Major factors consumer demands India China

Newgate India

Major factors that impact consumer demands
in India and China

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Table of Contents

Abstract .......................................................................................................................................... 6
1. Introduction ............................................................................................................................... 7
1.1 Overview ............................................................................................................................... 7
1.2 Purpose of conducting Research ........................................................................................... 8
1.3 Statement of problem ........................................................................................................... 8
1.4 Research topic ....................................................................................................................... 8
1.5 Research objective................................................................................................................. 9
1.6 Research Questions ............................................................................................................... 9
1.7 Proposed null hypothesis ....................................................................................................... 9
1.8 Need of study ...................................................................................................................... 10
1.9 Scope of study ..................................................................................................................... 10
1.10 Relevance to real world ..................................................................................................... 10
1.11 Limitations of the study..................................................................................................... 11
2. Review of Literature ............................................................................................................... 12
3. Research Methodology ........................................................................................................... 14
3.1 Research Framework .......................................................................................................... 14
Figure 3.1 .................................................................................................................................. 14
3.2 Type of Research ................................................................................................................. 15
3.2.1 Exploratory Research: .................................................................................................. 15
3.2.2 Causal Research :.......................................................................................................... 15
3.3 Sources of data ................................................................................................................... 15
3.3.1 Secondary data:............................................................................................................. 15
3.4 Sampling of data................................................................................................................. 16
3.4.1 Nature of Sampling ....................................................................................................... 16
3.4.2 Sampling Type .............................................................................................................. 16
3.4.3 Sample Size .................................................................................................................. 16
3.5 Target Sample ..................................................................................................................... 16
3.6 Primary scales used ............................................................................................................ 17
3.7 Analysis tool used ............................................................................................................... 17
3.7.1 Regression analysis....................................................................................................... 17

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3.7.2 Correlation Analysis .................................................................................................... 19
3.7.3. Descriptive statistics .................................................................................................... 20
3.7.4 Graphical Analysis ...................................................................................................... 20
3.8 Overview of work............................................................................................................... 20
4.Analysis ..................................................................................................................................... 21
4.1 Data set for China ........................................................................................................... 21
4.2 Regression Analysis for China ............................................................................................ 22
4.2.1 Dependent Variable: ..................................................................................................... 22
4.2.2 Independent variable..................................................................................................... 22
4.2.3 Regression Equation ......................................................................................................... 25
4.2.4 Interpretation .................................................................................................................... 25
4.3 Correlation Analysis for China ...................................................................................... 26
4.3.1 Correlation Variable: .................................................................................................... 26
Evaluation of Output ............................................................................................................. 27
Derived Result ....................................................................................................................... 27
4.3.2 Interpretation .................................................................................................................... 27
4.4 Descriptive Statistics for China ...................................................................................... 28
4.5 Graphical Analysis for China ......................................................................................... 29
4.6 Data set for India ............................................................................................................ 30
4.7 Regression Analysis for India ............................................................................................. 31
4.7.1 Dependent Variable: ..................................................................................................... 31
4.7.2 Independent variable..................................................................................................... 31
4.7.3 Regression Equation ......................................................................................................... 33
4.7.4 Interpretation .................................................................................................................... 33
4.8 Correlation Analysis for India ........................................................................................ 34
4.8.1 Correlation Variable: .................................................................................................... 34
Evaluation of Output ............................................................................................................. 35
Derived Result ....................................................................................................................... 36
4.8.2 Interpretation .................................................................................................................... 36
4.9 Descriptive Statistics for India ....................................................................................... 36
4.10 Graphical Analysis for India .......................................................................................... 37
4.11 Comparison of India and China ..................................................................................... 38

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6.Results & Discussion ................................................................................................................ 41
6.1 In case of China ................................................................................................................... 41
6.2 In case of India .................................................................................................................... 42
6.1 Hypothesis acceptance. ....................................................................................................... 43
7.Conclusion ................................................................................................................................ 44
8. References ................................................................................................................................ 45

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Abstract

The rapid economic growth in Indian and China is increasing and creating employment and better
business opportunities that in turn is increasing disposable incomes. Increase in disposable income
enhances buyer‟s efficiency and as a result consumer demand would rise. But many other macro-
economy variables like employment, inflation , CPI and interest rate set by bank would alter the
consequences of increase or decrease in consumer demand.

Industries in India and China is one of the competitive market catering to the global needs . This
research supports the facts that market patterns in China and India as emerging nations having
fluctuating due to factors like global slowdown, competitions and ability of consumers to accept
and afford a particular product/service. This overall regulates the demand pattern. Affordability
lies on several factors like inflation, annual disposable income and government expenditure to
push money supply in the cash chain. This entire research will give a clarity on how various
macro-economic variables, monetary policies that affects lively hood of consumers can result in
fluctuation of consumer demand in India and China. On the same time it will give an Idea on two
leading nations of Asia in terms of Economy and would help in summarizing the facts
accounting almost the entire Asia.

Results obtained from analysis supported the fact that two leading nations have lot of common in
their economy pattern and follow a similar trend. In both the cases consumer demand were impacted
by macro-economic variables however their relationship and degree of association differed from
each other. In case of expenditure pattern both were found same in India and China. In both the
cases The expenditure in education remained constant where expenditure in health care kept on
rising. All other trend too were similar like in both cases GNI, Annual savings, Consumer Demand
kept on rising where as CPI, Inflation and real interest rate kept on fluctuating based on the monetary
policies set by central bank. In India and China the GDP growth rate had been constantly rising from
1999-2008. In 2008 due to economic depression the GDP fell down put picked up again from 2009.

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1. Introduction
1.1 Overview

Over past few decades industries and venture capitalist looking to invest in Chinese and Indian
market have significantly risen. These vast market act as a driving force to stimulate the economic
growth in China and India and has also influenced several other macro-economic conditions like
inflation, employment and GDP of the country.

Both India and China has focused on privatization, liberalization and marketization for past 3
decades and as a result has gained a tremendous transformational change in its social, economic and
technical aspects of life. China and India being world‟s fastest growing nation and counted among
two big economies in Asia has undergone this major growth in the presence of increase in proportion
of private ownership which resulted in economy to grow rapidly.

With time big brands overseas realized the presence of big market which were untapped in India
and China with huge market potential. In the meantime government cleared of many FDI‟s . Many
new players came into market, employment rose up, disposable income increased and result
consumer demand kept on accelerating at a faster pace. In China consumer demand grew quite faster
than that of India.

Economist and political leaders in China‟s have always worry regarding consumer demand because
export driven growth is unsustainable in case of china. China‟s accompanies a vast rural area with
poverty and isolation from urban areas due to poor supply chain distribution.

A survey by Gallup has said that the consumer product which were previously considered as luxury
goods are being seen with increase in sales. Products like cameras, computers, cell phones have got
increased acceptance from this market. However India too has seen increased footfalls on service
and hospitality sector. China is more of a manufacturing driven economy where as India is R&D
dominated economy. Many global rating agencies are considering India as one of the key market
player in future and major leader in global technology innovation and IT infrastructure.

Growth in both India and China is primarily driven by consumer markets due to a favorable A
recent study by the McKinsey Global Institute (MGI) suggests that if both India and China keeps
growing at current and forms a bilateral trade in future effectively than the average household
income will triple in coming two decades with India being world‟s 5-th largest consumer economy
and China at number one. India is growing at a faster space with annual rate of 7.65 in past five
years and is forecasted to continue growing making it world‟s 3rd largest economy by 2020 where as
China‟s is already expected to be at top by that time.

The rapid economic growth is increasing and enhancing employment and business opportunities and
in turn increasing disposable incomes.

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1.2 Purpose of conducting Research

It is very vital for economist to understandthe various macro-economic pattern in any economy.
India and China being the two fastest growing nation with very largemarket, it is very important
to understand how consumer demand pattern would differ in this two nations. Observing the
factors that affects the consumer demand in these countries would give an overview that how on
different circumstances the consumer demand would vary. This research will give deeper insight
into various factors like GDP growth,GNI, expenditure on education and Health care, CPI, Gross
saving , Inflation and Real interest rate and their role in stimulating consumer demand in India
and China.The research will also clarify the degree to which each of this macro-economic
variable will be related to consumer demand with the help of regression equation. The obtained
results will be used as a standards for economist in this two countries to understand the demand
pattern and thus can control it by varying this macro-economic variables or monetary policies. At
times economist would be expecting either rise or fall in consumer demand to that of normal and
it can varied to certain extent by varying the indicators.

.

1.3Statement of problem

To understand how various set of macro-economic variables like GDP growth,GNI, expenditure on
education and Health care, CPI, Gross saving , Inflation and Real interest rate have an impact on overall
growth of economy and consumer demand. With time both private and public industries have
evolved at a faster pace in India and China but it is very important to understand how each of
these sector will influence the overall economic growth pushing a demand pattern. Private
industries in china have been playing major part with evolution in Manufacturing,Telecom,
Retail, Petroleum, Service and entertainment industries constituting a strong demand in china. In
India IT, Hospitality, Telecom and Manufacturing Industries and R&D are pushing a strong
demand. It is very critical for economist to underline whether increased growth in consumer
demand is significantly impacted by macro-economic indicators leading to efficient Industrial
operating performance in India and China and if it has impacted than to what degree it has an
affect on India‟s and china‟s overallconsumer demand.

1.4Research topic

To identify the major factors that impacts the consumer demands pattern in India and China

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1.5Research objective

Objective:1To compare the difference in statistics of consumption expenditures for people in
Indiaand China.

Objective:2To identify the factors that impact consumer demand in Indian and China.

Objective:3 To analyze and discover to what degree each of this factors influence the consumer
demand in India and China.

1.6Research Questions

1. What is the differences between the statistics of consumption expenditures in Indian and
China?

2. What leading factors may cause the situation associated with the difference in consumer

demand?

3. How do the factors have a profound impact on consumer demand and the National

Economy?

1.7 Proposed null hypothesis

Hypothesis Ha : Consumer Demand is correlated and dependent upon GDP growth in India and
China

Hypothesis Hb : Consumer Demand is correlated and dependent upon Expenditure in China
and India

Hypothesis Hc : Consumer Demand is correlated and dependent upon GNI per capital in China
and India

Hypothesis Hd : Consumer Demand is correlated and dependent upon Inflation in China and
India

Hypothesis He : Consumer Demand is correlated and dependent upon Gross Saving in China

Hypothesis Hf : Consumer Demand is correlated and dependent upon CPI in China and India

Hypothesis Hf : Consumer Demand is correlated and dependent upon Real Interest Rate in
China and India

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1.8 Need of study

 This study will help out in identifying different consumer trends in India and China , as
well as the demand pattern that affects the entire economy.
 Understanding the relationship between consumption,expenditure and annual consumer
demand.
 Gives an Idea that how variables like GDP,GNP , Inflation and real interest rate would
affect the consumer demand in this regions.
 Regression and correlation analysis will help in establishing a cause and effect relationship
to demonstrate the degree of association between independent variables ( indicators ) and
consumer demand.
 It will help economist to prioritize their indicators based on circumstances to achieve
optimum consumer demand in India and China

1.9 Scope of study

This research will give a broader and also in-depth scope for economist and central bank
managers to device an effective monetary policies to regulate various macro-economic indicators
such that the entire economy is benefited by an optimized consumer demand pattern. This
patterns will give an overall picture on its granular level to its ground reality that will help in
framing effective strategies to help achieve a booming economy.The cause affect analysis
influence decision makers to keep a track on economy and macro-economic indicators so that
they can set consumer demand on the right track in a balanced way keeping GDP and inflation
under control.

1.10 Relevance to real world

Industries in India and China is one of the competitive market catering to the global needs . This
research supports the facts that market patterns in China and India as emerging nations having
fluctuating due to factors like global slowdown, competitions and ability of consumers to accept
and afford a particular product/service. This overall regulates the demand pattern. Affordability
lies on several factors like inflation, annual disposable income and government expenditure to
push money supply in the cash chain. This entire research will give a clarity on how various
macro-economic variables, monetary policies that affects lively hood of consumers can result in
fluctuation of consumer demand in India and China. On the same time it will give an Idea on two
leading nations of Asia in terms of Economy and would help in summarizing the facts
accounting almost the entire Asia.

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1.11 Limitations of the study

 This research is not taking all macro-economic variables into considerations and takes only major
indicators into consideration for research.
 The entire research will be concluded based on observation made by these indicators over a period of
30 years without making any forecast for future projections.
 Marketing indicators like Competition, branding , marketing strategies, value for money and
customers perception upon is not considered in this case.Purely financial indicators have
been chosen.
 The research focuses only on top most factors and revolves around it.
 None of the micro-economic variables are taken into account with the fact that micro-
economic variable would differ from Industry to Industry and hence would make it more
complicated. Generalized data consisting only macro-economic variables has been
considered.
 No geographic or cluster wise observation has been made in controlled way to analyze if the
consumer pattern is influenced and vary over different geographical location in India or
China or if vary based on classes of cities,town etc.
 This research itself may not conclude to solutions that will help in taking effective
strategies, as the scope of this research generalizes on understanding determinants and
factors affecting consumer demand. Further extensive research has to be done cluster wise
based on geography, economy, market penetration, customer and macro-economic variable.


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2. Review of Literature

Shrabani Saha, Zhaoyong Zhang, (2012), mentions in his article “Do exchange rates affect consumer
prices? A comparative analysis for Australia,China and India” that an important factor for consumer
deamand in countries like Chian is highly influenced by exchange rate maechanism.In this case a
comparative study was made to explore the domestic prices in India,China and Australia and they found
that inflation and monetary plicies played a vital role in deciding the faith of consumer demand.

McConnell and Servaes in 1990 have published their study on Journal of Financial Economics. (1990)
1173 US firms in 1976 and 1093 US firms in 1986 listed on NYSE or AMEX has been chosen as
samples. Their study is similar with Holdemess and Sheehan‟s study in 1988, consumer demand is set as
Tobin‟s Q value and Inflation. By using OLS regression methods, their main results are “both measures
of inflation and Interest rate directly relates to consumer demand..”(McConnell and Servaes, 1990) they
also discover that there is a curve relationship between the consumer demand, shareholders and
performance of company (Tobin‟s Q value). The proportion of inside shareholders from 0-40%, this
curve is upward-sloping, but when the proportion reaches 40%-50%, this curve is downward-sloping.

Consumers‟ demand is influenced as per (Ho and Wu, 1999, and Kim and Lim, 2001) as the extent
to which consumers‟ perceptions of amount they want to spend confirm their against their disposable
income.. Most consumers form expectations of the product, vendor, service, and quality These
expectations influence their attitudes and intentions to shop at certain Internet store, and consequently
their decision making processes and purchasing behaviour. If expectations are met, customers achieve
a high degree of satisfaction, which influences their online shopping attitudes, intentions, decisions,
and purchasing activity positively. In contrast, dissatisfaction is negatively associated with these four
variables.

Schaupp and Be‟langer (2005) using a conjoint analysis of consumer demand based on data collected
from 188 young consumers found that the three most important attributes to consumers for online
satisfaction are privacy, merchandising and convenience. These are followed by trust, delivery,
usability, product quality, and security.

Himmelberg, Hubbard and Palia (1999) have found further evidence to show the demand pattern I any
country depends on the of ownership structure. They have used OLS and IV regressions; find
endogeneity of managerial ownership caused by unobserved heterogeneity as opposed to reverse
causality. After controlling for firm characteristics and firm fixed effects, they finally find no relation
between managerial ownership and performance. This study further proofs the endogeneity of ownership
structures.

Myeong-Hyeon Cho (1998) use the data of 500 manufacturing companies study the relationship between
consumer demand and the performance of company. The results from their simultaneous equation
regression show the investment will firstly influence the value of company, and then influence the

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demand for consumers Dahai Fu,Yanrui Wu,Yihong Tang,2009, "The effects of oil & gas ownership
structure and industry characteristics in china", The western Australian University.

The paper of Miyazaki and Fernandez (2001) explores risk perceptions among consumers of varying
level of internet experience and examine how these perceptions relate to their spending activity. The
study provides evidence of relationships among consumers‟ the use of alternate remote purchasing
methods, the perceived risks and purchasing activity.
In addition, GDP and GNI are vital prerequisite at the macro level. It is not merely a result, but also a
necessity for successful in a period of growing competition in financial markets. Thus, obtaining
consumer demand is the basic aim of the management of banks, which is the crucial requirement for
conducting any business (Bobáková, 2003: 21). At the macro level, a profitable banking sector is
contributing the financial system‟s stability and better able to overcome negative effect. Like demand,
suppy and consumer disposable income The importance of bank profitability at economy has made
researchers, academics, bank managements and bank regulatory authorities (Athanasoglou et al.,
2005: 5).

Hussain and Bhatti, (2010),Internal drivers of consumer demand can be defined as factors that are
influenced by a bank„s management decisions. Such management effects will definitely affect the
operating results of banks. Although a quality management leads to a good bank performance, it is
difficult, if not impossible, to assess management quality directly. In fact, it is implicitly assumed that
such a quality will be reflected in the operating performance. As such, it is not uncommon to examine
a bank„s performance in terms of those financial variables found in financial statements, such as the
balance sheet and income statement. External determinants of bank profitability are factors that are
beyond the control of a bank„s management. They represent events outside the influence of the bank.
However, the management can anticipate changes in the external environment and try to position the
institution to take advantage of anticipated developments. The two major components of the external
determinants are macroeconomic factors and financial structure factors.

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3. Research Methodology

3.1 Research Framework

Figure 3.1

To identify the major factors that have an impact on
Problem Definition consumer demand in India and China

1. To compare the difference in statistics of consumption
expenditures for people in India and China.

2. To identify what specific reasons may lead to different consumer
Research Objectives demand.

3. To analyze and discover how the factors influence consumer
demand in these regions.

Exploratory Research: Identifying the factors that impact
Research Design
consumer demand
Causative Research:Statistically stating relationship between
identified factors and consumer demand in India and china
identified factors influence
Source of Data Economy of other countries.
Secondary Data

Online Journals and review of literature
Data Collection 30 years data from The World Bank, National
Bureau of Statistics of China

Data Analysis
1. Linear Regression Analysis
(Primary) 2. Correlation Analysis
3. Descriptive statistics
4. Graphical Analysis

Results &
Discussion

Conclusion
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3.2 Type of Research

3.2.1 Exploratory Research:

In such kind of research the cause or the outcome is not known and is difficult to identify the
factors which may affect a particular variable. In such case a background research is done to
identify certain set of indicators that may actually effect desired variables. In this case
observations made from review of literature based on results derived by other authors in similar
context has been used to identify the key indicators that would impact the consumer demand
pattern in India and China. Once the identifiers were found further casual research was done to
find out the relationship of those indicators with consumer demand pattern

3.2.2 Causal Research :

In case of causal research a relationship is being established between dependent and independent
variable that helps to derive a cause affect relationship. It indicates how change in any of the
independent variable would significantly alter the dependent variable. In this type of research the
degree of dependency/association is derived to establish how effectively the relationship holds
true. In this research the motive is to find a relationship in between consumer demand and
several macro-economic variables identified as indicators.

It indicates how variable function F(Xt) affects variable Y(t)

*Dependent variable : Y(t) , Consumer demand

*Independent variable: F(Xt)

Y(t) = F(Xt) + C,Where C is the constant value

F(Xt) = Xt1 + Xt2 + Xt3 …… + Xtn

3.3 Sources of data

3.3.1 Secondary data:

 Secondary data were gathered from various reliable sources available on websites
through government portals like world bank, IMF, India and China statistics of Bureau
etc.
 Online journals were reviewed to frame the review of literature and find out what other
authors have to say for the same research problem in similar context. It also gave an in
depth- ideas on research, views, strategies , opinions and results derived by them.

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 IMF data helped in collecting several important macro-economic indicators in India and
China.
 Various monetary policies regulated by central bank in India and China helped out to
understand the economy pattern in both of this countries.

3.4 Sampling of data

3.4.1 Nature of Sampling

Probability Sampling:

In case of probability sampling every sample picked up from a given pool of population will
have likely equal chances to be selected. In other way in this sampling the population size is
always known before starting a research

3.4.2 Sampling Type

Fixed Sampling

In fixed sampling method samples are already organized and instead of getting chosen randomly
the samples are selected in an organized manner in a definite pattern/trend on given scale of
time, space or based on certain priority, symmetry, preferences or over interval of year or against
certain given interval of variables in increasing or decreasing order. The main fact which lies
over here is that every sample in fixed sampling has an equal likely probability of lying
anywhere on the pool of population.

3.4.3 Sample Size

Sample size considered is over period of 30 years from 1981-2010 to get accurate figures.

N = 30

3.5 Target Sample

Target sample were collected from various official sites in two leading nations of Asia that were
India and China.

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3.6 Primary scales used

1. Nominal Scale: This scale is meant to define name based objects. In statistics usually
strings like name, country, place etc. come under this category. This objects do not
indicate any value and also an not be compared. It just holds an identity. In this case Name
of country is a part of nominal scale.

2. Interval Scale : In this scale the object holds comparative values and are numerically
equally distant on a given space of scale. In this research GNP per capital,GDP growth rate
are measured on interval scale.

3. Ratio Scale In this the scale the objects holds a mathematical value which can be added,
subtracted, multiplied and divided on a given space of scale. In this research inflation,
annual savings, real interest rate, expenditure, CPI were measured on ratio scale.

3.7 Analysis tool used

3.7.1 Regression analysis
In case of regression analysis a regression equation is formulated and derived from the scatter
diagram plotted between dependent variable and independent variable where the equation
defined the most likely trend of the scatter diagram and help in establishing relationship
between dependent and independent variable. In this analysis it help to understand the scope of
relationship such that the coefficient along with intercepts would express the equation and the
value of “R” would clarify the degree of association, reliability and to what extent the
relationship holds true.

Equation is generally in the format

Y = C + A1X1 + A2X2 + A3X3……… + ANXN ; C is a constant

Chart 3.1 Regression plot

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Usual names for X and Y variables.
Table 3.1

Context X Y

General Predictors Responses

Multiple Linear Regression Independent Dependent
(MLR) Variables Variables

Factors, Design
Designed Data Responses
Variables

Spectroscopy Spectra Constituents

*Dependent variable : Y(t) , Consumer demand

*Independent variable: F(Xt) , macro-economic variables

Y(t) = F(Xt) + C, Where C is the constant value

F(Xt) = Xt1 + Xt2 + Xt3 …… + Xtn

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3.7.2 Correlation Analysis

Correlation analysis helps in identifying the degree of association between any two random
variables in given range of time or space. Correlation coefficient ( r) helps to express this degree
of association where r will always lie between +1 to -1.R value close to -1 or +1 indicates that
the two variables are highly associated, r=1 or -1 mean both the random variable are fully
correlated where r =0 means the random variables are not at all associated.

If value of r is positive than that means the slope of the equation of both the variables on time or
space is same, which means when one variable increases it will influence the rise of other
variable. In short both of them are directly proportional. If r value is positive than it shows that
both the variables are directly proportional.

If value of r is negative than that means that the slope of the equation of both the variables on
time or space is opposite to each other which means when one variable increases it will
influence fall of other variable. In short both of them are inversely proportional. In such cases it
is also called as to be inversely correlated.

Chart 3.2

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3.7.3. Descriptive statistics

Descriptive statistics help to define that how a particular pool of distribution of data would bear
certain characteristics. Central tendencies like mean, median, mode and dispersions like
standard deviations, variance, range would be given vital important in this research analysis.

There are three main central tendencies that are mean, median and mode. Mean is the
statistical average of a sample of distribution. Median is the point on scale where 50% of
observation lies above it and 50% of observation below it and mode is the data that have
maximum frequency or maximum occurrence on the distribution. It is possible that a set of
distribution may have more than one mode.

3.7.4 Graphical Analysis

It explains and figure outs the trend of any set of data over a given time or space to get clarity
on the nature of data through various graphical analysis and tools like pie chart, bar chart, scatter
diagram etc.

3.8 Overview of work

1. Secondary data were gathered from various reliable sources available on websites through
government portals like world bank, IMF, India and China statistics of Bureau etc. background
analysis were done from online journals. Articles from several authors were reviewed to frame the
review of literature and find out what other authors have to say for the same research problem in
similar context. It also gave an in depth- ideas on research, views, strategies , opinions and results
derived by them. IMF data helped in collecting several important macro-economic indicators in
India and China. Various monetary policies regulated by central bank in India and China helped out
to understand the economy pattern in both of this countries.
2. Research framework was developed that clearly outlined the problem statement, questions
frequently raised by economists, purpose of research, the scope, need and with clarified
objective. Null hypothesis were also formed to lay foundation to research approach.
3. Analysis tools were used to carry statistical modeling. Collected data were inserted as
input into spss software to analyze and undergo regression, correlation analysis and
descriptive statistics. Graphical analysis were also done.
4. All output obtained were inferred and results were discussed briefly to get an idea on the
research objective composed. Final Results, key findings , hypothesis acceptance and
conclusion were noted down with its implications to real world. Limitations were
mentioned and also the future scope for research were underlined.
5. All missing value in data while analysis will replaced by mean value using spss software.

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4.Analysis

4.1 Data set for China

Table 4.1

Average GNI per Adjusted Adjusted
Annual capital, savings: savings: Inflation,
Consumer GDP Atlas education Health CPI gross Consum Real
Demand growt Method expenditure expenditure growth savings er Price Interest
Year ($) h (%) ($) (% of GNI) per capita ($) (%) (% of GNI) % Rate
2.68588
1981 2534 5.2 220 2.1 2.4 1
7.46854
1982 2745 9.1 220 2.2 1.9 36.2 6
6.13838
1983 3216 10.9 220 2.1 1.5 35.9 4
2.15730
1984 3865 15.2 250 2.1 2.8 35.5 3
-
1985 4738 13.5 280 2.0 9.3 34.5 2.07249
3.02780
1986 5189 8.8 310 2.1 6.5 36.0 6
7.21998 2.62650
1987 6124 11.6 320 1.9 7.3 37.1 58 2
18.7364 -
1988 7986 11.3 330 1.9 18.8 36.7 27 2.74994
18.3330 2.60734
1989 8794 4.1 320 1.9 18.0 36.0 44 8
3.05831 3.32725
1990 9543 3.8 330 1.8 3.1 39.4 07 3
3.54357 1.67583
1991 10367 9.2 350 1.8 3.4 39.5 53 6
6.34034 0.37189
1992 12567 14.2 390 1.7 6.4 39.0 49 6
14.5832 -
1993 16457 14.0 410 1.7 14.7 41.9 66 3.59723
24.2370 -
1994 22398 13.1 460 2.1 24.1 43.5 88 7.98242
16.8970 -
1995 28765 10.9 530 2.0 21.3 17.1 42.8 64 1.47381
8.32401 3.42426
1996 34984 10.0 650 2.0 26.7 8.3 41.9 51 5
2.80684 7.02088
1997 27126 9.3 750 2.0 31.2 2.8 42.3 32 1
-
0.84462 7.31130
1998 39780 7.8 790 2.0 35.7 -0.8 40.9 6 3

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-
1.40789 7.19506
1999 42003 7.6 840 1.8 38.9 -1.4 38.8 2 7
0.25530 3.71124
2000 43632 8.4 930 1.8 43.7 0.4 37.3 48 1
0.72290 3.72073
2001 45994 8.3 1000 1.8 47.5 0.7 38.1 25 5
-
0.76594 4.69835
2002 48345 9.1 1100 1.8 54.4 -0.8 40.7 9 5
1.15590 2.62977
2003 49823 10.0 1270 1.8 61.4 1.2 44.2 97 6
3.88418 -
2004 63971 10.1 1500 1.8 70.3 3.9 46.9 26 1.24664
1.82164 1.58785
2005 70065 11.3 1740 1.8 80.6 1.8 48.4 78 1
1.46318 2.24930
2006 81995 12.7 2040 1.8 93.4 1.5 51.7 9 1
4.75029 -
2007 90003 14.2 2480 1.8 114.5 4.8 51.8 66 0.12265
5.86438 -
2008 110012 9.6 3040 1.8 156.6 5.9 53.0 37 2.30789
-
0.70294 5.93857
2009 121632 9.1 3620 1.8 191.3 -0.7 53.4 9 8
3.31454 -
2010 137321 10.4 4240 1.8 220.9 3.3 52.7 59 0.81934

Source: The World Bank, National Bureau of Statistics of China,TradeEconomics

4.2 Regression Analysis for China

4.2.1 Dependent Variable:
Cc : Annual Consumer Demand in China

4.2.2 Independent variable

Gc : GDP growth in China

GNc: GNI per capital in china

Ec : education Expenditure in china

Hc : Health Expenditure in china

CPc : CPI Index in china

22 | P a g e

Sc : Gross Saving in china

Ic : Inflation in china

Rc : Real Interest in china

Table 4.2

Model Summary(b)

Change Statistics
Adjusted R Std. Error of R Square
Model R R Square Square the Estimate Change F Change df1 df2 Sig. F Change
1 .997(a) .994 .986 4025.76863 .994 134.422 8 7 .000
a Predictors: (Constant), Rc, Ec, Hc, Gc, CPC, Sc, Gnc, Ic
b Dependent Variable: Cc

In this table we concentrate on R square value. We expect R square value to ne close to 0 and
less than 0.5 ( 0 < R square < 0.5 ). If the condition is satisfied than the entire regression
analysis established hold true and cause affect relationship among dependent and independent
variables can be derived and stated. In this case the relationship holds true up to 99.4% of the
cases.
Table 4.3

ANOVA(b)

Sum of
Model Squares df Mean Square F Sig.
1 Regression 174283782 2178547282.5
8 134.422 .000(a)
60.019 03
Residual 113447691
7 16206813.060
.419
Total 175418259
15
51.438
a Predictors: (Constant), Rc, Ec, Hc, Gc, CPC, Sc, Gnc, Ic
b Dependent Variable: Cc

In this table we concentrate on the significance level. The significance level p should always be
less than 0.05 ( p < 0.05 ). If the condition is satisfied then the established regression equation is
significant enough to support the relationship between dependent and independent variable. In
this case the regression equation is fully significant as t = 0.000 < 0.05.

23 | P a g e

Chart 4.1

Table 4.4

Coefficients(a)

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta T Sig.
1 (Constant) 63418.990 51796.577 1.224 .030
Gc -2387.999 2096.492 .122 -1.139 .022
Gnc 62.387 40.884 2.066 1.526 .001
Ec 29796.861 27039.841 .078 -1.102 .037
Hc -666.946 738.302 1.184 -.903 .039
CPc 3493.113 20303.581 .472 .172 .0868
Sc 680.146 580.817 .115 1.171 .0280
Ic -3382.086 20527.320 -.454 -.165 .0074
Rc -303.024 863.563 -.029 -.351 .0736
a Dependent Variable: Cc

24 | P a g e

Evaluation of entire output

 In this analysis R square value clearly states that the relationship holds true for 99.4% of cases.
 In case of T-test The significance level t should always be greater than 0.5 ( | t | > 0.5 ). If the
condition is satisfied then the established independent variable is significant enough to support the
relationship with independent variable. In this case except the variable CPc ( capital index ) all
other variables pass the criteria. Hence price index is rejected is not considered for regression
analysis at it does not hold true for the relationship.
 In case of F-test Significance Level the p-values should always be less than 0.05 ( p < 0.05) . In
the above Cpc, and Rc are rejected as they are not significant. However variable Gc,Gnc,Hc,Ec,Sc
pass on the criteria can readily establish a relationship among each other.
 B value & C Value: Independent variables Gnc, Ec,Sc are positively related with consumer demand
where as Gc,Hc, Ic are negatively related.

4.2.3 Regression Equation

Cc = F ( X) + C

Where C = 63418.990

F ( X ) == -2387.99 Gc + 62.37 Gnc + 29796.861 Ec – 666.946 Hc + 680.146 Sc – 3382.086 Ic

4.2.4 Interpretation

 Independent macro- economic factor like GNP per capital, expenditure in Education and
annual saving were found to be directly proportional to average annual consumer demand
in china. More is the GNP, Educational , expenditure and savings higher will be thedemand
level
Cc α Gnc,Ec,Sc

 Independent macro- economic factor like Factors like GDP, , expenditure in Health care and
Inflation were found to be inversely proportional to average annual consumer demand in
china. More is the GDP, health care , expenditure and inflation lesser will be the demand
level
Cc α 1 / ( Gnc,Ec,Sc)

 CPI and Real Interest have no impact on annual consumer demand in china.
 Inflation, , expenditure in education and GDP growth rate has the highest impact on the
consumer demand level and thus it has to be top preference when demand would
fluctuate. Moreover focusing on GNP per capital will have least impact on consumer
demand level.

25 | P a g e

4.3 Correlation Analysis for China

4.3.1 Correlation Variable:

Cc : Annual Consumer Demand in China

Gc : GDP growth in China

GNc: GNI per capital in china

Ec : Education Expenditure in china

Hc : Health Expenditure in china

CPc : CPI Index in china

Sc : Gross Saving in china

Ic : Inflation in china

Rc : Real Interest in china

Table 4.5

Correlations

Cc Gc Gnc Ec Hc CPC Sc Ic Rc
Cc Pearson
1 .072 .982(**) -.500(**) .989(**) -.316 .913(**) -.433(*) -.066
Correlation
Sig. (2-tailed) .704 .000 .005 .000 .089 .000 .035 .729
N 30 30 30 30 16 30 29 24 30
Gc Pearson
.072 1 .080 -.044 .264 .229 .153 .244 -.488(**)
Correlation
Sig. (2-tailed) .704 .675 .818 .322 .223 .428 .251 .006
N 30 30 30 30 16 30 29 24 30
Gnc Pearson
.982(**) .080 1 -.455(*) .998(**) -.300 .891(**) -.384 -.077
Correlation
Sig. (2-tailed) .000 .675 .011 .000 .108 .000 .064 .685
N 30 30 30 30 16 30 29 24 30
Ec Pearson
-.500(**) -.044 -.455(*) 1 -.509(*) .180 -.445(*) .464(*) .155
Correlation
Sig. (2-tailed) .005 .818 .011 .044 .342 .016 .023 .414
N 30 30 30 30 16 30 29 24 30
Hc Pearson
.989(**) .264 .998(**) -.509(*) 1 -.135 .841(**) -.132 -.373
Correlation
Sig. (2-tailed) .000 .322 .000 .044 .619 .000 .626 .155
N 16 16 16 16 16 16 16 16 16
CPC Pearson
-.316 .229 -.300 .180 -.135 1 -.160 1.000(**) -.739(**)
Correlation
Sig. (2-tailed) .089 .223 .108 .342 .619 .407 .000 .000
N 30 30 30 30 16 30 29 24 30

26 | P a g e

Sc Pearson
.913(**) .153 .891(**) -.445(*) .841(**) -.160 1 -.235 -.222
Correlation
Sig. (2-tailed) .000 .428 .000 .016 .000 .407 .269 .248
N 29 29 29 29 16 29 29 24 29
Ic Pearson
-.433(*) .244 -.384 .464(*) -.132 1.000(**) -.235 1 -.741(**)
Correlation
Sig. (2-tailed) .035 .251 .064 .023 .626 .000 .269 .000
N 24 24 24 24 16 24 24 24 24
Rc Pearson
-.066 -.488(**) -.077 .155 -.373 -.739(**) -.222 -.741(**) 1
Correlation
Sig. (2-tailed) .729 .006 .685 .414 .155 .000 .248 .000
N 30 30 30 30 16 30 29 24 30

Evaluation of Output
In above analysis we look for significance level first. The significance level p should always be less
than 0.05 ( p < 0.05 ). If the condition is satisfied then the established correlation between two
variable is significant enough to support the association among each other.
Next we look for coefficient value R which should be greater than ( R > 0.75 ) to support a strong
correlation. Pearson method of correlation was applied.

Consumer Demand :Cc

 Strongly & Positively correlated to Invsetment per head ( Ih )
 Strongly & Positively correlated to education spending per head( Eh )
 Strongly & Positively correlated to Internet uers( I )
 Positively correlated to L, urban population ( Up ), working population ( Wp ), Life
expectancy ( Lf ),SSE, SLE

Derived Result

The consumer demand in china is very strongly associated with , expenditure in healthcare
showing that utmost preference should be given to this factors with a success rate of 98.9%,
followed by GNP per capital, which also accounts for 98.35% suggesting and important
parameter to be taken care to have a control over annual consumer demand.
Also annual saving is strongly correlated with a correlation coefficient of 91.3% stating that
it is strongly associated and can alter any changes in consumer demand.


27 | P a g e

GNP per capital should be given the highest preference and should be the prime focused
indicator while keep track of consumer demand. , expenditure in health care and also annual
saving should be given priority as both of this variables are responsible for fluctuating consumer
demand to a large extent.

Other factors like , expenditure in education sector,CPI and inflation should be taken into
consideration but can be given low priority as they weekly influence consumer demand in china.

Certain variables like GDP growth rate and Real interest rate can be avoided and is not found to
influence consumer demand.

4.4 Descriptive Statistics for China

Table 4.6
Descriptive Statistics

Cc Gc Gnc Ec Hc CPc Sc Ic Rc
N Valid 30 30 30 30 16 30 29 24 30
Missing 0 0 0 0 14 0 1 6 0
Mean 38599.1 1031.00
10.0933 1.9000 80.5250 5.6067 41.9345 5.9830 1.9734
333 00
Median 27945.5 590.000
10.0000 1.8000 57.9000 3.2000 40.7000 3.4291 2.6169
000 0
Mode 2534.00(
9.10 220.00 1.80 21.30(a) -.80(a) 36.00(a) -1.41(a) -7.98(a)
a)
Std. Deviation 37971.6 1062.96
2.83110 .13896 60.69755 6.58975 5.95868 7.22095 3.66405
9462 381
Sum 1157974 30930.0
302.80 57.00 1288.40 168.20 1216.10 143.59 59.20
.00 0
a Multiple modes exist. The smallest value is shown

Interpretation

The average value ( mean value ) for consumer demand over a period of 1981-2010 has been
38399.1333 . The chances of dispersion that the value would vary or spread out from its mean is
37926 also called the standard deviation. In distribution more than 50% of observation lies above
27945.5and 50% of observation lies below 27945.5. The value with highest frequency ( mode )
is 2534.00.

The average value ( mean value ) for GDP growth over a period of 1981-2010 has been
10.0933The chances of dispersion that the value would vary or spread out from its mean is 2.83
also called the standard deviation. In distribution more than 50% of observation lies above 10
and 50% of observation lies below 10. The value with highest frequency ( mode ) is 9.2

The average value ( mean value ) for GNP per capital over a period of 1981-2010 has been 1031.
The chances of dispersion that the value would vary or spread out from its mean is
1062.96381also called the standard deviation. In distribution more than 50% of observation lies

28 | P a g e

above 590and 50% of observation lies below 590. The value with highest frequency ( mode ) is
220.00.
The average value ( mean value ) for Annual Saving over a period of 1981-2010 has been
41.9345. The chances of dispersion that the value would vary or spread out from its mean is
5.95868 also called the standard deviation. In distribution more than 50% of observation lies
above 40.7000 and 50% of observation lies below 40.7000. The value with highest frequency (
mode ) is 5.95868.
The average value ( mean value ) for Inflation over a period of 1981-2010 has been 5.9830. The
chances of dispersion that the value would vary or spread out from its mean is 7.22095 also called
the standard deviation. In distribution more than 50% of observation lies above 3.4291 and 50%
of observation lies below 3.4291. The value with highest frequency ( mode ) is -7.98.

4.5 Graphical Analysis for China

Chart 4.2

250.0
GDP growth (%)

200.0
Adjusted savings: education expenditure (%
150.0 of GNI)
Health expenditure per capita ($)
100.0
CPI growth (%)
50.0

Adjusted savings: gross savings (% of GNI)
0.0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 3009 2010
-50.0 Inflation, Consumer Price %
Year

Table 4.7

GNI per Adjusted Adjusted
capital, savings: Health savings:
Average Atlas education expenditure CPI gross Inflation,
Consumer GDP growth Method expenditure per capita growt savings Consumer
Year Demand (%) ($) (% of GNI) ($) h (%) (% of GNI) Price %
0.255304
2000 45632 8.4 930 1.8 43.7 0.4 37.3 8
3.314545
2010 137321 10.4 4240 1.8 220.9 3.3 52.7 9
Growth 355.9139 1198.270
% 200.93136 23.80952381 785 0 405.18176 725 41.235638 2

Interpretation

29 | P a g e

The trend analysis from 1999-2010 shows that GNP has grown significantlyand have almost
been 4.5 times of what it was in 1999. GDP growth rate had been constantly rising from 1999-
2008. In 2008 due to economic depression the GDP fell down put picked up again from 2009.
The expenditure in education remained constant where expenditure in health care kept on rising
and grew by 5 times .Annual saving have grown to 41% since 1999.However CPI, Inflation and
real interest rate kept on fluctuating based on the monetary policies set by central bank. GDP
growth rate also had grown by 23% in this period.

4.6 Data set for India

Table 4.8

Adjusted
GNI per Adjusted savings:
capital, savings: Health gross
Average GDP Atlas education expenditure CPI savings Inflation, Real
Consumer growth Method expenditure per capita growth (% of Consumer Interest
Year Demand (%) ($) (% of GNI) ($) (%) GNI) Price % Rate
1981 6.0 300 3.1 13.1 21.1 13.1151 5.118237
1982 3.5 290 3.1 7.9 20.5 7.887271 7.774707
1983 7.3 290 3.2 11.9 18.5 11.86886 7.320987
1984 3.8 290 3.4 8.3 20.8 8.32158 7.9471
1985 5.3 300 3.5 5.6 21.9 5.555556 8.681674
1986 4.8 320 3.4 8.7 21.8 8.730811 9.093224
1987 4.0 360 3.2 8.8 21.1 8.798689 6.56018
1988 1567 9.7 400 3.7 9.4 22.3 9.384776 7.638633
1989 1765 6.0 400 4.0 3.3 22.7 3.26256 7.435843
1990 1872 5.5 390 3.9 9.0 22.6 8.971234 5.269527
1991 1943 1.1 350 3.7 13.9 22.2 13.87025 3.624717
1992 1988 5.5 350 3.6 11.8 23.5 11.78782 9.132749
1993 2056 4.8 330 3.5 6.4 22.1 6.362039 5.814777
1994 2212 6.7 350 3.5 10.2 24.8 10.2115 4.33711
1995 2399 7.6 370 3.3 16.5 10.2 26.9 10.22489 5.864178
1996 2532 7.5 410 3.2 16.5 9.0 23.1 8.977149 7.792994
1997 2639 4.0 420 3.5 19.2 7.2 25.4 7.164254 6.909579
1998 2834 6.2 420 3.8 19.3 13.2 22.8 13.23084 5.121276
1999 2956 8.5 440 4.4 19.3 4.7 26.2 4.669821 9.398475
2000 3067 4.0 450 3.8 20.7 4.0 25.4 4.009434 8.332154
2001 3287 5.0 460 3.8 22.1 3.7 25.7 3.684807 8.625162
2002 3435 4.0 470 3.8 22.4 4.4 26.9 4.3922 7.911236
2003 3956 8.0 530 3.8 24.7 3.8 28.6 3.805866 7.287253
2004 4023 7.8 620 3.8 26.5 3.8 33.3 3.767238 4.705205

30 | P a g e

2005 5356 9.3 730 3.1 30.0 4.2 34.3 4.246353 6.248326
2006 5634 9.3 810 3.1 33.1 6.1 35.0 6.145522 4.477361
2007 6052 9.8 950 3.1 40.4 6.4 36.9 6.369997 6.869161
2008 6324 3.9 1030 3.1 43.1 8.4 33.0 8.351816 4.277249
2009 7126 8.2 1150 3.1 44.3 10.9 34.5 10.87739 5.872688
2010 7589 9.6 1260 3.1 54.2 12.0 34.0 11.9923 -0.13571

Source: The World Bank, National Bureau of Statistics of China,TradeEconomics

4.7 Regression Analysis for India

4.7.1 Dependent Variable:

Ci : Annual Consumer Demand in India

4.7.2 Independent variable

Gi : GDP growth in India

GNi: GNI per capital in India

Ei : ducation Expenditure in India

Hi : Health Expenditure in India

CPi : CPI Index in India

Si : Gross Saving in India

Ii : Inflation in India

Ri : Real Interest in India

Table 4.9

Model Summary

Adjusted R Std. Error of
Model R R Square Square the Estimate
1 .994(a) .987 .976 272.04337
a Predictors: (Constant), Ri, Gi, Ei, CPi, Hi, Si, Gni

In this table we concentrate on R square value. We expect R square value to be close to 0 and
less than 0.5 ( 0 < R square < 0.5 ). If the condition is satisfied than the entire regression

31 | P a g e

analysis established hold true and cause affect relationship among dependent and independent
variables can be derived and stated. In this case the relationship holds true upto 98.7% of the
cases which is far better than previous case.

Table 4.10
ANOVA(b)

Sum of
Model Squares df Mean Square F Sig.
1 Regression 45205839.
7 6457977.026 87.261 .000(a)
180
Residual 592060.75
8 74007.595
7
Total 45797899.
15
938
a Predictors: (Constant), Ri, Gi, Ei, CPi, Hi, Si, Gni
b Dependent Variable: Ci

In this table we concentrate on the significance level. The significance level p should always be
less than 0.05 ( p < 0.05 ). If the condition is satisfied then the established regression equation is
significant enough to support the relationship between dependent and independent variable. In
this case the regression equation is fully significant as t = 0.000 < 0.05.

Chart 4.3

Table 4.11
Coefficients(a)

32 | P a g e

Unstandardized Standardized
Coefficients Coefficients

Model B Std. Error Beta T Sig.
1 (Constant) 611.805 2131.490 .687 .058
Gi 28.501 44.067 .036 .647 .043
Gni 5.004 2.788 .842 1.795 .010
Ei -125.302 279.514 -.029 -.548 .066
Hi 15.887 65.818 .105 .241 .0815
CPi -47.501 47.937 -.088 -.991 .0351
Si 17.690 50.047 .048 .753 .0333
Ri 3.390 61.895 .004 .055 .0958
a Dependent Variable: Ci

Evaluation of entire output

 In this analysis R square value clearly states that the relationship holds true for 98.7% of cases.
 In case of T-test The significance level t should always be greater than 0.5 ( | t | > 0.5 ). If the
condition is satisfied then the established independent variable is significant enough to support the
relationship with independent variable. In this case except the variable Hi and Ri all other
variables pass the criteria. Hence expenditure in health care and Real Interest rate is rejected and is
not considered for regression analysis at it does not hold true for the relationship.
 In case of F-test Significance Level the p-values should always be less than 0.05 ( p < 0.05) . In
the above Hi,Ei and Ri are rejected as they are not significant. However variable Gi,Gni, Cpi,Si
pass on the criteria can readily establish a relationship among each other.
 B value & C Value: Independent variables Gi,Gni, Si are positively related with consumer demand
whereas Cpiis negatively related.

4.7.3 Regression Equation

Cc = F ( X) + C

Where C = 611.805

F(X) = 28.5 Gi + 5 Gni - 47.501 CPi + 17.690 Si


33 | P a g e

 Independent macro- economic factor like GDP growth rate, GNP per capital, and annual
saving were found to be directly proportional to average annual consumer demand in
India. More is the GDP growth, GNP, and savings higher will be the demand level
Cc α Gi,Gni,Si

 Independent macro- economic factor like CPI was found to be inversely proportional to
average annual consumer demand in India. More is the CPI lesser will be the demand level
Cc α 1 / CPi

 Real Interest rate, expenditure ine education and helath care have no impact on annual
consumer demand in India.
 CPI, GDP growth and annual saving has the highest impact on the consumer demand
level and thus it has to be top preference when demand would fluctuate. Moreover
focusing on GNP per capital will have least impact on consumer demand level.

4.8 Correlation Analysis for India

4.8.1 Correlation Variable:

Ci : Annual Consumer Demand in India

Gi : GDP growth in India

GNi: GNI per capital in India

Ei : Education Expenditure in India

Hi : Health Expenditure in India

CPi : CPI Index in India

Si : Gross Saving in India

Ii : Inflation in India

Ri : Real Interest in India

Table 4.12

34 | P a g e

Correlations

Ci Gi Gni Ei Hi CPi Si Ii Ri
Ci Pearson
1 .443(*) .966(**) -.413(*) .981(**) .011 .921(**) -.026 -.392
Correlation
Sig. (2-tailed) .030 .000 .045 .000 .958 .000 .906 .058
N 24 24 24 24 16 24 23 23 24
Gi Pearson
.443(*) 1 .494(**) -.069 .383 -.069 .561(**) -.097 -.136
Correlation
Sig. (2-tailed) .030 .005 .712 .143 .713 .001 .610 .465
N 24 31 31 31 16 31 30 30 31
Gni Pearson
.966(**) .494(**) 1 -.191 .992(**) .034 .886(**) -.027 -.443(*)
Correlation
Sig. (2-tailed) .000 .005 .302 .000 .857 .000 .889 .013
N 24 31 31 31 16 31 30 30 31
Ei Pearson -
Correlation -.413(*) -.069 -.191 1 -.611(*) -.103 -.261 .369( .454(*)
*)
Sig. (2-tailed) .045 .712 .302 .012 .582 .163 .045 .010
N 24 31 31 31 16 31 30 30 31
Hi Pearson
.981(**) .383 .992(**) -.611(*) 1 .312 .819(**) .310 -.710(**)
Correlation
Sig. (2-tailed) .000 .143 .000 .012 .240 .000 .243 .002
N 16 16 16 16 16 16 16 16 16
CPi Pearson 1.00
.011 -.069 .034 -.103 .312 1 -.282 -.284
Correlation 0(**)
Sig. (2-tailed) .958 .713 .857 .582 .240 .131 .000 .122
N 24 31 31 31 16 31 30 30 31
Si Pearson
.921(**) .561(**) .886(**) -.261 .819(**) -.282 1 -.281 -.412(*)
Correlation
Sig. (2-tailed) .000 .001 .000 .163 .000 .131 .132 .024
N 23 30 30 30 16 30 30 30 30
Ii Pearson
-.026 -.097 -.027 -.369(*) .310 1.000(**) -.281 1 -.423(*)
Correlation
Sig. (2-tailed) .906 .610 .889 .045 .243 .000 .132 .020
N 23 30 30 30 16 30 30 30 30
Ri Pearson -
Correlation -.392 -.136 -.443(*) .454(*) -.710(**) -.284 -.412(*) .423( 1
*)
Sig. (2-tailed) .058 .465 .013 .010 .002 .122 .024 .020
N 24 31 31 31 16 31 30 30 31

Evaluation of Output
In above analysis we look for significance level first. The significance level p should always be less
than 0.05 ( p < 0.05 ). If the condition is satisfied then the established correlation between two
variable is significant enough to support the association among each other.
Next we look for coefficient value R which should be greater than ( R > 0.75 ) to support a strong
correlation. Pearson method of correlation was applied.

Consumer Demand :Cc

35 | P a g e

 Strongly & Positively correlated to GNi per capital( Gni )
 Strongly & Positively correlated to , expenditure in Health care( Hi )
 Strongly & Positively correlated to Annual Saving( Si )
 Weekly & Negatively correlated to , GDP growth rate ( Gi )
 Weekly & Negatively correlated to expenditure on education( Ei )
 Weekly & Negatively correlated to Real Interest rate( Ri )
 Not correlated to GDP growth (Ii)

Derived Result

The consumer demand in china is very strongly associated with , expenditure in healthcare
showing that utmost preference should be given to this factors with a success rate of 98.1%,
followed by GNP per capital, which also accounts for 96.6% suggesting and important
parameter to be taken care to have a control over annual consumer demand.
Also annual saving is strongly correlated with a correlation coefficient of 92.1% stating that
it is strongly associated and can alter any changes in consumer demand.


GNP per capital should be given the highest preference and should be the prime focused
indicator while keep track of consumer demand. , expenditure in health care and also annual
saving should be given priority as both of this variables are responsible for fluctuating consumer
demand to a large extent.

Other factors like GDP growth rate , Real interest rate and expenditure in education sector,
should be taken into consideration but can be given low priority as they weekly influence
consumer demand in India.

Certain variables like Inflation and CPI can be avoided and is not found to influence consumer
demand.

4.9 Descriptive Statistics for India
Table 4.13
Descriptive Statistics

Ci Gi Gni Ei Hi CPi Si Ii Ri
Mean 498.709 28.268
3547.7500 6.1903 3.4419 7.8290 25.9300 8.0013 6.3868
7 8
Median 400.000 23.550
2895.0000 6.0000 3.5000 8.3000 24.1500 8.3367 6.8692
0 0
Mode 290.00(a 16.50(a
1567.00(a) 4.00 3.10 3.80(a) 21.10(a) 3.26(a) -.14(a)
) )
Std. Deviation 1810.9901 268.660 11.514
2.22206 .42093 3.33008 5.26689 3.23095 2.12824
3 16 26
Sum 15460.0
85146.00 191.90 106.70 452.30 242.70 777.90 240.04 197.99
0

36 | P a g e

Interpretation

The average value ( mean value ) for consumer demand over a period of 1981-2010 has been
above 2895.0000 and 50% of observation lies below 2895.0000. The value with highest
frequency ( mode ) is 1567.

The average value ( mean value ) for GDP growth over a period of 1981-2010 has been 6.1903.
The chances of dispersion that the value would vary or spread out from its mean is 2.22 also
called the standard deviation. In distribution more than 50% of observation lies above 6 and
50% of observation lies below 6. The value with highest frequency ( mode ) is 4

The average value ( mean value ) for GNP per capital over a period of 1981-2010 has been 1031.
The chances of dispersion that the value would vary or spread out from its mean is 1810.99013
also called the standard deviation. In distribution more than 50% of observation lies above
2895.0000 and 50% of observation lies below 2895.0000.. The value with highest frequency (
mode ) is 1567.00

The average value ( mean value ) for Annual Saving over a period of 1981-2010 has been
above 24.1500and 50% of observation lies below 24.1500. The value with highest frequency (
mode ) is 5.26689.
The average value ( mean value ) for Inflation over a period of 1981-2010 has been 8.0013. The
chances of dispersion that the value would vary or spread out from its mean is 8.3367 also called
the standard deviation. In distribution more than 50% of observation lies above 8.3367and 50%
of observation lies below 8.3367. The value with highest frequency ( mode ) is 3.26.

4.10 Graphical Analysis for India

Table 4.7

Adjusted
GNI per Adjusted savings:
capital, savings: Health gross
Average GDP Atlas education expenditure CPI savings Inflation,
Consumer growth Method expenditure per capita growth (% of Consumer
Year Demand (%) ($) (% of GNI) ($) (%) GNI) Price %
2000 3067 4.0 450 3.8 20.7 4.0 25.4 4.009434
2010 7589 9.6 1260 3.1 54.2 12.0 34.0 11.9923
Growth % 147 140 180 -18 162 200 34 199
Chart 4.4

37 | P a g e

60.0

50.0 GDP growth (%)

Adjusted savings: education expenditure
40.0
(% of GNI)
Health expenditure per capita ($)
30.0
CPI growth (%)
20.0
Adjusted savings: gross savings (% of
GNI)
10.0
Inflation, Consumer Price %

0.0 Real Interest Rate
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-10.0
Year

Interpretation

The trend analysis from 1999-2010 shows that GNI has grown significantly and have almost
been 2.8 times of what it was in 1999. GDP growth rate had been constantly rising from 1999-
2008. In 2008 due to economic depression the GDP fell down put picked up again from 2009.T
The expenditure in education remained constant where expenditure in health care kept on rising
and grew by 1.6 times .Annual saving have grown to 34% since 1999.However CPI, Inflation
and real interest rate kept on fluctuating based on the monetary policies set by central bank.

4.11 Comparison of India and China

38 | P a g e

Expenditure in India and China
250.0

200.0

150.0
Expenditure

Education expenditure China
Education expenditure India
100.0 Health Care expenditure China
Health Care expenditureIndia

50.0

0.0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year

Coming to expenditure pattern both were found same in India and China. In both the cases The
expenditure in education remained constant where expenditure in health care kept on rising.

All other trend too were similar like in both cases GNI, Annual savings, Consumer Demand kept on
rising where as CPI, Inflation and real interest rate kept on fluctuating based on the monetary policies set
by central bank.

Surprisingly in both India and China the GDP growth rate had been constantly rising from 1999-2008. In
2008 due to economic depression the GDP fell down put picked up again from 2009

In case of India GDP is positively related where as in case of China GDP growth is negatively related to
annual consumer demand.

In case of China , CPI and Real Interest have no impact on annual consumer demand in china. Inflation, ,
expenditure in education and GDP growth rate has the highest impact on the consumer demand level

39 | P a g e

and thus it has to be top preference when demand would fluctuate. Moreover focusing on GNP per
capital will have least impact on consumer demand level.

In case of India , GNP per capital should be given the highest preference and should be the prime focused
indicator while keep track of consumer demand. , expenditure in health care and also annual saving
should be given priority as both of this variables are responsible for fluctuating consumer demand to a
large extent.

Other factors like GDP growth rate , Real interest rate and expenditure in education sector, should be
taken into consideration but can be given low priority as they weekly influence consumer demand in
India. Certain variables like Inflation and CPI can be avoided and is not found to influence consumer
demand.

40 | P a g e

Major factors consumer demands India China

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