW, NDTXSH and LEV_STD of factor 1, while factor 2 constitutes SIZ, VOL and AG. Factor 3 constitutes COLASS and P.

Table IV. 53 Factor Analysis of Determinants of LEV of Medium Income Size Firms of Food

Industry in India

Factor Eigen value Variable

convergence

Factor

loadings

Factor 1 3.327

LEV_TD 0.920

LEV_LTD 0.918

GROW 0.630

NDTXSH 0.563

LEV_STD 0.465

Factor 2 1.865

SIZ 0.887

VOL -0.617

AG -0.601

Factor 3 1.664 COLASS -0.858

P 0.842




IV.17.3 Analysis of High Income Size Firms The trend line of high income size firms shows (see chart IV.N) that they

have a steady rise in external borrowings, which is needed to meet the growth opportunities. The level of P follows a steady line without much of fluctuations, which fact shows their well established position.

Chart IV.N Trend Line Showing Relation between P and LEV_TD of High Income Size

Firms


The descriptive statistics (see table IV. 54) of high income size firms show comparatively a lesser standard deviation in case of LEV when compared to that of the firms under other categories, which shows that firms grouped as higher income size firms have more or less same size of external borrowings. The P and GROW show a lesser standard deviation. The AG, however, shows a higher level of standard deviation, indicating that the firms under the category of higher income size firms are also varied with AG.

The correlation results of high income size firms (see table IV. 55) show that SIZ has significant positive correlation (0.70) with LEV_STD. AG has insignificant negative relation with LEV, while P has significant negative relation with LEV_LTD (-0.60) at 10% level, which reveals that the negative relation between P and LEV increases with SIZ (though the results are not significant at 1% and 5% levels).

0.658

0.573

0.406

0.446

0.492

0.479

0.751

0.758

1.0350.888

14.787

16.308

16.96117.401

21.12622.168

24.71426.207

35.76837.958

0

5

10

15

20

25

30

35

40

45

LEV_TD

P



Table IV. 54 Descriptive Statistics of Determinants of LEV of High Income Size Firms of Food

Industry in India


LEV_STD 10 1.475 41.859 11.426 1.176

LEV_LTD 10 .354 27.648 11.914 1.029

LEV_TD 10 6.009 69.506 23.340 2.020

VOL 10 .013 .207 .063 .058

COLASS 10 .165 .615 .347 .146

NDTXSH 10 .011293 .051 .030 .014

P 10 .211 1.389 .649 .357

SIZ 10 2.365 3.677 2.964 .365

AG 10 16 51 31.30 13.284

GROW 10 .070 .381 .167 .094


The regression result shows (see table IV. 56) that NDTXSH (-419.09) and P (-22.59) have significant negative coefficient with LEV_STD in model 2 after removing COLASS and AG. SIZ has highly significant coefficient with LEV_STD (25.86) in models 2 & 3 (28.13) after removing VOL in addition to the variables removed under model 2 of LEV_STD. P also has highly significant negative coefficient with LEV_STD (-20.15) under model 3 of LEV_STD with Adj-R2 value 0.75, in model 1 of LEV_STD, 0.84 in model 2 and 0.85 in model 3. Hence, the predictor variables could predict the dependent variable, LEV_STD above 50%, thereby it leads to conclude that P, SIZ and NDTXSH are the major predictors of LEV_STD in case of high income size firms.

P (-30.80) and GROW (-74.58) have highly significant negative co-efficient with LEV_LTD in model 2 after removing the predictor variables SIZ and AG, which fact shows that P and growth in total asset have negative effect on LEV when their income grows. NDTXSH (-590.83) and VOL (163.73) have significant negative coefficient with LEV_LTD in case of higher income size firms, which is supported by F-stat value (25.43). P also has highly significant negative coefficient with LEV_TD (-54.96) in model 2, after removing COLASS and AG, which emphasizes the impact of income size on the relation between P and LEV_TD. On the other hand, SIZ has highly significant positive coefficient



(26.88) in model 2 of LEV_TD. GROW (-114.88), NDTXSH (-911.17) and VOL (-170.75) on the other hand has negative coefficient with LEV_TD (GROW and NDTXSH significant at 1% level and VOL significant at 5% level). Adj R2 value is 0.95, which leads to infer that the model fit is over 90% in model 2 indicating the key predictors of LEV_TD in case of high income size firms.

Table IV. 55 Correlation Matrix of Determinants of LEV of High Income Size Firms of Food

Industry in India

Variables LEV _STD

LEV _LTD


LEV_STD 1

LEV_LTD .677* 1

(.03)

LEV_TD .927** .904** 1

(.00) (.00)

VOL 1

COLASS .789** 1

(.00)

NDTXSH .733* 1

(.016)

P 1

SIZ .701* 1

(.02)

AG 1

GROW 1




Table IV. 56 Results of Regression on Determinants of LEV of High Income Size Firms of


Variables


LEV in terms of short term debt LEV in terms of

long term debt

LEV in terms of total

debt

Model 1 Model 2 Model 3 Model 1 Model 2 Model 1 Model 2

(Constant) -28.657

(0.46)

-29.378

(0.22)

-41.526

(0.02)

61.335

(0.09)

65.966

(0.00)

32.678

(0.36)

36.329

(0.15)

VOL -63.497

(0.54)

-34.869

(0.44)

-

-160.963

(0.10)

-163.726*

(0.01)

-224.460

(0.10)

-170.745*

(0.01)

COLASS 14.186

(0.83)

-

-

19.542

(0.65)

18.035

(0.52)

33.728

(0.58)

-

NDTXSH -521.224

(0.29)

-419.090*

(0.04)

-416.39*

(0.02)

-604.593

(0.12)

-590.826*

(0.02)

-1125.817

(0.07)

-911.166**

(0.00)

P -24.750

(0.16)

-22.592*

(0.01)

-20.15**

(0.00)

-30.997

(0.05)

-30.803**

(0.00)

-55.747*

(0.03)

-54.959**

(0.00)

SIZ 24.350

(0.08)

25.866**

(0.00)

28.128**

(0.00)

1.254

(0.81)

-

25.603

(0.06)

26.876**

(0.00)

AG 0.142

(0.62)

-

-

0.017

(0.92)

-

0.159

(0.53)

-

GROW -35.622

(0.37)

-39.470

(0.18)

-30.058

(0.21)

-73.286

(0.06)

-74.576**

(0.00)

-108.908

(0.05)

-114.875**

(0.01)

R2 0.945 0.931 0.918 0.971 0.969 0.986 0.976

Adj-R2 0.751 0.844 0.853 0.872 0.931 0.935 0.946

F Stat 4.876

(0.18)

10.734*

(0.02)

14.080**

(0.00)

9.732

(0.09)

25.425**

(0.00)

19.490*

(0.05)

32.592**

(0.00)


However, factor analysis could not be conducted as the KMO result shows that the correlation coefficient is not positive definite.



IV.18 Sector-wise Analysis of Determinants of LEV

Since there are more number of sectors with less number of firms, the firms are grouped by combining into three sectors constituting related firms. Sector I constitutes 32 vegetable oil firms; Sector II constitutes 30 firms which include 9 firms of tea sector, 11 firms of dairy sector, and 10 firms of sugar sector; Sector III constitutes 24 firms comprising of miscellaneous sectors which include coffee, cocoa products & confectionery, bakery products, processed /packaged foods, starches, marine food, poultry & meat product, floriculture, milling products, and other agricultural products.

IV.18.1 Analysis of Sector I The trend line of relation between P and LEV_TD of sector I shows (see

chart IV.O) that the level of LEV_TD is quite steady without any sudden plunge though there is an abrupt fall in P during the year 2008-09, Therefore, the firms in sector I are able to meet circumstantial changes.

Chart IV.O Trend Line Showing Relation between P and LEV_TD of Firms of Sector I


The descriptive statistics (see table IV. 57) of the firms in sector I show that LEV_STD and LEV_LTD have higher standard deviation but the standard deviation of LEV_TD is comparatively very less, hence though the firms in sector I have varied level of STD and LTD, their TD remains more or less closer

0.165

0.218

0.239

0.265

0.29

0.194

0.241

0.351

0.6020.274

5.807

6.409

7.412

8.00211.23

11.265

11.183

12.47

17.1718.935

0

5

10

15

20

25

LEV_TD

P



to its mean values. The AG shows a higher standard deviation, indicating that a wide range of firms with different AG comes under sector I. The NDTXSH also has higher standard deviation, indicating the varied depreciation policy used by the firms.

Table IV. 57 Descriptive Statistics of Determinants of LEV of Firms of Sector I of Food

Industry in India


LEV_STD 32 .086 41.859 5.040 7.819

LEV_LTD 32 .093 27.648 5.948 7.069

LEV_TD 32 .326 69.506 10.988 1.374

VOL 32 .013 .570 .0744 .105

COLASS 32 .034 .739 .343 .137

NDTXSH 32 -4.89 16.005 .701 3.319

P 32 -.349 .923 .284 .258

SIZ 32 -.845 3.677 2.006 .847

AG 32 13 53 22.28 8.509

GROW 32 -.061 .422 .099 .129


The correlation matrix (see table IV. 58) of the firms in sector I shows that GROW has highly significant positive correlation with LEV_STD (0.48), LEV_LTD (0.65) and LEV_TD (0.61). SIZ also has highly significant positive correlation with LEV_STD (0.57), LEV_LTD (0.55) and LEV_TD (0.61), while the other variables have insignificant correlation with LEV, P has insignificant positive correlation with LEV.

The multiple regression result of sector I (see table IV. 59) shows that SIZ has significant positive co-efficient with LEV_STD in model 2 (3.75) after removing VOL, NDTXSH and P. The Adj-R2 value in model 2 of LEV_STD is 0.27, which shows that the model poorly fits for LEV_STD. GROW shows highly significant positive coefficient with LEV_LTD in models 1 (33.22) & 2 (27.20) after removing variables VOL, NDTXSH, P and AG. SIZ has significantly positive coefficient (2.98) with LEV_LTD in model 2. The model fit



is about 50% (the Adj– R2 value 0.49) showing that the predictor variables in the model 2 are 50% determinants of LEV_LTD of sector I. SIZ has significant positive coefficient in models 1 (6.80) & 2 (5.89) of LEV_TD. GROW has also a significant positive coefficient in models 1 (48.27) & 2 (44.49) of LEV_TD. The model fits only 40% (Adj-R2 value is 0.45) in case of LEV_TD. The F-stat value is however significant in all cases indicating that the variance of the explaining variables is significantly related to the variance in the LEV.

Table IV. 58 Correlation Matrix of Determinants of LEV of Firms of Sector I of Food Industry

in India

Variables LEV _STD

LEV _LTD


LEV_STD 1

LEV_LTD .703** 1

(.00)

LEV_TD .931** .914** 1

(.00) (.00)

VOL 1

COLASS 1

NDTXSH 1

P 1

SIZ .570** .547** .606** 1

(.00) (.00) (.00)

AG 1

GROW .480** .651** .608** -.396* .566** 1

(.00) (.00) (.00) (.02) (.00)




Table IV. 59 Results of Regression on Determinants of LEV of Firms of Sector I of Food

Industry in India

Variables






(Constant) -3.177 (0.65)

-3.744 (0.52)

-10.741 (0.04)

-7.575 (0.06)

-13.918 (0.21)

-9.356 (0.17)

VOL -1.245 (0.93)

-

10.280 (0.38)

-

9.035 (0.71)

-

COLASS -4.093 (0.68)

-3.727 (0.69)

13.278 (0.08)

14.153 (0.05)

9.185 (0.56)

-

NDTXS -0.042 (0.92)

-

0.136 (0.67)

-

0.093 (0.89)

-

P -.827 (0.89)

-

-3.221 (0.46)

-

-4.048 (0.66)

-

SIZ 3.712 (0.08)

3.749* (0.04)

3.088 (0.05)

2.977* (0.03)

6.801* (0.04)

5.887* (0.03)

AG .047 (0.76)

0.048 (0.74)

0.121 (0.29)

-

0.168 (0.49)

0.185 (0.41)

GROW 15.051 (0.27)

14.930 (0.20)

33.217** (0.00)

27.198** (0.00)

48.268* (0.03)

44.490* (0.019)

R2 0.369 0.367 0.576 0.538 0.497 0.484

Adj-R2 0.184 0.274 0.453 0.489 0.350 0.428

F Stat 2.001 (0.09)

3.918* (0.012)

4.663** (0.00)

10.890** (0.00)

3.382* (0.012)

8.738** (0.00)


The KMO result shows that the correlation was not positive definite, and the data are not appropriate for a factor analysis. IV.18.2 Analysis of Sector II

The trend line of relation between P and LEV_TD of firms of sector II shows (see chart IV.P) that the level of borrowing took a deep plunge in the year



2008-09 as in the case of small size and low income size firms, hence, sector I constitutes firms which are comparatively smaller in size, and their external borrowing is affected by environmental changes.

Chart IV.P Trend Line Showing Relation between P and LEV_TD of Firms of Sector II


The descriptive statistics (see table IV. 60) of firms in sector II show that

the standard deviation is high in case of LEV_STD but in case of LEV_LTD and

LEV_TD it is comparatively lesser. Therefore, the firms in sector II have wider

variation in their short term borrowings while the size of outside debt, however,

remains same among the firms in sector II. The other variables have

comparatively lesser deviation. AG, however, as that of the other categories,

shows a higher standard deviation.

The correlation matrix of firms in sector II shows (see table IV. 61) that

AG has highly significant positive correlation (0.56) with LEV_STD, while P

(0.39) and SIZ (0.42) have significant positive correlation with LEV_STD.

GROW has significant positive correlation with LEV_LTD (0.51) at 1% level,

and LEV_TD (0.42) at 5% level.

0.281

0.23

0.195

0.219

0.257

0.269

0.409

0.3940.431

0.542

7.408

8.321

8.3599.472

10.64

10.74

11.785

15.98

24.69321.475

0

5

10

15

20

25

30

LEV_TD

P



Table IV. 60 Descriptive Statistics of Determinants of LEV of Firms of Sector II of Food

Industry in India


LEV_STD 30 .081 25.727 5.008 5.693

LEV_LTD 30 .149 48.860 7.937 1.084

LEV_TD 30 .23 57.282 12.887 1.470

VOL 30 .009 .289 .063 .056

COLASS 30 .165 .836 .499 .177

NDTXSH 30 .007 .098 .031 .019

P 30 -.017 1.389 .323 .328

SIZ 30 -.905 3.385 1.976 .925

AG 30 15 52 29.03 11.340

GROW 30 -.066 .463 .093 .122


The multiple regression result of firms of sector II shows (see table IV. 62)

that AG has significant coefficient with LEV_STD (0.25*) in models 1 & 2

(0.24**). SIZ has significant coefficient (2.099) with LEV_STD. GROW has

significant coefficient with LEV_LTD (model 1 (57.54**) & 2 (57.55**)) as well

as in LEV_TD (model 1 (58.15*) & 2 (57.74*)), hence, GROW has been a

significant determinant of size of external borrowings of firms of sector II.

However, the Adj-R2 value is insignificant in all the models, revealing that the

regression model fits poorly for firms in sector II.

Kaiser-Meyer-Olkin measure (see table IV. 63) is not adequate (0.44) to

support a factor analysis. However, Bartlett’s Test of sphericity is significant at

1% level, indicating that the correlation matrix is not an identity matrix and

factor analysis could well be conducted.



Table IV. 61 Correlation Matrix of Determinants of LEV of Firms of Sector II of Food

Industry in India

Variables LEV _STD

LEV _LTD


LEV_STD 1

LEV_LTD .540** 1

(.00)

LEV_TD .785** .945** 1

(.00) (.00)

VOL 1

COLASS 1

NDTXSH .418* 1

(.02)

P .388* -.675** 1

(.03) (.00)

SIZ .415* -.554** .541** 1

(.02) (.00) (.00)

AG .556** .458* 1

(.00) (.011)

GROW .510** .418* .394* 1

(.00) (.02) (.03)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Figures in parentheses denote p value.



Table IV. 62 Results of Regression of Determinants of LEV of Firms of Sector II of Food

Industry in India

Variables






(Constant) -8.415 (0.18)

-5.546 (0.09)

-7.195 (0.56)

-4.839 (0.62)

-15.408 (0.36)

-15.505 (0.31)

VOL 7.477 (0.70)

-

6.617 (0.86)

- 16.737 (0.75)

-

COLASS 3.894 (0.66)

-

14.229 (0.42)

18.395 (0. .20)

18.003 (0.46)

21.920 (0.27)

NDTXS -43.570 (0.53)

-19.439 (0.69)

-197.064 (0.16)

-220.602 (0.07)

-254.113 (0.18)

-246.062 (0.15)

P 0.158 (0.97)

-

-4.630 (0.61)

-

-4.556 (0.71)

-

SIZ 2.517 (0.08)

2.099* (0.04)

2.510 (0.36)

2.526 (0.32)

5.054 (0.18)

4.723 (0.19)

AG 0.249* (0.02)

0.241** (0.00)

0.166 (0.41)

-

0.417 (0.14)

0.354 (0.12)

GROW 0.848 (0.92)

-

57.538** (0.00)

57.551** (0.00)

58.154* (0.02)

57.742* (0.02)

R2 0.424 0.415 0.375 0.355 0.370 0.364

Adj-R2 0.241 0.347 0.176 0.251 0.169 0.232

F Stat 2.318 (0.06)

6.147** (0.00)

1.883 (0.12)

3.436* (0.02)

1.844 (0.12)

2.751* (0.04)


The combination of variables as factors for sector II shows (see table IV. 64) that factor 1 constitutes LEV_TD, LEV_LTD, and LEV_STD. Factor 2 constitutes P, COLASS, and SIZ. Factor 3 constitutes NDTXSH, VOL, AG and GROW, indicating interrelation between the variables grouped in to factors.



Table IV. 63 KMO and Bartlett's Test of Determinants of LEV of Firms of Sector II of Food

Industry in India




df 45

Sig. .000


Table IV. 64 Factor Analysis of Determinants of LEV of Firms of Sector II of Food Industry in

India Factor Eigen value Variable

convergence Factor

loadings

Factor 1 3.696

LEV_TD .976

LEV_LTD .947

LEV_STD .724

Factor 2 1.823

P .889

COLASS -.853

SIZ .750

Factor 3 1.623

NDTXSH .813

VOL .700

AG -.541

GROW .502


IV.18.3 Analysis of Sector III The trend line revealing the relation between P and LEV_TD of sector III

shows (see chart IV.Q) that there is no steep rise in the size of external borrowings in case of firms in sector III as that of in case of sector II. However, the level of borrowings plunged down in the year 2008-09. The level of P has risen during the same period unlike in other sectors, hence the business environment in this sector has helped the firms to maintain its size of P during the period of crisis.



Chart IV.Q Trend Line Showing Relation between P and LEV_TD of Firms of Sector III


The descriptive statistics (see table IV. 65) of firms in sector III indicate a higher degree of standard deviation in case of LEV_LTD and LEV_TD, indicating that the external long term borrowings of the firms under sector III vary widely as per their requirements, whereas the standard deviation of LEV_STD is comparatively lower, indicating that the firms have more or less the same size of short term borrowings. The deviation in P is comparatively low for all the firms.

Table IV. 65 Descriptive Statistics of Determinants of LEV of Firms of Sector III of Food

Industry in India


LEV_STD 24 .036 3.904 .991 1.070

LEV_LTD 24 .089 18.249 4.008 5.428

LEV_TD 24 .186 20.047 5.005 6.261

VOL 24 .009 .759 .127 .166

COLASS 24 .099 .831 .477 .206

NDTXSH 24 .014 .125 .043 .029

P 24 -.278 1.008 .259 .281

SIZ 24 -.413 2.804 1.373 .823

AG 24 15 48 20.42 7.751

GROW 24 -.174 .316 .037 .122


0.248

0.285

0.163

0.1960.267

0.237

0.186

0.27

0.2710.373

3.888

3.822

3.961

4.1934.078

5.055.174

6.3427.226

6.319

012345678

LEV_TD

P



The correlation matrix of sector III shows (see table IV. 66) that SIZ has highly significant correlation with LEV_STD (0.54), with LEV_LTD (0.71) as well as with LEV_TD (0.71). The GROW has a significant correlation with LEV_LTD (0.46) as well as with LEV_TD (0.44). While P has significant positive correlation with LEV_LTD (0.41), NDTXSH has significant negative correlation (-0.42) with LEV_STD. P, having significant positive correlation (0.41) with LEV_LTD, matches with medium income size firms correlation result, therefore the firms in sector III earns medium income which is not adequate to utilize the growth opportunities and therefore they borrow from external sources as they grow (as their P increases).

Table IV. 66 Correlation Matrix of Determinants of LEV of Firms of Sector III Food Industry in India

Variables LEV _STD

LEV _LTD


LEV_STD 1

LEV_LTD .742 1 (.00)

LEV_TD .815 .993 1 (.00) (.00)

VOL 1

COLASS 1

NDTXSH -.424 -.415 .532 1 (.03) (.04) (.00)

P .405 -.518 -.644 1 (.05) (.01) (.00)

SIZ .544 .712 .710 -.498 -.598 .655 1 (.00) (.00) (.00) (.013) (.00) (.00)

AG 1

GROW .459 .441 -.486 -.437 .446 .676 1 (.02) (.03) (.016) (.03) (.02) (.00)




The regression result of firms in sector III shows (see table IV. 67) that though P is correlated with LEV, it is not a significant determinant of LEV. P has no significant coefficient with LEV, and it is not a significant determinant of LEV. The regression run shows that P has insignificant negative coefficient with LEV. SIZ has significant coefficient (0.77) with LEV_STD in model 2 after removing the effects of COLASS, NDTXSH and P. It also has significant positive coefficient (5.95) with LEV_LTD in model 1 and highly significant coefficient (5.45) with LEV_LTD in model 2. SIZ has significant coefficient with LEV_TD (6.74) in model 1 and highly significant coefficient with LEV_TD (6.50) in model 2 after removing COLASS, NDTXSH and GROW.

Table IV. 67 Results of Regression on Determinants of LEV of Firms of Sector III of Food


Un-standardized Coefficients Beta Value LEV in terms of short term debt



Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 (Constant) 0.192

(0.85) 0-.528 (0.42)

-3.650 (0.47)

-2.063 (0.36)

-3.481 (0.55)

-1.858 (0.47)

VOL -1.749 (0.26)

-1.571 (0.23)

-4.654 (0.52)

-3.787 (0.56)

-6.399 (0.45)

-5.973 (0.42)

COLASS -0.838 (0.59)

-

2.531 (0.73)

-

1.784 (0.83)

-

NDTXS -2.084 (0.83)

-

7.232 (0.88)

-

4.774 (0.93)

-

P -0.953 (0.44)

-

-2.556 (0.66)

-3.068 (0.49)

-3.468 (0.61)

-4.054 (0.43)

SIZ 0.792 (0.09)

0.771* (0.02)

5.951* (0.014)

5.454** (0.00)

6.743* (0.01)

6.499** (0.00)

AG 0.038 (0.18)

0.038 (0.14)

-0.029 (0.82)

-

0.008 (0.95)

-

GROW -2.635 (0.30)

-2.921 (0.19)

-5.159 (0.66)

-3.814 (0.71)

-7.848 (0.57)

-6.767 (0.56)

R2 0.473 0.436 0.531 0.523 0.533 0.530 Adj-R2 0.242 0.317 0.326 0.422 0.329 0.431 F Stat 2.050

(0.11) 3.666* (0.02)

2.587 (0.05)

5.205** (0.00)

2.610 (0.05)

5.357** (0.00)




The Kaiser-Meyer-Olkin measure (see table IV. 68) is 0.60, indicating that the factor analysis will be able to converge the variables into reasonable factors.

Bartlett’s Test also has highly significant χ2 value (308.44), encouraging to run of factor analysis.

Table IV. 68 KMO and Bartlett's Test of Determinants of LEV of Firms of Sector III of Food

Industry in India




df 45

Sig. .000


The factor analysis result shows (see table IV. 69) that LEV_TD, LEV_LTD and LEV_STD converges into factor1; P, COLASS, VOL, SIZ, GROW, NDTXSH converges into factor 2 and AG constitutes factor 3 as it has no significant interrelation with other variables.

Table IV. 69 Factor Analysis of Determinants of LEV of Firms of Sector III of Food Industry in India


Factor loadings

Factor 1 4.961

LEV_TD 0.935

LEV_LTD 0.910

LEV_STD 0.859

Factor 2 1.423

P .831

COLASS -.768

VOL -.679

SIZ .649

GROW .594

NDTXSH -.555

Factor 3 1.027 AG .803




IV.19 Conclusion

The analysis conducted to study the determinants of CS and the endeavor to

draw attention on the impact of size and sectoral difference have proved to be

interesting and some significant conclusion is drawn about the food industry.

IV.19.1 Overall Analysis

The overall results of equation I show that GROW, AG, and SIZ have

significant positive correlation with LEV_STD, LEV_LTD and LEV_TD while, P has

significant positive correlation with LEV_STD, which fact coincides with that of Long

and Malitz (1985)71, Pandey (2004)72 who found a positive relation between P and

LEV. However, the results of Myers (1984)73, Titman and Wessels (1988)74, Kester

(1986)75, Friend and Hasbrouch (1988)76, Friend & Lang (1988)77, and Chen and

Zhao (2004)78 who found a negative impact between P and LEV has been untrue in

case of food industry in India. Thus, the hypothesis Ho8 is rejected. While, SIZ, AG, and

GROW have highly significant positive correlation with LEV_STD, LEV_LTD, &

LEV_TD, COLASS has significant negative correlation with LEV_STD which coincides

with the findings of Titman and Wessels (1988) 79. On the other hand, VOL has significant

negative correlation with LEV_TD. Hence the hypotheses Ho10, Ho

11, and Ho12 are rejected

and Ho9 which states that, “there is no significant relation between non debt tax shield and

leverage” is accepted.



Table IV.70 Summary of Overall Results of the Determinants of LEV

Hypotheses LEV_STD LEV_LTD LEV_TD Supporting works

Ho8 = “There is no significant

relationship between profitability

and leverage”.

+ve*

Rejected Accepted

+ve*

Rejected

Long and Malitz (1985) and Pandey (2004) predicted a positive

relation between CS and P. Myers (1984), Titman and Wessels

(1988), Barton and Gordon (1988), Johnson (1998), Booth Collins

et al. (2001), and Fama and French (2002) argued that there is a

negative relationship between P and LEV.


relationship between non debt

tax shield and leverage”.

Accepted Accepted Accepted

Fisher, Heinkel, and Zechner (1989) provided evidence that tax

benefits to debt are mostly negligible.


relationship between collateral

assets and leverage”.

-ve*

Rejected Accepted Accepted

Hutchinson and Michaelas (1998) and Titman and Wessels (1988)

identified negative relation between collateralizable capital and debt

level.

Ho11= “There is no significant

relationship between growth and

leverage”.

+ve**

Rejected

+ve**

Rejected

+ve**

Rejected

Barton and Gordon (1988) work provided evidence that GROW

rate is positively correlated with debt.


relationship between volatility

and leverage”.

Accepted Accepted -ve*

Rejected

Johnson (1997), and Titman and Wessels (1988) identified negative

relation between volatility and debt level.




IV.19.2 Sales Size-wise Analysis

The sales size wise analysis shows that in small size firms GROW, AG and SIZ have significant correlation with LEV_STD, LEV_LTD and LEV_TD and hence the hypotheses Ho

11, “there is no significant relationship between growth and leverage” is rejected. P has insignificant positive correlation with LEV, hence the hypothesis Ho

8 “there is no significant relation between P and LEV” is accepted in case of small size firms, thus the findings by Leland (1994) 80, Kane, Marcus, and MacDonald (1984) 81 and Wiggins (1990) 82 in respect of LEV is invariant to changes in P in case of small size firms.

The predictor variable P has no significant correlation with the dependent variables in case of medium size firms; therefore the hypothesis Ho

8 is accepted. However, VOL has significant negative correlation with LEV_TD substantiating the rejection of Ho

12. SIZ, AG and GROW have significant coefficients with LEV_LTD in regression model 3, and the model fit is about 40% in LEV_LTD.

The correlation coefficient of large size firms shows that there exists no significant correlation between predictor variables and the dependent variables, except for SIZ; hence the hypotheses Ho

8, Ho9, Ho

10, Ho11, and Ho

12 are accepted. SIZ has a significant coefficient with LEV_STD in case of large size firms. The impact of size on the relation between P and LEV cannot be brought to light as their relation is insignificant in all the categories viz., small size, medium size and large size firms and therefore the hypothesis Ho

13 “there is no significant influence of size of firms in deviating the relationship between profitability and capital structure” is accepted in case of sales size wise analysis.



Table IV.71 Summary of Sales Size-wise Analysis of the Determinants of LEV

Hypotheses Small Size Firms’ LEV Medium Size Firms’ LEV Large Size Firms’ LEV STD LTD TD STD LTD TD STD LTD TD


significant relationship between profitability and leverage”.

Accepted Accepted Accepted Accepted Accepted Accepted Accepted Accepted Accepted


significant relationship between non debt tax shield and leverage”.



significant relationship between collateral assets and leverage”.


Ho11= “There is no

significant relationship between growth and leverage”.

+ve* Rejected

+ve** Rejected

+ve** Rejected

Accepted +ve*

Rejected Accepted Accepted Accepted Accepted


significant relationship between volatility and leverage”.

Accepted Accepted Accepted Accepted Accepted -ve*

Rejected Accepted Accepted Accepted




IV.19.3 Income Size-wise Analysis However, income size wise analysis put forth different results. SIZ, AG

and GROW have significant positive correlation with LEV_STD, LEV_LTD and LEV_TD in case of low income size firms. There exists an insignificant positive correlation between P and LEV as that of the small size firms; however the medium income size firms show that there exists a significant positive correlation between P & LEV_LTD, as well as between P & LEV_TD, which result supports the overall result. Thus, hypothesis Ho

8 is rejected. However, the analysis of high income size firms shows that P has insignificant negative correlation with LEV and significant negative coefficient with LEV_ LTD and LEV_TD in the regression models; hence the hypothesis Ho

13 that “there is no significant influence of size of firms in deviating the relationship between profitability and capital structure” is rejected. Thus, the negative relation between P and LEV increases with SIZ as pointed out by Rajan and Zingales (1995) 83 and Titman and Wessels (1988) 84. The regression model fits best in case of high income size firms with the adj-R2 value of above 80%. The summary of results is shown in table IV.72.

IV.19.4 Sector-wise Analysis The sector wise analysis shows that in sector I, GROW and SIZ are the

major determinants of LEV_STD, LEV_LTD and LEV_TD; thus the hypothesis Ho

11 is rejected. In sector II, GROW and SIZ are the major determinants of LEV_LTD and LEV_TD and hence the hypothesis Ho

11 is rejected, while SIZ, AG and P have significant positive correlation with LEV_STD leading to the rejection of Ho

8 in case of sector II firms. However, in sector III P has significant positive correlation with LEV_LTD. GROW and SIZ are other predictor variables having significant correlation with LEV. Thus, there are various determinants in each sector that are considered as the major determinants in each sector, indicating the significant impact of sectoral differences as indicated by Barton, Hill, and Sundaram (1989) 85. Therefore, Ho

14 that “there is no significant influence of sectoral differences of firms in deviating the relationship between profitability and capital structure” is rejected. The major findings are summarised in table IV.73.



Table IV.72 Summary of Income Size-wise Analysis of the Determinants of LEV

Hypotheses Low Income Size Firms’ LEV Medium Income Size Firms’ LEV High Income Size Firms’ LEV STD LTD TD STD LTD TD STD LTD TD



Accepted Accepted Accepted Accepted +ve*

Rejected +ve*

Rejected -ve*

Rejected -ve**

Rejected -ve**

Rejected



Accepted Accepted Accepted Accepted Accepted Accepted -ve*

Rejected -ve*

Rejected -ve*

Rejected






Accepted +ve**

Rejected +ve**


+ve* Rejected

-ve* Rejected

-ve** Rejected

-ve** Rejected



Accepted Accepted Accepted Accepted -ve*

Rejected -ve*





Table IV.73 Summary of Sector-wise Analysis of the Determinants of LEV

Hypotheses Sector I Firms’ LEV Sector II Firms’ LEV Sector III Firms’ LEV STD LTD TD STD LTD TD STD LTD TD



Accepted Accepted Accepted +ve*


+ve* Rejected

Accepted



Accepted Accepted Accepted Accepted Accepted Accepted -ve*

Rejected Accepted

-ve* Rejected






+ve** Rejected

+ve** Rejected

+ve** Rejected

Accepted +ve**

Rejected +ve*

Rejected Accepted

+ve* Rejected

+ve* Rejected







IV.20 Regression on Determinants of P (Equation II)

Equation II is formulated for the purpose of finding out the factors determining P. The term P has been defined as the average rate of return on assets (ROA) by Lowe Naughton, and Taylor (1994)86.

P_TASSET = PBITD / Total Assets

Profit margin also has been used as another dependent variable to measure profitability.

P_SAL = PBITD /Sales.

IV.21 Hypotheses Development

There are various variables determining P of the firm. The variables that are analysed as predictors in this study are aggressive investing policy (AIP), capital intensity (CAPINS), volatility (VOL), growth (GROW), and size (SIZ). AIP increases the profit of the firms hence, it becomes important to study the relation between AIP and P. CAPINS is associated with difference in P (Bettis 1981)87. P declines with CAPINS (Ghemawat and Caves 1986)88 and hence it is also analysed as a variable determining P. Volatility (VOL) in earning should be studied when considering consistent profit earning capacity. The relation between GROW and P is also analysed. SIZ of the firm also influences its P. Small enterprises are characterized by variability in profits and growth hence, SIZ is argued to be another predictor of P. The hypotheses are thus:

Ho15 = “There is no significant relationship between aggressive investment policy

and profitability of the firms”.

Ho16 = “There is no significant relationship between capital intensity and profitability

of the firms”.

Ho17 = “There is no significant relationship between volatility and profitability of the

firms”.

Ho18= “There is no significant relationship between growth and profitability of the

firms”.



To study the impact of SIZ and sectoral differences the following hypotheses are assumed in addition.

Ho19 = “There is no significant influence of size in deviating the relationship between

the predictor variables and profitability of the firms”.

Ho20= “There is no significant influence of sectoral differences in deviating the

relationship between predictor variables and profitability of the firms”.

The regression equation for analysis of determinants of P is as follows:

P = α + β1 AIP + β2 CAPINS+ β3 SIZ + β4 GROW + β5 VOL + Є

Table IV.74 Ratios of Independent Variables Determining P

Variables Description Inference P_TASSET PBITD / Total Assets It indicates the return on assets invested.

High value denotes large return on asset and vice versa

P_SAL PBITD / Sales It indicates the profit margin earned on turnover of firm. A high value implies a great profit margin and vice versa

AIP Current Assets / Total Assets It indicates the proportion of current assets to total assets. A low value indicates more aggressive use of assets for increasing earnings and vice versa

CAPINS Total Assets / Sales It indicates how intensively the assets are used to increase turnover. A low value indicates large turnover for the investment in assets and vice versa

SIZ Logarithm of Sales over Years Turnover adjusted for fluctuation over years GROW Compounded annual growth rate

(CAGR) of total assets The growth of total asset over years






IV.22 Overall Analysis of Determinants of P

The trend line between P_SAL as well as P_TASSET of firms in food industry shows (see chart IV.R) that P in terms of ROA has been steady over the period of study. However, the profit margin (P_SAL) had a steep descend for the years 2004-05 and 2005-06. This was due to a big drop in agricultural production during this period.

The descriptive statistics of the overall firms show (see table IV. 75) that the standard deviation of CAPINS is high and dependent P_SAL also has a higher standard deviation when compared to that of the other dependent variable, P_TASSET, which indicates that the intensity with which the assets are employed for production has a possible relation with the profit margin of the firms.

Chart IV.R Overall Trend Line Showing Relation between P_SAL and P_TASSET


The correlation result (see table IV. 76) shows that VOL has highly significant negative correlation (-0.33) with P_TASSET while GROW (0.42) and SIZ (0.50) have highly significant positive correlation with P_TASSET. The impact of VOL on P is put to light through this correlation matrix. On the other hand, CAPINS shows a highly significant negative correlation (-0.91) with P_SAL, and SIZ has significant positive correlations (0.27) with P_SAL.

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

P_SAL

P_TASSET



Table IV. 75

Overall Descriptive Statistics of Determinants of P of Food Industry in India


P_TASSET 86 -.28 .431 .084 .082

P_SAL 86 -7.644 2.239 .020 .877

AIP 86 .060 .894 .482 .194

CAPINS 86 .154 235.270 5.392 2.586

SIZ 86 -.905 3.677 1.819 .903

GROW 86 -.174 .463 .080 .126

VOL 86 .009 .759 .085 .115


Table IV. 76 Overall Correlation Matrix of Determinants of P of Food Industry in India

Variables P_TASSET P_SAL AIP CAPINS SIZ GROW VOL

P_TASSET 1

P_SAL 1

AIP 1

CAPINS -.910 1

(.00)

SIZ .502 .266 .447 -.430 1

(.00) (.01) (.00) (.00)

GROW .420 .276 .558 1

(.00) (.01) (.00)

VOL -.327 -.299 -.348 1

(.00) (.00) (.00)




The overall regression result shows (see table IV. 77) that AIP has highly

significant negative coefficient with P_TASSET in models 1 (-0.13) & 2 (-0.12) after

removing the effects of CAPINS and GROW. SIZ has highly a significant positive

coefficient with P_TASSET in model 1 (0.05) & 2 (0.05). CAPINS is one of the

major variables determining P_SAL, which has highly significant coefficient with

P_SAL in models 1(-0.03) & 2 (-0.03) after removing GROW and VOL. AIP has

significant negative coefficient (-0.43) with P_SAL in model 2. SIZ has a significant

negative coefficient (-0.11) with P_SAL in model 2 while it has a positive impact on

P_TASSET. The Adj-R2 value for P_TASSET is only about 33% (0.33 each in

model 1 and model 2 respectively), indicating that the predictor variables determine

the dependent variable P_TASSET to the extent of 33% only and there are other

predictors influencing P_TASSET. The Adj-R2 value for P_SAL is about 84% (0.85

in model 1 and 0.85 in model 2), indicating that the regression model fits above

84% for P_SAL. The F-stat is also highly significant in all the cases, indicating that

the variance in the explaining variables is assisted by variance in the dependent

variables.

The Kaiser-Meyer-Olkin measure (see table IV. 78) is 0.58, hence the factor

analysis conducted would give a statistically significant result. Bartlett’s test χ2 value

(284.84) is highly significant, stating that the correlation matrix is not an identity

matrix and factor analysis shall be conducted with these variables.

The factor analysis (see table IV. 79) converges P_TASSET, GROW, VOL

and SIZ as factor 1, indicating the interrelation among them. P_SAL and CAPINS

are converged as factor 2 stressing the relation between P_SAL and CAPINS which

was also put forth by the overall correlation and regression results. AIP constitutes

factor 3, indicating that it has a lesser relation with the other variables.



Table IV. 77 Overall Results of Regression on Determinants of P of Food Industry in India

Variables


Dependent variable P_TASSET Dependent variable P_SAL

Model 1 Model 2 Model 1 Model 2

(Constant) 0.064 (0.01)

0.062 (0.01)

0.534 (0.00)

0.593 (0.00)

AIP -0.125** (0.00)

-0.124** (0.00)

-0.412 (0.05)

-0.426 (0.04)

CAPINS 0.00004536 (0.88)

- -0.033** (0.00)

-0.033** (0.00)

SIZ 0.045** (0.00)

0.052** (0.00)

-0.086 (0.14)

-0.105* (0.04)

GROW 0.108 (0.13)

- -.088 (0.81)

-

VOL -0.129 (0.06)

-0.154* (0.02)

.277 (0.43)

-

R2 0.372 0.354 0.856 0.855

Adj-R2 0.333 0.331 0.847 0.849

F Stat 9.497** (0.00)

14.997** (0.00)

95.126** (0.00)

160.633** (0.00)


Table IV. 78 KMO and Bartlett's Test of Determinants of P of Food Industry in India





df 21

Sig. .000



Table IV. 79 Overall Factor Analysis of Determinants of P of Food Industry in India


Factor loadings

Factor 1 2.721

P_TASSET 0.825

GROW 0.723

VOL -0.679

SIZ 0.644

Factor 2 1.724 P_SAL 0.967

CAPINS -0.957

Factor 3 1.016 AIP 0.944


IV.23 Sales Size-wise Analysis of Determinants of P

The sales size-wise analysis is attempted to bring out the effect of size of firms on P of the firms. The firms are classified on the same basis as that of grouped for equation I. The firms with sales < Rs.100 crore are grouped as ‘small size firms’; the firms with sales > Rs.100 crore but < Rs.500 crore are grouped as ‘medium size firms’; and firms with > Rs.500 crore are grouped as ‘large size firms’. The average sales over a period of 10 years taken for the study is considered for this purpose.

The trend analysis of P_TASSET shows (see chart IV.S) that the P of small size firms show a fall in the period 2005-06, which is similar to the overall result, however, these firms could recover gradual rise in P thereafter. The contradictory is the fact in case of medium size firm which don’t show any deep plunge in the year 2005-06. However, there are constant fluctuations over the period with fall and rise persistently. The medium size firms show that they are not able to make use of their assets effectively during 2006-07, and there is a constant fall in its P_TASSET. The trend line of large size firms with regard to P_TASSET, however, does not show much of its steep fluctuations.



Chart IV.S Trend Line Showing Comparison between P_TASSET of Small Size Firms, Medium

Size Firms and Large Size Firms


The trend line of P_SAL of small size firms shows (see chart IV.T) that the profit margin of the firms has higher degree of fluctuation during the period of study, thus they have the tendency to use opportunities and increase the profit margin and when the situation is not good they reduce their profit margin. This is apparent through the trend line rising steeply during the period 2008-09 above the lines of medium size firms as well as large size firms. On the other hand, the trend lines of medium size firms and large size firms show that they have a stable profit margin over the study period.

Chart IV.T Trend Line Showing Comparison between P_SAL of Small Size Firms, Medium Size Firms

and Large Size Firms


00.020.040.060.08

0.10.120.140.160.18

0.2

P_TASSET FOR SMALL SIZE FIRMS

P_TASSET FOR MEDIUM SIZE FIRMS

P_TASSET FOR LARGE SIZE FIRMS

-1.4-1.2

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

P_SAL FOR SMALL SIZE FIRMS

P_SAL FOR MEDIUM SIZE FIRMS

P_SAL FOR LARGE SIZE FIRMS



IV.23.1 Analysis of Small Size Firms

Descriptive statistics of small size firms show (see table IV. 80) high standard deviation in case of CAPINS, showing validity in intensively employing their capital. The mean of P_SAL is negative, indicating a negative profit margin in case of small size firms. The deviation is, however, higher in case of P_SAL, indicating the varied level of profit margin earned by small size firms.

Table IV. 80 Descriptive Statistics of Determinants of P of Small Size Firms of Food Industry in

India


PTASSET 43 -.280 .179 .054 .075

PSAL 43 -7.645 2.239 -.055 1.240

AIP 43 .060 .894 .432 .202

CAPINS 43 .277 235.270 9.893 3.621

SIZ 43 -.905 1.945 1.154 .768

GROW 43 -.174 .463 .014 .109

VOL 43 .009 .759 .113 .150


The correlation matrix of small size firms shows (see table IV. 81) that VOL has significant negative correlation (-0.35) with P_TASSET, while GROW (0.35) and SIZ (0.37) have significant positive correlation with P_TASSET. With regard to P_SAL, CAPINS has highly significant negative correlation (-0.92) with P_SAL and SIZ has significant positive correlation (0.34) with P_SAL.

The regression result shows (see table IV. 82) that SIZ has highly significant co-efficient (0.04) with P_TASSET in model 2 after removing the effects of CAPINS and VOL. On the other hand, GROW has a significant negative coefficient (-0.16) with P_TASSET in model 2, which indicates that the small size firms use their assets ineffectively for increasing P. CAPINS has highly significant negative coefficient with P_SAL in models 1 (-0.03) & 2 (-0.03), and SIZ has significant



negative coefficient with (-0.28) P_SAL in model 2, indicating that as the sales increases they earn a profit with lesser margin or the small size firms increase their sales by reducing their profit margin. The Adj-R2 value is very low in cases of P_TASSET (models 1 (0.19) & 2 (0.22)), indicating that model fits poorly for P_TASSET in case of small size firms. On a contrary, Adj-R2 value is good in case of P_SAL (models 1 (0.85) & 2 (0.86)), indicating that the regression model fairly fits for P_SAL. The F-Stat in all the cases is highly significant, indicating that the variance in predictor variables is related to the variance in the dependent variable.

Table IV. 81 Correlation Matrix of Determinants of P of Small Size Firms of Food Industry in

India


P_TASSET 1

P_SAL 1

AIP 1

CAPINS -.916** 1

(.00)

SIZ .370* .335* .508** -.508** 1

(.02) (.02) (.00) (.00)

GROW .351* .417** 1

(.02) (.00)

VOL -.353* -.370* 1

(.02) (.02)




Table IV. 82 Results of Regression on Determinants of P of Small Size Firms of Food Industry in

India

Variables




(Constant) 0.065 (0.03)

.063 (0.02)

.632 (0.00)

.613 (0.00)

AIP -0.091 (0.14)

-.096 (0.11)

-.334 (0.45)

-

CAPINS -4.969 (0.88)

- -.034** (0.00)

-.034** (0.00)

SIZ 0.038 (0.06)

.044** (0.00)

-.207 (0.15)

-.284* (0.014)

GROW 0.089 (0.43)

- -.143 (0.86)

-

VOL -0.142 (0.06)

-.161* (0.02)

.288 (0.59)

-

R2 0.287 0.274 0.865 0.862

Adj-R2 0.190 0.21 0.847 0.855

F Stat 2.974* (0.02)

4.918** (0.00)

47.600** (0.00)

124.574** (0.00)


The Kaiser-Meyer-Olkin measure (see table IV. 83) is lesser than 0.60 (0.52) indicating that the factor analysis result may give only illusionary picture, however

the Bartlett’s test χ2 value (135.44) is highly significant, supporting for conduct of factor analysis, hence factor analysis (see table IV. 84) is conducted and P_SAL, and CAPINS converge to form factor 1. Factor 2 constitutes P_TASSET, VOL, and GROW; and factor 3 constitutes AIP, and SIZ showing the interrelation between variables grouped as one factor.



Table IV. 83 KMO and Bartlett's Test of Determinants of P of Small Size Firms of Food Industry in India




df 21

Sig. .000


Table IV. 84 Factor Analysis of Determinants of P of Small Size Firms of Food Industry in India


Factor loadings

Factor 1 2.608

P_SAL .965

CAPINS -.950

Factor 2 1.715 P_TASSET .773

VOL -.763

GROW .701

Factor 3 1.106 AIP .936

SIZ .682


IV.23.2 Analysis of Medium Size Firms The descriptive statistics of medium sized firms show (see table IV. 85) that

CAPINS shows a high degree of standard deviation while the other variables show a low degree of standard deviation. SIZ shows the least deviation, indicating that the firms falling under the category of medium size are quite related with regard to their size.

The correlation matrix of medium size firms shows (see table IV. 86) that GROW has highly significant positive correlation (0.52) with P_TASSET. On the contrary to the prior results, CAPINS has highly significant positive correlation (0.74) with P_SAL, which indicates that in case of medium sized firms, the less intensive employment of capital for production has increased their profit margin



while AIP has highly significant negative correlation (-0.55) with P_SAL, indicating that higher the proportion of current asset in their asset structure, the lower will be their profit margin, hence, effective employment of capital on fixed asset would help to improve profit margin. These contradictory inferences are because they have sacrificed their profit margin to have a higher turnover.

Table IV. 85 Descriptive Statistics of Determinants of P of Medium Size Firms of Food Industry

in India


P_TASSET 31 -.091 .235 .098 .060

P_SAL 31 -.020 .259 .087 .079

AIP 31 .138 .826 .503 .165

CAPINS 31 .154 4.003 .953 .850

SIZ 31 1.603 2.686 2.292 .217

GROW 31 -.054 .422 .131 .105

VOL 31 .009 .234 .062 .058


The multiple regression result, on the other hand, shows (see table IV. 87) that GROW has highly significant coefficients with P_TASSET (models 1 (0.29) & 2 (0.30)), SIZ has significant coefficient (0.09) with P_TASSET in model 2. The adj-R2 value (0.34 in model 1 and 0.35 in model 2) shows that the regression model fits to the extent of 30% in case of P_TASSET. With regard to P_SAL, CAPINS has highly significant positive coefficient (model 1 (0.07) & 2 (0.07)) with P_SAL. Thus, the intensity of capital investment in production process is one of the major determinants of the profit margin in medium size firms. SIZ has a significant positive coefficient with P_SAL (0.11 in model 1 & 0.10 in model 2 after removing AIP and VOL). The adj-R2 value is about 60% (0.58 in model 1 and 0.60 in model 2), indicating that the regression model fits the medium size firms about 60%. F-stat value is highly significant in both the models of P_TASSET and P_SAL.



Table IV. 86 Correlation Matrix of Determinants of P of Medium Size Firms of Food Industry in

India


P_TASSET 1

P_SAL .513** 1

(.00)

AIP -.548** 1

(.00)

CAPINS .744** -.593** 1

(.00) (.00)

SIZ 1

GROW .516** 1

(.00)

VOL -.406* 1

(.02)


The Kaiser-Meyer-Olkin measure (see table IV. 88) is (0.38) discouraging the

conduct of factor analysis. However, the χ2 value (88.42) of Bartlett’s test is highly significant, indicating that the correlation matrix is not an identity matrix, hence factor analysis is run. CAPINS, P_SAL, and AIP are grouped into factor 1(see table IV. 89). Factor 2 constitutes SIZ and VOL, and factor 3 constitutes GROW and P_TASSET, indicating the interrelation among the variables constituting the factors.



Table IV. 87 Results of Regression on Determinants of P of Medium Size Firms of Food Industry

in India Variables

Un-standardized Coefficients Beta Value Dependent variable P_TASSET Dependent variable

P_SAL Model 1 Model 2 Model 1 Model 2

(Constant) -.028 (0.82)

-.116 (0.25)

-.212 (0.12)

-.226 (0.03)

AIP -.088 (0.21)

-.070 (0.20)

-.047 (0.52)

-

CAPINS -.003 (0.82)

- .068** (0.00)

.073** (0.00)

SIZ .065 (0.16)

.091* (0.03)

.105* (0.03)

.100* (0.02)

GROW .294** (0.00)

.298** (0.00)

.099 (0.29)

.111 (0.21)

VOL -.234 (0.18)

- .074 (0.68)

-

R2 0.453 0.412 0.651 0.641 Adj-R2 0.344 0.346 0.582 0.602 F Stat 4.140**

(0.00) 6.300** (0.00)

9.344** (0.00)

16.104** (0.00)


Table IV. 88 KMO and Bartlett's Test of Determinants of P of Medium Size Firms of Food

Industry in India




df 21

Sig. .000




Table IV. 89 Factor Analysis of Determinants of P of Medium Size Firms of Food Industry in

India


Factor loadings

Factor 1 2.452 CAPINS .907

P_SAL .886

AIP -.799

Factor 2 1.705 SIZ .834

VOL -.806

Factor 3 1.309 GROW .912

P_TASSET .797


IV.23.3 Analysis of Large Size Firms The descriptive statistics of large size firms show (see table IV. 90) that the P

measured as P_TASSET as well as P_SAL have lesser standard deviation, indicating that the firms grouped as large size have more or less same level of P_TASSET and P_SAL while the other variables do not show much deviation.

Table IV. 90 Descriptive Statistics of Determinants of P of Large Size Firms of Food Industry in

India


P_TASSET 12 .051 .431 .154 .108

P_SAL 12 .011 .269 .113 .092

AIP 12 .225 .796 .604 .183

CAPINS 12 .227 2.102 .733 .543

SIZ 12 2.684 3.677 2.984 .284

GROW 12 .057 .381 .182 .108

VOL 12 .012 .111 .043 .029




The correlation matrix of large size firms show (see table IV. 91) that GROW has insignificant negative correlation with P_TASSET (-0.31) and P_SAL (-0.50 significant at 10% level). Thus, GROW may not contribute to increase the P in case of large size firms. CAPINS has highly significant positive correlation (0.81) with P_SAL as that of the medium size firms while AIP has highly significant negative correlation (-0.77) with P_SAL. Thus, the intensity of capital employment as fixed asset, used for production purpose, increases the profit margin of large size firms as that of the case in medium size firms.

Table IV. 91 Correlation Matrix of Determinants of P of Large Size Firms of Food Industry in

India


P_TASSET 1

P_SAL 1

AIP -.765 1

(.00)

CAPINS .808 -.625 1

(.00) (.03)

SIZ 1

GROW .585 1

(.04)

VOL 1


Regression results of large size firms show (see table IV. 92) that AIP has significant negative coefficient (-0.50) with P_TASSET in model 2 after removing the variables SIZ, GROW, VOL. The adj-R2 value is increased from 0.15 in model 1 to 0.31 in model 2 after removing these variables. However, the problem of



multicolinearity is high, and thus the value of adj-R2 value is very low when compared to R2 values of both the models. CAPINS has significant positive coefficient (0.10) with P_SAL in model 1; highly significant positive coefficient with P_SAL (0.09) in model 2, indicating that even large size firms have to strive hard to invest in fixed assets to improve their profit margin. VOL also has significant positive coefficient (1.14) with P_SAL. The adj-R2 value is above 80% in both the models of P_SAL (0.80 in model 1 and 0.83 in model 2 after removing SIZ and GROW), indicating that the model is 80% fit and the predictor variables in this model determine P_SAL above 80%. The F-stat value is highly significant in models 1 ((.94) & 2 (18.60).

Table IV. 92 Results of Regression on Determinants of P of Large Size Firms of Food Industry in

India Variables

Un-standardized Coefficients Beta Value Dependent variable P_TASSET

Dependent variable P_SAL

Model 1 Model 2 Model 1 Model 2 (Constant) 0.161

(0.72) .534 (0.00)

-.065 (0.72)

.086 (0.26)

AIP -0.434 (0.12)

-.498* (0.02)

-.149 (0.19)

-.150 (0.11)

CAPINS -0.092 (0.25)

-.108 (0.12)

.101* (0.015)

.094** (0.00)

SIZ 0.084 (0.48)

- .042 (0.39)

-

GROW 0.139 (0.72)

- .084 (0.60)

-

VOL 1.086 (0.39)

- 1.230* (0.04)

1.137* (0.02)

R2 0.538 0.436 0.892 0.875 Adj-R2 0.152 0.310 0.803 0.828 F Stat 1.396

(0.34) 3.472 (0.07)

9.942** (0.00)

18.603** (0.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: Figures in parentheses are ‘p’ values;**Significant at 0.01 level;*Significant at 0.05 level.



The Kaiser-Meyer-Olkin measure (see table IV. 93) shows a low value 0.43, which is less than the acceptable level (0.60), hence the factor analysis may give only illusionary results. However, Bartlett’s Test of sphericity shows a highly significant

χ2 value (49.28), hence, the factor analysis is run. P_SAL, CAPINS, AIP, GROW, and VOL constitutes factor 1, and P_TASSET and SIZ constitute factor 2, indicating high level of interrelation among the variables constituting a factor (see table IV. 94).

Table IV. 93 KMO and Bartlett's Test of Determinants of P of Large Size Firms of Food Industry

in India




df 21

Sig. .000


Table IV. 94 Factor Analysis of Determinants of P of Large Size Firms of Food Industry in India


Factor loadings

Factor 1 3.453

P_SAL .934

CAPINS .875

AIP -.837

GROW -.635

VOL .524


SIZ .769


IV.24 Income Size-wise Analysis of Determinants of P Income size-wise grouping is a better approach to analyze the impact of

income size on the P of firms, hence the firms are grouped into three sub-categories viz., ‘low income size firms’ with profit (PBITD) < Rs.10 crore; ‘medium income size



firms’ with profit > Rs.10 crore but < Rs.50 crore; ‘high income size firms’ with income >Rs.50 crore. The average income (PBITD) for the period of 10 years under study is considered as base for the purpose.

The trend line of P_TASSET (see chart IV.U) indicates the comparison of low income size firms, medium income size firms, and high income size firms. The low income size firms show a steep fall in the year 2005-06 and a rise thereafter, while the medium income size firms show a stable level of P_TASSET upto 2005-06, after which they have a gradual fall in their P_TASSET, indicating that their ability to effectively use the asset for productive purpose has decreased. Whereas the high income size firms show a greater fluctuation in establishing their ability to get stable return on their assets.

Chart IV.U Trend Line Showing Relation between P_TASSET of Low Income Size Firms,

Medium Income Size Firms and High Income Size Firms


The trend analysis of P_SAL shows (see chart IV.V) that the low income size firms follow differential profit margin policy and their profit margin keeps on fluctuating as that of small size firms (see chart IV.N). The trend line of medium income size firms and high income size firms has steady P_SAL, indicating their stable profit margin policy.

0

0.05

0.1

0.15

0.2

0.25

P_TASSET FOR LOW INCOME FIRMS

P_TASSET FOR MEDIUM INCOME FIRMS

P_TASSET FOR HIGH INCOME FIRMS



Chart IV.V Trend Line Showing Relation between P_SAL of Low Income Size Firms, Medium

Income Size Firms and High Income Size Firms


IV.24.1 Analysis of Low Income Size Firms The descriptive statistics of low income size firms show (see table IV. 95) that

the mean value of P_SAL is negative (-0.46), indicating the inference as that of the small size firms. The standard deviation of P_SAL is quite high, indicating that there is a wide variation in the profit margin of firms of low income size. CAPINS shows a wide difference between maximum and minimum values. The deviation from the mean value is also very high, indicating that different firms have different policies with regard to aggressive employment of capital in productive activities. However, P_TASSET does not have high standard deviation, indicating that the ROA employed falls within the closer range among the firms grouped as low income size.

The correlation matrix of low income size firm show (see table IV. 96) that VOL has highly significant negative correlation (-0.38) with P_TASSET, while GROW (0.34) and SIZ (0.29) have significant correlation with the dependent variables P_TASSET. CAPINS has highly significant negative correlation (-0.91) with P_SAL in case of low income size firms which is just contrary to that of the

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

P_SAL FOR LOW INCOME FIRMS

P_SAL FOR MEDIUM INCOME FIRMS

P_SAL FOR HIGH INCOME FIRMS



result of large size firms (which showed a highly significant positive correlation). Hence, the risk factor associated with the intensive employment of capital in asset is taken into consideration in case of low income size firms. The SIZ has significant positive correlation (0.29) with P_SAL.

Table IV. 95 Descriptive Statistics of Determinants of P of Low Income Size Firms of Food

Industry in India


P_TASSET 54 -.280 .179 .053 .071

P_SAL 54 -7.644 2.239 -.046 1.103

AIP 54 .060 .894 .473 .196

CAPINS 54 .154 235.270 7.911 3.247

SIZ 54 -.905 2.457 1.367 .812

GROW 54 -.174 .463 .036 .120

VOL 54 .009 .759 .104 .139


The regression result shows (see table IV. 97) that VOL has significant negative coefficient (-0.17) with P_TASSET in models 1 & 2 (-0.18) after removing the impact of predictor variables CAPINS and GROW. However, the adj-R2 value is very low (0.17 in models 1 and 0.18 in model 2), indicating that the regression model poorly fits in case of P_TASSET of low income size firms. CAPINS has highly significant negative coefficient with P_SAL in models 1 (-0.03) and 2 (-0.03), which is contrary to the results of medium size and large size firms (which has highly significant positive coefficient). SIZ has a significant negative coefficient with P_SAL in model 2 (-0.23) after removing the variables GROW and VOL, hence the low income size firms, adopt a policy of low profit margin to increase their turnover. The results of medium size firms, on the other hand, had positive coefficient, indicating that after getting established they rise their profit margin. The adj-R2

value is very high in both the models with P_SAL (0.85 in model 1 and 0.86 in model 2), showing a good model fit and the F-stat value is also highly significant, indicating the significance of the model.



Table IV. 96 Correlation Matrix of Determinants of P of Low Income Size Firms of Food

Industry in India


P_TASSET 1

P_SAL 1

AIP 1

CAPINS -.912** 1

(.00)

SIZ .287* .292* .599** -.488** 1

(.03) (.03) (.00) (.00)

GROW .338* .279* .437** 1

(.012) (.04) (.00)

VOL -.381** -.327* 1

(.00) (.012)


The Kaiser-Meyer-Olkin measure (see table IV. 98) is only 0.54, which is not

encouraging the model fit. The Bartlett’s test of sphericity is highly significant and

so factor analysis (see table IV. 99) is conducted, predictor variables P_SAL and

CAPINS constitute factor 1. Factor 2 constitutes AIP and SIZ while factor 3

constitutes P_TASSET, VOL, and GROW.



Table IV. 97 Results of Regression on Determinants of P of Low Income Size Firms of Food

Industry in India

Variables




(Constant) .078 (0.00)

.070 (0.01)

.600 (0.00)

.660 (0.00)

AIP -.077 (0.18)

-.084 (0.14)

-.237 (0.53)

-.254 (0.49)

CAPINS .000 (0.56)

- -.034** (0.00)

-.034** (0.00)

SIZ .020 (0.24)

.031* (0.03)

-.209 (0.06)

-.231* (0.02)

GROW .107 (0.21)

- -.162 (0.77)

-

VOL -.165* (0.02)

-.181** (0.00)

.251 (0.58)

-

R2 0.252 0.224 0.865 0.864

Adj-R2 0.174 0.177 0.851 0.855

F Stat 3.240* (0.013)

4.799** (0.00)

61.552** (0.00)

105.573** (0.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: The figures in parentheses are ‘p’ values;**Significant at 0.01 level;*Significant at 0.05 level.

Table IV. 98 KMO and Bartlett's Test of Determinants of P of Low Income Size Firms of Food

Industry in India




df 21

Sig. .000




Table IV. 99 Factor Analysis of Determinants of P of Low Income Size Firms of Food Industry in

India


Factor loadings

Factor 1 2.635 P_SAL .972

CAPINS -.947

Factor 2 1.726 AIP .914

SIZ .783

Factor 3 1.108

P_TASSET .825

VOL -.760

GROW .590


IV.24.2 Analysis of Medium Income Size Firms P_TASSET and P_SAL of medium income size firms have lower standard

deviation (see table IV. 100) unlike the case of low income size firms where the deviation of P_SAL is considerably high. CAPINS shows a higher deviation when compared to the other variables, showing the variation in investment policy.

Table IV. 100 Descriptive Statistics of Determinants of P of Medium Income Size Firms of Food

Industry in India


P_TASSET 22 .051 .235 .115 .048

P_SAL 22 .011 .293 .116 .0788

AIP 22 .138 .826 .478 .197

CAPINS 22 .227 4.003 1.195 .985

SIZ 22 1.945 2.947 2.408 .259

GROW 22 .018 .422 .148 .104

VOL 22 .010 .120 .047 .031




The correlation matrix of medium income size firms shows (see table IV. 101) that GROW has a significant positive correlation (0.49) with P_TASSET, VOL also has a significant positive correlation (0.49), indicating that medium income size firms are risk bearing firms and their P increase with the extent of risk borne by these firms though it increases VOL. AIP has a highly significant negative correlation (-0.67) with P_SAL, indicating that lesser investment in current assets increases the profit margin of the firms. CAPINS has a highly significant positive correlation (0.80) with P_SAL, and SIZ has a highly significant negative correlation (-0.76) with P_SAL. Thus, these firms reduce their profit margin to increase sales unlike the fact in case of low income size firms, which shows a significant positive correlation with P_SAL as they tend to maintain profit margin to survive. They don’t reduce the margin to boost sales, however, the medium income size firms, which have a reasonable level of income, bear the risk of reducing the profit margin to enhance the sales volume.

Table IV. 101 Correlation Matrix of Determinants of P of Medium Income Size Firms of Food

Industry in India


P_TASSET 1

P_SAL 1

AIP -.670 1 (.00)

CAPINS .797 -.630 1 (.00) (.00)

SIZ -.759 .636 -.656 1 (.00) (.00) (.00)

GROW .488 1 (.02)

VOL .488 -.429 1 (.02) (.04)




The regression result shows (see table IV. 102) that CAPINS has highly significant negative coefficient with P_TASSET in models 1 (-0.04) & 2 (-0.04) after removing VOL. As the sales increases, the ROA decreases. The adj-R2 value is above 60% (0.60 in model 1 and 0.60 in model 2), proving that the predictor variables could determine P_TASSET upto 60%. CAPINS has highly significant positive coefficient with P_SAL in both the models (0.05 in model 1 and 0.05 in model 2). The adj-R2 value of both the models of P_SAL is good (0.70 in model 1 and 0.72 in model 2) and the regression model is 70% fit. The F-stat in the models are highly significant, indicating that the variance in the predictor variables is related to the variance in the dependent variables P_TASSET, and P_SAL.

Table IV. 102 Results of Regression on Determinants of P of Medium Income Size Firms of Food


Un-standardized Coefficients Beta Value Dependent variable P_TASSET Dependent variable P_SAL

Model 1 Model 2 Model 1 Model 2 (Constant) .374

(0.00) .456

(0.00) .247

(0.16) .353

(0.01) AIP -.085

(0.09) -.094 (0.06)

-.027 (0.70)

-

CAPINS -.035** (0.00)

-.040** (0.00)

.051** (0.00)

.047** (0.00)

SIZ -.090 (0.05)

-.114** (0.00)

-.088 (0.16)

-.130* (0.01)

GROW .180* (0.02)

.175* (0.02)

.123 (0. .24)

.130 (0.18)

VOL .278 (0.30)

- .346 (0.37)

-

R2 0.698 0.677 0.775 0.758 Adj-R2 0.604 0.601 0.704 0.718 F Stat 7.400**

(0.00) 8.915** (0.00)

11.003** (0.00)

18.832** (0.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: Figures in parentheses are ‘p’ values;**Significant at 0.01 level;*Significant at 0.05 level.



The Kaiser-Meyer-Olkin measure (see table IV. 103) is nearly 0.6 (0.59),

which supports the execution of factor analysis. Bartlett’s Test of sphericity shows

that the χ2 value (91.22) is highly significant and therefore factor analysis is

conducted for medium income size firms, which shows (see table IV. 104) that the

variables converge into two factors. Factor 1 constitutes P_SAL, SIZ, CAPINS and

AIP, while factor 2 constitutes P_TASSET, GROW and VOL.

Table IV. 103 KMO and Bartlett's Test of Determinants of P of Medium Income Size Firms

of Food Industry in India




df 21

Sig. .000


Table IV. 104 Factor Analysis of Determinants of P of Medium Income Size Firms of Food

Industry in India


Factor

loadings

Factor 1 3.284

P_SAL .909

SIZ -.896

CAPINS .845

AIP -.814

Factor 2 1.872

P_TASSET .935

GROW .624

VOL .621




IV.24.3 Analysis of High Income Size Firms The descriptive statistics of high income size firms show (see table IV. 105)

that the standard deviation is comparatively low. CAPINS shows comparatively higher standard deviation while P_TASSET, and P_SAL show a lesser deviation, indicating that the firms with more or less similar level of P are grouped as high income size firms.

Table IV. 105 Descriptive Statistics of Determinants of P of High Income Size Firms of Food

Industry in India


P_TASSET 10 .079 .431 .179 .104

P_SAL 10 .032 .269 .161 .0805

AIP 10 .225 .789 .537 .186

CAPINS 10 .415 2.102 1.028 .634

SIZ 10 2.365 3.677 2.964 .365

GROW 10 .070 .381 .167 .094

VOL 10 .013 .207 .063 .058


The correlation matrix of high income size firms shows (see table IV. 106) that GROW has highly significant negative correlation (-0.84) with P_SAL, thus growth in assets will no more be helpful in boosting the profit margin of high income size firms. CAPINS has highly significant positive correlation 0.77 with P_SAL while AIP has highly significant negative correlation (-0.79) with P_SAL. Profit margin for high income size firms increases when the firms invest their capital for productive purposes rather than maintaining a higher liquidity level in the form of current assets, however, the variables are not significantly correlated with the variable P_TASSET.

The multiple regression result shows (see table IV. 107) that CAPINS has a significant negative coefficient with P_TASSET in models 1 (-0.20) & 2 (-0.18) after removing the predictor variables SIZ and VOL. The adj-R2 value is also good (0.64



in model 1 and 0.72 in model 2), indicating that the independent variables could predict the dependent variable P_TASSET upto 70%. GROW has significant negative coefficient (-0.51) with P_SAL whereas, CAPINS has significant positive coefficient (0.06) with P_SAL, which support the finding of correlation results. The adj-R2 value is also good (0.67 in model 1 and 0.80 in model 2), making the regression model fit in determining the dependant variable P_SAL upto 80%. The F- stat value is also highly significant in model 2 of P_SAL.

Table IV. 106 Correlation Matrix of Determinants of P of High Income Size Firms of Food

Industry in India

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. **. Correlation is significant at 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Figures in parentheses denote p value.


P_TASSET 1

P_SAL 1

AIP -.792** 1

(.00)

CAPINS .768** -.665* 1

(.00) (.03)

SIZ 1

GROW -.838** .779** 1

(.00) (.00)

VOL 1



The Kaiser-Meyer-Oklin measure (see table IV. 108) is 0.55, which is about 0.6 and therefore factor analysis would give a reasonable outcome. The Bartlett’s test

also shows highly significant χ2 value (40.26). The factor analysis result (see table IV. 109) of high income size firms show that GROW, AIP and P_SAL converges to form factor 1; and variables SIZ, P_TASSET, CAPINS and VOL converges to form factor 2 to represent reasonable interrelation between the variables constituting a factor.

Table IV. 107 Results of Regression of Determinants of P of High Income Size Firms of Food

Industry in India

Variables




(Constant) .894 (0.06)

.643 (0.00)

.290 (0.32)

.189 (0.00)

AIP -.361 (0.15)

-.353 (0.09)

-.042 (0.79)

-

CAPINS -.204* (0.02)

-.178** (0.00)

.046 (0.31)

.057* (0.03)

SIZ -.067 (0.47)

- -.021 (0.75)

-

GROW -.633 (0.17)

-.542 (0.13)

-.503 (0.14)

-.514* (0.011)

VOL -.098 (0.84)

- -.093 (0.80)

-

R2 0.839 0.813 0.853 0.846

Adj-R2 0.638 0.720 0.669 0.802

F Stat 4.177 (0.09)

8.697* (0.01)

4.637 (0.08)

19.180** (0.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: Figures in parentheses are ‘p’ values;**.Significant at 0.01 level;*.Significant at 0.05 level



Table IV. 108 KMO and Bartlett's Test of Determinants of P of High Income Size Firms of Food

Industry in India




df 21

Sig. .007


Table IV. 109 Factor Analysis of Determinants of P of High Income Size Firms of Food Industry

in India


Factor loadings

Factor 1 3.663

GROW -.943

AIP -.903

P_SAL .901

Factor 2 1.782

SIZ .830

P_TASSET .739

CAPINS -.734

VOL -.552


IV.25 Sector-wise Analysis of Determinants of P Sector wise analysis is done by grouping firms having same business

environment and then finding their P because it has a great impact on P of the

firms. Since there is more number of sectors with firms of few in numbers, the firms

are combined and grouped into three sectors constituting related firms. Sector I

constitutes 32 vegetable oil firms; Sector II constitutes 30 firms, which include 9

firms of tea sector, 11 firms of dairy sector, and 10 firms of sugar sector; Sector III

constitutes 24 firms comprising of miscellaneous sectors, which include coffee, cocoa

products & confectionery, bakery products, processed /packaged foods, starches,



marine food, poultry & meat product, floriculture, milling products, and other

agricultural products.

The trend analysis of Sector I, Sector II and sector III shows (see chart IV.W)

that there is a high degree of fluctuation in the size of P_TASSET over the period

under study for firms grouped under all sectors, however the firms belonging to

sector III show a rising trend in their efficiency to show a good return during the

end of the period of analysis. The trend line of the sector I shows a fall in the year

2008-09, whereas sector II shows considerable rise in P_TASSET.

Chart IV. W Trend Line Showing Comparison between P_TASSET of Firms of Sector I, Sector II and

Sector III


The trend line shows (see chart IV. X) that sector II constitutes firms that use

varied profit margin policy as that of the low income size as well as that of the small

size firms. Sector II shows a rise in their P_SAL as they had a rising trend in

P_TASSET, whereas sector I follows a steady profit policy, and their P_SAL is quite

steady over the years of analysis.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

P_TASSET SECTOR I

P_TASSET SECTOR II

P_TASSET SECTOR III



Chart IV. X Trend Line Showing Relation between P_SAL of Firms of Sector I, Sector II and

Sector III


IV.25.1 Analysis of Sector I The descriptive statistics of sector I show (see table IV. 100) that CAPINS

and P_SAL have higher standard deviation, revealing that sector I constitutes firms with varied profit margin and varied capital investment policy. The SIZ, however, does not show high standard deviation. The firms range from firms with negative GROW, to firms with high positive GROW which shows that the sector I constitutes both the group of firms that are capable of using their assets effectively and the firms which do not effectively use their assets for generating profit.

The correlation matrix result of sector I shows (see table IV. 110) that GROW has significant positive correlation (0.45) with P_TASSET, which shows that the firms use their assets efficiently to increase P. SIZ has highly significant negative correlation (-0.56) with P_SAL, indicating that profit margin reduces with increase in sales. CAPINS has highly significant positive correlation (0.91) with P_SAL, encouraging effective use of funds for the purpose of production while AIP has highly significant negative correlation (-0.60) with P_SAL.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

P_SAL SECTOR I

P_SAL SECTOR II

P_SAL SECTOR III



Table IV. 110 Descriptive Statistics of Determinants of P of Firms of Sector I of Food Industry in

India


P_TASSET 32 -.091 .250 .077 .061

P_SAL 32 -.022 .651 .0568 .114

AIP 32 .204 .768 .587 .122

CAPINS 32 .154 24.982 1.491 4.434

SIZ 32 -.845 3.677 2.006 .847

GROW 32 -.061 .422 .099 .129

VOL 32 .013 .570 .074 .105


Table IV. 111 Correlation Matrix of Determinants of P of Firms of Sector I of Food Industry in

India


P_TASSET 1

P_SAL 1

AIP -.597 1

(.00)

CAPINS .911 -.543 1

(.00) (.00)

SIZ -.557 .417 -.676 1

(.00) (.018) (.00)

GROW .445 .378 .566 1

(.011) (.03) (.00)

VOL -.396 1

(.02)




The regression result shows (see table IV. 112) that GROW has a significant positive coefficient with P_TASSET in model 1 (0.27*) and (0.27**) in model 2 after removing the effects of SIZ and VOL. On the other hand, AIP has a significant negative coefficient with P_TASSET in models 1 (-0.26*) & 2 (-0.26**) encouraging an aggressive investment policy for firms belonging to sector I. The adj-R2 value is low (0.27 in model 1 and 0.31 in model 2) for P_TASSET indicating that there are other variables which could better predict P_TASSET. CAPINS has highly significant positive coefficient with P_SAL (0.02 in model 1 and 0.02 in model 2) and AIP has significant negative coefficient with P_SAL (-0.20 in model 1 & 2) after removing SIZ and VOL. GROW has highly significant positive coefficient (0.18) with P_SAL in model 2, indicating that investments in assets increases the profit margin of the firms. Thus, sector I has high scope of growth opportunities and more investment increases the profit margin of the firms. The adj-R2 value is high in both the models of P_SAL (0.86 in model 1 and 0.87 in model 2), indicating that the regression model fits 86% and the variables are the major determinants of P_SAL.

Table IV. 112 Results of Regression on Determinants of P of Firms of Sector I of Food Industry in India

Variables

Un-standardized Coefficients Beta Value Dependent variable P_TASSET Dependent variable P_SAL

Model 1 Model 2 Model 1 Model 2 (Constant) .197

(0.01) .212

(0.00) .130

(0.05) .124

(0.01) AIP -.258*

(0.014) -.264** (0.00)

-.201* (0.02)

-.199* (0.015)

CAPINS -.004 (0.28)

-.004 (0.08)

.021** (0.00)

.022** (0.00)

SIZ .004 (0.83)

- -.003 (0.85)

-

GROW .267* (0.011)

.267** (0.00)

.184* (0.03)

.175** (0.01)

VOL .036 (0.72)

- .001 (0.98)

-

R2 0.383 0.380 0.878 0.878 Adj-R2 0.265 0.314 0.855 0.865 F Stat 3.234*

(0.02) 5.725** (0.00)

37.581** (0.00)

67.336** (0.00)




The Kaiser- Meyer- Oklin test (see table IV. 113) has a value above 0.60 (0.63) and therefore the conduct of factor analysis is acceptable. The Bartlett’s test

also shows highly significant χ2 value (130.17) and so factor analysis is run. P_SAL, CAPINS, AIP and SIZ converge to form factor 1(see table IV. 114). Factor 2 constitutes VOL and GROW. And factor 3 constitutes P_TASSET, showing that the dependent variable does not have much of interrelation with the other variables.

Table IV. 113 KMO and Bartlett's Test of Determinants of P of Firms of Sector I of Food Industry

in India




df 21

Sig. .000


Table IV. 114 Factor Analysis of Determinants of P of Firms of Sector I of Food Industry in India


Factor loadings

Factor 1 3.138

P_SAL .957

CAPINS .935

AIP -.736

SIZ -.677

Factor 2 1.664 VOL -.898

GROW .648



IV.25.2 Analysis of Sector II The descriptive statistics of sector II shows (see table IV. 105) that the mean

value of P_SAL is negative and the standard deviation in respect of it is also high, which indicates that the firms in sector II range from firms’ incurring loss to firms



earning good profit. The CAPINS value also shows a high degree of standard deviation indicating the varied investment policy followed by firms in sector II. The other variables, however, comparatively show a lesser standard deviation.

Table IV. 115 Descriptive Statistics of Determinants of P of Firms of Sector II of Food Industry in

India


P_TASSET 30 -.015 .431 .101 .086

P_SAL 30 -7.645 .293 -.138 1.421

AIP 30 .115 .808 .398 .181

CAPINS 30 .317 235.270 9.902 4.264

SIZ 30 -.905 3.385 1.976 .925

GROW 30 -.066 .463 .093 .122

VOL 30 .009 .289 .063 .056


The correlation matrix of sector II shows (see table IV. 116) that SIZ has highly significant positive correlations with P_TASSET (0.67) and with P_SAL (0.61). CAPINS has highly significant negative correlation (-0.99) with P_SAL while the other predictor variables have insignificant correlation with the two dependent variables.

The regression results of sector II show (see table IV. 117) that SIZ has highly significant coefficients with P_TASSET in both the models (0.09 in models 1 & 2 after removing the predictor variables GROW and VOL). The Adj-R2 value is about 40% (0.44 in model 1 and 0.47 in model 2), indicating that the regression model fits to about 40%. CAPINS has a significant negative coefficient with P_SAL in both the models (-0.03 in models 1& 2 after removing SIZ and GROW). AIP also has high significant negative coefficient with P_SAL in both the models (-0.43 in model 1and -0.42 in model 2).



Table IV. 116 Correlation Matrix of Determinants of P of Firms of Sector II Food Industry in

India


P_TASSET 1

P_SAL 1

AIP 1

CAPINS -.996** 1

(.00)

SIZ .669** .606** .402* -.621** 1

(.00) (.00) (.02) (.00)

GROW .394* 1

(.03)

VOL 1


The Kaiser-Meyer-Olkin measure (see table IV. 118) is only 0.53, which does

not support to conduct of factor analysis, however the Bartlett’s test χ2 value

(182.31) is highly significant, hence the correlation matrix is not an identity matrix

and so factor analysis can be carried out. The factor formation shows (see table IV.

119) that P_SAL and CAPINS converge to form factor 1. P_TASSET, SIZ, GROW

converge to form factor 2. Factor 3 comprises of the variables VOL and AIP.



Table IV. 117 Results of Regression on Determinants of P of Firms of Sector II of Food Industry

in India

Variables




(Constant) -.046 (0.29)

-.033 (0.39)

.379 (0.00)

.400 (0.00)

AIP -.105 (0.17)

-.111 (0.13)

-.431** (0.00)

-.416** (0.00)

CAPINS .001 (0.12)

.001 (0.12)

-.033** (0.00)

-.033** (0.00)

SIZ .087** (0.00)

.087** (0.00)

.016 (0.60)

-

GROW .002 (0.98)

- -.095 (0.59)

-

VOL .160 (0.47)

- -.628 (0.09)

-.657 (0.07)

R2 0.533 0.522 0.995 0.995

Adj-R2 0.436 0.467 0.994 0.995

F Stat 5.476** (0.00)

9.481** (0.00)

1025.626** (0.00)

1818.784** (0.00)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: The figures in parentheses are ‘p’ values;**.Significant at 0.01 level;*.Significant at 0.05 level.

Table IV. 118 KMO and Bartlett's Test of Determinants of P of Firms of Sector II of Food

Industry in India




df 21

Sig. .000




Table IV. 119 Factor Analysis of Determinants of P of Firms of Sector II of Food Industry in India


Factor loadings

Factor 1 3.006 P_SAL .981 CAPINS -.971


SIZ .710 GROW .705

Factor 3 1.076 VOL .860 AIP -.590


IV.25.3 Analysis of Sector III The descriptive statistics of firms of sector III show (see table IV. 120) that

CAPINS has high standard deviation. The firms, however, have negative and positive P, indicating that the firms falling under sector III comprises of firms with negative P to firms having reasonably good P. The other variables, however, don’t show much deviation.

The correlation matrix of sector III shows (see table IV. 121) that GROW has highly significant positive correlation (0.59) with P_TASSET, indicating that investment in asset would increase its P_ASSET. SIZ has significant positive correlation with P_TASSET, indicating that P increase with SIZ. On the other hand, there are no variables significantly correlated with P_SAL.

The multiple regression result shows (see table IV. 122) that VOL has significant negative coefficient (-0.30) with P_ASSET in model 1 and highly significant coefficient (-0.33) with P_TASSET in model 2 after removing the variables CAPINS and GROW. SIZ has significant positive coefficient (0.07) with P_TASSET in model 2 while AIP has significant negative coefficient (-0.19) with P_TASSET. The Adj- R2 values are 0.46 in model 1 and 0.51 in model 2 of P_TASSET. Hence, the model fit is good in case of P_TASSET, however, no other variables show significant coefficients with P_SAL. The R2 (0.13) and the Adj-R2 (-0.11) values are low in case of P_SAL, indicating that the model poorly fits for P_SAL in sector III.



Table IV. 120 Descriptive Statistics of Determinants of P of Firms of Sector III of Food Industry in

India


P_TASSET 24 -.280 .235 .071 .100

P_SAL 24 -.379 2.239 .167 .471

AIP 24 .060 .894 .446 .229

CAPINS 24 .337 49.051 4.956 1.020

SIZ 24 -.413 2.804 1.373 .823

GROW 24 -.174 .316 .037 .122

VOL 24 .009 .759 .127 .166


Table IV. 121 Correlation Matrix of Determinants of P of Firms of Sector III Food Industry in

India


P_TASSET 1

P_SAL 1

AIP 1

CAPINS 1

SIZ .491 .639 1

(.015) (.00)

GROW .585 .676 1

(.00) (.00)

VOL -.629 -.486 1

(.00) (.016) Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. *. Correlation is significant at 0.05 level (2-tailed). **. Correlation is significant at 0.01 level (2-tailed). Figures in parentheses denote p value.



Table IV. 122

Results of Regression of Determinants of P of Firms of Sector III of Food Industry in India

Variables



Model 1 Model 2 Model 1

(Constant) .108 (0.02)

.106 (0.01)

.213 (0.49)

AIP -.175 (0.07)

-.192* (0.03)

-.612 (0.34)

CAPINS .000 (0.91)

- .004 (0.70)

SIZ .053 (0.12)

.067* (0.011)

.084 (0.71)

GROW .137 (0.47)

- .091 (0.94)

VOL -.300* (0.011)

-.327** (0.00)

.686 (0.35)

R2 0.590 0.577 0.131

Adj-R2 0.475 0.514 -0.110

F Stat 5.170 (0.00)

9.099** (0.00)

0.543 (0.74)

Source: Computed results based on compiled data collected from CMIE prowess Pvt. Ltd. Note: Figures in parentheses are ‘p’ values;**.Significant at 0.01 level;*.Significant at 0.05 level

The Kaiser-Meyer–Olkin test (see table IV. 123) shows that the factor analysis may give only tentative result. But the Bartlett’s test (see table IV. 124) has

highly significant χ2 value, indicating that the correlation matrix is not an identity matrix and factor analysis shall be conducted. The factor analysis formation shows that P_TASSET, GROW, SIZ, VOL and CAPINS are interrelated and can be grouped as factor 1. Factor 2 constitutes P_SAL and AIP.



Table IV. 123 KMO and Bartlett's Test of Determinants of P of Firms of Sector III of Food

Industry in India




df 21

Sig. .000


Table IV. 124 Factor Analysis of Determinants of P of Firms of Sector III of Food Industry in

India


Factor loadings

Factor 1 3.032

P_TASSET .876

GROW .827

SIZ .770

VOL -.694

CAPINS -.452

Factor 2 1.430 P_SAL .855

AIP -.698


IV.26 Conclusion IV.26.1 Overall Analysis

The overall result of equation II, which aims at finding out the determinants of P in food industry shows that the GROW and SIZ have highly significant positive correlation with P_TASSET while VOL has highly significant negative correlation with P_TASSET, hence the hypotheses Ho

17, and Ho18 are rejected, and hypotheses

Ho15 and Ho

16 are accepted in case of P_TASSET. On the other hand, CAPINS has a significant negative correlation with P_SAL which coincides with the results of Ghemawat and Caves (1986)89, while SIZ has significant positive correlation with



P_SAL, thus the hypotheses Ho16 is rejected and the hypotheses Ho

15, Ho17, Ho

18 are accepted. The summary of the findings of the determinants of P is given in table IV. 125.

IV.26.2 Sales Size Wise Analysis The size wise analysis shows different results because SIZ, and GROW have

significant positive correlation with P_TASSET in case of small size firms while VOL has significant negative correlation with P_TASSET. CAPINS has a significant negative correlation with P_SAL while SIZ has a significant positive correlation with P_SAL, thus the hypotheses Ho

17, and Ho18 are rejected in case of P_TASSET

while, hypotheses Ho16 is rejected in case of P_SAL.

Table IV.125 Summary of Overall Results of the Determinants of P

Hypotheses P_TASSET P_SAL


relationship between aggressive investment policy and profitability”.

Accepted Accepted


relationship between capital intensity and profitability”.

Accepted +ve**

Rejected


relationship between volatility and profitability”.

-ve** Rejected

Accepted

Ho18= “There is no significant

relationship between growth and profitability”.

+ve** Rejected

Accepted


The correlation analysis result of medium size firms show that GROW has a

significant correlation with P_TASSET. SIZ, on the contrary, it has a highly

significant negative relation with P_SAL, which proves that they have sold the

products with low profit margin to increase the turnover. This explains why



CAPINS has a significant positive correlation with P_SAL unlike in the findings of

small size firms. AIP has a significant negative correlation with P_SAL. GROW has

a highly significant negative correlation with P_SAL in case of large size firms

proving that they can use the assets effectively only to certain extent. CAPINS has a

significant positive correlation with P_SAL and AIP has significant negative

correlation with P_SAL for the medium size firms and large size firms. The

summary of the findings of sales size-wise analysis is given in table IV. 126.

Table IV.126 Summary of Sales Size-wise Analysis of the Determinants of P

Hypotheses Small Size Firms Medium Size Firms Large Size Firms

P_TASSET P_SAL P_TASSET P_SAL P_TASSET P_SAL


significant relationship between aggressive investment policy and profitability”.

Accepted Accepted Accepted -ve**

Rejected Accepted

-ve** Rejected


significant relationship between capital intensity and profitability”.

Accepted -ve*

Rejected Accepted

+ve** Rejected

Accepted +ve**

Rejected


significant relationship between volatility and profitability”.

-ve* Rejected

Accepted Accepted Accepted Accepted Accepted


significant relationship between growth and profitability”.

+ve* Rejected

Accepted +ve*





IV.26.2 Income Size-wise Analysis

In income sizes wise analysis shows that SIZ, and GROW have significant

positive correlation with P_TASSET while VOL has significant negative correlation

with P_TASSET in low income size firms. CAPINS has a significant negative

correlation with P_SAL while SIZ has a significant positive correlation with P_SAL,

thus the hypotheses Ho17, and Ho

18, are rejected in case of P_TASSET while,

hypothesis Ho16 is rejected in case of P_SAL.

The analysis of medium income size firms shows that GROW and VOL have

significant correlation with P_TASSET. On the contrary to the prior result, SIZ has

significant negative correlation with P_SAL. CAPINS has a significant positive

correlation with P_SAL while AIP has highly significant negative correlation with

P_SAL.

The high income size firms result shows that GROW has highly significant

negative correlation with P_SAL, showing that the firms cannot effectively use asset

after certain level. CAPINS has a significant positive correlation with P_SAL while

AIP has significant negative correlation with P_SAL. These findings lead to

rejection of Ho19 that “there is no significant influence of size in deviating the

relationship between the predictor variables and profitability of the firms”. The

summary of the results of income size-wise analysis is given in table IV. 127.

IV.26.3 Sector-wise Analysis

The sector wise analysis shows that CAPINS has a significant positive

correlation with P_SAL while SIZ and AIP have significant negative correlation

with P_SAL in sector I. However, GROW has significant positive correlation with

P_TASSET. The analysis of sector II shows that SIZ has significant positive

correlation with P_TASSET and P_SAL whereas, CAPINS has significant negative

correlation with P_SAL. The analysis of sector III shows that SIZ, and GROW have

significant positive correlation with P_TASSET while VOL has a significant



negative correlation with P_TASSET which fact is similar to that of the small size

firms although there are no significant predictor variables influencing P_SAL. The

relation between the predictor variables and P has differed in respect of sectors to

which they belong to, and hence, the hypothesis Ho20 is rejected (see table IV. 128).

Table IV.127 Summary of Income Size-wise Analysis of the Determinants of P

Hypotheses Low Income Size Firms Medium Income Size Firms

High Income Size Firms



significant relationship between aggressive investment policy and profitability”.

Accepted Accepted Accepted -ve**

Rejected Accepted

-ve** Rejected


significant relationship between capital intensity and profitability”.

Accepted -ve**

Rejected Accepted

+ve* Rejected

Accepted +ve**

Rejected


significant relationship between volatility and profitability”.

-ve** Rejected

Accepted +ve*



significant relationship between growth and profitability”.

+ve* Rejected

Accepted +ve*


-ve** Rejected




Table IV.128 Summary of Sector-wise Analysis of the Determinants of P

Hypotheses Sector I Firms Sector II Firms Sector III Firms



relationship between aggressive investment policy and profitability”.

Accepted -ve**

Rejected Accepted Accepted Accepted Accepted


relationship between capital intensity and profitability”.

Accepted +ve**

Rejected Accepted

-ve** Rejected

Accepted Accepted


relationship between volatility and profitability”.

Accepted Accepted Accepted Accepted -ve**

Rejected Accepted

Ho18= “no significant

relationship between growth and profitability”.

+ve* Rejected

Accepted Accepted Accepted +ve**

Rejected Accepted

IV.27 Concluding Remarks

Food industry in India is a rising industry with greater scope for developing as a major contributor to the gross domestic product (GDP) of India. Research about this industry at this point of time would enhance its development. The preliminary analysis, which aims at finding out the relation between PBITD and the different constituents of CS have proved that PBITD is an insignificant determinant of CS in case of small size and low income size firms while, it is the significant determinant of equity capital in respect of large size firms and high income size firms.

The overall analysis of the determinants of LEV shows that P has significant positive correlation with LEV, proving that the firms of food industry rely more on external funds as their P increases. However, the relation between P and LEV is the



contrary as the size of the firm increases. The medium income size firms show that P has positive coefficient with LEV while high income size firms show that P has significant negative coefficient with LEV. So, as their size increases, they use retained earnings and rely less on external borrowings. The equation I regression model fits over 80% in case of high income size firms indicating that the explaining variables determines the variance in LEV over 80%. There are various factors influencing the LEV of different sectors.

SIZ, GROW, and VOL are considered as the important determinants of P_TASSET and CAPINS and AIP are found to be the major determinants of P_SAL. CAPINS and AIP have negative relation with P_SAL, indicating that intensive use of capital for production purpose will increase the profit margin of the firms. But CAPINS shows a positive correlation with P_SAL in case of medium size, large size, medium income size and high income size firms which is supported by a negative relation between SIZ and P_SAL. This fact shows that these firms have increased their sales by reducing their profit margin, which fact proves the positive relation between CAPINS and P_SAL indicating that the profit has decreased with intensive use of asset for increasing sales.



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Chapter V SUMMARY OF FINDINGS & CONCLUSION




CHAPTER V

SUMMARY OF FINDINGS, CONCLUSION, SUGGESTIONS, AND SCOPE FOR FURTHER STUDIES

This chapter gives a comprehensive summary of findings, suggestions for improvement of the food industry, conclusion and scope for further studies in this area. The analysis reveals important facts about the variables that determine the capital structure (CS) and profitability (P) of the firms, and brings to light the impact of size and sectoral differences on the relation between P and CS.

V.1 Origin of the Research Problem

Ascertaining the determinants of CS of firms is an area which has been studied intensively by many researchers but still remains as a puzzle to be solved. The ability of the firm to earn consistent profit is the deciding factor of a firm’s CS. “Debt capacity” depends on the future P and value of the firm; it may be able to increase borrowing if it does well, or be forced to pay down debt if it does poorly (Myers 2001)1. The works of Titman and Wessel (1988)2, Kester (1986)3, Chang (2003)4 and many others have considered P as one of the determinants of CS. Pecking Order Theory and Signaling theory have proved the importance of P in deciding CS. These theories claims that profitable firms use lesser LEV as they rely on internal funds, thus, there exists a negative relation between P and LEV. The earlier studies have put forth different views about the relation between P and CS. The relation between CS and P has been explicated in the works of Myers (1984)5, Kester (1986)6, Rajan and Zingales (1995)7, Jonson (1998)8, Booth Collins et al. (2001)9, Dogra and Gupta (2009)10. The works of Myers (1984)11, Kester (1986)12, Friend and Hasbrouch (1988)13, Friend & Lang (1988)14, Titman & Wessels (1988)15, and Long Chen et al. (2004)16 provide empirical evidences in support of the negative relation between P & LEV while Long & Malitz (1985)17 have pointed that LEV increases with increase in P but their result was insignificant. Though there are varied views regarding the type of relation, the works give strong evidence that there is a binding link between CS & P. All the more, Errol D'Souza (2000)18 proved that Indian firms rely much more heavily on external debt as a source of finance than do firms in advanced countries such as US firms. When this is the condition, it



becomes essential to study the determinants of CS and P and also to study the type of relation between P and CS.

India, being the world's second largest producer of food next to China, with potential of being the biggest with the food and agricultural sector, this analysis is carried out with particular reference to food industry in India. Food Industry in India faces various challenges such as lack of proper infrastructure facility in India which leads to loss of more than half of the food grains stored under the public distribution system. Another major problem is food inflation, a major factor contributing to the overall inflation in India which is ultimately the outcome of low production when compared to demand and due to insufficient infrastructural facilities. The growth of food industry will help to trounce the problem and will also enhance agricultural sector, a major sector in India. Researches about various financial variables would serve the purpose and hence this study is carried out.

To throw light into the forbidden areas in the CS puzzle and to analyze the financial variables of the food industry in India which would assist the growth of the prospering industry, the following objectives are set.

V.2 Objectives of the Study V.2.1 General Objectives

The general objective of the study is to analyze the determinants CS with particular focus on the impact of P on CS and to analyze the determinants of P.

V.2.2 Specific Objectives The specific objectives are to study the relationship between P and leverage

(LEV) in general and to analyze the impact of non debt tax shield (NDTXSH), collateral asset (COLASS), growth rate (GROW), size (SIZ), age (AG) and volatility (VOL) on LEV of the firms for the reason that these are the other variables considered crucial in determining the CS of the firm.

The study also focuses on analyzing the determinants of P and analyzing the impact of aggressive investment policy (AIP), capital intensity (CAPINS), growth rate (GROW), size (SIZ) and volatility (VOL) on P of the firms.



The influence of sales size, income size and sectoral differences of firms in deviating the relationship between P and CS is also intended to be analyzed.



V.3 Hypotheses of the Study The analysis is carried out under two heads viz., preliminary analysis and

core analysis in order to accomplish the objectives set. The preliminary analysis is considered as the base which gives an outline about the relationship between profit earned (PBITD) and the various constituents of CS viz., internal funds (equity capital) and external funds (short term debt (STD), long term debt (LTD), and total debt (TD) which is the sum of STD and LTD). The core analysis aims at analyzing the determinants of CS and P. The impact of sales size, income size and sectoral differences on the relation between the various predictors on LEV and P are also brought to light. The aggregate of hypotheses developed for the purpose are given below:

“There is no significant relationship between profit and different constituents of capital structure”.

“There is no significant impact of size on the relation between profit and the various constituents of capital structure”.

“There is no significant impact of sectoral differences on the relation between profit and the various constituents of capital structure”.

“There is no significant relationship between profitability and leverage in general”.

“There is no significant impact of size (sales-wise and income-wise) on the relationship between profitability and leverage”.

“There is no significant impact of sectoral difference on the relationship between profitability and leverage”.

“There is no significant relationship between non debt tax shield, collateral assets, growth as well as volatility and leverage”.

“There is no significant relationship between capital intensity, aggressive investment policy, volatility, growth as well as size and profitability”.

V.4 Methodology



The study is based on secondary data which are collected from Centre for Monitoring Indian Economy (CMIE) Prowess package for a period of 10 years on year to year basis ranging from 1999-2000 to 2008-2009.

Descriptive statistics such as mean, median and standard deviation are extensively used to neutralize the fluctuation in the value of explained as well as explaining variables. Correlation co-efficient is extensively used to study one-to-one relationship between the variables selected. Multiple regression is also used to determine the various variables that influence the debt ratio / leverage in a firm. Besides, factor analysis is also used to determine the factors influencing LEV and P. Different appropriate ratios are also used to compute individual relative properties for analysis.

V.5 Regression Analysis V.5.1 Regression for Preliminary Analysis

The preliminary study analyses the relation between PBITD and the various constituents of CS viz., STD, LTD, TD and equity. The analysis aims at exploring the nature of impact of PBITD on various constituents of CS in food industry in India, which is considered to be the base for the core analysis, focusing the nature of relation between P and LEV, hence the equation is:

CS = α +PBITD+ Є

V.5.2 Regression for Core Analysis The analysis has two main objectives and therefore has two regression

equations to accomplish the objectives. Regression equation I focuses on indentifying the determinants of CS which is analyzed using leveraging ratios of the firms as proxy. The dependent variable namely LEV is studied under three heads viz., short term debt (LEV_STD), long term debt (LEV_LTD) and total debt (LEV_TD). Patrick Hutchinson and Nicos Michaelas (1998), in their study analyzed CS in terms of short term debt, long term debt and total debt. Titman (1988) also analyzed the implications with regard to different types of debt instruments viz.



short-term, long-term and convertible debt rather than an aggregate measure of total debt.

VOL, COL ASS, NDTXSH, P, SIZ, AG and GROW are used as predictor variables for determining LEV. VOL is the fluctuation in the firm’s earnings which determines its borrowing capacity from external sources. COLASS is the fixed assets that the firms have to cushion external borrowing, while NDTXSH is the other source through which the firms can mitigate tax which is considered as main reason for firm’s preference for external sources of fund. P is another crucial predictor of LEV analyzed under the pecking order theory by many researchers. SIZ of the firms and AG are also considered as variables influencing the borrowing capacity of the firms. GROW determines the debt requirement of the firms for snatching the opportunities and hence the equation is:

LEV = α + β1 VOL + β2 COLASS+ β3 NDTXSH + β4 P + β5 SIZ + β6 AG + β7

GROW + Є

Regression equation II is to accomplish the second main objective i.e., to identify the determinants of P. The term P has been defined as the average rate of return on assets (ROA) by Lowe et al. (1994). This would help to find out the earnings in comparison to the investment made.

P_TASSET = PBITD / Total Assets

Profit margin also has been used as another dependent variable to measure profitability.

P_SAL = PBITD / Sales.

Independent variables AIP, CAPINS, SIZ, GROW and VOL are used as explaining variables that determine the changes in the dependent variables. AIP and CAPINS show how aggressively the funds are used for production purpose which would ultimately increase its P. SIZ shows the sales turnover which is also a major consideration in determining its P while GROW shows the flourishing existence of



the firm over the future period. VOL determines the variation in its earning capacity that influences its P.

P = α + β1 AIP + β2 CAPINS+ β3 SIZ + β4 GROW + β5 VOL + Є



Table V.1 Summary of Findings - Regression Results of Preliminary Analysis

Type of analysis

Preliminary analysis Co-efficient significance

level

Dependent Variables STD

LTD

TD

EQUITY

I.OVERALL ANALYSIS OF 86 FIRMS

Sig. 1% PBITD PBITD PBITD PBITD Sig. 5% - - - - R2 value 0.603 0.240 0.493 0.661

Adj. R2 value 0.598 0.231 0.487 0.657 F Stat 127.498** 26.505** 81.688** 163.893**

II. SALES SIZE- WISE ANALYSIS: Small Size Firms

Sig. 1% PBITD - - - Sig. 5% - PBITD PBITD - R2 value 0.245 0.118 0.140 0.086

Adj. R2 value 0.226 0.097 0.119 0.063 F Stat 13.283** 5.491* 6.652* 3.838

Medium Size Firms

Sig. 1% PBITD PBITD PBITD - Sig. 5% - - - - R2 value 0.449 0.527 0.612 0.122

Adj. R2 value 0.430 0.510 0.598 0.091 F Stat 23.644** 32.266** 45.702** 4.020

Large Size Firms Sig. 1% - - - PBITD Sig. 5% PBITD - - - R2 value 0.361 0.024 0.205 0.754

Adj. R2 value 0.297 0.074 0.125 0.730 F Stat 5.643* 0.243 2.571 30.695**

INCOME SIZE-WISE ANALYSIS: Low income firms

Sig. 1% PBITD - - PBITD Sig. 5% - - - - R2 value 0.199 0.009 0.036 0.272

Adj. R2 value 0.184 -0.010 0.018 0.258 F Stat 12.957** 0.476 1.944 19.429**

Medium income firms

Sig. 1% - PBITD PBITD - Sig. 5% - - - - R2 value 0.005 0.685 0.597 0.102

Adj. R2 value -0.045 0.669 0.577 0.057 F Stat 0.105 43.469** 29.620** 2.261



High income firms

Sig. 1% - - - PBITD Sig. 5% - - - - R2 value 0.310 0.001 0.123 0.797

Adj. R2 value 0.224 -0.124 0.014 0.772 F Stat 3.592 0.011 1.124 31.410**

SECTOR WISE ANALYSIS: Sector I

Sig. 1% PBITD PBITD PBITD PBITD Sig. 5% - - - - R2 value 0.761 0.797 0.793 0.448

Adj. R2 value 0.753 0.790 0.787 0.430 F Stat 95.317** 117.596** 115.254** 24.349**

Sector II Sig. 1% PBITD - PBITD PBITD Sig. 5% - - - - R2 value 0.949 0.115 0.718 0.709

Adj. R2 value 0.947 0.083 0.708 0.698 F Stat 517.552** 3.626 71.153** 68.154**

Sector III Sig. 1% PBITD PBITD PBITD - Sig. 5% - - - PBITD R2 value 0.695 0.702 0.722 0.248

Adj. R2 value 0.681 0.689 0.709 0.213 F Stat 50.084** 51.932** 57.064** 7.236*



Table V.2 Summary of Findings - Regression Results of Determinants of LEV

Type of analysis

Core analysis Equation 1

Co-efficient significance

level

Dependent Variables

LEV_STD

LEV_LTD

LEV_TD


Sig. 1% SIZ GROW GROW

Sig. 5% AG - AG, SIZ

R2 value 0.304 0.349 0.380

Adj. R2 value 0.241 0.290 0.325

F Stat 4.857** 5.964** 6.838**


Sig. 1% AG GROW GROW

Sig. 5% - AG AG

R2 value 0.406 0.513 0.507

Adj. R2 value 0.287 0.416 0.409

F Stat 3.420** 5.277** 5.147**

Medium Size Firms

Sig. 1% - SIZ -

Sig. 5% - - SIZ

R2 value 0.295 0.495 0.421

Adj. R2 value 0.080 0.342 0.245

F Stat 1.374 3.226* 2.394

Large Size Firms Sig. 1% - - -

Sig. 5% SIZ - -

R2 value 0.783 0.604 0.724

Adj. R2 value 0.404 -0.089 0.241

F Stat 2.064 0.872 1.498


Sig. 1% AG GROW, AG GROW, AG

Sig. 5% SIZ - -

R2 value 0.380 0.463 0.492

Adj. R2 value 0.285 0.381 0.415

F Stat 4.021** 5.660** 6.374**

Medium income firms

Sig. 1% - P

Sig. 5% - VOL (-ve) VOL (-ve)



R2 value 0.380 0.690 0.610

Adj. R2 value 0.070 0.535 0.414

F Stat 1.227 4.449** 3.122*

High income firms

Sig. 1% - - -

Sig. 5% - P (-ve) P (-ve)

R2 value 0.945 0.971 0.986

Adj. R2 value 0.751 0.872 0.935

F Stat 4.876 9.732 19.490*


Sig. 1% - GROW -

Sig. 5% - SIZ SIZ, GROW

R2 value 0.369 0.576 0.497

Adj. R2 value 0.184 0.453 0.350

F Stat 2.001 4.663** 3.382*

Sector II Sig. 1% - GROW -

Sig. 5% AG - GROW

R2 value 0.424 0.375 0.370

Adj. R2 value 0.241 0.176 0.169

F Stat 2.318 1.883 1.844

Sector III Sig. 1% - - -

Sig. 5% - SIZ SIZ

R2 value 0.473 0.531 0.533

Adj. R2 value 0.242 0.326 0.329

F Stat 2.050 2.587 2.610



Table V.3 Summary of Findings - Regression Results of Determinants of P

Type of analysis

Core analysis Equation 2

Co-efficient significance

level

Dependent Variable - P

Model 1 (PBITD/T. ASSET)

Model 2 (PBITD/SALES)


Sig. 1% AIP (-ve), SIZ CAPINS (-ve)

Sig. 5% GROW -

R2 value 0.398 0.858

Adj. R2 value 0.352 0.848

F Stat 8.700** 79.744**


Sig. 1% - CAPINS (-ve)

Sig. 5% SIZ -

R2 value 0.316 0.867

Adj. R2 value 0.202 0.845

F Stat 2.771* 39.219**

Medium Size Firms

Sig. 1% GROW CAPINS

Sig. 5% - -

R2 value 0.454 0.654

Adj. R2 value 0.318 0.588

F Stat 3.329* 7.565**

Large Size Firms Sig. 1% - -

Sig. 5% - CAPINS, VOL

R2 value 0.724 0.922

Adj. R2 value 0.392 0.829

F Stat 2.183 9.919*


Sig. 1% - CAPINS (-ve)

Sig. 5% VOL (-ve) -

R2 value 0.258 0.866

Adj. R2 value 0.163 0.849

F Stat 2.719* 50.565**



Medium income firms

Sig. 1% - -

Sig. 5% CAPINS (-ve), SIZ (-ve), GROW

CAPINS

R2 value 0.716 0.779

Adj. R2 value 0.602 0.691

F Stat 6.296** 8.834**

High income firms

Sig. 1% - -

Sig. 5% - -

R2 value 0.897 0.863

Adj. R2 value 0.690 0.589

F Stat 4.336 3.148


Sig. 1% - CAPINS

Sig. 5% AIP (-ve), GROW GROW

R2 value 0.391 0.878

Adj. R2 value 0.244 0.849

F Stat 2.671* 30.126**

Sector II Sig. 1% SIZ AIP (-ve), CAPINS (-ve)

Sig. 5% - -

R2 value 0.542 0.996

Adj. R2 value 0.422 0.995

F Stat 4.530** 930.913**

Sector III Sig. 1% VOL (-ve) -

Sig. 5% AIP (-ve) -

R2 value 0.624 0.139

Adj. R2 value 0.491 -0.165

F Stat 4.701** 0.456

V.6 Summary of Findings of the Analysis The analysis is carried out in two major parts viz., preliminary analysis

(part I) which attempts to bring out the relation between PBITD and the different constituents of CS and the main analysis (part II), which is further divided into two aspects. Two regression equations are developed to test the developed hypotheses. The first regression equation endeavors to find out the determinants of CS while the second regression equation attempts to find out the determinants of P. The summary of findings of the analysis are as follows:



V.6.1 Preliminary Analysis of the Relation between PBITD and Different Constituents of CS

The preliminary analysis has put forth the following findings which give an outline about how the earnings of the firms would determine the different constituents of CS of the firms.

PBITD has highly significant positive correlation with STD, LTD,TD and equity of the firms while the regression model fit is over 50% in case of STD (adj-R2 value 0.60) and equity (adj-R2 value 0.66), hence the earnings of the firms of food industry explain over 50% of the variance in STD and equity.

V.6.1a Sales Size-wise Analysis

In small size firms, PBITD is not found to be the major determinant of the constituents of CS (adj-R2 value is around 10% only), although it has significant correlation with STD, LTD and TD, hence earnings of these firms are quite insufficient to attract funds from external sources for their capital requirements.

PBITD explains above 50% variance in external borrowings of medium size firms (adj-R2 value is 0.43 (43%) for STD, 0.51 (51%) for LTD and 0.60 (61%) for TD) hence, the earnings of these firms are sufficient enough to attract funds from external sources for their capital requirements.

The large size firms show a highly significant correlation between PBITD and equity capital and this predictor variable explains over 70% (adj-R2

value is 0.73) of the variance in equity capital which fact states that large size firms rely more on internal funds rather than external borrowings which corraborates the pecking order hierarchy of Myers (1984).

V.6.1b Income Size-wise Analysis

The low income size firms also show a highly significant positive correlation between PBITD and STD as well as PBITD and equity. But the adj-R2 value is very low and so PBITD of low income size firms is



poor determinant of various constituents of CS whereas it explains above 60% of the variance in LTD (adj-R2 value 0.67) and above 50% of variance in TD (adj-R2 value 0.58) of medium income size firms.

As that of large size firms, in high income size firms also, PBITD determines the variance in equity over 70% (adj-R2value is0.77).

V.6.1c Sector-wise Analysis

The sector wise analysis shows that for firms in sector I, PBITD is the major determinant of STD, LTD, and TD and determines above 70% of the variance in the external borrowings; it increases with increase in PBITD. The PBITD of sector II firms determines above 90% of the variance in STD (adj-R2 0.95), above 70% (adjR2 0.71) in TD and above 60% (adj-R2 0.70) in equity. PBITD is over 60% (adj-R20.68 for STD, 0.69 for LTD and 0.71 for TD) determinant of external borrowings of firms of sector III.

V.6.2 Results of Analysis of Determinants of LEV

The regression equation I framed to study the explaining variables (viz., VOL, COL ASS, NDTXSH, P, SIZ, AG and GROW) of LEV and to explain the relation between P and LEV, has put forth the following findings:

The overall analysis proves that GROW, AG and SIZ have positive correlation with LEV, indicating that the debt capital increases as the firms grow. P has significant positive correlation with LEV_STD, which fact shows that the firms of food industry in India depend more on external borrowings when their profit increases, which fact coincides with that of Long & Malitz (1985), Pandey (2004) who found a positive relation between P and LEV.


The size wise analysis of firms shows an in depth insight about the explaining variables that determine the CS of the firms. GROW, AG and



SIZ have significant correlation with LEV_STD, LEV_LTD and LEV_TD in case of small size firms. AG and SIZ have significant positive correlation and VOL has significant negative correlation with medium size firms, however, the regression model fit is not good for medium size firms, indicating that there are other variables influencing the external borrowings. The sales size-wise analysis show insignificant relation between P and LEV and therefore, the impact of size on the relation between P and LEV could not be portrayed.


The income size-wise analysis shows that SIZ, AG and GROW have significant positive correlation with LEV and the predictor variable explains around 40% of the variance in LEV of low income size firms. While the medium income size firms have significant positive correlation between P and LEV, P has significant negative coefficient with LEV for high income size firms, which fact shows that as the income increases the firms use their internal funds and depend less on external borrowings. Thus, the negative relation between P and LEV increases with SIZ as pointed out by Rajan and Zingales (1995) and Titman (1988).

GROW has significant positive correlation with LEV_TD for medium income size firms, which exemplify that the growth of the firms in terms of total asset is sourced through external borrowings. On the contrary, high income size firms show that GROW has highly significant negative coefficient with LEV_LTD and LEV_TD. Therefore, these firms expand their business through internal sources rather than external borrowings, which fact coincides with that of the pecking order theory (Myers 1984). The regression model fits best for high income size firms and the predictor variable explains over 90% of variance in LEV for high income size firms.


The sector-wise analysis shows that GROW and SIZ have significant positive correlation with LEV_STD, LEV_LTD and LEV_TD. However,



the explaining variables predict only 40% of external borrowings. P, SIZ and AG have significant positive correlation with LEV_STD, indicating that the firms rely more on external borrowings as their P increases. GROW has significant positive correlation with LEV_LTD and LEV_TD. The adj-R2 value is low, showing that the regression model fits poorly for sector II.

For sector III SIZ has significant positive correlation and significant coefficient with LEV_STD, LEV_LTD and LEV_TD. P and GROW have significant positive correlation with LEV_LTD and NDTXSH has significant negative correlation with LEV_STD and LEV_TD. However, the variables other than SIZ do not have significant coefficient (adj-R2

value is around 40%) for sector III. Therefore, the firms of sector III which are growing in nature require more funds for their growth and hence rely on external source of capital.

V.6.3 Results of Analysis of Determinants of P

Equation I, which aims at analyzing the impact of size and sectoral differences on the relation between P and CS, shows interesting results. Equation II endeavors to reveal the determinants of P in food industry. The impact of size and sectoral differences are also brought to light.

The overall analysis of the industry shows that GROW and SIZ have significant positive correlation with P_TASSET while, AIP has significant negative coefficient with P_TASSET. The regression model explains about 30% of the variance in the dependent variable P_TASSET. Hence, the aggressive use of assets with lesser investment in current asset increases the P measured in terms of total assets. However, there are other variables that contribute to the variance in P_TASSET. CAPINS has significant negative correlation and coefficient with P_SAL, indicating that efficient use of assets to increase turnover increases the profit margin of firms. However, AIP does not have any significant correlation with P_SAL, indicating that the apportionment between current assets and fixed assets does not contribute to the increase in profit margin. The regression model



fits over 80%, revealing the significance of CAPINS in explaining the variance in profit margin. SIZ, on the contrary, it has significant negative coefficient with P_SAL which shows that the firms increase their sales by sacrificing their profit margin.


The sales size-wise analysis of small size firms reveals that the regression model fit is only 20% and the predictor variables explain only around 20% of the variance in P_TASSET. CAPINS is one of the major determinants of P_SAL and has a significant negative coefficient with P_SAL and the regression model fit is around 80%.

For medium size firms and large size firms (sales size-wise analysis) CAPINS has significant positive correlation with P_SAL, which shows that increase in sales does not lead to a proportionate increase in the profit margin. The medium size firms increase their turnover by decreasing their profit margin. VOL also has a significant positive coefficient with P_SAL, indicating that increase in VOL helps in increasing the profit margin of large size firms.


The income-wise analysis also portrays that for low income size firms, SIZ and GROW have significant positive correlation with P_TASSET while VOL has a negative correlation indicating that increase in VOL decreases the return on assets.

The analysis on medium income size firms show that CAPINS has a significant negative coefficient with P_TASSET and positive coefficient with P_SAL, which fact reveals that intensively employed capital for production increases the ROA but leads to a decrease in the profit margin.

The analysis on high income size firms proves that as the size increases the growth in assets would not lead to increase in P_SAL, which is exhibited through the significant negative correlation that GROW has with P_SAL. CAPINS has significant negative coefficient with



P_TASSET and significant positive correlation with P_SAL, which fact corroborates to that of the medium income size firms. The model fit is above 60% for medium income size firms and above 80% for high income size firms. Hence, the predictor variable in the regression model can explain over 80% of the variance in P_TASSET and P_SAL of high income size firms.


Firms in Sector I have the tendency to reduce their profit margin to increase their turnover which is reflected in positive correlation between CAPINS and P_SAL while AIP has significant negative correlation with P_SAL, insisting on aggressive use of assets to increase their profit margin.

Firms in Sector II have a significant negative correlation with P_SAL, which also insists on aggressive use of assets to increase turnover, which ultimately increases their profit margin. The predictor variables explain over 90% of the variance in P_SAL in case of firms in sector II. However, there are no significant predictor variables influencing P_SAL in case of firms in sector III.

V.7 Conclusion

The analysis carried out with the key objective of analyzing the financial variables for enhancing the development of the growing food industry in India has brought to significant findings that may be very useful to the industry. The results of the overall analysis portrays that P has significant positive correlation with LEV proving that the firms of food industry rely more on external funds as their P increases. However, size has influenced the relation between P and LEV. The medium income size firms show that P has positive coefficient with LEV while high income size firms show that P has significant negative coefficient with LEV, which fact proves that the firms rely on retained earnings as their income size increases. GROW has been an important variable determining the CS of small size firms and low income size firms. The return on asset is increased with the increase with SIZ and GROW, and with decrease in VOL. Aggressive



investment policy helps the firms to increase their P in terms of P_TASSET and P_SAL. Increase in VOL could help to increase the P of high income firms because they are quite established in their business. Therefore, these findings warrant constructive suggestions for enhancing the financial performance of food industry in India.

V.8 Suggestions for Improvement of the Food Industry

Based on the findings, the following suggestions are put forth, which may help to improve the firms’ profitability and to improve efficient use of funds available for productive purposes.

The firms of food industry (in general) should invest more in asset which enhances the growth of the firms by increasing the sales volume.

Less volatility in earning will enable the firms of food industry (in general) to increase their return on investment in assets.

Aggressive use of assets for earning profit with lesser liquidity in the form of current asset would help the firms of food industry (in general) to increase the return on assets (AIP shows a significant negative correlation with P_TASSET).

The small size firms should use their assets to increase the turnover, which ultimately led to an increase in their profit margin, because there is a significant negative relation between CAPINS and P_SAL.

CAPINS has a significant positive coefficient with P_SAL for medium size firms, large size firms and sector I firms. Therefore, the medium size firms, the large size firms and firms of sector I should see to that they earn a reasonable margin on the turnover instead of simply increasing their sales volume.

VOL of large size firms has a significant positive coefficient with P_SAL, exemplifying that taking risk increases their profit margin although they may lead to fluctuations in earnings.



GROW has significant negative coefficient with LEV_LTD, and LEV_TD in case of high income size firms, which elucidates that these firms do not rely on external borrowing for further investment in assets required for expansion of business, but they use their internal earnings. Such invested funds do not increase their P because GROW has negative correlation with P_SAL. So, the firms of high income category should make effective use of their retained earnings to increase their profit margin.

V.9 Limitations of the Study

Analysis of the study is based on financing data collected from CMIE Prowess Package. The quality of the study depends purely upon the accuracy, reliability and quality of secondary data.

The sample firms chosen for the study were restricted to 86 due to limitations such as lack of continuous listing, non-availability of data pertaining to those firms in the data source-Prowess Package.

The sector-wise classification has grouped the firms into three sectors out of which the second and third sectors constitute related firms as there are more number of sectors with less number of firms.

The firms of Beverages & Tobacco category are left out as they occupy a negligible share (only 175 out of 1747 of food manufacturing firms, recording a share of 10%) and the nature of social concern of these firms also differs, hence firms categorized under food products alone are included in the study.

V.10 Scope for Further Studies

The regression model proves to best explain the variance in the dependent variable in case of high income firms with Adj- R2 value > 80%. The regression models fit for small size firms, and low income size firms are poor, indicating that there are other explaining variables that determine the LEV of these firms, hence, leving scope for further research to identify



those variables that influence the small size firms and low income size firms to determine LEV.

Sector-wise analysis has been carried out after grouping the firms into three sectors by combining the selected firms with related business because, there are only few number of firms under each sector fulfilling the sample selection criteria. Therefore, the influence of business environment on the variables determining the LEV and P may not be much accurate for sector-wise analysis. Further studies may be carried out for each sector (pure sector-wise) to give faultless results.

The research studies may be extended to a further period, and also to the whole of the food industry as the present study has left the Beverage & Tobacco producing firms for analysis.

Across industry analysis could also be carried out to study if there are any industrial differences on the variables determining the P and LEV.



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A. WEBSITES

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www.indiainbusiness.nic.in/.../Food_Processing_and_Agribusiness.pdf

www.indianfoodindustry.net

B. NEWSPARERS

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