Table of Contents
2. Literature Review
3. Data and Methodology
4. Empirical results
This paper investigates the impact of economic indicators in their relationship to non-performing loans and the way the indicators change trough times using a cross-sectional analysis from the sample of 70, 69 and 63 world countries. Using the robust regression approach, economic indicators were analyzed to find their impact on non-performing loans for the period from 2015 to 2017. The results show that the economic indicators negatively relate to non-performing loans are population growth rate (PG), current balance account (CBA), claims on private sector (CPS) and domestic credit to private sector (DCPS). Economic indicators have a positive impact on non-performing loans are gross domestic product growth (GDPG) and inflation. The findings also show that economic indicators affecting non-performing loans are changing. During the periods analyzed, there is an increase in economic indicators affecting non-performing loans. They were only domestic credit to private sector, current balance account and claims on private sector in 2015. In 2016, another factor occurred in addition of those of 2015, inflation. In 2017, additional factors again occurred, population growth and gross domestic product growth.
Banks should take recovery measures to reduce non-performing loans. A better assessment of the repayment capacity of the future customers coupled with a permanent follow-up of the customer during the whole credit cycle should be enhanced. Further investigations are needed to understand the interactions, and the relationships between non-performing loans, and the different types of borrowers, namely; individuals, small and medium enterprises, and corporate borrowers for a better customer selection because an increase in domestic credit to private sector reduces non-performing loans. Banks should develop a credit risk assessment model combining internal and customer factors.
Keywords: Inflation, Population growth, Unemployment rate, Interest rate, Domestic credit to private sector, Current balance account, Bank capital asset ratio, Robust Regression, Claims on private sector, Strength of legal index, foreign direct investment and Gross domestic product growth.
The main objective of this article is to investigate the impact of economic indicators in their relationship to non-performing loans and the way the indicators change through times. Banks play an important role in financing economic growth in all countries, through loan granting. However, not all granted loans are reimbursed by borrowers. Knowing economic factors that are related to non- performing loans will help both bank managers and policy makers to take good decisions. According to Imola and Codruţa (n.d) banks perform financial services that reduce the costs of moving funds between borrowers and lenders, leading to a more efficient allocation of resources and faster economic growth. In modern economy, banks play an indispensable role, in financing the national economy. According to the Association for Financial Markets in Europe (2014), through making loans to customers, banks create the credit needed for infrastructure, education, investment and growth, and allow savers and investments to be linked. A country financial system supports economic development, and contributes to the improvement in living standards by providing various services to the rest of the economy. These include clearing and settlement systems to facilitate trade, channeling financial resources between savers and borrowers, and various products to deal with risk and uncertainty (Alan, 2011). However, credit given to economic agent is not always paid back according to the lending contract. Principal and or interest can remain due for a certain period of time. This can lead to serious problems to bank, customers and the whole economy. The trend of non-performing loan for some countries is high (International Monetary Fund, 2019).The following graphic shows the real situation of non-performing loans compared to total loans in 2017.
Graph: 1. Non-performing loans as percentage of total loans
Abbildung in dieser Leseprobe nicht enthalten
Source: Author’s computation
This graphic shows that some countries have very high rate of non-performing loans. At the end of 2017, Ukraine has 54,5% , San Marino 48,9%, Greece 45,6%, Cyprus 40,2%,etc. Banks use deposed money to give loans to borrowers. If the money is not paid back, banks are not only losing but also depositors can lose their money deposed in the banks. Knowing the factors that contribute to the increase of non-performing loans can help decision making and bank managers to reduce the rate of non-performing loans.
1.2. Problem Statement
As it is mentioned previously, in some countries, banking sectors present a high rate of non-performing loans. The consequences of non-performing loans are various. Literature shows that non-performing loans affect not only banks but also borrowers, financial stability and a country economic growth. Muhammad et al., (2016) concluded that non-performing loans hurt economic growth. There is a decline in economic growth when non-performing lows grow and capital requirements will increase as a result of the growth of non-performing loans as erosion of capital occurs due to funds being trapped in such entities, making it impossible for the banks to fund new, economically viable ventures (Akinola & Mabutho, 2016). Non-performing loans contract credit supply, distort allocation of credit; worsen market confidence and slow economic growth (Maria, Michel & Alexander, 2016). If the trend of non-performing loans is not decreased, many countries will not be able to develop their economy. Many researchers conducted studies in various countries or in some geographical areas on the determinant of non-performing loans. The results of the findings are presented in the literature review. Therefore, at the best of our knowledge, no study has been conducted including many countries to compare whether or not factors affecting non-performing loans remain the same in space and time. Due to this knowledge gap, this study addresses this knowledge gap by including many countries from different areas so as to identify their determinant of non-performing loans and analyses their variability in the time.
The main objective of this article is to investigate the impact of economic indicators in their relationship to non-performing loans and the way the indicators change through times. Specifically the study sought to establish:
a) To analyze the impact, if any, of some economic indicators on non-performing loans.
b) To identify whether or not the factors affecting non-performing loans remain the same or change annually.
Based on the specified objectives of the study, the research aims to answer the following questions:
a) What is the impact of economic indicators on non-performing loans?
b) Do indicators affecting non-performing loans remain the same or change annually?
1.3. Study Justification
At the best of the researcher’s knowledge, no study including a great number of banks from different countries is made to identify factors affecting non-performing loans. No study analyzed the dynamics or the stability of those factors through time. The research findings of this study will help in addressing the existing knowledge gap in literature of effects of micro and macroeconomic variables on non-performing loans by including more than 60 countries. It will also be a valuable addition to the existing knowledge and provide a platform for further research which will be useful to scholars. An understanding of the effects of micro and macroeconomic variables on non-performing loans is important to the senior management and investors of financial institutions in the world. The study findings will enable managers and investors make timely decisions on how to avoid non-performing loans in order to increase bank profitability. On the policy front, the study findings are also important to the government, regulatory bodies and to the commercial banks themselves. It will help the regulators to know exactly how non-performing loans are affected by micro and macroeconomic variables and how to enhance the baking sector in terms of regulation.
2. Literature Review
The investigation of the determinant factors of non-performing loans (NPL) has attracted the attention of scholars and policy makers through the world. Micro-economic and macro-economic factors have been identified in many different countries as the determinant of NPL. The following development shows various studies and their findings. Ibish and al. (2018) find that Gross Domestic Product growth and inflation are both negatively and significantly correlated with the level of NPL, while unemployment is positively-related to NPL in transition countries. El-Maude, Abdul-Rehman, and Ibrahim (2017) found positive significant relationship between Non-Performing loans and Loan to deposit and Bank size in Nigeria. Ozcan and Suleyman (2016) found that solvency, profitability, credit quality, diversification, economic growth and the recent financial crisis are essential indicators of non-performing loans rate in Turkish banking sector. Nikola and Jelena (2017) found that NPLs are explained by crucial macroeconomic factors, such as the gross domestic product and inflation rate, and bank-specific factors, such as return on asset, bank’s capital to assets and lagged NPLs rate in 25 emerging countries. Khaled (2016) found that the lagged NPLs, the ratio of loans total assets, economic growth, inflation affect negatively non-performing loans. He found also that global financial crisis lead to higher non-performing loans in Jordan. The findings of Antonio, Candida, Lavinia and Marisa (2018) reveal that lower gross domestic product growth and a higher unemployment rate can generate a lower loan quality in Europe. Sergey (2019) analyzed Armenian banks and found that problem loans are negatively correlated with the growth rate of gross domestic product, the profitability of banks' assets and positively with the unemployment rate, house prices. Ahlem and Fathi (2013) concluded that problem loans vary negatively with the growth rate of gross domestic product, the profitability of banks’ assets and positively with the unemployment rate, the loan loss reserves to total loans and the real interest rate. Dagne and Maru (2016) found that gross domestic product growth, foreign direct investment, and average exchange rate have significant positive association with the amount of non-performing loan on one hand. On the other hand, poor due diligence assessment, insufficient grace period given by the Bank for the repayment, non-credit worthy project financing, financing second hand machines, lack of proactive policies detecting sign of default, willful default, rent seeking character of borrowers, week financial bookkeeping system of borrowers, misfortune of borrower, unavailability labor force in the project area, lack of customer of the project, remoteness from market, and unsuitable agro-ecological condition are among the explanatory variables that contribute significantly to the occurrence of NPLs in Development Bank of Ethiopia. Makri, Tsagkanos and Bellas (2014) found strong correlations between NPL and public debt, unemployment, annual percentage growth rate of gross domestic product, capital adequacy ratio, rate of nonperforming loans of the previous year and return on equity factors in Eurozone. Irman (2014) found that gross domestic product and inflation negatively affect non-performing loan, while the liquidity of the bank positively affects non-performing loan Islamic banks in Indonesia. Ekanayake and Azeez (2015) found that NPLs vary negatively with the growth rate of gross domestic product and inflation. However, their result show also that NPLs positively vary with the prime lending rate in Sri Lanka. Base on the literature, the research hypotheses are as follows.
H0: There are economic indicators that affect non-performing loans in the countries under study.
H0: Factors affecting non-performing loans are changing through time.
3. Data and Methodology
The population of the study is made by 263 countries all over the world for the period from 2015 to 2017. A sample is made by 70 countries in 2015, 69 countries in 2016 and 63 countries in 2017. Included countries are those that have required dependent and independent variables for the study. Secondary data were retrieved from the World Bank web site. The analysis of the result was made using robust regression with R software. In other words, data on the selected countries were collected for a period of three years. Collected data were checked to identify missing data. After the analysis, only countries with all dependent and independent variables are used. Descriptive analysis was made for each variable. Data were checked for outliers by using univariate method that examine each variable individually and multivariate method that looks for unusual combinations on all the variables (Irad, 2005). Boxplot and boxplot.stats of the R software were used. The result shows that variables have outliers except population growth (PG) and strength for legal index (SL). An outlying observation, or outlier, is considered as one that appears to deviate markedly from other members of the sample in which it occurs (Irad, 2005). It is important to identify outliers/extreme variables before modeling because they can lead to adversely to model misspecification, biased parameter estimation and incorrect results, (Irad, 2005). A correlation analysis was also conducted in order to find the correlation among variables. To test statistically the impact of independent variables to dependent variable, a regression analysis was conducted using Ordinary Least Square (OLS) and robust regression with R software. Robust regression is better than OLS as it gives better coefficients and estimators protect against bias under contamination and breakdown point (Eva, 2017). These robust-regression methods were developed between the mid-1960s and the mid-1980s and are insensitive to outliers and possibly high-leverage points (John & Sanford, 2018). The regression used backward elimination. The first model includes all independent variables, and then variables less significant were removed progressively. The same operation was conducted until remaining with predictors that have a p-value under 5%. The final model includes variables that are significant. The final model was tested to see if it is robust. Some assumptions were tested to see if the model is adequate. The following assumptions were verified: mean residuals, homoscedasticity, normal distribution of residuals, independence of residuals, autocorrelation of residuals, multicollinearity, normality of residuals and linearity test of the model. All the models satisfy those assumptions. To test the dynamism of the determinant of non-performing loans, a model is built for each year. Robust regression is conducted for the period time 2015, 2016 and 2017 in order to found the factors that affect non-performing loans year by year. A comparison of factors is made so as to see if factors affecting non-performing loans remain the same or change trough time.
3.1. Variables description
Data collected are related to 12 economic indicators. One of them is used as dependent variable while 11 are used as independent variables.
Non-performing loans (NPL): Bank non-performing loans to total gross loans (%)
Inflation, GDP deflator (INF): Annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole.
Population growth (PG): Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
Unemployment (UNR): Unemployment refers to the share of the labor force that is without work but available for and seeking employment.
Interest rate (IR): Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator.
Strength of legal rights index (SL): Strength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending.
Domestic credit to private sector (DCPS): Financial resources provided to the private sector by financial corporations.
Current account balance (CBA): The sum of net exports of goods and services, net primary income, and net secondary income.
Foreign direct investment (FDI): It is the sum of equity capital, reinvestment of earnings, and other capita flows in an economy.
Bank capital asset ratio (BCAR): The ratio of bank capital and reserves to total assets.
Claims on private sector (CPS): Claims on private sector (annual growth as % of broad money)
Gross domestic product growth (GDPG): Annual percentage growth rate of GDP at market prices based on constant local currency does not included in the value of the products.
All these economic indicators are available on the web site of the World Bank. The choice of these variables is motivated by the following: a) some of those variables are identified by previous studies as impacting non-performing loans; b) data on all selected variables are available; c) there are contrast findings on the impact of those independent variables on non-performing loans in the literature; finally d) some variables have not been tested by the previous researchers.
- Quote paper
- Antoine Niyungeko (Author), 2020, Non-performing loans between 2015 and 2017. The impact of economic indicators, Munich, GRIN Verlag, https://www.grin.com/document/902029