Specific Factors Affecting Nonperforming Loans from Banks in Member Countries of the Economic and Monetary Community of Central Africa (CEMAC)

The banking system plays a significant role in economic activity. It connects agents with financial capacity and those with a need for funding. The objective of this study is to determine the specific factors that influence the nonperforming loans of CEMAC banks. This research covers the six (6) CEMAC countries over a period from 2004 to 2017. To achieve this objective, we used the dynamic ordinary least squares (DOLS) method. The obtained results show that factors such as the ratio of loans to total bank assets (RPA), return on assets (ROA) and the ratio of loans to deposits (RCD) increase nonperforming loans from CEMAC banks.


Introduction
Nonperforming loans have garnered great interest among researchers and policy-makers over the past four decades (Upal, 2009). Indeed, the increase in these loans causes banking crises that turn into bank failures (Barr, Seiford, & Siems, 1994). In addition, nonperforming loans were considered to be one of the main causes of the global financial crisis (2007)(2008)(2009), which affected the US economy and the economies of many countries (Adebola, Wan Yusoff, & Dahalan, 2011).
The magnitude of this crisis and its negative effects on some global economies have highlighted the need for researchers and monetary authorities to streng-then the management of nonperforming loans within banks. Indeed, the good functioning of the banking sector accelerates economic growth, while the poor functioning of the banking sector is an obstacle to economic progress and, therefore, aggravates poverty (Richard et al., 2011). The performance of the banking sector is a symbol of prosperity and economic growth in any country or region, and the poor performance of these institutions doesn't only hinder the economic growth and structure of a particular region but also affects the whole world (Khan, Senhadji, & Smith, 2001). Based on the traditional role of the bank, it can be noted that loans constitute the bulk of bank assets (Njanike, 2009). Indeed, the interest on loans contributes significantly to the interest income of banks (Kamunge, 2013). Therefore, despite the importance of loans to banks in terms of assets and income, the lending process is not as straightforward as one might imagine. Loans oblige banks to assess the creditworthiness of borrowers and their ability to repay on time according to the contract established between the two parties. However, these steps do not always lead to a successful transaction, as one cannot know what will happen in the future. Consequently, the management of factors affecting the banking book has been the subject of numerous theoretical and empirical analyses.
At the theoretical level, there are two approaches suggesting different factors that can influence nonperforming loans. The first approach shows that nonperforming loans are tied to conditions in the economy. Indeed, during booms, the financial profile of borrowers improves, asset prices rise, and this euphoric situation paves the way for increased demand for credit (Gertler & Bernanke, 1989;Pesaran, 2006). In contrast, the second approach considers that the increase in nonperforming loans is linked to the distinctive characteristics of the banking sector and to the political choice of individual banks, which exert a decisive influence on the increase in nonperforming loans (Berger & De Young, 1997;Keeton et al., 2003).
Empirically, Louzis et al. (2012) show that nonperforming loans are explained mainly by external factors, which is contrary to the results of Ahmad and Bashir (2013), who explain the increase in nonperforming loans by internal factors. These differences call for new research to shed more light on this debate, in particular by taking into account little-explored fields of investigation, such as that of the CEMAC, which may be a relevant field of research for at least three reasons.
First, the number of active banks increased from 30 to 52 between 2004 and 2017, an increase of 22 banks in 13 years, or an average of two (2) banks per year. This increase is expected to increase the competition between banks.
Then, according to COBAC reports, the deposits collected increased from 3,096,559 (in billion FCFA) to 9,934,504 (in billion FCFA), and in terms of credit distribution, the CEMAC banks granted loans amounting to 8106.788 billion CFAF in 2017 (COBAC, 2017) against 1711.623 billion CFAF in 2004 (COBAC, 2004), i.e., an increase of 373.63% between the two (2) years.
Finally, there is an exponential increase in bad debts, from 250.247 billion  Ghosh (2015), this study postulates that internal bank factors contribute to the increase in nonperforming loans of CEMAC banks.
In addition to the introduction, this article is structured as follows. In the second section, we review the literature. The third section is devoted to the methodology. In the fourth section, we present and interpret the results. The conclusions and policy implications are presented in the fifth section.

Literature Review
The factors of nonperforming loans have been the subject of theoretical analyses and empirical evidence that fall into two groups: the first group concerns factors related to economic conditions, and the second group concerns factors related to the individual specificities of banks. Several works focus on the relationship between NPLs and macroeconomic factors (Salas & Saurina, 2002;Nor et al., 2021). The latter that the literature suggests as being important determinants are: annual GDP growth, credit growth, real interest rate, annual inflation rate, real exchange rate, annual unemployment rate, the money supply (M2) and the GDP by capital. Besides the macroeconomics factors, several empirical studies suggest that bank-specific factors are important because they can trigger risky loans (Berger & De Young, 1997;Keeton, 1999;Ahmad & Bashir, 2013;Karadima & Louri, 2021). These factors are such as the size of the institution, profitability, capital, intermediation rate, risk profile and market power.
For proponents of factors related to economic conditions (Nkusu, 2011;Beck et al., 2013), during boom periods, the financial profile of borrowers improves, asset prices rise, and this euphoric situation paves the way for an increase in demand for credit. On the other hand, nonperforming loans are doomed to increase in the event of a recession. Thus, according to Ozili (2015), "the state of the economy is the most important systematic factor influenced by the diversi- The second group dealing with factors linked to the specific individual conditions of banks, in contrast, is based on the lending behavior of individual banks (Berger & Deyoung, 1997;Keeton et al., 2003). For these authors, the peculiarities of the banking sector and the political choices of each bank, especially with regard to their efforts to improve efficiency and risk management, are likely to influence the evolution of nonperforming loans. The actions of the borrower thus make it difficult to assess the creditworthiness of the borrower (Dell'Ariccia, 2001). Therefore, informational asymmetries cause adverse selection and moral hazard problems, which were first described by Akerlof (1970).
Empirical evidence has confirmed the analyses of this group. Ahmad and Bashir (2013)  Wanjala and Gachanja (2020) explore the determinants of nonperforming loans specific to Kenyan banks. Four bank-specific variables are considered.
These variables are the size of the bank as measured by total assets, the loanto-deposit ratio, the capital adequacy ratio and the interest rate. They use a causal research plan. Time regression is applied to data from 43 Kenyan banks. This study reveals that there is a positive relationship between the size of the bank and NPLs. The authors also observe that there is a negative relationship between the loan-to-deposit ratio and nonperforming loans. They further find that there is a negative correlation between the capital adequacy ratio and NPLs in Kenya.
Finally, the study establishes that there is a positive relationship between the interest rate and NPLs in Kenya.
This overview of the literature shows that most of the existing research on the determinants of nonperforming loans in the banking sector relates to developed countries (Skarica, 2014;Makri et al., 2014). This article aims to enrich the sparse literature on the determinants of nonperforming loans in the CEMAC banking sector.

Methodology of Non-Performing Loan Factors
This section describes the methodology for analyzing the factors that can influ- Several specifications exist, but in this study, we start with the models presented by different authors to specify the relation of concern. This specification is represented by Equation (1) below: where it y is the endogenous variable, representing nonperforming loans expressed as a proportion of bad debts on loans granted, and it V represents the vector of exogenous variables that can affect nonperforming loans. Index i designates the country, index t indicates the time, i γ is a constant, β is the coefficient of the exogenous variables, and it ε is the error term. Thus, by considering the variables retained for this study, the specification of the empirical model is presented in the following lines.
Hereafter, the reformulation of the specific factors of nonperforming loans will be specified as in Equation (2) below: , , , , β β β β β : The settings.

1) Definition of variables
The choice of variables and the period studied is made on the basis of previous research (Yanga, 2020)  The choice of variables is important when using an econometric model. By referring to previous empirical work (Rachman et al., 2018), we defined the variables used in this study. Table 2 summarizes the description of the variables and expected signs.  Saikkonenn (1991); Stock and Watson (1993) in the time series, then adapted by Kao and Chiang (2001) for panel data. Thus, the latter focused on the finite sample properties of the estimators of OLS, FM-OLS and DOLS. They show that the estimate of small sample OLS cointegration relationship present a problem of bias and the corresponding statistical tests do not follow a usual Student's law. For this purpose, they propose to use the FM-OLS method initiated by Phillips and Hansen (1990) and the DOLS method. But the estimator DOLS is more efficient than FM-OLS and MCO for estimating the cointegration relation according to the simulations of Kao and Chiang (2001).
To identify the specific factors of nonperforming loans of CEMAC banks, we will use the dynamic least squares method. The choice of this method is justified by the fact that the DOLS estimator is more efficient than the fully modified ordinary least squares (FM-OLS) and ordinary least squares (OLS) estimators for the estimation of the cointegration relation according to the simulations (Kao & Chiang, 2001). However, for estimation using panel data, it is necessary to carry out descriptive and correlational studies of the variables, test the dependence of variables, and consider the stationarity and cointegration relation of the variables.

3) Data sources and descriptive analysis
This study adopts a panel dataset from CEMAC banks over the period 2004-2018. This period is chosen according to the availability of data on nonperforming loans. The data come mainly from the COBAC reports, the reports of the franc zone and the BEAC. Table 3 lists the descriptive statistics of the variables used in this research. Source: Author's calculation. Table 3 presents the descriptive statistics of the dependent variable and the independent variables. In this study, the total number of observations is 84. Table 3 shows that the mean values of PNP, ROA, RFA, RPA and RCD are 13.01, 1.599, 0.086, 58.245 and 71.233, respectively. The standard deviation for PNP is 8.36, with a minimum value of 1.38 and a maximum value of 34.56. The minimum value of the intermediation rate is 25.93, with a maximum value of 177.31 and a standard deviation of 28.49. However, the standard deviation of return on assets is 1.75, with a minimum value of 1.84 and a maximum value of 14.66.

4) Stationarity of variables
The role assigned to the stationarity tests is to detect the existence of a trend (unit root) in distribution to determine the correct way to make a series that is not stationary, i.e., in case the existence of a unit root is revealed by the test. Table 4 implements the results of the stationarity tests.  (1)]. This means that to make the stationary of variables, it is important to differentiate them only once. Thus, the stationarity of the variables in the first difference makes it possible to understand the existence of a long-term cointegrating relation between the variables. However, this long-term relationship must be accepted through various tests called "cointegration tests".
Several tests are used to study stationarity in panel data (Ficher, Kao, Pedroni).
In the context of this study, the Kao test is used to achieve the results stated in Table 5 below.

Results and Discussion
The presentation of the results and the discussion are addressed in this section.
The estimation of the model by the dynamic ordinary least squares method yields the different results summarized in Table 6. Three results are obtained from the estimates. First, the RCD variable has a positive and significant effect on nonperforming loans from banks at the 1% threshold. This result suggests that with the growth of deposits, banks engage in extensive lending, which leads to an increase in bank lending relative to deposits.
Such behavior leads banks to grant loans to low-quality borrowers, increasing the risk of the loan portfolio and the level of nonperforming loans. This result converges with those of Swamy (2012) and Ahmad and Bashir (2013). A negative and significant relationship between ROA and PNP is also shown, which is explained by mismanagement leading to riskier activities and poor performance.
This result is consistent with the results of Louzis et al. (2012) and Radivojevic and Jovovic (2017). Finally, a higher loan-to-asset ratio represents a high level of credit and an increasing likelihood of credit risk. This indicates that the increase in loans increases the level of nonperforming loans, as found by Klein (2013) and Ekanayake and Azeez (2015). These results are consistent with the argument that poor management leads to riskier activities and poor performance. On the other hand, banks that are in a competitive situation can increase loans without too much demand, which can reduce the quality of the loan and therefore increase non-performing loans. Indeed, the information asymmetry between bor- rowers and banks often leads to "adverse selection" and "moral hazard", which leads banks to grant very risky loans (Wang, 2019). Thus, we note that variables such as: the return on assets (ROA), the intermediation ratio (RCD) and the ratio of loans to total assets (RPA) explain the increase in bad debts of banks in the CEMAC.

Conclusion and Implications of Economic Policies
Managing the factors that increase nonperforming loans is a critical issue for the healthy survival of banks. Therefore, knowing these factors is important. The objective of this study was to determine the specific factors of nonperforming loans of CEMAC banks. To do so, we used panel data for the period 2004-2017 and the dynamic ordinary least squares method. The results obtained generally show that nonperforming loans affect the portfolios of CEMAC banks.
The hypothesis supported by this study is verified in that the results found affirm that the increase in nonperforming loans is linked to the specific factors of CEMAC banks.
It would be desirable to have electronic platforms to verify whether an asset has been used as collateral by a client to borrow money from other financial institutions. It would also be desirable for banks to invest in strong credit information systems to reduce information gaps and increase access to complete, accurate and reliable information about borrowers. In addition, this problem can be studied with a focus on individual countries to control the type of factor according to the national context.

Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.