for India for a 31 year period starting from 1980-1981 up to 2010-2011 on six independent
variables: Gross Domestic Capital Formation, Net Domestic Savings, Exports, Employment,
Gross Enrolment Ratio and Imports.
Macroeconomic theory suggests that aggregate demand depends on economy’s investment,
savings, exports and imports. We have gone off the beaten track and included two
unconventional variables- the employment in the public and organized private sector and the
gross enrolment ratio. Our objective behind this has been to show how the spread of
education sector and increased employment opportunities affect the annual growth rate of
GDP and to find out if it has had a significant impact on the economic growth of India in the
past few years.
The reason that we have taken the data of our variables from the 1980s rather
than the 1991, post liberalization, when the major economic reforms took place is because
since 1980, India’s economic growth rate has more than doubled, rising from 1.7 percent (in
per-capita terms) in 1950-1980 to 3.8 percent in 1980-2000.India’s economic performance
during the first three decades since independence was christened the “Hindu” rate of growth,
a term connoting a disappointing but not disastrous outcome, and playing to the cliché of the
acquiescence in the present that the religion supposedly imbues, because of a greater
emphasis on the hereafter. Many studies have attributed the 1991 reforms for the spurge in
economic growth but as we have studied in or 2nd year that trigger for India’s economic
growth was an attitudinal shift on the part of the national government in 1980 in favor of
private business. The rhetoric of the reigning Congress Party until that time had been all
about socialism and pro-poor policies. When Indira Gandhi returned to power in 1980, she re-
aligned herself politically with the organized private sector and dropped her previous
rhetoric. The national government’s attitude towards business went from being outright
hostile to supportive. Indira Gandhi’s switch was further reinforced, in a more explicit
manner, by Rajiv Gandhi following his rise to power in 1984. This, in our view, was the key
change that unleashed the animal spirits of the Indian private sector in the early1980s. The
change in India in the early 1980s is accordingly best described as pro-business rather than
pro-market. Easing restrictions on capacity expansion for incumbents, removing price
controls, and reducing corporate taxes (all of which took place during the 1980s) are
examples of pro-business policies, while trade liberalization (which did not take place in any
significant form until the 1990s) is the archetypal market-oriented policy. This shift from the
socialist policies to the pro-business policies led to the economic boom after the 1980s which
were only sustained by the liberalization of the 1990s. Therefore, we aim to study the growth
of India and how the economic factors affect the annual growth rate of GDP from the 1980s
rather than 1990s, as has been done in the literature survey that we have found.
DATA AND MODEL SPECIFICATIONS
Source - The data for the dependent and all the independent variables was obtained for the 31
Reserve Bank of India. We have taken the annual growth rate of GDP as our dependent
variable and have regressed it on six independent variables,
- GROSS DOMESTIC CAPITAL FORMATION
Gross domestic capital formation is the addition to the capital stock within the domestic
territory of a country during a year. The surplus of production over consumption during a
economy, the faster an economy can grow its aggregate income. Increasing an economy's
capital stock also increases its capacity for production, which means an economy can produce
more. Therefore we expect a positive relationship between gross domestic capital formation
and rate of annual GDP growth because higher domestic capital formation leads to higher
investment which in turn leads to an increase in the aggregate demand.
- NET DOMESTIC SAVINGS
Net Domestic Savings are calculated as GDP less final consumption expenditure (total
consumption).Therefore, more savings means more investment, which implies increase in
production, which leads to more demand for factor inputs, which results in more income,
which implies more demand, that leads to more investment, leading to rapid economic
growth, this again leads to increased savings. It should be noted that sustained low saving
relative to investment translates into persistent current account deficit and a deteriorating
international investment position.
Export growth may affect output growth through positive externalities on non-exports,
through the creation of more efficient management styles, improved production techniques,
increased scale economies, improved allocated efficiency, and better ability to generate
dynamic comparative advantage. If there are incentives to increase investment and improve
technology this would imply a productivity differential in favor of the export sector (in other
words, marginal factor productivities are expected to be higher in the export sector than in the
other sectors of the economy). It is thus argued that an expansion of exports, even at the cost
of other sectors, will have a net positive effect on the rest of the economy.
- EMPLOYMENT IN PUBLIC AND ORGANISED PRIVATE SECTOR
The relationship between unemployment and GDP is called Okun's law. It is the association
of a higher national economic output with the decrease in national unemployment. This is
because in order to increase the economic output of a country, people will need to go back to
work, thus lowering unemployment. Historically, a 1 percent decrease in GDP has been
associated with a slightly less than 2-percentage-point increase in the unemployment rate.
Employment growth has a positive and significant impact on growth rate of GDP, but some
of the effects take a few quarters to be fully felt.
- GROSS ENROLLMENT RATIO
The Gross Enrolment Ratio (GER) is a statistical measure used in the education sector. The
United Nations uses it to determine the number of students enrolled in school at several
different grade levels to be able to compute an education index for different countries. A
combined gross enrolment ratio (CGER), incorporating all three levels of education, is used
member states. The expected impact of a higher gross employment ratio on the growth rate of
GDP is positive because in order to promote economic and industrial development in a
country, the essential requirement is the capacity to develop skilled manpower of good
quality in adequate number.
A good or service brought into one country from another is called imports. Along with
exports, imports form the backbone of international trade. The higher the value of imports
entering a country, compared to the value of exports, the more negative that country's balance
of trade becomes. Therefore according to the economic theory, an increase in the amount of
imports as compared to the amount of exports of that country will lead to a negative growth
in the gross domestic product of that country. That’s why we expect a negative relationship
between the imports and growth rate of GDP in our regression analysis.
We plotted the annual growth rate of GDP for India over the 31 year period with respect to
each of the independent variables and the scatter that we obtained had the following shape-
We converted our entire sample data set into log 10 and again plotted the scatter diagram and
we got the following scatter plot:
Only a straight line could best fit into scatter plot thus obtained. Thus we reached the
conclusion that the functional form of our model was double log.
MODEL AND RESULTS
The software that we used was Microsoft Excel 2007. As proved earlier the functional form
of our model is a double log model. This implies that the regression line obtained would be
analogous to the following:
Y = AX1B1. X2B2 .X3B3 .X4B4 .X5B5 . X6B6
X1 – Rate of Gross Domestic Capital Formation (in %)
X2 – Net Domestic Savings (in Billions)
X3 – Exports (in Billions)
X4 – Employment in the public and organized private sector (in Millions)
X5 – Gross Enrolment Ratio (for both boys and girls between the ages of 6-14)
X6 – Imports (in Billions)
Taking log on both sides of the equation, we would get
Log Y = log A + B1 log X1 + B2 log X2 + B3 log X3 + B4 log X4 + B5 log X5 + B6 log X6
Now taking log A = B0 , we get
Log Y = B0 + log X1 + B2 log X2 + B3 log X3 + B4 log X4 + B5 log X5 + B6 log X6
- Multiple R is the correlation between the actual and the predicted values of the
dependent variable which in our case is the annual growth rate of GDP.
The value of the multiple R statistics is coming out to be .8076 or 80.76%
- R2 is the coefficient of determination. It gives the explanatory power of the regression
line i.e. the percentage of variations in the total sum of squares (TSS) which is
explained by the regression.
Another way of interpreting R2 is the percentage of variations in the
actual and predicted values of the dependent variable that is explained by the
regression of the dependent variable on the independent variables jointly.
The R2 value obtained for the model is .652 which implies that 65.02% of the
variations in the TSS are explained by the regression. This value is sufficiently high.
- Adjusted R2 is used to compensate for the addition of variables in the model. As more
independent variables are added to the original model, unadjusted R2 will rise
indicating that a greater percentage of variations are explained by the regression.
variables in the model. The adjusted R2value is .565 or 56.5% (approx)
EXPLAINATION OF ANOVA TABLE
- The sum of squares due to regression is .653. This is also called the explained sum of
squares or ESS.
The mean sum of squares given in the 4th column is the sum of squares divided by the
numerator degrees of freedom i.e. ( k-1 ) , ‘k’ being the number of independent
variables including the intercept term.
The degrees of freedom here is 7-1=6
So the mean sum of squares is 0.653/6 = 0.1088
- The residual sum of squares or RSS is 0.348
The mean sum of squares is the RSS divided by the denominator degrees of freedom
i.e. (n-k ) , ‘k’ being the number of independent variables including the intercept term
and ‘n’ being the sample size.
The degree of freedom is 31-7 = 24
The mean sum of squares is 0.348/24 = 0.01513
- The last row gives the total sum of squares which is the sum total of explained sum
of squares (ESS) and residual sum of squares (RSS).
- The 5th column gives the Fcal value for the model :
Fcal = ESS/(k-1)
INTERPRETATION OF THE MODEL
log GROWTH RATE = 3.06599 +2.52338 log GDCF+0.9348 log D_SAV+1.29033 log
EXPORTS+2.2260007 log EMPLOYMENT+4.45250 log GER+0.490416 log IMPORTS
The slope coefficients in multiple regression are referred to as the partial regression
coefficients. They capture the effect of a particular independent variable on the dependent
variable when all other variables are kept constant.
Since our model is a double log model, the partial regression slope coefficients
are also called the partial elasticity coefficients. They measure the relative or percentage
change in the dependent variable due to a relative or percentage change in one of the
independent variables, when all others are kept constant.
- B1 = 2.523
An increase in the rate of gross domestic capital formation (GDCF ) by 1% increases the
annual growth rate of GDP by 2.523%, all other Xi’s remaining constant. The partial
elasticity coefficient of the annual growth rate of GDP w.r.t the rate of GDCF is
This is backed by macroeconomic theory as an increase in the GDCF or investment will lead
have a positive impact on the growth rate of GDP.
- B2 = 0.935
An increase in the net domestic savings by 1% increases the annual growth rate of GDP by
0.935%, all other Xi’s remaining constant.
The partial elasticity coefficient of the annual growth rate of GDP w.r.t net domestic savings
An increase in savings would mean an increase in the funds available to b transformed into
gross capital formation which would again have a positive impact on the annual growth rate
- B3 = 1.290
An increase in the exports by 1% increases the annual growth rate of GDP by 1.29%, all
other Xi’s remaining constant.
The partial elasticity coefficient of the annual growth rate of GDP w.r.t exports is 1.29.
This is inconsistent with economic theory that states that expansion of exports, even at the
cost of other sectors will have a positive impact on the economy.
- B4 = 2.226
An increase in the employment in the public and organised private sector by 1% increases the
annual growth rate of GDP by 2.226%, all other Xi’s remaining constant.
The partial elasticity coefficient of the annual growth rate of GDP w.r.t the employment in
the public and organised private sector is 2.226.
- B5 = 4.453
An increase in the gross enrolment ratio by 1% increases the annual growth rate of GDP by
4.453%, all other Xi’s remaining constant.
The partial elasticity coefficient of the annual growth rate of GDP w.r.t the gross enrolment
ratio is 4.453.
The expected impact of a higher gross enrolment ratio on the country’s GDP is positive
because in order to promote industrial and economic development, the population has to be
literate. Again the effect is consistent.
- B6 = -0.490
An increase in the imports by 1% decreases the annual growth rate of GDP by 0.490%, all
other Xi’s remaining constant.
The partial elasticity coefficient of the annual growth rate of GDP w.r.t imports is -0.490.
Again this is consistent with economic theory which states that an increase in imports will
have a negative impact on the growth rate of GDP.
- TESTS OF SIGNIFICANCE
- FOR B1
HO : B1 = 0
HA : B1 is not equal to 0
The p- value is 0.029967.
This means that the null will be rejected at 5%, 10% and 25% levels of significance. This
means that the rate of GDCF has a significant impact on the annual growth rate of GDP in
- FOR B2
HO : B2 = 0
HA : B2 is not equal to 0
The p- value is 0.230416.
The null will not be rejected at 1%, 5% or 10% levels of significance.
But the null will be rejected at 25% level of significance.
This means that net domestic savings have a significant impact on the annual growth rate of
GDP in India.
A possible reason why the null was rejected at 25% instead of the conventionally
used levels of significance is because of the existence of a high degree of multi collinearity in
the data set.
There was the existence of a strong linear relationship between net domestic savings and
GDCF and exports.
- FOR B3
HO : B3 = 0
HA : B3 is not equal to 0
The p- value is 0.100676.
The null hypothesis is rejected at 25% level of significance which implies that exports do
have a significant impact on the annual growth rate of GDP in India.
We think that this result can be again attributed to multicollinearity and the fact that India has
been running a trade deficit for the time period we have considered.
- FOR B4
HO : B4 =0
HA : B4 is not equal to 0
The p- value is 0.023244.
The null is rejected at 5%, 10% as well as 25% levels of significance indicating that
employment does have a significant impact on the annual growth rate of GDP in India.
- FOR B5
HO : B5 = 0
HA : B5 is not equal to 0
The p- value is 0.000558.
The null hypothesis is rejected at 1%, 5%, 10% as well as 25% levels of significance.
This implies that the gross enrolment ratio does have a significant impact on the annual
growth rate of GDP in India.
- FOR B6
HO : B6 = 0
HA : B6 is not equal to 0
The p- value is 0.343266 which is very high and is not rejected even at 25% level of
significance. This means that imports doesn’t have a significant impact on the annual growth
rate of GDP in India. Imports are thus insignificant.
- JOINT TEST OF SIGNIFICANCE
HO : R2 = 0
HA : R2 is not equal to 0
The joint test of significance or the F-test tests the null hypothesis which states that the
independent variables jointly do not have a significant impact on the dependent variable,
against the alternative which states that the independent variables jointly have a significant
impact on the dependent variable.
Numerator degrees of freedom = (k-1) = 7-1 = 6. Denominator degrees of freedom = (n-k) =
31-7 = 24. The tabulated F-statistic at is significant at 5% and 10% level of significance:
The independent variables are also jointly significant
We found that Gross Domestic Capital Formation, Net Domestic Savings, Exports,
Employment in the public and organised private sectors and Gross Enrolment Ratio have had
a significant impact on the annual growth rate of GDP for India during the period considered.
All the above variables are significant at 25% level of significance and a few of them are
significant at levels of significance as low as 1% and 5%, indicating a good fit.
The results for all the independent variables are consistent with economic theory except for
imports which came out to be insignificant. This was a surprising result as imports have a
negative effect on a country’s GDP according to macroeconomic theory. Dropping imports
which is why we chose to include imports in our final model despite its insignificance. We
observed a very high degree of multicollinearity between the imports and the rest of the
independent variables, its value ranging from 95% to 99%. We thus concluded that it was
possible that the presence of a high degree of multicollinearity in the data set and a small
sample size could have been the possible reasons as to why imports didn’t have a significant
impact on India’s GDP. On the whole, we found that India’s GDP has seen an upward trend
in the time period considered except for a few years in between when it experienced a
downward trend.This can be attributed to the ever increasing rate of GDCF, net domestic
savings, exports, employment and the gross enrolment ratio.
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