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8/14/2019 Telecom Scenario-Imperical Study
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TERM PAPER
ECONOMETRICS
An Empirical Study on Tele- Density UsingMultiple Regression Model
Submitted by: Subject Instructor:RAVI CHAUHAN Dr. SUJATA KAREnrollment No: R290108026 Assistant ProfessorMBA (IFM) ECONOMETRICSUPES Dehradun
University of Petroleum & Energy StudiesDehradun
INTRODUCTION:
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Telecom reforms across the world are energizing businesses and people. In fact, Telecom reforms havebeen among the most visible face of reforms in India in the past decade. Long considered a naturalmonopoly, recent technological developments have facilitated competition in this sector leading toincreased access to telecom services and gains in efficiency and quality of service. India has emerged asan international destination for processing and distribution of information. Availability of infrastructurefor electronically transferring and assessing information are critical to maintaining the competitiveadvantage that it currently enjoys and embracing telecom reforms is a part of achieving that goal. Thoughthe results of telecom reforms the world over have been positive on average; domestic political economyand institutions have impacted every country experience and India is no exception.
Tele-communication is important not only because of its role in bringing the benefits of communicationto every corner of India , but also in serving the new policy objectives of improving the globalcompetitiveness of the Indian economy.
Indian telecom market is growing at rapid pace. Despite attaining a subscriber base of 429.72 million,Tele-density in India is 36.98 (as on March 2009). And rural market is still under- tapped. Newgeneration telecommunication like 3-G is latest addition.
The Tele-density is an average regarding the overall Telecom scenario.
OBJECTIVE:
To study the impact of1. Population Density2. G.D.P Per Capita
On the Tele-density at the global level, using the multiple regression model.
LITERATURE REVIEW :
Sudeshna Ghosh (2004) has used state-level data to empirically analyze the impact of telecomprivatization on economic development in the Indian context. If privatizing telecom provides a stimulusto state economic development, we would expect to see positive effect of telecom reforms on GSDP, FDIand industrial productivity. We find evidence that telecom reforms have a positive effect on industrial
productivity as measured by number of exchanges and telephone network across states. The effect ofnetwork on industrial productivity is robust to different specifications. We also find that teledensity is asignificant determinant of gross domestic product, that is, higher number of telephones facilitatescommunication and raises the state domestic product. However, our results on the effect of teledensity on
state domestic product is not robust to all specifications. Telecom privatization does not appear to be animportant determinant of state level FDI in India. Instead, agglomeration effect is a more important andsignificant determinant of FDI inflows.Gurshaminder Singh Bajwa (from JNU) tries to bring out the role of Indian state as an actor inresponding to the ICT (Information and Communication Technology) policy with special reference to thetwo task force reports highlighted in for carrying out this study. Response is here understood as the role
played by the Indian state towards putting policy statements into practice. The underlying intent of thetheories is the belief that information would be the prime mover in information or knowledge societies. Itis strongly believed that ICT will propel India into the league of developed nations. At the same time theyraise some important issues and questions: What are the broad objectives of major policies on ICT for
development? To what extent policies formulated at the highest decision-making bodies find their wayfor implementation? Is there a gap between Theory and Practice? To what extent ICT policiesinfluenced different stakeholders in the society? And what kinds of responses have been generated by
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different policies? The aim focus of this study is on the responses of different stakeholders especially thestate with respect to the ICT policies and discourse. This will reveal developmental concern for theoverall built environment especially in education, health, energy and transportation systems sectors, butthe structure and orientation of ICT policy initiatives, in a large measure are directed towards market-oriented demands of globalisation.Swadesh Kumar Samanta(2007) did as study on Impact of price on mobile subscription andRevenue. Access price or fixed monthly fee for mobile services is the major factor that governs the
percentage of people subscribing (penetration) to the services. Empirical analysis shows a strongcorrelation between access price and penetration for developing and developed countries. Theydemonstrate a trade-off between price of access and per-minute-call and show how subscription andrevenue to the operator can be increased.Singh (2005), in his article The role of technology in the emergence of the information society in Indiadescribes the role that information and communication technologies are playing for Indian society toeducate them formally or informally which is ultimately helping India to emerge as an informationsociety. Though India has a huge population, the illiteracy rate is also huge in this country. The paper hastaken an approach to find the historical situation and present the prevailing scenario as well as the change
that are taking place with the application of ICT to the advantage of the society in different areasincluding daily life. India is making all out efforts to be counted among the developed nations of theworld. The article also describes the considerable attention India is taking for application of technology,development of infrastructure and human resource for meeting national needs. Basically India is buildingan information society. Technology has helped society to cut across the traditional boundaries for gettingconverted into an emerging information society. The study concludes that The Indian software andservices industry has significantly helped to boost the Indian economy. In IT-enabled services too,India has been clearly perceived to be the dominant hub. The Indian software sector is being recognizedas the single largest contributor to incremental market capitalization in India but the sector is still smallinterms of contribution to GDP, especially when compared to other large sectors in the economy like
agriculture and manufacturing.Similarly, the telecommunication sector has contributed a lot but still has a considerable way to go. The
paper also enforces that comparisons of Indias telecommunication statistics with those of developed andother emerging economies show that the country is still far behind its contemporaries.Manas Bhattacharya (2002) in his report Vision 2020 for Indian telecom sector bring about thefollowing observations.LDCs are experiencing fastest growth in telecom network. In the mid-90s, growth in total telephonesubscribers per 100 inhabitants of the LDCs surpassed that of the developed countries. Given therelationship between telecom expansion and growth, there is hope for narrowing down of digital-divide,
provided, LDCs are able to sustain growth momentum in the long run. Developing countries with liberalpolicies have much better opportunity to leapfrog than before. Mobile experience of the low-incomecountries bears testimony to this process. India is a participant in this global process. There is tremendousappetite to absorb new technology. At the higher end of the market, India will mimic the mostsophisticated telecom technology of the world. After the cross-over between fixed line and mobile
phones, the next cross-over would be between data and voice. In order to guess the time frame overwhich such technological and commercial cycles may run their courses in future it may be of interest tolook at the past experiences.
METHODOLOGY:
Quantitative analysis of Tele-density. In this analysis we are using Multi-variat Linear Regression. The variables considered are:
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Tele-density, Population density, per capita GDP Data for the year 2008 of 196 countries of the world is taken for this purpose. The data collected is secondary data. Method of data collection: Research papers published in reputed refereed Journals, Articles,
Newspapers, magazines, Internet
MULTIPLE REGRESSION MODEL:
Researchers in the physical and social sciences often want to determine a relationship among anumber of variables based on a set of data that are observations or measurements of these variablesover a number of instances. Usually, one variable is called the dependent variable while the othervariables are independent variables. A linear regression tries to determine the dependent variable asa linear function of the independent variables.
Linear regression is also called multiple regressions, or least squares estimation.
REGRESSION:
A statistical technique used to find relationships between variables for the purpose of predicting
future values.
MULTIPLE REGRESSION:
Multiple regression is a statistical technique that allows us to predict someones score on onevariable on the basis of their scores on several other variables.
In multiple regression we make the following assumptions:
E(ui) = 0 V(ui) = 2 for all i
Ui and uj are independent for all i j Ui and xj are independent for all i and j. Ui are normally distributed for all i. There are no linear dependencies in the explanatory variables, i.e. none of the explanatoryvariables can be expressed as an exact linear function of the others.
WHEN SHOULD WE USE MULTIPLE REGRESSION?
I. We can use this statistical technique when exploring linear relationships between thepredictor and criterion variables that is, when the relationship follows a straight line. (Toexamine non-linear relationships, special techniques can be used.)
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II. The criterion variable that we are seeking to predict should be measured on acontinuous scale (such as interval or ratio scale). There is a separate regression method calledlogistic regression that can be used for dichotomous dependent variables.
III. The predictor variables that we select should be measured on a ratio, interval, orordinal scale. A nominal predictor variable is legitimate but only if it is dichotomous, i.e. thereare no more than two categories. For example, sex is acceptable (where male is coded as 1 and
female as 0) but gender identity (masculine, feminine and androgynous) could not be coded as asingle variable. Instead, we would create three different variables each with two categories(masculine/not masculine; feminine/not feminine and androgynous/not androgynous). The termdummy variable is used to describe this type of dichotomous variable.
IV. Multiple regression requires a large number of observations. The number of cases(participants) must substantially exceed the number of predictor variables we are using in yourregression. The absolute minimum is that we have five times as many participants as predictorvariables. A more acceptable ratio is 10:1, but some people argue that this should be as high as40:1 for some statistical selection methods.
THE MULTIPLE REGRESSION EQUATION IN THIS CASE:
Yi = o+ 1X1i+ 2X2i+ i
o = Y (Tele- density) intercept1 = slope of Y with variable X1 (Population Density in Population per Km2) holding variableX2 constant2 = slope of Y with variable X2 (GDP per capita) holding variable X1 constanti = random error in Y for observation i.
Regression Statistics
Multiple R 0.663648587R Square 0.440429447Adjusted R Square 0.434630789Standard Error 44.67644633Observations 196
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 70.58297254 3.909558871 18.053948 2.543E-43 62.8720259278.2939191
7
X1 0.002543513 0.0013240341.92103302
4 0.05620112 -6.79211E-050.00515494
8
X2 0.002249268 0.00019148111.7466693
7 2.18978E-24 0.0018716040.00262693
3
Computed values of the coefficients with the help of Microsoft excel, we get;
0 = 70.582972541 = 0.0025435132 = 0.002249268
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Therefore the multiple regression equation can be expressed asi = 70.58297254+0.002543513X1i +0.002249268X2i
Interpretation:
Intercept (0): The value of the coefficient of intercept is 70.58297254. It is interpreted that 70.58297254is the average value of population density and per capita GDP is assumed to be 0.
For 1: The Tele-density is expected to increase by 0.002543513 for each unit increase in the populationdensity considering per capita GDP to be constant.
For 2 : The Tele-density is expected to increase by 0.002249268 for each unit increase in GDP percapita holding the population density constant.
Coefficient of determination (R square)
R square = SSR/SSTWhere; SSR = regression sum of squares
SST = total sum of squares
R2 = 0.440429447R2 is computed as 0.440429447, means that 44.0429447% of the variation in Tele-density can beexplained by the variation in the population density and GDP per capita.
i. Here R square is 44.04%, so it is clear that the regression power of independent orexplanatory variable(X1, X2,) is up-to-the mark in defining the dependent variable (Tele-density).
Adjusted R Square: When dealing with multiple regression models, some staticians suggest that the radj2
should be computed to reflect the number of explanatory variables (k) in the model and the sample size.We know that, radj2=1-1-R2n-1n-k-1.
n= total number of observations, here n = 196k = number of explanatory variables, here k = 2
Also, radj2 = 0.434630789Hence, 43.46 of the variation in the Tele-density is explained by the multiple regression model- adjustedfor the number of predictors and sample size.
RESIDUAL ANALYSIS FOR THE MULTIPLE REGRESSION MODEL:The residual analysis was used to evaluate whether the multiple regression model was appropriate for theset of data being studied.The first residual plot examines the pattern of residuals versus the predicted values of Y. If the residualsshow a pattern for different predicted values of Y, this provides evidence of a possible quadratic effect inat least one explanatory variable, a possible violation of the assumption of equal variance.In the plot obtained from Microsoft Excel, there appears to be no pattern in the relationship between theresiduals and the predicted values of Y.
The second and third plots involve the explanatory variables. Patterns in the plot of residuals versus anexplanatory variable may indicate the existence of quadratic effect and therefore indicate the need to adda quadratic explanatory variable to the multiple regression.
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In the above plots, there appears to be no pattern in the relationship between the residuals and the value ofX1 (Population Density), the value of X2 (GDP per capita). Therefore, we can conclude that the multipleregression model is appropriate for predicting the Tele-Density.
TESTING FOR THE SIGNIFICANCE OF THE MULTIPLE REGRESSION MODEL:
Now that the residual analysis has been used to determine whether the multiple regression model isappropriate, we can determine whether there is significant relationship between the dependent variableand the set of explanatory variables. Because there is more than one explanatory variable, the null andalternative hypotheses are as follows:
H0: 1 = 2 = 0 (No linear relationship between the dependent variable and the explanatory variables.)H1: At least one j 0 (Linear relationship between the dependent variable and at least one of theexplanatory variables.)This null hypotheses is tested with an F- test by using values summarised in the table below.
ANOVA
Degreesof
freedom(df)
Sum Of squares(SS) Mean Squares(MS)
F=MSRMSE
Significance F
Regression k=2SSR=303204.782
6MSR=SSRk=151602.
3913 75.95367809 4.65579E-25
Residualn-k-
1=193SSE=385225.077
4MSE=SSEn-k-
1=1995.98Total n-1195 SST=688429.86
The F statistic is equal to the regression mean square (MSR) divided by the error mean square.
F=MSRMSE
F= test statistic from an F distribution with k and n-k-1 degrees of freedom.The decision rule is
RejectH0 at the level of significance ifFcalculated > Fcrit(k,n-k-1)If a 0.05 level of significance is used, the critical value of the F distribution with k=2 and n-k-1=193degrees of freedom obtained from the table is
Confidence and PredictionEstimate Intervals
Data
Confidence Level 95%
1
X1 given value 373
X2 given value 947
X'X 196 74490
23325
90
744901.19E
+092.1E+
0923325 2.1E+ 8.35E
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90 09 +10
Inverse of X'X0.0076
58-1.1E-
07-2.1E-
07-1.1E-
078.78E-
10-1.9E-
11-2.1E-
07
-1.9E-
11
1.84E-
11
X'G times Inverse of X'X0.0074
182.03E-
07 -2E-07
[X'G times Inverse of X'X]times XG
0.007303
t Statistic1.9723
32
Predicted Y (YHat)73.66
176
For Average Predicted Y (YHat)
Interval Half Width7.530
386Confidence IntervalLower Limit
66.13137
Confidence IntervalUpper Limit
81.19215