Economics Assignment: Analyzing Indian Economy (2000-2019)

This economics assignment analyzes the performance of the Indian economy from 2000 to 2019, focusing on the impact of GDP, household consumption, exports, and imports. The report employs statistical tools, including descriptive statistics, coefficient of variation, correlation coefficient, simple linear regression, and multiple linear regression to examine the relationships between these variables. The study aims to provide insights into the key determinants of the Indian economy's growth, particularly for a rice producer, and uses quantitative analysis to assess the correlations and impacts of these factors. The findings include interpretations of data, such as the positive relationship between GDP and household consumption, the effect of trade liberalization, and the implications for the rice industry. The report concludes with a summary of the analyses and their potential consequences for the Indian economy.

Running head: ECONOMICS ASSIGNMENT
Economics Assignment
Name of the Student
Name of the University
Author Note

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Executive Summary
The report aims to find out the performance and impact of the four variables on the Indian
economy over the last twenty years (200 -2019). The study has been developed on the basis of
statistical modelling using the given sample population. As per the findings, the proposed rice
producer can evaluate its business strategy with the help of this analysis as these four variables
are the key determinants to analyze the productivity, demand and supply of the rice production.
The report further summarizes all the report in the concluding part.

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Table of Contents
2. Introduction..................................................................................................................................3
2.1. Understanding quantitative analysis in economics............................................................3
2.2. Quantitative analysis: informing business.........................................................................3
2.3. The objective of this report..................................................................................................4
2.4. Structure of this report........................................................................................................4
3.1.2.3 Interpretation of data to inform business analysis.......................................................4
3.1.3.1.1 CV for the variables..................................................................................................5
3.1.3.1.2 Interpretation of the coefficient of variation.............................................................5
3.1.3.2.1 Definition of the correlation coefficient (CC)..........................................................6
3.1.3.2.2 Interpretation of the coefficient of correlation (CC).................................................6
3.2.1 Definition of SLR...........................................................................................................7
3.2.2.2 Interpretation of the different terms of SLR model.....................................................8
3.2.2.3 Explanation of E(Y).....................................................................................................9
3.2.2.4 Interpretation of data to inform business decisions.....................................................9
Conclusion.....................................................................................................................................10
References......................................................................................................................................11

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2. Introduction
According to the findings, Indian has been facing an emerging growth in the Gross
Domestic Product over the last twenty years of span. The household consumption level, export
and import are considered as the key influential factors behind the exponential growth of GDP.
The paper attempts to apply some statistical tools like simple and multiple regression line in
order to do analyze the correlation impact of the concerned four variables, such that, Gross
Domestic Product (GDP), household consumption, export and import.
2.1. Understanding quantitative analysis in economics
Quantitative analysis is aimed at analyzing the performance of economics by means of
statistical and mathematical measurements. This analysis follows a logical approach in order to
get effective outcome in numerical terms (Kumar and Singh 2015). The researchers apparently
provide a pragmatic solution regarding the financial performance, such as, trend in Gross
Domestic Product, employability and investment in the economy (Faruqui, Ara and Acma 2015).
Further, the development of predictive modelling provides proactive solutions to the future
events.
2.2. Quantitative analysis: informing business
The quantitative approach observes the current data and formulates policy in accordance
with the anticipated market structure. The report provides the researchers collective information
about the market structure, wages, revenues and stakeholders (Pradhan 2017). Regarding the
objectives and possible outcome of the existing business structure, the report is considered as a
best analytical tool for the business analyst (Stanley and Doucouliagos 2015). Quantitative

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analysis seems like a comprehensive report of a large data set related to the definite segment of a
population.
2.3. The objective of this report
The objectives of the paper are mentioned as follows:
1. Determine the relationship between the GDP and household expenditure.
2. Determine the association between the household expenditure and export along with the
import
3. Examine the expected changes in these four variables.
2.4. Structure of this report
The paper has been structured followed by two sections. The first section analyses the
methodology used for the findings of the paper followed by the implications of the statistical
tools. In last section, the paper summarizes the report with probable consequence the country
will be going to face in the coming years.
3.1.2.3 Interpretation of data to inform business analysis
Referring to descriptive statistical analysis in table 2, the average consumption level
related to the household sector is reported to have large contributor to India’s gross domestic
product compared to export and import. Meanwhile, import exceeds the export on the average
level reflecting the country’s trade deficit condition (Moral‐Benito 2015). The reported mean
value is lower than the mean which ensures that data set is positively skewed, resulting in that,
the growth rate of the three sector is increasing at decreasing rate. In terms of the estimated value

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of the CV, the report supports the existence of large dispersion in the data source (Moberg 2015).
Here, the CV value is calculated around and more than 50% implying the larger dispersion from
the mean.
3.1.3.1.1 CV for the variables
Table 2 provides the required analysis in terms of the CV of the given four variables. The
CV shows the relative variation of the two different variable which are valued in the different
units. The result is delivered in the percentage term in order to avoid the measurement
complication of two different units (Maruthappu et al. 2016). The higher the CV, the more
invalidate association between the variables. The lower CV assures higher consistency of the
data set towards the mean value (Tandon and Ahmed 2016).
3.1.3.1.2 Interpretation of the coefficient of variation
In case of a rice producing firm, the analysis covering mean, median, standard deviation
and correlation coefficient will give a pragmatic market analysis to the rice producer for the last
19 years. Contextually, the value of the SD and CV supports the presence of significant amount
of dispersion (Kaushal and Pathak 2015). The trend of the variables is reported to follow a
positively skewed pattern reflecting that rice production is increasing at decreasing rate as mean
is greater than the median. The highest dispersion is reflected in GDP value. The household
expenditure is a key determinant of the market demand for the product like rice (Klees 2016). On
the other hand, excess import over the export leads the economy to a trade deficit situation.

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For GDP: The CV for GDP is stated as 50% reflecting the existence of considerable data
fluctuation from the average GDP.
For Household Consumption: This sector is also reported to score 50% in CV, resulting in that
all entities are not likely to contribute in the same proportion to the GDP.
For Export: The relative variation of the export sector asserts that 55% of the data set is
deviated from the mean value. The fluctuation invariably is present in the concerned dataset.
For Import: Around 56% data of the considerate data base is deviated from the mean in case of
the import sector (Jeon 2015). The higher the relative variation value the high fluctuation in the
export earnings.
3.1.3.2.1 Definition of the correlation coefficient (CC)
This statistical tool measures the degree of association between the two variables. This
analyses the change of one variable with respect to the other variable. The negative association
asserts the inverse relationship between the variables, whereas, the positive association indicates
direct relationship within the two variables (Jain, Nair and Jain 2015). The value of CC ranges
between +1 to -1. The value tends to +1 indicates proximity towards the positive linear
association. In contrast, the value towards -1 implies the negative linear connection between the
variables.
3.1.3.2.2 Interpretation of the coefficient of correlation (CC)
GDP – HH Consumption, GDP- Export, GDP Import
Considering table 3, there exists strong relationship between the GDP and the house hold
consumption. GDP is stated to have strong association with the three mentioned variables
including household consumption, export and import sector. The result shows that change in the

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variable will give considerable impact on the GDP (Ramesh and Vardhan 2015). The positive
association, value of almost 1, asserts that there is a high tendency of the existence of linear
relationship between GD and other three variables. Therefore, any kind of minor changes in the
domestic outlet, export and import sector will bring effective impact on the GDP growth
(Agrawal 2015). As far as the rice producer is concerned, the rise in the rice consumption at the
household level tends to the rise in the growth of GDP and vise-versa as CC is almost value at 1.
Apart from domestic consumption, the rice production can be expected to increase as rise in the
export and import (Ioannidis, Stanley and Doucouliagos 2017). The rise in export encourages the
producer to enlarge the production capacity of the rice firms. On the other hand, positive
association with the import implies that the trade liberalization has given an impressive outcome
to enhance the output growth of the economy.
HH con. – Export, HHC – import
In the case of domestic consumption, the value of CC is reported as around the value of 1
in relation to the level of export and import. This positive outcome suggests that change in export
earnings and import cost is directly related to the change in the domestic consumption (Dimos
and Pugh 2016). The rise in export apparently intensifies the GDP growth, which in turn,
aggravates the domestic expenditure (Gupta and Batra 2016). However, increase in the import
will diminish the domestic income, resulting in that contraction in the household outlet.
Export – import
The relation between import and export shows a significant positive association with
respect to the given data set. This outcome can be inferred as the consequence of the trade

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liberalization impact (Agarwal and Ghosh 2017). The strong positive association between the
export and import sector ensures positive
3.2.1 Definition of SLR
The simple linear regression is defined as a statistical measurement to analyze the
association between two quantitative variables. The change in independent variable determine
the change in the dependent variable. The slope of the SLR indicates the trend line of the
association between the two variables (Ma 2015). The negative slope of SLR implies negative
association, whereas, the positive slope defines positive link between the variables
(Chaikumbung, Doucouliagos and Scarborough 2016). The error term indicates the sum of the
deviations with respect to the regression line. Referring to the paper, there is a positive relation
between GDP and domestic consumption as slope of the regression line is around 1.7 which
denotes negligible association between the variables (Varian 2016). The domestic expenditure as
a dependent variable tends to grow as increase in the GDP as an independent variable.
3.2.2.2 Interpretation of the different terms of SLR model
1.Y-intercept (Constant B0) = 7842.302444
The positive Y-intercept of the regression line results the projected value of y irrespective
of the data value of independent variable (Dzhumashev, Mishra and Smyth 2016). The intercept
term denotes the constant impact on the dependent variable.
2. Regression Coefficient (Slope of Regression Line B1) = 1.713451253
The regression slope defines the positive coefficient value though the reported value is
insignificant in terms of numerical value. The insignificant positive coefficient value asserts that
change in GDP will give negligible impact on the domestic consumption.

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3. Random Variable (standard error) = 48259.28821
The standard error explains how much the estimated outcome variates from the actual
value. The larger the value of standard error, the larger the difference between the actual and
predicted outcome (Grüne, Semmler and Stieler 2015). The large estimated value of standard
error denotes that original outcome differs at a large size from the actual result.
4. R2 = 0.995749423
R2 statistics measures the coefficient of determination of the regression model. This
estimates the how well the regression model has been explained by the given variables in order
to do the required analysis (Forbes et al. 2015). R2 = 100% implies that variability of the model
is completely explained by the predictor variables, however, 0% denotes that the set of given
variables entirely fails to explain the predictability of the data set (Tripathi, Seth and Bhandari
2015). In this case, the variability of the data set has been completely explained by the model as
value is almost tends to 1.
3.3. Multiple Linear Regression
3.3.1. Regression Model 2: HH Consumption =84605+5.55Export-2.61Import+e
3.3.2.1 Dependent Variable = Y
So, HH consumption = Y
Independent variable = X
X1 = Export
X2 = Import
HH Consumption =84605+5.55Export-2.61Import+e

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3.2.2.3 Explanation of E(Y)
The conditional expectation of a random variables gives expected value given the value
of another random variable. The standard equation for the conditional mean is used as follows.
E(Y|X) = b+Xb+e, where, X is an independent variable, Y is a dependent variable, e is random
error and b is constant part. Referring to the given equation Y= 7842.302+1.713451X, the
following chart can be prepared.
3.2.2.4 Interpretation of data to inform business decisions
According to the report, GDP acts as a dependent variable as Y and the household
consumption is taken as an independent variable in terms of X. Applying the conditional mean
theory, the firm can generate a expected mean report of rice production with respect to the given
information about the household consumption (Bel and Warner 2016). As the slope of the linear
equation is positive, the GDP will be increasing with respect to the rise in the domestic outlet.
Meanwhile, another regression equation is derived taking household consumption as a dependent
variable on the basis of two independent variables, such that, import and export. The negative
E (Y|X= 0) 7842.302
E (Y|X= 1) 7844.02
E (Y|X= 2) 7845.73
E (Y|X= 3) 7847.45

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intercept in the case of import regression line indicates that improvement in the import will pull
down the domestic consumption level (Debnath 2017). However, the positive intercept of the
export regression line suggests that growth in the export will enhance the household
consumption (Tarmizi, Daulay and Muda 2017). The predictability model is completely
supported by its both high R2 and adjusted R2 value implying that 99% of the model is explained
by the predictor variables and the model id good fitted.
Conclusion
In a concluding note, the country’s the country’s GDP growth invariably is affected by
the household consumption level. Emerging growth of the country’s output level is the
consequence of the rise in the household consumption level. Further, rising import declines the
level of disposable income, which in return, lowers the household consumption level. However,
increase in the export earnings boost the country’s income level giving positive impact on the
domestic consumption level. Unfortunately, the country has been experiencing more growth in
the import sector compared to the export sector and this is a serious concern to the economists.

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References
Agarwal, M. and Ghosh, S., 2017. Structural Change in the Indian Economy. In THE
ECONOMIES OF CHINA AND INDIA Cooperation and Conflict: Volume 3: Economic Growth,
Employment and Inclusivity: The International Environment (pp. 113-141).
Agrawal, P., 2015. The role of exports in India's economic growth. The Journal of International
Trade & Economic Development, 24(6), pp.835-859.
Bel, G. and Warner, M.E., 2016. Factors explaining inter-municipal cooperation in service
delivery: a meta-regression analysis. Journal of Economic Policy Reform, 19(2), pp.91-115.
Chaikumbung, M., Doucouliagos, H. and Scarborough, H., 2016. The economic value of
wetlands in developing countries: A meta-regression analysis. Ecological Economics, 124,
pp.164-174.
Debnath, P., 2017. Assaying the Impact of Firm's Growth and Performance on Earnings
Management: An Empirical Observation of Indian Economy. International Journal, 30.
Dimos, C. and Pugh, G., 2016. The effectiveness of R&D subsidies: A meta-regression analysis
of the evaluation literature. Research Policy, 45(4), pp.797-815.
Dzhumashev, R., Mishra, V. and Smyth, R., 2016. Exporting, R&D investment and firm survival
in the Indian IT sector. Journal of Asian Economics, 42, pp.1-19.
Faruqui, G.A., Ara, L.A. and Acma, Q., 2015. TTIP and TPP: Impact on Bangladesh and Indian
Economy. Pacific Business Review International, 8(2), pp.59-67.
Forbes, M.G., Patwardhan, R.S., Hamadah, H. and Gopaluni, R.B., 2015. Model predictive
control in industry: Challenges and opportunities. IFAC-PapersOnLine, 48(8), pp.531-538.

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Grüne, L., Semmler, W. and Stieler, M., 2015. Using nonlinear model predictive control for
dynamic decision problems in economics. Journal of Economic Dynamics and Control, 60,
pp.112-133.
Gupta, V.K. and Batra, S., 2016. Entrepreneurial orientation and firm performance in Indian
SMEs: Universal and contingency perspectives. International Small Business Journal, 34(5),
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Ioannidis, J.P., Stanley, T.D. and Doucouliagos, H., 2017. The power of bias in economics
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Klees, S., 2016. Inferences from regression analysis: Are they valid. Real World Economics
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Kumar, D. and Singh, D., 2015. Export Competitiveness of Indian Textile Industry. Abhinav-
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2277-1166), 4, pp.1-5.

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Ma, C., Rogers, A.A., Kragt, M.E., Zhang, F., Polyakov, M., Gibson, F., Chalak, M., Pandit, R.
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Appendix
Table 1: GDP and sectoral contribution in India (in million dollars): 2000-2019

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Table 2: Descriptive statistics of data from Table 1, 2000-2015
Source: The Author, 2019.
Table 3: Correlation coefficient
Co-Relation
coefficient
GDP House Hold
Consumption
Exports Imports
GDP 1.000 0.998 0.969 0.954
House Hold
Consumptio
n
0.998 1.000 0.956 0.937
Exports 0.969 0.956 1.000 0.994
Imports 0.954 0.937 0.994 1.000
Source: Created by the author

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Table 4: Simple linear regression results
1 Y-intercept
(Constant B0) 7842.302444
2
Regression
Coefficient (Slope of
Regression Line B1) 1.713451253
3 Random Variable
(standard error) 48259.28821
4 R2 0.995749423
Source: created by the author
Table 5: Simple linear regression results
Assumption
House hold
consumption
(X)
Assumption
GDP (Y)
GDP (In Million)
(From year 2000
to 2018)
House Hold
Consumption (In
Million) (Y) (From
year 2000 to 2018)
0 7842.302
468,394.95 298,635.06
100 8013.302
485,441.03 311,444.01
200 8184.302
514,937.96 324,575.63
300 8355.302
607,699.30 373,740.77
400 8526.302
709,148.53 413,799.14
500 8697.302
820,381.67 470,724.54
600 8868.302
940,259.89 527,579.31
700 9039.302 1,216,735.43 678,457.90

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800 9210.302
1,198,895.50 679,495.84
900 9381.302
1,341,886.70 750,918.25
1000 9552.302
1,675,615.31 916,978.11
1,823,049.93 1,024,685.66
1,827,637.86 1,031,901.77
1,856,722.12 1,070,321.69
2,039,127.45 1,185,298.23
2,103,587.81 1,241,269.97
2,290,432.08 1,359,101.98
2,652,551.20 1,564,550.22
2,726,322.62 1,621,631.94
0 0
27298827.33 15845110.01
Source: Created by the author

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Table 6: Multiple regression results
EXPORT IMPORT
Y-intercept (Constant B0) 84605
Regression Coefficient
(Slope of Regression Line
B1) 5.55 -2.61
Random Variable (standard
error) 121245
R2
0.929699714
Source: Created by the author, 2019