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purpose of the study is to investigate the factors affecting the NPL in the for
bank based in the European Union of the total 28 Countries.

Literature review provides evidence that both aggregate and disaggregate

bank) data are used for similar investigations. Nevertheless, according to

Taktak, and Jellouli (2009b), aggregate data for the whole banking system

of each
country (in contrast to the examination of individual data for each bank) are

preferable as the risk of non-representativeness of the sample is reduced.

aggregate data were used by Rinaldi and Sanchis-Arellano (2006) in order

to overcome
the obstacle of disaggregate data unavailability in the euro area. For

reasons, we chose to examine exclusively aggregate data in our research.

extracted our data from the databases of the International Monetary Fund

the World Bank and the Eurostat. Our main objective was to collect data from

all 17
countries of the Eurozone, for the longest possible period. However, the nature

of the
research and the multitude of the examined variables created difficulties in

the required data for all countries. The main target of our study was to

determinants of NPL ratio exclusively on the pre-crisis period. In this

the final sample consisted of an unbalanced panel of 14 countries with 120

for the period 2000-2008. According to Rinaldi and Sanchis-Arellano

unbalanced panel data include more observations and their results are less

on a particular period. The distribution of observations is presented in Table



2.2 Methodology


As mentioned
above, this study identifies the factors that affect positively or negatively

the NPL
rate in 14 of the 17 Eurozone countries. Based on the merits of studies

investigate NPLs, we use a set of explanatory variables that are commonly

in such
models. However, one of our novelties is the inclusion of public

variables. Additionally, contrary to Boudriga, Taktak, and Jellouli (2009a),

Khemraj and Sukrishnalall Pasha (2009), Cotugno, Stefanelli, and Torluccio

we used a dynamic panel regression method for our analysis. Specifically, in

to provide consistent and unbiased results, we implemented the difference

of the Moments (GMM difference) estimation, which is based on

differences and was introduced by Manuel Arellano and Stephen Bond (1991).

choice of this estimation is also in line with the empirical investigations of

and Jesus Saurina (2006), Louzis, Vouldis, and Metaxas (2010) and De

and Demyanets (2012). However, we investigate the effect of banking and

on NPLs for two separate periods, t and t-1. Our fist

is expressed as follows:

NPLit = a0 + aiXi,t + aiMi,t + ?i,t (1)

where NPL is the aggregate
non-performing loans to total gross loans, X denotes the

specific variables and M the macroeconomic factors, as presented on Table 2.

that i corresponds to the
examined country of the sample and t to the year.

with the purpose of extending our investigation we use one lag

both bank-specific and macroeconomic regressors, targeting to capture the

explanatory variables over the previous year. Generally, the inclusion of

lags is commonly used in the literature e.g. Jimenez and Saurina (2006),

and Torluccio (2010), Louzis, Vouldis, and Metaxas (2010). Therefore,

second econometric model is expressed as follows:

NPLit =a1+aiXi,t-1 +aiMi,t-1+ ?i,t-1. (2)

order to obtain deeper insight into the relevance of explanatory variables, we

(1) and (2) in three different versions; we begin by examining only

variables as regressors, secondly only macro variables and finally both micro


For the
GMM estimation, we employed first and second period lagged variables

instruments for the explanatory variables, which are in line with the results

Sargan test. In order to check whether our series are autoregressive, we

panel cointegration test. The results indicated that the null hypothesis

(H0 = no cointegration) is not rejected (p-value = 0.2547).

One of
the examined bank-specific factors is the capital adequacy ratio (CAP).

measures the risk that a bank can undertake. Generally, regarding capital

although they are widely used in similar studies, the results are not clear

they affect positively or negatively the NPL index (Sinkey and Greenawlat

1991; Bertrand Rime






Research Design and Approach

 Research design is a master plan specifying
the methods and procedures for collecting and analyzing the required data. The
choice of research design depends on objectives that the researchers want to
achieve (John, 2007). Since this study was designed to examine the
relationships between NPLs and its determinants, a logical reasoning either
deductive or inductive is required. Deductive reasoning starts from laws or
principles and generalizes to particular instance whereas inductive reasoning
starts from observed data and develops a generalization from facts to theory.
Besides, deductive reasoning is applicable for quantitative research whereas
inductive reasoning is for qualitative research. Thus, due to quantitative
nature of data, the researcher used deductive reasoning to examine the cause
and effect relationships between NPLs and its determinants in this study.

The objective
to be achieved in the study is a base for determining the research approach for
the study. In case, if the problem identified is factors affecting the outcome
having numeric value, it is quantitative approach (Creswell, 2003). Therefore,
the researcher employed quantitative 31 research approach to see the regression
result analysis with respective empirical literatures on the determinants of
Nonperforming loans. Thus, the researcher was used a panel data from 2002 to
2013 period.


Dependent variable

Nonperforming Loan

A performing loan is the one that generate
profitability for the bank and make it able to extend new loans. When borrowers
are not able to meet theirs’ payment obligations for 90 days or more, the bank
must set aside capital equal to the reaming amount of the loan, both in
principal and interest, under the assumption of that the loan will not be paid
back. That loan from now on is characterized a non-performing loan (NPL). Non-performing
loans as commonly used as a measure in order to assess the quality of the loan
portfolio of a financial institution. Deterioration of the quality of a bank’s assets is essential issue as,
beside everything else, is a common cause of bank failure. NPLs can seriously
damage a bank’s financial position having on banks operation. As European
Central Bank mention “To be successful in
the long run, banks needs to keep the level of bad loans at a minimum so they
can still earn a profit from extending new loans to customers” (ECB, 2016). In
our study will use the following independent variables in order to examine if
they have impact to the amount of NPL, in the form of logarithm.



Independent Variables

 Independent variables are explanatory
variables that explain the dependent variable of NPLs. In our study, we both
include bank specific and macroeconomic independent variable. Bank specific
variables are the indictors of bank profitability (ROA and ROE), total
liabilities to assets ratio, capital adequacy ratio (CAR) and the logarithm of total
Assets (Size). The macroeconomic independent variables are the Gross Domestic
Product (GDP), inflation rate (INF), unemployment rate (UNEMP) and interest rate
(INT). The majority of these variables adopted from previously empirical studies,
based on the extent of their effect on nonperforming loan. whereas one of these variable, that is
effective tax rate is added from the researcher’s own perception.

Return on Asset (ROA):

 ROA is
qualitative ratio that represents the efficiency of assets to generate net
income. It provides a measurement of the ability of bank management to exploit their
assets to generate profits. Since assets are the “investments” of the bank in
order to produce revenue, this ratio helps bank management and investors to
monitor how well the bank convert assets into profits. Thus, the higher the ROA
ratio the better performance of the management to utilize assets to a
profitable way. Bank’s profitability in terms of Return On Assets is might a
result from high interest rates, commission and fees of services that provide
to the bank growth in size and

Researchers like
Messai (2014), Kjosevski (2016) and Khan
(2016) proved that the ROA has negative relationship with NPL and as Godlewski
(2004) mentioned “The lower the return on
asset the higher will be the NPLs and vice versa”.


Return on Equity (ROE):

 ROE is a
qualitative ratio that represents the ability of equity to generate net income.
Is one of the most important profitability metrics as it reveals the after-tax
income in comparison to the total shareholder equity. Profitability in terms of
Return On Equity is a result of the amount of money shareholders have invested
to the bank.  

Calculation of ROE comes for the Equity=Assets-Liabilities and Net EQUATION

In many
relative studies, the results concerning NPLs and bank profitability measure, in
terms of ROE, are as expected. For instance: Kjosevski (2016) and Makri et
al.(2014)found negative relationships between ROE and NPLs.



Total Liabilities to Total Assets Ratio

The total liabilities to Total Assets Ratio or
as it commonly used as debt ratio. Debt is the part of the balance sheet that
shows the obligation of the company that been monitored, debt ratio is interpreted
as the leverage that a company has due to it obligations. In case of commercial
banks’ balance sheet though, liabilities (obligation to depositors or debt) is
consisted mostly by the deposits of the clientele of the bank. Generally total
debt to total assets ratios gives a comparison measure that shows the bank’s
assets that are financed by deposits (or bank’s loans), rather than equity.

Louzis et al (2010), in their study concerning
determinants of NPLs for the Greek banking sector, they did not find the expected signs of neither the variable was statistically
significant (for all types of loans of the study). In our research we want to examine if the
level of leverage in terms of assets, for our segment, is significant and able
to determine the level of NPLs of a bank’s loan portfolio.





Capital Adequacy Ratio (CAR)

Adequacy Ratio is also known as Capital to Risk (Weighted) Assets Ratio (CRAR),
is a way of measure bank`s financial strength since it shows the ability about
the toleration of operational and abnormal losses. As noted by Makri et al.(2014), CAR determines
risk behavior of banks. It is a measure of banks capital and it is
expressed as percentage in respect of risk weighted credit exposure, as it is
shows the bank’s solvency and ability to absorb risk. Thus, this percentage of
capital is the amount that used to protect depositors, promote efficiency and stability
of financial system. According
to Makri et al.(2014), there is negative relationship with NPLs indicating a
risky loan portfolio is marked by a high NPL (equivalent to high credit risk).

However, Djiogap
and Ngomsi (2012) found positive association between NPLs and capital adequacy
ratio. It is 37 measured by total Equity to total asset ratio. However, it is expected to have negative association with NPLs in this
study. This implies that well capitalized banks are less incentive to take





 Inflation Rate

Inflation rate is interpreted as the rate in
which the purchasing power is decreased or increased, in terms of the currency,
and consequently the overall level of prices rising or falling respectively. We
can say that is the situation in which the economies overall price level is
rising. So, if the inflation is high and unexpected, it can be very costly for
the country. At the same time, inflation generally shifts cost from borrowers
to lenders and savers, since borrowers can repay their loans with less worthy
amount of money. Thus, in theory inflation reduce the value of dept. as it
reduce the real value of a currency hence make lending easier. However, in case
of high inflation rates the nominal lending interest rates may increase in
order to maintain the debt in its actual value. Additionally, due to the
impacts of high inflation rates, as the reduce of purchasing power, individual
hold less cash and try to counteract through interest rates of time deposits.
Finally, inflation can also determine as the general consumer price index (CPI)
as they are highly correlated. High changes in CPI requires monetary regulators
to use necessary measures by for example, increasing the interest rate in order
to control inflation which later increase the cost of borrowing and ultimately
cause NPLs. Based on this, the relationship between NPLs and inflation is
expected to be negative for this study.

According to Jordan et al. (2013) ……………. they
found a positive relationship between NPLs and Inflation rate.


The expected results of the increase of NPLs while
Inflation rate rises, is confirmed also by the literature  regarding the relationship between NPLs and
inflation rate. According to Farhan et al.(2012), Skarica(2013), Klein(2013)
and Tomak(2013) found as there is a positive relationship between NPLs and
Inflation rate.



Unemployment rate is the percentage of the
working force that stay unemployed. Individuals who would like to work but they
are not able due to disability for example, are not considered as unemployed.
In periods of economic crisis and recession, a high unemployment rate is almost
inevitable. The positive impact of unemployment, to the increase of
non-performing loans is relatively expected, as the increase of unemployment
rate lead to the general decline of households’ income and finally cause
individuals not able to pay their loan’s obligations.

expected positive impact of unemployment rate to the increase of NPLs, is also
confirmed between other, by the Jordan et al. (2013), Messai 2013 and Makri


Domestic Product (GDP)



Several empirical studies have found a negative
association between NPL and real GDP growth (Salas and Saurina 2002; Fofack,
2005; Jimenez and Saurina, 2006; Khemraj and Pasha, 2009; Dash and Kabra,


independent variable we consider is the Gross Domestic Product of the country
that each bank is based. GDP is the best measure to measure a country’s economy
(Amadeo, 2017), as it summarizes everything that being produced by all
individuals and companies between the boundaries of the country, despite the
origin of the producer. GDP rate is the percentage increase or decrease of the
GDP among the years and provide us the growth rate of each country. An economy
in growth is favorable to a decrease of financial distress and to an increase
of revenues. A high positive GDP rate habitually entails a higher level of
household income and subsequently a better capacity of the borrower to meet his
obligations and pay his debts. As a result, a negative impact of GPD to NPLs is

empirical studies have found the negative association between GDP and NPLs, as
expected (Clichici, ????, Messai 2013 and Makri 2014)


Interest Rates

Interest rate is the price a borrower pays for the use of money they borrow
from a lender/financial institutions or fee paid on borrowed assets (Crowley,
2007). It is “rent of money” fundamental to a
‘capitalist society’ and normally expressed as a percentage rate over the period of
one. Interest rate as a price of money reflects market information regarding
expected change in the purchasing power of money or future inflation (Ngugi,

of market interest rates exert significant influence on the activities of
commercial banks. Banks determine interest rates offered to consumers, the
mortgage production line ends in the form of purchased by an investor. The free
market determines the market clearing
prices investors will pay for mortgage-backed securities. These prices feedback
through the mortgage industry to determine the interest rates offered to



The aim of this study is to examine the determinants of
NPLs of commercial banks in Ethiopia. Similar to the most noticeable previous
research works conducted on the nonperforming loans of financial sectors, this
study used nonperforming loans ratio as dependent variables whereas Loan to
deposit ratio, capital adequacy ratio, return on asset, return on equity,
Average lending rate, inflation rate and effective tax rate as explanatory
variables. These variables were chosen since they are widely existent for the
commercial bank in Ethiopia. Accordingly, this study examined the determinants
of NPLs of commercial banks in Ethiopia by adopting a model that is existed in
most literature. The regression model which is existed in most literature has
the following general form; Yit= ?o + ?Xit + ?it Where: – Yit is the dependent
variable for firm ‘i’ in year ‘t’, ?0 is the constant term, ? is the
coefficient of the independent variables of the study, X it is the independent
variable for firm ‘i’ in year ‘t’ and ?it the normal error term. Thus, this
study is based on the conceptual model adopted from Fawad and Taqadus (2013).
Accordingly, the estimated models used in this study are modified and presented
as follow; NPLit= ?0 +?1(LTD)it + ?2(CAR)it+ ?3(ROA)it+?4(ROE)t+ ?5(ALR)it+ ?6(INFR)it+ ?7(ETR)it+?it Where; § ?0 is an intercept § ?1, ?2, ?3, ?4, ?5, ?6, and ?7represent estimated coefficient for
specific bank i at time t , § LTD,CAR, ROA, ROE, ALR, INF and ETR
represent Loan to deposit ratio, capital adequacy/Solvency ratio, return on
asset, return on equity, Average lending rate, inflation rate and effective tax
rate respectively § ?it represents error terms for
intentionally/unintentionally omitted or added variables. It has zero mean,
constant variance and non- auto correlated. The coefficients of explanatory
variable were estimated by the use of ordinary least square (OLS) technique.