AN ANALYSIS ON THE RELATIONSHIP BETWEEN GROSS FIXED CAPITAL
FORMATION (CURRENT LCU) AND GDP PER CAPITA (CONSTANT LCU)
Submitted byName: Mohammad Areeb Arshad
Faculty Number: 20ECB072
Enrolment Number: GJ7978
2023-2024
Project Submitted for ECB6S2
Department of Economics
Aligarh Muslim University,
Aligarh
Table of Content-
1
ABSTRACT ............................................................................................................................ 1
2
INTRODUCTION .................................................................................................................... 2
3
RESEARCH OBJECTIVE ........................................................................................................... 3
4
METHODOLOGY.................................................................................................................... 4
5
6
4.1
Data Source ............................................................................................................................. 4
4.2
Mean ....................................................................................................................................... 4
4.3
Median .................................................................................................................................... 4
4.4
Standard Deviation ................................................................................................................. 4
4.5
Correlation .............................................................................................................................. 4
4.6
Regression ............................................................................................................................... 4
4.7
Intercept.................................................................................................................................. 4
4.8
Regression slope ..................................................................................................................... 5
4.9
Coefficient of Determination .................................................................................................. 5
ANALYSIS OF RESULT ............................................................................................................ 6
5.1
Line Graph ............................................................................................................................... 6
5.2
Descriptive Statistics ............................................................................................................... 7
5.3
Correlation Analysis ................................................................................................................ 8
5.4
Regression Analysis ................................................................................................................. 9
REFERENCES ......................................................................................................................... 1
List of TablesTable 1: Descriptive Statistics ................................................................................................................. 7
Table 2: Correlation Analysis .................................................................................................................. 8
Table 3: Regression Analysis ................................................................................................................... 9
List of FiguresFigure 1: Gross Fixed Capital Formation (Current LCU) And GDP Per Capita (Constant LCU) ................ 6
Analysis on the Relationship between Gross Fixed Capital Formation (Current LCU) and GDP
Per Capita (Constant LCU) – Mohammad Areeb Arshad1
1 ABSTRACT
This study examines the relationship between Gross Fixed Capital Formation (Current LCU)
and GDP per Capita (Constant LCU) in India from the year 1960 to 2021, using data from the
World Development Indicators.
The study uses time-series data for India to examine the relationship between these two
variables. The data is analyzed using Descriptive Statistics, Correlation and Regression
analysis techniques to identify any significant patterns or trends in the relationship between
GFCF and GDP per Capita.
The results of this study indicate a positive correlation between Gross Fixed Capital
Formation and GDP per Capita in India, suggesting that higher levels of GFCF are associated
with higher levels of GDP per Capita. Regression analysis further confirms this relationship,
and shows that GDP per Capita is dependent on Gross Fixed Capital Formation.
The study's findings have important implications for policymakers and stakeholders
interested in promoting economic growth and development in India. Investing in capital
formation may lead to long-term economic benefits and contribute to the country's
economic growth.
1
Department of Economics, Aligarh Muslim University
1
2 INTRODUCTION
India is one of the fastest-growing economies in the world, with a GDP that has been
steadily increasing over the past few decades. The country has experienced significant
progress in reducing poverty and improving living standards, thanks to various policies
implemented by the government and private sector investment. One of the key drivers of
India's economic growth is its investment in Gross Fixed Capital Formation (GFCF).
Gross fixed capital formation (GFCF), is defined as the acquisition of produced assets
(including purchases of second-hand assets), including the production of such assets by
producers for their own use, minus disposals. The relevant assets relate to assets that are
intended for use in the production of other goods and services for a period of more than a
year.
Another important measure of economic development is GDP per Capita, which reflects the
economic output of a country divided by its total population. It is an essential indicator of a
country's standard of living, reflecting the average income of individuals in a country. The
relationship between GFCF and GDP per Capita has been widely studied, with many
research studies suggesting that higher levels of investment in fixed capital formation can
lead to increased economic growth and development.
In India, investments in GFCF have played a significant role in the country's economic
growth, particularly in the manufacturing and infrastructure sectors. Therefore,
understanding the relationship between GFCF and GDP per Capita in India is crucial for
policymakers and stakeholders interested in promoting sustainable economic growth and
development.
This seminar paper aims to analyze this relationship using data from the World
Development Indicators. The study will use correlation and regression analysis techniques to
identify any significant patterns or trends in the relationship between these two variables,
with a focus on the period from 1960 to 2021. Next, we will describe the data sources and
methods used for the analysis and then finally present the results of the analysis and discuss
their implications.
2
3 RESEARCH OBJECTIVE
The primary objective of this seminar paper is to analyze the relationship between Gross
Fixed Capital Formation (GFCF) at current LCU and GDP per Capita in India at constant LCU
from 1960 to 2021. Specifically, the research objectives are as follows:
I.
II.
III.
IV.
To determine the nature and strength of the relationship between GFCF and GDP
per Capita in India over the period 1960-2021.
To identify any significant trends or patterns in the relationship between these two
variables over time.
To explore the potential drivers of any observed relationship between GFCF and GDP
per Capita in India, including factors such as government policies, institutional
quality, and economic conditions.
To discuss the implications of the relationship between GFCF and GDP per Capita for
policymakers and stakeholders interested in promoting sustainable economic growth
and development in India.
These research objectives will guide the analysis of the relationship between GFCF and GDP
per Capita in India and help to identify any significant patterns or trends in this relationship.
3
4 METHODOLOGY
4.1 Data Source
The data used in this paper is from World Development Indicator. (Bank)
4.2 Mean
Mean is nothing but the average of the given set of values. It denotes the equal distribution
of values for a given data set. To calculate the mean, we need to add the total values given
in a data and divide the sum by the total number of values. (Gupta)
4.3 Median
The median is the middle number in a sorted, ascending or descending list of numbers and
can be more descriptive of that data set than the average. It is the point above and below
which half (50%) the observed data falls, and so represents the midpoint of the data.
(Gupta)
4.4 Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its
mean and is calculated as the square root of the variance. If the data points are further from
the mean, there is a higher deviation within the data set, thus, the more spread out the
data, the higher the standard deviation. (Gupta)
4.5 Correlation
Correlation examines the relationship between two or more variables. It is a measure that
describes the strength and direction of a relationship between two variables. It is commonly
used in statistics, economics and social sciences for budgets and business plans.
The correlation coefficient is denoted by the letter "r" and takes values between -1 and +1.
(Gujrati)
4.6 Regression
Regression analysis is concerned with the study of the dependence of one variable, the
dependent variable, on one or more other variables, the explanatory variables, with a view
to estimating and/or predicting the (population) mean or average value of the former in
terms of the known or fixed (in repeated sampling) values of the latter. (Gujrati)
4.7 Intercept
Consider a regression model
𝒀 = 𝒂 + 𝒃𝑿
Here,



a =the intercept of the model.
Y= dependent variable
X= independent variable
The intercept is calculated by below formula
𝑎 = 𝑌̅ − 𝑏𝑋̅
The intercept which is a constant is the point where the function crosses the y-axis. (Gujrati)
4
4.8 Regression slope
The slope of the line of regression of Y on X is also called the coefficient of regression .
𝒀 = 𝒂 + 𝒃𝑿
Here,



b= slope of the regression model
Y= dependent variable
X= independent variable
The slope formula is
𝑏=
∑(𝑋 − 𝑋̅) ∑(𝑌 − 𝑌̅)
∑(𝑋 − 𝑋̅ )2
The slope (b) tells how much change will be there in dependent variable Y, when there is a
unit change in the independent variable X. (Gujrati)
4.9 Coefficient of Determination
The coefficient of determination r2 (two-variable case) or R2 (multiple regression) is a
summary measure that tells how well the sample regression line fits the data and is the
most commonly used measure of the goodness of fit of a regression line.
It measures the proportion or percentage of the total variation in Y (dependent variable)
explained by X (independent variable). (Gujrati)
𝐫𝟐 =
𝐄𝐒𝐒
𝐓𝐒𝐒
Also,
𝒓𝟐 = 𝟏 −
=𝟏−
𝑹𝑺𝑺
𝑻𝑺𝑺
𝟐
∑𝒖
̂
𝟐
∑𝒚
̂
where,
 ESS= Explained sum of sqaure
 TSS= Total sum of square
 RSS=Residual sum of square
 𝑟 2 = coefficient of determination
5
5 ANALYSIS OF RESULT
5.1 Line Graph
GDP per capita(constant LCU) and Gross Fixed Capital Formation(current LCU)
120000,000
80000000000000,000
70000000000000,000
100000,000
60000000000000,000
80000,000
50000000000000,000
60000,000
40000000000000,000
30000000000000,000
40000,000
20000000000000,000
20000,000
10000000000000,000
0,000
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
2014
2017
2020
0,000
GDP per capita (constant LCU)
Gross fixed capital formation (current LCU)
Figure 1: Gross Fixed Capital Formation (Current LCU) And GDP Per Capita (Constant LCU)
6
5.2 Descriptive Statistics
Descriptive Statistic
Gross fixed capital formation (current LCU)
GDP per capita (constant LCU)
Mean
11059884470445.50
40311.47
Standard Error
2246798105039.64
3375.37
Median
1565370555848.33
28736.89
Standard Deviation
17691305970396.90
26577.68
Sample Variance
312982306938202000000000000.00
706372823.49
Kurtosis
1.86
0.25
Skewness
1.71
1.20
Range
67572410896762.80
88424.10
Minimum
25490476637.21
16527.33
Maximum
67597901373400.00
104951.42
Sum
685712837167623.00
2499310.97
Count
62.00
62.00
Table 1: Descriptive Statistics
7
5.3 Correlation Analysis
Gross fixed capital formation (current LCU)
Gross fixed capital formation (current LCU)
1
GDP per capita (constant LCU)
0.975
GDP per capita (constant LCU)
1
Table 2: Correlation Analysis

The Correlation between Gross Fixed Capital Formation (current LCU) and GDP per capita (constant LCU) is 0.975.

This indicates there is a strong positive correlation between the two variable.

This means that as GFCF increases, so does GDP per capita, indicating that investment in fixed assets leads to higher economic growth and
development. This finding is consistent with economic theory, which suggests that investment in physical capital is an important driver of
economic growth.
8
5.4 Regression Analysis
Regression Statistics
R Square
Multipe R
Adjusted R Square
Standard Error
Observations
0.950669271
0.975022703
0.949847092
5952.029174
62
ANOVA
Regression
Residual
Total
df
1
60
61
SS
40963143155
2125599078
43088742233
MS
40963143155
35426651.29
F
1156.280418
Significance F
6.53437E-41
Intercept
Gross fixed capital formation (current LCU)
Coefficients
24111.1911
1.46478E-09
Standard Error
893.5180479
4.30765E-11
t Stat
26.98455969
34.00412354
P-value
2.97627E-35
6.53437E-41
Lower 95%
22323.88889
1.37861E-09
Table 3: Regression Analysis
From the above table
 R square is 0.950669271 which indicates that 95% of variation in the dependent variable GDP per capita is explained by the independent
variable Gross Fixed capital formation, rest is determined by other variables
 The intercept is 24111.1911, which means that when Gross Fixed Capital Formation is zero, the GDP per capita is 24111.1911.
 Multiple R which is the correlation coefficient is 0.975022703, larger the value stronger is the relationship. So there two variables have strong
relationship
 Standard error which shows the average distance that the data fall from regression line is 5952.029174
9
6 REFERENCES

https://databank.worldbank.org/source/world-development-indicators

Gupta, S.C. and Kapoor, V.K. (1997) Fundamentals of Mathematical Statistics. Sultan
Chand and Sons

Gujarati, D.N. (2004) Basic Econometrics. 4th Edition, McGraw-Hill Companies.
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