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
5.5 Impact of fluctuations on Profitability of
TATA Consultancy Services Ltd 114
5.6 Impact of fluctuations on Profitability of
Infosys Ltd 138
5.7 Impact of fluctuations on Profitability of
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
9.2 Hypothesis 2 204
9.3 Hypothesis 3 205
9.4 Hypothesis 4 207
9.5 Hypothesis 5 208
9.6 Hypothesis 6 209
9.7 Hypothesis 7 211
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
31. Regression scatter plot for PBDIT of Manufacturing w.r.t. EBIT of
ITC.
243
32. Regression scatter plot for PBDIT of Manufacturing w.r.t. EBIT of
Glenmark
244
33. Regression scatter plot for PBDIT of Manufacturing w.r.t. EBIT of
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
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.
65
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.
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.
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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
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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 ^a & ^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)
a denotes constant qty i.e. intercept of line o Y axis
b denotes coefficient of X
X denotes PBDIT of service 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 ^a & ^b , where hats indicate that the qty is an
estimate of a model parameter – an estimate that is computed from the observed data.
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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.
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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.
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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
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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.
Company Performance in 2004-05:
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.
Company Performance in 2005-06:
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.
Company Performance in 2006-08:
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.
Company Performance in 2008-09:
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.
Company Performance in 2009-10:
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.
Company Performance in 2010-11:
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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.
Company Performance in 2011-12:
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
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(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
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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
regression model is as follows:
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%.
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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.
In the model, the dependent variable Y is Profit before income ratio whereas
independent variable X earnings before income ratio of ITC. The estimated
regression model is as follows:
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.
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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
is statistically significant.
The significance of the individual coefficients can be tested using t-statistic,
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
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 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 company performance 2003-04:
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,
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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 company performance 2004-05:
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 company performance 2005-06:
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 company performance 2006-07:
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
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before interest, depreciation & tax increased to ` 2172.23 million from `
1086.02 million, an increase of ~100% over the previous year.
The company performance 2007-08:
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 company performance 2008-09:
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.
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The company performance 2011-12:
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.
In the model, the dependent variable Y is Profit before income ratio whereas
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
Squares df Mean Square F Sig.
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
concluded that 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) 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 $107 million in the previous fiscal.
Company performance 2003-04:
Net sales increased by 9 per cent from ` 15,286 million in 2002-03 to `
16,666 million in 2003-04. Total income grew by 11 per cent from ` 15,751 million
in 2002-03 to ` 17,424 million in 2003-04. Profit before depreciation, interest and tax
(PBDIT) calculated net of other income declined by 33 per cent from ` 4,491 million
in 2002-03 to ` 3,008 million in 2003-04. Operating profit margin (Operating
PBDIT/Net Sales) declined from 29 per cent in 2002-03 to 18 per cent in 2003-04.
Post-tax profit (PAT) decreased by 28 per cent from ` 3,924 million in 2002-03 to `
2,833 million in 2003-04.
Company performance 2005-06:
Consolidated revenues decreased by 23% to ` 50,006 million, or U.S.$. 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
Company performance 2006-07:
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.
Company performance 2007-08:
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.
Company performance 2008-09:
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
Company performance 2009-10:
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.
Company performance 2010-11:
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.
Company performance 2011-12:
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.
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e) Objective 5: To study the impact of Profit before income ratio of
Manufacturing sector on earnings before income ratio of Dr. Reddy.
In the model, the dependent variable Y is Profit before income ratio whereas
independent variable X earnings before income ratio of DR. REDDY. The estimated
regression model is as follows:
Y (Profit before income ratio of Manufacturing sector) = 7.332 + (0.711) (earnings
before income)
The results indicated that the independent variable i.e. 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
Squares df Mean Square F Sig.
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
concluded that that at 5% level of significance R2
is statistically significant.
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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) 7.332 4.012 3.112 0.011
DR.
REDDY
_PBDIT
0.711 0.097 0.812 2.612 0.021
From the above table it was observed that at 5 % level of significance p < α
(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
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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:
Company Performance in 2004-05:
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.
Company Performance in 2005-06:
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.
Company Performance in 2006-07:
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.
Company Performance in 2007-08:
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.
Company Performance in 2008-09:
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.
Company Performance in 2009-10:
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.
Company Performance in 2010-11:
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.
Company Performance in 2011-12:
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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Company Performance in 2012-13:
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
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 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
14,306.27 crores was higher by 25.7% over last year (` 11,385.72 crores in 2011-12).
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).
132
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
133
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
is statistically significant.
The significance of the individual coefficients can be tested using t-statistic,
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
In the model, the dependent variable Y is Profit before income ratio whereas
independent variable X earnings before income ratio of TCS. The estimated
regression model is as follows:
Y (Profit before income ratio of Service sector) = 6.441 + (0.621) (earnings before
income)
The results indicated that the independent variable i.e. 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
income ratio by 0.621 units.
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
5% level of significance R2
is statistically significant.
The significance of the individual coefficients can be tested using t-statistic,
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
From the above table it was observed that at 5 % level of significance p < α
(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.
In the model, the dependent variable Y is Profit before income ratio whereas
independent variable X earnings before income ratio of Infosys. The estimated
regression model is as follows:
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
income ratio by 0.655 units.
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.
The significance of the individual coefficients can be tested using t-statistic,
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
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
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.
The company performance in 2005-06:
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.
The company performance in 2006-07:
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.
The company performance in 2008-09:
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.
The company performance in 2009-10:
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 company performance in 2010-11:
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 performance in 2011-12:
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.
In the model, the dependent variable Y is Profit before income ratio whereas
independent variable X earnings before income ratio of RELIANCE. The estimated
regression model is as follows:
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
From the above it was observed that R2 value for the model is 0.608 which indicated
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
5% level of significance R2
is not 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) 7.750 6.527 1.187 0.269
Reliance_
PBDIT 0.308 0.253 0.395 1.218 0.258
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. 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
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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
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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
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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
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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.
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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
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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
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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:
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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 –
Summary Statistics Actual Values
Mean 8.03
Median 7.41
Standard Deviation 3.55
First Quartiles 6.34
Third Quartiles 10.28
Skewness -0.21
Coefficient of Variation 44.28
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 –
Summary Statistics Actual Values
Mean 13.3
Median 12.1
Standard Deviation 9.39
First Quartiles 8.20
Third Quartiles 23.38
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 –
Summary Statistics Actual Values
Mean 14.94
Median 16.3
Standard Deviation 10.37
First Quartiles 3.46
Third Quartiles 19.59
Skewness 0.582
Coefficient of Variation 69.41 %
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 –
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 26.09
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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 –
Summary Statistics Actual Values
Mean 8.89
Median 9.67
Standard Deviation 1.63
First Quartiles 6.96
Third Quartiles 10.27
Skewness -0.35
Coefficient of Variation 18.33 %
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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 –
Summary Statistics Actual Values
Mean 8.02
Median 8.14
Standard Deviation 3.13
First Quartiles 4.95
Third Quartiles 10.00
Skewness 0.35
Coefficient of Variation 39.02 %
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).
Y (GDP Growth rate) = 8.303 + (0.044) (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)
a denotes constant qty i.e. intercept of line o Y axis
b denotes coefficient of X
X denotes PBDIT of service sector (yearly)
te is residual term of the model.
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 ^a & ^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:
In the model, the dependent variable Y is GDP growth rate whereas independent
variable Inflation. The estimated regression model is as follows:
Y(GDP Growth rate) = 4.255 + (0.082) * (Inflation)
176
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
From the above it was observed, 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 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
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 is statistically significant.
The significance of the individual coefficients can be tested using t-statistic,
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
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 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
In the model, the dependent variable Y is GDP growth rate whereas independent
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
that 79.5 % of the variations in the GDP Growth rate are explained by change in profit
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
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
is statistically significant.
The significance of the individual coefficients can be tested using t-statistic,
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
In the model, the dependent variable Y is GDP growth rate whereas independent
variable X is change in profit ratio in service sector. The estimated regression model
is as follows:
Y (GDP Growth rate) = 8.303 + (0.044) (Change in profit ratio)
The results indicated that the independent variable i.e. change in profit ratio has a
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
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. 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
that at 5% level of significance R2
is not statistically significant. The significance of
the individual coefficient can be tested using t-statistic,
183
Coefficients (a)
Model Unstandardized
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
Model R R2 Std. Error of the Estimate
1 0.789(a) 0.622 1.24
From the above it was observed, The R2 value for the model is 0.622 which indicated
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
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 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:
Y (Change in profit ratio) = 5.422 - (0.033) (Inflation)
The results indicated that the independent variable i.e. change in profit ratio has a
negative impact on inflation. So, one unit increase in profit ratio will decrease
inflation by 0.033 units in service sector.
Model Summary
Model R R2 Std. Error of the Estimate
1 0.842(a) 0.708 1.06
189
From the above it was observed, The R2 value for the model is 0.708 which indicated
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
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.
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
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 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
Wald statistics for the joint hypothesis:
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
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 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
GROWTH_RATE_OF_SERVICES
9 0.02362 0.9768
GROWTH_RATE_OF_SERVICES
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
Pairwise Granger Causality Tests:
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
Null Hypothesis Obs F-Statistic Prob
PROFIT_AND_LOSS_FOR_MANU does not
Granger Cause 9 0.01405 0.9861
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. 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.
Null Hypothesis Obs F-Statistic Prob
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
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 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 –
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
Wald statistics for the joint hypothesis:
1 2 ........ 0l
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.
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
P_AND_LOSS_FOR_SER does not
Granger Cause INFLATION 9 0.98152 0.45
From the above table it was observed that at 5 % level of significance p > α
(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.
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
Wald statistics for the joint hypothesis:
1 2 ........ 0l
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.
Null Hyothesis Obs F-Statistic Prob
P_AND_LOSS_FOR_SER does not
Granger Cause INFLATION 9 0.98152 0.45
PROFIT_AND_LOSS_FOR_MANU does
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
Null Hyothesis Obs F-Statistic Prob
PROFIT_AND_LOSS_FOR_MANU does
not Granger Cause INFLATION 9 38.6459 0.0024
197
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 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
From the above it was observed, The R2 value for the model is 0.795 which indicated
that 79.5 % of the variations in the GDP Growth rate are explained by change in profit
ratio in manufacturing sector. Curve was fitted with the estimated regression. The
equation, thus formulated as follows:
Y (GDP Growth rate) = 4.473 + (0.238) (Change in profit ratio)
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
Regression Statistic
R 0.781
R2 0.608
Standard error of estimate 1.67076
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:
Y (GDP Growth rate) = 8.823 + (0.044) (Change in profit ratio)
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:
H03: Manufacturing sector has insignificant contribution in the growth of GDP.
H13: Manufacturing sector has significant contribution in the growth of GDP.
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 Deviation 2.39390
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
Discussion and comment:
Here, p < 0.05.
Hence, H03 is rejected.
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:
H04: Service sector has insignificant contribution in the growth of GDP.
H14: Service sector has significant contribution in the growth of GDP.
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 Deviation 1.14043
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
Discussion and comment:
Here, p < 0.05.
Hence, H04 is rejected.
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:
H05: Inflation rate has no effect on Profit ratio of manufacturing sector.
H15: Inflation rate has effect on Profit ratio of manufacturing sector.
209
Regression Statistic
R 0.789 (a)
R2 0.622
Standard error of estimate 1.24
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:
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
From the above table 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)
Hence, H05 is accepted.
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:
H06: Inflation rate has no effect on Profit ratio of service sector.
H16: Inflation rate has effect on Profit ratio of service sector.
210
Regression Statistic
R 0.842(a)
R2 0.708
Standard error of estimate 1.06
From the above it was observed that R2 value for the model is 0.708 which indicated
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:
Y (Change in profit ratio) = 5.422 - (0.033) (Inflation)
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)
Hence, H06 is accepted.
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:
H07: Inflation has no significant effect on GDP.
H17: Inflation has significant effect on GDP.
In the model, the dependent variable Y is GDP growth rate whereas independent
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.
Regression Statistic
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)
Hence, H07 is accepted.
H17 is rejected.
212
b) Test of Granger Causality –
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, 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.
Hence, H07 is accepted.
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
Manufacturing sector
has insignificant
impact on GDP
growth rate.
Alternative
Hypothesis
H11: Change in profit
ratio of
Manufacturing sector
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
H12: Change in profit
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
The following regression equation has been plotted.
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.
Source: Author’s Calculation
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
The following regression equation has been plotted.
Y (Profit before income ratio of Manufacturing sector) = 9.772 + (0.252)
(earnings before income tax for Glenmark)
Fig. 32 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of
Glenmark.
Source: Author’s Calculation
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
The following regression equation has been plotted.
Y (Profit before income ratio of Manufacturing sector) = 7.332 + (0.711)
(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.
Source: Author’s Calculation
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
The following regression equation has been plotted.
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.
Source: Author’s Calculation
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
The following regression equation has been plotted.
Y (Profit before income ratio of Services sector) = 15.218 - (0.006) (earnings
before income tax for Infosys)
Fig. 35 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of
Infosys.
Source: Author’s Calculation
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
The following regression equation has been plotted.
Y (Profit before income ratio of Services sector) = 7.75 + (0.308) (earnings
before income tax for Reliance Capital)
Fig. 36 Regression plot for PBDIT of Manufacturing w.r.t. EBIT of
Reliance Capital.
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
The ACF plot indicates that ACF is positive in Lag 1, 2 and 3. After lag 3, the ACF
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
Source: Author’s Calculation
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
Source: Author’s Calculation
The ACF plot indicates that ACF is positive in Lag 1, 3 and 4. After lag 4, the ACF
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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.
Source: Author’s Calculation
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.
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation
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
Source: Author’s Calculation