84520340 factors influencing the gdp of india

7/29/2019 84520340 Factors Influencing the Gdp of India

1/16

TION

FACTORS

INFLUENCING

THE GDP OF

INDIA


2/16

Contents

1. OBJECTIVE ............................................................................................................................................. 2

STEPS INVOLVED IN ANALYSIS ...................................................................................................................... 3

VARIABLES USED FOR REGRESSION ANALYSIS .............................................................................................. 4

REGRESSION ANALYSIS ................................................................................................................................. 5

TRANSFORMATION ................................................................................................................................... 7

CONFIDENCE LEVEL ................................................................................................................................... 7

TEST OF FIT OF MODEL ................................................................................................................................. 8

CORRELATION ............................................................................................................................................. 10

SIGNIFICANCE TEST ..................................................................................................................................... 12

STANDARDISED COEFFICIENTS ................................................................................................................... 13

R-SQUARED AND OVERALL SIGNIFICANCE OF THE REGRESSION ............................................................... 14

VALIDATION OF OUR MODEL ...................................................................................................................... 15

CONCLUSION ............................................................................................................................................... 16

1. OBJECTIVE:

ANALYSIS OF FACTORS AFFECTING GDP OF A NATION

In this project, I tried to research and find out, how the various quantitative factors that affect

GDP are correlated to it. This will be useful in predicting an estimated GDP for future years,when we know the values of our variable.

These factors are not directly accounted for in GDP calculations, hence we try to establish a

relationship between these factors and GDP and how they could affect it.

As GDP is sum of the Consumption, Investments, Government Expenditure and Net Exports,

Y= C + I + G + (X-M)

Our factors are the various factors affecting each of these components.

1.1 TOOLS USED FOR OUR RESEARCH ANALYSIS


3/16

Moving ahead with our objective of finding out what are the various factors contributing to GDP

of a nation, we use some statistical tools, namely REGRESSION and CORRELATION.

Prediction or estimation is one of the major problems in almost all the spheres of human activity.

The estimation or prediction of future production, consumption, prices, investments, sales,profits, income etc. are of very great importance to business professionals. Similarly, population

estimates and population projections, GNP, Revenue and Expenditure etc. are indispensable for

economists and efficient planning of an economy.

Regression analysis was explained by M. M. Blair as follows:

Regression analysis is a mathematical measure of the average relationship between two or more

variables in terms of the original units of the data. If two variables are significantly correlated,

and if there is some theoretical basis for doing so, it is possible to predict values of one variablefrom the other. This observation leads to a very important concept known as Regression

Analysis.

STEPS INVOLVED IN ANALYSIS

1) Our first step was screening through secondary data sources in order to understand whatare the factors that affect GDP of nation.

Once all the factors are listed, we had to again screen through secondary data sources to

find out the values of these factors for 20 years, and then see its affect on GDP of nation,

which was again found through secondary data.

Our secondary data sources were as following:

a) Business Beaconb) Website www.rbi.orgc) websitewww.imf.org((International Monetory Fund)d) www.indexmundi.com
http://www.imf.org/http://www.imf.org/http://www.imf.org/http://www.indexmundi.com/http://www.indexmundi.com/http://www.indexmundi.com/http://www.imf.org/


4/16

2) The data on all factors once collected and summarized in a proper way, was ready forregression analysis.

3)

The regression analysis was done with the SPSS software used for Business ResearchAnalysis.

VARIABLES USED FOR REGRESSION ANALYSIS

1) Interest rate:With the increase in the interest rate of the country the bend will be towards saving since

the cost of holding money increases resulting into less consumption decrease in the GDP

and secondly the money supply in the market also falls and the GDP goes down.

2) Population of the country:with the rise in population the availability of the cheap labor increases and attracts more firms to

invest in the country increasing the FIIs or FDIs that boosts the GDP of the country

3) Population of country getting Primary Education:higher level of literacy in the country generates more job opportunities in the economy

which in turns contribute significantly in the GDP.

4) Rainfall:Agriculture and allied sectors accounted for 16.6% of the GDP in and despite a steady

decline of its share in the GDP; agricultural of the country significantly depends upon the

rainfall.


5/16

5) Exchange Rate:With the appreciation in the value of the national currency the revenue generated from

the exports is less which in turn affects the GDP of the country

6) Foreign direct investment:It is theoretically straightforward to argue that inflows of FDI have a potential for increasing the

rate of Economic growth in the host country. Inflows of physical capital resulting from FDI

could also increase the rate of economic growth and the GDP of the country.

7) People Employed:More employment results into greater expenditure, which in turns affects the GDP of the

country.

There are various other factors, like Business Confidence, Market Risk which affect GDP

significantly, but we have dropped them from regression analysis because of their qualitativenature.

We also found that People Employed is a Lagging Variable, which means that the change in

this variable in one year, will affect the GDP of the subsequent year. Hence, the value of say,

2001 employment will be regressed against 2002 GDP.

REGRESSION ANALYSIS

OurDependent variable in the regression analysis, is GDP of the country.

OurIndependent variables are the various factors found from secondary data analysis.

The equation is in the form:

Y = b1X1 + b2X2 + ... + A

where Y is the dependent variable you are trying to predict, X1, X2 and so on are the

independent variables you are using to predict it, b1, b2 and so on are the coefficients

or multipliers that describe the size of the effect the independent variables are having

on your dependent variable Y, and A is the value Y is predicted to have when all the

independent variables are equal to zero.

In our case, it looks like following equation :


6/16

Where F1 to Fn = various factors acting as independent variables

Steps :

1. Data tabularized in MS Excel 2003.Data is then imported to SPSS Data View.2. Run regression :PATH

Analyse Regression Linear Enter Dependent and Independent variables and run

regression.

GDP = a + b1F1 + b2F2 + b3F3 + bnFn


7/16

TRANSFORMATION

Since our degree of freedom is 9, with data being collected for 10 years, therefore in order to

have a better regression, and to maintain uniformity of variables throughout, we transformed

them using the Ln ( Log Natural) function in excel.

These variables were

1. GDP2. Population of Country3. NRI Deposits4. Population5. FDIOther variables since had no great variation among these 10 years, were taken as it is.

CONFIDENCE LEVEL

We have taken a confidence level of 80% for our F test and 70% for T test.


8/16

TEST OF FIT OF MODEL

The most important part comes where we need to test whether the model developed by us is right

or not. This is checked by carrying out the F test or ANOVA test.

A good model means that all the variables together, significantly explain our dependent

variables behavior.

This test starts with the null hypothesis that

R2

= 0

Which means that correlation is 0.

In order to prove our model good, we need to reject the null hypothesis. When the significance or

P value of F test is less than .20, as our confidence level is 80%, we can neglect the null

hypothesis.

Our output was as follows:

Significance value of .019 rejected the null hypothesis and proved that the model developed by

us is right. Hence we moved ahead with further analysis.

Regression (Explained Sum of Squares)


9/16

Explained SS gives you the variance that is explained by the Model. This gives you the

deviation of fitted regression value around mean. Our value for the same is 1.488.

The part which couldnt be captured by our model orResidual Sum of Squares is .008.

The total variation in our model which is given by the Total Sum of Square is 1.496.

Second column represents degree of freedom.

The model degree of freedom corresponds to the number of predictors minus n (K-n) where n

is the number of parameters estimated. Data taken are for 10 years , so are degree of freedom= 101 = 9


10/16

CORRELATION

As we are running regression on a single dependent variable. i.e. GDP , our correlation

coefficients tell us by how much does GDP increase or decrease when the corresponding

variables increase by 1 unit.

Here we can deduce that:

1. Increase in 1 unit of organized sector employment by activity results in a decrease inGDP by .906 units.

This is because In organized sector with the advent of modern technology, automation, is

highly needed and hence increase in the labour workforce is decreasing the GDP of the

country.

2. Increase in exchange rate results in a decrease in GDP by .164 unitsSince our money depreciates with increase in exchange rate, the Imports are affected and

hence GDP.

3. Increase in 1 unit of NRI Deposits results in a increases in GDP by .985 unitsDeposits in turn increase the investment in our country and hence increasing the GDP.


11/16

4. Increase in 1 unit of Actual Rainfall results in a increase in GDP by .315 unitsOur country is greatly influenced by the agricultural sector, hence an increase in the

rainfall results in increase in agricultural productivity and hence the GDP.

5. Increase in 1 unit of Population results in a increase in GDP by .981 unitsPopulation increases the available workforce in the country and hence the GDP of nation.

6. Increase in 1 unit of bank interest rate results in a decrease in GDP by .739 unitsIncrease in bank rate decreases the investment in the country, as loans become expensive

for investors. Hence the GDP is directly affected.

7. Increase in 1 unit of FDI results in a increase in GDP by .932 unitFDI is a direct component of investment, and hence increase in FDI increases GDP.

This output table not only shows how the independent variables are correlated to GDP, but

also correlations among them.

On analyzing we find that high correlation of .917 between NRI deposits and Population of

the country, which results in multi colinearity of these variables.


12/16

SIGNIFICANCE TEST

In order to find out whether the coefficients of our independent variables are really different

from 0 or if alternatively any apparent differences from 0 are just due to random chance.

The null (default) hypothesis is always that each independent variable is having absolutely no

effect (has a coefficient of 0) and you are looking for a reason to reject this theory.

To carry out this null hypothesis test, we carry out T test on the independent variables.

The t statistic is the coefficient divided by its standard error. The standard error is an estimate of

the standard deviation of the coefficient, a measure of the precision with which the regression

coefficient is measured.

When the test carried out at 70% significance level, significance value less than .30 rejects null

hypothesis and shows that variables have significant contribution to dependent variable.

From the outpur generated, we find the following variables to fulfil this condition and qualify as

significantly affecting variables.

1) Organised Sector Employment by Activity2) Population of Country3) Bank rate or Interest rate


13/16

The other variables are not unimportant, they are just not having significant INDIVIDUAL effect

on GDP of India, but as a whole, they make a significant impact as calculated from ANOVA

Test

STANDARDISED COEFFICIENTS

Our variables are not in uniform unit. While some are in Monetory terms, others are in number

of people, or mm as in case of rainfall. To carry out a objective comparisons among the

variables, we should make them unit less, or try and convert them into same unit. Thestandardized co efficient in the above table, do this for us.

These betas are better correlations for predictions.


14/16

R-SQUARED AND OVERALL SIGNIFICANCE OF THE REGRESSION

The R-squared of the regression is the fraction of the variation in our dependent

variable, GDP that is accounted for (or predicted by) our independent variables.

The R-squared is generally of secondary importance, unless our main concern is using

the regression equation to make accurate predictions.

Our output is as follows for the Model Summary:

R2 = .

975 which means 97.5% of the variation in GDP is explained by the factors taken.

This is a highly satisfying score for making use of model in making predictions.


15/16

VALIDATION OF OUR MODEL

YEAR ACTUAL GDP PRIDICTED GDP

PERCENTAGE

CHANGE

2000 6.131622063 6.55968 6.981153

2001 6.169294783 6.08729 -1.32924

2002 6.228885687 6.25516 0.421814

2003 6.396030918 6.55636 2.506696

2004 6.580512916 6.32619 -3.86479

2005 6.730057018 6.91114 2.69066

2006 6.855611096 6.99721 2.065445

2007 7.117205503 6.99221 -1.75624

2008 7.101675972 8.05146 13.37408

2009 7.177782416 7.95121 10.7753

By running our model on the past data and finding the value of the dependent variable, we

clearly see that our model is fit for the first 8 years. In the year 2008, it shows a large deviation

of 13%.

On analyzing this particular Year, we found out that, this year was affected by global economy,

especially the recession times in USA.

Our model hence can Predict GDP with fair accuracy, provided that the global economy remains

reasonably stable, not affecting the Indian Economy. This is because our model takes care of

variables that are confined to Indian Economy context.


16/16

CONCLUSION

Since our model is fit, we can use this model to predict the GDP values with

knowledge of increase or decrease in the factors known.

We can obtain future predicted data of rainfall from Meteorological Department,

Interest rate from RBI, Population and other variables from the respective

departments, and hence predict the future growth in GDP of a Nation.

Since these values are not open to common public, we cannot predict the future

GDP of 2012-2013. But our model will be useful, once the data is available.

84520340 factors influencing the gdp of india

Documents