PUBLIC DEBT AND ECONOMIC GROWTH : INDIAN
EXPERIENCE IN POST REFORM SCENARIO
Project report submitted to CMS College (Autonomous), Kottayam, (Affiliated to MG University Kottayam) in
partial fulfillment of the requirement for the award of the degree of
MASTER OF ARTS
IN
ECONOMICS
By
ABHIJITH PAUL THOMAS
UPRN : 211216101
Under the guidance of
NIBU VARGHESE
Assistant Professor
CMS COLLEGE KOTTAYAM (AUTONOMOUS)
DEPARTMENT OF ECONOMICS
JUNE 2023
CMS COLLEGE KOTTAYAM
(AUTONOMOUS)
DEPARTMENT OF ECONOMICS
CERTIFICATE OF THE SUPERVISOR
This is to certify that the project entitled “PUBLIC DEBT AND ECONOMIC GROWTH :
INDIAN EXPERIENCE IN POST REFORM SCENARIO " submitted for the award of
the degree of Master of Arts in Economics by ABHIJITH PAUL THOMAS is based on
the original work done by him in the Department of Economics, CMS College
Kottayam(Autonomous) affiliated to Mahatma Gandhi University and that the research has
not previously formed the basis for the award of any Degree, Associateship, Fellowship or
any other similar title and it represents entirely an independent work on the part of the
candidate.
Place: Kottayam
Date: 30/06/2023
NIBU VARGHESE
( Supervisor)
Counter Ssigned
HOD
2
ABHIJITH PAUL THOMAS
MA Economics
Department of Economics
CMS College Kottayam
Kottayam - 686001
DECLARATION
I hereby declare that this written document entitled "
PUBLIC DEBT AND
ECONOMIC GROWTH : INDIAN EXPERIENCE IN POST REFORM
SCENARIO" submitted by me for the award of Degree of Master of Arts in Economics,
is done by me under the guidance and supervision of Mr. Nibu Varghese, Assistant Professor,
Department of Economics CMS College Kottayam during the period 2021-2023
I declare that this project has not been submitted by me or others fully or partially for the
award of any other degree, diploma, title or recognition before in this institution or any other
institution.
Place: Kottayam
ABHIJITH PAUL
THOMAS
3
4
ACKNOWLEDGEMENT
I take this opportunity to express my heartfelt gratitude to one and all without
whom the thesis wouldn’t have been completed.
First and foremost, I sincerely thank God almighty for his grace for the
successful completion of the project work.
I acknowledge and extend my deep gratitude to my guide, Mr. Nibu Varghese ,
Assistant Professor, Department of Economics, whose valuable guidance,
technical advice, constant moral support and encouragement has been the ones
that helped me patch this project and make it full proof success. I shall remain
grateful to him for his personal care and attention and for the valuable time that
he spared in spite of his busy work schedule for the successful completion of the
work within stipulated time. I also thank other faculty members of the
Department for their academic help and support.
I would like to express my gratitude towards my parents Mr. Thomas Pailo and
Mrs. Bindhu V.M and my sister Aparna Rose Thomas for their constant support,
blessings and encouragement which helped me in the completion of this project.
My thanks and appreciations also go to my friends in developing the project and
to the people who have willingly helped me out with their abilities.
I am also thankful to all my fellow colleagues, senior colleagues and juniors for
their kind help and support in my study.
I dedicate this work to my parents, sister and all my friends; my constant source
of support and enthusiasm.
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Contents
Page Number
Chapter 1 : Introduction and Overview
10 - 25
1.1 Introduction
10 - 11
1.1a Types of Public Debt
12 - 17
1.1b Causes of Public Debt
17 - 20
1.1c Objectives of Public Debt
20 - 22
1.1d Background of the study
22 - 23
1.2 Objectives
23
1.3 Hypothesis
23 - 24
1.4 Data of the study
24
1.5 Limitations of the study
24 -25
Chapter 2 : Review of Literature and Theoretical perspectives
26 - 34
2.1 Review of literature
26 - 33
2.2 Theoretical framework
34
Chapter 3 : Data and Methodology
35 - 40
3.1 Data
35
3.2 Methodology
35 - 40
Chapter 4 : Analysis
41 - 49
4.1 Unit Root Test
41 - 42
4.2 To analyse Causal relation between Debt, GDP,
Credit of all Scheduled Commercial Banks and Total Revenue Receipts
42 - 43
4.3 To examine the correlation of GDP, Credit of all Scheduled
Commercial Banks and Total Revenue Receipts with Debt
43 - 44
4.4 To analyse short run dynamic relationship between Debt, GDP, Credit
of all Scheduled Commercial Banks and Total Revenue Receipts
44 - 45
4.5 To analyse the long-term association of Debt with the independent
variables GDP, Credit of all Scheduled Commercial Banks and
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45 - 49
Total Revenue Receipts
Chapter 5 : Summary and Conclusion
50 - 52
5.1 Summary
50 - 51
5.2 Major Findings of the study
51 - 52
5.2 Policy Suggestions
52
Bibliography
53 - 55
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List of Tables
Page Number
Table 1: Unit root test
42
Table 2: Granger Causality Test
43
Table 3: Pairwise Correlation
44
Table 4: IRF
45
Table 5: Johansen cointegration test results between Debt and GDP
46
Tables 6: Johansen cointegration test results between Debt and
47
Credit of all scheduled commercial banks
Table 7: Johansen cointegration test results between Debt and
Total Revenue Receipts
8
48
PUBLIC DEBT AND ECONOMIC GROWTH :
INDIAN EXPERIENCE IN POST REFORM SCENARIO
9
CHAPTER 1
INTRODUCTION AND OVERVIEW
1.1 Introduction
In countries like India where incurring public debt is a method to undertake investment in the
much desired economic development. In such cases question whether public debt is a drag on
the economic growth of a state or if it lends a vital nudge to economic growth is of great
significance. This makes an analysis into the relationship between the public debt and
economic growth, urgent and imperative. The continuous rise in government spending widens
the gap of fiscal deficit, and thereby forces the government to depend on public debt from both
internal and external sources. Though the Indian government tries hard to reduce the fiscal
deficit by promoting an inflow of foreign investment and disinvestment, sustaining a lower
fiscal deficit becomes challenging, mainly due to high subsidies on food and fertilizer. The
economic consequences of high fiscal deficits result in heavy public debt, which is likely to
affect the economic growth of the nation. The public debt scenario of the Indian government
in the post-reform period was worse than in the pre-reform period.
India after independence opted for a path of economic development based on centralised
planning with a leading role assigned to public debt on the consideration that it could be
instrumental to achieve rapid economic growth through planned investment. However, large
amount of public debt may crowds out private investment by raising interest rate. Policymakers
need to be aware of the effect of public debt on economic growth for effective policy
formulations. As per the recommendations of the FRBM review committee (2017) headed by
N.K. Singh, a prudent medium-term ceiling for the general government debt is 60% of the
GDP, a ceiling of 40% for the Centre and the balance 20% for the States. In this context, the
management of debt plays a pivotal role in stimulating economic growth.
India, with its vibrant economy and diverse population, has emerged as one of the fastestgrowing economies in the world. Over the past few decades, the country has witnessed
significant economic growth, driven by various factors such as globalization, liberalization,
and domestic reforms. However, like many other nations, India has also had to grapple with
the challenge of public debt and its impact on economic growth.
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Public debt refers to the accumulated borrowings of the government to finance expenditures
when its revenue falls short. It is an essential tool for governments to bridge the fiscal gap and
meet their financial obligations. However, excessive public debt can have adverse effects on
the economy, hindering growth and stability. Public debt may be acquired from both internal
as well as external sources. An internal debt involves only transfer of wealth within the
borrowing community. In case of external loans, it involves firstly, a transfer of wealth from
the lending to the borrowing community, when the loan is raised and secondly, a transfer in the
reverse direction when the interest on principal is repaid (Meade, 1958). Public debt includes
domestic and external liabilities excluding small savings and total liabilities. Internal debt
includes market loans and bonds, treasury bills and special securities issued to RBI etc. Other
liabilities include Small Savings, Provident Funds, Reserve Funds and Deposits.
Understanding the dynamics of public debt and its influence on economic growth is crucial for
policymakers, economists, and citizens alike. It helps us grasp the complexities of fiscal
management and enables us to assess the long-term sustainability of the Indian economy.
The public debt scenario of the Indian government in the post-reform period was worse than in
the pre-reform period. Post-reform, the central government debt was 68.3% of GDP in 1992–
1993, increasing to 72.3% in 2002–2003, and then slightly declining in consecutive years till
2010-11 which reached 89.6% in 2019-20, but declined to 84.68% in 2020-21. Further, the
combined central and state governments’ average debt (public debt plus other liabilities) during
the post-reform period was 79%. Although there has been marked improvement in repaying
external debt (principal and interest payment) by the central government, particularly after
2000, the internal debt and other liabilities, such as the National Small Saving Fund (NSSF),
the Provident fund, and the Deposit and Reserve funds are on the rise, particularly after the
global financial meltdown in 2007-2008.
The present study aims to analysis the effect of public debt on economic growth in India from
1990 to 2021. Pair wise correlation technique, is employed in order to analyse the effect of
Public Debt on GDP, Revenue Receipt and Credit of all Scheduled Commercial Banks. For
causality analysis Clive Granger’s Granger causality test is employed. And for analysing the
long run effect Johansen-Juselius cointegration test is also employed.
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1.1a Types of Public Debt
It is the total amount of money that is owed to the public by the government to meet the
development funds. In public finance, it is also known as public interest, government debt,
national debt and sovereign debt. The public debt can also be owed to lenders within the
country. Even foreign leaders can owe money to the public, but this type of debt is called
external debt. By handing out government bonds and bills, the government can create public
debt. The debt that is the addition of all prior deficits is also termed public debt. If one person
wants to analyse the public debt, the common method to do so is to check the duration until the
repayment is due. Debt can be either short-term, medium-term or long-term. A debt of one year
or less is generally considered short-term debt. In contrast, the ones that go for around ten years
or longer come under long-term debt. The one that is between these two ranges is the mediumterm debt. Economists have divided debt on the bases of use, target, time limit and terms of
payment. The different types of public debt are following –
a. Internal and External Debt
Internal debts are those public debts taken from the country inside, but the external debt is a
debt taken from foreign governments. Foreign people and international organizations, In
Dalton's words, “A debt is internal if given by those people or organizations living in that area
that is controlled by the local officer of taking debt. Governments often borrow money by
issuing government securities such as treasury bills, bonds, and notes to finance their
expenditures when tax revenues and other sources of income are insufficient. These securities
are sold to individuals, banks, pension funds, insurance companies, and other entities within
the country. The individuals and institutions that purchase these securities become creditors to
the government and hold the internal public debt. The government uses the borrowed funds for
various purposes, including infrastructure development, social welfare programs, defence
spending, and debt refinancing. The government pays interest on the debt periodically and
repays the principal amount at maturity.
Government borrowing can help stabilize the economy during times of recession or financial
crisis by injecting money into the system and stimulating demand. Internal public debt is often
considered less risky compared to external debt because the government borrows in its
domestic currency and has more control over repayment terms. Funds raised through internal
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borrowing can be invested in infrastructure projects, education, healthcare, and other sectors
that promote economic growth.
A debt is external, if given by those people and organization living outside of that area”. By
the payment of interest on foreign debt, there is a reduction in net income of debtor country
because their income’s big part goes to the foreign country, but it doesn’t effect at the time of
paying interest on internal debts. Whether the interest on internal debts leave on tax payers or
taken from them and paid as a form of interest on war debts, it does not affect the national
income of the country, that becomes stable like before. This is form of method by which money
is taken from the taxpayer one pocket is been debt in another pocket. Governments often
borrow money from foreign sources to finance various activities, such as infrastructure
development, social programs, budget deficits, or to stabilize their economies. The borrowed
funds are typically obtained through issuing bonds, notes, or other debt instruments in foreign
currencies.
External debt is usually denominated in a foreign currency. This exposes the government to
currency risk, as fluctuations in exchange rates can affect the debt burden. Depreciation of the
local currency can increase the cost of servicing the debt and make repayment more
challenging. The interest rates on external debt may be influenced by global market conditions,
credit ratings, and the perceived risk associated with the borrowing country. Governments with
weaker credit ratings or uncertain economic conditions may face higher interest rates,
increasing the cost of borrowing. External debt also carries the risk of default. If a government
is unable to repay its foreign obligations, it may face severe economic and financial
consequences, including damage to its creditworthiness, restricted access to international
capital markets, and loss of investor confidence. Servicing external debt requires making
regular interest payments and repaying the principal amount. These payments are typically
made in foreign currency, necessitating the government to maintain adequate foreign exchange
reserves. The accumulation of excessive external debt can strain a country's economy. High
debt levels may lead to reduced investor confidence, increased borrowing costs, and limited
fiscal flexibility, potentially hindering economic growth and development.
Managing external public debt requires careful debt management strategies and fiscal
discipline. Governments may employ measures such as prudent borrowing practices,
diversification of funding sources, managing exchange rate risks, and implementing policies to
promote economic stability and growth.
International financial institutions such as the International Monetary Fund (IMF) and the
World Bank provide assistance to countries facing debt-related challenges. They may offer
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financial support, debt restructuring programs, and policy advice to help countries manage their
external debt burdens effectively.
b. Productive and Unproductive Debt
This classification depends upon on the use of public debt. Debts can be used for the production
works and unproductive debt.
Productive debts are those debts which are used in those plans which provide income, like
railway, plans of electricity and the plans of irrigation. The income got from these plans can be
used to the payment of yearly interest and for the payment of Principle. So, productive or
reproductive debts are those debts where are same costs or the assets of more cost kept. By
this, productive debt never put pressure on government and taxpayer.
Unproductive debt are those debts used in that plans, no income is provided, for example,
war. So, unproductive debts are those debts, no assets is in the back. The main reason of
unproductive debt is not only on war but at some point the losses of interest is also the reason.
c. Redeemable and Irredeemable Debt
Redeemable debts are those debts the government promises that he will pay back the debt on
a fixed date. These debts are also called terminable debt. Redeemable debt often includes a call
provision, which gives the issuer the right to redeem the debt at a specified price or within a
specific period. The call price may include a premium or penalty, depending on the terms of
the debt agreement. Some redeemable debt may have a sinking fund provision. A sinking fund
requires the issuer to set aside regular payments into a fund that will be used to retire the debt
gradually over time. The issuer can then redeem a portion of the outstanding debt using the
funds accumulated in the sinking fund. The issuer may have the flexibility to repay the debt
early without a specific call provision or sinking fund requirement. This can occur if the issuer
has excess cash or decides to refinance the debt at more favourable terms.
Redeemable debt provides flexibility for issuers by allowing them to manage their debt
obligations based on their financial conditions, interest rate environment, or other strategic
considerations. It can help reduce interest expenses, lower refinancing risks, or adjust the debt
structure to align with changing needs. For investors or lenders, redeemable debt introduces
the possibility of early repayment, which may impact their investment return or interest income.
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However, redeemable debt may offer higher yields or premiums compared to non-redeemable
debt to compensate investors for the potential early repayment risk.
Irredeemable Debts are those debts which are without any promise they are called
irredeemable or perpetual debt. When debts are not returned then the governments have to do
same arrangement to paid back the debt. If government decides that these debts will be paid
back from the tax income, which is the best way in almost all the situations for this work they
have to put new taxes. So in the condition of redeemable debts government have to pay both
interest and principle amount on a fixed future coming date.
d. Funded and Non-Funded Debt
Government debt can also be divided in the form of funded and non-funded debt.
Funded debts are long term debts. Payment of these debts can be done within one year or it
can be possible, not to give any promise regarding this in other words funded debts are those
debts, in which the payments are given with in, one year.
Treasury bonds are unfunded debts, because these debts are given for three or six months and
their time period is not more than one year. Even then, this is clear that in the condition of
funded debts, government is responsible to pay the regular payment of interest to the debt
payer; yes, their basic money payment is totally left on the government.
e. Voluntary and Compulsory Loans
Government debts are normally of voluntary nature and to person and organizations controlled
by the government bonds are voluntary.
Today compulsory loans are not much popular but in the condition of war, government are can
put pressure on people to give loans. Government can also help in the condition of depression,
so that work power from the hands of people could be reduced and stop the increasing rates.
f. With Rate of Interest and without Rate of Interest
On loans with rate of interest, government gives interest on a fixed rate to the loan taker after
a fixed time period. debt without rate of interest, loans government don’t have to pay any
interest.
Debt without rate of interest, loans government don’t have to pay any interest.
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g. Purchasable and Non–Purchasable Debt
Purchasable debts includes government securities; whose sale and purchase is not possible
independently. Purchasable debt could refer to debt instruments or obligations that are available
for purchase or trade in financial markets. These are typically debt securities that investors can
acquire from the original debt holders, such as governments or corporations, through primary
or secondary markets. The tradability of purchasable debt provides liquidity and flexibility for
investors to manage their portfolios and potentially earn returns by buying debt instruments at
one price and selling them at a higher price in the secondary market.
Non-purchasable debt could refer to debt obligations that are not available for purchase or
trade in financial markets. These are debts that are not structured as tradable instruments or are
not intended to be sold to external investors. Non-purchasable debt could include various types
of debt obligations that are not publicly traded or are held by specific entities without being
offered for sale. Non-purchasable debt may have limited liquidity, as it is not easily transferable
or tradeable. The holders of such debt instruments may not have the option to sell or exchange
them with other investors.
h. Total Debt and Net Debt
Total debt refers to the overall amount of debt owed by an entity. It represents the sum of all
outstanding liabilities, including both short-term and long-term obligations. Total debt includes
both borrowings from external sources (such as loans, bonds, and credit lines) and internal
borrowings (such as intercompany debt). It represents the absolute amount of money owed by
the entity at a specific point in time.
Total debt is an important indicator of the entity's overall financial obligations and its ability
to repay its creditors. It is often used to assess the entity's leverage or debt burden. High levels
of total debt relative to its income or assets may indicate a higher risk of default or financial
instability.
Net debt is a measure that takes into account an entity's total debt but adjusts it for any available
cash or cash equivalents. It represents the actual debt burden after considering the entity's
available liquid assets that can be used to offset a portion of the outstanding debt.
To calculate net debt, you subtract the entity's cash and cash equivalents from its total debt.
Cash equivalents typically include highly liquid assets such as bank balances, short-term
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investments, and marketable securities that can be readily converted into cash. Net debt
provides a more accurate picture of the entity's financial position and its ability to service its
debt obligations. It reflects the entity's net indebtedness after considering its available
resources. A lower net debt indicates a stronger financial position and greater capacity to handle
debt repayments.
i. Short Term and Long Term Debt
When government takes debt for a short period, then this is called short term debt. These
debts are paid back in the time period with in a year that is to be taken to complete the tenure
of debts. Short-term debt, also known as current or near-term debt, refers to debt obligations
that are due for repayment within a relatively short period, typically within one year or less. It
represents debt that is expected to be settled or refinanced within the short term. Short-term
debt is commonly used to finance working capital needs, cover temporary cash flow shortfalls,
or address immediate funding requirements. It is generally associated with shorter repayment
terms and higher interest rates compared to long-term debt. However, short-term debt can
provide flexibility and can be more easily adjusted or refinanced as business needs change.
When governments take debt for a very long period then this is called long term debit. The
time of giving it back is not fixed. At that time the debt is paid back, the debt giver got regular
interest. Long-term debt is often used to finance major investments, capital expenditures,
infrastructure projects, or acquisitions that require an extended repayment period. It provides
stability and allows borrowers to spread the repayment over an extended period, typically with
fixed or variable interest rates. Long-term debt typically carries lower interest rates compared
to short-term debt due to the longer repayment period. Lenders and investors may have more
confidence in providing long-term financing since it aligns with the expected life of the
financed asset or project.
1.1b Causes of Public Debt
Government can borrow because it can possible that local income was not enough for their
expenditure due to incidental expenditure government could have to borrow because it is not
possible to increase the tax income at that point. Government can borrow finance arrangement
of capital expenditure because current revenue will not be enough to fulfil the target. At the
time of depression, when private demand is not enough then government borrow, the extra
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savings of people which is not in use and spends it to increase the effective demand and by this
gives birth to the extra income and employment in the society. These extra amounts from
government taxes are supplementary to each other.
a. Small Share of Taxes in National Income: After India got independence, there is an
increase in national income four times more. In present, there is a part of tax less than 20% in
the national income but this percentage in America is 22.42%, in Sweden 26.3%, in Australia
27.9%, in Nederland 29.2% and in England 30.4%. Except this, in these countries, the most
part of income from taxes got from direct taxes and in India the most part of the tax income is
from indirect taxes. So, in this condition, fiscal policy cannot be able to help in the increase in
the development of Economy.
b. Burden of Indirect Taxes: In Indian tax system, there is a burden of indirect taxes that is
not just. In the economy there is an inflation increase in indirect taxes that the complete tax
arrangement has become imbalance and unjust. Most of the pressure of taxes are from in
indirect taxes the lower class people who have to face as comparison to rich section so this
increase, economical problems in society. Regarding this, Prof. K.T. Shah says right, “Even
there is more tax ability in rich section and there is less tax pressure on them. In its opposite,
on lower section the tax pressure is equal to the part of lion and the ability to face is equal to
the baby sheep”.
c. Fiscal Deficit: One of the primary causes of public debt in India is the fiscal deficit, which
occurs when the government's expenditures exceed its revenues. The government often resorts
to borrowing to cover the deficit, leading to an increase in public debt. In India, if the fiscal
deficit is consistently high over time, it leads to a continuous accumulation of public debt. The
government's borrowing to cover the deficit adds to the outstanding debt, and the interest
payments on the borrowed funds contribute to the debt burden. Failure to effectively manage
fiscal deficits can result in an unsustainable debt burden and affect the country's
economic stability. Rangarajan and Srivastava (2005) argued that a large fiscal deficit and
interest payments to GDP adversely affected economic growth.
d. Government Spending: The government's expenditure on various sectors such as
infrastructure development, social welfare programs, defence, and subsidies contributes to
public debt. If the government spends more than it can generate through tax revenues, it needs
to borrow to finance its spending. India has been investing heavily in infrastructure
18
development projects such as roads, railways, ports, and power plants. Financing these projects
often involves borrowing, which can contribute to the accumulation of public debt.
e. Imperfect Tax System: The Indian tax system is not work perfect. In India, there is very
high tax evasion because our tax system is full of error. According to the idea of Prof. Kaldor,
in India there are tax evasion of ` 200/- to ` 300/- crore, every year. Except this, the complete
part of income tax is never collected. An imperfect tax system can results in a shortfall in tax
revenue. If the tax system is ineffective in generating sufficient revenue, the government may
rely more heavily on borrowing to meet its financial obligations. This increased borrowing to
compensate for the revenue shortfall adds to the overall public debt burden. Further, an
imperfect tax system can lead to distortions and inequities in the distribution of the tax burden.
f. Subsidies and Welfare Programs: The government provides subsidies and welfare
programs to support various sections of society, including farmers, the poor, and marginalized
communities. While these initiatives are crucial for social welfare, they can strain the
government's finances and lead to higher levels of public debt. The government provides
subsidies to various sectors, such as agriculture, food, fuel, and exports, with the aim of
supporting specific industries, promoting social welfare, and addressing economic challenges.
While subsidies are essential for equitable development and supporting vulnerable sections of
society, they can strain the government's finances and contribute to public debt. If the
expenditure on subsidies is not effectively managed and targeted, it can lead to increased
borrowing to finance these programs, thus adding to public debt. Subsidies and welfare
programs contribute to the fiscal deficit, which is the difference between government
expenditure and revenue. If the expenditure on subsidies exceeds the revenue generated
through taxes and other sources, it creates a fiscal deficit. To bridge this deficit, the government
may resort to borrowing, thereby increasing public debt.
g. External Borrowing: India may borrow from international institutions, such as the World
Bank and International Monetary Fund (IMF), or issue bonds in global financial markets.
External borrowing can be a cause of public debt if the government is unable to generate
enough foreign exchange earnings to service these debts. The interest payments on previously
borrowed funds form a significant portion of public debt. If the government continually
borrows without adequately repaying its debts, the interest obligations can accumulate and
contribute to the overall debt burden.
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h. State Government Debt: In addition to the central government, individual state
governments in India also contribute to public debt. State governments borrow to finance their
own development projects and fulfill their financial obligations, which can add to the overall
public debt burden.
i. Misuse of Public Income: In India, a big part of public income misuses. A big part is spent
on undeveloped plans, except this some plans are started on the basis of standard. A big quantity
is spent on them even public got no gain from these. There is a big quantity spent on
government departments where there is corruption, bribe, and red tapism available and work is
completed with very difficult. By this reason, there is a reduction in production.
1.1c Objectives of Public Debt
a. Income and Revenue: The target of public debt normally is to cover the ditch that developed
in any year between proposed expenditure and expected revenue. Whenever because of
increased administrative expenditure or flood, feminine, earth quake and communicable
diseases like unexpected problem government's income becomes less because they have to
spend it to covers these problems then government cover it by taking Indian and Foreign debts.
This is the government; whose income is different from all the taxes and revenue sources.
b. In Times of Depression: Depression is the condition when costs reduce, there is a lack of
courage in people for spending money on industries and in future there is no possibility of
getting gain. This condition can be removed when there is increase in the demand of things and
services and that is possible when in the country there is an increase in the expenditure of public
construction work or most important public use and infra-structure services.
c. To Curb Inflation: Inflation is the name of that condition at the time of increased cost. So,
government by taking debt can take back a big quantity of work power from the hands of people
but modern economists believe that as comparison to government tax, taxation is said to be
more important will to remove inflation, because if the debted government money is never used
in productive use, it increase the responsibility for government to give it back. But waste tax income can easily to be debited in the government fund so the pressure can be removed from
production in economy.
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d. To Finance Development Plans: In undeveloped economy, there is always a lack. In these
countries, as the ability to pay the bill is less. So, government cannot take shelter on heavy
taxation. But to remove poverty from the country, this is also most needed and important to do
arrangements of development plans. In this condition, the only way is to take public debt. So,
the governments of undeveloped countries take debts from within the country or from foreign
governments or from people to do finance arrangements.
e. The Finance Public Enterprises: Government also takes debts for the arrangement of
finance for the commercial enterprises running by itself. This is very common in India since
independence.
f. Expansion of Education and Health Services: Government can also take debt for the
construction and development of education and health services and other services like this That
helps to increase normal social welfare but does not give any direct finance and that is not
productive from the angle of currency.
g. To Finance War: Government can take debt for the self-defence work. In the present
century of increased international pressure and atomic war, there is a need of money in big
amount to save the country from foreign attacks and for self-defence services and to do the
arrangement of modern decoration. But it is very difficult to collect the money for modern wars
only by taxation because it affects the production unfavourably. So, to cope up with this type
of situation government can take shelter from public debt from inside and outside the country.
h. For the Establishment of Social Society: For the establishment of socialist society,
government is doing nationalism of industry and business in present time and running it
themselves, but to run modern industries, there is a need of big quantity of money government
can only fulfil this by taking debts.
i. To Cover the Expenditure on Administrative Work till Getting Income: The income
which government got from taxes that is available at the end of the year but expenditure is from
the starting of the year so at the beginning of the year government spends money by taking
debt and pays the debts when it got the income in the last of the year.
21
j. To Make the Public Verdict Favourable: When the citizens are not able to pay the tax then
the government have to take debt. Sometimes even then the more capability of public, the
government never increase taxes because the public verdict sticks to favourable.
1.1d Background of the study
The public debt scenario of the Indian government in the post-reform period was worse than in
the pre-reform period. Post-reform, the central government debt was 68.3% of GDP in 1992–
1993, increasing to 72.3% in 2002–2003, and then slightly declining in consecutive years till
2010–2011 (Handbook of Statistics on Indian Economy, 2012). However, it remains an
alarming fact that the average public debt of the central government during the post-reform
period was 65%, which is higher than the debt of the pre-reform period. In the past decade,
and after the COVID-19 pandemic, there is a substantial rise in public debt, which reached
84% of the GDP in 2021. The continuous rise in government spending widens the gap of fiscal
deficit, and thereby forces the government to depend on public debt from both internal and
external sources. Though the Indian government tries hard to reduce the fiscal deficit by
promoting an inflow of foreign investment and disinvestment, sustaining a lower fiscal deficit
becomes challenging, mainly due to high subsidies on food and fertilizer. The economic
consequences of high fiscal deficits result in heavy public debt, which is likely to affect the
economic growth of the nation.
Public debt in India can be classified into external and internal debt. The internal public debt
of India has increased in terms of gross domestic product (GDP) from 36.8% in 1960 to 41.2%
in 1970, and from 41.6% in 1980 to 55.3% in 1990. Although it registered a declining trend
from 1995 to 1998–1999, it rose further to 53.4% in 1999, and even more in 2010 at 66%. It
further increased to 89.2% in 2020. However, if other liabilities are taken into consideration
along with the internal debt, the figures are much higher, both in pre-reform and post-reform
periods. The trends in India’s external debt also increased from USD 261 billion at the end of
March 2010 to USD 305.9 billion at the end of March 2011. The increase can be directly linked
to the higher external commercial borrowing and short-term flows. The share of commercial
borrowing in total external debt increased from 19.7 % by the end of March 2005 to 28.9% by
the end of March 2011. The long-term external debt accounted for 78.8% of the total external
debt and the remaining 21.2% was short-term debt.
22
During 1990-91, total debt as percentage of GDP was 53.7% which rose up to 61.5% during
2004-05. Although in the last few years, total debt has slightly come down, it still amounted to
50% of GDP during the year 2014-15 and it further increased to 84% in 2020-21. Public debt
(sum of internal debt and external debt) amounted to 42.3% of GDP during 2002-03 from 31%
during 1990-91. During 2014-15, Public debt was still high (close to 39%). The average central
government debt was 53.1% of GDP during the early reform period from 1992-93 to 2003-04
while in the later reform period (2004-05 to 2014-15) it was 55% (Handbook of Statistics on
Indian Economy, 2015). Similarly, the average public debt was 34.4% during the early reforms
period which soared up to 39% during the later reforms period.
In the past decade, and after the COVID-19 pandemic, there is a substantial rise in public debt.
India's general government debt (Centre and states) to GDP, which 75 % in 2018-19, rose
sharply to 88.5% in 2019-20. This large rise in Government debt to GDP is due to the COVID19 pandemic. In year 2020, due to government's policies in response to Covid -19, India's
external debt grew unprecedently. By the end of March 2022, India's external debt raised to
$620.7 Billion, this was $47.1 Billion higher than previous financial year.
1.2 Objectives
This study aims at the following:
a. To analyse the relationship of Public Debt with Gross Domestic Product, Total Revenue
Receipts and Total Credit of all Scheduled Commercial Banks of India.
b. To study the causality between Public Debt and Gross Domestic Product, Total Revenue
Receipts and Total Credit of all Scheduled Commercial Banks of India.
c. To study about the long-term association of Public Debt with Gross Domestic Product,
Total Revenue Receipts and Total Credit of all Scheduled Commercial Banks of India.
d. To analyse short run dynamic relationship between Debt, GDP, Credit of all Scheduled
Commercial Banks and Total Revenue Receipts of India
1.3 Hypothesis
a. There is no relation between Public Debt and Gross Domestic Product, Public Debt and
Total Revenue Receipts and Public Debt and Total Credit of all Scheduled Commercial
Banks.
23
b. There is no causality between Public Debt and Gross Domestic Product, Total Revenue
Receipts and Total Credit of all Scheduled Commercial Banks
c. There is no long-term association of Public Debt with Gross Domestic Product, Total
Revenue Receipts and Total Credit of all Scheduled Commercial Banks
d. There is no short run dynamic relationship between Debt, GDP, Credit of all Scheduled
Commercial Banks and Total Revenue Receipts of India
1.4 Data of the study
To fulfill the above mentioned objectives the times series data have been collected from various
secondary sources. This study uses time series data from 1990 to 2021. Main reason for opting
this period is because from the year 1991 it is marked as the period of reforms in India.
Government of India implemented significant economic reforms during this period. These
reforms aimed to liberalize markets, promote privatization, encourage foreign investment, and
enhance trade openness, promoting changes in financial sectors etc… Gross Domestic Product
data are collected from the World Economic Outlook Database. Outstanding liability data and
Credit of all Scheduled Commercial Banks data are collected from the Hand Book of Statistics
on Indian Economy published by the Reserve Bank of India. Data on Total Revenue Receipts
are taken from Centre for Monitoring Indian Economy Pvt . Ltd Economic Outlook.
Outstanding liabilities has been taken as debt in the present study. Growth form of Outstanding
liability and Credit of all Scheduled Commercial Banks had been calculated for conducting the
analysis.
This work had been undertaken by applying different econometric techniques which includes,
Pairwise Correlation matrix, Vector Autoregression (VAR), Granger Causality and Johansen
Juselius Cointegration technique
1.5 Limitations of the study
The study has caught on with various limitations. Since this work solely depends upon
secondary sources of data, it poses all the disadvantages of secondary data. Furthermore, the
study takes into account certain factors such as GDP, Outstanding Liabilities, Total Revenue
Receipts, Total Credit of all Scheduled Commercial Banks but there are many other significant
factors are also available. In the study we use the time series data of only 31 years hence
24
insufficient data leads to non-reliable conclusion. In addition, the work possesses time
constraints which could have negatively affect the detailed examination of the study.
25
CHAPTER 2
REVIEW OF LITERATURE AND THEORETICAL PERSPECTIVES
2.1 Review of literature
Pattillo, Poirson, and Ricci (2002) in their study attempted to assesses the non-linear impact
of external debt on growth using a large panel data set of 93 developing countries over 196998. Results are generally robust across different econometric methodologies, regression
specifications, and different debt indicators. For a country with average indebtedness,
doubling the debt ratio would reduce annual per capita growth by between half and a full
percentage point. The differential in per capita growth between countries with external
indebtedness below 100 percent of exports and above 300 percent of exports seems to be in
excess of 2 percent per annum. For countries that are to benefit from debt reduction under
the current HIPC initiative, per capita growth might increase by 1 percentage point, unless
constrained by other macroeconomic and structural economic distortions. Their findings
also suggest that the average impact of debt becomes negative at about 160-170 percent of
exports or 35-40 percent of GDP. The marginal impact of debt starts being negative at about
half of these values. High debt appears to reduce growth mainly by lowering the efficiency
of investment rather than its volume.
Mohanty, Asit, Suresh Kumar Patra, Satyendra Kumar, and Avipsa Mohanty (2016) in their
empirical study attempted to examine the causal nexus between public debt and economic
growth for 15 NSC states of India for the period 1991-2015 using Dumitrescu Hurlin causality
test. The panel causality test identified the endogeneity issue as it revealed the bidirectional
causality between these two variables. Further, they revisited the effect of public debt on
economic growth for NSC states for the same period by incorporating other controlled variables
in the model. Understanding the potential endogeneity issue, they employed FMOLS which
solves the endogeneity as well as serial autocorrelation problem in the model. The results of
their study revealed that public debt, total revenue receipts and total credit have positive effect
on economic growth. As regards policy implications, the government should adopt proper tax
reform strategies to minimize tax leakages. Further, it should implement effective credit and
risk management practices to improve the asset quality. Lastly, suitable debt management
26
strategy should be adopted to utilize debt in the most effective and proficient way to expand
productivity capacity of the economy.
R J Barro (1990) found out that the growth and saving rates fall with an increase in utility-type
expenditures, the two rates rise initially with productive government expenditures, but
subsequently decline. With an income tax, the decentralized choices of growth and saving are
"too low," but if the production function is Cobb-Douglas, the optimizing government still
satisfies a natural condition for productive efficiency.
Forslund, Lima and Panizza (2011) in their study used a new dataset on the composition of
public debt in developing and emerging market countries to look at the correlation between
country characteristics and domestic debt share. They found that most variables have the
expected sign and also country characteristics cannot explain regional differences in the
composition of public debt. Moreover, the paper found a weak correlation between inflationary
history and the composition of public debt. They found out that in countries with moderate or
no capital controls there is a statistically significant negative correlation between domestic debt
share and inflation.
Kumar and Woo (2010) founded an inverse relationship on initial debt and economic growth.
Their analysis was based on a panel of advanced and emerging economies over almost four
decades, takes into account a broad range of determinants of growth as well as various
estimation issues including reverse causality and endogeneity. In addition, threshold effects,
nonlinearities, and differences between advanced and emerging market economies are
examined. The empirical results from their study suggest an inverse relationship between initial
debt and subsequent growth, controlling for other determinants of growth. On average a 10
percentage point increase in the initial debt-to-GDP ratio is associated with a slowdown in
annual real per capita GDP growth of around 0.2 percentage points per year, with the impact
being somewhat smaller in advanced economies. There is some evidence of nonlinearity with
higher levels of initial debt having a proportionately larger negative effect on subsequent
growth. Analysis of the components of growth suggests that the adverse effect largely reflects
a slowdown in labour productivity growth mainly due to reduced investment and slower growth
of capital stock.
27
Rangarajan and Srivastava (2005) argued that a large fiscal deficit and interest payments to
GDP adversely affected economic growth. They also pointed out that public debt negatively
affected the growth of the Indian economy.
Kannan and Singh (2007) conducted a study in order to trace out the evolution of public debt
and deficits over a medium term horizon and its dynamic interaction with other key
macroeconomic variables such as interest rate, inflation, trade gap and output. Their study
implies that fiscal adjustment with compositional shifts in expenditure to achieve convergence
not only leads to acceleration in the investment rate in the economy, it also facilitates monetary
management by moderating inflation expectations and contributing to stable interest rate
regime. The adjusted converging debt path is consistent with the higher growth trajectory. Such
corrections also do not pose the challenge of growth inflation trade-off.
Debi Prasad Bal, Badri Narayan Rath (2014) conducted a empirical study for examining the
effect of public debt on economic growth in India between 1980 and 2011. Using the
autoregressive distributed lag ARDL model, they traced a long-run equilibrium relationship
between public debt and economic growth. The error correction model (ECM) results show
that central government debt, total factor productivity (TFP) growth, and debt-services are
affecting the economic growth in the short-run, and that the results are consistent with our a
priori expectation. It is recommended that the government should follow the objective of intergenerational equity in fiscal management over the long term in order to stabilize debt-GDP
ratio, particularly, after the global financial crisis.
Mohanty and Mishra (2016) conducted a study in order to examine the impact of public debt
on economic growth by taking other control variables like institutional credit and commercial
electricity consumption. It uses panel data of 14 major (non-special category) States in India
for the period 1980-81 to 2013-14. After establishing long-run relationship among the
variables, panel long-run estimates are drawn using both DOLS and FMOLS methods. Results
from both the methods suggest positive and statistically significant impact of all the variables
on economic growth. To test causal relationships among the variables, Dumitrescu-Hurlin
pairwise causality test is employed. The results indicate existence of bi-directional causality
between public debt and economic growth. One way causality is revealed from economic
growth to electricity consumption and from economic growth to credit.
28
A Elbadawi (1997) conducted an empirical study that directly considers nonlinear effects of
debt on growth. They present fixed and random effects panel estimates of a growth regression
in which debt to GDP enters both in linear and quadratic form. The results imply growth
maximizing debt to GDP ratio of 97 percent.
Several studies have found an inverse linear relationship between total debt and economic
growth both across countries and at a single country-level analysis. The empirical work by
Mitchell (1988), Baro (1989), and Camen and Rogoff (2011) used UK data to show that public
debt has a significant impact on economic growth.
B Fincke, A Greiner (2015) empirically studied the relationship between public debt and
economic growth for selected emerging market economies by performing panel data
estimations. They use the data of eight emerging countries - Brazil, India, Indonesia,
Malaysia, Mexico, South Africa, Thailand and Turkey. The results reveal a statistically
significant positive correlation between public debt and the subsequent growth rate of per
capita gross domestic product (GDP). Population and investment are also positively correlated
with per capita growth, whereas the initial level of real GDP per capita exerts a negative
influence on growth, implying conditional convergence. Other variables such as the inflation
rate, the trade balance or the exchange rate do not yield a statistically significant effect with
respect to economic growth.
C Singh (1999) study investigates the relationship between domestic debt and economic growth.
The traditional view considers that in the long run, domestic debt has a negative impact on economic
growth while the Ricardian equivalence hypothesis implies the neutrality of domestic debt to
growth. In India, domestic debt has been incurred mainly on the consideration that it shall be used
for investment purposes. The issue is empirically examined using the cointegration test and the
Granger causality test for India over the period 1959-95. Cointegration and the Granger causality
tests support the Ricardian equivalence hypothesis between domestic debt and growth.
A Kaur, B Kaur (2015) analyse the empirical relationship between public debt and economic
growth in India. Their study covers the period of 32 years (i.e., from 1981-82 to 2012-13).
Granger's causality analysis has been carried out in order to examine the cause and effect
relationship between economic growth and public debt. Multiple Regression has been worked
out to investigate the indirect relationship between economic growth and public debt. The study
provides the evidence of positive, but indirect relationship between public debt and economic
growth via investment. The results show the positive and statistically significant relationship
29
between public debt and investment and also public debt effects the economic growth
significantly.
A Barik, JP Sahu(2022) examines the long run relationship between public debt and economic
growth in India for the period 1980–2018 using the autoregressive distributed lag (ARDL)
approach. The bounds test results suggest that gross domestic product (GDP) per capita,
internal and external public debt, fixed investment and trade openness are cointegrated
implying the existence of a long run equilibrium relationship between these variables. They
find that both internal and external public debt have significant negative effect on economic
growth in the long run whereas, investment has significant positive effect on the growth rate
of output per capita. The error-correction model shows that the long run equilibrium
relationship is stable and the adjustment towards equilibrium is relatively high. Their findings
suggest that too much reliance on public debt must be discouraged since it has adverse effect
on economic growth in the long run. The government should adopt investment-supportive
policies to enhance economic growth of the country.
N Manik (2016) attempted to investigate the important issue of “cause-effect” relationship
between public debt and economic growth for Indian economy over the period of 1980-81 to
2013-14. The statistical description provides evidence that the causal issue between these
variables is undefined and inconclusive. To address this irresolvable issue, he employs the
time series techniques like unit root test (ADF and PP), VAR lag selection criteria, Johansen
cointegration test, VECM, and VEC granger causality test. Empirical results suggest that
reliance on debt for development purposes is not a safe option, even though the presence of no
feedback relationship between the said variables.
DP Bal, BN Rath (2018) attempted to investigate the impact of public debt on economic growth
using key macroeconomic channels for the period 1970–2013 in the context of India. The
analysis is undertaken in two different steps: first, it examined whether public debt has any
nonlinear impact on economic growth and second, determines the key channels through which
public debt affects economic growth. The results derived from 2SLS model show that public
debt positively affects economic growth in the short run, while it shows a negative impact in
the long run. Further, by using Nonlinear ARDL approach, they supported the existence of a
nonlinear impact of public debt on economic growth. The channels through which public debt
significantly affects economic growth are households saving, public investment and total factor
productivity growth. From policy perspective, they suggest that government should target the
30
public investment and productivity channels for utilizing the public debt in India, and the
government should opt for borrowings as long as it leads to capital formation of the country.
Ntshakala (2015) examines the effect of both public external and domestic debt on economic
growth in Swaziland including variables such as inflation and government expenditure to the
model to avoid spuriousness of the results. Advanced econometric techniques were used to
analyse the time series data spanning1988-2013. Ordinary Least Square (OLS) method has
been used to determine the nature and extent of each relationship as all variables were found
to be normally distributed and stationary at level. The study found that there is no significant
relationship between external debt and economic growth in Swaziland for the period under
study, while on the other hand, domestic debt was found to have a significant positive
relationship with economic growth at 5 percent level of significance. In view of this, the study
recommends that the government of Swaziland should encourage sustainable domestic and
external borrowing and utilize the funds in productive economic activities.
Hameed, M. R. and Quddus M. A. (2020) examine the structure of public debt its implications
on economic growth in of SAARC economies of Bangladesh, India, Pakistan and Sri Lanka.
For this purpose the penal data of selected countries for the period of 1990-2018 has been used.
From the literature several factors through which public indebtedness effect economic growth
have been identified - public debt to GDP ratio, debt servicing as a ratio of export earnings, net
foreign financing as a ratio of total deficits, private and public investment as ratio of GDP.
Besides these variables some policy, fundamental and shock variables like inflation, exchange
rate, terms of trade and population growth. For analysis of data the econometrics techniques
like Fixed Effect Model, Hausman Test and PMG/ Panel ARDL approach have been applied
to analyse the long run association among the regressors. The results of the study reveal that
public debt negatively affected the economic performance both in short period and long period.
The study concludes that presently, simultaneous achievement of desirable level of economic
growth and public debt stock seems to be difficult and could remain elusive if some serious
measures are not taken.
Akram, N. (2016) examines the consequences of public debt for economic growth and poverty
regarding selected South Asian countries, i.e., Bangladesh, India, Pakistan and Sri Lanka, for
the period 1975–2010. It develops an empirical model that incorporates the role of public debt
into growth equations and the model is extended to incorporate the effects of debt on poverty.
The model is estimated by using standard panel data estimation methodologies. The results
31
shows that although public debt has a negative impact on economic growth, neither public
external debt nor external debt servicing has a significant relationship with income inequality,
suggesting that public external debt is as good/bad for poor as it is for rich. However, domestic
debt has a positive relationship with economic growth and a negative relationship with the
GINI coefficient, indicating that domestic debt is pro-poor.
Joy, J., & Panda, P. K. (2020) attempted to analyse the pattern of public debt in Brazil, Russian
Federation, India, China and South Africa (BRICS) in a comparative perspective. Besides, an
attempt is made to verify the existence of debt overhang as suggested by Krugman (1988)
among BRICS nations. Annual panel data for BRICS for the period 1980-2016 has been used
for the analysis. Percentage ratio method has been used to analyse the pattern of debt. Panel
covariate Augmented Dickey–Fuller test has been used to verify the time series properties of
the variable, while panel cointegration test of Pedroni (1999) is used to check the existence of
any co-integrating vector among the variables. Panel Granger causality test is used to check
the causality between the variables. Co-integration result suggests that there exists a strong
long-run equilibrium relationship between debt service, domestic savings, capital formation
and economic growth of BRICS nations. From Granger causality test, it is observed that
domestic savings and capital formation are Granger caused by debt servicing. The coefficients
from fully modified ordinary least squares measure a negative impact of debt service on gross
capital formation and gross domestic saving. This suggests that the payment for debt service
affects capital formation and gross domestic savings adversely. Thus, it gives primary signals
for debt overhang effect in BRICS nations.
Lau, J. M. de Alba and K. H. Liew (2022) empirically investigates the impact of debt levels by
estimating the appropriate threshold of external debt to GDP on economic growth for a sample
of 16 Asian countries during the years 1980 to 2016. The outcomes indicate that external debt
negatively and significantly impacts growth in most of these countries. Debt to GDP threshold
construction revealed ten countries with a threshold below 30%, three countries in the range
between 30%–60%, two countries in the range between 60%–90%, with only Thailand
exceeding a 90% threshold. The fiscal discipline of targeting an appropriate debt to GDP ratio
can serve as a guide to optimizing sustainable economic growth for countries in the Asian
region.
Kumarasinghe and Purankumbura (2018) analyses the long run association as well as cause
and effect of external debt and debt service on economic growth in South Asian countries
32
including variables such as interest payment, foreign direct investments, gross savings and net
export to the model to prevent spuriousness of the outcomes. This study uses secondary data
that were collected from the World Bank (WB) and International Monetary Funds (IMF) by
casing period from 1990 to 2015. The stationary of the data set has been tested by applying
panel unit root tests. The long run association of the public debt and the economic growth were
checked via applying Pedroni Residual Co-integration and cause and effect of the public debt
on economic growth in short run check through Granger Causality test. The results show that
external debt negatively impacts economic growth in countries of Afghanistan, Bangladesh,
Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka are the South Asian countries that are
examined in the study. Panel data show that there is a co-integration between external debt and
economic growth for South Asian countries.
Bökemeier and Greiner (2014) attempted to analyse the relationship between public debt and
economic growth for selected emerging markets performing panel data estimations. Several
regressor variables are included, but the main focus is on public debt. The results reveal a
significant positive correlation between public debt and the subsequent growth rate of per
capita GDP. Population and investment also yield a significant positive influence on
subsequent growth, whereas the initial real GDP per capita gives a negative influence. Other
variables such as the inflation rate, the trade balance or the exchange rate do not render a
significant effect with respect to economic growth.
MK Agarwal and S Ansari (2022) examines both the short- and long-run impact of public debt
on the economic growth of Uttar Pradesh during the post-reform period of 30 years by
employing the vector error correction model. The empirical analysis revealed that the increase
in public debt-to-gross state domestic product ratio and interest payments burden would have
an adverse impact on the long-run economic growth of UP, while having no significant impact
on the short-run growth. It is also notable that the effective interest rate has negatively
correlated with the gross capital formation in UP, and the latter has shown significant positive
long-run association with the economic growth. In order to attract investments and economic
growth, the state Government of UP should continue a countercyclical fiscal stance that would
help in adhering to fiscal sustainability rules by smoothing out the repercussions of the COVID19 pandemic.
33
2.2 Theoretical framework
Various schools of thoughts propose distinct relationship between public debt and economic
growth. In the Classical sense, Ricardian Equivalence states that individuals anticipate future
tax increases to finance public debt. Therefore, if individuals believe that future taxes will be
raised to repay the debt, they may increase their savings to offset the expected tax burden. As
a result, the theory suggests that public debt does not significantly impact revenue receipts as
individuals adjust their behaviour accordingly (Ricardo, 1817). In the Keynesian framework,
during times of economic downturn or recession, governments should increase public spending
and run budget deficits to stimulate economic growth. According to this theory, public debt
can have a positive impact on economic growth by boosting aggregate demand and creating
jobs. In a Neo-Classical set up Diamond (1965) formally brought the public debt as a variable
explaining growth. Endogenous growth theory, pioneered by economists like Paul Romer,
suggests that public debt can have positive effects on economic growth if it is used to finance
productive investments in education, infrastructure, and research and development. When
public debt is channelled into these areas, it can enhance the economy's productive capacity
and lead to sustained economic growth. These theories provide a theoretical framework for this
study.
34
CHAPTER 3
DATA AND METHODOLOGY
3.1 DATA
In order to examine the above-mentioned objectives, the analysis use time series data collected
from different secondary sources. Data spanning from 1991 to 2021 are used for conducting
the analysis. Main reason for opting this period is because from the year 1991 it is marked as
the period of reforms in India. Government of India implemented significant economic reforms
during this period. These reforms aimed to liberalize markets, promote privatization, encourage
foreign investment, and enhance trade openness, promoting changes in financial sectors etc….
This study use variables such as Gross Domestic Product, data regarding government
Outstanding liabilities, Credit of all Scheduled Commercial Banks and Total Revenue
Receipts. Gross Domestic Product data are collected from the World Economic Outlook
Database. Outstanding liability data and Credit of all Scheduled Commercial Banks data are
collected from the Hand Book of Statistics on Indian Economy published by the Reserve Bank
of India. Data on Total Revenue Receipts are taken from Centre for Monitoring Indian
Economy Pvt . Ltd Economic Outlook. Outstanding liabilities has been taken as proxy for debt
in the present study. However, Growth form of Outstanding liability and Credit of all
Scheduled Commercial Banks had been calculated for conducting the analysis.
3.2 METHODOLOGY
This work had been undertaken by applying different econometric techniques which includes,
Correlation matrix, Vector Autoregression (VAR), Granger Causality and Johansen-Juselius
Cointegration technique. A brief note on the econometric techniques used in this work can be
summarised as follows.
35
Unit Root Test
A unit root test, also known as a stationarity test, is a statistical test used to determine whether
a time series variable is stationary or non-stationary. Stationarity refers to the property of a
time series where its statistical properties, such as mean, variance, and autocovariance, remain
constant over time. A non-stationary time series, on the other hand, exhibits statistical
properties that change over time. Non-stationarity can arise due to trends, seasonality, or other
forms of systematic patterns in the data. Non-stationary time series can be problematic for
statistical analysis because standard techniques assume stationarity or can lead to spurious
relationships.
The most commonly used unit root test is the Augmented Dickey-Fuller (ADF) test. The study
uses the Augmented Dickey-Fuller (ADF) test to check stationarity of time series data. The
ADF test examines whether a time series variable has a unit root, which implies nonstationarity. The null hypothesis of the ADF test is that the variable has a unit root, while the
alternative hypothesis is that the variable is stationary.
The ADF test estimates a regression model of the form:
Δyt = α + βyt-1 + γt + εt
where Δyt is the first difference of the time series variable, yt-1 is the lagged value of the
variable, γt represents deterministic trend terms, and εt is the error term. The test examines the
significance of the coefficient β to determine whether the variable has a unit root or not.
If the ADF test statistic is significantly negative and the corresponding p-value is below a
chosen significance level (e.g., 0.05 or 0.01), then the null hypothesis of a unit root is rejected,
suggesting that the variable is stationary. On the other hand, if the test statistic is not
significantly negative, the null hypothesis of a unit root is not rejected, indicating that the
variable is non-stationary.
There are other variations of unit root tests, such as the Phillips-Perron (PP) test and the
Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, which have slightly different methodologies
36
and assumptions. These tests may be used as alternatives or in conjunction with the ADF test
to provide more robust results.
Unit root tests are commonly employed in time series analysis, econometrics, and financial
modelling to diagnose the presence of non-stationarity and guide the appropriate modelling
and analysis techniques.
Vector Autoregressive Model
Vector Autoregressive (VAR) model is a statistical time series model that allows for the
analysis of multiple variables simultaneously. It is an extension of the univariate autoregressive
model and is widely used in econometrics and other fields to capture the dynamic relationships
among variables. In a VAR model, each variable is regressed on its own lagged values and the
lagged values of all other variables in the system. This allows us to account for the
interdependencies and feedback effects among the variables over time.
One of the key advantages of VAR models is their ability to capture the dynamic interactions
among the variables. By estimating the coefficients of the lagged values, we can analyze how
shocks or innovations in one variable affect the others in the system. This leads us to the
concept of impulse response functions.
Impulse response functions (IRFs) are used to examine the short-term dynamic responses of
variables in a VAR model to an exogenous shock. An exogenous shock refers to an unexpected
change or disturbance in one of the variables. The IRF allows us to analyse how this shock
propagates through the system and affects the other variables over time. To compute the IRF,
we start by estimating the VAR model using historical data. Once the model is estimated, we
can introduce a shock to one variable by setting its value to a different value than what it would
have been based on the historical data. We then trace out the dynamic response of the variables
over time as the shock propagates through the system.
The IRF provides insights into the direction, magnitude, and duration of the effects of the shock
on each variable in the VAR model. It allows us to understand the short-term dynamics of the
system and how variables respond to changes in their own past values and the past values of
other variables. For example, in an economic VAR model, we can use the IRF to analyse how
a monetary policy shock affects variables such as output, inflation, and interest rates. By
examining the IRF, we can determine the immediate and lagged responses of these variables
to the shock, providing valuable information for policymakers and analysts.
37
In summary, the VAR model is a powerful tool for analysing the dynamic relationships among
multiple variables. It captures the interdependencies and feedback effects in the system, and
the impulse response function allows us to understand the short-term dynamic responses to
exogenous shocks. Together, these tools provide valuable insights into the behaviour of
complex systems and help in making informed decisions.
Correlation Matrix
Correlation matrices are commonly used in statistics and data analysis to explore relationships
between variables, identify patterns, and determine the strength of associations. They can help
in detecting multicollinearity (high correlation between predictor variables) or finding
variables that are strongly related to the target variable in regression analysis.
A correlation matrix is a table that shows the correlation coefficients between multiple
variables. Each cell in the matrix represents the correlation coefficient between two variables.
The correlation coefficient measures the strength and direction of the linear relationship
between two variables. It can range from -1 to +1. In a correlation matrix, the diagonal
elements (cells on the main diagonal) always have a correlation coefficient of 1 because they
represent the correlation of a variable with itself. The upper triangle and lower triangle of the
matrix contain the same correlation coefficients since the correlation between variable A and
variable B is the same as the correlation between variable B and variable A. Examine the
correlation matrix to understand the relationships between variables. Positive values indicate
positive correlations, negative values indicate negative correlations, and values close to zero
indicate weak or no correlation.
Pairwise correlation analysis is widely used in various fields, including statistics, finance,
economics, social sciences, and many other disciplines. It helps identify associations between
variables, uncover patterns, and guide further analysis or decision-making processes. However,
it's important to note that correlation does not imply causation, and other factors may influence
the observed relationships between variables.
Granger Causality test
The Granger causality test is a statistical tool used to assess whether one time series variable
can predict or "Granger-cause" another time series variable. It helps determine if the past values
of one variable provide useful information for forecasting the future values of another variable.
38
The underlying idea of the Granger causality concept is that if variable X significantly
improves the prediction of variable Y when compared to a model that only uses past values of
Y, then we can say that X Granger-causes Y.
The null hypothesis assumes that the past values of X do not have any significant predictive
power for Y. In other words, X does not Granger-cause Y. The alternative hypothesis is that X
does have predictive power for Y. Determine the number of lagged terms to include in the
model. Lagged terms capture the past values of both X and Y, and the choice of lag length
depends on the specific dataset and the time frame under investigation.
Construct an autoregressive model that includes both X and Y as predictor variables, along
with their respective lagged terms. The model may look like this:
Y(t) = α + ∑(β_i * Y(t-i)) + ∑(γ_i * X(t-i)) + ε(t)
Here, Y(t) represents the value of variable Y at time t, X(t) represents the value of variable X
at time t, α is the intercept term, β_i and γ_i are the coefficients for the lagged terms of Y and
X, and ε(t) is the error term.
Use suitable estimation techniques (e.g., ordinary least squares regression) to estimate the
coefficients of the model. This involves finding the values of α, β_i, and γ_i that minimize the
sum of squared errors.
In order to conduct hypothesis testing calculate the F-statistic or chi-square statistic based on
the estimated model. This statistic compares the fit of the model that includes the lagged values
of X to the fit of a reduced model that excludes X. The null hypothesis assumes that the
excluded variable (X) has no effect on the prediction of Y. Determine the critical values for the
chosen level of significance (e.g., 5% or 1%). These critical values are used to determine
whether the calculated test statistic is statistically significant or not.
Compare the test statistic with critical values: If the calculated test statistic exceeds the critical
value, we reject the null hypothesis and conclude that X Granger-causes Y. On the other hand,
if the test statistic is lower than the critical value, we fail to reject the null hypothesis, indicating
that there is no evidence of Granger causality between X and Y.
It is important to note that the Granger causality test is a purely statistical test and does not
establish a definitive causal relationship. It only suggests that there is a statistical association
between variables.
39
Johansen Cointegration Test
Johansen cointegration test, developed by Søren Johansen, is a statistical test used to determine
whether there is a long-term relationship, or cointegration, between two or more non-stationary
time series variables. It is widely used in econometrics and time series analysis to examine the
presence of a stable equilibrium relationship among multiple variables.
Cointegration refers to the situation where a linear combination of non-stationary variables
results in a stationary series. In other words, even though the individual variables may be nonstationary (i.e., they have a unit root), their combination may have a stationary behaviour,
indicating a long-term equilibrium relationship. Johansen cointegration test estimates the
number of cointegrating vectors, which represent the long-term relationships among the
variables. The test allows for the identification of both the presence of cointegration and the
number of cointegrating vectors, which indicates the dimension of the cointegrating space.
The Johansen cointegration test is based on the Vector Error Correction Model (VECM)
framework. It estimates the parameters of the VECM and performs likelihood ratio tests to
determine the rank, or the number of cointegrating vectors. The test evaluates whether the
estimated cointegrating vectors are statistically significant or not. The result of the Johansen
cointegration test provides valuable information for analysing the dynamics of the variables
and their long-term relationships. It helps in understanding the underlying economic
relationships and can be used for forecasting, policy analysis, and modelling purposes.
It's important to note that the Johansen cointegration test assumes that the time series variables
are integrated of the same order (i.e., they have the same order of integration). If the variables
have different orders of integration, a prior transformation such as differencing may be required
before conducting the test.
40
CHAPTER 4
ANALYSIS
This chapter focuses on analysing the results obtained. Data analysis is a process of inspecting,
cleansing, transforming and modelling data with the goal of discovering useful information,
informing conclusions and supporting decision making. It is systematically applying statistical
tools or logical techniques to describe and illustrate, condense, recap and evaluate data. The
relation existing between these variables can be identified through the above-mentioned
techniques. The objectives that are to be analysed are: to study the relationship of public debt
with Gross state domestic product, Total revenue receipts and Total credit of all scheduled
commercial banks, whether there exists a long run association between these four variables,
whether there exists short run dynamic relationship between these four variables and whether
there exists causality among them. In order to analyse these various methods are adopted.
Techniques like Unit root test, Johansen Juselius Cointegration, Granger causality and Vector
Autoregressive (VAR) model are used here. Data are processed to obtain results for the abovementioned objectives.
4.1 Unit Root Test:
Before doing any tests, it is necessary to identify whether the series or the variables are
stationary or not. This test lets to know at what difference does the variables become stationary.
A series being stationary implies that it has a constant mean and variance which is independent
of time. A series being stationary is an important assumption of many timeseries analysis
techniques. There are several tests for stationarity like Augmented Dickey-Fuller (ADF) test,
Phillips-Perron tests, Elliot-Rothenberg-Stock (ERS) test etc.
Here, ADF test is used for testing stationarity. The hypothesis set are:
•
Null hypothesis: Series is non stationary.
•
Alternative hypothesis: Series is stationary.
41
If the p value is less than 0.05 then the null hypothesis is rejected showing that the series is
stationary.
Table 1: Unit root test
Variables
I st
difference
Level
II nd
difference
Inference
Debt
0.0005
I(0)
GDP
0.0020
I(0)
0.0001
I(0)
Credit of all Scheduled
Commercial Banks
Total Revenue Receipts
0.0003
I(2)
It is clear from the above table that the variables under study are not stationary at the same
order. The Debt, Gross Domestic Product and Credit of all Scheduled Commercial Banks are
found to be stationary only at first difference I(0) while Total Revenue Receipts are stationary
at level I(2). Debt, Gross Domestic Product and Credit of all Scheduled Commercial Banks are
stationary at level itself. For Debt and Credit of all Scheduled Commercial Banks their growth
form have been used for the study.
4.2 To analyse Causal relation between Debt, GDP, Credit of all Scheduled Commercial
Banks and Total Revenue Receipts.
Granger causality is a statistical tool used to analyse whether one time series can be used for
predicting the other. By testing causality, it is attempted to find whether either one of the series
causes the other. For this also there are two hypotheses:
•
Null hypotheses: No causality between the variables.
•
Alternative hypotheses: Causality exists between the variables.
42
The result of causality is as follows:
Table 2: Granger Causality Test
Pairwise Granger Causality Tests
Date: 06/25/23 Time: 21:38
Sample: 1 31
Lags: 2
Null Hypothesis:
Obs
F-Statistic
Prob.
GDP does not Granger Cause DEBT
DEBT does not Granger Cause GDP
29
1.18745
2.47659
0.3223
0.1052
BANK_CREDIT does not Granger Cause DEBT
DEBT does not Granger Cause BANK_CREDIT
29
0.54088
2.23513
0.5892
0.1288
REVENUERECEIPT_1 does not Granger Cause DEBT
DEBT does not Granger Cause REVENUERECEIPT_1
27
0.35129
2.10526
0.7077
0.1457
BANK_CREDIT does not Granger Cause GDP
GDP does not Granger Cause BANK_CREDIT
29
0.06263
0.14226
0.9394
0.8681
REVENUERECEIPT_1 does not Granger Cause GDP
GDP does not Granger Cause REVENUERECEIPT_1
27
1.48005
4.96531
0.2494
0.0166
REVENUERECEIPT_1 does not Granger Cause BANK_CREDIT
BANK_CREDIT does not Granger Cause REVENUERECEIPT_1
27
0.04300
0.44188
0.9580
0.6484
The tables can be used to identify whether any causal relationships exist between the selected
variables. From the above table we can identify no causal relationship between the dependent
variable Debt and the independent variables GDP, Credit of all Scheduled Commercial Banks
and Total Revenue Receipts. Only GDP have a causal relationship with Total Revenue Receipts
since the p value is 0.0166 which is less than 0.05. Thus we can reject the null hypothesis.
Therefore, GDP have a unilateral relationship with Total Revenue Receipts. Except for this we
cannot identify any causal relationship between the variables.
4.3 To examine the correlation of GDP, Credit of all Scheduled Commercial Banks and
Total Revenue Receipts with Debt.
Correlation matrices are commonly used in statistics and data analysis to explore relationships
between variables, identify patterns, and determine the strength of associations. In the study
pair wise correlation is used to explore relationships between the dependent variable Debt and
43
independent variables - Gross Domestic Product, Credit of all Scheduled Commercial Banks
and Total Revenue Receipts.
Table 3: Pairwise Correlation
Correlation
DEBT
GDP
BANK_CREDIT
REVENUE_
RECEIPTS
1.000000
0.004417
0.004417
1.000000
-0.284058
-0.085765
-0.003906
0.999318
BANK_CREDIT -0.284058
-0.085765
1.000000
-0.085850
REVENUE_
RECEIPTS
0.999318
-0.085850
1.000000
DEBT
GDP
-0.003906
The table displays the pairwise correlation results between the variables Debt, Credit of all
Scheduled Commercial Banks and Total Revenue Receipts. According to the correlation
matrix, the dependent variable Debt have a positive correlation of 0.004417 with GDP,
implying that while GDP grows, Debt also grows and vice versa. With Debt and GDP has a
very weak positive relationship which is almost nearly to zero suggesting that there is not a
strong linear association between these two variables. Furthermore, there is a moderate
negative association between Debt and Credit of all Scheduled Commercial Banks, whereas
there is a weak negative association between Debt and Total Revenue Receipts.
When coming to the cross-correlation analysis we could observe that GDP have a weak
negative correlation with Credit of all Scheduled Commercial Banks and GDP have a strong
positive correlation with Total Revenue Receipts. We can also identify a weak negative
correlation between Credit of all Scheduled Commercial Banks and Total Revenue Receipts.
4.4 To analyse short run dynamic relationship between Debt, GDP, Credit of all
Scheduled Commercial Banks and Total Revenue Receipts of India
The dynamic relationships among variables are analysed using VAR which stands for Vector
Autoregression introduced by Christopher sims. The VAR facilitates impulse response
44
analysis, which measures the effects of a shock or an innovation in one variable on the entire
system. It allows researchers to examine how the shocks propagate through the system and how
variables respond to these shocks over time.
Table 4: IRF
The given Impulse Response Function graph shows the impulse response function of India for
each of the selected variables in the model. Here from this graph, it is clear that valid results
exist only for the response of Total Revenue Receipts to the unit standard deviation increase in
GDP. Here it implies for positive response of Revenue Receipt immediately to rise in GDP,
which dies out after the end of first period.
4.5 To analyse the long-term association of Debt with the independent variables GDP,
Credit of all Scheduled Commercial Banks and Total Revenue Receipts
Johansen cointegration test is use is used for analysing whether the variables have a long term
association between them. Johansen cointegration test, developed by Søren Johansen, is a
statistical test used to determine whether there is a long-term relationship, or cointegration,
between two or more non-stationary time series variables. It is widely used in econometrics
and time series analysis to examine the presence of a stable equilibrium relationship among
multiple variables.
45
There are two hypotheses:

Null hypotheses: No long term cointegration.

Alternative hypotheses: Long term cointegration exists.
Result for the Johansen cointegration test is as follows:
Debt and GDP
Table 5: Johansen cointegration test results between Debt and GDP
Date: 06/26/23 Time: 11:42
Sample (adjusted): 3 31
Included observations: 29 after adjustments
Trend assumption: No deterministic trend (restricted constant)
Series: DEBT GDP
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.495345
0.221606
27.09768
7.265164
20.26184
9.164546
0.0049
0.1131
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.495345
0.221606
19.83252
7.265164
15.89210
9.164546
0.0114
0.1131
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
DEBT
-37.72640
70.25192
GDP
-3.65E-05
-3.26E-05
C
4.634717
-7.959435
Unrestricted Adjustment Coefficients (alpha):
D(DEBT)
D(GDP)
0.001255
2103.845
1 Cointegrating Equation(s):
-0.007720
337.3421
Log likelihood
-186.0047
Normalized cointegrating coefficients (standard error in parentheses)
DEBT
GDP
C
1.000000
9.69E-07
-0.122851
(2.6E-07)
(0.00848)
Adjustment coefficients (standard error in parentheses)
D(DEBT)
-0.047365
(0.12170)
D(GDP)
-79370.52
(16581.9)
46
From this table we can identify long term cointegration between the dependent variable Debt
and the independent variable GDP. Since the p value is 0.0049 which is less than 0.05 we can
reject the null hypothesis. Thus we can conclude that there is long term cointegration between
Debt and GDP.
Debt and Credit of all Scheduled Commercial Banks
Tables 6: Johansen cointegration test results between Debt and Credit of all scheduled
commercial banks
Date: 06/26/23 Time: 11:52
Sample (adjusted): 3 31
Included observations: 29 after adjustments
Trend assumption: No deterministic trend (restricted constant)
Series: DEBT BANK_CREDIT
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.434633
0.188803
22.60623
6.068099
20.26184
9.164546
0.0233
0.1855
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.434633
0.188803
16.53813
6.068099
15.89210
9.164546
0.0396
0.1855
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
DEBT
-41.21521
78.82189
BANK_CREDIT
-4.18E-05
2.21E-06
C
5.112418
-9.273461
Unrestricted Adjustment Coefficients (alpha):
D(DEBT)
D(BANK_C...
-0.002113
32136.08
1 Cointegrating Equation(s):
-0.007091
1245.560
Log likelihood
-267.3808
Normalized cointegrating coefficients (standard error in parentheses)
DEBT
BANK_CREDIT
C
1.000000
1.02E-06
-0.124042
(2.0E-07)
(0.00564)
Adjustment coefficients (standard error in parentheses)
D(DEBT)
0.087107
(0.13333)
D(BANK_C...
-1324495.
(297161.)
47
From table above we can identify a long term cointegration between the dependent variable
Debt and the independent variable Credit of all scheduled commercial banks. The null
hypothesis is rejected because the p value is 0.0233 which is less than 0.05. Thus we can
conclude that there is a long term cointegration between Debt and Credit of all scheduled
commercial banks.
Debt and Total Revenue Receipts
Table 7: Johansen cointegration test results between Debt and Total Revenue Receipts
Date: 06/26/23 Time: 11:59
Sample (adjusted): 3 31
Included observations: 29 after adjustments
Trend assumption: No deterministic trend (restricted constant)
Series: DEBT REVENUE_RECEIPTS
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None
At most 1
0.423049
0.094400
18.82549
2.875554
20.26184
9.164546
0.0778
0.6042
Trace test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.423049
0.094400
15.94994
2.875554
15.89210
9.164546
0.0490
0.6042
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
DEBT
-67.81912
23.33249
REVENUE_...
7.83E-08
-1.36E-08
C
8.341174
-1.385439
Unrestricted Adjustment Coefficients (alpha):
D(DEBT)
D(REVENU...
0.007103
552263.5
1 Cointegrating Equation(s):
-0.003845
225683.4
Log likelihood
-359.4921
Normalized cointegrating coefficients (standard error in parentheses)
DEBT
REVENUE_...
C
1.000000
-1.16E-09
-0.122991
(4.5E-10)
(0.00517)
Adjustment coefficients (standard error in parentheses)
D(DEBT)
-0.481706
(0.19968)
D(REVENU...
-37454023
(1.3E+07)
48
This table suggest that there is no long term cointegration between the dependent variable Debt
and the independent variable Total Revenue Receipts. We cannot reject the null hypothesis
since the p value is 0.0778 which is greater than 0.05. From this table we can conclude that
there is no long term cointegration between Debt and Total Revenue Receipts.
49
CHAPTER 5
SUMMARY AND CONCLUSION
5.1 SUMMARY
This study, on the whole, focuses on estimating the impact of public debt upon economic
growth of India for the time spanning from 1990 to 2021. This study use variables such as
Gross Domestic Product, Outstanding liability data, Credit of all Scheduled Commercial Banks
and Total Revenue Receipts. Outstanding liabilities has been taken as proxy for Debt in the
present study. The growth form of Outstanding liability and Credit of all Scheduled
Commercial Banks are used in the analysis. In the study Debt is taken as the dependent
variables and GDP, Credit of all Scheduled Commercial Banks and Total Revenue Receipts as
independent variables. In order to explore the causality and correlation between these factors,
pairwise correlation matrix and Granger causality test has been employed. And to explore the
long-term association of the dependent variables with the independent variables JohansenJuselius cointegration technique is employed. Additionally, the vector autoregression model
has been used for examining the short run dynamics of these variables.
The study uses pairwise correlation matrix and Granger causality for understanding the
direction of movement and the prediction power of one variable upon the other. Correlation
results suggests a weak positive correlation between dependent variable debt and independent
variable GDP. A negative correlation is identified between debt and Credit of all scheduled
commercial banks as well as debt and Total Revenue Receipts. Besides a weak negative
correlation is noted between GDP and Credit of all Scheduled Commercial Banks and GDP
has a strong positive correlation with Total Revenue Receipts. A weak negative correlation is
identified between Credit of all Scheduled Commercial Banks and Total Revenue Receipts.
Nevertheless, no causality is identified between the dependent variable Debt and independent
variables. Only the independent variables GDP and Total Revenue Receipts have a causal
relation. Moreover, a vector autoregression model has been employed for estimating the short
run dynamics between the concerned variables. Using impulse response function, helps to
capture the interaction between variables in the VAR model. They represent the reaction of
variables to the shocks hitting the system. As compare to cointegration which exhibits the long
run relationship, VAR models are more relaxable in terms of stationarity requirements. The
results obtained from the VAR model suggests that there is no short run dynamics between the
50
dependent variable and independent variables. The study uses Johansen-Juselius cointegration
technique which identify a long run association between the dependent variable Debt and
independent variables GDP and Credit of all Scheduled Commercial Banks. Augmented
Dickey-Fuller unit root test indicates that Debt, GDP and Credit of all Scheduled Commercial
Banks are stationary at level I(0) and Total Revenue Receipts are stationary at second
difference I(2).
This empirical study aims at examining the impact of GDP, Credit of all Scheduled
Commercial Banks and Total Revenue Receipts on Debt for India for the time period 1990 to
2021. By applying econometric techniques of Johansen cointegration, Granger causality,
correlation and VAR , the study came up with a conclusion that there exist a long-run
relationship between Debt and GDP as well as Debt and Credit of all Scheduled Commercial
Banks. However, no causality between the chosen variables is identified for this time period.
Furthermore, short run dynamics reveals that Debt is not responding to any of the shocks
produced in the other variables GDP, Credit of all Scheduled Commercial Banks and Total
Revenue Receipts. Since the analysis is conducted on the basis of 31 years data, the results
obtained is not that reliable and further studies in the future can be conducted to explore more
aspects of the topic.
5.2 Major Findings of the Study

A weak positive correlation is identified between Debt and GDP. A negative correlation
is identified between Debt and Credit of all scheduled commercial banks as well as debt
and Total Revenue Receipts. Besides a weak negative correlation is noted between GDP
and Credit of all Scheduled Commercial Banks and GDP has a strong positive
correlation with Total Revenue Receipts. A weak negative correlation is identified
between Credit of all Scheduled Commercial Banks and Total Revenue Receipts.

No causality is identified between the dependent variable Debt and independent
variables GDP, Credit of all Scheduled Commercial Banks and Total Revenue Receipts.
Only the independent variables GDP and Total Revenue Receipts have a causal relation.

Impulse response identifies short run relationship between the selected variables in the
study. The study came up with a conclusion that there is no short-run relationship
51
between dependent variable Debt and independent variables GDP, Credit of all
Scheduled Commercial Banks and Total Revenue Receipts. However, Total Revenue
Receipts responds to the unit standard deviation increase in GDP. It implies for positive
response of Revenue Receipt immediately to rise in GDP, which dies out after the end
of first period.

A long run association is identified between the dependent variable Debt and
independent variables GDP and Credit of all Scheduled Commercial Banks.

The variables Debt, GDP and Credit of all Scheduled Commercial Banks are stationary
at level itself and Total Revenue Receipts are stationary at second difference.
5.3 Policy Suggestions

The government should create an investor-friendly environment for additional source
of financing to replace the high public debt.

Since the public debt has been translated into lower level of new fixed assets, the
government needs to focus on investment in infrastructure and human capital for
realising the multiplier effect.

The government may think of public private partnership (PPP) model in social schemes
such as Deen Dayal Upadhyay Grameen Kaushalya Yojna (DDU-GKY). Essentially,
contribution in cash or kind, from the project implementing agencies in private sector
may be thought of when the captive/champion employers are sourcing the skilled
trainees for their operations in order to reduce the fiscal burden of the government.

India imports nearly 80 per cent of its domestic requirement of crude oil. India can
become a $5 trillion economy by 2025 if it gives more thrust on renewable energy by
reducing its reliance on fossil fuels, thereby saving on foreign exchange. Further, the
government should enhance efficiency of public debt management by adhering to the
canons: low cost, risk mitigation and market development.
52
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