DETERMINANTS OF NONPERFORMING LOAN IN AN
ECONOMY
Research Topic
Determinants of the Non-Performing Loan in an Economy
Course Name: Research Methodology
Course Code: 4105
Prepared for
Md. Atiqur Rahman
Assistant Professor
Department of Accounting & Information Systems
Jahangirnagar University
Prepared by
Group Number: 01 (ID ending with 0)
Serial No.
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2
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Name
Mostarina Khandaker
Arpita Karmaker
Md. Fayshal Hossain Nawshad
Md. Abid Arif
Md. Kayes Ahamed Shaon
Md. Akramuzzaman Moon
Date Of Submission: October 30, 2022
BBA 8th Batch
Department of Accounting & Information Systems
Jahangirnagar University
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ID No
1570
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1610
2040
2610
Acknowledgement of Individual Tasks Completion
Group 01
Serial
No
Chapter Name
Name
ID No.
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Abstract
Md. Abid Arif
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Introduction
Arpita Karmaker
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Md. Abid Arif
1610
Mostarina Khandaker
1570
Md. Kayes Ahamed Shaon
2040
Md. Fayshal Hossain
Nawshad
1600
Md. Akramuzzaman
Moon
2610
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Literature Review
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Research Methodology
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Findings from the Data
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Data Interpretation and
Discussion
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Conclusion
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Signature
Abstract
This report is conducted to find out the possibilities of opening new paths that describe the
economic states of countries, and how they are influenced, affected or manipulated through
macroeconomic determinants. In this report, we have tried to find out the impacts of different
variables on non-performing loans based on different economies of the world. We have selected
GDP growth rates, unemployment rates, real interest rates, inflation rates and corruption control
scores as key determinants of non-performing loans. This report is prepared based on the secondary
dataset of 70 countries. For data analysis, the most accessible tool we found was Microsoft Excel.
For primary analysis of the report, we have used descriptive statistics, correlation matrix and
ordinary least squared (OLS) regression output. We found some extreme values that form biasness
in the entire output of the report. We have tried to come up with results that are significant and
unique to the purpose. The dataset we have used is not sufficient. We were only able to use the
data from 70 countries which might not represent the entire world economy. In the dataset,
countries’ names were missing which was not helpful to indicate a specific area's economy. We
could use only five determinants of NPLs. Other determinants could show a bigger picture.
Insufficient access to analysis tools and dataset restricts greater findings of the report but leaves
an opportunity for further research.
Keywords: Non-performing loans, GDP growth, Unemployment, Inflation, Real Interest Rates,
Corruption Control Score
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Table of Contents
Abstract .......................................................................................................................................... iii
Chapter 1: Introduction ................................................................................................................... 1
1.1 Background of the Research Topic ....................................................................................... 1
1.2 Research Rationale................................................................................................................ 2
1.3 Aim of the Research.............................................................................................................. 3
Chapter 2: Literature Review .......................................................................................................... 4
Chapter 3: Research Methodology.................................................................................................. 9
3.1 Research Purpose .................................................................................................................. 9
3.2 Research Theory Building .................................................................................................... 9
3.3 Research Methodology ......................................................................................................... 9
3.4 Research Instruments .......................................................................................................... 10
3.5 Sampling ............................................................................................................................. 10
3.6 Data Collection Resources .................................................................................................. 10
Chapter 4: Findings from the Data................................................................................................ 11
4.1 Samples and Data Collection .............................................................................................. 11
4.2 Study Variables ................................................................................................................... 11
4.3 Dependent Variable ............................................................................................................ 11
4.4 Explanatory Variables ......................................................................................................... 11
4.5 Conceptual framework ........................................................................................................ 14
4.6 Econometric framework...................................................................................................... 14
Chapter 5: Data Interpretations and Discussion............................................................................ 16
5.1 Descriptive statistics ........................................................................................................... 16
5.2 Correlation matrix ............................................................................................................... 17
5.3 Ordinary Least Squared (OLS) Regression Output ............................................................ 18
iv
Chapter 6: Conclusion................................................................................................................... 22
6.1 Recommendation ................................................................................................................ 22
Appendices .................................................................................................................................... 24
References ..................................................................................................................................... 25
v
Chapter 1: Introduction
1.1 Background of the Research Topic
The goal of the study is to thoroughly assess the macroeconomic variables that have a substantial
impact on non-performing loans. A nonperforming loan is in default because the borrower has
failed to make the necessary payments for the stipulated amount of time (NPL). Even though the
specifics of nonperforming status may vary based on the conditions of a certain loan, it is generally
known that no payment means that there have been no payments of that particular principal and
interest. Nonperforming loans frequently occur for several reasons, such as inadequate financial
management and supervision, a lack of urgency on the side of lenders, a frail legal system, and a
dearth of effective debt-resolution strategies.
As noted by Fajar & Umanto (2017), a loan is declared non-performing when at least 90 days have
passed without either the principal or the interest being paid. Furthermore, Ghosh (2015),
discovered that loans are no longer able to "undertake" or accrue interest when they are routinely
past due for more than 90 days. On the other hand, non-performing loans are those for which the
banks are unable to collect the whole principle or interest, most often by the dates provided, and
for which there is no probability that settlement will occur soon. But this research is based on the
IMF's notion of NPLs. Additionally, due to the high inflation rate, shaky fiscal and monetary
policies, and growing bank exposure to credit risk, which threatens financial stability, and
economic activity.
Since rising NPLs have a direct effect on the whole banking system, they are regarded as a
significant proxy for credit risk. The NPLs ratio is one of the strong markers of the start of the
financial crisis since it significantly undermines general economic stability by reducing loan
expansion. An increasing NPLs ratio is an indication of a financial system that is vulnerable,
whereas a declining NPLs rate is a sign of sound finances. As per the idea of Sthembiso Msomi
(2022), High NPLs hurt each country's commercial banks as well, which ultimately puts the
system as a whole and the country's infrastructure at risk due to the commercial banks'
concentrations up to credit risk. Undoubtedly, a steady increase in NPLs hurts financial efficiency.
Consequently, the possibility of a banking crisis is raised. More specifically, NPLs lessen
investment opportunities, limit interest income, and exacerbate the financial collapse that is
1
primarily to blame for an economic system's failure. To ensure economic and financial stability, it
is necessary to identify the factors that influence NPLs.
According to the analysis conducted by Beck et al. (2013) even though the global financial system
experienced the Global Financial Crisis (GFC) in 2007–2008, the major recession primarily hit the
United States, countries in the EU nations, Latin American nations such as Argentina, and some
countries in Africa.
An analysis of the EU28 nations from 2000 to 2013 by Chaibi (2016), found that fiscal
consolidation results in a large budget surplus rather than a low budget deficit, which hurts bank
portfolios. The study also found a strong positive correlation between NPLs and both the
unemployment rate and the budget of government. Khafid et al. (2020) investigated the
macroeconomic implications of non-performing loans on the quality of bank portfolios between
the debt economies of France and Germany between the years 2005 and 2011. showed a link
between the rate of inflation and non-performing loans that were negative, although Singgih et al.
(2014) found a negligible positive correlation between the two among the nations that make up
Europe.
1.2 Research Rationale
According to the authors' understanding, numerous in-depth studies have been undertaken that
have looked at the factors that influence non-performing loans across the globe, but none of them
has included corruption control score as a contributing factor. The claim explains that this region's
financial market differs significantly from that of other nations since it does not suffer from the
same problems with corporate governance and financial inclusion that other developed and
growing nations do.
The study's remaining organizational structure consists of the following. In the literature review
section, the research methodology describes the purpose of the research, sampling strategy, and
data sources. After that, empirical studies of the macroeconomic factors that influence nonperforming loans are discussed. Incorporating data sources and research variables, creating
hypotheses, demonstrating the econometric framework, and talking about analytical
methodologies are all covered in the methodology section. While the conclusion part brings the
investigation to a close, the results section presents the findings and evaluates them.
2
1.3 Aim of the Research
It is feasible to ascertain the important causes of the issue and identify a solution by researching
nonperforming loans. This study will assist banks and other local lenders in comprehending how
the macro environment affects a nation’s and the global economy, enabling them to take action.
Additionally, it would help the borrowers understand how nonperforming loans are a drawback to
a robust financial system. In particular, research on nonperforming loans covers a country's whole
macroeconomic system.
3
Chapter 2: Literature Review
The purpose of the chapter is to bring about the determinants that influence non-performing loans
in an economy. In this chapter, we try to investigate the drivers that influence the non-performing
loan rates in the banking sectors, and the challenges and limitations from the previous books and
journals. As noted by Dewailly (2019), among them, a few thoughts have been conducted on the
issuance of credits, the NPL and the comparative default rate.
The text is divided into three sections: The main focus of the work has been on describing the NPL
using a credit mechanism to highlight the role of macroeconomic variations, administrative
excellence, and political decisions. The following section of the article that analyzes the connection
between NPL and macro-financial conditions shows that NPL has a favourable impact on the risk
of crisis and, thus, highlights the critical role that NPL plays in foreseeing financial emergencies
or bank crises. The third area of literature focuses on explaining or forecasting the NPL at a broad
scale. These summaries may refer to the entire requirement of credits in one economy or specific
categories of credits.
Practically speaking, there is no worldwide consensus on what constitutes non-performing loans.
According to Nichols & Shibut (2021), non-performing loans (NPLs) are quantities of borrowed
money for which the borrower has not made all of the agreed-upon instalment payments for at
least 90 days. A nonperforming credit is in default or extremely near to it. Once a credit becomes
nonperforming, it is believed to have a much lesser chance of being fully repaid. Though the
borrower resumes making payments on a nonperforming advance, the loan becomes a performing
loan even if the debtor has not made up all of the past-due instalments. According to Manz (2019),
There is noteworthy observational proof concerning the anti-cyclical behaviour of the NPLs. the
common clarification is that higher genuine GDP development ordinarily interprets into more
income which moves forward the obligation serving capacity of borrowers. The findings are
consistent with Tanasković & Jandrić (2015), when there's a deceleration, within the economic
level of NPLs is likely to extend as unemployment rises and borrowers confront more noteworthy
challenges to reimburse their obligation.
As per Pesola (2007), An increase in genuine GDP development leads to a decay in nonperforming loan ratios. Slack in GDP development moreover essentially influences NPL
development but with a positive sign. This finding loans bolster the idea that bank resource quality
4
breaks down with slack in reaction to positive development due to free credit guidelines connected
amid the boom period. At the same time, the by and large effect of GDP development (the whole
of the lagged and the contemporaneous coefficient) is negative as anticipated. Typically, a decay
in financial movement tends to influence non-performing advances with a time slack of a few
quarters.
According to Klein (2013), unemployment encompasses a positive effect on NPLs. The common
thought is that an increment in the unemployment rate diminishes the acquiring control of families.
For example, they are incapable to meet their obligation commitments. According to Ahmed et al.
(2021), Today’s economy is characterised by an intense population, high vulnerability to natural
disasters and floods, and highly fluctuating political stability.
Indeed, if the proportion of NPL doesn’t influence straightforwardly inflation, it influences the
loaning arrangement of the banks, such that it is an imperative determinant of the transmission
component and is likely to affect the last reaction to changes within the intercession intrigued rates.
Regarding particular variables chosen for the return on resources, the change in credits and the
credit loss savings to add up to the loan ratio, the macroeconomic parameters include the GDP
growth rate, unemployment rate, and real interest rate. After reviewing the previously published
journals and books, we discovered that there is a negative correlation between the NPLs and the
rate of GDP growth, the productivity of bank resources, and, most notably, the unemployment rate,
the amount of credit loss savings that add up to credits, and the real interest rate. The reduction of
NPLs can be a crucial requirement for advancing financial growth. The findings of Mileris (2014)
disclose that when NPLs retained permanently, these will affect the assets that are encased in
unprofitable areas. Hence, NPLs are likely to obstruct financial development and decrease
financial proficiency. The stuns to the monetary framework can emerge from components
particular to the company or macroeconomic variations. In common, we found that the developed
economies have affirmed that macroeconomic conditions influence credit chance.
According to Magdalinos & Tsakalos (2021), the integration of non-performing advances and their
determinants has greatly expanded since we experience more distributed information at the bank,
nation, and total keeping money framework level. The outcomes are about uncovering profitable
experiences that approximate the quality of advanced portfolios and the delicacy of banks. The
driver of components that influence money-related defencelessness may be a cause of
5
contradiction due to the uncontrolled increment of problem credits. Numerous analysts consider
NPL as “financial pollution” with hurtful effects on both financial advancement and the economy.
There is another factor that plays a role in affecting bad debt. Meaning that, if there is a positive
effect of interest rates on bad loans, this results in a climb up in the debt due to the increase in
payments of interest rate. As a result, the amount of non-performing loans rise. The real interest
rate is one of the crucial factors driving the non-performing loans down. And there is a wife of
interest rates the volume of net loans also increases. As a result, it makes the payment of loans
more expensive and difficult. Eventually, this triggers the borrowers to go incapable of paying
loans in time as it increases the burden on the borrower.
Castro (2013) showed an argument that mentioned about economic growth and real interest rates
are crucial determinants of non-performing loans in sub-Saharan countries of the African
continent. He showed the interconnection of doubtful loans with the macroeconomic factors in an
environment that represents an undiversified culture. Kocisova & Pastayriková (2020) appeared
with evidence that GDP growth, credit conditions and real interest rates can be explained by the
rate of non-performing loans. Suzuki & Miah (2017) sought to identify the factors that contribute
to non-performing loans in various nations. They came to the same conclusion. Due to the real
effective exchange rate, they discovered the reverse effect on damaged loans. These findings imply
a negative relationship between GDP growth and non-performing loans. It eventually causes the
amount of non-performing loans to decline. The findings also showed that wine banks have high
anticipation of acquiring defective loans and give out too many loans at high-interest rates.
Kennedy (2021), who employed a dynamic panel of 80 nations, concluded that higher interest rates
and weak economic development both contribute to an increase in non-performing loans. The link
with non-performing loans is improved by the effective interest rate.
For further research, Mitchell (2019) used panel data from 75 countries, stock prices, foreign
currency exchange rates, real GDP growth and lending interest rate and found a significant effect
on NPL ratios. Based on the research they stated that they were able to have a sufficient amount
of empirical evidence that interprets the anticyclical behaviour of non-performing loans.
Commonly, all the studies directed by these researchers reach a similar conclusion that nonperforming loans are likely to rise when there is a drawback appears in the economy. Like the
other factors that influence net-performing loans, the effect of real interest rates is also significant.
6
While GDP growth, unemployment problems and real interest rates play roles as important
determinants of affecting the amount of non-performing loans, other factors also need to come into
consideration that is associated with this loan crisis. As a purpose of the report, we have tried to
bring the idea of decreasing the value of the currency of a country. In an economy, inflation is a
common factor that affects the monetary value of its currency. The effect is commonly adverse.
Currency depreciation can have a particularly negative and unfavourable effect on the economy
when out of the total loans, there is a large amount of money from foreign countries. Studies by
Qianying (2017) show that providing loans in countries in foreign currencies to unhedged
borrowers might increase non-performing loans. The reason is they earn money typically in local
currency so it gives rise to more difficulties for borrowers to pay their debts. On the other hand, in
countries that have little mismatches of their local currencies, faceless depreciation and they are it
might lead to an increase in their export activities and volumes resulting in considerable
improvement in the financial position in the corporate sector and also reducing non-performing
loans.
According to Shkodra & Ismajli (2017), the effect of inflation on debt is equivocal and ambiguous.
When the inflation rate is higher, it can decrease the real value of debts making the loan services
easier but at the same time, it can also decrease the actual income of the borrowers and make their
wages sticky. Peek et al. (2000) say that, when debt rates vary in countries with higher inflation
rates, it can increase the real interest rate resulting from the country's monetary policies.
The final determinant affecting non-performing loans to be discussed in this report's literature is
the corruption control score. The few studies online have investigated the impact of corruption
control of a country on its non-performing loans. Empirically investigated the relationship between
non-performing loans and corruption. One of the researches conducted by Gorter et al. (2001)
pointed out that when legal institutions enforce legal contracts, a triggers the willingness of the
financial institutions to lend more. The reason behind this is the financial institutions are ensured
that there will have the legal power to take action against the borrowers to recover the credit
amounts using legal powers in case the debtor go defaults. On the other hand, the corruption inside
the financial institutions cast the uncertainty of the imposition and payment of legal contracts and
on recovery of debited money.
7
Used panel data from 129 countries over 25 years. Their studies showed the strength of the legal
protection rights of the creditors. They classified their data: as rich countries and poor countries.
They found different results in a different types of countries. The wealthy country possesses strong
legal protection rights wherein the poor countries, the creditors have almost no or weak legal
protection rights. Extended studies done by Avetisyan (2019), found that strong legal protection
rights keep the amount of non-performing loans low. Poor countries where the legal system is
weak and vulnerable tend to take advantage of the corrupted system and avoid loans. It is also
found that countries that have lower corruption control scores have higher non-performing loans.
These studies are an indication that a strong legal system can in hands a firm's market competition
and reduce the lending activities derived from corrupted intentions.
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Chapter 3: Research Methodology
3.1 Research Purpose
Towards finding ambiguity of the determinants of nonperforming loans and creating scope to
convert those uncertainties into opportunities we are following the exploratory and conclusive
pattern of the research type here. As the nonperforming loan is something that seems already turned
defaulted or is close to being defaulted, there is less possibility of getting the interest or the
principal amount back, according to Khafid et al. (2020). Through exploratory research and
conclusive feature, any financial organization will be able to clarify the absolute determinants of
nonperforming loans and the way to find a new opportunity to collect those amounts by developing
new agreements over its business environment as per the idea of Qianying (2017). And following
the liquidity ratio, capital adequacy ratio, bank credit, and return on assets scenario the
organizations may their requirement in that agreement. This will lessen the uncertainty of those
borrowed amounts at a large margin and that’s the purpose of this assessment.
3.2 Research Theory Building
The exploratory pattern doesn’t provide anything conclusive. So, the further research approach is
quite mandatory. In that case there for ensuring the amount which has been quite announced as
default, the organizations have to ensure the proper development of new thoughts and concepts.
And Deductive and Inductive are the results of that need. Singh (2020)conducts that Here for the
dataset Deductive build-up will show a specific, true feature against a no-responding scenario. And
Inductive reasoning establishes a general proposition based on the observations and this also will
help to reduce the loss of a huge amount from nonperforming or standby figures.
3.3 Research Methodology
The way that researchers follow to go through their proceeding of collecting, analyzing, and
interpreting data is called the research approach. Generally Qualitative, Quantitative, and Mixes
these three approaches are followed around. For this assessment, we went for the Quantitative
approach and a concern checkout at those data. As per the findings of Chiorazzo et al. (2017), as
with the increases in the number of data sets, there is a high chance to get the result at a more
accuracy. And here with more and more data about the intention of not being tended to repay the
loan amount, just being defaulted, or close to defaulting conquer a great solution to reduce the
uncertainty about interest income and the principal figure.
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3.4 Research Instruments
There was descriptive analysis, Correlation, Regression, etc. of those data all around. The
descriptive analysis part features a genuine view of the total impact on the bank or a business
organization and other parts of society. As conducted by Ahmad & Ariff (2008) that correlation
sets the connection and impact on one another where being defaulted seems a great loss in the long
run. And regression is there to identify which variables have an impact on a topic of interest.
3.5 Sampling
Here the Random Sample is taken from 70 countries’ data about Non-performing Loans,
Unemployment Rate, GDP Growth Rate, Corruption Control Score, Real Interest Rate and
Inflation Rate.
3.6 Data Collection Resources
Here the information is the secondary basement. A secondary way of collecting data is the process
of using a support that helps to collect data. The data are collected from a financial report of a bank
but are not physically present in their environment. The data have been taken from their website.
So, this is via information that constructs the structure of the secondary data collection process.
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Chapter 4: Findings from the Data
4.1 Samples and Data Collection
For a certain period, the article evaluates 70 developing countries. The information was gathered
from official websites and websites that give these statistics for several foreign banks. To analyze
the outcomes, a panel data technique is utilized. Data, where entity behaviour is tracked over time,
are the subject of panel data analysis. Finding patterns in data gathered from many sources and
over time is the fundamental goal of panel data analysis.
An imbalanced panel data set is used to account for the structural breakdowns in part of the data.
Within this time frame, it is analyzed how the independent variable has changed both before and
mostly during the financial crisis.
4.2 Study Variables
This study takes five macroeconomic factors into account to assess the non-performing loans in
these 70 nations. Based on empirical research that significantly affects NPLs that was reviewed in
the literature review part, all the study's factors were chosen. The selection of these variables was
made to determine if non-performing loans in these nations are causally related to GDP,
unemployment rate, inflation, real interest rate, and corruption control score.
4.3 Dependent Variable
The amount of borrowed money on which the borrower is unable to make their instalments for at
least 90 days is represented by the non-performing loans (NPLs) ratio, according to Khafid et al.
(2020), which serves as the dependent variable in the study. When the planned payment is passed
late and is no longer anticipated to be made soon, advances and instalment loans cease to be
performing. NPLs are the key component for calculating loan loss as a result. This study uses the
non-performing loans ratio as a measurable variable and as a stand-in for credit risk because it
assesses the bank's financial stability, credit portfolio management, and capital adequacy.
4.4 Explanatory Variables
As explanatory variables, the GDP growth rate, unemployment rate, corruption control score
(WGI), inflation rate and real interest rate are all taken into consideration. Numerous studies
conducted by a large number of scholars focused on the elements that determine NPL based on
either macroeconomic conditions or bank-specific characteristics. For bank-specific issues, panel
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data analysis from the sample of the largest banks in one region or one nation is frequently used,
although researchers frequently use panel information across different countries to explain the
effects of macroeconomic variables.
The following criteria served as a guide for choosing the variables: 1) That factors that are
important for NPLs, particularly in emerging markets, should be considered 2) that data are
available for all the variables for all the chosen nations; and 3) that the literature has contradictory
findings regarding their effect on NPLs.
As per the study of
Limajatini et al. (2019), the empirical studies demonstrate the close
relationship between NPLs and the economic and business cycles, demonstrating that
macroeconomic elements, such as declines in overall economic activity, are always present in
every financial crisis.
As identified in the literature, a key component of the macroeconomic environment that gauges
economic progress is the GDP Growth Rate (GDP). The economic cycle and banks' exposure to
credit risk has a dialectical connection. Financial intermediaries often face more credit risk during
economic stagnation or recessions because it is more difficult for the economy to sustain targeted
levels of employment, prices, and outputs. On the other hand, during an economic boom, the rising
economic activity causes both families' and enterprises' cash volumes to rise. Once again,
increased income levels for borrowers enhance their ability to repay the debt and raise confidence
among depositors and investors for investment projects. Consequently, the cumulative effects
result in a decrease in credit risk for the banking industry. Hence, this study formulates the
following hypothesis,
H1 GDP growth rate has negatively related to NPLs.
One of the most significant measures of the NPLs ratio is UNR. The UNR variable shows the
fraction of the labour force that is made up of unemployed people. As per the findings of Siakoulis
(2017) A greater unemployment rate is anticipated to cause the rate of NPLs to increase. The
economic downturn and banks' exposure to credit risk have a dialectical connection. Financial
intermediaries often face more credit risk during economic stagnation or recessions because it is
more difficult for the economy to sustain targeted effects on employment, prices, and supplies. On
12
the other hand, during an economic boom, the rising economic activity causes both families' and
enterprises' cash volumes to rise. So, the purpose of the study is,
H2 Unemployment rate is positively related with NPLs
A price rising for commodities and services over a given period in a specific economy is referred
to as inflation, as per the literature. As a result of depreciating the initial worth of money, high
inflation raises borrowing costs and loan interest rates, increasing the borrower's obligations and
raising the risk of default. According to Hong (2016), whereas low inflation promotes economic
growth, excessive inflation lowers the borrower's real income and reduces their capacity to repay
the loan. In contrast, a rising inflation rate reduces the total amount of loans and, as a result,
enhances the borrower's capacity to make timely payments on their debts, thus lowering the chance
of default. Because of this, it is possible that non-performing loans do not have a clear connection
with inflation. It formulates the following hypothesis,
H3 Inflation is either positively or negatively related to NPLs.
The real interest rate (RIR) imposes an implicit penalty on bank-issued credit that has an impact
on loan defaults. As a result of many banks refusing to lend, a large percentage of non-performing
loans (NPLs) will impede economic growth. Thus, it formulates,
H4 Real Interest Rate is positively related to NPLs
Another important concern for banks in developing nations is the Corruption Control Score (WGI).
It encompasses views on the extent to which public authority is utilized for personal gain, including
both small-scale and large-scale corruption, as well as the "ownership" of something like the
government by people and business interests. This variable is incorporated into the study as a
result. This results in the following hypothesis,
H5 Corruption Control Score is negatively related to NPLs
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4.5 Conceptual framework
The following is an illustration of the conceptual framework which is appropriate for the paper:
Figure 1: Conceptual Framework
Source: Author’s Compilation
4.6 Econometric framework
The determinants of NPLs have been estimated using the following equation:
NPLit = β0 + β1UNPit + β2GDPit + β3RIit + β4INFit + β5CCSit + εit
Here,
NPL = Non-Performing Loans
UNP = Unemployment Rate
GDP = GDP growth rate
RI = Real Interest Rate
INF = Inflation Rate
CCS = Corruption Control Score
Subscript i = The examined country
Subscript t = The examined time
14
According to the analysis of Cetin (2019), The equation was further transformed into a logarithm
model in this study to condense the information because a huge sample cannot predict an important
outcome. Log transformation occasionally produces very accurate results. Due to the tiny dataset's
low variance, the result is consistent and cohesive. As a result, the natural log of NPLs, where
NPLs, UNP, GDP, RI, INF, and CCS are concerned, is used in the study. The Intercept is 0, the
respective coefficient terms are 1, 2,…., 6, the country count is 1, 2,..., 8, and the time interval is
1, 2,..., 12. It reads: Random Error
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Chapter 5: Data Interpretations and Discussion
5.1 Descriptive statistics
Table 1 illustrates the descriptive analysis of both dependent and explanatory drivers for 70
developing nations with a total of 70 observations. According to descriptive data, the average
amount of non-performing loans (NPLs) in the nations was 6.79%, with an SD of 0.901% and a
range of significant differences across the countries between 0.95% and 49.9%.
Table 1 | Descriptive Statistics of the Data Set in Brief
Variables
Non-Performing
Loan
Unemployment
Rate
Observations
Mean
Std. Dev
Minimum
Maximum
70 6.791369867 0.901195894
0.953674 49.90132291
70 10.03665714 5.435399825
3.38 21.02899933
GDP growth Rate
70 3.907200206 3.005250082
-5.717683 11.34339697
Real Interest Rate
70
Inflation Rate
Corruption
Control Score
(WGI)
5.89032 3.635151303
70 4.770663826 4.569674408
0.0932
12.14
-1.403608 30.69531299
70 0.011270753 1.164665019 -1.636177 1.611609817
Source: Author’s calculations
The GDP had a substantial-high degree of variability, with lowest and maximum values of 5.71%
and 11.34%, respectively, and a lower standard deviation of 3.0% and an average GDP growth
rate of 3.91%. Since the mean value of the GDP growth rate is relatively low and the minimum
value is negative, some of those nations likely had decreasing trend throughout the database period.
The average inflation rate (INFL) was reported to be 4.77%, with a standard deviation of 4.57%
and a range for the inflation rate of -1.40% to 30.69%. Moreover, during the same period, the
unemployment rate is relatively high which will take values in the range of 3.38% and 21.03%.
The data for the inflation rate has a standard deviation of 5.43% and varies from -1.4% to 30.7%.
Among other things, the large oscillations in the economies of the chosen nations may be seen as
a result of the standard deviation of macroeconomic indicators.
16
Figure 2: Bar Chart of Mean
Source: Author’s Compilation
The fact that the real interest rate is lower, at 0.09%, indicates that some institutions did not make
the best choices. The standard deviation of the real interest rate was 3.63%, while the average was
5.89%. The Corruption Control Score (WGI) average, which runs from a minimum of -1.63% to
a high of 1.61% with a standard deviation of 1.16%, indicates that nearly all of the nations have
lower levels of corruption control.
5.2 Correlation matrix
The correlation between the independent variables is described in Table 2. The multi-collinearity
issues in the dataset are shown by the correlation matrix.
Table 2 | The Correlation Matrix
Non-Performing Loan
(%)
Unemployment Rate (%)
GDP growth Rate (%)
Real Interest Rate (%)
Inflation Rate (%)
Corruption Control Score
(WGI)
NPLs
(%)
UNP
(%)
GDP
(%)
1
0.2382
-0.135
0.3476
-0.0424
1
-0.139
0.0839
-0.053
1
-0.25
0.0202
1
0.1851
1
-0.4337
-0.071
-0.222
-0.603
-0.4474
17
RI
(%)
INF
(%)
CCI (%)
1
Source: Author’s calculations
As shown in Table 2, the link between non-performing loans and the unemployment rate and real
interest rate is favourable. That indicates that changes in these two factors proportionally
influenced the likelihood of having NPLs. The rate of NPLs will rise if the rate of these factors
rises. And if the rate of these factors falls, so will the rate of NPLs. The non-performing loan, on
the other hand, has a negative correlation with the GDP growth rate, inflation rate, and corruption
control score (WGI). However, there is only a very weakly negative correlation with the inflation
rate (-0.04). Therefore, it rarely harms the likelihood of having a non-performing loan. But there
is a significant inverse relationship (-0.43) between the corruption control score and the NPL.
Accordingly, there is a good chance that when the WGI rate rises, the non-performing loan will be
reduced to practically half of its present ratio, which is now 6.91% on average. Additionally, the
GDP growth rate has a negative correlation (-0.135) with non-performing loans.
Again, in the correlation matrix, the relationships of the variable with each other are also shown.
Table 2 has shown how the variables are affecting each other with their positive and negative
increment. The GDP growth rate, corruption level, and inflation rate are all negatively correlated
with the unemployment rate. Thus, a rise in the unemployment rate results in a decline in the rates
of all three variables. Additionally, it positively correlates with the real interest rate. The only two
variables that have a positive correlation are inflation and GDP growth rates, thus when GDP
increases, inflation also rises. Additionally, it is negatively correlated with the other three factors.
The real interest rate and GDP have a positive association with the inflation rate. Additionally,
there is a poor association between the unemployment rate and the corruption control score.
Additionally, all of the factors that were employed as the independent variable had a negative
association with Corruption Control Score It is because only the increasement in the rate of
corruption control scale can reduce all of the problems related to the non-performing loan. It highly
affects the inflation rate and the non-performing loan. Though it has a weak relationship with the
unemployment rate, an increase in the corruption control scale decreases its rate.
5.3 Ordinary Least Squared (OLS) Regression Output
Although a pooled OLS model was used in the past to investigate the multi-collinearity issue, this
model has some serious drawbacks since it overlooks the uniqueness and heterogeneity of the data.
Dependent Variable: Non-Performing Loans (NPL)
18
Table 3 | Results from the Panel Regression of All Countries
Intercept
Unemployment Rate
(%)
GDP growth Rate (%)
Real Interest Rate (%)
Inflation Rate (%)
Corruption Control
Score (WGI)
R2
Adjusted R Square
Standard Error
Significance F
Observations
Coefficient
s
11.18
Standard
Error
t Stat
3.563 3.1381
0.196
-0.706
-0.178
-0.524
0.143 1.3706
0.307 -2.298
0.307 -0.579
0.191 -2.737
-4.402
0.35
0.299
6.311
1.057
ρ -value
0.003
-4.165
0.175
0.025
0.564
0.008
0.00009
5
Lower
95%
4.0626
Upper
95%
18.3
-0.09
-1.32
-0.792
-0.906
0.482
-0.09
0.436
-0.14
-6.513
-2.29
0.00003322
70
Refers ρ<0.05
Source: Author’s Calculation
As per the regression model, the model for the overall dataset is,
NPLit = β0 + β1UNPit + β2GDPit + β3RIit + β4INFit + β5CCSit + εit
= 11.28 + 0.196 UNPit + (-0.706) GDPit + (-0.178) RIit + (-0.524) INFit + (-4.402) CCSit
Here is the ρ-value for the overall F test, defined as significance F. The table also shows the
coefficients, intercepts and slopes. here is a ρ-value for the partial slop. These will use to do the
individual partial slope t-test. In this table are the confidence intervals for the partial slopes for
coefficients in general and some more kinds of multiple things.
In the table, there is a result of the r square and it got a very low value which is 0.35 which means
that roughly 35% of the variability of the dependent variable which is the non-performing loans
can be explained by the entire set of independent variables that was the unemployment rate GDP
growth, corruption control score, real interest, and inflation. Though 35% result in the r square is
not the greatest result, in some fields that might not be the worst thing. Generally, a higher r square
is considered to be a great result for any kind of data set. According to Guerard (2012), A high r
19
square suggests that the multiple regression model has some predictive potential. Except for
adjusting the r square for the sample size and the number of independent features utilized in
independent variables, adjusted R squares are relatively comparable. So, increasing the sample
size can give a better result in this case. So, the adjusted R Square is always smaller than the r
square.
Observation for the research was 70 which means 70 different countries’ data has been taken to
conduct the research.
The ρ-value, although it doesn't get labelled ρ-value for the overall F. This number is Ho and H1.
The null hypothesis was that in all the partial slopes that means in this case there are five
independent features so all the partial slopes there are 5 of them are equal to zero and the alternative
is at least One of these variables is not equal to zero.
Ho: β1UNPit = β2GDPit = β3RIit = β4INFit = β5CCSit =0
H1: β1UNPit = β2GDPit = β3RIit = β4INFit = β5CCSit ≠0
So then now the hypothesis says that there is no useful linear relationship between any of those 5
independent features. And the alternative hypothesis says that there is at least one useful
relationship between one of them or at least one of the independent features. In this case, the ρvalue is 0.00003322. So, if the level of significance is, α = 0.05, the ρ-value is lower than the value
of α. That means there is no sufficient evidence to reject the null hypothesis. So, it can be said that
there is at least one useful relationship between one of the variables or at least one of the
independent features.
As the test failed to reject the null hypothesis, the individual t-test can be done through the ρ-value
that is given in the table. For t-test of each of the independent variables follows the same hypothesis
as before,
So, for the t-test, the individual variables are likely to be considered by the following hypothesis
Ho: βi=0; have no significant relationship with NPL
H1: βi≠0; have some significant relationship with NPL
20
The ρ-value indicates whether or not the results are statistically significant if it is less than the
significance threshold (0.05), otherwise, it will be considered that the results are statistically
insignificant. From the table, we found that the ρ-value for the GDP growth rate, Inflation rate and
corruption control score is 0.025, 0.008 and 0.000095 respectively which are less than the
significance level of 0.05. That means for these variables the results are statistically significant.
So, there has some significant relationship. But for the unemployment rate and real interest rate
ρ-value are 0.175 and 0.564 which are greater than the significance level. And that makes to think
that the results are statistically insignificant and these variables have no significant relationship
with non-performing loans. As per the literature review, it has been said that the unemployment
rate and the interest rate significantly influence non-performing loans. But from the database, it
has been found that there is so significant relationship between these two variables with the NPLs.
From the findings, we have found that there is an extreme value of the rate of NPLs in the country
with the code number 1 which is 49.90132291%. This value is much higher than the entire dataset
of the sample. In this case, we try to recalculate the ρ-value by eliminating the data of country code
1 (see Appendix A) and the results show that without the extreme value, The results are statistically
significant since each independent variable's ρ-value is below the 0.05 threshold of significance.
Even if the unemployment rate's ρ-value is 0.052, which is higher but nearer the α = 0.05 level of
significance, it is still greater than this figure. The conclusion in this instance will be that there is
insufficient evidence to rule out the null hypothesis. Additionally, the R2 value is much greater
than the previous value, which was 45.4%. This indicates that by excluding that particular data,
10% more of the dependent variable's variability can be explained by all of the independent
variables. As a result, the entire dataset is skewed because of the high NPLs rate that country code
1 has.
21
Chapter 6: Conclusion
The study explores the effect of some macroeconomic factors on non-performing loans. As nonperforming loans affect the economic stability of a country, this study is significant for finding the
main reasons behind non-performing loans to get out of this situation. So, we considered five
factors into account to study non-performing loans in 70 countries. These factors have a positive
and negative relation with non-performing loans and also among them. GDP growth rate has
negative relation with non-performing loans. So, if the GDP growth rate increases, the nonperforming loans will decrease. Similarly, the corruption control score has also negative relation
with non-performing loans. But the corruption control score is weak in our represented countries.
So, increasing the GDP growth rate and corruption control score will decrease non-performing
loans. On the other hand, the Unemployment rate and real interest rate have a positive relation
with non-performing loans. So, increasing unemployment and interest rates will result in higher
non-performing loans in a country. From the study, we can also see that the inflation rate doesn't
have much significant impact on non-performing loans. The relations among the variables show
that the unemployment rate has negative relation with the inflation rate, GDP growth rate and
corruption control score. Inflation and GDP growth rate show a positive relationship between
them. But GDP has negative relation with corruption control score and real interest rate. The
corruption control score shows a negative relationship with all the independent variables. So, the
target is to increase the negatively related variables and decrease the positively related variables.
So that, the non-performing loans can be reduced to a minimum. After all, this study can also be
continued further with much more samples and considering other variables that affect nonperforming loans.
6.1 Recommendation
From our study on factors that affect non-performing loans, we have some recommendations to
improve the condition of non-performing loans in a country.
1. As reducing unemployment will reduce non-performing loans, we recommend focusing on
this factor. Reducing unemployment is a challenge for a country. As per the study, by
increasing the GDP growth rate and corruption control score, the unemployment rate can
be reduced. It follows as lowering the interest rate will result in a lower unemployment
22
rate. Our recommendation is to take different policies and steps which will reduce the
unemployment rate.
2. Another recommendation for reducing non-performing loans is to increase the corruption
control score in a country. It has negative relation with non-performing loans. Controlling
corruption is a challenge for developing countries. Making sure transparency and
accountability can reduce corruption. It will also have a significant effect on other variables
like GDP and the unemployment rate. So, our recommendation is to control corruption.
3. Interest rate should be managed and set with much more caution because it has an impact
on non-performing loans. Reducing interest rates will reduce non-performing loans. But it
affects other factors too. So, the recommendation is to reduce the interest rate and manage
it properly regarding other socio-economic factors.
4. Increasing the GDP growth rate will reduce non-performing loans. So, we recommend
taking steps that increase the GDP growth rate. To increase GDP lowering the interest rate,
unemployment etc. are recommended. As the factors are correlated and one affects another,
we finally recommend increasing the GDP growth rate along with reducing unemployment
and interest rates and making sure of better corruption control.
23
Appendices
Appendix A: Pooled OLS estimation (without Country 1)
Table 4
Particulars
Intercept
Unemployment
Rate (%)
GDP growth Rate
(%)
Real Interest Rate
(%)
Inflation Rate (%)
Corruption
Control Score
(WGI)
Multiple R
R Square
Adjusted R
Square
Standard Error
Observations
Coefficients
11.884
Standar
d Error
2.373444
t Stat
5.007097
P-value
0.0000047
Lower
95%
7.141114
Upper
95%
16.627
0.1886
0.095235
1.980202
0.05205
-0.00173
0.3789
-0.83
0.205018
-4.04963
0.00014
-1.23994
-0.421
-0.428
-0.365
0.206377
0.128693
-2.07323
-2.83887
0.04225
0.00609
-0.84028
-0.62252
-0.015
-0.108
-3.969
0.6737
0.4539
0.705323
-5.62766
4.5E-07
-5.3788
-2.56
0.4106
4.2022
69
Refers ρ<0.05
Source: Author’s Calculation
24
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