Consumer Demand

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Consumer Demand

  1. 1. Newgate IndiaMajor factors that impact consumer demands in India and China 1|Page
  2. 2. Table of ContentsAbstract .......................................................................................................................................... 61. 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..................................................................................................... 112. Review of Literature ............................................................................................................... 123. 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 2|Page
  3. 3. 3.7.2 Correlation Analysis .................................................................................................... 19 3.7.3. Descriptive statistics .................................................................................................... 20 3.7.4 Graphical Analysis ...................................................................................................... 20 3.8 Overview of work............................................................................................................... 204.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 3|Page
  4. 4. 6.Results & Discussion ................................................................................................................ 41 6.1 In case of China ................................................................................................................... 41 6.2 In case of India .................................................................................................................... 42 6.1 Hypothesis acceptance. ....................................................................................................... 437.Conclusion ................................................................................................................................ 448. References ................................................................................................................................ 45 4|Page
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  6. 6. AbstractThe rapid economic growth in Indian and China is increasing and creating employment and betterbusiness opportunities that in turn is increasing disposable incomes. Increase in disposable incomeenhances 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 theconsequences of increase or decrease in consumer demand.Industries in India and China is one of the competitive market catering to the global needs . Thisresearch supports the facts that market patterns in China and India as emerging nations havingfluctuating due to factors like global slowdown, competitions and ability of consumers to acceptand afford a particular product/service. This overall regulates the demand pattern. Affordabilitylies on several factors like inflation, annual disposable income and government expenditure topush money supply in the cash chain. This entire research will give a clarity on how variousmacro-economic variables, monetary policies that affects lively hood of consumers can result influctuation of consumer demand in India and China. On the same time it will give an Idea on twoleading nations of Asia in terms of Economy and would help in summarizing the factsaccounting almost the entire Asia.Results obtained from analysis supported the fact that two leading nations have lot of common intheir economy pattern and follow a similar trend. In both the cases consumer demand were impactedby macro-economic variables however their relationship and degree of association differed fromeach other. In case of expenditure pattern both were found same in India and China. In both thecases The expenditure in education remained constant where expenditure in health care kept onrising. All other trend too were similar like in both cases GNI, Annual savings, Consumer Demandkept on rising where as CPI, Inflation and real interest rate kept on fluctuating based on the monetarypolicies set by central bank. In India and China the GDP growth rate had been constantly rising from1999-2008. In 2008 due to economic depression the GDP fell down put picked up again from 2009. 6|Page
  7. 7. 1. Introduction1.1 OverviewOver past few decades industries and venture capitalist looking to invest in Chinese and Indianmarket have significantly risen. These vast market act as a driving force to stimulate the economicgrowth in China and India and has also influenced several other macro-economic conditions likeinflation, employment and GDP of the country. Both India and China has focused on privatization, liberalization and marketization for past 3decades and as a result has gained a tremendous transformational change in its social, economic andtechnical aspects of life. China and India being world‟s fastest growing nation and counted amongtwo big economies in Asia has undergone this major growth in the presence of increase in proportionof private ownership which resulted in economy to grow rapidly.With time big brands overseas realized the presence of big market which were untapped in Indiaand China with huge market potential. In the meantime government cleared of many FDI‟s . Manynew players came into market, employment rose up, disposable income increased and resultconsumer demand kept on accelerating at a faster pace. In China consumer demand grew quite fasterthan that of India.Economist and political leaders in China‟s have always worry regarding consumer demand becauseexport driven growth is unsustainable in case of china. China‟s accompanies a vast rural area withpoverty 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 luxurygoods are being seen with increase in sales. Products like cameras, computers, cell phones have gotincreased acceptance from this market. However India too has seen increased footfalls on serviceand hospitality sector. China is more of a manufacturing driven economy where as India is R&Ddominated economy. Many global rating agencies are considering India as one of the key marketplayer 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 Arecent study by the McKinsey Global Institute (MGI) suggests that if both India and China keepsgrowing at current and forms a bilateral trade in future effectively than the average householdincome will triple in coming two decades with India being world‟s 5-th largest consumer economyand China at number one. India is growing at a faster space with annual rate of 7.65 in past fiveyears and is forecasted to continue growing making it world‟s 3rd largest economy by 2020 where asChina‟s is already expected to be at top by that time.The rapid economic growth is increasing and enhancing employment and business opportunities andin turn increasing disposable incomes. 7|Page
  8. 8. 1.2 Purpose of conducting ResearchIt 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 importantto understand how consumer demand pattern would differ in this two nations. Observing thefactors that affects the consumer demand in these countries would give an overview that how ondifferent circumstances the consumer demand would vary. This research will give deeper insightinto various factors like GDP growth,GNI, expenditure on education and Health care, CPI, Grosssaving , Inflation and Real interest rate and their role in stimulating consumer demand in Indiaand China.The research will also clarify the degree to which each of this macro-economicvariable will be related to consumer demand with the help of regression equation. The obtainedresults will be used as a standards for economist in this two countries to understand the demandpattern and thus can control it by varying this macro-economic variables or monetary policies. Attimes economist would be expecting either rise or fall in consumer demand to that of normal andit can varied to certain extent by varying the indicators..1.3Statement of problemTo understand how various set of macro-economic variables like GDP growth,GNI, expenditure oneducation and Health care, CPI, Gross saving , Inflation and Real interest rate have an impact on overallgrowth of economy and consumer demand. With time both private and public industries haveevolved at a faster pace in India and China but it is very important to understand how each ofthese sector will influence the overall economic growth pushing a demand pattern. Privateindustries in china have been playing major part with evolution in Manufacturing,Telecom,Retail, Petroleum, Service and entertainment industries constituting a strong demand in china. InIndia IT, Hospitality, Telecom and Manufacturing Industries and R&D are pushing a strongdemand. It is very critical for economist to underline whether increased growth in consumerdemand is significantly impacted by macro-economic indicators leading to efficient Industrialoperating performance in India and China and if it has impacted than to what degree it has anaffect on India‟s and china‟s overallconsumer demand.1.4Research topicTo identify the major factors that impacts the consumer demands pattern in India and China 8|Page
  9. 9. 1.5Research objectiveObjective:1To compare the difference in statistics of consumption expenditures for people inIndiaand 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 consumerdemand in India and China.1.6Research Questions1. What is the differences between the statistics of consumption expenditures in Indian andChina?2. What leading factors may cause the situation associated with the difference in consumerdemand?3. How do the factors have a profound impact on consumer demand and the NationalEconomy?1.7 Proposed null hypothesisHypothesis Ha : Consumer Demand is correlated and dependent upon GDP growth in India andChinaHypothesis Hb : Consumer Demand is correlated and dependent upon Expenditure in Chinaand IndiaHypothesis Hc : Consumer Demand is correlated and dependent upon GNI per capital in Chinaand India Hypothesis Hd : Consumer Demand is correlated and dependent upon Inflation in China andIndiaHypothesis He : Consumer Demand is correlated and dependent upon Gross Saving in ChinaHypothesis Hf : Consumer Demand is correlated and dependent upon CPI in China and IndiaHypothesis Hf : Consumer Demand is correlated and dependent upon Real Interest Rate inChina and India 9|Page
  10. 10. 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 China1.9 Scope of studyThis research will give a broader and also in-depth scope for economist and central bankmanagers to device an effective monetary policies to regulate various macro-economic indicatorssuch that the entire economy is benefited by an optimized consumer demand pattern. Thispatterns will give an overall picture on its granular level to its ground reality that will help inframing effective strategies to help achieve a booming economy.The cause affect analysisinfluence decision makers to keep a track on economy and macro-economic indicators so thatthey can set consumer demand on the right track in a balanced way keeping GDP and inflationunder control.1.10 Relevance to real worldIndustries in India and China is one of the competitive market catering to the global needs . Thisresearch supports the facts that market patterns in China and India as emerging nations havingfluctuating due to factors like global slowdown, competitions and ability of consumers to acceptand afford a particular product/service. This overall regulates the demand pattern. Affordabilitylies on several factors like inflation, annual disposable income and government expenditure topush money supply in the cash chain. This entire research will give a clarity on how variousmacro-economic variables, monetary policies that affects lively hood of consumers can result influctuation of consumer demand in India and China. On the same time it will give an Idea on twoleading nations of Asia in terms of Economy and would help in summarizing the factsaccounting almost the entire Asia. 10 | P a g e
  11. 11. 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.  11 | P a g e
  12. 12. 2. Review of LiteratureShrabani Saha, Zhaoyong Zhang, (2012), mentions in his article “Do exchange rates affect consumerprices? A comparative analysis for Australia,China and India” that an important factor for consumerdeamand in countries like Chian is highly influenced by exchange rate maechanism.In this case acomparative study was made to explore the domestic prices in India,China and Australia and they foundthat 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 assamples. Their study is similar with Holdemess and Sheehan‟s study in 1988, consumer demand is set asTobin‟s Q value and Inflation. By using OLS regression methods, their main results are “both measuresof inflation and Interest rate directly relates to consumer demand..”(McConnell and Servaes, 1990) theyalso discover that there is a curve relationship between the consumer demand, shareholders andperformance of company (Tobin‟s Q value). The proportion of inside shareholders from 0-40%, thiscurve 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 extentto which consumers‟ perceptions of amount they want to spend confirm their against their disposableincome.. Most consumers form expectations of the product, vendor, service, and quality Theseexpectations influence their attitudes and intentions to shop at certain Internet store, and consequentlytheir decision making processes and purchasing behaviour. If expectations are met, customers achievea high degree of satisfaction, which influences their online shopping attitudes, intentions, decisions,and purchasing activity positively. In contrast, dissatisfaction is negatively associated with these fourvariables.Schaupp and Be‟langer (2005) using a conjoint analysis of consumer demand based on data collectedfrom 188 young consumers found that the three most important attributes to consumers for onlinesatisfaction 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 anycountry depends on the of ownership structure. They have used OLS and IV regressions; findendogeneity of managerial ownership caused by unobserved heterogeneity as opposed to reversecausality. After controlling for firm characteristics and firm fixed effects, they finally find no relationbetween managerial ownership and performance. This study further proofs the endogeneity of ownershipstructures.Myeong-Hyeon Cho (1998) use the data of 500 manufacturing companies study the relationship betweenconsumer demand and the performance of company. The results from their simultaneous equationregression show the investment will firstly influence the value of company, and then influence the 12 | P a g e
  13. 13. demand for consumers Dahai Fu,Yanrui Wu,Yihong Tang,2009, "The effects of oil & gas ownershipstructure and industry characteristics in china", The western Australian University.The paper of Miyazaki and Fernandez (2001) explores risk perceptions among consumers of varyinglevel of internet experience and examine how these perceptions relate to their spending activity. Thestudy provides evidence of relationships among consumers‟ the use of alternate remote purchasingmethods, 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 anecessity for successful in a period of growing competition in financial markets. Thus, obtainingconsumer demand is the basic aim of the management of banks, which is the crucial requirement forconducting any business (Bobáková, 2003: 21). At the macro level, a profitable banking sector iscontributing 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 maderesearchers, 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 areinfluenced by a bank„s management decisions. Such management effects will definitely affect theoperating results of banks. Although a quality management leads to a good bank performance, it isdifficult, if not impossible, to assess management quality directly. In fact, it is implicitly assumed thatsuch a quality will be reflected in the operating performance. As such, it is not uncommon to examinea bank„s performance in terms of those financial variables found in financial statements, such as thebalance sheet and income statement. External determinants of bank profitability are factors that arebeyond 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 theinstitution to take advantage of anticipated developments. The two major components of the externaldeterminants are macroeconomic factors and financial structure factors. 13 | P a g e
  14. 14. 3. Research Methodology 3.1 Research Framework Figure 3.1 To identify the major factors that have an impact onProblem 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 consumerResearch 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 14 | P a g e
  15. 15. 3.2 Type of Research3.2.1 Exploratory Research:In such kind of research the cause or the outcome is not known and is difficult to identify thefactors which may affect a particular variable. In such case a background research is done toidentify certain set of indicators that may actually effect desired variables. In this caseobservations made from review of literature based on results derived by other authors in similarcontext has been used to identify the key indicators that would impact the consumer demandpattern in India and China. Once the identifiers were found further casual research was done tofind out the relationship of those indicators with consumer demand pattern3.2.2 Causal Research :In case of causal research a relationship is being established between dependent and independentvariable that helps to derive a cause affect relationship. It indicates how change in any of theindependent variable would significantly alter the dependent variable. In this type of research thedegree of dependency/association is derived to establish how effectively the relationship holdstrue. In this research the motive is to find a relationship in between consumer demand andseveral 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 …… + Xtn3.3 Sources of data3.3.1 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. 15 | P a g e
  16. 16.  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 data3.4.1 Nature of SamplingProbability Sampling:In case of probability sampling every sample picked up from a given pool of population willhave likely equal chances to be selected. In other way in this sampling the population size isalways known before starting a research3.4.2 Sampling TypeFixed SamplingIn fixed sampling method samples are already organized and instead of getting chosen randomlythe samples are selected in an organized manner in a definite pattern/trend on given scale oftime, space or based on certain priority, symmetry, preferences or over interval of year or againstcertain given interval of variables in increasing or decreasing order. The main fact which liesover here is that every sample in fixed sampling has an equal likely probability of lyinganywhere on the pool of population.3.4.3 Sample SizeSample size considered is over period of 30 years from 1981-2010 to get accurate figures. N = 303.5 Target SampleTarget sample were collected from various official sites in two leading nations of Asia that wereIndia and China. 16 | P a g e
  17. 17. 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 used3.7.1 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 17 | P a g e
  18. 18. 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 18 | P a g e
  19. 19. 3.7.2 Correlation Analysis Correlation analysis helps in identifying the degree of association between any two randomvariables in given range of time or space. Correlation coefficient ( r) helps to express this degreeof association where r will always lie between +1 to -1.R value close to -1 or +1 indicates thatthe two variables are highly associated, r=1 or -1 mean both the random variable are fullycorrelated 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 orspace is same, which means when one variable increases it will influence the rise of othervariable. In short both of them are directly proportional. If r value is positive than it shows thatboth the variables are directly proportional.If value of r is negative than that means that the slope of the equation of both the variables ontime or space is opposite to each other which means when one variable increases it willinfluence fall of other variable. In short both of them are inversely proportional. In such cases itis also called as to be inversely correlated. Chart 3.2 19 | P a g e
  20. 20. 3.7.3. Descriptive statisticsDescriptive statistics help to define that how a particular pool of distribution of data would bearcertain characteristics. Central tendencies like mean, median, mode and dispersions likestandard 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 thestatistical average of a sample of distribution. Median is the point on scale where 50% ofobservation lies above it and 50% of observation below it and mode is the data that havemaximum frequency or maximum occurrence on the distribution. It is possible that a set ofdistribution may have more than one mode.3.7.4 Graphical AnalysisIt explains and figure outs the trend of any set of data over a given time or space to get clarityon the nature of data through various graphical analysis and tools like pie chart, bar chart, scatterdiagram etc.3.8 Overview of work1. 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. 20 | P a g e
  21. 21. 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 InterestYear ($) h (%) ($) (% 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 21 | P a g e
  22. 22. - 1.40789 7.195061999 42003 7.6 840 1.8 38.9 -1.4 38.8 2 7 0.25530 3.711242000 43632 8.4 930 1.8 43.7 0.4 37.3 48 1 0.72290 3.720732001 45994 8.3 1000 1.8 47.5 0.7 38.1 25 5 - 0.76594 4.698352002 48345 9.1 1100 1.8 54.4 -0.8 40.7 9 5 1.15590 2.629772003 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 1.587852005 70065 11.3 1740 1.8 80.6 1.8 48.4 78 1 1.46318 2.249302006 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 5.938572009 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
  23. 23. 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 SquareModel 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
  24. 24. Chart 4.1 Table 4.4 Coefficients(a) Unstandardized Standardized Coefficients CoefficientsModel 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
  25. 25. 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
  26. 26. 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 RcCc 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 30Gc 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 30Gnc 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 30Ec 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 30Hc 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 16CPC 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
  27. 27. 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 29Ic 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 24Rc 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. 4.3.2 Interpretation 27 | P a g e
  28. 28. 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 RcN Valid 30 30 30 30 16 30 29 24 30 Missing 0 0 0 0 14 0 1 6 0Mean 38599.1 1031.00 10.0933 1.9000 80.5250 5.6067 41.9345 5.9830 1.9734 333 00Median 27945.5 590.000 10.0000 1.8000 57.9000 3.2000 40.7000 3.4291 2.6169 000 0Mode 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 381Sum 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
  29. 29. 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 ConsumerYear 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 9Growth 355.9139 1198.270% 200.93136 23.80952381 785 0 405.18176 725 41.235638 2 Interpretation 29 | P a g e
  30. 30. 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 InterestYear 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
  31. 31. 2005 5356 9.3 730 3.1 30.0 4.2 34.3 4.246353 6.2483262006 5634 9.3 810 3.1 33.1 6.1 35.0 6.145522 4.4773612007 6052 9.8 950 3.1 40.4 6.4 36.9 6.369997 6.8691612008 6324 3.9 1030 3.1 43.1 8.4 33.0 8.351816 4.2772492009 7126 8.2 1150 3.1 44.3 10.9 34.5 10.87739 5.8726882010 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
  32. 32. analysis established hold true and cause affect relationship among dependent and independentvariables can be derived and stated. In this case the relationship holds true upto 98.7% of thecases 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: CiIn this table we concentrate on the significance level. The significance level p should always beless than 0.05 ( p < 0.05 ). If the condition is satisfied then the established regression equation issignificant enough to support the relationship between dependent and independent variable. Inthis 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
  33. 33. 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 4.7.4 Interpretation 33 | P a g e
  34. 34.  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 India4.8.1 Correlation Variable:Ci : Annual Consumer Demand in IndiaGi : GDP growth in IndiaGNi: GNI per capital in IndiaEi : Education Expenditure in IndiaHi : Health Expenditure in IndiaCPi : CPI Index in IndiaSi : Gross Saving in IndiaIi : Inflation in IndiaRi : Real Interest in India Table 4.12 34 | P a g e
  35. 35. Correlations Ci Gi Gni Ei Hi CPi Si Ii RiCi 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 24Gi 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 31Gni 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 31Ei 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 31Hi 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 16CPi 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 31Si 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 30Ii 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 30Ri 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
  36. 36.  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. 4.8.2 Interpretation 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 RiMean 498.709 28.268 3547.7500 6.1903 3.4419 7.8290 25.9300 8.0013 6.3868 7 8Median 400.000 23.550 2895.0000 6.0000 3.5000 8.3000 24.1500 8.3367 6.8692 0 0Mode 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 26Sum 15460.0 85146.00 191.90 106.70 452.30 242.70 777.90 240.04 197.99 0 36 | P a g e
  37. 37. Interpretation The average value ( mean value ) for consumer demand over a period of 1981-2010 has been 3547.7500. The chances of dispersion that the value would vary or spread out from its mean is 2895.0000 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. 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 24.1500. 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 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 ConsumerYear 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 11.9923Growth % 147 140 180 -18 162 200 34 199 Chart 4.4 37 | P a g e
  38. 38. 60.050.0 GDP growth (%) Adjusted savings: education expenditure40.0 (% 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

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