Data

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The

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.

Eurozone.

Literature review provides evidence that both aggregate and disaggregate

(individual

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

Boudriga,

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

considered

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

Moreover,

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

these

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

We

extracted our data from the databases of the International Monetary Fund

(IMF),

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

obtaining

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

investigate

the

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

context,

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

observations

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

(2006),

unbalanced panel data include more observations and their results are less

dependent

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

1.

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

that

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

examined

in such

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

finance

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

Tarron

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

(2010),

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

order

to provide consistent and unbiased results, we implemented the difference

Generalized

Method

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

first

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

The

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

Gabriel

Jimenez

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

Bock

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

macroeconomic

factors

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

econometric

model

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

bank

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

Note

that i corresponds to the

examined country of the sample and t to the year.

Furthermore,

with the purpose of extending our investigation we use one lag

for

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

dynamics

of

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

time

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

Cotugno,

Stefanelli,

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

our

second econometric model is expressed as follows:

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

In

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

estimate

Equation

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

micro

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

and

macro.

For the

GMM estimation, we employed first and second period lagged variables

as

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

of

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

implemented

Kao

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).

CAP

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

adequacy

ratios,

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

whether

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

1991; Bertrand Rime

2001).

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

profitability.

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.

The

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)

Capital

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

risk.

Size

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

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.

The

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

2014.

Gross

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,

2010).

Another

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

expected.

Several

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,

2001).

Fluctuations

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

consumers

http://erepository.uonbi.ac.ke/bitstream/handle/11295/95462/Mwangi_Effect%20of%20interest%20rates%20on%20non-performing%20loans%20in%20Commercial%20Banks.pdf?sequence=5

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.

amadeo

https://www.thebalance.com/what-is-gdp-definition-of-gross-domestic-product-3306038

ECB

https://www.ecb.europa.eu/explainers/tell-me/html/npl.en.html