Impact of the Fluctuations in Indian Economy on the ... · PDF fileA comparative study of 2008 economic ... TATA Consultancy Services Ltd 114 5.6 Impact of fluctuations on Profitability

Impact of the Fluctuations in Indian Economy on the

Profitability of an Industry w. r. t. Manufacturing

Sector and Service Sector (for the Period 2003-12)

Thesis Submitted to the D. Y. Patil University,

Department of Business Management

in partial fulfillment of the

requirements for the award of the Degree of

DOCTOR OF PHILOSOPHY

In

BUSINESS MANAGEMENT

Submitted by

DEEPALI M. GARGE (Enrollment No. DYP-PhD- 110100012)

Research Guide

Prof. Dr. R. Gopal

Director, Dean and Head of the Department

D.Y. PATIL UNIVERSITY,

DEPARTMENT OF BUSINESS MANAGEMENT,

Sector 4, Plot No. 10,

CBD Belapur, Navi Mumbai 400 614.

December 2014

i

IMPACT OF THE FLUCTUATIONS IN INDIAN

ECONOMY ON THE PROFITABILITY OF AN

INDUSTRY W. R. T. MANUFACTURING

SECTOR AND SERVICE SECTOR (FOR THE

PERIOD 2003-12)

ii

DECLARATION

I hereby declare that the thesis entitled “Impact of the Fluctuations in Indian Economy on

the Profitability of an Industry w. r. t. Manufacturing Sector and Service Sector (for the

Period 2003-12)” submitted for the award of Doctor of Philosophy (PhD) in Business

Management at the D.Y. Patil University, Department of Business Management is my

original work and the thesis has not formed the basis for the award of any degree, associate

ship, fellowship or any other similar titles.

The material borrowed from other sources and incorporated in the thesis has been duly

acknowledged.

I understand that I myself could be held responsible and accountable for plagiarism, if any,

detected later on.

The research papers published based on the research conducted during the course of the study

are also based on the study and not borrowed from any other sources.

Place : Navi Mumbai Signature of the Student

Date : Enrollment No. DYP-PhD- 110100012

iii

CERTIFICATE

This is to certify that the thesis entitled “Impact of the Fluctuations in Indian Economy on

the Profitability of an Industry w. r. t. Manufacturing Sector and Service Sector (for the

Period 2003-12)” submitted by Deepali M Garge is a bonafide research work in partial

fulfilment of the requirements for the award of the Doctor of Philosophy in Business

Management at the D.Y.Patil University, Department of Business Management, and that the

thesis has not formed the basis for the award previously of any degree, diploma, associate-

ship, fellowship or any other similar title of any University or Institution.

Also it is certified that, the thesis represents an independent work on the part of the candidate.

Place: Navi Mumbai,

Date:

Dr. R. Gopal Dr. R. Gopal

Signature of the Signature of Guide

Head of the Department

iv

ACKNOWLEDGEMENT

First and foremost, I owe thanks to the Almighty God for giving me strength to pursue this

study.

I am greatly indebted to the D.Y. Patil University, Department of Business Management

which has accepted me for the Doctoral Program and provided me with an excellent

opportunity to carry out the present research work.

I am grateful to my guide, mentor, philosopher Prof. Dr. R. Gopal who encouraged me during

the entire study. His esteemed guidance at every step made my research more directional and

focused. I am grateful to him, for guiding me throughout the research period and for

providing his constructive criticism which made me to bring my best. I would also like to

thank sir for being there at any point of time without considering his own precious personal

time.

I would like to thank my senior most colleague Ranjeev Manrao and all the Industry experts

who spent their valuable time for me and helped me to complete research work.

Very special thanks to my dear husband, my sweet little child and my parents for their

support throughout the course of this study. I am grateful to my in-laws for their immense

support in completing the thesis.

Lastly, I also wish to thank all my near and dear ones who have been directly and indirectly

instrumental in the completion of my dissertation.

v

Contents

Chapter

No. Subsection Title

Page

No.

List of Tables Viii

List of Figures ix-xi

List of Abbreviations Xii

EXECUTIVE SUMMARY xiii –

xxii

1.

Introduction: Indian Economy and

Fluctuations 1 – 51

1.1 Global Economic Fluctuations 14

1.2

Phases of economic fluctuation

a. 2000-2005

b. 2005-2010

c. 2010 & after

16

1.3

A comparative study of 2008 economic

meltdown with reference to impact on India

and USA economy

21

1.4 Manufacturing Industries in India 40

1.5 Service Industries in India 49

2.

Literature Review and Research Gap 52 – 61

2.1 Studies on Economic Fluctuations 52

2.2 Studies on service sector 57

2.3 Studies on manufacturing sector 60

2.4 Research Gap 60

3.

Statement of the Problem 62 – 73

3.1 Objectives of the Study 63

3.2 Hypotheses 64

3.3 Defining variables for the Study 65

3.4 Operational Definition of the variables 65

3.4.1 GDP 65

3.4.2 Inflation 68

3.4.3 GDP deflator 71

3.4.4 PBDIT 72

4.

Research Methodology 74 – 86

4.1 Conceptual Framework 74

4.2 Research Design 75

4.3 Sources of Data 75

4.4 Econometric Modelling for the Hypothesis 77

4.5 Details of statistical tools Used for the study 81

4.6 Limitations of the study 85

5. Case Study on Manufacturing and Service

Sector Industry

87 –

152

5.1

Impact of fluctuations on Profitability of

Hindustan Unilever Ltd 87

5.2 Impact of fluctuations on Profitability

vi

Imperial Tobacco Company (ITC) Ltd 100

5.3 Impact of fluctuations on Profitability of

Glenmark Pharmaceuticals 103

5.4 Impact of fluctuations on Profitability of Dr

Reddy’s Laboratories Ltd 109


TATA Consultancy Services Ltd 114


Infosys Ltd 138


Reliance Capital Ltd 141

5.8

Summary of Impact of fluctuations on

Profitability of sample companies of

Manufacturing and Services Sector

149

6.

Impact of Inflation 153 –

165

6.1 Impact of Inflation on Profitability of

Manufacturing Industries 160

6.2 Impact of Inflation on Profitability of

Service Industries 163

6.3 Recommendations to industries 164

7.

Data Analysis, Interpretations and Model

Estimations

166 –

197

7.1

Model 1: (GDP and profitability of

manufacturing sector) Karl Pearson’s

correlation coefficient

166

7.2 Descriptive Analysis of Two variables 168

7.3 Model 2: : (GDP and profitability of service

sector) Karl Pearson’s correlation coefficient 174

7.4 Model 3 (GDP and Inflation) Granger’s

Causality Test 175

7.5 Inferential Statistics 179

7.6 Tests of Granger’s Causality 190

8. Validation of Data through Industry Experts 198 -

202

9.

Results and Discussion 203 –

212

9.1 Hypothesis 1 203







10. Summary of Hypothesis, Statistical Tools

Used and Results

213 -

217

11. Conclusion 218

vii

12. Recommendations & Future scope of the

Study 223

13. Suggestions 226

References 228

Annexure 234

viii

List of Table

Table No. Title of Table Page No.

1. Sectoral growth rate in the year 2009-11 47

2. Growth Rate of GDP Vs Inflation in India, 1951-2011 158

ix

List of Figures

Figure

No.

Title of Figure Page

No.

1. Sectoral Composition of GDP of India 2

2. Growth Rate of GDP in percentage YoY 5

3. Ten Years Study of GDPfc at constant prices 7

4. Growth rate of IIP since 2009 11

5. Nasdaq fall in 2002 18

6. Varieties of crises-World Aggregate-1932 to 2010 19

7. GDP trend of India since year 1960 to 2010 23

8. Calculated trend of GDP of India 25

9. Author’s Prediction of GDP growth rate of India for the year 2013

in 2011

26

10. GDP trend of USA since year 1960 to 2010 27

11. Author’s Prediction of GDP growth rate of USA for the year 2013

in 2011

28

12. Comparative study of India and USA Business Cycle since year

1990 to 2010

29

13. Factory forward process 33

14. GDPfc quarterly growth rate since Q. 2, 2009 38

15. Use-based classification of India’s manufacturing sector 45

16. Classification of FMCG Industry 46

17. GDP growth rate yearly, at factor cost & market price. 67

18. Earnings Per Share of TCS since 2005 to 2013 132

19. EBITDA of TCS since 2005 to 2012 134

20. Fit for better future for industries 165

21. Recommendations to Industries in turbulence time 201

22. Growth Rate of GDP at Factor Cost (Bar Diagram) 234

23. GDP at Factor cost with Actual values. 235

24. Inflation yearwise trend 236

25. GDP Growth rate at various inflation values. 237

26. Growth rate of Manufacturing Sector 238

27. Growth Rate of Services Sector 239

28. PBDIT of Manufacturing Sector 240

x

29. PBDIT of Service Sector 241

30. Regression scatter plot for PBDIT of Manufacturing w.r.t. EBIT of

HUL.

242


ITC.

243


Glenmark

244


Dr Reddy’s Laboratories

245

34. Regression scatter plot for PBDIT of Services w.r.t. EBIT of TCS 246

35. Regression scatter plot for PBDIT of Services w.r.t. EBIT of

Infosys

247

36. Regression scatter plot for PBDIT of Services w.r.t. EBIT of

Reliance Capital

248

37. ACF plot for GDP growth 249

38. PACF plot for GDP growth 250

39. ACF of Growth Rate of Manufacturing Sector 251

40. PACF of Growth Rate of Manufacturing Sector 252

41. ACF for Growth Rate of Services Sector 253

42. PACF for Growth Rate of Services Sector 254

43. ACF for Inflation 255

44. PACF for Inflation 256

45. ACF for PBDIT of Manufacturing Sector 257

46. PACF for PBDIT of Manufacturing Sector 258

47. ACF for PBDIT of Service Sector 259

48. PACF for PBDIT of Service Sector 260

49. ACF for EBIT of HUL 261

50. PACF for EBIT of HUL 262

51. ACF for EBIT of ITC 263

52. PACF for EBIT of ITC 264

53. ACF for EBIT of TCS 265

54. PACF for EBIT of TCS 266

55. ACF for EBIT of Reliance Capital 267

56. PACF for EBIT of Reliance Capital 268

57. Regression scatter plot for GDP Growth Rate w.r.t. PBDIT of

Manufacturing sector

269

58. Regression scatter plot for GDP Growth Rate w.r.t. PBDIT of 270

xi

Service sector

59. Regression scatter plot for change in PBDIT of Manufacturing

sector w.r.t. change in Inflation

271

60 Regression scatter plot for change in PBDIT of Service sector w.r.t.

change in Inflation

272

xii

List of Abbreviations

ACF

Auto Correlation Function

CAGR Compound Annual Growth Rate

CMIE Centre for Monitoring Indian Economy

CPI Consumer Price Index

CRR Cash Reserve Ratio

CSO Central Statistics Organisation

EBIT Earnings Before Interest & Taxes

EBITDA Earnings Before Interest, Taxes, Depreciation

and Amortisation

FDI Foreign Direct Investment

FICCI Federation of Indian Chamber of Commerce &

Industry

FMCG Fast Moving Consumer Goods

GDP(fc) Gross Domestic Product at factor cost

GNDM Global Network Delivery Model

ICRA Investment Information and Credit Rating

Agency

ICRIER Indian Council for Research on International

Economic Relations

IMF International Monetary Fund

IIP Index of Industrial Production

MOSPI Ministry of Statistics & Programme

Implementation

NASSCOM The National Association of Software and

Services Companies

NAASDAQ National Association of Securities Dealers

Automated Quotations

NDP Net Domestic Product

NNP Net National Product

PACF Partial Auto Correlation Function

PBDIT Profit Before Depreciation, Interest & Taxes

RBI Reserve Bank of India

SLR Statutory Liquidity Ratio

UNESCO United Nations Economic and Social Cooperation

WPI Wholesale Price Index

xiii

EXECUTIVE SUMMARY

GDP had been the prime calculator of economic growth and also vital to sustenance in

economic fluctuations. Future economic growth crucially depends upon positive performance

of GDP. Stability and continuity in performance of GDP, controlled creeping Inflation and

favourable economic conditions are responsible for progress towards growth in performance

of all sectors of economy. Primary sector of economy is Agriculture, secondary is

Manufacturing and tertiary sector is Services sector. In India out of total population, highest

share is engaged with Agriculture. In present scenario, agriculture is the only sector engaged

in highest percentage of population in Indian economy. After primary sector, the tertiary

sector and then industry sector provides highest employment. During economic fluctuations,

the less affected sector is agriculture sector since there is less impact of global economic

activity on the performance of agriculture. Whereas, manufacturing sector and services sector

are the most affected sectors during global economic fluctuations. Severe ups and downs in

macro economic variables like GDP, Export-Import, Rupee currency devaluation, Inflation,

Employment have direct impact on performance and profitability of manufacturing sector and

service sector. Precisely, the effects of the global crisis have directly impacted on some

important macroeconomic variables.

Three general indicators stand out in terms of their quite sudden deterioration during

any downfall of an Economy. The decline in the foreign exchange reserves held by the

Reserve Bank of India, the fall in the external value of the rupee especially in exchange of the

US dollar and the decrease in stock market indices are these three indicators. Similar

observations were noted down in these three indicators during downfall of year 2008. GDP is

considered as the broadest indicator of economic growth and output. Real GDP considers

inflation into account, which allows for comparisons against some other historical time

xiv

periods. There was severe decline in GDP percentage growth rate in 2008-09 (which was

6.77% only). In its previous year (2007-08), GDP growth rate was 9.32%. Further, because of

inflation and rupee devaluation vis-a-vis in exchange of US dollar, performance of Economy

deteriorated and reflected in terms of GDP. So, in 2012-13 GDP growth rate was just 4.99%.

Economic fluctuations or frictions are always present in economy but get worsened in

crisis period. Profitability of manufacturing sector and service sector had serious impact in

economic downfall. Several steps were also taken by Reserve Bank of India (RBI) through

monetary policy. Changes in repo rate, reverse repo rate, rise and fall in CRR, and SLR were

done by RBI. To control money supply in economy, these steps were effective during

financial frictions. Fluctuations in economy are not always harmful but, improve the working

of the economy.

In the present study, introduction of Global economic fluctuations and phases of

Global and Indian economic fluctuations was studied in Chapter 1. In these fluctuations,

impact on macroeconomic variables like GDPfc, Inflation, and IIP have been described.

Manufacturing process comprises of converting raw materials, components

or parts into finished or semi finished goods that meet a customer's specifications or

expectations has been considered. Manufacturing generally employs a man-

machine setup with division of labor in a large scale production. In its earliest form,

manufacturing was usually carried out by a single skilled artisan with assistants. According

to economists, manufacturing had been a wealth-producing sector of an economy. In chapter

1, impact of fluctuation on manufacturing sector had also been covered by the researcher. The

three phases of economic frictions were considered in this research study. The three focused

phases were marked as follows:

First: From 2002 to 2005,

Second: From 2005 to 10 and

xv

Third: From 2010 and thereafter.

In 2002, the “dotcom bubble” busted and declined the value of dollar steadily. Sharp

drop in stock prices in Nasdaq was observed, which continued till 2004. In the year 2008,

sub-prime crisis which was also known as financial crisis arose and it had impact global

economies till 2010. After Great Recession of 2008-09, problem of Unemployment was faced

by major developed economies. Recovery was very difficult for them in post recession

period. Almost all countries were adjusting their economies with monetary policy measures.

Germany was the country where recovery after recession was prompt and strong. Germany

used its previous experience of WWII to stabilise the economy.

In this chapter, an analytical study which was carried out in 2011 by the researcher

has also been presented with predictions of GDP for the year 2013 for India and USA. In the

year 2014, the predictions based on the study were also cross checked by least squares

method by the researcher. Further, the two major sectors of economy manufacturing and

service sector and their contribution in GDP had been described in this study. Impacts of

economic fluctuation on these two sectors were also studied in this section.

In chapter 2, detailed study of available research related to this topic has been done by

the researcher. To give gist of all available research papers and thesis, its literature review has

been classified in three different categories. The first has been about Research on economic

fluctuations. Here, all global and Indian economic fluctuations and their impact on

macroeconomic variables were studied. Second sub point of Literature review chapter was

focused for service sector i.e. economic fluctuations and impact on Indian service sector has

been studied in detail. Here, the focus was mainly on IT sector, which has been considered to

be the highest affected sector in global economic fluctuation of 2008 and also taken as a case

study in this research further. Moving ahead, the last point of Literature review has been

xvi

about manufacturing sector i.e. impact of economic fluctuations on Indian manufacturing

sector. Here, the focus was based on FMCG sector since according to the researcher, this

sector has been least affected sector in 2008 global economic crisis.

In chapter 3, the statement of problem, objectives, Hypotheses and Variables studied

by the researcher have been discussed.

Objectives of the Study were finalised as follows:

1. To study the impact of change in GDP growth rate due to change in profit ratio of

manufacturing sector.

2. To study the impact of change in GDP growth rate due to change in profit ratio of

Service sector.

3. To examine and understand the growth rate of manufacturing sector in comparison

with growth rate of the GDP.

4. To examine and understand the growth rate of service sector in comparison with

growth rate of the GDP.

5. To analyse consequences of Inflation on Profit ratio of manufacturing sector.

6. To analyse consequences of Inflation on Profit ratio of service sector.

7. To study the impact of Inflation on GDP.

Development of Hypotheses for the study:

In order to evaluate and quantify the objectives, null and alternate Hypothesis for each of

the objective were developed. Null Hypothesis and Alternate Hypothesis were denoted by

first suffix, either zero or one respectively. The developed hypotheses have been listed as

follows:

Hypothesis: 1

H01: Change in profit ratio of Manufacturing sector has insignificant impact on GDP

growth rate.

xvii

H11: Change in profit ratio of Manufacturing sector has significant impact on GDP

growth rate.

Hypothesis: 2

H02: Change in profit ratio of Service sector has insignificant impact on GDP growth

rate.

H12: Change in profit ratio of Service sector has significant impact on GDP growth

rate.

Hypothesis: 3

H03: Manufacturing sector has insignificant contribution in the growth of GDP.

H13: Manufacturing sector has significant contribution in the growth of GDP.

Hypothesis: 4

H04: Service sector has insignificant contribution in the growth of GDP.

H14: Service sector has significant contribution in the growth of GDP.

Hypothesis: 5

H05: Inflation rate has no effect on Profit ratio of manufacturing sector.

H15: Inflation rate has effect on Profit ratio of manufacturing sector.

Hypothesis: 6

H06: Inflation rate has no effect on Profit ratio of service sector.

H16: Inflation rate has effect on Profit ratio of service sector.

Hypothesis: 7

H07: Inflation has no significant effect on GDP.

H17: Inflation has significant effect on GDP.

Finalization of the variables to be used for the study:

In this study, variation in GDP growth rate has been studied and compared with

variation in PBDIT of manufacturing and services sector. Change in inflation has impact on

xviii

various factors such as commodity price, demand, market fluctuations and so on. Change in

GDP growth rate with respect to Inflation has also been considered. Based on the objectives

and the Hypotheses formulated, finalization of the variables to be used for the study has been

carried out. Dependent and Independent variables were listed as follows:

Independent Variables:

i. Inflation,

ii. PBDIT or EBIT of Manufacturing Industries and Service Industries.

Dependent Variable: GDP growth rate.

Research Methodology Adopted for the present study has been elaborated in Chapter

4. Primary and secondary data were considered in this study. The information of GDP,

Inflation, IIP, PBDIT or EBIT of Manufacturing industries and Service industries was

obtained from secondary data. Annual reports of RBI, statistical data base of RBI, reports of

CSO and planning commission were referred for data collection purpose. Data of some of the

Industries was also collected from CMIE reports. The primary data was collected from the

Industry experts. In the Interview session of around 20 Industry experts, data validation had

been done for the present study. Econometric models were developed by the researcher and

have been solved in this study by using various statistical tools available. The models were

explained thoroughly in this chapter.

Corporate cases had been studied in Chapter 5 as case studies to demonstrate the

actual impact on the specific companies during the Economic slowdown between 2003 and

2012. Seven companies were considered as a representative of that particular sector and

analysed their profitability in the period from year 2003 to 2012. Hindustan Unilever Limited

(HUL) and ITC have been considered as a representative of FMCG sector. Glenmark and Dr.

Reddy’s have been considered from pharmaceutical sector of manufacturing sector. From IT

sector TATA Consultancy Services (TCS) and Infosys Ltd have been considered as a

xix

representative of Service sector. Reliance Capital Ltd has been considered for service

provider in financial activities. Impact of economic fluctuations on the performance and

profitability of these companies has been studied thoroughly.

Inflation is another important macroeconomic variable. GDP had impact on

profitability of Manufacturing industries and Service industries. Hence, impact of this

variable on profitability of Manufacturing industries and Service industries were studied

separately in chapter 6.

Data Analysis, Interpretation and Model estimation were presented in chapter number

7. Three different models were developed by the researcher and proved by analysis of the

data obtained. The data was analysed with the help of the statistical package SPSS 17. The

mean scores arrived would be put to various statistical analysis using various statistical tools

in order to test the research hypothesis.

Model 1: Karl Pearson's Correlation Coefficient based on two variables: X (PBDIT of

manufacturing sector) and Y (GDPfc at constant prices with base year 2004-05).

Model 2: Karl Pearson's Correlation Coefficient based on two variables: X (PBDIT of

service sector) and Y (GDPfc at constant prices with base year 2004-05).

Model 3: Granger’s causality test has been used for this model for comparison of GDP and

Inflation.

Various statistical tools had been used for all these models. First two models are

purely based on Regression and Correlation analysis. Third model of Granger’s causality test

was used to check the impact of Inflation on GDP. It was assumed whether, GDP is a

Granger Cause of Inflation or not. The calculated Mean of GDP at factor cost at constant

prices at 2004-05 base year was 7.51% and Median was 7.97% for 11 years duration that is

2002-03 to 2012-13. Skewness was also measured to check whether distribution of the series

xx

is left skewed or right skewed.GDP at factor cost for ten years was observed to be negatively

skewed. This inferred that the series is normally distributed.

To validate secondary data which was analysed with the use of different statistical

tools, interviews of around 20 Industry experts were conducted by the researcher. This

comprised VP of Companies, Executive Officers of Companies and some were

Entrepreneurs.

The following were the findings of the Study:

1. The impact on GDP growth rate on the profitability of manufacturing sector has been

shown by the study. The change in profit ratio of manufacturing sector has a positive

impact on GDP growth rate. So, one unit increase in profit ratio of manufacturing sector

was observed to depict increase GDP growth rate by 0.238 units. R2 value for the model

was observed to be 0.795 which indicated that 79.5 % of the variations in the GDP

Growth rate have been explained by change in profit ratio in manufacturing sector and

Karl Pearson Coefficient was observed to be positively correlated. Therefore, the

hypothesis “Change in profit ratio in manufacturing sector is a significant variable in

influencing GDP growth rate” was accepted.

2. In Model-2 with the use of Correlation and Regression, it has been observed that 60.8 %

of the variations in the GDP Growth rate are explained by change in profit ratio in

service sector. R2 value for the model was 0.608. Therefore, the hypothesis “Change in

profit ratio of Service sector has insignificant impact on GDP growth rate.” was

accepted.

Paired Differences (paired sample test) and Paired Correlation Coefficients (Standard

error mean) 0.72 which was more than 0.01, and Correlation coefficient was 0.77 so it

was inferred that there was no significance difference in the growth rate of

manufacturing sector with respect to growth rate of the GDP. It was also seen that,

xxi

Manufacturing sector has significant contribution in the growth of GDP” was accepted.

Paired Differences and Paired Correlation Coefficients (Standard error mean: 0.34 >

0.01, and Correlation coefficient: 0.81 so there has been no significance difference in the

growth rate of service sector with respect to growth rate of the GDP. Therefore, “Service

sector has significant contribution in the growth of GDP” was accepted. Regression and t

test (R= 0.789(a), R2= 0.622, t-Test significance level = 0.83) Inflation was observed to

be not a significant variable in influencing change in profit ratio in manufacturing sector.

Therefore, the Hypothesis “Inflation rate has no significant impact on profit ratio of

manufacturing sector” was accepted. Regression and t test R= 0.842(a), R2= 0.708, t Test

significance level = 0.668). Inflation has not been a significant variable in influencing

change in profit ratio in service sector. Therefore, “Inflation rate has no effect on profit

ratio of service sector” was accepted.

3. Granger’s Causality Test Model 3, (R = 0.822, R2 = 0.675) P = 0.4152, the pair wise

Granger causality test showed probability 0.4. (p) > 0.05(α) Therefore, “Growth rate of

GDP does not granger cause on inflation” was accepted.

Recommendations emerged from the study have been enumerated as follows:

Down turn in economy was observed to be not always a negative aspect of economy,

whereas it gives the required pumping up to the economy for the future.

In recessionary phase, it has been advised that businesses may channelize resources or

money into new opportunities.

For the Giant industries or settled industries in market the recommendation would be

to acquire new business, as their valuations are lower and attractive in these times.

The present study was found to have the following limitations:

The study was carried out with assumptions regarding time, study area and sample

size. The study was confined to the duration of 2002-03 to 2012-13 only.

xxii

The study was focused only to the impact of economic fluctuations and inflation on

profitability of manufacturing and service sector industries on GDP growth rate of

Indian economy.

In the present study, only two sectors of economy had been considered, whereas

Agriculture sector also can get some consideration to check the impact of fluctuation.

The restriction of the research only to Profitability and performance of manufacturing

sector and service sector on the basis of only PBDIT/EBDIT has been another major

limitation of the study.

Future scope of the Study:

The present study would bring greater nuances in the study of economic fluctuations

in India with reference to profitability of industries by focusing on manufacturing sector and

service sector. The present study was expected to open up avenues for further research on

macro economic variables other than GDP at factor cost and inflation. The profitability of

industries of manufacturing sector and service sector has been analysed in this study. It would

also open avenues to expand the study to measure other indicators of performance of

industries. There has been greater impact of global economic fluctuations in India which

affected many industrial sectors of the economy. The study would open a new door to look at

those sectors which were also severely affected in terms of employment pattern, export

earnings.

1

Chapter 1

Introduction

Indian Economy brief description:

Indian Economy is a developing economy and is dependent on global economy.

Overall growth performance and stability in the global economy is very important for

the growth of the Indian economy. Developing economies are dependent on

developed economies hence; economic fluctuations in world market have severe

impact on the developing economies like India. This impact is on Trade Pattern,

Export, Import and on Domestic market, too. Domestic market is the market of all

three sectors of economy viz; Agriculture, Manufacturing, and services sector.

Growth of these sectors reflects growth in GDP also. Therefore, performance pattern

of these sectors decides health and growth of economic variables. Performance of

Manufacturing and Services sectors can be measured on the basis of Sales, Export,

Employment Generation and Profitability.

However, Agriculture sector is completely dependent on the monsoon and the

rainfall and independent (to a certain extent) on economic fluctuations. Share of

agriculture in Indian economy had progressively declined to less than 15% due to the

focused and high growth rates of the industrial and service sectors. The exisiting

scenario is that majority of India’s around 70% population is in rural areas. India’s

food supply depends on production of cereal crops, as well as production of fruits,

vegetables and milk to fulfil the demands of the growing population. Therefore, a

productive, sustainable, competitive and diversified agricultural sector will need for

growth of an economy at an accelerated pace. India is the world’s largest producer of

2

milk, pulses and spices and has the world’s largest cattle herd. India also has the

largest area under cultivation for wheat, rice and cotton. It is the second largest

producer of rice, wheat, cotton, sugarcane, farmed fish, sheep & goat meat, fruit,

vegetables and tea.

The agriculture subsector like dairy sector has high potential to grow and

expand. The livestock sector, basically due to dairy sector, contributed over a quarter

of agricultural GDP and is a main source of income for around 70% of India’s rural

families, most of them are poor and the dairy sector at local level is headed by

women. Growth in milk production is at around 4% per annum, but future domestic

demand might be expected to grow by at least 5% per annum.

Fig.1. Sectoral Composition of GDP of India

Source: RBI annual report, 23/8/13

The highest share in GDP was seen by Service sector in the year 2010-11,

which was 65.2 %. Industrial sector has been contributed 20.3 % in the GDP growth

rate and 14.5 % contributed by agriculture sector in the same year. There was classical

3

journey pattern for any under developed economy towards a developed economy. For

a developing economy, when per capita income is started to grow from a very low

level, the share of agriculture in total GDP is declined as the proportion of people

employed in agriculture. This decline is continued till a minuscule share of population

is employed in agriculture at the high-income level. Manufacturing has been the

leading sector in growth at low-income levels, share of this sector in GDP and the

proportion of people employed in it has been rising. This share eventually stabilised

and then started to decline as the share of services sector increased at high-income

levels.

India has followed the standard pattern with respect to contribution of various

economic sectors in GDP but not with respect to the share of employment. The share

of labour force in agriculture employment remains too high and that in manufacturing

to be too low relative to the standard pattern of the economic development. The

services sector in India has always grown faster during the last 55 years than the

tradable goods sector (manufacturing, agriculture and mining). Part of this is due to

the traditionally slower growth of the Agriculture sector that underlies the

conventionally expected structural transformation from agriculture to manufacturing.

In India, in the eighties, however, the rate of growth of services has accelerated above

that of the manufacturing and the growth rate gap has widened in the nineties. This

was mainly due to the phenomenal growth of exports of software and IT enabled

services. This has raised expectations from the services sector. During 2012-2013,

GDP growth rate was only 4.5 % due to insignificant growth rate of Agriculture

(1.5%) and 1% of Industry, whereas, the services sector recorded a good growth rate

of 7%. In 2013-2014, Industrial growth rate was expected only 0.7% (negligible

4

growth rate of Manufacturing sector) and services are expected to record 6.9% growth

rate.

Flashback of Growth of Indian Economy in last 50 years.

Indian economy had experienced some major policy changes in early 1990s. The new

economic reforms, which are known as, Liberalization, Privatization and

Globalization (L-P-G) aimed at making the economy as fastest growing economy in

the world. The series of introduction of reforms undertaken with relation to industrial

sector and trade sector as well as financial sector aimed at making the Indian

Economy more efficient.

From the analysis of Indian Economy in the last 50 years, cyclical fluctuation

in GDP has been observed after every 6-7 years during the Pre-liberalisation period.

There was huge downfall of GDP in 1965-66 as in third five year plan (1961-66).

GDP growth rate was reported to be -3.65%. In that scenario, contribution of

Agriculture sector was very less. The Annual growth rate of Agriculture was -13.47%

according to planning commission of India. GDP improved gradually in three annual

plan period i.e. in 1966-67 with the help of services sector. Annual growth of services

sector was 2.80 % in 1965-66 which improved up to 3.105 % in 1966-67. There was

fast growth in GDP in 1967-68 when it reached to 8.14%. The highest contribution

was from agriculture sector but performance of manufacturing sector was not up to

the mark. There was only 3.03 % annual growth rate in manufacturing sector which

was 2.36 % in 1966-67. Again in 1968-69, because of monsoon failure, the

performance of agriculture sector fell and ultimately, the GDP went down to 2.6%.

5

Fig.2: Growth Rate of GDP in percentage YoY:

Source: RBI

It was seen that, the sharp deterioration in the economic situation of the

country in 1979-80. Large part of the country was gripped by severe drought and

heavy rainfall and resulted in decline in agricultural production. The sharp reduction

in agriculture production by 10% compared to its previous year has been observed.

Average Annual Growth Rate of GDP at factor cost at Constant 1993-94 Prices was -

5.2% in annual plan of 1979-80. There had been severe inflationary pressure during

1980s. In 1980, (Wholesale Price Index) WPI increased by 19.9%. The BOP (Balance

of Payment) was under pressure in 1979-80 due to sharp rise in crude oil prices. The

exports were reduced by higher import prices and domestic constraints.

In 1979-80, difficulties had been faced in almost every sector, partially in

domestic market and partially in international market by economy. While the growth

rate of the primary sector in the 80’s was slightly higher than that of the 60’s and 70’s,

a steady increase in growth rate over the successive decades had been witnessed by

tertiary sector. In 80’s the growth rate of GDP was 4.9% per annum. In the decade of

1980-81 to 1990-91, the growth rate of tertiary sector was recorded 6.7% per annum

and the growth rate of manufacturing sector was seen to be 7% per annum. The

6

tertiary sector had been the fastest growing sector in 90’s. However, moderate growth

was observed for secondary sector.

With the decision of reforms and liberalize the Indian economy in July of

1991, a new chapter for India dawned and for her billion plus population. This period

of economic transition had a tremendous impact on the overall Indian economic

development. Almost all major sectors of the economy had its effects. In post

liberalisation period, growth rate of service sector was noticeable. On an average, the

services sector grew slower than Industry sector from 1950 to 1990. Growth in service

sector picked up in 1980s and accelerated in 1990s when it averaged 7.5% per annum.

The averaged growth rate of services sector was 7.1% for the period 1980-81

to 1989-90. In July 1991, with the announcement of liberalisation, India opened the

economy with dismantled import controls, lowered custom duties and devalued the

currency. Indian Economy abolished licensing controls on private inputs, dropped tax

tare and broke public sector monopolies. Subsequent to that, the growth during 1990’s

was stronger and less volatile. Impact of reforms was also seen in terms of higher

industrial growth. The five year averages of growth rates during 1992-93 to 2001-02

ranged from 5.5% to 6.7%. The slowdown in the Ninth plan (1997-2002) was related

to Agriculture and Industry sector. During this period, the services sector registered a

remarkable growth rate of 7.9% per annum. The expansion of services accelerated

hence 2002-03. In 10th

Five Year Plan (2002-2007) services revealed growth at a rate

of 8.8% per annum. The first year of 11th

plan (2007-2012), the rate of growth was

8.8% in GDP. However, in second year of 11th

plan (2008-09), it had been only 6.7%

due to global economic recession and slowdown in Indian Economy.

7

Growth pattern of services sector was more robust. For service sector, 10.3%

and 10% growth rate respectively in the above years was witnessed. Services sector

was the only significant contributor to achieve a growth rate of 6.7% in 2008-09. The

growth rate of this sector was 10.5% in 2009-2010 and 9.7 % in 2010-2011. But it

decelerated to 6.6% in 2011-2012. Overall, in 11th plan, the growth rate of services

sector was recorded 9.4% as annual growth rate. This was higher than the growth rate

of Agriculture an Industry sector in the same period. Indian economy expanded by

5.7% in the first quarter (Apr-Jun) of Financial year 2014-15 which has been the

highest in last 2 years.

In this study, profitability of industries and GDP growth rate with special

reference to Indian economic fluctuations has been described by the researcher.

India’s economic progress for, the period between 2003 to 2012-13 has been an

important phase in this study.

Fig.3. Ten Years Study of GDPfc at constant prices:

Source: CSO

Four broad phases have been carved out by the researcher for this period:

I. FY-03 to FY-08 where GDP averaged growth was 8.68%,

8

II. A year of low growth in FY-09 when GDP growth was 6.72%,

III. Two years of recovery with 8.59% and 8.91% growth in FY 2009-2010 and

FY 2010- 2011 respectively.

IV. Followed by two years of low growth with an average rate of 5.58%.

The Indian economy was seen a typically services oriented till 2011. The

government focused on industry in its New Manufacturing Policy in 2011.

This had aimed to take the manufacturing sector to a growth path of 15-16%

such that its share in GDP would increase to 25% by 2022.

Various Macro Economic variables had been considered in this study. The

variables were described briefly as follows:

GDP:

GDP is one of the major macroeconomic and important variables, recognised

worldwide. It is an aggregate measure of total economic production for a country.

Gross Domestic Product represents the market value of all goods and services

produced by the economy during the period measured, normally one year. It is

considered as one of the most important measures of how well or poorly an economy is

performing. It comprises of personal consumption, private inventories, government

purchases, paid-in construction costs and the foreign trade balance (exports are added,

imports are subtracted). In this research, GDP as a macroeconomic variable has been

specifically considered by the researcher rather than Net Domestic Product (NDP) and

Net National Product (NNP). GDP is considered to be the broadest indicator of

economic output and growth. The NDP equals the GDP minus depreciation on a

country's capital goods.

9

NDP accounts for capital that has been consumed over the year in the form of

housing, vehicle or machinery deterioration. The depreciation accounted for it is often

referred to as "capital consumption allowance" and represents the amount of capital

that would be needed to replace those depreciated assets. If the country is not able to

replace the capital stock lost through depreciation, then the GDP will decline. In

addition to this, a growing gap between GDP and NDP indicates increasing

obsolescence of capital goods, whereas a narrowing gap means that the condition of

capital stock in the country is improving. The Bureau of Economic Analysis issues its

own analysis document with each GDP release, which had been a great investor tool

for analyzing figures and trends, and reading highlights of the very lengthy full

release.

“Factor cost GDP (GDP(fc)) generally provides a more accurate picture of

economic developments” as stated by IMF in Economic Times, 10th

October 2013.

Central Statistical Office of India has also considered GDP(fc) as a major indicator to

calculate GDP growth rate. Accordingly, in this study, GDP has been considered as

GDP(fc) unless mentioned explicitly. Real GDP takes inflation into account, allowing

for comparisons against other historical time periods.

GDP(fc): Economic growth rate =

100

1

12

YearGDP

YearGDPYearGDP

Inflation:

Inflation means a persistent rise in price levels of commodities and services,

which leads to a decline in currency’s purchasing power. Inflation can be measured in

Consumer Price Index (CPI) or Wholesale Price Index (WPI). A consumer price

index measures the changes in the price level of consumer goods and services

purchased by households. A CPI can be used to index (i.e. to adjust for the effect of

10

inflation) the real value of wages, salaries, pensions, for regulating prices and for

deflating monetary magnitudes to show changes in real values.

CPI=

* 100

The Wholesale Price Index or WPI is the price of a representative basket of

wholesale goods. The WPI focuses on the price of goods traded between corporations,

rather than goods bought by consumers, which is measured by the Consumer Price

Index. The purpose of the WPI is to monitor price movements that reflect supply and

demand in industry, manufacturing and construction. In this study, Inflation (CPI) has

been considered. The new Consumer Price Index (CPI) (combined) as the key

measure of inflation came in force since April 2014.

IIP:

Index of Industrial Production (IIP) is a composite indicator to measure the

short term changes in the volume of production of basket of industrial products during

a given period with respect to chosen base period. The All India Index of Industrial

Production (IIP) was first released by Office of Economic Adviser under the Ministry

of Commerce & Industry with considering 1937 as a base year. The Central Statistical

Organisation (CSO) started compiling & releasing IIP since 1950 with consideration

of 1946 as a base year. The base year of IIP was since revised successfully to 1951,

1960, 1970, 1981, 1993-94 and currently to 2004-05. IIP comprises 682 items which

include 61 from mining & quarrying, 620 from manufacturing and 1 from electricity

sector and having the weightage of 14.16%, 75.53%, and 10.32% respectively in the

all India IIP. On the basis of recommendations of Standing Committee on Industrial

Statistics (SCSI), IIP gets revised periodically by changing its base year time to time

to capture the changes in structure and composition of industry due to technological

11

changes, economic reforms and change in consumption pattern of people. This gives a

realistic approach for computation of IIP.

Measuring economic performance over a span of time has been a key factor in

economic analysis and a fundamental requirement for policy-making. Short-term

indicators play an important role in providing comparison indicators. Among these

short-term indicators, the Index of Industrial Production (IIP) has historically been

one of the most well known and well-used indicators to measure the real growth of

industrial sector. United Nations Statistics Division (UNSD) (formerly known as

United Nations Statistical Office, UNSO) recommends quinquennial revision of the

base year of IIP to capture the changing composition of industrial production and

emergence of new products and services.

Fig.4.IIP Growth rate in % since 2009

(Source: Economic Survey: 2013-14)

In the above graph it has been depicted that, the IIP growth was 8.20% in

2010-11 with very good performance of Manufacturing sector and service sector.

Since inflation was high in 2011-12, it had impact on IIP growth rate which declined

in 2011-12 up to 2.9% further it worsen in 2013-14 to -0.1%.

12

The IIP measures volume changes in the production of an economy, and

therefore provides a measurement that is free of influence of price changes, making it

an indicator of choice for many applications. An index is a composite indicator, an

absolute number free of units of measurement and generally expressed as a percentage

with reference to a chosen point. It is a number that shows the percentage change(s) in

a variable or group of variables during a particular period with respect to a chosen

reference period, called the base period.

Industrial production refers to the outputs of all industrial activities, which are

part of the International Standard Industrial Classification (ISIC). In India, National

Industrial Classification (NIC) is developed in harmony with the ISIC. NIC is the

basis for many classifications of all economic activities within the boundary of the

country. The term ‘industry’ is used in a restricted sense of production of tangible

commodities, excluding agricultural goods and services. However, in the collection of

IIP, the sectors are limited and thus industrial production for the purpose of IIP means

that of the sectors of Manufacturing, Mining and Electricity.

Computation of IIP: IIP is generally computed as the weighted average of

production related to all the industrial activities. Laspeyre’s fixed-base formula has

been used for the calculation of the index, which can be expressed mathematically as

follows:

Lt =

× 100

Where,

Wi0= Weight of the ith

item in the base year

Ri = Production relative of the ith

item = Pit/Pi0

Pit = Production of the ith

item in the period t

Pi0= Production of the ith

item in the base period

13

The all India IIP is a composite indicator that measures the short-term changes

in the volume of production of a basket of industrial products during a given period

with respect to that in a chosen base period so that the year is assigned an index level

of 100. It is compiled and published monthly by the Central Statistics Office (CSO)

with the time lag of six weeks from the reference month.

Year 2008 was a destructive year for global manufacturing sector. Industrial

production dropped in last three months of 2009 by 3.6% and 4.4% respectively in

America and Britain (equivalent to annual declines of 13.8% and 16.4%). Half-empty

freighters are just one sign of a worldwide collapse in manufacturing sector. In

Germany, in December 2008, machine-tool orders were 40% lower than a year

earlier. China's 50% of the orders got cancelled and toy exporters faced heavy loss.

Export of Taiwan’s notebook computers fell thrice in the month of January. In

America, car assembling reduced by 60% in the month of January, 2008.

Contributions of these sectors reflect in growth rate of GDP and profitability of the

industries. Profit Before Interest and Tax (PBIT) of last 10 yrs of manufacturing &

service industry was considered in this study. The growth in (PBIT) has been

examined. There appear to be two phases of growth in profit. The first period was up

to Financial Year 2007-08 where growth in profits was robust and the sector was

upbeat. This was also the period when the Industrial Production growth rate was

buoyant and was reflected in sales. However, subsequently, the sector did come under

pressure. While there was a recovery in FY-10 after the decline in profit in FY-09,

growth in profits has been low and uneven with negative growth once again in FY-12.

14

1.1 Global Economic Fluctuations:

Many developed and underdeveloped countries underwent a few severe economic

downturns in the 20th century. The decade long stagnation of Japanese economy and

East Asian crises had serious impact on world trading system. Contribution by these

economies in world GDP is high and therefore this regional economic slowdown

affected world trade and it had been considered as global slowdown.

GDP of USA was 3.3% in 1991 which was improved only up to 6% in that

decade. Finland underwent severe economic depression in 1990–93. Financial

regulation was managed properly by central bank in the 1980s, particularly removal

of bank borrowing controls and liberation of foreign borrowing, combined with

strong currency. Hong Kong, Malaysia, Laos and the Philippines were also hurt by

the slump. Less affected countries were

China, Taiwan, Singapore, Brunei and Vietnam, as all suffered from a loss of

demand and confidence throughout the region.

In late 90s there was Asian Financial Crisis, which started in Thailand with

financial collapse of Thai Baht. As the crisis spread, most of Southeast Asian and

Japanese currency, devalued stock markets and other asset prices also. This resulted

in a precipitously rise in private debt. The most affected countries by this crisis were

Indonesia, Thailand, and South Korea.

In 1998, the growth rate of Philippines dropped virtually to zero. Only Singapore

and Taiwan proved relatively insulated from the shocks of downturn, but both

countries suffered serious hits of recession in passing. By 1999, analysts saw signs

that the economies of Asia were starting to recover. After the year of the 1997 Asian

15

Financial Crisis, some economies in the region were working towards financial

stability on financial supervision.

GDP growth rate of Thailand which was -1.4% in 1997, became worst in 1998 as

it touched -10.5%. But with the increase volume in export of goods and services

country could achieve 4.8% GDP growth rate in 2000. The Philippine GDP contracted

by 0.6% during the worst part of the crisis, but grew by 3% by 2001.

Bank borrowing increased at its peak over 100% a year and asset prices

skyrocketed. Real GDP of Finland contracted about 14% and unemployment rose

from 3% to 20% in four years. The collapse of the Soviet Union in 1991 led to a 70%

drop in trade with Russia.

When several European countries faced the collapse of financial institutions and

organisations, huge government debt and continuously rising bond yield spreads in

government securities the then European sovereign debt crisis started at the end of

year 2008. With the collapse of banking system of Iceland, the crisis spread

primarily to Greece, Portugal and Ireland during 2009. Concerns were being raised

over Italy, Spain and the European banking system. Moreover, there were

imbalances within the euro zone. In the (European Union) EU, especially in some of

the countries, sovereign debt increased sharply due to bank bailouts. The debt crisis

was mostly centred on condition of events in Greece, where the cost of financing for

government debt had increased. The public debt shot to 121 percent of GDP in 2010

from 113.4 percent in 2009. In 2010, EU forecasted Greece worse, with the deficit

seen at 12.2 percent of GDP and national debt reaching 124.9 percent. This was the

highest debt to GDP ratio in the EU that led Greek Government into debt crisis. The

Euro zone crisis resulted from a set of combination of complex factors, which was

16

including the globalisation of finance. Easy credit conditions during the 2002–2008

period encouraged high-risk lending and borrowing practices. The financial crisis of

2007–08 being the outcome of bursting real estate bubbles resulted into international

trade imbalances and. The Great Recession of 2008–2012 then followed.

To fight the crisis, raising taxes and lowering expenditures were incurred by

Governments. This took place due to social unrest and significant debate among

economists. In mid-2012, due to successful fiscal consolidation and implementation

of structural reforms in the countries being at most risk and various policy measures

taken by EU leaders and the European Central Bank (ECB). The measures were

responsible for reducing volatility in the financial markets and improving liquidity

for financial stability. Euro Zone improved significantly and interest rates dropped

steadily in October 2012.

1.2 Phases of Economic Fluctuation:

The worst financial crisis in the history of the World Economy, obstruct the USA

and many other countries which started in 1929 and the Great Depression followed.

The second-worst struck world economy in 2008 and then Great Recession followed.

Economic fluctuations are powerful determinants of economic activity.

a) 2000-2005:

In 2002, “the stock market crash" or "the dotcom bubble bursting" and 9/11

event of 2001, witnessed the sharp drop in stock prices during 2002 in stock markets

across the United States, Canada, Asia, and Europe. The dollar declined steadily

against the Euro, reached a 1-to-1 valuation which was not ever seen since the Euro's

introduction. The International Monetary Fund (IMF) had expressed concern about

instability in United States stock markets in the months leading up to the sharp

17

downturn. The technology-heavy National Association of Securities Dealers

Automated Quotations (NASDAQ) stock market peaked on 10th March, 2000, which

hit an intra-day at high of 5,132.52 and closing at 5,048.62. The Dow Jones Industrial

Average (DJIA), a price-weighted average (adjusted for splits and dividends) of 30

large companies on the New York Stock Exchange, peaked on January 14, 2000 with

an intra-day high of 11,750.28 and a closing price of 11,722.98. In 2001, the Dow

Jones Industrial Average (DJIA) was largely unchanged overall but had reached at

11,337.92 (11,350.05 intra-day) on May 21st which was secondary peak.

The downturn might be viewed as a reversion to average stock market

performance in a longer-term context. From 1987 to 1995, the Dow increased each

year by about 10%, but from 1995 to 2000, the Dow saw a rise of 15% a year. While

the bear market began in 2000, by July and August 2002, the index had only dropped

to the same level it would have achieved if the 10% annual growth rate followed

during 1987-1995 had continued up to 2002. NASDAQ found difficult to progress in

year 2002. After the dot-com bubble, the asset inflation had taken the form of a

housing boom in the United States. United States housing boom had a special toxic

element for future troubles in the form of sub-prime mortgages, which were sold

aggressively, in particular since 2002-03, to low-income people with no down

payments. The lending was often packaged at floating or adjustable flexible rates,

with a one- or two-year clause. Wick Simmons, Chairman and Chief Executive

Officer of The NASDAQ Stock Market stated on 10th

March 2003, that, Economic

conditions, regulatory delays and fundamental changes in the complexion of market

ultimately created challenges for them. In view of this, further it was stated by him

that, in such an environment also, NASDAQ improved the transaction quality and

transparency of its market for investors. NASDAQ supplied new, value added

18

services for listed companies. This reduced the effective cost of running the business

it was seeking to increase investor trust.

Fig.5: Nasdaq fall in 2002

Source: http://en.wikipedia.org/wiki/Stock_market_downturn_of_2002

b) 2005-2010:

The global financial crisis had its roots in the mid of the year 2007 and it

accelerated in the year 2008. The root cause for the financial crisis was the subprime

crisis i.e. unbridled lending by the major financial institutions.

The Indian Financial System had been robust and was not so adversely affected

by the economic meltdown (as compared to the western economies). Indian

Economy could survive primarily because the tight control mechanism of the

Reserve Bank of India (RBI), the Statutory Authority.

In the graph below, a composite Index of banking, currency, sovereign default,

and inflation crises, (BCDI index) and stock market crashes (weighted by their share

of world income) has been depicted.

19

Fig. 6: Varieties of Crises: World Aggregate, 1932–2010.

A composite Index of banking, currency, sovereign default, and inflation crises, and stock market

crashes (weighted by their share of world income) Source for Data: IMF working paper, December

2013.

The Index can take a value between 0 and 5 (for any country in any given

year) depending on the varieties of crises taking place in that year. (For example, in

1998; the index increased by a value of 5.0 for the Russian Federation, because there

was a currency crash, a banking and inflation crisis and a sovereign default on both

domestic and foreign debt obligations existed in the same year). The index was then

weighted by the country’s share in world income. To compile the BCDI+ index in the

IMF research paper, many of the countries were considered in their sample (a subset

of the 66-country as a sample, except for Switzerland) for the period 1864–2006. In

United States, downturn in index in 2008 because banking crisis and stock market

20

crash. For Australia and Mexico it also posted a reading of 2 (currency and stock

market crash). For every country, the reading of the BCDI + index can range from

zero crises per year to a maximum of six (banking, currency, inflation, domestic debt

crisis, external debt crisis and equity market crash).

In the above diagram, it was observed that major economic shocks since 1932

were explained graphically. The great depression started in 1932, which continued till

1938 and then WWII in 1941. The repercussions of WWII continued till 1955 on

index. Another major downfall in Index was in 1970’s due to rise in Oil Prices. The

latest significant drop in index took place in 2008 with global economic crisis.

2010 and After:

Major economies, which ramped up fiscal stimulus after the recession, faced

debt loads that made further government spending to be difficult. After Great

recession in 2008-09, US unemployment was near to 10% in 2009 and started

becoming worse. Germany, UK and France also experienced stretched unemployment

rates of 8.0-10%. Spain, Portugal, and Italy saw youth unemployment shooting up to

25-50%. Greece was a battered economy on many fronts. Therefore, the focus of the

recovery in all these countries was on generation of employment.

According to World Bank, Economic news the first two months of 2012 were

positive, but the global recovery remained fragile. After expanding 4.2 percent in the

year 2010, global GDP slowed down to 2.7 percent in the year 2011. With a current

situation of economy, the world economy had missed the V- and U-shaped recovery.

In Germany, the recovery after the Great Recession was prompt and strong. This

showed the strong boom after 2005 until the eve of the downturn and the V-

shaped recession. This corroborated the view that the recession was mostly demand

driven. Since the competitiveness of German industries was high, the recovery

21

process of the world economy slump quickly after 2009 and transformed into higher

demands for the German firms. Moreover, because part of their experienced work

force was idle, it was possible for German companies to respond to these demand

impulses immediately. After several months of heightened uncertainty, conditions in

financial markets improved significantly. During the first three months of the year

2012, with a stretch on sovereign debt, both high-spread European and developing

economies came off their late 2011 highs in response to ECB policy steps, and the

successful restructuring of Greek debt problem.

In emerging market economies and Euro Zone countries, multilateral

institutions like the IMF were pushing for structural reforms in developed countries.

For a country like Japan, structural reforms in labour market called for a radical

departure from long held traditions which include US recoveries. This proved slow

pace in creation of jobs with consumption accounting for about 70 percent of demand.

Federal Reserve Bank had pressure to revive the economic forces and try to

discourage household savings in downturns by keeping policy rates at ultra-low levels

for sustained periods of time. Especially in developing economies, an even stronger

tightening of monetary policy triggered at the initial stage.

1.3 A comparative study of 2008 economic meltdown with reference

to impact on India and USA economy:

The slowing down of growth in the Indian economy, specifically in service

sector & industrial sector raised significant interest in business cycle indicators. The

analysis of the GDP data for India and US has been carried out. The outcome of the

analysis indicated that there exists a co relation between the GDP growth curve of the

22

US and India. Additionally, this data was also correlated with the economic

conditions and the happenings of India and US.

The curve fitting that has been used is generalized from a polynomial y = a +

bx to a polynomial of degree m.

Where

‘y’ = GDP growth rate,

‘x’ =corresponding year.

Thus the equation is

Where, m is the degree of polynomial considered for regression analysis / Least

squares method.

And depends on (m+1) parameters b0, …., bm.

Which give a system of (m+1) equations.

In case of a quadratic polynomial,

The normal equations are (summation from 1 to n).

23

MACRO Level Impact:

The Growth rate of India’s GDP has been compared with US GDP in the same

time period specifically since 1960 to 2010. The impact of recession in 2008 was

focused with GDP growth. The trend in GDP growth has also been calculated in

respect of US & Indian economy.

Fig.7: GDP trend of India since 1960 to 2010

Source: Reserve Bank of India, Handbook of Statistics on the Indian Economy 2006

A number of methods have been used by the researcher to relate Business

Cycle analysis to GDP growth rate & averaged trend of GDP over a period from 1960

24

to 1995. The statistical research indicated that in 1960 when GDP was 7.1% which

sharply dropped down in 1961 to 3.1% because of Huge Deficit Budget. From FY

1951 to FY 1979, the economy grew at an average rate of about 3.1% a year in

constant prices. In 1965, Devaluation of Rupee currency was the vital reason of

falling GDP. This picture continued till 1987 when GDP was only 3.8%. Again in

1991 when India adopted Liberalisation – Privatisation - Globalisation (L-P-G) in

balance of payment, the crisis led to increase foreign reserves, the GDP went down to

1.3% as expected. Subsequent to that, from 1992 to 1996 there was continuous growth

in GDP. The Observed figures were: 5.1% in 1992 which increased to 7.8% in 1996.

Reforms since 1991 in manufacturing, production, trade, and investment provided

new opportunities for Indian entrepreneurs.

India GDP 1996 to 2010:

In 1997, due to East Asian Currency Crisis, India’s GDP dropped drastically

to 4.8%. But in that crisis, the intervention of RBI controlled the fluctuations of

currency & repercussions were that in 1998, the GDP again increased to 6.5%. FY

2000 saw a global rise in prices, which was focused especially in commodities and

housing. As a result, in 2001, the GDP was only 3.7%.

The rupee dollar exchange rate appreciated from 48.6 to 41.3 (`/$) from 2002

to 2007. This boosted GDP rate from 8.7% to 9.8% in the same period. The trend

below depicted Lehman Brother’s Bankruptcy resulting into fall in GDP in 2008,

which led to world recession. In the following Business Cycle, it was observed that in

20 years, Trough stage took place for 5 times; i.e. in 1991, 1997, 2000, 2002 & 2008.

Peak was achieved in 1996, 2003 & 2007.

25

Fig.8: Calculatd trend of GDP of India

Source: RBI Annual Report Aug, 2011.(Author’s Calculation for trend)

In the above diagram, it has been observed that upward sloping trend line but

in 2002 & 2008 because of recession it fell down sharply. In these 20 yrs time span

fall in GDP occurred 4 times i.e. in 1991, 1997, 2002, & 2008.

The analysis also indicated that a recession could occur in the year 2013 and

thereafter again in the year 2014. The analysis also showed that the drop in GDP

could be lower than the current estimated level of 7% per annum. (Predicted analysis

was based on least square method.) The results of the analysis had been plotted as can

be seen in following figure Fig.9.

26

Fig.9: Author’s Prediction of GDP growth rate of India for the year 2013 in 2011

US GDP Trend:

In the figure 10, below, it was seen that the time-series of GDP growth in

percentage for USA. The trend of GDP was continuously decline since 1984 to 1998.

In that period, the fluctuations in GDP were between 3 and 6%. The overall of

macroeconomic conditions affected GDP growth of USA. In 1960 to 1975, there were

many ups and downs in US GDP. But in 2009, the trough which has been observed by

US business cycle was severe.

-4

-2

0

2

4

6

8

10

12

1990 1995 2000 2005 2010 2015

GD

P

Year

Business Cycle GDP

calculated trend Poly. (GDP )

Predicted for Year 2013

27

Fig. 10: GDP trend of USA from the year 1960 to 2010

Source: Bureau of economic analysis, US dept of commerce. World Bank, World

Development Indicators (based on least square method)

The analysis of US GD trend yielded that a recession could occur in the year

2013 and may long last till the year 2014 which would be quite severe (Predicted

analysis based on least squares method).

28

Fig.11: Author’s Prediction of GDP growth rate of USA for the year 2013 in

2011

Source: calculations Based on least square method by the Author

In the Analysis of INDIA- US Business cycle, (Fig. 12), it has been observed

that Global recession occurred every after 5 years, however, its severity was always

less in India as compared to US. In 2007 also it has been viewed that there 2008-2009

was worst for US economy. Overall there are more fluctuations & ups & downs in

US economy in comparison of Indian economy.

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1960 1970 1980 1990 2000 2010

US

GD

P P

ERC

ENTA

GE

YEAR

US GDP TREND FROM 1960 TO 2010

GDP percent change based on current dollars

Predicted for Year 2013

29

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1990 1995 2000 2005 2010 2015

BU

SIN

ESS

CYC

LE T

REN

D

YEAR

INDIA-US BUSINESS CYCLE COMPARISON US BUSINESS CYCLE INDIA BUSINESS CYCLE

Poly. (US BUSINESS CYCLE) Poly. (INDIA BUSINESS CYCLE)

Predicted Year 2013

Fig. 12: Comparative Analysis of India-US Business Cycle:

Source: Author’s Calculation.

MACRO Level Impact:

The discussions revealed that at the macro level, the impact of the economic

meltdown would result in the following:

1. Depletion of the Foreign Exchange Reserves

2. Increase in the Current Account Deficit.

3. Depreciation of the Rupee

4. Losses due to exposure of some private banks operating in the West

5. The “Mark to Market” losses of the national banks was estimated to be around

US $ 90”0 as against that for the private banks at US $ 360”0 (as on Oct 2008)

6. Drop in Market Capitalization.

30

7. Drop in the rate of lending by banks for both the automobile sector as well as

the housing sector.

8. Purchase deferment by the consumers for consumer durables and houses etc

9. Lending by private unofficial financial sector will see a tremendous increase

in the interest rate.

The net impact of the economic meltdown at the Macro Level was stated as follows:

1. Spending by the consuming class may come down

2. Staff downsizing

3. Fiscal Deficit likely to touch a high of 5% of the GDP inclusive of the

budgetary and off budgetary items.

4. The sectoral growth rates of the GDP are likely to fall dramatically. Capital

Goods sector likely to be the worst hit followed by the consumer durables

sector.

5. The service rate also likely to decelerate. The growth rates likely to be a

modest (say 10 % p.a).

Experts expected that this economic meltdown and its impact was likely to continue

through the year 2010-11 to 2013-14

Detailed discussions with the expert persons revealed that at the macro level, the

possible reaction could be:

1. Relaxation of norms on the capital account

2. Additional cuts likely in the SLR and the CRR

3. Reduction in the interest rates so as to stimulate the demand

4. Intervention in the foreign exchange to check the depreciation of the Indian

Rupee.

31

5. Increasing pressure from the Government on the private sector to maintain the

price line and not to increase the prices of commodities

6. Tax Cuts, Reduction in Administered Prices (e.g. in Petrol, ATF etc)

7. Increase in spending in Infrastructure Projects especially the AAM AADMI

projects.

In Conclusion, indicated by experts that at the macro level, governments and other

fiscal authorities have to follow a policy of:

Flexibility

Awareness Creation and finally

Resilency

MICRO Level Impact

Detailed discussions were also held to find out the ways and means for the Indian

companies to overcome this environment conditions.

The discussions revealed that the possible responses could be stated as follows:

ONE TIME ACTIVITY

Restructuring the balance sheet,

Voluntary Retirement Schemes,

Selling off businesses or assets etc.

The initial reaction of most of the organizations would be to cut manpower

and improve the manpower productivity.

Rescheduling of bank loans was another area which the managers felt was a

must. This according to them would help in reducing the overheads – interests cost

32

and thus bring down the cost of the product. Thus the product may be available at a

lower price – competitively.

ACHIEVE REDUCTION IN COST

Reducing operating costs

Increasing productivity

Reduce all capital expenditures

Restructure the Work force

Quit certain unfavourable markets

Introduce new products / new services.

The exploratory survey with the top brass of organizations mainly medium and large

organizations also revealed that the strategies followed by them were as follows:

STRATEGIES TO BE FOLLOWED:

Reduce operating costs (70%)

Increase productivity (50%)

Redesign Processes (45%)

Reduce all capital expenditures (45%)

Restructure the Work force (50%)

Quit certain unfavourable markets (20%)

Introduce new products / new services (30%)

(Figures in brackets indicate % of CEOS adopting that particular strategy. Not

mutually exclusive. Total number of CEOs interviewed ~ 35).

The detailed discussions also revealed that in any organization, changing the

processes that were used was a highly complicated task as many of the processes were

33

interlinked with one another and any change in one of the sub process impacted the

rest of the measures.

The processes that were followed in these organizations could be classified into two

major categories:

Factory Processes

Factory Forward Processes (i.e. Market driven processes).

A typical factory forward process comprises of the following:

Fig. 13: Factory forward process

Source: Research Paper Presented By Dr. R. Gopal, in 2011 International Conference, DY

Patil University.

Each of these processes has several sub processes which needs to be revisited.

This meant that for firms, in order to survive will have to achieve operational

Factory Forward Process

1

4

3

5

2

6

Bac

k U

p P

roc

ess

1=Customer Database Management

2=Infrastructure Management

3=People Management

4=Accounting System Management

5=Industry / Environment Monitoring

6=Marketing Communication

FACTORY PROCESSES

Business Development Processes Sales Processes

Business

Competency

Channel

Management

Customer Account Order Execution

Further Sub Processes

CUSTOMERS

Main

Pro

ces

s

on

Factory Forward Process

1

4

3

5

2

6

Bac

k U

p P

roc

ess

1=Customer Database Management

2=Infrastructure Management

3=People Management

4=Accounting System Management

5=Industry / Environment Monitoring

6=Marketing Communication

FACTORY PROCESSES

Business Development Processes Sales Processes

Business

Competency

Channel

Management

Customer Account Order Execution

Further Sub Processes

CUSTOMERS

Main

Pro

ces

s

on

34

excellence. These were summarised as follows:

Institutionalizing standard operations procedure.

Strengthening product management capabilities (e.g. through training)

Sharpening cost and delivery competencies (e.g. through supply chain

management, outsourcing non-core manufacturing processes)

Value Engineering through redesigning products and services to offer

better value to the customer. Bringing in new technology product was one

of the key success areas.

Talent Management through behavioural skill improvement programs like

leadership development programmes, performance improvement programmes

and most importantly higher emoluments

Risk Management: The Business managers have to classify risk in terms of the

probability of occurrence and their potential to cause repercussion.

Corporate Social Responsibility (CSR) was another area which most of the

managers felt was important. However, CSR practices were not very visible

especially in the case of medium sized organizations. In the larger organizations,

CSR was more demonstrated as seen in areas of Health, Safety, Environment and

Education.

Innovation and finally

Communication. Invariably all the CEOs expressed that Communication

was the key to success.

The detailed discussions also revealed that the high oil prices would be the

single biggest challenge which major economies of the world would have to face.

This would affect several industries but the worst hit would be the airline industry.

35

According to Brian Pearce, (International Air Transport Association) IATA

chief economist, the outlook from IATA included a $5 billion loss for the industry in

2008, followed by a $2.5 billion loss in 2009. This would increase tremendously in

the years to come. It was also said by him that in USA, there was a major need to

replace the existing fleet but however because of the credit crunch, it was getting

increasingly difficult to finance the new aircrafts.

The airline industry was facing basically unprecedented weakness in revenue

and air travel. The year 2009 was a very difficult year for Airlines as far as revenues

were concerned. There was fall much more dramatic than in the downturn of

September 2001. When the price of a barrel of crude shot up to a record $147 in July

2008, it proved to be the worst setback for the Aviation Industry. Recession was more

concerned than high oil prices because this was the first truly global recession faced

by the Airline industry. Industrial revenues were at their bottom. Year 2009 was the

most challenging revenue environment for Airlines since last 50 years.

The Indian economy was affected by the sharp increase in global commodity

and crude oil prices. While domestic prices of petrol and diesel witnessed a one-off

upward adjustment in February 2008. The aviation sector which witnessed a double-

digit growth in past years, had begun resorting to reorganization for better efficiency.

The recession, as in many other sectors, was the mediocre from the competent in the

aviation sector too. Private Indian airlines, which in the past have experienced

massive growth, have demanded a “bailout” in the form of reduction in taxes and

airport charges etc from the government and even threatened to ground their planes if

their demands are not met. There had been a case of Air India. The Maharaja (known

36

for) had piled up accumulated losses of over ` 7,000 crore and debt exceeded `

16,000 crore. It was forced to cut salaries and cancel order for new jets.

All major airlines in India complained about the exorbitant prices of Air

Turbine Fuel (ATF). According to an estimate, the ATF prices in India were 60-70

percent higher than those at the international level. While in other countries fuel cost

was usually 10-15 percent of an airline’s total operating cost, when in India it

accounts for around 40 percent. Sales tax was averaging 26-30%. Similarly, airport

charges, landing and parking fees were very high. Following was a brief study

regarding impact on this industry due to Economic Meltdown.

Air India: A national carrier of India was the recognition of Air India when it

established in 1932 by J. R. D. Tata. In 1946, Air India turned into a public limited

company. The government of India acquired 49 per cent stake in the airline in 1948.

Till 1994, Air India enjoyed the monopoly in the Indian aviation sector and earned

profits because in mid nineties liberalisation in airline industry allowed private

airlines to operate in India.

Decrease in Demand: In 2007, when oil prices increased exorbitantly &

swine flu disease at international level reduced demand in airline industry

drastically. To meet the requirements of their increasingly discerning

customers, some airlines invested heavily in the quality of service that they

offer, on the ground and in the air too. Better entertainment options, ticketless

travel, new interactive entertainment systems, more variety in food and more

comfortable seating are just some of the product enhancements being

introduced to attract and retain customers.

37

Huge Losses: Air India faced losses in 2008-09. Therefore, the cost reduction

steps were taken by Government. This move was comprised of pay cuts, and

changing the system of incentives, delay in salary payments and flying

allowances.

Kingfisher Airlines: Kingfisher Airlines started operation in year 2005. It had

incurred huge losses & it was suffering till 2012. The after-effects of recession

attributed to the grounding of 15 aircraft and de-leasing of another two to

“rationalization” of its network to counter deep financial losses. Private Indian

airlines, which in the past have experienced massive growth, demanded a

“bailout” in the form of reduction in taxes and airport charges etc from the

government. The airline industry in India was going through a tough period

due to high costs and lower yields. Kingfisher planned to cut first class seating

and increase economy offerings across its fleet. Kingfisher Airlines dropped

unprofitable flights and expedited fleet reconfiguration.

Huge Losses: Kingfisher Airlines fell to fifth place in domestic market share

during November 2011, from third in the previous month. It was once India's

second largest carrier by passengers. Kingfisher's market share fell to 14%

primarily due to less capacity offered in November.

The Data showed, the country's largest airline, Jet Airways, and its unit JetLite

had the largest market share of 27.1 percent, after that budget carrier IndiGo with 19.8

percent and government controlled Air India with a 17.4 percent in the year 2011.

The aim of this study had been to identify where the global financial crisis fitted

in the larger picture of India’s business cycle. In the process, part of the research

38

8.5

7.5

5.4

4.4

8.6

7.6

6.5

5.2

4.8

6.5

8.3

6

4.7

8.6

7.8

5.1

4.8

0 2 4 6 8 10

2009-10

2010-11

2011-12

2012-13

2013-14

GDP growth in %

Year

GDPfc(2004-05 prices) Quarterly Growth

Q4

Q3

Q2

Q1

contributed to the existing literature on Indian business cycles, by identifying the

cycles from 1960-2010. The study was complimented by the construction of US GDP

trend using data from 1960 to 2010. It was inferred that as per the literature review,

this was the most critical analysis with reference to US GDP and India GDP on

business cycles so far.

Predictions about next recession were also focussed as part of this study. With the

Least Square Method, the results were obtained. It showed that after every 5 years

recession may occur in the economy. So according to the analysis, next downfall was

predicted that may occur in 2013-2014 with respect to GDP was predicted.

The data released by the Central Statistics Office (CSO) in May, 2014

confirmed that both the manufacturing and mining sectors shrunk in 2013-14 with fall

in output. Spiritless infrastructure activity dampened construction growth as well. The

manufacturing sector contracted (-) 0.7 per cent in 2013-14 against 1.1 per cent in

2012-13. Sectors like Mining and quarrying also declined to 1.4 per cent against 2.2

per cent in 2012-13.

Fig.14: GDPfc quarterly growth rate since Q2, 2009

Source: Ministry of Statistics & Programme Implementation

39

The study further depicted how these airline companies will be performed

better in near future. It was suggested that, the airlines need to retain the star

employees, having good skills and willingness to stay in the organization. Similarly,

the airlines also had to concentrate on those customers having potential to travel by

the airline (e.g. frequent fliers discounts programme, etc) and extract revenues from

them. When the recession was faced by the world and, there was slow down in India,

the domestic services has been enhanced by airlines. Airlines must also identify the

key elements in selecting the sales channels. This has been because, in turbulent

times, the airlines had to go for low margins but higher sales volumes.

Actual in 2014 and Predictions in research study:

In this case study, the GDP at factor cost for USA and India with least square

method for the period of 2013 had been predicted by the researcher. Prediction of

down fall in 2013 in GDP growth of both the countries had been carried out by the

Researcher in the year 2011. The predicted values were very close with the actual

values, showing prediction were right. For India GDP growth rate was predicted to

fall below 7%. In actual, the GDP growth rate was 8.9% in 2010-11 which fell in

2011-12 up to 6.7% and in 2013-14 it was 4.7 % (according to Economic Survey

2013-14). This shows that the predictions were exactly correct for India GDP at factor

cost at constant prices with 2004-05 as base year. The Predicted value for USA GDP

growth rate was, fall in GDP growth rate below 2% where as in actual GDP growth

rate for 2013-17 was 2-2.5% and higher growth has been forecasted closer to year

2017.

As predicted by the researcher in this case study in 2011, that airline

companies will perform better in near future. The then India’s civil Aviation Minister

told that Air India had posted a loss of `6,865 crore in 2010-11. Which reduced

40

approximately to `4,270 crore in the 2012-13 fiscal year. Two Air India executives

confirmed the estimated loss figures, but refused to disclose the net loss in the first six

months of the year 2012-13 ended September.

1.4 Manufacturing Industries in economic fluctuation:

Adam Smith, Father of Economics, mentioned in 18th century, referring to

China, Egypt and India acknowledged that they were "the wealthiest countries in the

world, and mainly renowned for their superiority in agriculture and manufacturing.

He also mentioned that they were much richer than Europe. The neo-classical models

of growth which prevailed in the 1950s and 1960s were pioneered by Solow (1956).

The highlight of these neo-classical models of growth was “the property of

convergence of growth rate”. The models also predicted that the countries those are

with lower real per capita GDP would have higher levels of growth rate. This was

derived from the assumption of diminishing returns to capital. Whenever a change

growth rate in labour and technology was zero, the growth rate in output is a function

of capital accumulation, Capital Flows and their Macroeconomic Effects in India.

Before the Industrial Revolution in India, most manufacturing business

occurred in rural areas. The manufacturing sector is closely connected

with engineering and industrial design. Manufacturing sector holds a key position in

the Indian economy and employs about 12 per cent of India’s labour force. Growth in

this sector has been matching the strong pace in overall GDP growth in last few years.

For example, when real GDP expanded at a CAGR of 8.4 per cent in the FY05-FY12,

the growth in the manufacturing sector was only marginally higher at around 8.5 per

cent in the same period.

Global economic slowdown reduced sales and production of Manufacturing

sector. The slowdown period was observed since 2008 in almost all sectors and

41

industries. The tough time for all manufacturing industries was 2008-2009. The

decrease in demand in market created decline in production which ultimately led fall

in employment and this vicious circle moved on rolling for almost six quarters.

In the pre slowdown period, the growth rate achieved by industries was

28.2%. While most of the small, medium and large enterprises faced with the problem

of decline in demand in the affected sectors/sub-sectors. Inter-firm growth

performance was also surely changed. Year 2008 was a destructive year for global

manufacturing sector. Industrial production fell in last three months of 2009 by 3.6%

and 4.4% respectively in America and Britain (equivalent to annual declines of 13.8%

and 16.4%).

Contributions of these sectors reflect in growth rate of GDP and profitability of

the industries. Profit Before Interest and Tax (PBIT) of last 10 yrs of manufacturing &

service industry was considered in this study. The growth in (PBIT) has been

examined. There appear to be two phases of growth in profit. The first period has

been up to Financial Year 2007-08 where growth in profits was robust and the sector

was upbeat. This was also the period when the Industrial Production growth rate was

buoyant and was reflected in sales. However, subsequently, the sector did come under

pressure. While there was a recovery in FY-10 after the decline in profit in FY-09,

growth in profits has been low and uneven with negative growth once again in FY-12.

There was rise in potential output in 2002 to 2007, which ultimately increased

GDP growth rate in same period. The potential growth observed in same tenure was

7.6%. India’s real gross domestic product (GDP) growth rate fell to 5.3 per cent in the

third quarter of 2008 and slightly improved to 5.8 per cent in January to March 2009,

recording the most dismal performance since 2005. The recent deceleration in India’s

real GDP growth has impact on potential growth rate of the economy and the size of

42

the output gap. According to data of Central Statistical Organization (CSO), India’s

annual growth rate of real GDP ( at market prices) has fallen to 7.0 % in the calendar

year 2011 from 10.5 % in 2010 indicating a sharp decline of 3.5 % points in a single

year.

The first quarter of 2012 was also marked by a decline in growth to 5.6

percent. The decline in growth had been accompanied by a slowdown in investment

(both gross fixed capital formation and infrastructure investment). Therefore, India’s

ranking in the overall global competitiveness index by the World Economic Forum

slipped five positions in 2011-2012.

After the 2008 global financial crisis, GDP growth of India declined

significantly from 9.3 per cent in the year 2007-08 to 6.7 per cent in the year 2008-09

due to the impact of external demand shocks on domestic economy. With

expansionary monetary policy and fiscal policy, growth of GDP recovered quickly

during the year 2009-10 and the year 2010-11 to 8.6 per cent and 9.3 per cent,

respectively. Subsequently, due to domestic and external factors, GDP growth rate

slowed down to 6.5 per cent in 2011-12. This decline in GDP was broad based and

was more affected to industrial sector. Considering the same period, inflation rate in

India increased from 4.7 per cent in 2007-08 to 8.1 per cent in 2008-09 and also

dropped to 3.8 per cent in 2009-10. Afterwards, the inflation rate increased and stayed

near double digits during 2010-11 and 2011-12. Growth rebounded strongly in the

year 2010-11, after the deep fall in 2008-09 in the wake of the global financial crisis

and the recovery in 2009-10.

Besides elevated headline inflation, it is been a kind of generalised price

pressure kept non-food manufactured products inflation at a higher level.

43

Corresponding to GDP growth trajectory, performance of Indian corporate was also

diverse. Their profitability as can be measured by the operating profit margin i.e.

‘Earnings Before Interest, Taxes, Depreciation, and Amortisation’ (EBITDA) to sales

ratio declined steadily from its highest level at 17.5 per cent in 2009-10 (Q1) to 11.5

per cent in 2011-12 (Q3). This discussion brought out the fact that profitability of

industries in India had declined in First quarter of 2009-10. While production had

fallen sharply since third quarter of 2010-11, WPI inflation had been sticky and

remained at a relatively higher level. Reserve Bank of India, hiked the policy interest

rate by a cumulative 375 basis points between March 2010 and October 2011.

Manufacturing sector with reference to FMCG:

The decline in production of manufacturing sector due to global economic

slowdown was expected in the survey of Federation of Indian Chambers of

Commerce and Industry (FICCI). The performance of manufacturing sectors in terms

of major parameters like growth, exports and employment was assessed the survey. In

the study conducted by the Reserve Bank of India on the balance sheets of private

sector has been found that profitability declined across most sectors of economy in

2008-09, as global recession came up. Therefore, aggregate net profits of

manufacturing companies declined by 24.3%. These sectors were far less exposed in

developed markets and relying more on the domestic market. But India considered

growth in manufacturing sector for the overall development of economy. Government

was supporting this sector by providing training programmes for availability of skilled

workforce. For the encouragement of FDI in this sector, several measures were also

introduced by the Government. Therefore, India was ranked second in the world

according to 2012 FDI, a confidence index developed by A. T. Kearney.

44

The Growth of India's manufacturing sector was at its slowest pace in July

2012 since Nov 2011. Manufacturing sector especially the export oriented

manufacturing units was affected due to the continuous depreciation of the rupee

currency along with weaker global demand due to sovereign debt crisis.

The FMCG industry has been one of the largest industries in manufacturing sector

in the Indian economy, which registered an astonishing double-digit growth rate in

sales in the past couple of years. FMCG sector characterised by healthy distribution

network, strong and giant MNC presence and low operational costs, which had been

one of the rapid growing sectors in India with a total market size US $13.1 billion in

2011. Among sub-sectors in manufacturing sector, the top five were FMCG and food

products, basic metals, rubber, petrochemicals, chemicals, and electrical machinery,

combined account for over 66 per cent of total revenues of the manufacturing sector.

Examples include non-durable goods like soft drinks, toiletries, over-the-counter

(OTC) drugs, toys, processed food items and many more other consumables.

Thus, this sector had been chosen as one of the sectors to be analyzed with

reference to contribution for GDP in India. Fast-moving consumer goods (FMCG)

or consumer packaged goods (CPG) are products that are sold quickly and at a

relatively low cost.

Market potentiality of FMCG industry was observed to be high because low

operational cost, well-known FMCG companies, Population growth and strong

distribution networks. These are the factors responsible for growth of FMCG industry

in India. The profit margin made on FMCG products was relatively small (mainly for

retailers than the producers or suppliers), those who are generally sold in large

quantities. FMCGs have generally a short shelf life, either because of high consumer

demand or because the product deteriorates rapidly. FMCG industry has been

45

considered the best because, even during the recession times, the demand for house

hold goods didn’t fall much so profit and balance sheet of FMCG was hardly affected.

Fig. 15: Use-based classification of India’s manufacturing sector

Source: Reserve Bank of India.

The most common consumables under FMCG can be classified as House hold

products, Personal care products and foods and beverages and extends to certain

electronic goods. The further description has been shown in the following table

Confederation of Indian Industry (CII).

There were Thirteen major industries in manufacturing sector namely textiles,

capital goods, textile machinery, metals, chemicals, cement, electronics, automotive,

leather & footwear, machine tools, Food processing, Paper and tyres. In the Quarterly

survey by FICCI in March, 2011, it had been observed that very few respondents have

reported that uncertainty in economic environment had increased. Around over 40%

respondents reported that economic uncertainty acting as a major constraint for the

46

growth of the sector. Another 28% respondents said economic uncertainty a moderate

constraint for the growth of the sector.

Fig. 16: Classification of FMCG Industry

Source: http://www.itnext.in

In a series of survey by FICCI, it had been observed that the growth

expectations in different sectors, five major sectors out of fourteen in the survey were

witnessed strong growth of over 10% in Q-4 of 2010-11. These sectors were Capital

Goods, Automotive, Machine Tools and Consumer Durables. For three sectors

namely Chemicals, Forging and Tyre growth were likely to be moderate (between 5 to

10%) in Q-4 of 2010-11 as compared to last Year 2009-10. Sectors like Cement,

Paper, Steel, Metals, Textiles and Miscellaneous witnessed low growth of less than

5% in Q-4 of 2010-11.

47

Table 1: Sectoral growth rate in the year 2009 to 2011

Sector Growth

Cement Low

Paper Low

Steel & Metals Low

Textiles Low

Miscellaneous Low

Chemicals Moderate

Forging Moderate

Tyre Moderate

Machine Tools Strong

Automotive Strong

Electronics & Consumer Durables Strong

Leather & footwear Strong

Capital Goods Strong

(Note: Strong > 10%; 5% < Moderate < 10%; Low < 5%). Source: FICCI Survey

In the Annual Report of Department of Industrial Policy and Promotion, 2012-

13, Ministry of Commerce and Industry, Government of India, mentioned that

National Manufacturing Policy (NMP) had objective of increase in share of

manufacturing in GDP to 25% and creation of jobs for 100 million people in a

decade or so.

NMP was based on some of the important features:

National Investment and Manufacturing Zones (NIMZs)

Reorganisation and simplification of business regulation

SMEs Incentives

48

Skill up gradation and Industrial Training measures

For technology development Financial and Institutional mechanisms including

green technologies

Government Procurement

Special focus sectors

To boost manufacturing sector, the government of India has been announced

setting up of sixteen National Investment and Manufacturing Zones (NIMZs). The

NMP provided for promotion of clusters and aggregation, mainly through the creation

of national investments and manufacturing zones (NIMZ). According to Economic

Survey (2013-2014), 16 NIMZs will be getting set up till 2013-14. Out of these, eight

were along with the Delhi Mumbai Industrial Corridor (DMIC). Besides, eight other

NIMZs have been given in-principle approval: (i) Nagpur in Maharashtra, (ii)

Chittoor in Andhra Pradesh, (iii) Medak in Andhra Pradesh (now Telengana), (iv)

Prakasam in Andhra Pradesh (v) Tumkur in Karnataka, (vi) Kolar in Karnataka, (vii)

Bidar in Karnataka, and (viii) Gulbarga in Karnataka. NIMZs are concept focused

integrated industrial townships of at aroud 50 sq km (5,000 hectares) with world class

infrastructure. NIMzs had land use on the basis of clean and energy efficient

technology and important social infrastructure.

According to Economic Survey 2013-14, to push the share of manufacturing

sector in the overall GDP growth, there seemed a need to focus the global market in

the sectors which are showing a rising trend in demand. Such sectors are largely high

technology sectors and capital intensive. To gain a sizable amount in these sectors, the

policy should focus on pushing up the level of public and private expenditure on

technology up gradation, research and development, innovation, and skill

development. Manufacturing sector constitutes over 75 % of the index of industrial

49

production (IIP), which declined 1.2 percent in March, 2014 against a growth of 4.3

percent in March, 2013. During the financial year of 2013-14, the output of

manufacturing sector contracted 0.8 percent compared with 1.3 percent growth in last

financial year 2012-13.

1.5 Service Industries in economic fluctuation

Service sector had been one amongst largest of the contributors of India's

economic growth. Service was the fastest growing sector in India, which has been

contributing significantly to GDP at factor cost and market prices, GDP growth rate,

employment, total trade and investment. Labour productivity in services sector was

observed to be highest in India and it has increased continuously. India is a major

promoter of liberalizing services both in the WTO and in its bilateral trade

agreements. The Service sector recorded the highest contribution to India's economy

during the period of 1978 to 2007. Services now contribute 59% to India’s GDP and

also have contributed around 60% of India’s growth in last decade. There has been a

rapid increase in the share of services from 38% in 1981 to 42.7% in 1991 in GDP. It

was 59.9 % in 2013-2014. During the period of Economic Reforms (post 1991), the

share of services increased very rapidly. Therefore, share of service sector in GDP

was 50.4% in 2000-2001.

The Service sector grew drastically after 1991, the year when economic

reformation began. In most of low income economies, Agriculture was the prominent

sector. With the economic progress, industrial sector increased. The development of

the industries promotes activities in services sector like Banking and Insurance,

Trade, Communication and Transportation and many others.

The Information Technology (IT) field in India has been developing at a fast

rate, its IT engineers were outsourced to many international firms, organisations and

50

some are even being outsourced to other countries like: Malaysia, Singapore and

Australia, for instance. Hence, Services sector acted as a major contributor for

economic growth of India since the 1980s. According to income & expenditure

summary of service sector by Centre for Monitoring Indian Economy Pvt Ltd

(CMIE), in 2002-03, PBDIT of service sector being in negatives, depicted losses in

the industry. However, PBDIT started improving immediately after 2003 and showed

a profit of 23.38%. With estimated revenues of US$ 36.3 billion in FY 2005-06, the

Indian Information Technology – Information Technology Enabled Services (IT-

ITES) Industry continued to grow 5 times as fast as the global IT services industry,

clocking a Compounded Annual Growth Rate (CAGR) of 28% since FY 1999-2000.

In 2008-09, global recession affected Services sector as there was a sharp

decrease in PBDIT to 8.23% which was 23.43% in previous year. NASSCOM in 2009

had forecasted that the growth rate of over 25% has been expected to continue and

will help Indian IT-ITES exports exceed US$ 60 billion by FY 2010.

Service sector comprised financial institutions also. Capital market and

financial institutions were badly affected in global economic recessions. These

institutions were highly dependent on FDI in India. The size of a capital market of a

nation is directly proportional to the size of its economy. The United States, which has

been the world’s largest economy, has the biggest and deepest capital markets. Capital

markets are increasingly interconnected in a globalized economy, which means that

ripples in one corner can cause major waves elsewhere. The disadvantage of this

interconnection was best illustrated by the global credit crisis of 2007-09, which was

triggered by the collapse in U.S. mortgage-backed securities. Lehman Brothers was

the cause for this collapse. The effects of this meltdown were globally transmitted by

capital markets because banks and institutions in Europe and Asia held trillions of

51

dollars of these securities. Performance of Reliance Capital Ltd had been considered

in the present study in economic slowdown for the period of 2003-12.

52

Chapter 2

Literature Review and Research Gap

GDP is an important component of an economy. Growth rate of GDP matters

to the health of an economy, whether it is developed economy or developing

economy. Despite consistent ups and downs in global economy due to economic

fluctuations, GDP growth rate plays a significant role in shaping global economic

development. Performance of components of GDP like Manufacturing sector and

Service sector seems to have a significant impact on growth rate of GDP. Discussion

from major and contemporary perspective, an attempt can be made to explore the

applicability of such theorization into empirical enquiries with regards to western as

well as Indian studies that would help to develop a theoretical framework for the

present study.

2.1. Studies on economic Fluctuations

Global economic slowdown and impact on different macro economic variables

had been studied by Chitre Vikas (2003) in this paper. It has been pointed out that

2001 slowdown affected all major industrial countries including developed countries.

The European Union got affected during 2000 and 2001 and the United States in 2002

and 2003. The study was extended with focus on IIP growth rate, Information

Technology revolution, Unemployment rate and Inflation rate.

Growth cycles in the Indian economy and examines short-term fluctuations for

the period 1951-1976 using annual data on a large number of variables was also

studied by Chitre Vikas (1992). The growth cycle in the index of industrial

production (IIP) for North America was observed to be showing perfect conformity

with the Indian growth cycle over 1950-1975 as per the author.

53

Excess growth of Tertiary sector of Indian Economy Issues and Implications

revealed by Bhattacharya BB and Mitra Arup(1990). Pattern of growth of tertiary

sector in India in post Independence period of 1950-51 to 1986-87 was studied. It has

been investigated in this research that National Domestic Product (NDP), changes in

occupation and production structure. For this, the period between 1960 and 1980 was

considered. Distribution of GDP on the basis of majorly three sectors of economy

(agriculture, Industry and Services) was studied. Further, it was concluded that service

sector in India grew faster than commodity sector.

In the IMF economic review, Global input-output framework to quantify

demand was considered by Bems Rudolfs, Johnson Robert C and Kei-Mu- yi

(2010) with reference to the USA and European Union (EU) which spilled over and

the elasticity of world trade to GDP during the global recession of 2008–09. It was

found that the US and EU had 20–30 percent of the decline in final demand by foreign

countries in which North American Free Trade Agreement (NAFTA) and emerging

Europe hit hardest. Therefore, in and around 70 percent of the trade collapsed. The

change in demand for durables in global recession was measured in this paper.

In a Comparative study of industrial production since 1980 and 2008

recession, which was carried out by Imbs, Jean (2010). In IMF economic review, it

was mentioned that the degree of international correlation in national business cycles

since the end of 2008 was unprecedented in three decades. In this study, the cycle

synchronization between business cycles of advanced economies was correlated. It

was pointed out that both goods and assets trade contributed to this synchronization.

The significant synchronization among (Organisation for Economic Co-operation and

Development) OECD economies was associated with financial openness and weaker

54

synchronization was among developing economies which tends to take place between

trade partners.

Common Component of International Economic Fluctuations with a new

approach was identified by Lumsdaine Robin L and Eswar S. Prasad (2003). An

aggregation procedure using time-varying weights for constructing the common

component of international economic fluctuations was developed. The model

developed was on cyclical and seasonal fluctuations and also the Dynamic

propagation of shocks across countries. In this regard, data was referred for a ‘world

business cycle’ as well as for a distinct European common component. In conclusion

it was mentioned that macroeconomic fluctuations became more closely linked across

industrial economies in the period after 1973.

The pattern and causes of economic growth was studied by Kaushik Basu

and Annemie Maertens (2007). As per them, India needed to sustain and even raise

its current growth to a new high, with the focus on the important bottlenecks in the

Indian economy. It was also focused on the current erratic and low growth pattern of

the agricultural sector, and the increasing inequality—between states and that between

rural and urban areas. Inequality within urban and rural areas mainly since the 1990s

was also considered. The components of the Indian growth and the relative

importance of the different policies in the 1980s and 1990s were studied.

In the article titled “The right timing” all the phases of Business Cycle were

revealed by Michael O'Sullivan (2010). A thought “recession phase is a

depressionary phase and recovery phase is a hope of improvement” has been

presented thoroughly. Appropriate economic explanation of phases of the Trade Cycle

in a flow was described. The Mystery of the Indian Growth Transition, Hindu Growth

to Productivity was focussed by Rodrik, Dani; Subramanian, Arvind (2005).

55

Emerging Market Business Cycles as the Cycle has been the Trend measured by

Mark Aguiar, Gita Gopinath(2011). It was indicated that the trend in Business

cycle with existing accounts, volatility in consumption that exceeds income volatility

and "sudden stops" in capital inflows was truly indicated in this study.

A regular tradeoff between inflation and output or unemployment with

inflationary expectations based on the 1950-2009 was reviewed by Ravindra H.

Dholakia & Amey A. Sapre, IIMA, Research and Publications. Regression

equation representing the conventional Phillips curve was estimated in their study. An

in depth study had been carried out by Aurodeep Nandi (2011) on India’s cosine

curve, a Business Cycle approach of analyzing growth. The findings were envisaged

where the global financial crisis fitted in the Indian business cycle. A business cycle

and a composite leading economic indicator index with the Index of Industrial

Production (IIP) were constructed in this research.

The impact of purchasing more output by the Government on the increase in

GDP was commented and discussed by Robert Hall. A useful overview on

government purchases and GDP was provided. An interesting and thought-provoking

paper which compels further thought about the channels through which government

purchases might affect output, both in normal times and in the very abnormal current

scenario was presented by R. Hall.

Proper check on investment activity by Banking & non-banking financial

institutions was proposed by TT Ram Mohan (2009). Ten regulatory lessons were

learnt from the sub-prime crisis with monetary policy. Control on money supply in

economic phases has been an important factor as per him. A very popular book by

Rudiger Dornbusch , Stanley Fischer & Richard Startz, on Macroeconomics by

Tata-McGraw-Hill (2007), was referred containing all macroeconomic variables. It

56

was stated that it has been all about Aggregate Demand, Unemployment, Aggregate

Supply, National Income, Fiscal & Monetary policy, Phillips curve and Inflation.

Book of Dominick Salvatore (2004), on International Economics, (2004)

was referred. This book has been covered Various topics associated with International

Trade, Free Trade, Protection policy, Custom union, exchange rate determination,

Terms of Trade, Gains from Trade were covered in this research. Diagrammatical

explanation with appropriate formulas have been described in this book for terms of

trade, gains of trade. Favourable and unfavourable terms of trade had also been

discussed. An article on Rethinking Economic Theory & Policy was written by The

Editor of Economic & Political Weekly in 2009. The Implementation of useful

economic theories & public expenditure policies after recession for economic stability

were debated in this article.

Two frictions of financial activity in his research paper under the title “The

high sensitivity of economic activity to financial frictions” was studied by Robert

Hall (2011). One of the frictions raises the rental cost of capital to firms and the other

friction raises the rental cost of housing and durable goods to consumers. Effects of

dynamic model with investment in business and housing sectors were further

investigated. It was concluded that financial crisis of end of 2008 and early 2009 lead

deep recession. This article focused exclusively on the key issue of how financial

frictions affect economic activity.

A rigorous statistical testing on the long term trend of growth rate of GDP

India for the period of 1950-51 to 1987-88 was carried out by Nagaraj (1990). In this

study of 38 years, GDP growth rate excluding public administration and defence

(PAD) and including public administration and defence was measured. It was further

57

observed that GDP growth rate decreases and increases because of performance of

primary, secondary and tertiary sector in this 38 years time span.

2.2. Studies on Service Sector:

The labour market and its opportunities of Services and Manufacturing sectors

were compared in the research paper of K.V. Ramaswamy and Tushar Agrawal

(2012). Urban labour market has been elaborated in this paper. An era when Indian

Economy saw economic reforms and liberalisation has been considered. In the

broader context of economic development and structural changes, the observed

sequence was that manufacturing followed agriculture while the service sector

became prominent only at a later stage. India’s experience appeared to be different

with the share of services sector in GDP sharply going up in the 1990s, beginning

with a share of 43 % 1990-91, to reach a high share of 57 % in 2009-10. This has

raised the expectation in development policy discussions of the possibility of India

skipping the traditional sequence and the service-sector assuming the role of the lead

sector in India’s growth path. An article ‘Understanding India’s Services

Revolution’ by Gupta, Singh and Eichengreen (2004) was referred. It was

explained that in such scenario, labour shifting out of agriculture will get directly

absorbed in services rather than in manufacturing. While there has been broad

agreement about the dynamism of the service-sector, questions have been raised about

the sustainability of services output growth by many others on several grounds in this

regard.

Dominance of informal sector and the associated low productivity of the

service-sector was being a key concern undermining the optimistic viewpoint was

elaborated by Acharya, Shankar (2002). It was pointed out that there exists

statistically significant contribution of modern segments of services to GDP growth

58

and was suggested a complementary relationship between manufacturing and services

as both are required to absorb India’s large additions to the labour force. In the other

context of employment growth, structure and changes in skill/education composition

of workforce in urban India in manufacturing and services sectors in the last decade

and recent developments and future prospects has been studied by Eichengreen,

Barry and Gupta Poonam (2011).

In the Working paper of Indian Council For Research On International

Economic Relations (ICRIER) Banga Rashmi and Goldar Bishwanath (2004) was

focused on Contribution of Services to output growth and productivity in Indian

Manufacturing during pre and post reforms period. Connection of service industry as

a prefix-suffix of manufacturing industry has been considered. In this study, using the

available data from the Annual Survey of Industries, a production function for

registered manufacturing was estimated which explicitly includes services as an input

along with energy and materials (and labour & capital). These estimates have been

used to evaluate the contribution of different inputs to overall manufacturing output

(sources of growth). It was observed that though service inputs contributed little to

production of the registered manufacturing sector during 1980s, the contribution of

services has increased dramatically during the nineties. Total Factor Productivity

(TFP) has been evaluated in this research. A more significant positive relationship

was noticed between technology acquisition and productivity. A multiple regression

analysis was undertaken with KLEMS (Capital-Labour-Energy-Material-services) as

variables to understand what caused the use of services in manufacturing to go up in

the1990s. It was pointed out by the results of the analysis that the trade reforms

played an important role in increasing the use of services in the manufacturing sector.

The inference of the research findings was that the services sector in India has got

59

augmented its own demand by raising output growth and productivity of the

manufacturing sector in the post-reforms period. This was confirmed for the services

sector to sustain its growth performance to a considerable extent.

Based on the number of studies it has been observed by Pais Jesim (2014), in

the working paper of ISID, that high productivity sector may provide better quality

employment. It was also said that with a few caveats, high productivity employment

has been more sustainable and stable. A turnaround analysis of service sector GDP,

employment, and productivity were carried out in this research. Performance of

services sector including its all sub sectors was analysed. Total productivity and

employment engage in service sector was also analysed by researcher. Total period

for analysis considered for this study was from 1950 to 2010.

A research paper being on inflation in India, a topic of current interest to the

public, policy makers and research community has been discussed by Lulla Jharna,

(2011). The current state of inflationary condition in India was thrust of this research

paper with the detail discussion on CPI and WPI Inflation index. Impact of inflation

on Manufacturing sector and service sector was also discussed. A special focus on the

issues in the measurement of inflation in India along with the analysis of inflationary

trends and its current status had been discussed in this research. The study of the

impact and relationship of inflation rate on the performance of agricultural growth,

Industry growth and service growth was carried out by Mani Saurabh, Mishra and

Dhar Ashish (2014). With this, impact on GDP growth rate of Inflation was analysed.

With the analysis of data obtained, it was found that the relationship between GDP (at

factor cost) and inflation rate is quite low significant, low positively related. Thus,

GDP is less affected by inflation rate was brought out. GDP and inflation rate are

interdependent to each other. The t-test analysis and Regression analysis were used

60

for all variables and their results. Impact of inflation on agricultural growth,

manufacturing sector growth and service sector growth were also considered

separately.

2.3. Studies on manufacturing sector:

Total Factor Productivity Growth (TFPG) was highlighted by Pradhan

Gopinath, Barik Kaustuva(1998) and presented the data at macro level only. In this

regard an acceleration test on aggregate registered manufacturing industries was

performed. Accordingly, in conclusion, it had been mentioned that there had been a

positive trend of TFPG for the aggregate manufacturing sector in India for the period

of 1963 to 1992. The Study of the Indian Manufacturing Industry related to Utilities,

Infrastructure and Economic Development discussed Indian data on the basis of direct

effect of roads and electricity purchased by manufacturing industries was carried out

by Hulten Charles R, Bennathan Esra and Shrinivasan Shilaja (2006).

Infrastructural development and economic development has been analysed in this

paper. A period of 20 years from 1972 to 1992 was considered to check the growth of

road and electricity generation capacity in India.

In one of the research papers by Chandra Nirmal Kumar (1991), issues like

foreign capital inflow in recent past, importance of the foreign sector in private

corporate manufacturing and its significance in organised manufacturing have been

pointed out. Furthermore, major three enactments namely (Monopoly Restrictive

Trade Practices) MRTP, (Foreign Exchange Regulatory Authority) FERA and

(Transnational Corporations) TNCs were focused.

2.4. Research Gap

From the above literature review, it was concluded that the studies in

economic fluctuations in India and impact on industries available have been limited.

61

In the area of Indian economic fluctuations and impact on Manufacturing sector and

Service sector industries with reference to their profitability has not been studied

deeply yet. GDP growth rate is an essential indicator to check the health and progress

of economic growth in any economy whether it is developed or developing economy.

GDP growth rate has direct impact of economic fluctuation which also affects

profitability of economic sectors like Manufacturing sector and Service sector.

Economic fluctuations have less impact on Agriculture sector.

Whenever downfall in economy occurs, profitability of manufacturing sector

and Service sector was expected to decrease and consequent performance of

manufacturing sector and Service sector reduce their rate of profit. Ultimately it got

reflected in GDP growth rate as GDP comprises Agriculture, Manufacturing and

Service sector.

Four companies from Manufacturing sector and three companies from

Services sector were considered as case studies. The change in profitability rate of

these companies with reference to fluctuations in GDP at factor cost at constant prices

growth rate were studied by the researcher.

Hence, the research has been particularly committed to the study the impact of

economic fluctuations on profitability rate of Manufacturing sector industries and

Service sector industries for ten years time span. Here, Hindustan Unilever Ltd

(HUL), Imperial Tobacco Company (ITC), Glenmark Pharmaceuticals and Dr

Reddy’s Laboratories as case studies for Manufacturing sector and TATA

Consultancy Services (TCS), Infosys and Reliance Capital Limited as case studies for

Service sector were considered by the researcher.

62

Chapter 3

Statement of the Problem, Objectives, Hypothesis

Indian economy is one of the emerging economies which are growing

although facing economic fluctuations. GDP growth rate is an important parameter for

measuring health of economy, because it is a vital input for economic development.

GDP growth rate of India has slowed down after 2008-09 global recession. GDP

comprises basically performance of the three sectors of Indian Economy. The biggest

contributors in the growth of economy are manufacturing industries & service

industries in terms of output and revenue, however, Agriculture sector remains the

biggest contributor in economy in terms of employment generation.

The intent of the study has been to understand, how change in profit of

Manufacturing & Service industries occur due to economic fluctuations and also to

find impact of macroeconomic variables like GDP, Inflation on Profit of

Manufacturing & service industries during economic fluctuations.

The essence of the study has been to garner the understanding of the causal

relationship with the phenomenon of complexity of historic facts in Profitability of

Manufacturing industries and Service industries and reality of economic growth with

GDP. The study has been an essential effort to know about the changes in Economic

conditions, predictions and effect on Industries and also to help in solving problems of

businesses arising out due to inflation, predicting the future price signal in relation to

the business environment and economic growth.

No similar research initiative has been undertaken in India that has focused on

causal study and the impact of economic fluctuations on the economic indicators like

63

the inflation and GDP at factor cost growth of the economy. Many studies have been

taken which consider variables for analysis i.e. GDP at market price since GDP

growth based on market prices dropped much more sharply during the global financial

crisis than that based on factor costs. Similarly, in many studies, WPI Inflation index

of India for analysis was considered whereas in this research CPI Inflation Index of

India has been considered by the researcher. The period of study has been from 2002-

03 to 2012-13 financial years. The relationship between economic fluctuations and

profitability of industries of manufacturing firms and service industries operating in

India has been examined in this research.

3.1 Objectives of the Study

3.1.1 To study the impact of change in GDP growth rate due to change in profit

ratio of manufacturing sector.

3.1.2 To study the impact of change in GDP growth rate due to change in profit

ratio of Service sector.

3.1.3 To examine and understand the growth rate of manufacturing sector in

comparison with growth rate of the GDP.

3.1.4 To examine and understand the growth rate of service sector in comparison

with growth rate of the GDP.

3.1.5 To analyse consequences of Inflation on Profit ratio of manufacturing sector.

3.1.6 To analyse consequences of Inflation on Profit ratio of service sector.

3.1.7 To study the impact of Inflation on GDP.

64

3.2 Hypotheses

Following Hypothesis are developed:

Hypothesis: 1

H01: Change in profit ratio of Manufacturing sector has insignificant impact on GDP

growth rate.

H11: Change in profit ratio of Manufacturing sector has significant impact on GDP

growth rate.

Hypothesis: 2

H02: Change in profit ratio of Service sector has insignificant impact on GDP growth

rate.

H12: Change in profit ratio of Service sector has significant impact on GDP growth

rate.

Hypothesis: 3



Hypothesis: 4



Hypothesis: 5


65


Hypothesis: 6



Hypothesis: 7



3.3 Defining Variable for study:

Independent Variable: Inflation, PBDIT or EBIT of Manufacturing Industries and

Service Industries.

Dependent Variables: GDP growth rate.

3.4 Operational Definition of the Variables:

3.4.1 GDP:

Macroeconomic variables are associated with uncertainty and that has impact

on Economy. GDP is one of the major macroeconomic and important variables which

are recognised worldwide. It is an aggregate measure of total economic production for

a country. GDP represents the market value of all the goods and services produced by

the economy during the period measured, normally one year. The change in a nation's

Gross Domestic Product (GDP) from one period of time (usually a year) to the next.

The economic growth rate indicates that by how much GDP has grown or shrunk in

raw dollar or rupee amounts or in the currency of that country. It comprises personal

66

consumption, purchases by government, private inventories, construction costs which

are paid-in and the foreign trade balance (exports are added, imports are subtracted).

GDP growth based on market prices fluctuated much more sharply during the global

financial crisis than that based on factor costs.

Difference between GDP at Factor Cost and GDP at Market Price:

According to Handbook of Statistics on Indian Economy published by RBI on

15th September 2014, the Components of Gross Domestic Product (At Market Prices)

are Government final consumption expenditure, Private final consumption

expenditure, Valuables, formation of Gross fixed capital, Changes in Stocks, Exports

of Goods and Services, Import of Goods and Services, Discrepancies. The

components of Gross Domestic Product (At Factor cost) are Agriculture, Industry,

Agriculture & Allied Activities, Manufacturing, Mining & Quarrying, Electricity,

Trade, Community, Services, Construction, Hotels, Gas & Water Supply, Transport &

Communication, Real Estate, Financing, Insurance, Business Services, Personal

Services and social services.

Growth rate of Exports till 2008-09, was contributing 14.6% in GDP growth

rate at market price. Whereas, in 2009-10, exports growth was -4.7% with the

decrease in imports also. Import was -2.1% in same year.

In post liberalisation period, share of Service sector in India’s GDP at factor

cost has been continuously increasing. In 2007-08, service sector contributed 54.4%

in the growth rate of GDPfc. The share of service sector was continuously rising every

year since 2007. Contribution of Agriculture sector was reducing steadily. Since

2007-08, it was observed to be decreasing. It was 16.8% in year 2007-08 and reached

67

2

3

4

5

6

7

8

9

10

11

GD

P G

row

th R

ate(

%)

Year

GDP at MP

GDP at FC

at 13.9% in 2012-13. Share of manufacturing sector was almost steady for the same

tenure.

In this research, GDP at factor cost as macroeconomic variable has been

considered by the researcher rather than Net Domestic Product (NDP), Net National

Product (NNP) because GDP is considered to be the broadest indicator of economic

output and growth. The NDP equals the GDP minus depreciation on a country's

capital goods.

The clear path of GDP at factor cost and GDP at market prices with 2004-05

prices has been depicted in the following table. Indian GDPfc has been more stable

than GDPmp in the financial year 2005-06 to 2012-13.

Net domestic product accounts for capital that has been consumed over the

year in the form of machinery deterioration, deterioration of housing, depreciation of

vehicle, etc. The depreciation accounted for has often been referred to as "capital

consumption allowance" and was represented as the amount of capital that would be

needed to replace those assets which have depreciated. If the country is unable to

replace the capital stock lost through depreciation, then the GDP will decrease.

Fig. 16: GDP growth rate yearly, at factor cost and market price.

Source: CSO, Databook 30/5/2014

68

In addition to this, a growing gap between GDP and NDP indicates increasing

obsolescence of capital goods. The other condition of narrowing gap means that the

condition of improvement of capital stock in the country. “Factor cost GDP (GDP(fc))

generally provides a more accurate picture of economic developments” as stated by

IMF in Economic Times, Oct 10, 2013. Central Statistical Office of India has also

considered GDP(fc) as a major indicator to calculate GDP growth rate. Accordingly, in

this study GDP is considered as GDP(fc) unless mentioned explicitly. Real GDP takes

inflation into account, thus, allowing for comparisons against other time periods in

history. The Bureau of Economic Analysis issues its own analysis document with

each GDP release, which is a significant investor tool for analyzing figures, predicting

trends, and highlighting the very lengthy full release.

GDP(fc): Economic growth rate =

100

1

12

YearGDP

YearGDPYearGDP

3.4.2 Inflation:

Inflation means a persistent rise in price levels of commodities and services,

which leads to a dip in currency’s purchasing power. Inflation is a rise in the general

level of price of goods and services in an economy over a time period. When the

general price level increases, each unit of currency can buy fewer goods and services.

Consequently, inflation also gets reflected in erosion in the purchasing power of

money – a loss of real value in the internal medium of exchange and unit of account

in an economy. A significant measure of price inflation is inflation rate. The inflation

rate is the annualized percentage change in the general price index (normally the

consumer price index) over time.

69

Inflation's effects on an economy are countless and can be simultaneously

negative and positive. Negative effects of inflation comprise a decrease in the real

value of money and other monetary items over a period of time, uncertainties over

future inflation which may discourage investment and savings. If inflation is rapid

enough, shortages of goods as consumers start hoarding out of concern that prices will

increase in the future. Positive effects of inflation include ensuring central banks can

adjust nominal interest rate (intended to counter recession), and encouraging

investment into non-monetary capital projects.

If Government of India needs to control inflation, it reduces rate of interest for

manufacturing sector to revive growth in manufacturing sector. Inflation can be

measured in Consumer Price Index (CPI) or Wholesale Price Index (WPI). The WPI

can be interpreted as an index of prices paid by producer for their inputs. CPI is the

money outlay required to purchase a given basket of consumption goods and services.

A consumer price index measures changes in the price level of consumer goods and

services purchased by households. A CPI can be used for regulating to index (i.e.,

adjust for the effect of inflation) the real value of wages of labourors, salaries,

pensions of retirees, commodity prices and can be used for deflating monetary

magnitudes to show changes in real values.

CPI= * 100

The Wholesale Price Index or WPI is the price of a representative basket of wholesale

goods. The WPI focuses on the price of goods traded between corporations than

goods bought by consumers. This trading of goods is measured by the Consumer

Price Index. The primary objective of the WPI is to monitor price movements that

reflect supply and demand in industry, manufacturing and construction. In this study,

70

Inflation (CPI) has been considered. The new Consumer Price Index (CPI)

(combined) as the key measure of inflation came in force since April 2014.

Historically, the wholesale price index (WPI) has been the main measure of inflation

in India. However, in 2013, Raghuram Rajan, the governor of The RBI announced

that the consumer price index is a better measure of inflation. As per this article, in

India, the most important constituents in the consumer price index are Food,

beverages and tobacco (49.7 percent of total weight). Fuel and electricity contribute to

9.5 percent, Housing 9.8 percent, communication and transport for 7.6 percent,

Medical care for 5.7 percent, Clothing-bedding and footwear account to 4.7 percent

and most importantly the education contributes to (only) 3.4 percent.

In general, inflation is measured by calculating the percentage rate of change

of a price index, which is called the inflation rate. The price index is an indicator of

the average price movement over time of a fixed basket of goods and services. There

are many possibilities for the measurement of inflation: annualized/fixed base; annual

point-to-point/average, where the frequency could be annual / quarterly / monthly /

weekly for the price index. There are different indictors used to measure inflation

namely Wholesale Price Index (WPI), Consumer Price Index (CPI) and the GDP

Deflator which is constructed from the National Income Data.

CPI is a statistical time-series measure of a weighted average of prices of a

specified set of goods and services purchased by consumers. CPI is the price index

that tracks prices of a specified basket of consumer goods and services, providing a

measure of inflation. India is the only major country that uses a wholesale index to

measure inflation. Many of the countries use the CPI as a measure of inflation since

CPI actually measures the increase in price that a consumer will ultimately has to pay

for.

71

3.4.3 GDP Deflator:

The GDP deflator (implicit price deflator for GDP) is a measure of the level of

prices of all new goods, domestically produced goods, final goods and final services

in an economy. GDP (gross domestic product) is the total value of all the final goods

and final services produced within that economy during a specified period. The GDP

price index, as opposed to CPI index which measures the average price level of all

goods and services included in the GDP estimates. Prices are always in fluctuation

mode, however, they generally move upwards over a period of time. Therefore, a

change in prices can yield the impression of an increase in the gross domestic product

(GDP -- a measure of national income) even without an increase in the quantity of

goods and services produced by an economy. Also the base year for the GDP price

index was year 2000. The closer the year in consideration to the base year, the more

precise is the measure of real GDP. This is being the reason that the GDP price index

is cascaded frequently and called the GDP deflator to reflect the change in price level

of the goods and services produced. Although GDP price deflator is not reported as

frequently as the CPI (quarterly Vs monthly), it does provide a better comprehensive

measure of the price level and thus, the inflation. Hence, in the aggregate market

analysis, the GDP price deflator has been used to measure the price level. The impact

of prices has to be removed to arrive at a true measure of economic growth. Deflator

is used to restate consequent estimates at current prices into what they would be if

calculated with reference to prices in an earlier year. It has been given an idea of the

real growth in economy, minus price effect.

Economists, business leaders, and government policy makers often find it

useful to convert present indicators, or nominal economic indicators to real terms, this

is to nullify any inflationary increase of the nominal values. In fact, the "deflator" part

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of the GDP price deflator comes about because it is used to deflate nominal GDP to

real GDP. The GDP Deflator is measured as follows:

GDP Deflator = Nominal GDP X 100 Real GDP

The measurement of inflation using WPI frequently produced unrealistic

results as the ongoing WPI series in India suffered from a number of defects. The

WPI did not properly measure the exact price rise. It has been calculated at the

wholesale level, and more over, the services, which have assumed of so much

important, did not come under the ambit of WPI. In fact, service sector forms an

essential part of the consumption of everyone in the country and currently accounts

for more than 52 percent of Indian GDP.

In India, a combination of WPI and CPI has been used as deflator. The usage

of deflator is dependent on the particular estimate to deflate. For private consumption

and government consumption, different deflators were used. It is seen that the

difference in the value of quarterly deflators and year-end deflators. This was true

since the prices were not constant. For the year-end deflators, an overall measure of

WPI/CPI, have been used appropriately. Therefore, the year-end estimates of GDP are

more reliable than quarterly estimates. Therefore, in this study, a year on year GDP

growth rate, and inflation rate has been considered rather than considering quarterly

growth.

3.4.4 PBDIT:

PBDIT is an acronym for profit before depreciation, interest, and taxes. Profit

is the difference between revenues and expenses over a period of time. Profit is the

final output of a company. Financial managers continuously evaluate efficiency of

73

company in terms of profits earned by the companies. The profitability ratios are

calculated to quantify the operating efficiency of the company. Not only managers of

company but creditors and owners are also interested in the profitability of the firm.

Creditors want to get returns on invested capital and owners want returns on their

investment. This can be explaining further, with the two major types of profitability

ratio:

a) Profitability in relations to sales

b) Profitability in relations to Investment

PBIT also referred to as earnings before interest & taxes (EBIT) is the

operating profit of the firm plus any non-operating surplus less any non- operating

loss. According to I. M. Pandey, Author of Essentials of Financial Management, the

Chapter of Ratio Analysis explained that, if the firms profit has to be examined from

the point of view of all investors namely lenders and owners, the appropriate measure

of profit is operating profit. Operating profit also means Earnings Before Interest and

Taxes (EBIT). This measure of profit has been shown earnings arising directly from

the commercial operations of the business without the effect of financing. It has been

a measure of profit before considering interest expense & tax burden. It abstracts

away the effect of debt policy (which determines the interest expense) as well as the

tax code (which determines the tax burden) Hence, it was pre-eminently suitable for

comparing profitability of firms with different debt policies & tax obligation. In the

present study, it has been considered only w. r. t the manufacturing sector & services

sector.

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Chapter 4

Research Methodology

4.1 Conceptual Framework:

Indian economy is one of the emerging economies which are growing although

facing economic fluctuations. GDP growth rate is the fundamental building block

among development of economy which dictates the overall economic growth. This

study has been to understand change in profitability of Manufacturing & service

industries in economic fluctuations and also to find impact of macroeconomic

variables like GDP, Inflation on Profit ratio of Manufacturing & service industries

during economic fluctuations.

The period of study was considered from 2002-03 to 2012-13 financial years. In

this period, FY 2008-09 experienced a severe downfall in Indian economy. When in

the same tenure there was Global economic recession in which USA, UK and

European countries had a big hit. Indian economy is developing economy therefore

business of Indian economy is mostly with these developed economies. So, when

there was recessionary phase in these developed economies, India’s Export and

Import also got affected to considerable extent. Ultimately this effect was reflected in

the balance sheets of all manufacturing industries and service industries. The impact

was long lasting for 2 to 3 years in case of some of the service sector industries.

This has been a quantitative and analytical research. Data analysis has been

done mainly by statistical and econometrics methods (deductive process) to find out

correlation between the dependent and independent variables, also empirical

75

relationship of the variables based on the objective and hypotheses followed with

Granger’s causality tests.

4.2 Research Design:

Research design is a blue print of the study conducted, which includes steps of

data collection, Interaction with Industry Experts for Primary data collection, process

of data and finally interpretation of the data. The period of study is important in

collecting the secondary data.

4.3 Sources of Data:

Secondary data sources have been used to collect information about the Indian

Inflation rate and GDP growth. Information collected from Secondary data sources

include Central Statistical Organization (CSO) data of Indian Economy, RBI reports,

Indian Economic survey reports, and websites of IMF, FICCI (Federation Of Indian

Chambers Of Commerce & Industry) .

For deriving relationship between Profitability of manufacturing sector and

Service sector with GDP growth rate from 2003 to 2013 data have been used from

CSO and Income & Expenditure Summary of Manufacturing Sector: Centre for

Monitoring Indian Economy (CMIE) Data on WPI has been taken from CSO data.

The EBITDA/ PBDIT of HUL, ITC, Glenmark, Dr. Reddy’s for manufacturing sector

and TCS, Infosys, Reliance Capital Ltd for services sector have been collected from

their respective Annual reports available on their official websites. Gross Domestic

Product (GDP) at factor cost at constant prices growth rate (base year 2004-05) has

been used from data source of Reserve Bank of India and its publications. GDP

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growth based on market prices changes much more sharply during the global financial

crisis than that based on factor costs. Data for inflation rate has been collected from

International Monetary Fund, World Economic Outlook Database dated 15th May,

2014. The data for this research was selected from World Bank Database since; it is

standard source of data and is used in most of the research studies.

4.3.1 CRISIL: CRISIL is a global analytical company which has been providing

ratings, risk, research and policy advisory services. CRISIL is one of the India's

leading ratings agency. CRISIL is also the top provider of high-end research to the

world's largest banks and leading corporations. In competitive advantage which arises

from their strong brands, unmatched credibility, market leader across all businesses,

and large customer base, CRISIL delivers analysis, opinions, and solutions which

make markets function better.

The defining trait is the ability of CRISIL to convert data and information into expert

judgments and forecasts across a wide range of domains, with deep expertise and

complete objectivity.

The majority shareholder of CRISIL has been Standard and Poor's (S&P). Standard &

Poor's, a part of McGraw Hill Financial (formerly The McGraw-Hill Companies)

(NYSE:MHFI) (New York Stock Exchange: McGraw-Hill Financial Inc), is the

world's renowned provider of credit ratings.

4.3.2 CMIE: Centre for Monitoring Indian Economy (CMIE), is a leading business

information company. It has been providing services to the entire spectrum of

business information consumers that includes government organisations, academic

77

institutions, financial markets, business enterprises, business professionals and the

media.

CMIE has been producing economic and business databases and develops

specialized analytical tools to deliver its customers for decision making and for

research purpose. CMIE analysed the data to read and interpret trends in the Indian

economy. It has built India's largest database on the financial performance of

individual companies. CMIE is a privately owned and professionally managed

company.

4.4 Econometrics Modeling for the Hypotheses:

The principal statistical tools considered for data analysis are using the Karl

Pearson’s Correlation Co-efficient, followed with econometrics modeling of

regression, ANOVAs and causation. Correlation means a statistical relationship

between sets of variables none of which has been experimentally manipulated i.e.

(GDP growth rate and Profitability of manufacturing and service sector), (Inflation

and growth rate of manufacturing and service sector). Therefore, Correlation means a

relationship between un-manipulated variables. It measures the strength of linear

association between two variables. Karl Pearson’s Correlation Co-efficient is used

to study correlation between two variables GDP growth rate and Profitability of

manufacturing and service sector, Inflation and growth rate of manufacturing and

service sector. Often in practice, correlation is followed by regression. The tacit

assumption being, if the relation between two variables whether linearly or log

linearly related has been established, then one variable can be predicted based on data

of one variable. The purpose of regression is also to study the model relationship

between variables, describing the relationship between the explanatory and response

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variable and has been addressed using the modeling framework and followed by

Granger’s causality tests.

4.4.1 Model-1 (GDP growth rate & PBDIT of manufacturing sector).

Y (GDP Growth rate) = a + (b) (Change in profit ratio)

To determine influence of change in profit of manufacturing sector on GDP

growth rate of Indian Economy. The following time series regression equation is to be

fitted:

tt ebXaY .............(1)

Y denotes GDP(fc) base year (2004-05)

a denotes constant qty i.e. intercept of line o Y axis

b denotes coefficient of X

X denotes PBDIT of manufacturing sector (yearly)

te is residual term of the model.

The observed data has been used to estimate the two parameters, ‘a’ & ‘b’ of

the model & te is the stochastic term or noise. The actual numerical estimates of the

intercept & the slope are written as â & ^b , where hats indicate that the qty is an

estimate of a model parameter – an estimate that is computed from the observed data.

The above equation can be written as Y= a+bX in absence of error term. i.e.

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te = 0

In the equation the parameter ‘a’ is the intercept, it gives the qty of GDP (fc)

without the influence of MI (PBDIT) i. e. When X=0 & constant ‘b’ is the coefficient

of Y in relation of X or the slope. The slope, a summary of the relationship between X

& Y answers the equation, when X changes by 1 unit, y changes by ‘b’ units.

4.4.2 Model 2: (GDP growth rate & PBDIT of service sector).

Y (GDP Growth rate) = a + (b) (Change in profit ratio)

To determine influence of change in profit of service sector on GDP growth

rate of Indian Economy. The following time series regression equation is to be fitted:

tt ebXaY .............(1)

Yt denotes GDP(fc) base year (2004-05)



X denotes PBDIT of service sector (yearly)


The observed data has been used to estimate the two parameters, ‘a’ & ‘b’ of

the model & te is the stochastic term or noise. The actual numerical estimates of the

intercept & the slope are written as â & ^b , where hats indicate that the qty is an

estimate of a model parameter – an estimate that is computed from the observed data.

80

The above equation can be written as Y= a+bX in absence of error term (et= 0).

In the equation the parameter ‘a’ is the intercept; it gives the qty of GDP (fc)

without the influence of Service sector (PBDIT) i. e. When X=0 & constant ‘b’ is the

coefficient of Y in relation of X or the slope.

The slope, a summary of the relationship between X & Y answers the

equation, when X changes by 1 unit, y changes by ‘b’ unit.

4.4.3 The Granger causality Test:

Granger (1969) proposed a time – series data based approach in order to

determine causality. In the sense of Granger ‘x’ is a cause of ‘y’ if it is useful in

forecasting y. In this framework “useful” means that x is able to increase the accuracy

of prediction of y with respect to a forecast, which considers only past values of y.

The Granger causality test assumes that the information relevant to the

prediction of the respective variables, GDP growth and inflation rate, inflation rate

and rate of change in profit ratio of manufacturing sector are contained solely in the

time series data of the above mentioned variables. The test involves estimating the

following pair of regressions.

(i) Yt(inflation) = Σni=1αi X t-i (rate of change in profit ratio of manufacturing

sector) + Σnj=1 βjYt-j(inflation) + u1t

(ii) Xt (rate of change in profit ratio of service sector) = Σni=1λiXt-i (rate of

change in profit ratio of service sector) + Σnj=1 δjYt-j(inflation) + u2t

Similarly,

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(i) Yt(GDP) = Σni=1αi Xt-i (inflation) + Σnj=1 βjYt-j(GDP) + u1t

(ii) Xt (inflation) = Σni=1λiXt-i(inflation) + Σnj=1 δjYt-j(GDP) + u2t

Where, disturbance terms u1t, u2t are uncorrelated.

Based on the estimated OLS (Ordinary Least Squares ) coefficients for the two

sets of equation different hypotheses about the relationship between rate of change in

profit ratio of manufacturing sector, service sector and inflation also the relationship

between GDP growth rate and inflation can be formulated.

Different companies (for manufacturing and service sector) also had been

studied by the researcher to see the Impact of Economic fluctuation on Individual

Industry. The basic source of data for the study was obtained from Prowess database

of the Centre for Monitoring Indian Economy (CMIE). Top performer of

manufacturing firms & service industries listed on the Bombay Stock Exchange

(BSE) with data available in the Prowess database was selected as a sample for the

case studies. HUL, ITC have been leading organisations in FMCG manufacturing

sector. Glenmark and Dr. Reddy’s have been giant players in Pharmaceutical

manufacturing sector. TCS and Infosys are the big performers in IT service sector

from last ten years. Reliance Capital Ltd is known for financial services in capital

market. Therefore these seven companies were studied by researcher to check direct

impact of Economic fluctuation on the profitability of these industries.

4.5 Details of statistical tools Used for the study:-

The data was analyzed in SPSS version 16 using different statistical tools viz:

1. Descriptive Statistics

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2. Regression

3. Paired t Test

4. Granger Causality Test

The data was collected from Secondary sources. To draw the logical

inferences from the data, descriptive and inferential statistics techniques were

used. The type of statistical techniques were used i.e. Univariate and Bivariate

analysis was used based upon the level of measurements of the data. The Univariate

procedure was dealt with one variable at a time, so for that purpose, descriptive

statistics with percentages to understand the idea about the data has been carried out.

So testing the different assumption or hypothesis, inferential statistics has been used

by the researcher. The first inferential statistics used was paired t test. This test is

applicable only for the ratio scale and only for the two populations at a time, as it also

helps to understand the comparison between two variables.

Regression Analysis:

Regression is another technique for measuring the linear association between a

dependent and an independent variable. Regression analysis assumes the dependent

(or criterion) variable, Y, is predicatively or “causally” linked to the independent (or

predictor) variable, X. These ideas can be investigated through regression models

using the notion of Granger causality. Regression analysis attempts to predict the

values of a continuous and interval-scaled dependent variable from the specific values

of the independent variable. Forecasting of sales is by far the most common

application of regression analysis.

Bivariate linear regression investigates a straight-line relationship of the type

Y = a + X, where Y is the dependent variable, X is the independent variable, and a

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and are two constants to be estimated. The symbol a represents the Y intercept, and

is the slope coefficient. The slope is the change in Y due to the change in one

unit of X.

Least-Squares Method of Regression Analysis

The task of the researcher is to find the best means for fitting a straight line to

the data. The least-squares method is a relatively simple mathematical technique that

ensures that the straight line will be the most representative of the relationship

between X and Y. Any straight line drawn will generate errors, but the method of

least-squares uses the criterion of attempting to make the least amount of total error in

predictions of Y from X. More technically, the procedure used in the least-squares

method generates a straight line that minimizes the sum of the squared deviations of

the actual values from this predicted regression line. Using the symbol e to represent

the deviations of the dots from the line, least-squares criterion is as follows:

where e = Yi (the “residual”)

Yi = actual value of the dependent variable

iY = estimated value of the dependent variable (Y “hat”)

n = number of observations

i = number of the observation

e is minimum

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The general equation for a straight line is Y = a + X where a more

appropriate estimating equation includes an allowance for error:

eXaY ˆ

Test of Statistical Significance

The regression line has been fitted, this error can be reduced; the error is

reduced by using Yi - iY rather than Yi - . This is the “explained” deviation due to

the regression.

Thus the total deviation can be partitioned into two parts:

= )ˆ( iYY + )ˆ( ii YY

where = mean of the total group

iY

= value predicted with regression equation

Yi = actual value

An F-test, or an analysis of variance, can be used to determine if there is more

variability explained by the regression or unexplained by the regression. The

coefficient of determination, r2, reflects the proportion of variation explained by the

regression line.

Y

Y Y

Y

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4.6 Limitations of the study:

The study was carried out with some assumptions regarding time, study area and

sample size.

The study was confined to the duration of 2002-03 to 2012-13 only. The limitations of

the study are as follows –

The data for the study of the impact of economic fluctuations on

profitability of manufacturing and service sector industries on GDP growth

rate of Indian economy. Data from RBI annual reports and CMIE reports for

different types of analysis have been used to enable a comparative analysis.

The restriction of the research only to some macroeconomic and

microeconomic variables was another important limitation of the study.

The study was confined to the impact of economic fluctuations and

inflation on profitability of manufacturing and service sector industries on

GDP growth rate of Indian economy. The study would have had greater

accuracy if data has been collected on employment rate, Government

revenues, tax structure also.

The major limitation in terms of trends and intercepts, was present in the time

series data that was collected. Researcher has been considered time series data which

pertained to economy and so the effect of recessions and other disturbances were

occurred. These disturbances could lead to period of opposite or unexplained

relationships and could distort the final results. In the present study, researcher had

found the relationship between GDPfc at constant prices and other independent

variables like PBDIT of manufacturing sector and service sector.

86

The study was carried out with some assumptions regarding time, study area and

sample size.

The study was confined to the duration of 2002-03 to 2012-13 only. The

limitations of the study are as follows –

The data for the study of the impact of economic fluctuations on profitability

of manufacturing and service sector industries on GDP growth rate of Indian

economy. Data from RBI annual reports and CMIE reports for different types

of analysis have been used to enable a comparative analysis.

The restriction of the research only to some macroeconomic and

microeconomic variables was another important limitation of the study.

The study was confined to the impact of economic fluctuations and inflation

on profitability of manufacturing and service sector industries on GDP growth

rate of Indian economy. The study would have had greater accuracy if data has

been collected on employment rate, Government revenues, tax structure also.

The major limitation in terms of trends and intercepts, was present in the time series

data that was collected. Researcher has been considered time series data which

pertained to economy and so the effect of recessions and other disturbances were

occurred. These disturbances could lead to period of opposite or unexplained

relationships and could distort the final results. In the present study, researcher had

found the relationship between GDPfc at constant prices and other independent

variables like PBDIT of manufacturing sector and service sector.

87

Chapter 5

Case Studies on Manufacturing and Service Sector Industries during

the year 2002-03 to 2011-12

5.1 Impact of fluctuation on profitability of HUL:

About HUL:

Hindustan Unilever Limited (HUL) is India's largest Fast Moving Consumer

Goods (FMCG) Company with a heritage of over 80 years in India and touches the

lives of 2 out of 3 Indians. More than 35 brands spread over 20 distinct categories like

soaps, detergents, toothpastes, shampoos, skin care products, deodorants and so on.

The Company is a part of the everyday life of millions of consumers across India. The

Company has employed more than 16,000 employees and has an annual turnover of

around ` 25,206 crores (in financial year 12 - 13). HUL is a subsidiary of Unilever,

which is one of the world’s leading suppliers and producer of fast moving consumer

goods with strong local roots in more than 100 countries across the globe with annual

sales of €51 billion in 2012. Unilever has about 67% shareholding in HUL. As per

Nielsen market research data published in 2012, every 3rd

Indian uses HUL products.

CRISIL rated HUL AAA, on 14th

Oct, 2014. (CRISIL AAA: Instruments with this

rating are considered to have the highest degree of safety regarding timely servicing

of financial obligations. These instruments carry lowest credit risk.)

Hindustan Unilever's distribution covers over 2 million retail outlets across

India directly and its products are available in over 6.4 million outlets in the

country. It is headquartered in Mumbai, India and employs more than 16,500 workers.

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In 1888- Lever brothers came to India. In 1895-Lifebuoy soap was launched. In 1930-

Unilever was formed on January 1st through merger of Lever Brothers and Margarine

Unie. 2007-Name formally changed to Hindustan Unilever Limited. On 17th October

2008-HUL completed 75 years of corporate existence in India. Current Chairman of

HUL, Mr. Harish Manwani assumed charge as the Non-Executive Chairman of the

Company in 2005. Current CEO & Managing Director of HUL Mr. Sanjiv Mehta

joined the Board of the Company in October 2013.

Reasoning behind selection of HUL from FMCG:

According to CRISIL Study in May, 2014, Top Brands in Indian FMCG market

are ITC Ltd, HUL, Nestle India Ltd, Britannia industries, and Dabur India Ltd.

Internationally, P&G (Procter and Gamble) is a much bigger FMCG company having

turnover of $ 83.7 bn. Unilever had commenced operations almost a decade later than

the former in 1885. Both of the companies market home care and personal care

products. Even geographically, the two companies differ in their core markets. In case

of HUL a huge share of 55% of its total sales comes from emerging economies.

Whereas, P&G derives a majority that is 62% of its overall sales from developed

markets.

• In India there is a price war between HUL And P&G:

• HUL Increase Price For Wheel

• P&G Increase The Price Of Tide of 25%

• P&G cut the price of Ariel

• HUL Reduces The Price Of Surf Excel

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• In the year 2010 HUL has reduced 11-17%price in detergents, 7-17% in toilet

soaps and 6-7% in the toothpastes.

• HUL cut the price of RIN by 30% (price war with P&G).

• Due to which P&G reacts by cutting 20%indirect price (25% hike) in TIDE.

In 2010, around 4000 HUL employees had participated in this mission to interact

with the trade and consumers covering 14,000 outlets.

Bush Fire has resulted in a 40% spike in sales in store wherever the initiative

has been implemented, with reference to internal company estimates. Mission

Bushfire of company is an HUL employee led initiative that aims to create 'Perfect

Stores.' It is seen in the research by HUL, that 70% of the brand purchase decisions

were made at point of purchase. Perfect store programmed was an initiative

started in 2010 with objective of bringing the HUL brands from consideration set to

the purchase basket of the shopper. This project was driven through two initiatives

called IQ and Better Stores. Project IQ had been instrumental in increasing the

assortment and driving placement of new products through strong analytics and

customization of tasks at an outlet level. Better Stores Project derived the visibility of

the brands at the point of purchase through the right visibility mix, focus on

plannograming (share of shelf) and advanced merchandising process using hand-held

terminal (HHT) technology.

Robust consumption in the domestic economy is one of the key drivers of the

FMCG industry. A large number of FMCG products of company derive a significant

proportion of their overall sales from outside the top 100 towns/cities.

Despite the slowdown in 2008 and after, demand for premium products in the

health and wellness space were rising, encouraging companies to launch more

90

premium products. Furthermore, demand for sophisticated personal care products

were also on the rise as people become beauty and health conscious.

The biggest challenge for companies due to high inflation was both on the cost

side and demand side in India, where value sensitive market has arrived. Massive

consumption deceleration in urban markets was anticipated due to incessant consumer

inflation and paltry income increase. The middle class population of the country

continuously remained the main growth driver. Commodity prices fluctuate, which

make it difficult to finalize raw material prices, which also affects the final price of

the product. It was very difficult for Company to pass on the increased costs to

customers without compromising sales volumes. The Marketing costs of company

were also continuously rising.

Company was expected to incur high advertising and promotion costs on

account of rising intensity of competition. Brands of MNCs, despite being household

names, continue to spend heavily on A&P (Advertising and Promotion) to increase

consumer awareness and gain market share.

Foreign exchange fluctuations i.e. Fluctuation in rupee value in exchange of

all currencies had severe impact on export of HUL. Rupee depreciation hit margins of

companies which was dependent on imported Materials. Infrastructure bottlenecks

were the crucial issues for company. Power cost and availability, transportation

challenges, inadequate market connectivity, fragmented nature of the demand, and

lack of sufficient storage Infrastructure for manufacturers and distributors are also big

problems faced by HUL in domestic market.

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GROWTH and PERFORMANCE OF COMPANY Y-o-Y BASIS:

Company Performance in 2003-04:

Indian economy was performing well in initial quarters of 2004, but FMCG

market was in continuous decline turn in last few years. HLL significantly improved

their food business in last four years. In 2000, the profitability of food business was

very low because high cost structures, low price completion. Therefore, HLL

strengthened their key brands, reduced costs, and reengineered supply chain.

Company also sharpened and strenghthened her brand portfolio in 2004. Totally HLL

had 35 brands in 2004. Company always had focus on marketing and innovation for

growth.

In 2003-04 the growth rate in PBDIT was 27.9% but growth in net sale was

only 2.1% in the same duration. Gross turnover of HLL for the year declined by 1.9

% and net turnover declined by 2.1% primarily due to business disposals.

For a FMCG company economic growth has a direct impact on its

performance.


Indian economy was continuously performing well with real GDP growth of

8% in 2005-06 and remained one of the fastest growing economies in the world.

Many of the economic parameters remained strong and positive. FMCG sector

witnessed a scenario of poor or no growth in the past few years. But it began to

change for the better, with good growth numbers posted across various categories

from the last quarter of 2004 onwards and throughout the year 2005. The sales growth

of company was 11% in 2005. Gross turnover for the year increased by 10% and net

turnover increased by 11.04% due to higher production in fiscal benefit zones.

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Growth in PBDIT was only 0.5% from the year 2004 to 2005. The segment profit of

the business grew by 6.6%, the profit impacted by cost and higher brand investments.

A&P (Advertisement and Promotion) investments for brand increased by 20.3% to

strengthen their competitiveness.


By the end of the financial year 2005-06, EBIT growth of HUL was 16.2%.

EBIT margin was only 0.8% in the same year. According to A. C. Nielsen market

survey, HUL was leading brand for some specified products in the year 2006. In

products like, Dishwasher, Talcum powder and Jams HUL was the leader, had highest

market share with 35 powerful brands. In 2006, India was a growing economy with

increasing trend in GDP. Per capita income also started growing which was

favourable for FMCG industry. In the year, 2007, HUL was selected as the top

company in the Indian FMCG sector for the Dun & Bradstreet by American Express

Corporate Awards.


India's longest-running soap opera came up with new name in 2007. The

Company migrated to its new name Hindustan Unilever Ltd. In 2007, the growth of

company was with full swing, but in 2008 global slowdown reduced pace of growth

of Agriculture, when Manufacturing and service sector were better. The Indian

economy has grown with a healthy rate 8% + level for the 2004 to 2007. This growth

was driven by a strong performance by the industry and service sectors, with

agriculture slated to register a positive growth of 3%.Overall growth of economy was

slow due to inflationary pressures from petroleum crude, vegetables, oils and food

grains. FMCG market sustained strong growth in 2007.

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For the year 2007, Company achieved an overall turnover growth of 13.3%;

both Home and Personal Care (HPC) and Foods businesses grew by 12.3% and 20.2%

respectively. Profit After Tax registered a growth of 14.9%. Growth in PBDIT was

14.7% in this financial year. Growth of Net Profit (after exceptional items) was by

3.8%. Company celebrated its Platinum Jubilee Year in 2008.


The global economy suffered a slowdown in this particular financial year,

afflictions that started as financial sector issues in the US spread fast to the real

sectors of the economy across the globe. Although India’s domestic economy grew

strongly for the whole year, many sectors like automotives, capital goods, consumer

durables and realty dampened significantly towards the second half of the year.

Company recorded an overall sales growth of 15.5% with FMCG categories

growing at 18.3%. Operating margins expanded by only 0.4% from 14.1%. The

performance of the exports sector was also observed to have reduced than expected.

Major issue faced by company during the year, where was unprecedented volatility in

the price of commodities, driven largely by the swings in petroleum crude prices.

Managing volatile commodity cost environment was difficult task. The impact of this

was managed dynamically through a combination of judicious pricing, aggressive cost

savings programmes and tight control of indirect costs. This allowed HUL to deliver

margin improvements.

Severe and continued rise during the year, the precipitous fall towards the later

part of the year, and the associated uncertainties in material price movements needed

very careful management. Many public policies were implemented to squarely

address some of the issues in the economy. As compare to other products, the FMCG

markets were generally held well.

94

In an around 700 million people in domestic market are the customers of

HUL. The brands of company continued to feature high in India’s most trusted brands

lists. During the year HUL won many awards for Corporate Social Responsibility,

Innovation and Human Resource practices. HUL volumes dipped 4 per cent in the

quarter ended March 2008-09. In 2008-09 the market situation was such where price

inflation and deflation happened very rapidly. HUL has lost some ground to small and

mid-sized companies and lost market share in March 2009 in some key product

categories. For instance, in market of soaps, market share in value was downward to

47.5 per cent in March 2009 from 53.4 per cent in the same period of previous year.

HUL’s market share dipped to 45.9 % from 47.3 %, in toothpaste and detergents too.


In the year 2009-10 there were many pressure points to sustain in the global

market. FMCG markets continued to grow albeit at a slower pace. In addition, the

strong growth potential of the Indian market attracted many new competitors resulting

in a substantial increase in the competitive intensity across categories. Food Inflation

was the key issue in this financial year, therefore FMCG market was continuously

growing with slow pace rate.

In the challenging environment in 2009-10, HUL registered an overall growth

of 6.4% in 2009-10 while the domestic consumer business grew by nearly 9%. The

growth momentum improved through the year with double-digit volume growth in the

last quarter. During this year, operating margin of company was improved by 15 basis

points compared to the last year, despite a significant increase in investment behind

brand support. In last five years, Company has performed well with a CAGR of 10%

for total sales and 11.5% for FMCG sales.


95

In the environment where Commodity and food inflation appear sticky and

GDP growth was moderate. The year 2010-11 was eventful and satisfying year for the

company. HUL managed to grow in volumes and strengthen position in a tough and

competitive environment. Company ended March 2011 with five consecutive quarters

of double-digit volume growth.

During the year, the domestic consumer business grew by 10.9 per cent driven

by a strong 13 per cent volume growth. PBIT margins decreased by 190 basis points

on account of higher input cost inflation and 60 basis points increase in brand

investment. Net Profit increased by 4.7 per cent to ` 2306 crore for the full year.

During 2010-11, HUL significantly increased their direct retail coverage by adding

over 600,000 outlets. With this coverage in rural India increased by company, and

contributing to 50 per cent of rural growth of company.

HUL has won six EMVIES Awards across Categories. HUL has emerged as the top

‘Dream Employer’ as well as the top company considered for application in the

annual B-School Survey conducted by A.C. Nielsen in November 2010. HUL brands

continued to dominate India’s Most Trusted Brands Survey rankings.

Six of company brands – Lux, Pepsodent, Pond’s, Fair & Lovely, Lifebuoy,

Clinic Plus, in the top 10 and eight in the top 20. In all there are 17 HUL brands

among the ‘100 most trusted brands’ in India. HUL has won UNESCO Water Digest

Award.


The domestic consumer business grew by 18% with 9% underlying volume

growth. Profit Before Interest and Tax (PBIT) grew by 25% with PBIT margin

improved 140 basis points. Profit After Tax (PAT) but before exceptional items, PAT

96

(bei), grew by 20% to `2,592 crore with Net Profit at `2,691 crore growing 17%.

Growth with Innovations was the pattern in the development of portfolio.

Company further expanded their direct retail coverage in 2011. In last two

years, company added one million new stores, which doubled their coverage and

taken HUL products and services to some of the remotest corners of the Country.

HUL had in 2011 over one million outlets enrolled in their ‘Perfect Stores’

programme, which focused on better availability and visibility of all their key brands

in retail stores.

The after effects of the global financial crisis of 2008 continued to cast their

shadow on the economies around the world even in 2012. Because of crisis

vulnerabilities of the systems of regulation and operation of the financial and fiscal

processes came in front. The unprecedented scale of fiscal stimulus that was required

to manage this crisis has meant that bringing the fiscal deficit back to acceptable

levels was an equally difficult challenge.

For India, the weak external demand conditions have been increased by the

high crude oil prices. Slow export growth and rising import bill have led to rising

current account deficit in 2011. In the union Budget, Central Government subsidies

were to be capped at 2% of GDP and some measures to widen tax net were taken. The

area in which there has been relief was the decline in inflation rate from the near

double digit rates seen in the past two years. Although there were risks associated

with the petroleum sector prices and some of the food sector prices, the non-food

manufactured products prices had shown deceleration. The opening up and expansion

of the economy, increasing income levels and changing consumer beliefs and

behaviours have led to an increase in consumption. This was indicating tremendous

opportunity in the market for HUL.

97

Data Analysis of EBDIT of HUL for the period of 2002-03 to 2012-13:

a) Objective 1 - To study the impact of earnings before income ratio of HUL on

GDP growth rate.

In the model, the dependent variable Y is GDP growth rate whereas independent

variable X earnings before depreciation, interest and tax of HUL. The estimated

regression model is as follows:

Y (GDP Growth rate) = 20.37 - (0.947) (earnings before income)

The results indicated that the independent variable i.e. earnings before

depreciation, interest and tax of HUL has a negative impact on GDP Growth rate. So,

one unit increase in earnings before depreciation, interest and tax of HUL will

decrease in GDP growth rate by 0.947 units.

Model Summary

Model R R2

Std. Error of the

Estimate

1 0.801(a) 0.641 2.69776

From the above it was observed, The R2 value for the model was 0.641 which

indicated that 64.1 % of the variations in the GDP Growth rate were explained by

earnings before depreciation, interest and tax of HUL ratio. The significance of R2

was tested with the help of F statistic has been shown in below table,

ANOVA (b)

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 3.228 1 3.228 0.546 0.001(a)

Residual 20.913 8 2.614

Total 24.141 9

98

From the above table it was observed that the, the p < (0.05), so it was

concluded that that at 5% level of significance R2

has been statistically significant.

The significance of the individual coefficients was tested using t-statistic,

Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

1 (Constant) 20.377 11.260 1.810 0.108

EBIT of

HUL -0.947 0.852 -0.366 -1.111 0.299

From the above table it was observed that at 5 % level of significance p > α (0.05),

so the null hypothesis was accepted and alternative was rejected, so the coefficient of

earnings before depreciation, interest and tax of HUL was not statistically significant.

Therefore, it was concluded that earnings before depreciation, interest and tax of HUL

is not a significant variable in influencing GDP growth rate. This was also found

using t statistic.

Test of Granger Causality –

Growth rate of GDP growth rate and income ratio of HUL

aH 0 : Growth rate does not Granger Cause on income ratio of HUL

aH1 : Growth rate has Granger Cause on income ratio of HUL

Pairwise Granger Causality Test:

Null Hypothesis Obs F-statistic Prob

GDP does not Granger

Cause EBIT_I_HUL 8 3.32654 0.1733

From the above table it was observed that at 5 % level of significance p > α

(0.05), so the null hypothesis was accepted and alternative was rejected, so the

99

Growth rate does not granger cause on EBDIT of HUL. Hence, Granger Causality test

points out that the growth rate does not have significant impact over the income ratio

of HUL.

b) Objective 2 - To study the impact of Profit before income ratio of

Manufacturing sector on earnings before income ratio of HUL.

In the model, the dependent variable Y is Profit before income ratio whereas

independent variable X earnings before income ratio of HUL. The estimated


Y (Profit before income ratio of Manufacturing sector) = 5.331 + (0.221) (earnings

before income)

The results indicate that the independent variable i.e. earnings before income ratio of

HUL has a positive impact on profit before income ratio of Manufacturing sector. So,

one unit increase in earnings before income ratio of HUL will increase in Profit before

income ratio by 0.221 units.

Model Summary

Model R R Square

Std. Error of the

Estimate

1 0.801(a) 0.641 9.221

From the above it was observed that R2 value for the model 0.641 which indicated

that 64.1 % of the variations in the Profit before income ratio can be explained by

earnings before income ratio of HUL ratio. The significance of R2 was tested with the

help of F statistic, which is shown in below table,

100

ANOVA (b)

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 256.41 1 3500 7.415 0.012(a)

Residual 100.11 8 80.16

Total 356.52 9

From the above table it was observed that the, the p < (0.05) it was concluded that at

5% level of significance R2

is statistically significant.

The significance of the individual coefficients can be tested using t-statistic,

Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std.

Error Beta

1

(Constant) 0.692 4.102 1.132 0.000

HUL

_PBDIT 0.7125 0.096 0.903 4.122 0.001

From the above table it was observed that at 5 % level of significance p < α (0.05).

The null hypothesis was rejected and alternative was accepted, so the coefficient of

earnings before income ratio of HUL is statistically significant. Therefore, it has been

found that earnings before income ratio of HUL is significant variable in influencing

profit before income ratio of Manufacturing sector. This was also found using t

statistic.

5.2 Impact of fluctuation on profitability of ITC:

About ITC: ITC was Registered under the name Imperial Tobacco Company on

24th August, 1910.

FMCG Market: The growth rate of FMCG market had been 16.2% in the

period of 2006-12. In the year 2013, the share of Rural FMCG market was 33%.

101

Estimated share of modern trade had been expected 10-12% for FMCG market by the

year 2016. Out of total revenue from FMCG sector 63% revenue had been come from

personal care and food products in the year 2013. In the year 2013, FMCG sector had

been contribute 2.4% in India’s GDP. FMCG sector is the 4th

largest sector in Indian

economy, with a total market size of US $ 44.9 Bn. The sector grew at a CAGR of

16.2% during 2006-13.

Food products had been the largest FMCG segment, which constituted approx

43% of the total market. Personal care contributed 22%. Branded products had been

accounted for 93-95% share in modern trade sales in India in 2013 while private

labels accounted 5-7 % share only. The top five FMCG categories in India for the

year 2012-13 were tissue papers, floor cleaners, packaged rice, packaged atta, and

packaged pure ghee.

c) Objective 3 - To study the impact of Profit before income ratio of

Manufacturing sector on earnings before income ratio of ITC.


independent variable X earnings before income ratio of ITC. The estimated


Y (Profit before income ratio of Manufacturing sector) = 0.262 - (0.655)

(earnings before income)

The results indicated that the independent variable i.e. earnings before income

ratio of ITC has a negative impact on profit before income ratio of

Manufacturing sector. So, one unit increase in earnings before income ratio of

ITC will decrease in Profit before income ratio by 0.655 units.

102

Model Summary

Model R R Square

Std. Error of the

Estimate

1 0.801(a) 0.641 7.9621

From the above it was observed that the R2 value for the model to be 0.641

which indicated that 64.1 % of the variations in the Profit before income ratio

were explained by earnings before income ratio of ITC ratio. The significance

of R2 is tested with the help of F statistic, which is shown in below table,

ANOVA (b)

Model Sum of Squares Df Mean Square F Sig.

1 Regression 618.401 2 309.200 4.868 0.047(a)

Residual 444.582 7 63.512

Total 1062.983 9

From the above table it was observed that p < (0.05), so it can be concluded

that at 5% level of significance R2



Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

1

B Std. Error Beta

(Constant) 20.994 13.162 1.595 0.149

ITC_PBDI

T -0.440 0.872 -0.176 -0.505 0.627

103



coefficient of earnings before income ratio of ITC is not statistically

significant. Therefore, it has been found that earnings before income ratio of

ITC is not significant variable in influencing profit before income ratio of

Manufacturing sector.

5.3 Impact of fluctuation on profitability of Glenmark

Pharmaceuticals Ltd:

The Pharmaceutical industry in India was the world's third-largest in

terms of volume in the year 2010. According to the Department of

Pharmaceuticals of the Indian Ministry of Chemicals and Fertilizers, the total

turnover of India's pharmaceuticals industry between 2008 and September

2009 was US$ 21.04 billion while the domestic market was worth US$ 12.26

billion. Glenmark was a research driven, global and integrated pharmaceutical

company. The global pharma industry had been changed with a new economic

reality, one in which growth shifted from mature markets to emerging ones;

new product adoption was not keeping pace with loss of patent protection by

established products; while specialty and niche products began to play a larger

role.

Glenmark was established in India in 1977 by Glen Saldenha and

Mark Saldenha. It had been 14 manufacturing facilities in 4 countries (India,

South Africa, UK and USA) and had totally 6 R&D centres. It manufactured

and marketed generic formulation products and active pharmaceutical

104

ingredients (API). In India, it had manufacturing plants in Goa, Nashik,

Ankleshwar and Solapur.

GROWTH and PERFORMANCE OF COMPANY Y-O-Y BASIS:

The company performance 2002-03:

The Indian pharmaceutical industry was working as a niche market.

Growth of company was estimated to be worth US $ 4.5 billion in the year

2002-03. This highly organised sector had been grown at a rate of 8-9 per cent

annually. It ranked very high in the third world countries, in terms of

technology, quality and range of medicines manufactured. From simple

headache pills to sophisticated antibiotics and complex cardiac compounds,

almost every type of medicine had been made indigenously. The Company

had reported a profit after tax of ` 3,921 million in the financial year 2002-03

as compared to ` 4,597 million in the year 2001-02. The reasons for this

significant decrease in profit after taxation was primarily due to a decline in

profits generated from sale of Flouxetine 40 mg during the year in US market.


The financial year under review was a favourable one for Glenmark

not only because company reported an all-round increase, but because

company strengthened the competitive ability of the business for the long

term. The market had seen severe price competition; four key products of

Glenmark witnessed severe price erosion owing to competitive pressures. The

Indian market grew by approximately 8 per cent in volume terms, it translated

into a meagre 5 per cent growth in value. The operating profit before interest,

105

depreciation and other income increased to `734.89 million from ` 645.43

million in the year 2002-03, an increase of 13.86 per cent over the previous

year.


The financial year under review had been an eventful year for

Glenmark. The out-licensing of GRC 3886 to Forest Laboratories and Teijin

Pharma Ltd had changed the play field for the Company. It was done for the

necessary cash flow for future expansion and growth. It also validated

company’s business model, and demonstrated that India can indeed deliver on

its potential of discovering new molecules at significantly low costs. The

operating profit before interest, depreciation and tax increased to ` 1186.50

million from ` 734.89 million in 2003-04, an increase of 61.45 per cent over

the previous year.


The operating profit before interest, depreciation & tax declined to `

1,086.02 million from ` 1,174.68 million in 2004-05, a decrease of 7.55%

over the previous year.


The Consolidated operating profit before interest, depreciation and tax

increased to ` 4419.85 in 2006-07 from `1500.26 for the year 2005-06, an

increase of 195% over its previous year. The standalone operating profit

106

before interest, depreciation & tax increased to ` 2172.23 million from `

1086.02 million, an increase of ~100% over the previous year.


The Company achieved consolidated Gross revenue of `20092.01

million which was registered to be `12515.34 million in previous year. This

registered a growth of 60.5% over the previous year. On standalone basis, the

Company achieved gross revenue of ` 14048.24 million which was ` 8371.18

million in the year 2006-07. This registered an increase of 67.8% over the

previous year. The growth was mainly attributed to the entry into new

markets. Addition of new products and extension of original products in the

existing markets also increased sales of company. There was tremendous

increase in 2008 on account of 7 novel molecules in different stages of clinical

development, out of which one molecule completed phase 3 trials.


The Asia pacific business unit at Glenmark demonstrated a steady

growth in primary and secondary sales in the financial year. While the overall

primary sales grew by 40%, the growth number for secondary sales stood at a

healthy 34% for that year market. The profit contribution also grew by 32%

over FY 2007-08. Due to economic recession and credit squeeze, the

Company borrowed short-term funds to meet its obligations. However, the

Company took appropriate measures and sought shareholders’ approval for

raising of funds.

107


There was marginal increase in Profit after tax. Profit after tax for the year was

at ` 4643.07 million as against ` 4578.33 million in the previous year. On

standalone basis, the company achieved gross revenue of ` 15,646.65 millions

and the standalone operating profit before finance costs, depreciation and tax

was ` 3,660.79 million as compared to ` 3,575.31 millions in the previous

year. On Consolidated basis, Glenmark Company achieved gross revenue of `

40,206.43 millions and the consolidated operating profit before finance costs,

depreciation and tax was ` 7,236.24 million as compared to ` 7,327.72

millions in the previous year. The domestic pharma market had grown at a

14% CAGR over the past 18 years. However, drug consumption per capita in

India was still among the lowest globally.

d) Objective 4: To study the impact of Profit before income ratio of

Manufacturing sector on earnings before income ratio of Glenmark.


independent variable X earnings before income ratio of GLENMARK. The

estimated regression model is as follows:

Y (Profit before income ratio of Manufacturing sector) = 9.772 + (0.252)

(earnings before income)

The results indicated that the independent variable earnings before income

ratio of GLENMARK had a positive impact on profit before income ratio of

Manufacturing sector. So, one unit increase in earnings before income ratio of

GLENMARK will increase in Profit before income ratio by 0.252 units.

108

Model Summary

Model R R2

Std. Error of the

Estimate

1 0.780(a) 0.6084 8.0331

From the above it was observed that R2 value for the model is 0.608 which

indicates that 60.8 % of the variations in the Profit before income ratio are

explained by earnings before income ratio of GLENMARK ratio. The

significance of R2 was tested with the help of F statistic, as shown in below

table,

ANOVA (b)

Model

Sum of


1

Regression 546.723 1 546.723 8.472 0.020(a)

Residual 516.260 8 64.532

Total 1062.983 9

From the above table it was observed that the, the p < (0.05), so it can be




Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients

T Sig. B

Std.

Error Beta

1

(Constant) 9.772 3.034 3.221 0.012

Genmark_

PBDIT 0.252 0.087 0.717 2.911 0.020

From the above table it was observed that at 5 % level of significance p < α

(0.05), so the null hypothesis was rejected and alternative was accepted, so the

109

coefficient of earnings before income ratio of GLENMARK is statistically

significant. Therefore, Earnings before income ratio of GLENMARK is a

significant variable in influencing profit before income ratio of Manufacturing

sector. This was also found using t statistic.

5.4 Impact of fluctuation on profitability of Dr. Reddy’s

Laboratories Ltd:

GROWTH and PERFORMANCE OF COMPANY Y-O-Y BASIS:

Company performance 2002-03:

Total revenues grew by 9% to $ 380 million as against $ 350 million in the

previous fiscal Fluoxetine capsules 40mg contributed $ 40 million in revenues. This

compared with revenues of $ 77 million in the previous fiscal, which included one-

time marketing exclusivity revenues. Excluding fluoxetine in both the years, total

revenues grew by 25% over the previous fiscal. Revenues outside India grew by 10%

to $ 244 million as against $ 222 million in the previous fiscal driven primarily by the

growth in key markets of Europe, Russia and other US markets. Revenues in North

America were at $ 123 million as against $ 127 million in the previous fiscal, a

marginal decline of 3%. Growth in APIs by 54% as well as the contribution from the

launch of generic tizanidine in July 2002 offset the decline in revenues from

fluoxetine, post expiry of one-time marketing exclusivity in January 2002. Revenues

in Europe grew by 79% to $ 29 million as against $ 16 million in the previous fiscal.

This growth was driven primarily by the acquisition of BMS and Meridian in the UK,

presently known as Dr Reddy’s Laboratories, EU and Dr. Reddy’s Laboratories, UK

respectively. Revenues in the CIS markets including Russia grew by 30% to $ 44

million as against $ 34 million in the previous fiscal. Driven by the improved business

110

mix, the gross profit margin for the year under review was at 57% of total revenues.

R&D expenditure increased by 85% to $ 29 million as against $ 16 million in the

previous fiscal. As a % of revenues, the R&D spent was at 7.6% as against 4.5% in

the previous fiscal. Net Income was at $ 74 million (20% of revenues) as against $

104 million (30% of revenues) in the previous fiscal. Final dividend of 11 cents per

share was recommended by the Board. Cash and cash equivalents increased to $ 153

million from $107 million in the previous fiscal.


Net sales increased by 9 per cent from ` 15,286 million in 2002-03 to `

16,666 million in 2003-04. Total income grew by 11 per cent from ` 15,751 million

in 2002-03 to ` 17,424 million in 2003-04. Profit before depreciation, interest and tax

(PBDIT) calculated net of other income declined by 33 per cent from ` 4,491 million

in 2002-03 to ` 3,008 million in 2003-04. Operating profit margin (Operating

PBDIT/Net Sales) declined from 29 per cent in 2002-03 to 18 per cent in 2003-04.

Post-tax profit (PAT) decreased by 28 per cent from ` 3,924 million in 2002-03 to `

2,833 million in 2003-04.


Consolidated revenues decreased by 23% to ` 50,006 million, or U.S.$. 1.25

billion in 2007–08 from ` 65,095 million in 2006–07. Operating Income decreased by

70% to ` 3,358 million in 2007–08 from ` 11,331 million in 2006–07. Profit before

tax and minority interest decreased by 67% to ` 3,438 million in 2007–08 from `

10,500 million in 2006–07. Profit after tax decreased by 50% to ` 4,678 million in

2007–08 from ` 9,327million in 2006–07. Fully diluted earnings per share decreased

to ` 27.73 in 2007–08 from ` 58.56 in 2006–07.

111


Consolidated revenues grew by 168 per cent, from ` 24,267 million in 2005–

06 to ` 65,095 million, or US $ 1.51 billion. Operating income increased almost

eight-fold from ` 1,442 million in 2005–06 to ` 11,224 million in 2006–07. PBDIT

Grew by more than 450 per cent from ` 1,888 million in 2005–06 to ` 10,500 million,

or US $ 243.6 million. PAT Profit after tax increased almost five-fold from ` 1,629

million in 2005–06 to ` 9,327 million, or US $ 216 million.


Consolidated revenue decreased by 23% to ` 50,006 million, or US $ 1.25

billion in 2007–08 from ` 65,095 million in 2006–07. Operating Income decreased by

70% to ` 3,358 million in 2007–08 from ` 11,331 million in 2006–07. Profit before

tax and minority interest decreased by 67% to ` 3,438 million in 2007–08 from `

10,500 million in 2006–07. Profit after tax decreased by 50% to ` 4,678 million in

2007–08 from ` 9,327 million in 2006–07.


Consolidated revenues increased by 39% to ` 69,441 million, or US $ 1.37

billion in 2008-09 from ` 50,006 million in 2007-08. Gross profit increased by 44%

to ` 36,500 million in 2008-09. As a percentage of revenue, gross profit stood at 53%

in 2008-09, versus 51% in 2007-08. EBIDTA stood at ` 14,529 million in 2008-09,

compared to 9,661 million in 2007-08, showing a growth of 50%. There was net loss

of ` 5,168 million in 2008-09 as against net profit of ` 3,836 million in 2007-08.

Adjusted net income increased by 44% to ` 8,855 million in 2008-09 from ` 6,937

million in 2007-08.

112


2009-10 has been a satisfactory year for the Company. Starting with the

financial results, consolidated revenues for 2009-10 was ` 70,277 million. Excluding

revenues from sumatriptan — the Company’s Authorized Generic version of

Imitrex® which was launched in 2008-09 — revenue grew by 9%. In US dollar terms,

2009-10 revenue was US$ 1.56 billion. The Company’s revenue has been rising at a

CAGR of 23% over the last decade. That is a creditable performance by any standard.

EBITDA of ` 15,828 million was the highest among pharmaceutical companies in

India. Return on Capital Employed (RoCE) in 2009-10 was 17%, as against 14% in

2008-09.


2010-11 has been a very good year for the Company. Here are the key

consolidated financial results. Consolidated revenue for 2010-11 grew by 6% to

`74,693 millions, or US$ 1.7 billion. In the ten years between 2000-01 and 2010-11,

revenue of Company had been rising at a CAGR of 21%. Company’s EBITDA in

2010-11 was 16,789 millions, which was higher than the previous year’s EBITDA of

5,828 millions. Profit after tax at 11,040 millions in 2010-11 was also significantly

greater than what it was in the previous year.


FY 2012 has been a good year for the Company. Consolidated revenues

increased by 30% to ` 96.7 billion in FY2012. Earnings before interest, taxes,

depreciation and amortization (EBITDA) rose by 55% to ` 25.4 billion. Profit after

Tax (PAT) grew by 45% to ` 15.3 billion. Diluted Earnings per Share (EPS)

increased from ` 64.9 in FY2011 to ` 83.8 in FY2012.

113

e) Objective 5: To study the impact of Profit before income ratio of

Manufacturing sector on earnings before income ratio of Dr. Reddy.


independent variable X earnings before income ratio of DR. REDDY. The estimated


Y (Profit before income ratio of Manufacturing sector) = 7.332 + (0.711) (earnings

before income)


ratio of DR. REDDY has a positive impact on profit before income ratio of

Manufacturing sector. So, one unit increase in earnings before income ratio of DR.

REDDY will increase in Profit before income ratio by 0.711 units.

Model Summary

Model R R2

Std. Error of the

Estimate

1 0.820(a) 0.6725 9.011

From the above it was observed, The R2 value for the model is 0.672 which

indicated that 67.2 % of the variations in the Profit before income ratio were

explained by earnings before income ratio of DR. REDDY ratio. The significance of

R2 was tested with the help of F statistic, which has been shown in below table,

ANOVA (b)

Model

Sum of


1 Regression 346.723 1 5440.66 9.785 0.021(a)

Residual 214.125 8 77.321

Total 560.848 9

From the above table it was observed that the, the p < (0.05), so it can be



114


Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B

Std.

Error Beta

1

(Constant) 7.332 4.012 3.112 0.011

DR.

REDDY

_PBDIT

0.711 0.097 0.812 2.612 0.021


(0.05), so the null hypothesis rejected and alternative is accepted, so the coefficient of

earnings before income ratio of DR. REDDY is statistically significant. Therefore, it

was found that earnings before income ratio of DR. REDDY is significant variable in

influencing profit before income ratio of Manufacturing sector. This was also found

using t statistic

5.5 Impact of fluctuations on Profitability of TATA Consultancy

Services Ltd (TCS)

Introduction of Service sector with reference to IT:

The services sector has been the major growth propeller of the Indian

economy with the highest sectoral contribution in India’s Gross Domestic Product

(GDP). However, in recent quarters, in line with general slowdown of the economy,

the growth of services sector has also decelerated. Services export growth has also

decreased since 2011-12 and was at 3.4 per cent both in 2012-13 and of 2013-14.

However ‘Net Services’ growth which decelerated to 1.4 % in 2012-13, picked up to

12.6 % in of 2013-14. The country’s strengths in Information Technology as well as

115

the industry’s adeptness in providing the right solutions at the right time and cost

highlights the increasingly important role of Indian companies in providing high-

quality services to global corporations at the best possible value through a

combination of onsite and off-shore services.

An attempt to analyze the recent performance services sector and list out some

important issues of IT sector in this sector has been made. In IT and ITeS sector, the

growth of total IT and BPM services revenue moderated to 7.5 per cent in 2012-13,

though The National Association of Software and Services Companies (NASSCOM)

has forecasted a growth in revenue of 13-15 per cent for software sector in 2013-14.

Teledensity which is an important indicator of telecom penetration, increased from

18.22 per cent in March 2007 to 73.01 per cent as on 30th Sept 2013.

Reasoning behind selection of TCS from IT Industry:

TCS is one of the largest Indian IT Services Company in terms of revenues

and profits. The Company pioneered the concept of offshore IT Services in 1974 and

continues to provide a comprehensive range of IT Services across geographies and

from different locations across the globe. The Company is well-positioned to take

advantage of the global and domestic opportunities. India remains the preferred

offshore destination for IT Services, both from the point of view of capacity and cost

effective servicing capability. Foreign IT Services players and big foreign

corporations are establishing or expanding their offshore base in India. These trends

both establish India as a chosen offshore delivery destination and create competitive

pressure on the Indian vendors. TCS is uniquely positioned in this market as the only

service provider with an integrated service offering across the entire engineering

value cycle.

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TCS was rated as the best employer by Hewitt CNBC TV18 in 2004. The

Company strives to retain that position and to continue to improve on the employee

satisfaction benchmarks set out in the year – both at the workplace and away. The

Company’s revenues are largely denominated in foreign currency, predominantly

US$, GBP and Euro. Given the nature of the business, a large proportion of costs are

denominated in Indian rupees. This exposes the Company to profit/loss on currency

fluctuations. In financial year 2012-13, the Company remained the highest recruiter in

the industry, with a gross addition of 69,728 and net addition of 37,613 employees

across the globe.

Profit before tax (PBT) has grown by almost 7 times in the last nine years. The

Company had been successful in pursuing profitable growth over the years.TCS has

the distinction of being one of the most valuable companies in India and one of the

top ten IT services companies in the world.

Impact of fluctuation on profitability of TCS

About TCS: Tata Consultancy Services Limited (TCS) is one of the world’s

leading information technology consulting, business process outsourcing, service, and

engineering services organizations, offering services to clients across 55 countries. It

has envisioned and pioneered the adoption of the flexible global business practices

that today enable companies to operate more efficiently and produce more value. TCS

achieved this by creating and mastering a unique method of global deployment and

delivery of high value services of very high quality, and products in IT consulting and

business process outsourcing. “Global Delivery Model,” being the strategic services

delivery concept, has been reshaped by the IT services industry.

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The inception of year of TCS is 1968. TCS is highly respected for its

community practice and workplace principles and this is a key reason for TCS for the

lowest turnover of staff in the Indian industry amongst the main players in this sector.

TCS draws its strength from a highly engaged and motivated workforce, whose

commitment and collective passion has helped the organisation to scale new heights.

The Company has a diverse, technical workforce of 2,76,196 employees from 118

odd countries. The UK Prime Minister, Tony Blair, awarded TCS’ “Outstanding

Contribution to UK Knowledge Industry” in the year 2005. The TCS was awarded by

the World Council for Corporate Governance’s Golden Peacock Global Award for

CSR in February 2007. The Company achieved annual enterprise-wide ISO

certification for ISO 20000: 2011 (Services Management), ISO 9001:2008 (Quality

Management) and ISO 27001:2005 (Security Management). The Company is

enterprise-wide certified for ISO 14001:2004 (Environmental Management) and BS

OHSAS 18001:2007 (Occupational Health and Safety Management) which

demonstrates TCS’ strong commitment to the environment and the occupational

health & safety of its employees and business partners.

TCS has identified the following service offerings for achieving its growth

aspirations:

i. IT Solutions and Services: TCS offers application development and

maintenance services over the entire IT application lifecycle, including

reengineering and migration, e-commerce and internet services, systems

integration, testing services, architecture and technology consulting, as well as

the packaged software implementation across multiple industry and

technology domains.

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ii. Asset based IT Services: TCS utilises its proprietary software assets to

deliver solutions to clients in specific industries and has licensed several

software intellectual property rights (IPR). TCS also develops and markets a

variety of products across diverse industries. TCS has developed products

such as the Hospital Management System (HMS), eIBS, Quartz, NCS, FIG,

etc. for the banking and financial services. CemPac for the cement industry,

and also software development tools such as Assent, MasterCraft, , DataClean

and Infrex were also developed.

iii. Engineering and Industrial Services: TCS offers a range of embedded

softwares, engineering services, and R&D services to diverse clients, thereby

assisting in new product development and product lifecycle management

through its services in the areas of simulation, product design, engineering

drafting, computer-aided engineering services like design and manufacturing,

customisation of engineering software and product data management.

iv. IT Infrastructure Services: The Company offers services that include

hardware support and installation, complete outsourcing of IT networks,

consulting and integration, infrastructure management.

v. Business Process Outsourcing: TCS offers a variety of transaction-based IT-

enabled services. These comprise inbound call centres, engineering services

and database services, back office support. TCS’s focus is to provide

transactional services 24x7 for client needs from various geographies and

ensuring business continuity and disaster recovery.

vi. Global Consulting Services: One of the first companies to set up an

independent consulting division. TCS business includes consulting as an

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integrated part of any assignment to its customers in different industry

segments.

Profitability of TCS:

TCS was India’s largest IT Services company in terms of both revenues and

profits. TCS achieved revenue of ` 62,989 Cr in financial year 2013; a growth of

28.8% over the previous year i.e. 2012. The Net Profit of the company was ` 13,917

Crore a growth of 33.6% over the previous year 2012. The earnings per share of the

company increased to ` 70.99 in Financial Year 2013 from 53.07 in year Financial

Year 2012. Global presence with operations is in effect in 44 countries. Till March 31,

2013, TCS had applied for 1,280 patents. 81 of them have been granted till date.

In the financial year 2012-13, on consolidated basis, the Company achieved

well-rounded growth with steady profitability. TCS had excellent growth across

markets - United Kingdom (44%), Latin America (40%), North America (27%),

Europe (21%), Asia Pacific (27%), Middle East Africa (28%) and India (16%). All

the industry segments of TCS have registered double digit growth. Even the Company

crossed USD 3 billion revenue in a quarter during Q-4 of the financial year 2012-13.

On consolidated basis, revenue from operations for the financial year 2012-13 at `

62,989.48 crores was higher by 28.8% over last year (` 48,893.83 crores in 2011-12).

Earning before interest, tax, depreciation and amortisation (EBITDA) at ` 18,039.91

crores was higher by 25.0% over last year (` 14,435.31 crores in 2011-12). Profit

after tax (PAT) for the year at ` 13,917.31 crores was higher by 33.7% over the

previous year (` 10,413.49 crores in 2011-12).

Expenditure incurred in the R&D centres and innovation centres of TCS

(consolidated) during the financial year 2012-13 and 2011-12 are ` 164.18 and 143.70

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respectively. Total R&D and innovation expenditure was ` 776.58 Crore in 2012-13

which was ` 602.99 Crore in 2011-12.

GROWTH and PERFORMANCE OF COMPANY Y-o-Y BASIS:


2004-05 was a milestone year for Tata Consultancy Services. It made an entry

in the stock markets in August last year. TCS became the first Indian IT company to

cross the $ 2 billion annual revenue mark to reinforce its position as a pioneer and

leader in this sector. TCS institutionalized its best practices across subsidiaries

making customer centricity a guiding principle. Synergies within the sister

organisations are being explored to enable joint go to market strategies and a common

face to the customer to the advantage of all concerned. In 2004-05 the unexplored

opportunities or TCS were Eastern Europe, Russia and China. TCS was well

positioned to face the challenges of the future. The immense support, dedication,

innovativeness of over 40,000 TCSers continues to be the Company’s greatest asset.

TCS continued the tradition of being a pioneer in the development of the country and

the industry. Pursuant to a Prospectus dated August 11, 2004, the Company made an

IPO (Initial Public Offer) of 5,54,52,600 equity shares of ` 1/- each for cash at a price

of ` 850/- per equity share summing up to ` 4,713.47 crores. This consisted of a

Fresh Issue of 2,27,75,000 equity shares by the Company and an Offer for Sale of

3,26,77,600 equity shares by some of shareholders of the Company. In addition, there

was a Green Shoe Option of 83,17,880 equity shares offered by shareholder of the

Company at the price of ` 850/- per equity share aggregating ` 707.02 crores. The

IPO was over-subscribed 8.03 times on an overall basis. The amount of ` 1,935.88

crores realized by the Company from the Fresh Issue of shares was fully utilized in

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paying, in part, the purchase consideration of ` 2,300 crores to TSL as per the objects

of the IPO, and the balance amount was paid out of the Company’s internal resources.

The net profit of the Company for the year amounted to ` 1,831.4 crores or

22.6% of total revenue. Excluding the onetime charges, net profit for the year was `

2,027.9 crores (25.0% of the revenue for the year ended March 31, 2005). The net

profit of the Company as per the Consolidated Accounts was ` 1,976.9 crores

representing 20.1% of the consolidated revenue.


TCS was listed with stock market in Aug, 2004. TCS, leader of the Indian IT

industry for the last 35 years, the Company has just commenced its journey as a

public company in 2004 and made a smooth and successful transition by adopting

governance measures as well as enhancing its communication and brand building

activities with the analysts and investors, media and ensuring regulatory compliance.

Company accelerated its activities along these dimensions. The internal systems for

knowledge management, customer relationship management and data-driven decision

making had matured, creating a responsible, profitable and empowered global

organization. The robust foundations combined with an aggressive growth focus saw

the Company entering new business segments and new markets, and helped sustain

the growth rate of 36 per cent in 2004, which continued to be higher than the industry

average. Through the sustained rapid growth company built intellectual assets and

creating a learning organization, confident about its abilities and talents. More than

60,000 culturally and ethnically diverse professionals from 53 nationalities, located

across 35 countries, used technology to collaborate and share ideas and create

innovative solutions. Company also defined the next level of internal digitization.

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Innovation-led change was the common thread binding the Company’s overall

strategy. TCS functioned in a highly dynamic business environment which demanded

almost immediate responsiveness. Through a series of innovative steps, using organic

and inorganic methods, Company had emerged as a scale player in the IT services

industry worldwide. Company’s ability to deliver total solutions from conception to

implementation, its knowledge of technology and business as well as its world-class

project management capabilities have propelled it into the top league of global

consultants.

Around 6 per cent of Company’s employees being non-Indian, creating a

multi-cultural global organization that operates in an enriched and inclusive

atmosphere of collaboration and excellence. Higher brand awareness is attracting

talented people to the Company and it is increasingly being recognized as a preferred

employer in key world markets. In India, TCS continued to be among the largest

employers in the private sector with over 60,000 employees and has added over

21,000 employees in the year 2004-05. TCS remains the employer of choice in a fast-

paced industry and has the lowest attrition rate in the industry. TCS offered the

services to large customers by offering specialized and emerging services like

consulting, business process outsourcing, management of infrastructure services,

software assurance services as well as engineering and industrial services. These

services are growing rapidly and some have the potential to become billion dollar

businesses in the medium term.

The revenue potential of the Indian IT industry was estimated to be $60 billion

by 2010 and Company was well poised to take advantage of this opportunity. A

virtualised organization, increasing domain specialization, building a global scale and

increasing global marketing and communication efforts are measures taken with an

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eye on the future. There are many opportunities in Latin America, North America,

Europe, Asia-Pacific and India that the Company must tap be it in the form of

potential markets or sources of talent. TCS continues to examine growth via mergers

and acquisitions that are a strategic fit. The Company’s performance in 2005-06 was

dominated by profitable and strong growth in key markets around the world. The

Company emerged as a global full-services player in the IT sector with the ability to

handle large customers and complex engagements.

For the year ended March 31, 2006, the Company bagged total income of `

11282.81 crores (previous year ` 8122.81 crores) and ` 13386.23 crores as per the

Consolidated Accounts (previous year ` 9844.60 crores). The net profit of the

Company for the year amounted to ` 2716.87 crores or 24.08% of total income (`

2966.74 crores or 22.16% of total income as per the Consolidated Accounts) and for

the previous year it amounted to ` 1831.42 crores or 22.55% of total income (`

1976.90 crores or 20.08% of total income as per the Consolidated Accounts).

The Company continued its multi-pronged strategy to establish itself among the top

global IT services and consulting companies by providing solutions to real business

problems to corporations around the world by leveraging its excellence in technology,

domain knowledge and processes. The growing strength of the Company’s core

business of IT services including application development and maintenance (ADM)

was underscored in its significant win from ABN Amro Bank, who awarded the

Company a five-year ADM assignment in excess of Euros 200 million to be executed

through its centres in Mumbai, Bangalore, Budapest, Luxembourg and Campinas,

Brazil.

According to NASSCOM McKinsey Report of December 2005, IT

outsourcing services such as software and hardware maintenance, network

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administration and help desk services were expected to account for more than 45% of

the total addressable market for off-shoring of US$ 150 to 180 billion, and are likely

to be the drivers of growth.


In the 2006-07 Company took some significant steps on the journey to be

among the top IT companies globally. With consolidated revenues of $4.3 billion TCS

stood at global number 11 and on the threshold of the top ten global IT firms. On

other parameters like profits, market capitalization and employee strength, the

Company was well established in the global top 10. Just in the 11 Quarters duration

the Company had been publicly listed, with market capitalization had more than

trebled from around $8 billion at the IPO price to $28 billion at the end of March

2007. Growth rates had also rise steadily year-on-year since the company went public

-- from 37% in 2004-05 to 41% in the year 2006-07 on an ever increasing revenue

base. As Company continued to deepen their links in mature markets and established

a strong presence in emerging markets, it remained confident of pursuing growth rates

higher than the industry average, with a keen eye on profitability and new

opportunities.

The TCS strategy is being driven by three key differentiators. One is the

unique Global Network Delivery Model (GNDM) which is much more than having an

India-centric delivery model with near-shore centres. The GNDM allows their teams

to collaborate on projects, leverage all our assets, work on a follow-the-sun model, if

necessary, and above all, through their homogeneity in terms of quality, skills and

look-n-feel, give customers the same experience of certainty, irrespective of

geography and market.

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Another key driver of growth had been their Acquisitions. The intent had not

been to buy revenues, but to create capabilities that would drive higher levels of

synergistic growth. The value being generated by these acquisitions had been

tremendous and it was seen that only the beginning of the synergistic revenue

opportunities. For instance, acquisition of FNS (Sydney-based Financial Network

Services) in Australia had given them a world-class core banking product which they

had been able to leverage to enter new markets such as China and Latin America. The

power of GNDM as well as strategic acquisitions was truly unleashed by the

integrated full-services that now capture the entire value chain of IT – from consulting

to products and solutions and from implementation to support.

Five of the twelve large deals of over $50m won by TCS in 2006-07 used

more than one service line which shows that an integrated offering has been validated

by global customers. Opportunities for TCS in the global market place and growth

strategy were:

Increase market share in established markets like the US, the UK and Europe

In India, concentrate on projects with scale and complexity like the National

Stock Exchange, the third-largest global exchange by trading volumes, or

mission critical projects like the Ministry of Company Affairs’ MCA-21

initiative

Establish TCS as a leading player in new markets like China, Japan, Latin

America as well as the Middle-east and Africa

Add new offerings to portfolio such as infrastructure services and new BPO

platforms and increase traction for the financial products portfolio in key

markets.

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Innovation can take many forms, but these must result in better productivity of

employees through continuous improvement in processes, systems, methodologies

and capabilities, resulting in higher revenues and profits per employee. Our internal

focus on operational excellence had seen innovation in the form of extensive

digitization, creating real time dashboards which had brought in visible efficiencies,

growth and margin enhancements. Increase in the use of automation in software

development and reducing the linear increase in manpower by greater use of IT

development-to-deployment cycle was another key focus of the innovation efforts.

The ultimate test of success is probably talent management.

The average age of an employee in TCS is 27 years. Young employees constantly

push it hard to meet their global career aspirations and to nurture them into world-

class professionals. TCS is becoming truly global with over 8,000 employees from

over 67 nationalities in the TCS fold. The immense professionalism, dedication and

support of over 85,000 TCSers globally continue to be Company’s greatest asset.


In the year 2007-08, Company’s consolidated revenues had grown by 22% to

` 22,863 crore or US$ 5.7 billion. Consolidated profits of TCS were at ` 5,026 crore

or US$ 1.25 billion, a growth of 19.3 % in the financial year 2007-08, and this

performance came against the backdrop of the Indian Rupee’s rapid appreciation by

11 % against the US dollar during FY08. Despite this, Company maintained its

profitability with net margins stable at around 22 %. For the year ended March 31,

2008, the Company earned a total income of ` 18979.67 crore, an increase of 25.22 %

over previous year’s ` 15156.52 crore. As per the Consolidated Accounts the total

income was ` 23349.45 crore, an increase of 23.45% over the previous year’s `

18914.26 crore. The net profit of the Company for the year increased to ` 4508.76

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crore (23.76% of the total income) as compared to ` 3757.29 crore (24.79% of total

income) in the previous year. As per the Consolidated Accounts the net profit for the

year was ` 5026.02 crore (21.53% of total income) as compared to ` 4212.63 crore

(22.27% of total income) in 2006-07.

A significant feature of fiscal 2008 was the volatility in foreign currency

exchange rates adversely affected the export oriented industries. TCS Limited earns

its revenues in US Dollar, GB Pound, Euro and multiple other foreign currencies.

During the fiscal 2008, vis-à-vis Indian Rupee, US$ fell by 11.05%, GBP fell by

5.64% and Euro fell by 1.76% on the basis of average daily closing prices. Revenues

in foreign currencies constituted 91.5% in fiscal 2008. Revenues in US$ were 60.7%

of total revenues. Consequently, revenues in Indian Rupee got adversely affected.

Expenditure in foreign currencies constituted 36.9% of the total expenditure in fiscal

2008 - providing a relatively narrow natural hedge to the exchange rate risk in the

business. These factors resulted in relatively lower growth in revenues.


For the year 2008-09 Company's consolidated revenues grew by 23 per cent to

` 27,813 crore, thereby helping Tata Consultancy Services cross the $ six billion

revenue milestone. Company's consolidated operating profits grew by 26 per cent to `

7,170 crore and its operating margins improved by 109 basis points to 23.73 per cent.

The net profit was ` 5,256 crore, a growth of 5 per cent over 2007-08, due to external

factors like extreme currency volatility. The Company continued to see profitable

growth in the financial year 2008-09 across all markets in existing and new areas of

business.

For the year ended March 31, 2009, the Company earned a total income of `

21947.76 crore, an increase of 15.64% over previous year ` 18979.67 crore. As per

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the Consolidated Accounts the total income was ` 27385.89 crore, an increase of

17.30% over the previous year's ` 23347.81 crore. The net profit of the Company for

the year increased to ` 4696.21 crore (21.40% of the total income) as compared to `

4508.76 crore (23.76% of total income) in the previous year. As per the Consolidated

Accounts the net profit for the year was ` 5256.42 crore (19.19% of total income) as

compared to ` 5026.02 crore (21.53% of total income) in 2007-08.

TCS is amongst the leading global IT companies and continues to retain its

leadership position in the Indian IT Industry. With Consolidated Revenues at `

27812.88 crore for the year ended March 31, 2009, TCS has, over the last five years

as a listed company, recorded a CAGR of 23.33%. TCS operates extensively in the

global market and the global economic slowdown in general, and the particular

difficulties that the key global markets and the customers of TCS have faced during

this year on account of the economic conditions.

TCS was recommended enterprise wide for ISO 9001:2008 (new version of

Quality Management standard) certification. TCS was recommended enterprise wide

for continuation of the ISO 27001:2005 (Security Management) and ISO 20000:2005

(Service Management) certification. TCS was re-certified for domain specific quality

certification TL 9000 for the Telecom business. TCS also continues to maintain the

domain specific certifications AS9100 (for Aerospace industry) and ISO 13485 (for

Medical Devices), thus further reinforcing the industry domain focus within the

organization.


Revenue growth was translated into higher profitability at the operating and

net levels on the back of good execution. On a consolidated basis, operating profits

(EBT before Other Income) grew to ` 8,018 crore, an increase of 21.91 per cent

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during the year. As a result, operating margins increased to near historic highs of 26.7

per centupfrom23.66 per cent last year. Profits after tax increased to ` 7,001 crore, a

growth of 33.2 per cent in 2009-10. Net margin stood at 23.32 per cent for 2009-10 up

from 18.91 per cent in the previous year. Our Earnings per share for the year 2019-10

were ` 35.67. The Company was aggressive in its quest for new contracts, executed

on its full services strategy and maintained pricing discipline. This helped to deliver

8% revenue growth for the year along with improvement in margin. On a

Consolidated basis, in 2009-10 TCS revenues were at ` 30,028.92 crore, a growth of

7.97% over 2008-09. Operating margin (Profit before taxes excluding other income)

grew 304 basis points to 26.70% and net margin grew 441 basis points to 23.31%.

This stellar performance was well received by investors, with the market

capitalisation increasing from ` 52,845 crore ($10.4 billion) in March 2009 to `

152,820 crore ($34 billion) in March 2010.


The net profit of TCS grew by 29.05% to 9068 ` Crores in 2010-11. Over the

last 12 months company have added around 140 new customers. They have

continuously increased their share of IT spend across their key customers base by

providing integrated solutions designed to propel their business forward. While the

core IT service business continuously performing in double digit growth, strong

annual growth of other sectors like: assurance service (67%), infrastructure

management (40%), global consulting (41%) and intellectual property based products

(38%) helped the company to post an industry leading performance for fiscal year

2011. There were several macroeconomic challenges in the Financial year 2010-11,

slow GDP growth and employment growth as well as rising level of public debt in

mature markets like USA, Europe and high commodity prices, inflation and currency

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movement dampened the growth in emerging markets like India, China, and Brazil.

On consolidated basis for the year 2010-11, revenues were at 37,324.51 crores were

higher by 24.30% over the previous year’s revenues of 30,028.92 crores. Operating

profit (profit before taxes excluding other income) at 10,416.62 crores was higher by

29.92% over the previous year’s operating profit of 8,017.56 crores. Net profit for the

year at 9,068.04 crores was higher by 29.53% over the previous year’s net profit of

7,000.64 crores.


In the financial year 2011-12, TCS was able to capture the business growth

with all markets and industries in which it operated with growth of double digit. On

consolidated basis, revenue for 2011-12 grew by 31% to 48,894 crores. Net profits

grew by 15% from the previous financial year. Over the last 12 months, company had

added around 141 new customers across the world to take its active customer base to

1076. Company has increased significantly their patents filing, and 460 patents were

filed in several countries in 2011-12. Out of which 72 patents have been grated till

2012. Macroeconomic fluctuations faced by company in this financial year also but

that affect the growth of company only in short run, because company learn to adapt

these fluctuations of Rupee, inflation and slow GDP growth rate. In the financial year

2011-12, the Company continued its strong growth momentum across major markets.

Revenue growth in the year remained high in North America (29.62%), UK (29.16%),

Europe (41.62%), Asia Pacific (50.67%) and Middle East & Africa (43.38%). Other

geographies also witnessed double digit growth rates. On consolidated basis,

operating profit at ` 13,517.37 crores was higher by 29.44% (` 10,443.10 crores in

2010-11) and the net profit for the year at ` 10,413.49 crores was higher by 14.84%

(` 9,068.04 crores in 2010-11).

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During the financial year 2012-13, the global economy was on a slow growth

path. There were signs of faster growth in certain geographies, primarily in the

emerging markets. The prevailing uncertainties in the economy were challenging. In

the financial year 2012-13, on consolidated basis, the Company has achieved well-

rounded growth with steady profitability. The Company had excellent growth across

markets - United Kingdom (44%), Latin America (40%), North America (27%),

Europe (21%), Asia Pacific (27%), Middle East Africa (28%) and India (16%). All

the industry segments have registered double digit growth. For the first time, the

Company crossed USD 3 billion revenue in a quarter during Q4 of the financial year

2012-13.

On consolidated basis, revenue from operations for the financial year 2012-13

at ` 62,989.48 crores was higher by 28.8% over last year (` 48,893.83 crores in 2011-

12). Earning before interest, tax, depreciation and amortisation (EBITDA) at


Profit after tax (PAT) for the year at ` 13,917.31 crores was higher by 33.7% over last

year (` 10,413.49 crores in 2011-12).

On unconsolidated basis, revenue from operations for the financial year 2012-

13 at ` 48,426.14 crores was higher by 27.1% over last year (` 38,104.23 crores in

2011-12).Earnings before interest, tax, depreciation and amortisation (EBITDA) at


Profit after tax (PAT) for the year at ` 12,786.34 crores was higher by 16.5% over last

year (` 10,975.98 crores in 2011-12).

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Impact on TCS of Global economic situation:

The economic slowdown in the US and Europe has eased, but uncertainty

remained. The US economy had shown signs of stability; still uncertainties remain

with respect to debt ceiling, which could lead to further economic challenges in US.

Unemployment situation was a worry in US and Europe in 2013 also. Possible

sovereign default in Europe was also an area of concern. TCS has Strategic focus on

new services in the portfolio of service offerings and revenues from new services. It

achieved 21 fold increase in revenue from new services which was 7.30% of total

revenue in 2006 was increased upto 31.66 of total revenue in 2013. The strategic

investment in Asia-Pacific, Latin America and Middle East & Africa markets in order

to derisk geographical concentration gave 14 fold increase in revenue which was `

602 Crores, 6.18% of total revenue which rose to ` Cr.8150 In 2013 which is of

12.94% of total revenue.

The Earnings Per Share (EPS) of TCS had a continuous rise since 2003 to 2013.

Fig. 18: Earning Per Share of TCS since 2005 to 2013

Source: Annual Reports of TCS

The year 2005-06 was a defining year for TCS. Total income earned in 2004-05

was ` Cr. 8122.81 which climbed upto ` Cr. 11282.81 in 2005-06. The net profit of

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the Company for the year amounted to ` 2716.87 crores or 24.08% of total income as

per the Consolidated Accounts and for the previous year it amounted to ` 1831.42

crores or 22.55% of total income as per the Consolidated Accounts. In fiscal 2006, the

Company’s consolidated total income aggregated ` 13,386.23 crores as compared to `

9,844.60 crores in fiscal 2005, recording a growth of 35.98%. The Company’s

consolidated profit before taxes aggregated ` 3,506.62 crores in fiscal 2006 as

compared to ` 2,633.69 crores in fiscal 2005 - a growth of 33.14%.EPS units were `

53.63 Units in 2008-09 Fiscal Year which fall up to ` 35.67 Units in 2009-10 Fiscal

Year. But there is less impact of global slowdown on profitability of TCS. So,

PBDITA was ` Cr. 7,169.8 in 2008-09 which increase upto ` Cr. 8,694.55 in 2009-

10. Growth in PBDITA from FY 10 vs 09 was 23.09%. The increase in the PBIDT of

4.88% as a percentage of revenues during fiscal 2010 is attributable to:

Decrease in total employee cost 0.99%

Decrease in the cost of services rendered by business associates by 0.25%

Decrease in overseas business expense other than employee cost 0.47%

Decrease in operating cost and other expenses 0.37%

Increase in other income

Economic slowdown in 2008 hit Europe to a large extent therefore, revenue from

European countries dropped sharply in 2009-10 upto ` Cr. 8,009.57 from ` Cr.

8,212.22 in 2008-09. In domestic market no severe impact had been observed.

Revenue from India was ` Cr. 2,597.90 in 2009-10 which was ` Cr. 2,182.12 in

2008-09.

134

Fig.19: EBITDA of TCS since 2005 to 2012

Source: Annual Reports of TCS

With the above diagram, Earnings before interest, tax, depreciation and

amortisation (EBITDA) excluding other income have grown by more than five times

in the last eight years. There was decrease in EBITDA excluding other income margin

in Financial year 2008-09 to Financial year 25.20% from 27.50% in 2007-08.

EBITDA in fiscal 2012 was ` 14,435.31 crores ` 11,178.36 crores in fiscal 2011).

There was a drop of 0.43% in EBITDA as percentage of revenue. The decrease was

primarily attributable to

increase in employee and (business associates) BA related costs 0.35%

increase in operation and other expenses 0.15%

offset by a decrease in overseas business expenses 0.07%

f) Objective 6: To study the impact of earnings before depreciation, interest and

tax of TCS on GDP growth rate.

In the model, the dependent variable Y is GDP growth rate whereas

independent variable X earnings before depreciation, interest and tax of TCS.

The estimated regression model is as follows:

135

Y (GDP Growth rate) = 6.625 + (0.050) (earnings before depreciation, interest

and tax)

The results indicated that the independent variable i.e. earnings before

depreciation, interest and tax of TCS has a positive impact on GDP Growth rate. So,

one unit increase in EBDIT of TCS will increase in GDP growth rate by 0.050 units.

Model Summary

Model R R

2

Std. Error of the

Estimate

1 0.799(a) 0.638 2.69776

From the above it was observed, that R2 value for the model is 0.638 which

indicated that 63.8 % of the variations in the GDP Growth rate are explained by

earnings before depreciation, interest and tax of TCS ratio. The significance of R2

was tested with the help of F statistic, which is shown in below table,

ANOVA (b)

Model

Sum of

Squares Df

Mean

Square F Sig.

1

Regression 2.383 1 2.383 0.876 0.003(a)

Residual 21.759 8 2.720

Total 24.141 9

From the above table it was observed that the, the p < (0.05), so it can be concluded

that that at 5% level of significance R2



Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

1 B

Std.

Error Beta

(Constant) 6.625 1.435 4.616 0.002

EBIT of

TCS 0.050 0.053 0.314 0.936 0.377

136

From the above table it was observed that at 5 % level of significance p > α (0.05), so

the null hypothesis was accepted and alternative was rejected, so the coefficient of

earnings before depreciation, interest and tax of TCS is not statistically significant.

Therefore, it was observed that earnings before depreciation, interest and tax of TCS

is not a significant variable in influencing GDP growth rate. This was also found

using t statistic.

Test of Granger Causality –

Growth rate of GDP growth rate and income ratio of TCS–

aH 0 : Growth rate does not Granger Cause on income ratio of TCS

aH1 : Growth rate has Granger Cause on income ratio of TCS

Pairwise Granger Causality Tests

Null Hypothesis Obs F-Statistic Prob

GDP does not Granger Cause

EBIT_IN 8 1.19528 0.4152

From the above table it was observed that at 5 % level of significance p > α (0.05),

so the null hypothesis was accepted and alternative was rejected, so the Growth rate

does not granger cause on income ratio of TCS. Hence, Granger Causality test pointed

out that the growth rate does not have significant impact over the income ratio of

TCS.

g) Objective 7 - To study the impact of Profit before income ratio of Service

sector on earnings before income ratio of TCS.

137


independent variable X earnings before income ratio of TCS. The estimated


Y (Profit before income ratio of Service sector) = 6.441 + (0.621) (earnings before

income)


ratio of TCS has a positive impact on profit before income ratio of Service sector. So,

one unit increase in earnings before income ratio of TCS will increase in Profit before


Model Summary

Model R R Square

Std. Error of the

Estimate

1 0.800(a) 0.640 10.112

From the above it was observed that R2 value for the model is 0.640 which indicated

that 64 % of the variations in the Profit before income ratio have been explained by

earnings before income ratio of TCS ratio. The significance of R2 is tested with the

help of F statistic, which is shown in below table,

ANOVA (b)

Model

Sum of

Squares Df Mean Square F Sig.

1

Regression 359.63 1 4400 8.332 0.011(a)

Residual 112.56 8 73.21

Total 472.19 9

From the above table it is observed that the, the p < (0.05), so we conclude that that at




138

Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B

Std.

Error Beta

1

(Constant) 6.441 2.112 2.161 0.010

TCS

_PBDIT 0.621 0.094 0.916 3.214 0.011


(0.05) hence, the null hypothesis was rejected and alternative was accepted, so the

coefficient of earnings before income ratio of TCS is statistically significant.

Therefore, it has been found that earnings before income ratio of TCS is significant

variable in influencing profit before income ratio of Service sector. This was also

found using t statistic.

5.6 Impact of fluctuation on profitability of Infosys

In1981, seven engineers started Infosys Limited with just US$ 250. Infosys is a

global leader in consulting, technology, and outsourcing solutions. It provides a range

of software services namely application development and maintenance, corporate

performance management, independent validation services, infrastructure services,

packaged application services and product engineering and systems integration. It has

digital marketing strategy for their products and services. It has provided locally

relevant business value – It has understood client-specific needs and value preposition

has been key to this. It has region specific services. It has vast experience in the

software industry. The company has business innovation in global delivery model

which is coupled with technology and industry experience in finance, manufacture

and telecommunications, transportation and logistics industries.

139

FLEXIBILITY AND SCALABILITY – Due to its ability to distribute

engagements and capacity across centres worldwide. Also, it can control the scale of

production depending upon the current market demand.

SIMPLICATION OF ADOPTION -The procedure for adoption of its

various products and services is very simple. In fact, during its early years there had

been 200 marketers and 50 agencies which adopted their services easily.

COST AND EFFICIENCY - In annual report of 2014, it has ensured a 33%

reduction in the operating costs which led to huge profit margins.

RELIABILITY –Due to digital marketing strategy consumers are able to

know the products and services provided by the company better and so reliability

value has increased and so the brand loyalty towards the company which has helped

the company even at high pricing of its products.

h) Objective 8: To study the impact of Profit before income ratio of Service

sector on earnings before income ratio of Infosys.


independent variable X earnings before income ratio of Infosys. The estimated


Y (Profit before income ratio of Service sector) = 15.218 - (0.006) (earnings before

income)

The results indicated that the independent variable i.e. earnings before income ratio of

Infosys has a negative impact on profit before income ratio of Service sector. So, one

unit increase in earnings before income ratio of Infosys will decrease in Profit before


140

Model Summary

Model R R2

Std. Error of the

Estimate

1 0.799(a) 0.638 8.1151

From the above it was observed that R2 value for the model was 0.638 which

indicated that 63.8 % of the variations in the Profit before income ratio are explained

by earnings before income ratio of INFOSYS ratio. The significance of R2 was tested

with the help of F statistic, which is shown in below table,

ANOVA (b)

Model

Sum of

Squares df

Mean

Square F Sig.

1

Regression 0.368 1 0.368 0.006 0.942(a)

Residual 526.846 8 65.856

Total 527.214 9

From the above table it was observed that p > (0.05), so it can be concluded that at 5%

level of significance R2

is not statistically significant.


Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

1

B

Std.

Error Beta

(Constant) 15.218 2.670 5.700 0.000

Infosys

PBDIT -0.006 0.078 -0.026 -0.075 0.942


the null hypothesis was accepted and alternative was rejected, so the coefficient of

141

earnings before income ratio of Infosys is not statistically significant. Therefore, it

was found that earnings before income ratio of Infosys is not significant variable in

influencing profit before income ratio of Service sector.

5.7 Impact of fluctuations on Profitability of Reliance Capital Ltd

(RCL):

Introduction of Capital Market Service Sector:

Capital Market is the market for buying and selling of equity and debt

instruments. Capital markets channel savings and investment between suppliers of

capital like retail investors and institutional investors, and users of capital like

individuals, businesses and government. Capital markets are vital to the functioning

of an economy, because capital is a critical component for generating economic

output. Capital markets comprise of primary markets, where new stock and bond

issues are sold to investors. Secondary markets are the markets which trade existing

securities.

Capital Market works under three different categories:-

Government Securities Market,

Corporate Debt Market and

Equity Market.

Government Securities Market is regulated by the RBI. Corporate Debt

Market operated in regards to debentures floated by corporate in the Equity

Market shares can be bought and sold which provides liquidity to markets.

Capital Market considers lending and borrowing of medium and long term

funds. The demand for long-term funds is mainly from industry, trade,

agriculture and central and state governments. The supply for funds comes

142

from individual savings, corporate savings, banks, insurance companies,

specialized financial institutions and government. The size of a capital market

of a nation is directly proportional to the size of its economy. The United

States, which is the world’s largest economy, has the biggest and deepest

capital markets. Capital markets are increasingly interconnected in a

globalized economy, which means that ripples in one corner can cause major

waves elsewhere. The disadvantage of this interconnection is best illustrated

by the global credit crisis of 2007-09, which was triggered by the collapse in

U.S. mortgage-backed securities. Lehman Brothers was the cause for this

collapse. The effects of this meltdown were globally transmitted by capital

markets because banks and institutions in Europe and Asia held trillions of

dollars of these securities.

Reliance Capital Ltd:

Reliance Industries is a core company of Reliance group founded by

Dhirubhai Ambani in the Year 1996. It is one of the largest private sector enterprises

and largest financial services provider companies by market capitalization in India.

Anil Ambani, promoter of Reliance Group is the Chairman of Reliance Capital.

Reliance Capital Limited (BSE: 500111, NSE: RELCAPITAL) is an Indian

diversified financial services holding company promoted by Reliance Group.

Reliance Capital has diversified interests like in asset management, mutual

funds; life insurance, and general insurance; commercial finance; stock

broking; wealth management services; private equity; asset reconstruction;

distribution of financial products; proprietary investments and many more activities in

financial services.

143

The company operated throughout India and has more than ` 20 Crore

customers and employees of around 18,500 as of March 31, 2014. Reliance has

encouraged capital formation in Indian society which helped in speedy Indian

Economy Development. The turnover of Reliance Capital has a continuously rising

trend since 2005 to 2009. In 2005, the turnover of company was ` 296 Crore which

rose to 3014 in 2009. In 2010, there was sharp fall in turnover and was down upto `

2390 Crores. It could improve only in 2012 and reached at ` 3317 Crore. Any

slowdown in Indian economic growth could cause the business of the company to

suffer. In today’s world growth in industrial production has been variable. Any

downfall in Indian economy particularly the demand for housing and infrastructure

could adversely affect company’s business and performance. Similarly, sustained

volatility in global commodity prices including rise in prices of oil and petroleum

products could also create inflationary phase in economy which reduces purchasing

power of people. Reliance Capital Ltd managed these risks by maintaining a

conservative financial profile and risk management practices. Reliance Private equity

was continuously involved in evaluation of investment opportunities in the fast

growing sectors of Indian Economy.

The EBDIT of Reliance Capital Ltd was not in a growing stage till 2005.

Rather company faced loss till 2004-05 financial year. But in the year 2005-06, it had

been observed that company could earn considerable profit. And EBDIT was 53%

which was high as compared to the previous year. After that, there was steady growth

observed in the total income of company. 2007-08 was also a significant year when

company could achieve 52% growth rate in EBDIT. That was paramount year for the

company because after that year again in 2009-10 and 2010-11 the EBDIT showed

negative figures with downfall in economy.

144

GROWTH and PERFORMNACE OF COMPANY Y-O-Y BASIS:

The company performance in 2002-03:

Reliance Capital Ltd. (RCL) which was one of India's leading private sector

non-banking financial services companies (NBFCs), had reported satisfactory

financial and operating performance during the period 2002-03. Gross income for the

year was ` 458.78 crores compared to ` 548.59 crores in the financial year 2001-02.

Interest expenses for the year were also lower at ` 252.81 crores, compared to `

373.43 crores of its previous year.


Gross income of Reliance Capital Ltd for the financial year 2005-2006

increased to ` 652.02 crores, from Rs 295.69 crores in 2004-05 which registered a

growth of over 120 percent. The PBDIT (operating profit) of the Company increased

113 percent to ` 619 crores during the year, up from ` 290.06 crores in the previous

financial year. The expectations of company for their future were increase in the

infrastructure sector which included power, roads, ports, telecom and other urban

infrastructure projects. This would provide excellent investment opportunities in the

future. The services sector, which was also growing at rapid pace and Contributed

substantially to GDP, may provide many new opportunities for the financial services

industry in India.


Gross income of RCL for the financial year ended March 31, 2007 increased

to ` 883.86 crore, from ` 652.02 crore in the previous year, registered a growth of

over 35.56 per cent. The operating profit (PBDIT) of the Company increased 26.48 %

to ` 782.88 crore during the year 2006-07 from ` 619 crore in the previous year. GDP

had increased on an average by about 8.5 per cent annually since 2003 to 2006. This

145

growth was seen due to productivity gains in both the sectors namely industry and

services allowed the country to increase its participation in international trade and

investment. Exports of goods and services had grown at an average of 30 % annually

since 2003. Average net foreign investment inflows increased to about US$ 15 billion

annually between 2003 and 2006.


During this financial year, Reliance Consultants (Mauritius) Ltd., Reliance

Equities International Pvt. Ltd., Reliance Home Finance Pvt. Ltd., Reliance Capital

Services Pvt. Ltd., Reliance Capital (Singapore) Pte. Ltd., Reliance Consumer

Finance Pvt. Ltd., Reliance Securities Ltd., Reliance Prime International Ltd.,

Reliance Commodities Ltd., Reliance Financial Ltd., Reliance Alternative

Investments Services Pvt. Ltd. and Reliance Capital Pension Fund Ltd. became

subsidiaries of the Company. The company’s gross income for the financial year

2008-2009 increased to ` 3,017.29 ` crore from ` 2,079.79 crore in 2007-08

registering a growth of over 45.08 %. The operating profit (PBDIT) of the Company

also increased 46.24 per cent to ` 2,334.99 crore during the year, up from ` 1596.69

crore in the previous year.


Total income increased from 6019 ` Cr.in 2008-09 to 6141 ` Cr. in 2009-10.

There was just rise of 2% in the total income of Reliance Capital Ltd. The emergence

of a set of new regulatory changes in some financial services affected industrial

growth in the year 2009-10. Short term borrowing program of Company got Highest

credit ratings A+ by ICRA. (formerly Investment Information and Credit Rating

Agency of India Limited)

146

Indian economy continuously showed resilience in 2009-10. Measures taken

by RBI and policy makers improved situation. In the third quarter of 2009-10, the

GDP growth rate was extremely low due to decline in agricultural output because of

poor monsoon. In the year 2008 and 2009, inflation (WPI) was also high. It had

impact of high global oil prices and commodity prices. Thereafter, drought in 2009

increased food prices in India. Inflation touched 9.9% in March 2010 by increased in

the price of Fuel and manufactured goods. The PBDIT of RCL (Reliance Capital Ltd)

showed fall in the year 2009-10, by 35%. The (PBT) Profit Before Tax also fall by

51% as compared to previous year.


The gross income of Company for the financial year 2010- 2011 decreased to

1,934.01 ` crore from 2,389.88 crore in the previous year showed decline of 24 per

cent. The operating profit (PBDIT) of the Company decreased by 17 per cent to

1,471.70 ` crore during the year from 1,723.69 ` crore in the 2009-10. Interest

expenses for the year decreased by 2 per cent. The GDP growth in the first two

quarters of the financial year 2010-11 was 8.9% and moderated to 8.2% in the third

quarter due to lower industrial growth. Inflation remained a primary policy concern

and the principal threat to economic stability. The average inflation in financial year

2010-11 was very high and stood at 9.5 per cent.


The company’s Gross Income for the financial year 2011-12, increased to

3317 ` Crore from 1971 ` Crore in 2010-11 showing 68% rise in gross income. The

PBDIT of company (Operating profit) increased by 84% which was 1472 ` Cr in

2010-11 to 2712 ` Cr. in 2011-12. India’s economic growth slowed to 6.5% in the

year 2011-12 due to weakening industrial growth. Uncertainty in global environment

147

had impact on manufacturing sector. Agriculture and service sector was continuously

performed well. The service sector had performed well as its share in GDP increased

from 58 % in 2011-12 to 59% in 2011-12. The global financial and economic crisis

had its severe effect on cross border FDI flow and to maintain earlier level of foreign

investment in economy.

i) Objective 9: To study the impact of Profit before income ratio of Service

sector on earnings before income ratio of RELIANCE.


independent variable X earnings before income ratio of RELIANCE. The estimated


Y (Profit before income ratio of Service sector) = 7.750 + (0.308) (earnings before

income)

The results indicate that the independent variable i.e. earnings before income ratio of

RELIANCE has a negative impact on profit before income ratio of Service sector. So,

one unit increase in earnings before income ratio of RELIANCE will decrease in

Profit before income ratio by 0.308 units.

Model Summary

Model R R Square

Std. Error of the

Estimate

1 0.780(a) 0.608 5.78112


that 60.8 % of the variations in the Profit before income ratio are explained by

earnings before income ratio of RELIANCE ratio. The significance of R2 was tested

with the help of F statistic, which is shown in below table,

148

ANOVA (b)

Model

Sum of

Squares df

Mean

Square F Sig.

1

Regression 82.459 1 82.459 1.483 0.258(a)

Residual 444.755 8 55.594

Total 527.214 9

From the above table it is observed that the, the p > (0.05), so we conclude that that at


is not statistically significant.


Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B Std.

Error Beta

1

(Constant) 7.750 6.527 1.187 0.269

Reliance_

PBDIT 0.308 0.253 0.395 1.218 0.258


(0.05) so, the null hypothesis was accepted and alternative was rejected. The

coefficient of earnings before income ratio of RELIANCE is not statistically

significant. Therefore, it has been found that earnings before income ratio of

RELIANCE is not a significant variable in influencing profit before income ratio of

Service sector. This was also found using t statistic.

149

5.8 Summary of the case studies:

Sr.No. Hypothesis Statistical

Tools

Results Comments

1

H0=Change In

Profit Ratio Of

HUL Has

Insignificant

Impact On

Profit Ratio Of

Manufacturing

Sector

H1= Change In

Profit Ratio Of

HUL Has

significant

Impact On

Profit Ratio Of

Manufacturing

Sector.

Regression

F Statistics

t Statistics

R2 = (0.641)

= 64%

F = P < 0.05

(0.01 <

0.05)

t = p < 0.05

(0.01<

0.05)

H0=Rejected

H1 = Accepted

2.

H0=Change In

Profit Ratio Of

ITC Has

Insignificant

Impact On

Profit Ratio Of

Manufacturing

Sector

H1= Change In

Profit Ratio Of

ITC Has

significant

Impact On

Profit Ratio Of

Manufacturing

Regression

F Statistics

t Statistics

R2 = (0.641)

= 64.1%

F = P < 0.05

(0.04 < 0.05)

t = p > 0.05

(0.6 > 0.05)

H0= Accepted

H1= Rejected

150

Sector.

3.

H0=Change In

Profit Ratio Of

Glenmark Has

Insignificant

Impact On

Profit Ratio Of

Manufacturing

Sector.

H1= Change In

Profit Ratio Of

Glenmark Has

significant

Impact On

Profit Ratio Of

Manufacturing

Sector.

Regression

F Statistics

t Statistics

R2 = (0.608)

= 60.8%

F = P < 0.05

(0.02 <

0.05)

t = p < 0.05

(0.02 <

0.05)

H0=Rejected

H1 = Accepted

4.

H0=Change In

Profit Ratio Of

Dr. Reddy’s

Has

Insignificant

Impact On

Profit Ratio Of

Manufacturing

Sector.

H1= Change In

Profit Ratio Of

Dr. Reddy’s

Has significant

Impact On

Profit Ratio Of

Manufacturing

Sector.

Regression

F Statistics

t Statistics

R2 = (0.672)

= 67.2%

F = P < 0.05

(0.02 < 0.05)

t = p < 0.05

(0.02 < 0.05)

H0 = Rejected

H1 = Accepted

151

5.

H0=Change In

Profit Ratio Of

TCS Has

Insignificant

Impact On

Profit Ratio Of

Service Sector

H1= Change In

Profit Ratio Of

TCS Has

significant

Impact On

Profit Ratio Of

Service Sector.

Regression

F Statistics

t Statistics

R2 = (0.640)

= 64%

F = P < 0.05

(0.01 <

0.05)

t = p < 0.05

(0.01 <

0.05)

H0=Rejected

H1 = Accepted

6.

H0=Change In

Profit Ratio Of

Infosys Has

Insignificant

Impact On

Profit Ratio Of

Service Sector

H1= Change In

Profit Ratio Of

Infosys Has

significant

Impact On

Profit Ratio Of

Service Sector.

Regression

F Statistics

t Statistics

R2 = (0.638)

= 63.8%

F = P > 0.05

(0.9 > 0.05)

t = p > 0.05

(0.9 > 0.05)

H0= Accepted

H1= Rejected

7.

H0=Change In

Profit Ratio Of

Reliance Capital

Ltd Has

Insignificant

Regression

R2 = (0.608)

= 60.8%

H0= Accepted

H1= Rejected

152

Impact On

Profit Ratio Of

service sector

H1= Change In

Profit Ratio Of

Reliance Capital

Ltd Has

significant

Impact On

Profit Ratio Of

Service Sector.

F Statistics

t Statistics

F = P > 0.05

(0.2 > 0.05)

t = p > 0.05

(0.2 > 0.05)

153

Chapter 6

Impact of Inflation on manufacturing and service industries

Introduction to Inflation:

The long-run rate of growth is determined by real factors: the savings

rate, technical progress and demographics. Inflation on the other hand is a

monetary phenomenon. Prima facie it is expected to be not related. Some

qualifications of Inflation can be brought out:

Inflation is a tax on money holders. Change in the rate of inflation may

therefore change wealth-holders’ preference between holding their wealth in

the form of money or in the form of real assets and thus affects the growth

rate. However, a small proportion of total wealth which is held in the form of

money, such effects are likely to be small. Inflation volatility increases

uncertainty in a money-using economy thereby increasing the riskiness of

investment projects and affecting the growth rate adversely. Apart from

hyperinflationary situations the additional inflation risk is likely to be small

compared to other sources of risk such as variation in exchange rate, labour

and infrastructure environment, political and climate uncertainty.

Inflation makes debtors better off because debt repayment now

imposes a smaller burden in real terms. For the similar reason, it makes

creditors worse off. Any rise in expenditure by the former would be cancelled

in part by the decrease in expenditure by the latter and only the small residual

that would remain one way or the other would affect growth. If the tax system

imposes taxes at different rates based on money income, inflation changes the

154

burden of taxes. Rising incomes put people in higher tax slabs even though the

real purchasing power of that income might have been eroded in the

meanwhile by inflation. This effect would last till the time tax slabs are

revised. This effect is likely to be small except in hyperinflation. Thus, for

moderate rates of inflation, the rate of inflation is unlikely to be related to the

rate of long-run growth.

Demand-side inflationary pressures arise from excess demand pushing

actual output above the economy’s long-term output level, increasing the

marginal cost of production of firms. Firms in turn increase prices to cover

their marginal costs. Cost-side inflationary pressures come from rising prices

of domestic raw materials and imported goods. Inflationary pressures

determine the rate of change of the inflation rate. Excess demand is

responsible for accelerating the rate of inflation compared to the expected rate

of inflation. Deficient demand slows it down. Even without any demand-side

or cost-side pressures firms increase prices if they expect other firms to do so.

With increase in price, workers demand increase in their wages. Managing

inflationary expectations is essential for inflation control. For example,

following an oil price shock, if firms expect monetary policy to accommodate

the shock they would immediately raise the price of non-oil commodities even

before the exhaustion of their existing oil stocks. Monetary policy affects

inflation through its effect on aggregate demand.

Since present inflation depends on inflation expected in future, what

matters is not just present policy but also the expected policy response to

future events. The policy regime matters more than particular decisions. A

credible anti-inflationary stance makes monetary policy more effective by

155

anchoring inflationary expectations. If the monetary authority is committed to

its inflation targets there is much less danger of a temporary inflation shock

turning into a persistent wage-price spiral. On the other hand, if the monetary

authority is seen as being willing to accommodate inflationary pressures, the

private sector starts to expect any inflationary trend to persist and it becomes

harder to fight inflation.

Historically, the wholesale price index (WPI) has been the main

measure of inflation in India. However, in 2013 Raghuram Rajan, the

governor of RBI had announced that the consumer price index is a better

measure of inflation. In India, the most important category in the consumer

price index is Food, beverages and tobacco (49.7 % of total weight). Fuel and

light accounts for 9.5 %; Housing for 9.8 %; Transport and communication for

7.6 %; Medical care for 5.7 %; Clothing, bedding and footwear for 4.7 % and

education for 3.4 %.

Recent Trends in Inflation:

In Oct 2014, there was drop in prices of food and commodity which

reduced WPI inflation to 1.77% in this month. It was 2.8% in the month of

Sept, 2014. This decline in WPI inflation is 5 years low which may put

pressure on RBI, to cut the rates for stimulation of demand and investment in

economy. According to commerce Department, Oct 2014 is the sixth

successive month of decline in WPI inflation and which is also lowest since

Sept 2009.

Inflation rate was relatively stable in 2001-02. In 2002-03, Due to

drought condition in 14 states, there was a sharp increase in prices of food

156

grains followed by rise in general price level. The inflation rate in the initial

two and the half months of the year 2004-05 was moderate but on 52 weeks

average basis it was 6.5%. During 2005-06, the average annual rate of

inflation was 4.3%. Under Indian conditions, if the inflation rate remains in

the range of 4.5% to 5% it would be consider as a satisfactory position.

Towards the last quarter of 2007-08, inflationary pressures started building up

again, and inflation rate rose to 8.02% in March, 2008. This was a matter of

serious concern and a number of measures were taken. Reserve Bank of India

raised the Cash Reserve Ratio (CRR) in phases from 7.50% in April, 2008 to

8.75% in July 2008. But situation worsened and the Inflation rate touched

12.6% in Aug, 2008. RBI further hiked CRR to 9.0% in Aug, 2008. In the last

quarter of financial year 2008-09, the Inflation rate started coming down due

to decline in commodity prices and crude oil prices. Due to economic

slowdown a number of manufactured goods and global recessionary trends

pulled down the prices of crude oil.

The overall average inflation was 9.6% in 2010-11. RBI was

continuously increasing the repo and reverse repo rates. Repo rate was 7.25 %

in 2011. Inflation fell to a three year low of 7.18 percent in December 2012,

raising expectations of rate cut by the reserve bank of India in its monetary

policy review meet on January 29.

This wholesale price index (WPI) based inflation which is India main

gauge for inflation was lower than the 7.24 percent recorded in the previous

month and 7.74 percent in December 2011. The inflation, based on movement

in wholesale prices, reached the three-year low of 7.18 % in December, the

retail inflation continued to remain in double digit at 10.56 %. It only indicates

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easing WPI was not providing any relief to the consumers from spiralling

prices. The WPI inflation at 7.18 per cent was also much above the central

bank’s comfort level of 4-5 %. The inflation has not declined to the expected

levels despite tight monetary stance pursued by the RBI to check price rise.

With industrial output contracting by 0.1 % in November, the industry has

stepped up its demand for interest rate cut by the RBI in its forthcoming

policy. The economic growth, which slipped to nine-year low of 6.5 % in

2011-12, was expected to decline further to 5.7-5.9 per cent in the current

fiscal. Rising for the third consecutive month, retail inflation breached the

double-digit mark at 10.56 per cent in December, driven by higher prices of

vegetables, edible oil, pulses and cereal.

Inflation was in control for almost a decade, between 2000 and 2010,

in spite of high economic growth. But from 2009 it galloped beyond the thresh

hold level of the Reserve Bank of India, forcing measures for monetary

contraction. Inflation rate (CPI) was 10.83% in 2009 which increased to

12.22% in 2010. The Bank increased the interest rates by more than 350 basis

points in less than 24 months. The rate hike was not accepted by the industry

as hike increased costs and discouraged investments in turn leading to

declining output. However, economists observed that the RBI was acting in

the way it should in the absence of any concrete measure from the government

either to ease supply constraints or to curb demand.

India's wholesale price inflation rate slipped to 8.9% year on year in

the week ending November 8th. This was the second consecutive week of

single-digit price growth, Prices for manufactured products, which account for

nearly two-thirds of the index, rose by 8% in the week ending November 8th

.

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The cost of fuel, power, lighting and lubricants rose by 8.2% after expanding

by 9.2% in the week ending November 1st, although prices of primary goods

maintained their double-digit pace of expansion, rising by 11.7% compared

with an 11% gain in the previous week. The Reserve Bank of India (RBI, the

central bank) plans to lower its inflation target from 7% in 2008/09 to 5-5.5%

in 2009/10.

Table 2: Growth rate of GDP vs. Inflation: India, 1951-2011

Period

Average annual growth

rate of GDP at constant

prices (%)

Average annual rate of

WPI inflation (%)

2005-06 to 2010-11 8.47 6.55

2000-01 to 2005-06 6.93 4.68

1995-96 to 2000-01 5.92 5.07

1990-95 to 1995-96 5.38 10.18

1980-81 to 1990-91 5.64 8.51

1970-71 to 1980-81 3.16 10.28

1960-61 to 1970-71 3.75 6.24

1950-51 to 1960-61 3.94 1.75

Source:http://www.slideshare.net/ChandanKumar71/inflation-growth-1417383

In the above table, in a comparative study, Real GDP growth rate and WPI

inflation of all was considered for the period of 1950-51 to 2009-10. The

variations in the price level in India are usually measured in terms of the

Wholesale Price Index (WPI). A new WPI series with 2004-05 as base was

released on 14th

Sept, 2010. A representative commodity basket comprising 676

159

items has been selected. The total number of price quotations has also increased

from 1918 in the old series (which had 1993-94 as base) to 5482 in the new series.

This indicated better representation of the price in the wholesale market. WPI

does not include services and non tradable commodities. Moreover it only

measures “Headline inflation”. The Headline inflation includes the entire set of

commodities in the general price index.

The retail inflation was 9.90 per cent in November and 9.75 per cent in

October 2010. The vegetables basket in December recorded the highest inflation

of 25.71 per cent among all the constituents that make the consumer price index

(CPI). Wholesale price based inflation for November was at 7.24 per cent, much

higher than the RBI comfort level of 5-6 per cent.

Consumer Price Index reflects the cost of living for homogeneous group of

consumers. Since CPI considers cost of living it is the retail prices that are taken

into account. There are four consumer price indices in India: The CPI for

Industrial Workers (CPI-IW), CPI for Agricultural Labour (CPI-AL), CPI for

Rural Labour (CPI-RL), and CPI for Urban Non Manual Employees (CPI-

UNME). With effect from 2006, the base year for CPI-IW was revised to 2001. In

Jan, 2011 The CSO introduced the new series on CPI-Urban, CPI- Rural and CPI

(urban and rural) combined. Therefore, CPI-UNME has been discontinued.

India's headline inflation slowed to its lowest level in three years, thereby

hardening expectations for an interest rate cut by the RBI later this month to boost

an economy that is set to post its slowest growth in a decade. The wholesale price

index (WPI), India's main inflation indicator, rose an annual 7.18 % in December.

It was the slowest since December 2009 and below analysts' forecast of 7.4 %

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rise. Wholesale prices rose 7.24 percent in November. The bad news was the

hidden inside the good news. Seemingly comforting headlines showed that

inflation had hit a three-year low, with wholesale price increases slowing down to

7.2% in December. But the common man had taken a hit with prices of food

products shooting up by 11.2% - the highest increase in almost two years. Food

prices were attributed to a sharp pick-up in prices of high value food products like

milk, eggs, meat and fruits. The current surge has been mainly on account of

soaring prices of essentials products like rice, wheat, pulses and coarse grains like

bajra, maize, barley, ragi and vegetables.

6.1 Impact of Inflation on Profitability of Manufacturing Industry:

Business sentiment is bound to be hit by the rapid deterioration in many of the

world's largest developed and developing economies. This is likely to prevent any

recovery in investment growth that might otherwise have been expected to follow

the sharp fall back in global oil prices and other input costs. Efficiency of any

organization can be judged through its profitability. The profitability of the firm is

highly influenced by internal and external variables, i.e., the size of organizations,

component of costs, liquidity management, inflation rate, and growth of

organizations. In addition, the crisis has led to further significant weakening of the

rupee. This may increase non-commodity import prices and may keep wholesale

price inflation sufficiently high that the Reserve Bank of India has no choice but

to keep interest rates unchanged until mid-2009. Surging food prices will not only

hit consumers, who have less money left for other purchases, but also industry

which is forced to pay higher raw material prices and shell out higher wages to its

employees. They also impact financial savings of households adversely, with

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negative consequence on investments. Rising food prices pose a big question mark

for government procurement policies which corner huge stocks and truncate the

markets.

Growth in business volume represents the change similar to capacity

utilization in a manufacturing enterprise. Growth in volume of business is likely to

generate more revenue and hence a direct bearing on profitability of the

organization. A review of empirical literature (Dess and Robinson, 1984) depicted

that the most used measures for growth have been compounded annual growth

rate of sales and total assets. Operating profit ratio being the ratio of operating

profit to business income is certainly one of the significant explanatory variables

to explore the financial efficiency of an organization.

It has been observed that the overall stability and growth of the global

economy and ultimately domestic economy has become extremely important for

the growth performance of Indian firms. In fact, the sales and profitability growth

of Indian manufacturing and IT firms were significantly reversed with the

condition of global market turning adverse since late 2008 year. It was interesting

that those Indian firms were relatively young in age and more focused on global

market have been better off in terms of sales and profit growth than other firms.

Also large firms and the firms having higher advertising intensities have not faced

losses much in this period.

India's manufacturing-sector activity in November, 2013 expanded after a long

time in this quarter of financial year as new orders increased, raising hopes for the

country's economy, which has been struggling with high inflation and weakening

growth. India's gross domestic product data showed that the economy grew a

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better-than-expected of 4.8% during the June to September. It was much quicker

than the 4.4% expansion in the preceding three months. Rising inflation and

slower consumer spending being the main engine of economic growth had

devoured profit margins and sales also fall. The retail sector tumbled in downturn

of economy. The manufacturing sector had sustained a severe hit. The rise in

prices paid by manufacturers for procuring inputs rose at the weakest pace in the

second quarter of 2013, while prices paid by customers to manufacturers grew at a

slower rate than the previous month.

According to the recent data, wholesale inflation, India's main gauge of price

increases, accelerated to an eight-month high of 7.0% in October 2013, much

above the 5.0% level that the central bank believed is acceptable, while consumer

inflation hit a double-digit rate during the month. Sustained inflationary pressures

over the past few years that are Inflation since 2005 have forced the central bank

to raise borrowing costs, contributing to the economic slowdown. So, once the

growth of manufacturing is on track, the problem of inflation will also be under

control. So finally, it can be concluded can say that Manufacturing Policy 2011 is

a good effort.

A fall in input prices, inflation and softer interest regime may lead to increse

profit margins for businesses. The Reserve Bank of India in its monetary policy

every year since 2008 lowered the interest rates by 25 to 50 basis points to

encourage growth in the economy. As the cost of finance comes down, profit

margins and ultimately investments were expected to go up. The RBI in 2011-12

fiscal year had hiked interest rates 13 times to control the spiralling inflation. With

moderate inflation it is possible to boost growth in manufacturing sector and in

economic activity.

163

6.2 Impact of Inflation on Profitability of Service Industry:

India, the 12th largest and the second fastest growing major economy in the

world, has been experiencing significant price instability in the recent past. India

was the only major country that uses a wholesale index to measure inflation until

March 2014. Since April 2014, according to Governor of RBI Mr. Raghuram

Rajan, India also started using CPI as a measure of Inflation Index. CPI is a

statistical time-series measure of a weighted average of prices of a specified set of

goods and services purchased by consumers. It is a price index which tracks the

prices of a specified basket of consumer goods and services, providing a measure

of inflation. Most countries use the CPI as a measure of inflation, as it actually

measures the increase in price that a consumer will ultimately have to pay. CPI is

the official barometer of inflation in many countries such as the United States, the

United Kingdom, Japan, France, Canada, Singapore and China.

After contraction, India's service sector recovered in November, 2011 on the

back of surge in new businesses received by Indian private sector companies

despite persistent inflationary pressures. All the three major components of the

WPI, "primary articles", "fuel, power, electricity and lubricants" and

"manufactured products" showed a downturn in annual inflation during 2007-08.

There was a sharp decline in inflation of primary articles to 3.8 per cent on

January 19, 2008, compared to 10.2 per cent a year ago. These commodities

contributed 22 per cent to overall inflation as against 35.4 per cent in the previous

year. With regard to inflationary impact of services sector expansion, Rath and

Raj (2006) found that growing service sector share in GDP has coexisted with low

and stable inflation on account of inflation moderating forces operating, inter alia

164

through the synergy between the two growth drivers. A high growth in service

sector without a corresponding growth in manufacturing sector leads to a

divergence in growth in incomes in the these two sectors, which was reflected in

demand and supply gap of consumer goods in the economy and ultimately had an

inflationary impact on the economy (Bhattacharya and Mitra,1990). The

contemporary growth in economy is characterized by the co-existence of high

services growth with low and stable inflation. These situations are possible with

the credible monetary policy run by RBI in recessionary and inflationary

situations. In which the focus was on the adjustments in Repo rate, Reverse repo

rate and CRR.

6.3 Recommendations to industries:

1. It is commonly postulated that innovation is a key to success of firms hi the

pre-slowdown period and acts as a survival strategy in the slowdown phase.

Thus, unlike innovative firms that continue to offer new products and services,

non-innovative firms are likely to face relatively greater growth loss.

2. Manufacturers that have heavily invested in differentiating themselves and

building brand loyalty are expected to suffer less from the crisis than firms

with weak differentiation in die market place.

3. Export-dependent Indian firms were likely to be more vulnerable to the falling

export opportunities than their domestic market-oriented counterparts.

4. As we can see in the diagram below, (Presented in the lecture delivered at

Narsee Monjee Educational Trust, established a recognized Management

institute of the Mumbai University by Dr. R. Gopal) that for better future

165

industries must restructure themselves and must respond faster with the

change in economic and environmental activity.

Fig.20: Fit for better future for Industries

Source: Research paper presented by Dr. R. Gopal at Narsee Monjee,

Mumbai, 2010

166

Chapter 7

Data Analysis, Interpretations and Model Estimations

The study reported data analysis elaborately and step by step with statistical

methods followed by interpretations and estimation of econometrics models.

7.1 Model 1: KARL PEARSON'S CORRELATION

COEFFICIENT (r):--

The Karl Pearson correlation coefficient (r) is used to measure the correlation

between variables X (PBDIT of manufacturing sector) and Y (GDPfc at constant

prices with base year 2004-05). The Karl Pearson coefficient is designated by

the letter "r" and is sometimes called "Pearson's r." Pearson's correlation reflects

the degree of linear relationship between two variables. It ranges from +1 to -1.

A correlation of +1 means that there is a perfect positive linear relationship

between variables. A correlation of -1 means that there is a perfect negative

linear relationship between variables. A correlation of 0 means there is no linear

relationship between the two variables.

Mathematical Formula:--

The quantity r, called the linear correlation coefficient, measures the strength

and the direction of a linear relationship between two variables. The linear

correlation coefficient is sometimes referred to as the Pearson product moment

correlation coefficient in honour of its developer Karl Pearson.

The mathematical formula for computing r is:

167

Where, N = numbers of the observations

ΣXY = Sum of the products of paired variables.

ΣX = Sum of X variables

ΣY = Sum of Y variables

ΣX² = Sum of squared X variables.

ΣY² = Sum of squared Y variables.

Therefore, Karl Pearson Correlation Coefficient ‘r’= .892

And r2 = .795

In the model, the dependent variable Y was GDP growth rate whereas

independent variable X was change in profit ratio in manufacturing sector. The

estimated regression model is as follows:

Model 1: (GDP Growth rate & PBDIT of Manufacturing Sector)

Y (GDP Growth rate) = 4.473 + (0.238) (Change in profit ratio)

The results indicated that the independent variable i.e. change in profit ratio has

a positive impact on in manufacturing sector. So, one unit increase in profit ratio

will increase GDP growth rate by 0.238 units in manufacturing sector.

Model Summary

Model R R Square Std. Error of the

Estimate

1 0.892(a) 0.795 2.69776

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From the above it was observed that R2 value for the model is 0.795 which

indicates that 79.5 % of the variations in the GDP Growth rate are explained

by change in profit ratio in manufacturing sector.

7.2 Descriptive Analysis of 2 variables-

a) GDPfc Growth rate at Constant Prices –

Summary Statistics Actual Values

Mean 7.51

Median 7.97

Standard Deviation 1.96

First Quartiles 6.69

Third Quartiles 9.32

Skewness -0.82

Coefficient of Variation 26.17

The estimates were obtained Indian growth rate in GDPfc data for the time

period from Financial Year 2002-03 to 2012-13 yielding 11 observations of each

variable. All the data was obtained from various issues of the CMIE and other

publications of the Reserve Bank of India.

From the above table, it was observed that the average rate of Growth of GDP

by Industry of Origin at Factor Cost & at 2004-05 Prices (Constant) was 7.51, and

50% of GDP growth rate at constant prices was less than the 7.97 and 50% was

greater than 7.97. In first Quartile, 25 % of GDP growth rate at constant prices was

less than the 6.69 and 75 % was more than 6.69 and in third quartile 75 % of GDP

growth rate at constant prices was less than the 9.32 and 25 % was more than 9.32

from coefficient of variation. There was 26.17 % fluctuation in the GDP growth rate

in the manufacturing sector. Growth of GDP by Industry of Origin at Factor Cost & at

2004-05 Prices was negatively skewed data. The negative value of Skweness (-0.82).

This indicated that the distribution of the inflation series was skewed to the left with

169

small tails and the series was highly leptokurtic which means that the series was

normally distributed.

In order to interpret standard deviation, Chebyshev’s rule was used. It can be

written as: 3,2 . 75% of the Growth of GDP by Industry of Origin at

Factor Cost & at 2004-05 Prices from year 2002 to 2014 was between (3.23, 11.07)

and 89.9 % of the Growth of GDP by Industry of Origin at Factor Cost & at 2004-05

Prices from year 2002 to 2014 was between (1.27, 13.03).

b) Growth Rate at Factor Cost at Constant Prices of Manufacturing Sector –


Mean 8.03

Median 7.41




Skewness -0.21


Firstly, some of the descriptive statistics for the Growth rate of manufacturing

sector were examined and the results were presented in the form of above table. It was

observed that the average rate of Growth Rate at Factor Cost at Constant Prices of

Manufacturing Sector was 8.03, and 50% of Growth Rate at Factor Cost at Constant

Prices of Manufacturing Sector was less than the 7.41 and 50% was greater than 7.41.

In the first quartile 25 % of Growth Rate at Factor Cost at Constant Prices of

Manufacturing Sector was less than the 6.34 and 75 % was more than 6.34 and in

third quartile 75 % of Growth Rate at Factor Cost at Constant Prices of Manufacturing

Sector was less than the 10.28 and 25 % was more than 10.28 from coefficient of

variation. There was 44.28 % fluctuation in the Growth Rate at Factor Cost at

Constant Prices in Manufacturing Sector. Growth Rate at Factor Cost at Constant

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Prices of Manufacturing Sector at 2004-05 Prices is negatively skewed data so to

interpret standard deviation, Chebyshev’s rule was followed. 75% of the Growth of

GDP by Industry of Origin at Factor Cost & at 2004-05 Prices from year 2002 to 2014

is between (0.91, 6.97) and 89.9 % of the Growth Rate at Factor Cost at Constant

Prices of Manufacturing Sector at 2004-05 Prices from year 2002 to 2014 is between

(0, 6.97). The negative value of Skweness (-0.21) indicated that the distribution of the

inflation series is skewed to the left with small tails and the series is highly leptokurtic

which means that the series is normally distributed.

c) Profitability : Service sector (Other Than Financial) Industry –


Mean 13.3

Median 12.1




Skewness -0.429

Coefficient of Variation 70.69 %

From the above table, we observed that the average rate of Profitability of Service

Sector is 13.3, and 50% profitability of Service Sector is less than the 12.1 and 50%

is greater than 12.1. In the first quartile, 25 % profitability of Service Sector is less

than the 8.20 and 75 % is more than 8.20 and in third quartile 75 % profitability of

Service Sector is less than the 23.38 and 25 % is more than 23.38 from coefficient of

variation it can be said that there is 70.69 % fluctuation in the profitability in Service

Sector. Profitability in service sector is negatively skewed data so to interpret standard

deviation, chebyshev’s rule was followed. 75% of the profitability in Service Sector is

between (-0.58, 32.08) and 89.9 % of the profitability in Service Sector of Service

Sector from year 2002 to 2014 is between (-14.87, 41.47).

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d) Profitability: Manufacturing sector –


Mean 14.94

Median 16.3




Skewness 0.582


From the above table, it was observed that the average profitability of Manufacturing

is 14.94, and 50% profitability is less than the 16.3 and 50% is greater than 16.3. In

the first quartile, 25 % profitability of Manufacturing Sector is less than the 3.46 and

75 % is more than 3.46 and in third quartile 75 % profitability is less than the 19.59

and 25 % is more than 19.59 from coefficient of variation it can be said that there is

69.41 % fluctuation in the profitability in Manufacturing Sector. Profitability in

Manufacturing Sector is positively skewed data i.e 0.582 so to interpret standard

deviation, chebyshev’s rule was followed. 75% profitability in Manufacturing Sector

is between (0, 35.68) and 89.9 % of the profitability in Manufacturing Sector from

year 2002 to 2014 is between (0, 46.0).

e) Rate of Growth of GDP by Industry of Origin at Factor Cost & at 2004-05 Prices

(Constant) in service sector –


Mean 7.51

Median 7.97




Skewness -0.82


172

From the above table, it was observed that the average rate of Growth of GDP by

Industry of Origin at Factor Cost & at 2004-05 Prices (Constant) in service sector is

7.51, and 50% rate of Growth of GDP by Industry of Origin at Factor Cost & at 2004-

05 Prices (Constant) in service sector is less than the 7.97 and 50% is greater than

7.97. In the first quartile, 25 % rate of Growth of GDP by Industry of Origin at Factor

Cost & at 2004-05 Prices (Constant) in service sector is less than the 6.69 and 75 % is

more than 6.69 and in the third quartile 75 % rate of Growth of GDP by Industry of

Origin at Factor Cost & at 2004-05 Prices (Constant) in service sector is less than the

9.32 and 25 % is more than 9.32 from coefficient of variation it can be said that there

is 26.09 % fluctuation in the rate of Growth of GDP by Industry of Origin at Factor

Cost & at 2004-05 Prices (Constant) in service sector. The negative value of

Skweness i.e -0.82 indicates that the distribution of the inflation series is skewed to

the left with small tails and the series is highly leptokurtic which means that the series

is normally distributed. It is negatively skewed data so to interpret standard deviation;

we will use chebyshev’s rule i.e. 3,2 . 75% rate of Growth of GDP by

Industry of Origin at Factor Cost & at 2004-05 Prices (Constant) in service sector is

between (3.59, 11.43) and 89.9 % of the rate of Growth of GDP by Industry of Origin

at Factor Cost & at 2004-05 Prices (Constant) in service sector from year 2002 to

2014 is between (1.63, 13.39).

f) Growth Rate at Factor Cost at Constant Prices of Services –


Mean 8.89

Median 9.67




Skewness -0.35


173

From the above table, it was observed that the average rate of Growth Rate at Factor

Cost at Constant Prices of Services is 8.89, and 50% Growth Rate at Factor Cost at

Constant Prices of Services is less than the 9.97 and 50% is greater than 9.97. In the

first quartile, 25 % Growth Rate at Factor Cost at Constant Prices of Services is less

than the 6.69 and 75 % is more than 6.69 and in the third quartile, 75 % Growth Rate

at Factor Cost at Constant Prices of Services is less than the 10.27 and 25 % is more

than 10.27 from coefficient of variation it can be said that there is 18.33 % fluctuation

in the Growth Rate at Factor Cost at Constant Prices of Services and is negatively

skewed data i.e. -0.35 so to interpret standard deviation, Chebyshev’s rule was

followed. 75% Growth Rate at Factor Cost at Constant Prices of Services is between

(12.15, 14.49) and 89.9 % Growth Rate at Factor Cost at Constant Prices of Services

is between (4, 13.78).

g) Inflation from 2002-2013 –


Mean 8.02

Median 8.14




Skewness 0.35


From the above table, it was observed that the average rate of Inflation in 2002-2013

is 8.02, and 50% Inflation in 2002-2013 is less than the 8.14 and 50% is greater than

8.14. In the first quartile, 25 % Inflation in 2002-2013 is less than the 4.95 and 75 %

is more than 4.95 and in third quartile, 75 % Inflation in 2002-2013 is less than the

10.00 and 25 % is more than 10.00 from coefficient of variation it can be said that

there is 39.02 % fluctuation in the Inflation in 2002-2013 and is positively skewed

174

data i.e. 0.35 so to interpret standard deviation, Chebchev’s rule was followed. 75%

Inflation in 2002-1013 is between (1.76, 14.28) and 89.9 % Inflation in 2002-1013 is

between (0, 17.41).

7.3 Model 2: KARL PEARSON'S CORRELATION

COEFFICIENT:

Model 2: (GDP growth rate & PBDIT of service sector).


To determine influence of change in profit of service sector on GDP growth rate of

Indian Economy. The following time series regression equation is to be fitted:

tt ebXaY .............(1)

Yt denotes GDP(fc) base year (2004-05)



X denotes PBDIT of service sector (yearly)


The observed data are used to estimate the two parameters, ‘a’ & ‘b’ of the model &

te is the stochastic term or noise. The actual numerical estimates of the intercept &

the slope are written as â & ^b , where hats indicate that the qty is an estimate of a

model parameter – an estimate that is computed from the observed data.

The above equation can be written as Y= a+bX in absence of error term. i.e. te = 0

175

In the equation the parameter ‘a’ is the intercept, it gives the qty of GDP (fc) without

the influence of Service sector (PBDIT) i. e. When X=0 & constant ‘b’ is the

coefficient of Y in relation of X or the slope.

The slope, a summary of the relationship between X & Y answers the equation, when

X changes by 1 unit, y changes by ‘b’ units.

Model Summary:

Model R R Square Std. Error of the

Estimate

2 0.781(a) 0.608 1.67076

From the above it was observed, The R2 value for the model is 0.608 which indicated

that 60.8 % of the variations in the GDP Growth rate are explained by change in profit

ratio in service sector.

7.4 Model 3: Granger’s Causality test: GDP and Inflation

Granger (1969) proposed a time – series data based approach in order to determine

causality. In the Granger-sense x is a cause of y if it is useful in forecasting y. In this

framework “useful” means that x is able to increase the accuracy of prediction of y

with respect to a forecast, considering only past values of y.

The Granger causality Test:


variable Inflation. The estimated regression model is as follows:

Y(GDP Growth rate) = 4.255 + (0.082) * (Inflation)

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The results indicated that the independent variable i.e. Inflation has a positive impact

on GDP Growth rate. So, one unit increase in inflation will increase in GDP growth

rate by 0.082 units.

Model Summary:

Model R R2 Standard error of estimate

1 0.822 (a) 0.672 3.2556


that 67.5 % of the variations in the GDP Growth rate are explained by Inflation. The

significance of R2 is tested with the help of F statistic, which was shown in below

table,

ANOVA (b):

Model Sum of squares Df Mean Square F Sigma

Regression 1.322 1 3.235 0.998 0.005 (a)

Residual 22.3228 2.786

Total 24.1419


that that at 5% level of significance R2 is statistically significant.


Coefficients (a):

Model Unstandardised

Coefficients

Standardised

coefficients T Sigma

1 Β

Standard

Error Β

(Constant) 7.785 2.200 6.514 0.005

Inflation 0.040 0.073 0.414 0.775 0.667

177



coefficient of Inflation is not statistically significant. Therefore, it was found that

Inflation is not a significant variable in influencing GDP growth rate. This was also

found using t-statistic.

b) Test of Granger Causality –

Growth rate of GDP growth rate and Inflation –

EViews runs bivariate regressions of the form:

For all possible pairs of (x, y) series in the group. The reported F-statistics are the

Wald statistics for the joint hypothesis:

For each equation, the null hypothesis is that does not Granger-cause in the first

regression and that does not Granger-cause in the second regression.

Data of GDP growth rate and Inflation have been used. The test results were given

by:

Pairwise Granger Causality Tests:

Null Hypothsis Observations F-Statistic Probability

GDP does not Granger Cause

Inflation 8 1.19528 0.4152

From the above table, p > 0.05. So, the hypothesis that GDP growth rate does not

Granger cause Inflation cannot be rejected.

The Granger causality test assumes that the information relevant to the prediction of

the respective variables, GDP growth and inflation rate, inflation rate and rate of

change in profit ratio of manufacturing sector are contained solely in the time series

data on these variables. The test involves estimating the following pair of regressions.

178

(i) Yt (inflation) = Σni=1αi X t-i (rate of change in profit ratio of manufacturing

sector) + Σnj=1 βjYt-j(inflation) + u1t.

(ii) Xt (rate of change in profit ratio of service sector) = Σni=1λiXt-i( rate of change

in profit ratio of service sector) + Σnj=1 δjYt-j(inflation) + u2t‟

Similarly,

(i) Yt(GDP) = Σni=1αi Xt-i(inflation) + Σnj=1 βjYt-j(GDP) + u1t

(ii) Xt(inflation). = Σni=1λiXt-i(inflation) + Σnj=1 δjYt-j(GDP) + u2t‟

Where, disturbance terms u1t, u2t are uncorrelated.

Based on the estimated OLS coefficients for the two sets of equation different

hypotheses about the relationship between rate of change in profit ratio of

manufacturing sector, service sector and inflation also the relationship between GDP

growth rate and inflation can be formulated.

Regression Analysis:

General mode = Y=b0+b1*X

Estimated model = (GDP factor cost) = 6.01 + 0.16 * inflation rate

In this table it was observed that inflation rate has a significant, positive effect on

GDP (factor cost at constant price). As inflation increase by 1 unit, GDP increase 0.16

units.

Interpretation of Hypothesis:

In this case, 2 T - test was taken: one for the intercept and other for slope parameter.

The Null Hypothesis is

179

H0: b0 = 0 (intercept)

H0: b1 = 0 (slope)

T- score for intercept (6.34598) and for slope (0.09485) were calculated (MS Excel).

Here null coefficient is Zero (0). Because t- value is relatively high (6.35 & 0.09

respectively), hence, null hypothesis was rejected. This means that neither intercept or

nor slope are Zero (0).

The relationship between inflation and economic growth in India, using annual

data set on real GDP and CPI, The empirical evidence demonstrates that there exists a

statistically significant long-run negative relationship between inflation and economic

growth for the country as indicated by a statistically significant long-run negative

relationship between inflation rate and real GDP. Level of inflation for India using an

annual data set employed the Granger Causality test as an application of the threshold

model and finally, the relevant sensitivity analysis of the model. The model suggests

that an inflation rate beyond 9-percent is detrimental for the economic growth of

India. This in turn, suggests that inflation rate below the level of 9-percent is

favorable for the economic growth.

7.5 Inferential Statistics –

Here, the analysis of the objectives and confirmation of the

acceptance/rejection of the Hypothesis has been presented.

a) Objective 1 - To study the impact of change in GDP growth rate due to

change in profit ratio of manufacturing sector.

180


variable X is change in profit ratio in manufacturing sector. The estimated regression

model is as follows:

Y (GDP Growth rate) = 4.473 + (0.238) * (Change in profit ratio)

The results indicated that the independent variable i.e. change in profit ratio has a

positive impact on in manufacturing sector. So, one unit increase in profit ratio will

increase GDP growth rate by 0.238 units in manufacturing sector.

Model Summary

Model R R2

Std. Error of the

Estimate

1 0.892(a) 0.795 2.69776

From the above it was observed, that R2 value for the model is 0.795 which indicated


ratio in manufacturing sector. The significance of R2 is tested with the help of F

statistic, which is shown in below table,

ANOVA (b)

Model Sum of

Squares Df Mean Square F Sig.

1

Regression 60.963 1 60.963 8.376 .018(a)

Residual 65.501 9 7.278

Total 126.464 10


that that at 5% level of significance R2



181

Coefficients (a)

Model Unstandardized

Coefficients

Standardized

Coefficients t Sig.

1

B Std. Error Beta

(Constant) 4.473 1.474 3.035 0.014

Change in profit

ratio 0.238 0.082 0.694 2.894 0.018

From the above table it was observed that at 5 % level of significance p < α (0.05), so

the null hypothesis was rejected and alternative was accepted, so the coefficient of

change in profit ratio in manufacturing sector is statistically significant. Therefore, it

was found that change in profit ratio in manufacturing sector is significant variable in

influencing GDP growth in manufacturing sector. This was also found using t-

statistic.

Further, the relative importance of the independent variable in influencing GDP

growth was determined by examining the standardized coefficients (called β). These

were reported in above table. The standardized coefficients were obtained by running

the regression of standardized values of dependent variable on the standardized values

of the independent variables i.e. 69.4%. The standardized values of a variable were

obtained by subtracting from the variable its mean value and dividing by its standard

deviation. Higher the value of absolute standardized coefficient, higher is the

importance of that variable in influencing GDP growth. Therefore, finally it was

concluded that change in the profit ratio in manufacturing sector is the most important

variable for impacting GDP growth rate in manufacturing sector.

b) Objective 2 - To study the impact of change in GDP growth rate due to

change in profit ratio of Service sector -

182


variable X is change in profit ratio in service sector. The estimated regression model

is as follows:



positive impact on GDP growth rate in service sector. So, one unit increase in profit

ratio will increase GDP growth rate by 0.044 units in service sector. This change in

GDP with corresponding change in the profit ratio was observed to be very

insignificant.

Model Summary

Model R R2 Std. Error of the Estimate

1 0.781(a) 0.608 1.67076



ratio in service sector. The significance of R2 was tested with the help of F statistic,

which is shown in below table,

ANOVA (b)

Model Sum of

Squares Df

Mean

Square F Sig.

1

Regression 1.721 1 1.721 0.617 0.452(a)

Residual 25.123 9 2.791

Total 26.844 10

From the above table it was observed that the, the p > (0.05), so it was concluded that


is not statistically significant. The significance of

the individual coefficient can be tested using t-statistic,

183

Coefficients (a)


Coefficients

Standardized

Coefficients t Sig.

1

B Std.

Error Beta

(Constant) 8.303 0.905 9.173 0.000

Change in

profit ratio 0.044 0.056 0.253 0.785 0.452

From the above table, at 5 % level of significance, p > α (0.05). So the null hypothesis

was accepted and alternative was rejected, so the coefficient of change in profit ratio

in service sector is not statistically significant. Therefore, it was found that change in

profit ratio in service sector is not a significant variable in influencing GDP growth in

service sector. This was also found using t statistic.

c) Objective 3 - To examine and understand the growth rate of

manufacturing sector in comparison with growth rate of the GDP –

To understand the relationship between growth rate of manufacturing sector with

growth rate of GDP, two-paired test was applied to see the relationship,

0H : There is no significance difference in the average growth rate of manufacturing

sector with growth rate of the GDP ))(0 :( GDPofRateGrowthSectoruringofManufactRateGrowthH )

1H : There is significance difference in the average growth rate of manufacturing

sector with growth rate of the GDP ))(1 :( GDPofRateGrowthSectorcturingofMananufaRateGrowthH )

184

Paired Samples Test

Paired Differences t df

Sigma

(2-

tailed)

Mean

Std.

Deviatio

n

Std.

Error

Mean

99%

Confidence

Interval of the

Difference

Lower Upper

GDP Growth

Rate & Growth

Rate of

Manufacturing

sector

-0.51636 2.39390 0.7217 -2.803 1.771 -0.715 10 0.491

From the above table, it was observed that at 99% confidence interval p > α (0.005)

(0.4 > 0.01). So, null hypothesis was accepted and alternative hypothesis was rejected.

It was concluded that there is no significant difference in the growth rate of

manufacturing sector with respect to growth rate of the GDP. So it can be said that the

growth rate of manufacturing sector with respect to GDP is same. Now, further the

relationship between growth rate of manufacturing sector and growth rate of GDP was

tested. For that, correlation was used.

0H : There is no significant relationship between growth rate of manufacturing sector

and growth rate of GDP

1H : There is significant relationship between growth rate of manufacturing sector

and growth rate of GDP

Paired Samples Correlations:

Model N Correlation Sig.

GDP Growth rate & Growth Rate of

Manufacturing sector 11 0.771 0.003

185

From the above table, it was observed that at 99% confidence interval p < α (0.01) so

null hypothesis was rejected and alternative hypothesis was accepted. So there is

significant relationship between growth rate of manufacturing sector and growth rate

of GDP. Since the correlation coefficient is 0.77, they are strongly positively related

with each other. So, finally it can be concluded that if growth rate of manufacturing

sector increases than growth rate of GDP will also increase and vice versa.

d) Objective 4 - To examine and understand the growth rate of service sector

in comparison with growth rate of the GDP in service sector –

To understand the relationship between growth rate of service sector with growth rate

of GDP, two-paired test was applied.

0H : There is no significance difference in the growth rate of service sector with

growth rate of the GDP ))(0 :( GDPofRateGrowthSectorServiceofRateGrowthH )

1H : There is significance difference in the growth rate of service sector with growth

rate of the GDP ))(1 :( GDPofRateGrowthSectorServiceofRateGrowthH )

Paired Samples Test

Paired Differences

t df Sig.(2-

tailed) Mean Std.

Deviation

Std.

Error

Mean

99% Confidence

Interval of the

Difference

Lower Upper

GDP

Growth rate-

Growth rate

of Services

sector

-1.380 1.14043 0.3438 -2.469 -0.290 -4.013 10 0.002

186

From the above table, it was observed that at 99% confidence interval p < α (0.01)

(0.002 < 0.01), so null hypothesis was rejected and alternative hypothesis was

accepted. It can be concluded that there is significance difference in the growth rate of

service sector with respect to growth rate of the GDP. Further, to check whose

performance is better sample statistics table was referred.

Paired Samples Statistics

Model Mean N Std. Deviation Std. Error Mean

Pair 1 GDP Growth rate 7.5136 11 1.96634 0.59287

Growth rate of

Services sector 8.8936 11 1.63842 0.49400

From the above table, it was observed that the GDP growth rate is lesser than the

growth rate of service sector.

Finally, the relationship between growth rate of service sector and growth rate of GDP

was tested with the use of correlation.

0H : There is no significant relationship between growth rate of service sector and

growth rate of GDP

1H : There is significant relationship between growth rate of service sector and

growth rate of GDP

Paired Samples Correlations

N Correlation Sig.

GDP Growth rate - Growth rate of

Services sector 11 0.815 0.002

187

From the above table, it was observed at 99% confidence interval p < α (0.01). So,

null hypothesis was rejected and alternative hypothesis was accepted. This depicted

that there is significant relationship between growth rate of service sector and growth

rate of GDP. Since the correlation coefficient is 0.81, they are strongly positively

related with each other. So, finally it was concluded that if growth rate of service

sector increases than the growth rate of GDP will also increase and vice versa.

e) Objective 5 - To analyse impact of Inflation on Manufacturing Sector -

In the model, the dependent variable Y is Change in profit ratio whereas independent

variable X is Inflation. The estimated regression model is as follows:

Y (Change in profit ratio) = 11.123 - (0.230) (Inflation)

The results indicated that the independent variable i.e. Inflation has a negative impact

on Change in profit ratio. So, one unit increase in Inflation will decrease Change in

profit ratio by 0.230 units in manufacturing sector

Model Summary


1 0.789(a) 0.622 1.24


that 62.2 % of the variations in the Inflation are explained by Change in profit ratio in

manufacturing sector. The significance of R2 was tested with the help of F statistic,

which was shown in below table,

ANOVA (b)

Model Sum of

Squares Df

Mean

Square F Sig.

1

Regression 32.123 1 32.567 4.256 0.063(a)

Residual 57.898 9 7.566

Total 90.021 10

188

From the above table it was observed that the, the p > (0.05), so we conclude that that

at 5% level of significance R2

is not statistically significant. The significance of the

individual coefficient was tested using t-statistic,

Coefficients (a)

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

1 (Constant) 11.123 1.286 7.228 0.000

Inflation -0.231 0.087 -0.562 -2.156 0.083


the null hypothesis was accepted and alternative was rejected. So the coefficient of

inflation is not statistically significant. Therefore, it was found that inflation is not a

significant variable in influencing change in profit ratio in manufacturing sector. This

was also confirmed using t statistic.

f) Objective 6 - To analyse impact of Profit ratio of service sector on

Inflation -

In the model, the dependent variable Y is Change in profit ratio whereas independent

variable X is Inflation. The estimated regression model is as follows:



negative impact on inflation. So, one unit increase in profit ratio will decrease

inflation by 0.033 units in service sector.

Model Summary


1 0.842(a) 0.708 1.06

189


that 70.8 % of the variations in the Change in profit ratio of service sector are

explained by in Inflation. The significance of R2 was tested with the help of F

statistic, which is shown in below table,

ANOVA (b)

Model Sum of Squares Df Mean Square F Sig.

1

Regression 4.266 1 4.334 0.216 0.422(a)

Residual 93.742 9 12.586

Total 97.899 10

From the above table it was observed that the, the p > (0.05), so it was concluded that



the individual coefficient was tested using t-statistic,

Coefficients (a)


Coefficients

Standardized

Coefficients T Sig.

1

B Std. Error Beta

(Constant) 9.222 1.522 6.032 0.004

Inflation -0.052 0.322 -0.512 -0.534 0.668


the null hypothesis was accepted and alternative was rejected. So the coefficient of

Inflation is not statistically significant. Therefore, it was observed that inflation is not

a significant variable in influencing change in profit ratio in service sector. This was

also found using t statistic.

190

g) Objective 7 - To analyse impact of Inflation on GDP

The R2 value for the model is 0.675 which indicated that 67.5 % of the variations

in the GDP Growth rate are explained by Inflation. The significance of R2 was

tested with the help of F statistic, which is shown in below table,

ANOVA (b)

Model Sum of

Squares Df Mean Square F Sigma

Regression 1.322 1 3.235 0.998 0.005 (a)

Residual 22.3228 2.789

Total 24.1419

From the above table it was observed that the, the p < (0.05), so it was concluded that

that at 5% level of significance R2 is statistically significant.

The significance of the individual coefficients was tested using t-statistic,

Coefficients (a):

Unstandardised

Coefficients

Standardised

coefficients t Sigma

Β Standard Error Β

(Constant) 7.785 2.200 6.514 0.005

Inflation 0.040 0.073 0.414 0.775 0.667

From the above table it was observed that at 5 % level of significance p > α (0.05). So

the null hypothesis was accepted and alternative was rejected. So, the coefficient of

Inflation is not statistically significant. Therefore, it was stated that Inflation is not a

significant variable in influencing GDP growth rate. This was also found using t-

statistic.

7.6 Tests of Granger Causality –

The Granger (1969) approach to the question of whether causes is to see how

much of the current can be explained by past values of and then to see whether adding

191

lagged values of can improve the explanation. It is said to be Granger-caused by if it

helps in the prediction of, or equivalently if the coefficients on the lagged’s are

statistically significant. Note that two-way causation is frequently the case; Granger

causes and Granger causes. It is important to note that the statement “Granger causes”

does not imply that is the effect or the result of Granger causality measures

precedence and information content but does not by itself indicate causality in the

more common use of the term. When Granger Causality view is selected, a dialog

box is seen asking for the number of lags to use in the test regressions. In general, it is

better to use more rather than fewer lags, since the theory is couched in terms of the

relevance of all past information. A lag length has to be picked that corresponds to

reasonable beliefs about the longest time over which one of the variables could help

predicted the other. EViews runs bivariate regressions of the form:

0 1 1 1 1

0 1 1 1 1

... ...

... ...

t t t t l t l l t

t t t t l t l l t

y y y x x

x x x y y

For all possible pairs of (x, y) series in the group, the reported F-statistics are the


1 2 ........ 0l

For each equation, the null hypothesis is that which does not Granger-cause in the

first regression and that does not Granger-cause in the second regression.

(a) Growth rate of Manufacturing Sector and Service Sector –

aH 0 : Growth rate of Manufacturing does not Granger Cause on growth rate of service

sector

aH1 : Growth rate of Manufacturing does have Granger Cause on growth rate of

service sector

192

Null Hypothsis Obs F-Statistic Prob

MANUFACTUING does not

Granger Cause

GROWTH_RATE_OF_SERVICES

9 0.02362 0.9768


(0.05). So the null hypothesis was accepted and alternative was rejected, so the

Growth rate of manufacturing does not granger cause on growth rate of service sector.

Hence, Granger Causality test pointed out that the growth rate of manufacturing

sector does not have significant impact over the growth rate of service sector.

Null Hypothsis Obs F-Statistic Prob

MANUFACTURING does not

Granger Cause


9 0.02362 0.9768


does not Granger Cause

MANUFACTURING

9 3.62314 0.1265

From the above table, the hypothesis that growth rate of manufacturing does not

Granger cause growth rate of service sector can not be rejected. The hypothesis that

growth rate of service sector does not Granger cause growth rate of manufacturing

was rejected. Therefore it appears that Granger causality runs two-way from growth

rate of manufacturing to growth rate of service sector.

b) Growth rate of Service Sector and Manufacturing sector –

bH 0 : Growth rate of service does not Granger Cause on growth rate of manufacturing

sector

193

bH1 : Growth rate of service have Granger Cause on growth rate of manufacturing

sector


Null Hypothesis F-Statistic Prob

GROWTH_RATE_OF_SERVICES_ does not Granger

Cause MANUFACTUING 3.62314 0.1265

From the above table it was observed that at 5 % level of significance p > α (0.05). So

the null hypothesis was accepted and alternative was rejected, so the Growth rate of

service does not granger cause on growth rate of manufacturing sector. Hence,

Granger Causality test pointed out that the growth rate of service sector does not have

significant impact over the growth rate of manufacturing sector.

c) Profit ratio of Manufacturing Sector and Service Sector –

aH 0 : Profit ratio of Manufacturing does not Granger Cause on growth rate of service

sector

aH1 : Profit ratio of Manufacturing have Granger Cause on growth rate of service

sector


PROFIT_AND_LOSS_FOR_MANU does not

Granger Cause 9 0.01405 0.9861


(0.05). So, the null hypothesis was accepted and alternative was rejected. Thus, the

profit ratio of manufacturing does not granger cause on profit ratio of service sector.

194

Hence, Granger Causality test pointed out that the profit ratio of manufacturing sector

does not have significant impact over the profit ratio of service sector.


PROFIT_AND_LOSS_FOR_MANU does

not Granger Cause

P_AND_LOSS_FOR_SER

9 0.01405 0.9861

P_AND_LOSS_FOR_SER does not

Granger Cause

PROFIT_AND_LOSS_FOR_MANU

9 0.67391 0.5595

From the above table, the hypothesis that Profit ratio of Manufacturing does

not Granger cause Profit ratio of service sector can not be rejected and the hypothesis

that Profit ratio of service sector does not Granger cause Profit ratio of manufacturing

was rejected. Therefore it appears that Granger causality runs two-way from Profit

ratio of manufacturing to Profit ratio of service sector.

d) Profit ratio of Service Sector and Manufacturing sector –

bH 0 : Profit ratio of service does not Granger Cause on growth rate of manufacturing

sector

bH1 : Profit ratio of service have Granger Cause on growth rate of manufacturing

sector

Null Hypothesis F-Statistic Prob

P_AND_LOSS_FOR_SER does not Granger

Cause PROFIT_AND_LOSS_FOR_MANU 0.67391 0.5595


(0.05). So the null hypothesis was accepted and alternative was rejected. So the Profit

195

ratio of service does not granger cause on profit ratio rate of manufacturing sector.

Hence, Granger Causality test pointed out that the profit ratio of service sector does

not have significant impact over the profit ratio of manufacturing sector.

Profit ratio of Manufacturing and Service Sector –


0 1 1 1 1

0 1 1 1 1

... ...

... ...

t t t t l t l l t

t t t t l t l l t

y y y x x

x x x y y



1 2 ........ 0l

For each equation, the null hypothesis is that does not Granger-cause in the first


e) Profit ratio of Manufacturing Sector and Inflation –

aH 0 : Profit ration of Manufacturing does not Granger Cause on inflation

aH1 : Profit ration of Manufacturing have Granger Cause on inflation

Null Hyothesis Obs F-Statistic Prob


Granger Cause INFLATION 9 0.98152 0.45


(0.05). So, so the null hypothesis was accepted and alternative was rejected. So, the

profit ratio of manufacturing does not granger cause on inflation. Hence, Granger

196

Causality test pointed out that the profit ratio of manufacturing sector does not have

significant impact over the inflation.


0 1 1 1 1

0 1 1 1 1

... ...

... ...

t t t t l t l l t

t t t t l t l l t

y y y x x

x x x y y



1 2 ........ 0l

For each equation. The null hypothesis is that does not Granger-cause in the first




Granger Cause INFLATION 9 0.98152 0.45


not Granger Cause INFLATION 9 38.6459 0.0024

From the above table, the hypothesis that Profit ratio of Manufacturing Sector

does not Granger cause Inflation can not be rejected but the hypothesis that Inflation

does not Granger cause Profit ratio of Manufacturing Sector was rejected. Therefore it

appears that Granger causality runs one-way from Inflation to Profit ratio of

Manufacturing Sector and not the other way.

f) Profit ratio of Service Sector and Inflation –

aH 0 : Profit ratio of Service does not Granger Cause on inflation

aH1 : Profit ratio of Service have Granger Cause on inflation



not Granger Cause INFLATION 9 38.6459 0.0024

197


(0.05). So the null hypothesis was rejected and alternative was accepted, so the profit

ratio of manufacturing have granger cause on inflation. Hence, Granger Causality test

points out that the profit ratio of service sector has significant impact over the

inflation.

198

Chapter 8

Validation of Data through Industry Experts

In terms of primary data, the researcher has collected information from various

Industry experts and grabbed knowledge from their experiences which they shared in

the Interviews. The interviews of around 20 industry experts were based on economic

downfall in Indian economy in 2002 to 2013 and its impact on manufacturing

industries and service industries. In the interview session, they also shared the

measures and steps taken by their company to overcome the downfall. Almost all

experts expressed that there was only downfall in Indian Economy, it was not a

recession or a severe recession. In the downfall of 2008, since it was originated in

USA, the impact was there on Indian economy basically on employment of both the

industries i.e. manufacturing and service. But very few companies took steps of

termination of employees. According to them, retrenchment of staff was the last

option.

There was severe impact on profit ratio of companies. According to these

industry experts, profit ratio would have severely gone down but the policy of

company was to reduce their overheads. The focus was on rationalisation of cost,

rationalisation of infrastructure, reduction in overhead cost. There was no new

recruitment in slowdown period, rather promotions and incentives were stopped.

Annual reports of the companies also revealed that, employment expenses were cut

down by company. A vice president of a well known blue chip manufacturing

company, shared his 40 years of work experience. In which he expressed that, in India

normally the policies of company are in favour of labour and employees. Whenever

such downturn came, focus of company was on to reduce discretionary expenditures

199

first. According to Businessdictinary.com, discretionary expenditures are the unusual

purchases or fees for the course of business operations. For example, a meal at a 4 star

restaurant for a perspective client, Free meals to employees or subsidised meals to

employees may consider as discretionary expenditures of business. Therefore,

discretionary expenses focused by all the companies to reduce operating cost. There

was more focus on “needs” of the company instead of “want” of the company.

In the process of production, packaging, transportation the key issue was

reduction in wastage. Wastes may be generated during the extraction of raw materials,

the processing of raw materials into intermediate and final products or the

consumption of final products, and other human activities like packaging, distribution,

handling. Similarly, there was re-engineering of packaging with which company can

reduce packaging cost for proper and timely distribution of goods.

In the detail discussion of downfall of economy which steps companies should

take, Dr. R. Gopal who has around 30 years of industry experience expressed that A

three step program is normally developed to turnaround a company. According to

him, In Short run,

There should be Tight control on implementation, especially capital

expenditure of a company. Therefore there should be postponement of

purchase of fixed assets. So expenditures such as buildings, machinery and

equipment, vehicles which could be postpone for short term must focus by

company.

Rather than purchasing it is better to Sale of fixed assets, current assets

(inventories), and even business.

200

Realization of debtors and strict control on the time duration is advisable in

downfall of economic conditions. Because the sooner debtors pay the business

the better, so a short debtor’s collection period is good. If debtors pay quickly,

it helps cashflow and reduces the risk of business not paying the money they

owe. Rescheduling of liabilities with time is also required in economic crisis

of company and bad economic environment of nation.

Reduction of manpower, Selective booking of orders if excess staff is

disguised. If excess staff is not giving productivity up to the mark there should

be cut down of these disguised unemployed people.

Introduction of new features in company’s product to increase value addition

of product. Innovations in sales, production and marketing are also possible to

attract customers.

For the Long Term Survival of Company Dr. R. Gopal further expressed that,

companies should take some harsh decisions. Some of the possible solutions

are :

Replacement of the CEO of Company

Introduction of new products all together. Diversification in product line

is also possible in long term survival.

Introduction of a strong, accurate and reliable MIS and streamline

organizational structure is another technical requirement of every

company to survive in economic crisis.

Centralize Cash Management and if necessary injection of funds for long term

is also suggested by Dr. R. Gopal. These long term utilizations of funds could

201

be mainly for Research and development activity, with which company can

come up with innovations in product. On the basis of discussions with

Industry experts, the researcher has developed a flow chart with the help of

Dr. R. Gopal.

Fig. 21: Recommendations to Industries in turbulence time

Source: Research paper presented by Dr. R. Gopal in International Conference at

Dr. D. Y. Patil University, 2011

In The above chart, it was explained by the researcher that some actions are one

time to improve initial conditions. Like financial restructuring, adjustments of existing

manpower, staff shifting within the departments, sale of unproductive assets.

In the second stage company can give importance to asset efficiencies for

reduction in breakeven at all levels of production. At the same time reduction of

202

break-even is also possible at logistics level, distribution and marketing. Ultimately

residing sales and distribution process must be there for betterment of company.

In Long run and medium run, with appropriate tenure company should focus on

exploration of profitable market opportunities. Repositioning of profile of company in

the market with new products, and existing products also is possible only in long run.

Identification of unserved markets is again another requirement for company to face

next economic downturn.

The data analysis has shown that there was some impact of slowdown on profitability

ratio of manufacturing sector. But impact on manufacturing is not much severe in all

sectors specifically in the period of 2008-09. Results have shown that if the growth

rate of manufacturing sector increases then only increase in GDP growth rate was

seen. The industry experts namely from textile, automobile and petrochemicals also

shared that, fall in GDP growth rate observed in those years when there was fall in

performance of manufacturing companies.

From the data analysis and experiences of experts from service industry namely IT

and Apparel expressed that due to due to recession in USA in 2008, the downfall in

India was for 2-3 years. The FY 2008-09 and FY 2009-10 was bad for these sectors.

Indian IT sector has maximum clients from USA, UK, and Europe. When there was

recession in 2008, in the world, these clients faced financial crunch. Most of the

business got bankrupt. So payments of Indian service providers were in trouble and

then there was low business with these country clients.

The analysis of data and experiences of industry exerts go hand in hand.

203

Chapter 9

Results and Discussion

Based on the data and methodology discussed, researcher had applied statistical tools

of to obtain the results of the models.

9.1. Hypothesis 1:

H01: Change in profit ratio of Manufacturing sector has insignificant impact on

GDP growth rate.

H11: Change in profit ratio of Manufacturing sector has significant impact on

GDP growth rate.

In the analysis of data for testing hypothesis 1, firstly, calculated Karl Pearson’s

correlation co-efficient between GDP growth rate and Profitability of Manufacturing

sector was calculated. The results indicated that the independent variable i.e. change

in profit ratio had a positive impact on in manufacturing sector.

Following table represents the results of the analysis:

Regression Statistic

R 0.892

R2 0.795

Standard error of estimate 2.69776



ratio in manufacturing sector. Curve was fitted with the estimated regression. The

equation, thus formulated as follows:


204

The equation showed that one unit increase in profit ratio will increase GDP growth

rate by 0.238 units in manufacturing sector.

This gives confirmation that profit ratio of Manufacturing Industries has significant

impact on GDP growth rate. The significance of R2 was tested with the help of F

statistic. Calculated value of F statistic is: 8.376.

Discussion and Comment:

Table value of F (95% confidence) at (dfn1=1, and dfn2=9), i.e. F0.95 (1, 9) = 5.12.

F CALCULATED > F0.95 (1, 9)

Hence, H01 is rejected.

H11 is accepted.

Thus, the Hypothesis H11 “Change in profit ratio of Manufacturing sector has

significant impact on GDP growth rate” is Accepted.

9.2. Hypothesis 2:

H01: Change in profit ratio of Service sector has insignificant impact on GDP

growth rate.

H11: Change in profit ratio of Service sector has significant impact on GDP

growth rate.

In the analysis of data for testing hypothesis 2, firstly, Karl Pearson’s

correlation co-efficient between GDP growth rate and Profitability of Services sector

was calculated. The results indicated that the independent variable i.e. change in profit

ratio has a positive impact on service sector. Following table represents the results of

the analysis:

205


R 0.781

R2 0.608


From the above it was observed, The R2 value for the

model is 0.608 which indicated that 60.87 % of the variations in the GDP Growth rate

are explained by change in profit ratio in service sector. Curve was fitted with the

estimated regression. The equation, thus formulated as follows:


The equation showed that one unit increase in profit ratio will increase GDP growth

rate by 0.044 units in service sector.

This gives confirmation that profit ratio of Service sector Industries does not have

significant impact on GDP growth rate. The significance of R2 is tested with the help

of F statistic. Calculated value of F statistic is: 0.617

Discussion and Comment:

Table value of F (95% confidence) at (dfn1=1, and dfn2=9), i.e. F0.95 (1, 9) = 5.12.

F CALCULATED < F0.95 (1, 9)

Hence, H02 is accepted.

H12 is rejected

Thus, the Hypothesis H02 “Change in profit ratio of Service sector has

insignificant impact on GDP growth rate.” is Accepted.

9.3. Hypothesis 3:



206

To understand the relationship between growth rate of manufacturing sector with

growth rate of GDP, We will apply two-paired test to see the relationship.

Paired Differences (Paired Sample Test)

GDP Growth Rate and Growth Rate of Manufacturing sector

Mean -0.51636


Standard error Mean 0.72179

99% confidence interval of the difference

Lower -2.803

Upper 1.771

T -0.715

Df 10

2-tailed sigma 0.491

Discussion and comment:

Here, p > 0.01. Therefore, it was concluded that there is no significant difference in

the growth rate of manufacturing sector with respect to growth rate of the GDP. So, it

can be said that the growth rate of manufacturing sector with respect to GDP is same.

Now, further the relationship between growth rate of manufacturing sector and growth

rate of GDP was tested. For that, Paired samples correlation was used.

Paired Sample Correlation

GDP Growth rate and Growth Rate of Manufacturing sector

N 11

Correlation 0.771

Sigma 0.03

207


Here, p < 0.05.


H13 is accepted.

Since the correlation coefficient is 0.77, they are strongly and positively related with

each other.

Thus, the Hypothesis H13 “Manufacturing sector has significant contribution in

the growth of GDP” is Accepted.

9.4. Hypothesis 4:



To understand the relationship between growth rate of service sector with growth rate

of GDP, two-paired test was applied to see the relationship,

Paired Differences (Paired Sample Test)

GDP Growth Rate and Growth Rate of Service sector

Mean -1.380


Standard error Mean 0.34385

99% confidence interval of the difference Lower -2.469

Upper -0.290

T -4.013

Df 10

2-tailed sigma 0.002

208

Here, p > 0.01. Therefore, it was concluded that here is no significance difference in

the growth rate of service sector with respect to growth rate of the GDP. So, it can be

said that the growth rate of service sector with respect to GDP is same. Now, further

we will test the relationship between growth rate of service sector and growth rate of

GDP, for that we will use Paired samples correlation.

Paired Sample Correlation

GDP Growth rate and Growth Rate of Service sector

N 11

Correlation 0.815

Sigma 0.002


Here, p < 0.05.


H14 is accepted.

Since the correlation coefficient is 0.815, they are strongly and positively related with

each other.

Thus, the Hypothesis H14 “Service sector has significant contribution in the

growth of GDP” is Accepted.

9.5. Hypothesis 5:



209


R 0.789 (a)

R2 0.622


From the above it was observed that the R2 value for the model to be 0.622 which

indicated that 62.2 % of the variations in the Inflation are explained by Change in

profit ratio in manufacturing sector. Curve was fitted with the regression analysis. The

equation, thus formulated becomes:


The results indicated that the independent variable i.e. Inflation has a negative impact

on Change in profit ratio. So, one unit increase in Inflation will decrease Change in

profit ratio by 0.230 units in manufacturing sector

From the above table it was observed that the, the p > (0.05). So, it was concluded

that at 5% level of significance, R2


the individual coefficient was tested using t-statistic. From calculations from the t-

statistic table, it was inferred that at 5 % level of significance, p > α (0.05)


H15 is rejected.

Therefore, it was found that inflation is not a significant variable in influencing

change in profit ratio in manufacturing sector.

Thus, the Hypothesis H05 “Inflation rate has no significant effect on Profit ratio of

manufacturing sector” is Accepted.

9.6. Hypothesis 6:



210


R 0.842(a)

R2 0.708



that 70.8 % of the variations in the Change in profit ratio of service sector are

explained by in Inflation. Curve was fitted with the regression analysis. The equation,

thus formulated becomes:


The results indicated that the independent variable i.e. inflation has a negative impact

on change in profit ratio of service sector. So, one unit increase in inflation will

decrease change in profit ratio by 0.033 units in service sector

The significance of R2 was tested with the help of F statistic. It was observed that the,

the p > (0.05), so it was concluded that at 5% level of significance, R2

is not

statistically significant. The significance of the individual coefficient was tested using

t-statistic.

From calculations from the t-statistic table, it was inferred that at 5 % level of

significance,

p > α (0.05)


H16 is rejected.

Therefore, it was found that inflation is not a significant variable in influencing

change in profit ratio in service sector. This was also found using t statistic.

211

Thus, the Hypothesis H06 “Inflation rate has no significant effect on Profit ratio of

service sector” is Accepted.

9.7. Hypothesis 7:




variable Inflation. The estimated regression model is as follows:

Y (GDP Growth rate) = 4.255 + (0.082) * (Inflation)

The results indicated that the independent variable, Inflation has a positive impact on

GDP Growth rate. So, one unit increase in inflation will increase in GDP growth rate

by 0.082 units.


R 0.822 (a)

R2 0.675

Standard Error of Estimate 3.2556

From the above it was observed, that R2 is 0.675. The significance of R

2 was

tested with the help of F statistic.

Table value of F (95% confidence) at given degrees of freedom = 12.5.

F CALCULATED < F0.95 (1, 9)


H17 is rejected.

212

b) Test of Granger Causality –


Null Hypothsis Observations F-Statistic Probability

GDP does not Granger Cause Inflation 8 1.19528 0.4152

From the above table, 0.4152 (p) > 0.05 (α). Hence, Null hypothesis was

accepted and alternative hypothesis is rejected. So, it was concluded that growth rate

GDP does not Granger cause on Inflation.


H17 is rejected.

Therefore, it has been found that inflation does not play a significant role in

GDP growth rate.

Thus, the Hypothesis H17 “Inflation has insignificant effect on GDP” is

Accepted.

213

Chapter 10

Summary of Hypothesis, Statistical Tools Used and Results

Table 10.0

Sr.

No Hypothesis

Statistical /

Econometric

tools

Results Comments

1

Null Hypothesis

H01: Change in profit

ratio of


has insignificant

impact on GDP

growth rate.

Alternative

Hypothesis


ratio of


has significant

impact on GDP

growth rate.

Correlation

and

Regression, t

test

Model-1

( R = 0.892 , R2

= 0.795 , P =

0.018) P < 0.05

i.e 0.01 < 0.05

79.5 % of the

variations in the

GDP Growth

rate are

explained by

change in profit

ratio in

manufacturing

sector.

Therefore,

“Change in

profit ratio in

manufacturing

sector is a

significant

variable in

influencing

GDP growth in

manufacturing

sector” is

accepted

2

Null Hypothesis

H02: Change in

Correlation

and

Regression, t

( R = 0.781(a),

R2 = 0.608, P =

0.452) P > 0.05

Therefore

“Change in

profit ratio of

214

profit ratio of Service

sector has

insignificant impact

on GDP growth rate.

Alternative

Hypothesis


ratio of Service

sector has significant

impact on GDP

growth rate.

test

Model-2

i.e 0.4 > 0.05

60.8 % of the

variations in the

GDP Growth

rate are

explained by

change in profit

ratio in service

sector.

Service sector

has insignificant

impact on GDP

growth rate.” is

Accepted.

3

Null Hypothesis

H03: Manufacturing

sector has

insignificant

contribution in the

growth of GDP.

Alternative

Hypothesis

H13: Manufacturing

sector has significant

contribution in the

Paired

Differences

(paired

sample test)

and Paired

Sample

Correlation

(Standard error

mean is 0.72

which is more

than 0.01, and

Paired Sample

Correlation is

0.77 , P < 0.01

0.003 < 0.01 so

here is no

significance

difference in the

growth rate of

manufacturing

Therefore,

“Manufacturing

sector has

significant

contribution in

the growth of

GDP” is

Accepted.

215

growth of GDP. sector with

respect to growth

rate of the GDP.

4

Null Hypothesis

H04: Service sector

has insignificant

contribution in the

growth of GDP.

Alternative

Hypothesis

H14: Service sector

has significant

contribution in the

growth of GDP.

Paired

Differences

(Paired

Sample Test)

and Paired

Sample

Correlation

(Standard error

mean is 0.34 &

P < 0.01, (0.002

< 0.01) and

Paired Sample

Correlation is

0.81 so here is

no significance

difference in the

growth rate of

service sector

with respect to

growth rate of

the GDP.

Therefore,

“Service sector

has significant

contribution in

the growth of

GDP”is

Accepted.

5

Null Hypothesis

H05: Inflation rate

has no effect on

profit ratio of

manufacturing

sector.

Alternative

Regression

and t test

(R= 0.789(a),

R2= 0.622, t

Test P > 0.05 i.e.

0.08 > 0.05)

Inflation is not a

significant

variable in

influencing

Therefore,

“Inflation rate

has no

significant

impact on profit

ratio of

manufacturing

sector” is

216

Hypothesis

H15: Inflation rate

has effect on profit

ratio of

manufacturing

sector.

change in profit

ratio in

manufacturing

sector.

Accepted

6

Null Hypothesis

H05: Inflation rate

has no effect on

profit ratio of service

sector.

Alternative

Hypothesis

H15: Inflation rate

has effect on profit

ratio of service

sector.

Regression

and t test

R= 0.842(a),

R2= 0.708, t

Test P > 0.05 i.e.

0.6 > 0.05)

Inflation is not a

significant

variable in

influencing

change in profit

ratio in service

sector.

Therefore,

“Inflation rate

has no effect on

profit ratio of

service sector”

is Accepted

7

Null Hypothesis:

H07: Inflation has no

significant effect on

GDP.

Alternative

Regression,

t statistics,

Granger’s

Causality

Test Model 3

( R = 0.822 , R2

= 0.675 , P >

0.05 i.e 0.6 >

0.05 , P > 0.05

i.e 0.4 > 0.05

Inflation is not a

Therefore,

“Growth rate of

GDP does not

Granger Cause

Inflation” is

217

Hypothesis

H17: Inflation has

significant effect on

GDP.

significant

variable in

influencing GDP

Growth rate.

Accepted

218

Chapter 11

Conclusion

An economy which grows over a period of time tends to slow down the

growth as a part of the normal economic cycle. An economy typically expands for 6-

10 years and tends to go into a recession for about six months to 2 years. There

always remains an uncertainty in economy. Financial fluctuations are powerful

determinants of economic activity.

GDP growth rate is the most important ingredient which shows the condition

of economy. Performance and Profitability of Manufacturing sector and Services

sector remain an important economic variable inflicting inflation and cause substantial

change to GDP growth of the economy of India.

This study adds to the existing literature by bringing an awareness of the

importance of the impact of PBDIT of Manufacturing sector and Services sector on

GDP growth rate of Indian economy. The objectives and the hypotheses of the study

have brought about certain conclusions with respect to the study. The study confirms

that GDP has no inflationary effect, which plays a significant role in Indian economy.

Profit ratio in manufacturing sector is significant variable in influencing GDP growth

in manufacturing sector, 79.5 % of the variations in the GDP Growth rate are

explained by change in profit ratio in manufacturing sector. Karl Pearson Correlation

coefficient between profitability of manufacturing sector and GDP growth rate is

positively correlated and is equal to 0.795. F = 8.376, P = 0.018(a).

The analysis of variance indicates that F- statistic is = 8.376 and p-value is

0.018, which is highly significant. Therefore, the null hypothesis is rejected and the

219

alternative hypothesis “Change in profit ratio in manufacturing sector is significant

variable in influencing GDP growth” is accepted.

Profit ratio of Service sector has insignificant impact on GDP growth rate. In

service sector, 60.8 % of the variations in the GDP Growth rate are explained by

change in profit ratio in service sector. Karl Pearson Correlation coefficient between

profitability of service sector and GDP growth rate is positively correlated and is

equal to 0.608. F = 0.617, P = 0.452(a)). However, the analysis of variance indicates

that F- statistic is = 0.617, P = 0.452(a)) is not statistically significant. Therefore, the

null hypothesis is accepted and the alternative hypothesis is rejected. “Change in

profit ratio of Service sector has insignificant impact on GDP growth rate.” is

accepted.

Objectives of study were also correlated with the performance of Industries.

Seven companies have been considered as case in the present study. Out of these

seven companies four were representing manufacturing sector and three were service

sector. Manufacturing is vast sector therefore in this study out these four companies

two were from FMCG sector and two were from Pharmaceutical sector. In service

sector two companies have been considered from IT sector and one from financial

services.

Profit ratio of HUL and ITC of 10 years has been considered. Here, R2

=

(0.641) which is 64% In F statistics, P < 0.05. That is p value is less than alpha (0.01

< 0.05). In t statistics, p < 0.05 here (0.01< 0.05) therefore, Alternative hypothesis

was accepted. “Change in profit ratio of HUL has significant impact on profit ratio of

manufacturing sector”. Whereas, ITC which was another company from FMCG

sector, has insignificant impact on profit ratio of manufacturing sector. The reason

220

behind this being the profit of ITC mainly comes from Tobacco business. This profit

was not affected much in the prescribed duration of study specifically, 2003-2012.

Here, R2

= (0.641) i.e. 64%. In F statistics, P < 0.05. That is p value is less than alpha.

(0.04 < 0.05). In t statistics, p > 0.05 here (0.6 > 0.05). That is p value is greater than

alpha. Therefore, Null hypothesis was accepted. “Change in profit ratio of ITC has

insignificant impact on profit ratio of manufacturing sector”.

According to Ministry of Chemicals and Fertilizers, Drugs and pharmaceutical

is another significant industry showed considerable progress over the years. India

holds fourth position in terms of volume and thirteenth position in terms of value of

production in pharmaceuticals. Pharmaceutical manufacturing industry is another

major industry in India, which got affected severely in economic downturn. In this

study, two companies have been studied with their performance for ten years on the

basis of profitability. Change in profit ratio of Glenmark Pharmaceutical Ltd and Dr.

Reddy’s Pharmaceutical Ltd has significant impact on profit ratio of manufacturing

sector. Here, R2

value for Glenmark was 0.608 (60.8%) and for Dr. Reddy’s was

0.672 (67.2%). In F statistics, P < 0.05. That is p value is less than alpha. (0.02 <

0.05). In t statistics, P < 0.05 That is p value is less than alpha. (0.02 < 0.05) for both

the companies. Therefore, Alternative hypothesis was accepted. “Change in profit

ratio of Glenmark and Dr. Reddy’s have significant impact on profit ratio of

manufacturing sector”.

Service sector was also affected due to global economic slowdown in the

period 2003-12. TCS has been considered as a case in this study to check profitability

in the given duration. TCS has significant impact on profit ratio of service sector.

Because, R2

value for TCS was 0.640 (64%). In F statistics, P < 0.05. That is p value

is less than alpha. (0.01 < 0.05). In t statistics, P < 0.05 That is p value is less than

221

alpha. (0.01 < 0.05) therefore, Alternative hypothesis was accepted. Similarly, Infosys

is the another IT company which has been considered in the present study. But,

Infosys Ltd has insignificant impact on profit ratio of service sector in the year 2003-

12. The performance company was good during downfall phase of economy. Because,

R2

value for Infosys Ltd was 0.638 which was (63.8%). In F statistics, P > 0.05. That

is p value is greater than alpha. (0.9 > 0.05). In t statistics, P > 0.05 That is p value is

greater than alpha. (0.9 > 0.05) therefore, Null hypothesis was accepted. 3.0 strategy

of Infosys was big failure in the year 2011. The 3.0 strategy was about providing not

only software services and solutions but also lot of transformational projects. Along

with the IT services Infosys has worked with the business side of clients with the aim

to transform into a business solutions provider. Company has made groups providing

learning solutions and business platform solutions. Because of which its revenue

growth was slower than the industry and it was losing market share. Its operating

profit dropped down after 2012. Many giant service industries faced losses in

economic downfall due to short supply of FDI in 2008-09. Capital market of India,

had been facing financial crunch. But this was temporary phase for Reliance Capital

Ltd. Here, R2

value for Reliance Capital Ltd was (0.608) which was 60.8%. In F

statistics, P > 0.05. That is p value is greater than alpha. (0.2 > 0.05). In t statistics, P

> 0.05 That is p value is greater than alpha. (0.2 > 0.05) therefore, Null hypothesis

was accepted. Change in profit ratio Reliance Capital Ltd has insignificant impact on

profit ratio of services sector. Inflation rate of selected years and GDP factor cost

fluctuated during last 10 years. On the basis of the data researcher obtained during

analysis it is obvious that relationship between GDP and Inflation rate is quite less

significant and having low positive relations. The impacts of inflation on agricultural

growth, industrial growth, service growth are very less significant. Data also indicates

222

that the relationship and impact between regression value of inflation rate and GDP

factor cost is quite low. GDP factor cost and inflation rate are interdependent to each

other. There are some other factors that influence agricultural growth and industrial

growth and service growth, and on the other side several other factors which influence

the inflation rate.

Fluctuation rate of inflation was on an average around 4% from 2002 to 2005.

On the other side fluctuation rates were on an average around 6 % from 2005-06 to

2007-08 and 9 % 2010-11 to 2011-12. Fluctuation rate of inflation rate were very

sharp on an average around 11% from 2008-09 to 2009-10 and 10% 2011-12 to 2012-

13.

The correlation between Inflation rate and Profitability of Industries (both

manufacturing and service) are of very low positive degree of correlation and

regression is highly low positive so it can be stated that impact of inflation on industry

growth is very low.

The correlation between Inflation rate and Service sector growth has positive

moderate degree of correlation and regression is insignificantly positive. This imparts

the impact of inflation on service growth is very low.

The correlation and regression value of inflation and GDP factor cost are

insignificantly positive. The extent of relationship is not considerable. Here, (R =

0.822, R2 = 0.675) P = 0.4152, the pair wise Granger causality test showed

probability 0.4. (p) > 0.05(α) Therefore, “Growth rate of GDP does not granger cause

on inflation” was accepted. This infers that GDP and inflation are independent and

not affected by ups and downs of one of them.

223

Chapter 12

Recommendations and Future scope of the study

In this study, profitability of Manufacturing and Services sector has been studied.

The relationships between profitability and GDP growth rate have been quantified.

Also, as a measure, inflation rate was compared with GDP growth rate. From various

findings and conclusions, the author recommends a few measures to the concerned.

Implementation of these recommendations may result in positive changes in business

activities. The author’s recommendations are as listed follows:

Down turn or Recession is not always a negativity for any economy, as it

gives the required push to the economy to cut the flab and create efficiencies

in all the three sectors of economy.

Maximization of the resources of industry, improved synergies and reduction

in the discretionary costs are the requirements for all industries. In this

situation, Business units may not reduce cost or profit. They may instead

channelize resources or money into new opportunities.

No travel by business class for any employee, only economy class fair

should be reimbursed.

Encourage video or telephonic interviews to avoid travelling expenses,

save electricity, restrict usage of printing papers.

May adopt “5S” method to reduce wastages and optimise productivity.

(5S includes sort, set in order, shine, standardize and sustain. It

provides a methodology for organizing, cleaning, developing and

224

sustaining a productive work environment. It is also known as lean

method.)

It is also recommended that in the troughs of the business cycle, investments

may be made in starting manufacturing units having low initial capital.

New Industries or infant industries may correct operational efficiency and

pricing rationale to survive in the market.

For giant industries/business units, the recommendation could be acquisitions

of the new business, as their valuations are lower and attractive in these times.

With a positive note, it is also time for industries to revaluate markets,

consumers, understand where the lacuna exists and incursion or entry into new

avenues of business and opportunities.

Future scope of the Study:

The present study would bring greater nuances in the study of economic

fluctuations in India with reference to profitability of industries by focusing on

manufacturing sector and service sector. The present study was expected to

open up avenues for further research on macro economic variables other than

GDP at factor cost and inflation. The profitability of industries of

manufacturing sector and service sector has been analysed in this study. It

would also open avenues to expand the study to measure other indicators of

performance of industries. There has been greater impact of global economic

fluctuations in India which affected many industrial sectors of the economy.

The study would open a new door to look at those sectors which were also

severely affected in terms of employment pattern, export earnings. There are

225

ample scope of future research in the field of economic fluctuations and

impact on industries related to their performance, profitability, sales,

employment ratio, demand and supply.

226

Chapter 13

Suggestions

In Suggestions, the researcher indicated that at the macro level, governments

and other fiscal authorities may follow a policy of:

Tax Cuts, to stimulate demand.

Policy of flexibility may follow by the Government.

Pump-priming by government measures for generating more demand

in downturn of economy.

At macro level, Monetary policy also may follow:

Cuts in interest rates to stimulate demand.

Cuts in SLR & CRR.

To reduce inflation rate different steps should be taken by fiscal and

monetary authorities.

Detailed discussions were also held with Industry experts and Data analysis to

find out the ways and means for the Indian companies to overcome this economic

fluctuations.

Restructuring the balance sheet

Voluntary Retirement Schemes

Selling off businesses or assets partially

They should also focus on reduction in cost

227

This involves

Reducing operating costs

Increasing productivity

Reduce all capital expenditures

Restructure the Work force

Quit certain unfavourable markets

Introduce new products / new services.

228

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234

0

2

4

6

8

10

12

GD

P G

row

th R

ate

(%

)

Year

GDP Growth rate at Constant Prices

Fig. 22 Growth rate of GDP at Constant Prices

Source: CSO

GDP growth rate at constant prices has been plotted. This shows the various ups and

downs in GDP in the three phases (2002-03, 2008-09 and 2010 afterwards)

considered in this study.

235

Fig. 23 GDP at Factor cost with Actual values.

Source: CSO

Actual Values of GDP have been plotted yearwise. The values are in ` (crores)

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

` (

Cro

res)

Year

GDP at FC (Actual Values)

236

0

2

4

6

8

10

12

14

16

2002-03 2004-05 2006-07 2008-09 2010-11 2012-13

Infl

atio

n (

%)

Year

Inflation

Fig. 24 Inflation yearwise trend

Source: International Monetary Fund, World Economic Outlook Database

This plot shows the trend of inflation in the period (2002-2013) considered in

this study.

237

0

2

4

6

8

10

12

3.49 4.17 4.95 7.46 6.94 9.09 14.29 10 9.38 10.39 8.14

GD

P G

row

th R

ate

(%

)

Inflation (%)

GDP Growth rate at FC

Fig. 25 GDP Growth rate at various inflation values.

Source: Author’s calculation

238

0

2

4

6

8

10

12

14

16

2002-03 2004-05 2006-07 2008-09 2010-11 2012-13

Gro

wth

rat

e o

f M

anu

fact

uri

ng

Sect

or

(%)

Year

Growth rate of Manufactuing Sector

Fig. 26 Growth rate of Manufacturing Sector

Source: CSO

Growth rate of Manufacturing Sector (%) has been plotted. This shows the

various ups and downs in GDP in the three phases (2002-03, 2008-09 and

2010 afterwards) considered in this study.

239

4

5

6

7

8

9

10

11

12

Gro

wth

rat

e o

f Se

rvic

es

Sect

or

(%)

Year

Growth Rate of Services sector

Fig. 27 Growth Rate of Services Sector

Source: CSO

240

0

5

10

15

20

25

30

35

40

PB

DIT

of

Man

ufa

ctu

rin

g Se

cto

r (%

)

Year

PBDIT of Manufacturing sector

Fig. 28 PBDIT of Manufacturing Sector

Source: CMIE, Income & Expenditure Summary

PBDIT of Manufacturing Sector (%) has been plotted. This shows the various

ups and downs in GDP in the three phases (2002-03, 2008-09 and 2010

afterwards) considered in this study.

241

-10

-5

0

5

10

15

20

25

30

PB

DIT

of

Serv

ice

s Se

cto

r (%

)

Year

PBDIT of Service sector

Fig. 29 PBDIT of services Sector (%)

Source: CMIE, Income & Expenditure Summary

PBDIT of Services Sector (%) has been plotted. This shows the various ups

and downs in GDP in the three phases (2002-03, 2008-09 and 2010

afterwards) considered in this study.

242

0

2

4

6

8

10

12

14

-15 -10 -5 0 5 10 15 20 25 30 35

PB

DIT

of

Man

ufa

ctu

rin

g Se

cto

r (%

)

EBIT of HUL (%)

Regression Scatter plot of PBDIT of Manufacturing sector w.r.t. EBIT of HUL

The following regression equation has been plotted.

Y (Profit before income ratio of Manufacturing sector) = 5.331 + (0.221) (Earnings

before income tax for HUL).

Fig. 30 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of HUL.

Source: Author’s Calculation

243

-14

-12

-10

-8

-6

-4

-2

0

0 5 10 15 20 25

PB

DIT

of

Man

ufa

ctu

rin

g se

cto

r (%

)

EBIT of ITC (%)

Regression Scatter plot of PBDIT of Manufacturing sector w.r.t. EBIT of ITC


Y (Profit before income ratio of Manufacturing sector) = 0.262 - (0.655)

(earnings before income tax for ITC)

Fig. 31 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of ITC.


244

0

5

10

15

20

25

30

35

40

0 20 40 60 80 100 120

PB

DIT

of

Man

ufa

ctu

rin

g Se

cto

r (%

)

EBIT of Glenmark (%)

Regression Scatter plot of PBDIT of Manufacturing sector w.r.t. EBIT of Glenmark



(earnings before income tax for Glenmark)

Fig. 32 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of

Glenmark.


245

0

50

100

150

200

250

300

0 50 100 150 200 250 300 350

PB

DIT

of

Man

ufa

ctu

rin

g Se

cto

r (%

)

EBIT of Dr Reddy's Laboratories (%)

Regression Scatter plot of PBDIT of Manufacturing sector w.r.t. EBIT of Dr Reddy's

Laboratories



(earnings before income tax for Dr Reddy’s Laboratories)

Fig. 33 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of Dr

Reddy’s Laboratories.


246

6

6.5

7

7.5

8

8.5

9

0 5 10 15 20 25 30 35 40

PB

DIT

of

Serv

ice

s Se

cto

r (%

)

EBIT of TCS (%)

Regression Scatter plot of PBDIT of Services sector w.r.t. EBIT of TCS


Y (Profit before income ratio of Services sector) = 6.625 + (0.05) (earnings

before income tax for TCS)

Fig. 34 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of TCS.


247

14.95

15

15.05

15.1

15.15

15.2

0 10 20 30 40 50

PB

DIT

of

Serv

ice

s Se

cto

r (%

)

EBIT of Infosys (%)

Regression Scatter plot of PBDIT of Services sector w.r.t. EBIT of Infosys


Y (Profit before income ratio of Services sector) = 15.218 - (0.006) (earnings

before income tax for Infosys)


Infosys.


248

-5

0

5

10

15

20

25

30

-40 -20 0 20 40 60

PB

DIT

of

Serv

ices

Sec

tor

(%)

EBIT of Reliance Capital (%)

Regression Scatter plot of PBDIT of Services sector w.r.t. EBIT of Reliance Capital


Y (Profit before income ratio of Services sector) = 7.75 + (0.308) (earnings

before income tax for Reliance Capital)


Reliance Capital.


249

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1 2 3 4 5 6 7 8

ACF

Fig. 37 ACF of GDP growth Rate


The ACF plot indicates that ACF is positive in Lag 1, 4 and 8. After lag 5 the ACF

declines sharply.

250

Fig. 38 PACF of GDP Growth Rate


The PACF plot indicates that PACF is positive in Lag 1, 3 and 4, from Lag 5 to Lag 7

the PACF declines.

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1 2 3 4 5 6 7 8

PACF

251

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8

ACF

Fig. 39 ACF of Growth Rate of Manufacturing Sector


The ACF plot indicates that ACF is positive in Lag 1, 3 and 4. After lag 4, the ACF

declines sharply.

252

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8

PACF

Fig. 40 PACF for Growth Rate of Manufacturing Sector


The PACF plot indicates that PACF is positive in Lag 1, 3 and 8, from Lag 4 to Lag 7

the PACF declines.

253

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9

ACF

Fig. 41 ACF for Growth Rate of Services Sector


The ACF plot indicates that ACF is positive in Lag 1, 2, 8 and 9. After lag 3, the ACF

declines sharply.

254

Fig. 42 PACF for Growth Rate of Services Sector


The PACF plot indicates that PACF is positive in Lag 1 and 9. From Lag 2 to Lag 8

the PACF declines.

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9

PACF

255

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 2 3 4 5 6 7 8 9

ACF

Fig. 43 ACF for Inflation



declines sharply.

256

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 2 3 4 5 6 7 8 9

PACF

Fig. 44 PACF for Inflation


The PACF plot indicates that PACF is positive in Lag 1 and 7. From Lag 2 to Lag 6

the PACF declines.

257

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8

ACF

Fig. 45 ACF for PBDIT of Manufacturing Sector



declines sharply.

258

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8

PACF

Fig. 46 PACF for PBDIT of Manufacturing Sector


The PACF plot indicates that PACF is positive in Lag 1, 3 and 8. From Lag 4 to Lag 7

the PACF declines.

259

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

1 2 3 4 5 6 7 8

ACF

Fig. 47 ACF for PBDIT of Service Sector


The ACF plot indicates that ACF is positive in Lag 1 and 2. After lag 2, the ACF

declines sharply.

260

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

1 2 3 4 5 6 7 8

PACF

Fig. 48 PACF for PBDIT of Service Sector


The PACF plot indicates that PACF is positive in Lag 1, 2 and 8. From Lag 4 to Lag 7

the PACF declines.

261

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8

ACF

Fig. 49 ACF for EBIT of HUL


The ACF plot indicates that ACF is positive in Lag 3 and 6.

262

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

1 2 3 4 5 6 7 8

PACF

Fig. 50 PACF for EBIT of HUL


The PACF plot indicates that PACF is positive in Lag 3, 4 and 6.

263

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1 2 3 4 5 6 7 8

ACF

Fig. 51 ACF for EBIT of ITC.


The ACF plot indicates that ACF is positive in Lag 4, 5 and 6. From 1to 3 and for lags

7 and 8, ACF declines sharply.

264

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

1 2 3 4 5 6 7 8

PACF

Fig. 52 PACF for EBIT of ITC.


The PACF plot indicates that PACF is positive only in Lag 8, from Lag 1 to Lag 7 the

PACF declines sharply.

265

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1 2 3 4 5 6 7 8

ACF

Fig. 53 ACF for EBIT of TCS


The plot indicates that ACF declines from lag 1 to 4. The ACF for lags 5 to 8 are on

positive side.

266

-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

1 2 3 4 5 6 7 8

PACF

Fig. 54 PACF for EBIT of TCS


From the PACF plot, it indicates that PACF values are remaining show a decline.

267

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8

ACF

Fig. 55 ACF for EBIT of Reliance Capital


The ACF plot indicates that ACF is positive at lag 2 only. At other lags, ACF declines

sharply.

268

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8

PACF

Fig. 56 PACF for EBIT of Reliance Capital


PACF plot indicates that PACF is positive for 2, 7 and 8. At other positions, PACF

declines sharply.

269

0

2

4

6

8

10

12

14

0 5 10 15 20 25 30 35 40

GD

P g

row

th r

ate

(%

)

PBDIT of Manufacturing Sector (%)

GDP growth rate

Fig. 57 Regression scatter plot for GDP Growth Rate w.r.t. PBDIT of

Manufacturing Sector


270

8

8.2

8.4

8.6

8.8

9

9.2

9.4

9.6

-10 -5 0 5 10 15 20 25 30

GD

P G

row

th R

ate

(%

)

PBDIT of Services Sector (%)

GDP Growth Rate

Fig. 58 Regression scatter plot for GDP Growth Rate w.r.t. PBDIT of Services

Sector


271

6

6.5

7

7.5

8

8.5

9

9.5

10

10.5

11

0 2 4 6 8 10 12 14 16

Pro

fit

rati

o o

f M

anu

fact

uri

ng

Sect

or

Change in Inflation (%)

Change in Profit Ratio of Manufacturing sector (%)

Fig. 59 Regression scatter plot for change in PBDIT of Manufacturing sector

w.r.t. change in Inflation


272

4.9

4.95

5

5.05

5.1

5.15

5.2

5.25

5.3

5.35

0 2 4 6 8 10 12 14 16

Pro

fit

Rat

io o

f Se

rvic

es

Sect

or

(%)

Change in Inflation (%)

Change in Profit Ratio of Service sector (%)

Fig. 59 Regression scatter plot for change in PBDIT of Services sector w.r.t.

change in Inflation


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