JUSTIFICACIÓN E INTERÉS DE LA INVESTIGACIÓN

5.3.2.1. Loan Loss Provisions (LLP)

LLP is the dependent variable (Leventis et al., 2011; Bushman and Williams, 2012; Curcio and Hasan, 2015), which is deflated by beginning total assets (i.e., LLPit/TAi,t-1) following the approach of Kilic et al. (2012) and Bushman and William (2012) to take into account known values of bank characteristic. Data for loan loss provisions is obtained from Bankscope database.

5.3.2.2. Earnings before tax and loan loss provisions (EBTP)

The earnings variable (EBTP) is the ratio of earnings before tax and loan loss provisions divided by beginning total assets. The earnings before tax and loan loss provisions variable is mechanically derived by adding-back loan loss provisions to the profit before tax number. The literature commonly focuses on the relation between LLP and EBTP to detect whether banks use loan loss provisions to smooth reported earnings. A positive (and significant) relationship between LLP and EBTP is commonly taken as

evidence to indicate smoothed earnings (see. Kanagaretnam et al., 2004; Liu and Ryan, 2006; Curcio and Hasan, 2015; Kilic et al., 2012; Bushman and William, 2012), and imply that banks lower loan loss provisions to increase low earnings and increase loan loss provisions to decrease high earnings in the current period.

I perform additional sensitivity test based on the earnings distribution of African banks to detect whether African banks use loan loss provisions to smooth specific earnings pattern rather than the entire earnings

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distribution. In other words, I test whether African banks use loan loss provisions to smooth earnings when they are more profitable. El Sood (2012) observe that US banks use loan loss provisions to smooth earnings when banks are more profitable, that is, when they have high earnings. I extend El Sood (2012) by introducing two proxies to capture ‘higher profitability’, i.e., non-negative earnings and above-the- median earnings. I introduce POS dummy variable that take the value of one if EBTP ratio is positive and zero otherwise, and HIGH dummy variable that take the value of one if EBTP ratio is above-the-median EBTP and zero otherwise. The latter is consistent with El Sood (2012). POS and HIGH dummies are then interacted with EBTP to detect whether African banks use loan loss provisions to smooth reported earnings when they are more profitable. Finally, data for bank earnings is also obtained from Bankscope database.

5.3.2.3. Non-Performing Loan (NPL)

Non-performing loan (NPL) is the ratio of impaired loans to beginning total asset (Bushman and William, 2012). NPL captures specific loan loss provisions that banks set aside for loan losses that are highly probable to occur or that are 90-days past due. Beaver and Engel (1996) and Ahmed et al. (1999) posit that non-performing loans is an ex-post measure of the quality of bank loan portfolio because banks will generally increase loan loss provision when they expect higher loan default, implying a positive relation between loan loss provisions and non-performing loans. Beaver and Engel (1996) and Curcio and Hasan (2015) also predict and find a positive relation for the NPL coefficient. Other studies use change in NPL to control for the quality of bank loan portfolio (e.g. Ahmed et al., 1999; Bushman and William, 2012). A closer look at the NPL data for African banks in the data shows that the time series data for NPLs has some missing values. The missing values reduce the number of NPL observations for the analysis. An attempt to take the change in NPL values would further reduce the observations and reduce the degree of freedom in the econometric analysis. For this reason, I did not incorporate change in NPL variable as an explanatory variable in the model in the analysis. Finally, data for non-performing loan is obtained from

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5.3.2.4. Loan growth (LOAN)

The relationship between loan loss provisions and loan growth (or change in gross loan outstanding) is often used to capture loan loss provisioning decisions that depend on contemporaneous credit risk arising from increased bank lending. Increase in bank lending may give rise to credit risk arising from changing economic conditions that affects the credit quality of bank loans which would require higher provisioning (Laeven and Majnoni, 2003). To support this view, Laeven and Majnoni (2003) find a positive

relationship between loan loss provisions and loan growth. Lobo and Yang (2001), on the other hand, suggest that a negative relation between loan loss provision and loan growth may be expected because improved quality of incremental loans would require fewer loan loss provisions. Also, Cavallo and Majnoni (2002) and Bikker and Hu (2002) suggest that, during periods of economic prosperity commonly associated with increased bank lending (i.e., loan growth), banks may underestimate credit risk by

keeping fewer loan loss provisions due to aggressive lending practices and lax loan screening standards, implying a negative association between provisions and bank lending. Given these mixed arguments, I do not have a definite prediction for the LOAN variable for the case of African banks. Data for loan growth is obtained from Bankscope database.

5.3.2.5. Capital Adequacy Ratio (CAP)

CAP ratio is the ratio of total equity to beginning total asset. The CAP variable is included to control for capital management incentives to manipulate provisions estimate. Bonin and Kosak (2013) and Kilic et al. (2012) argue that bank managers can increase loan loss provisions when they have low capital levels to compensate for their weak capital levels, and reduce loan loss provisions when they have higher capital levels. The link between loan loss provisions and bank capital is expected to be stronger if banks view loan loss provisions as a form of capital to compensate for weak bank capitalization. Hence, a negative relation between LLP and CAP is predicted.

Additionally, I test whether earnings smoothing behaviour is pronounced when African banks are under- capitalised or well-capitalised. To do this, I use a simple criterion and introduce UC dummy variable that take the value of one if CAP ratio is less than 25% and zero otherwise, and WC dummy variable that take

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the value of one if CAP ratio at least 50% and zero otherwise. Other studies use regulatory capital to risk- weighted asset ratio rather than equity to asset ratio, to capture capital management or regulatory capital management. While regulatory capital to risk-weight assets ratio is considered to be a better measure to capture capital management incentives (e.g. Ahmed et al., 1999; Leventis et al., 2011), many African banks in the sample do not report time series data for regulatory capital ratio because some African countries in the sample do not adopt Basel capital regulation or do not follow Basel rules strictly. For African banks that report data on regulatory capital ratio, data for this ratio is not reported for some years, and when reported it yields a relatively small number of observations which drastically reduce the degree of freedom for the econometric analysis. Rather, I use equity to asset ratio because it offers a better coverage of African banks and yield almost twice as many observations as the regulatory capital ratio. Bonin and Kosak (2013) also use the ratio of total equity to total asset variable. Data for equity to total asset ratio and regulatory capital to risk-weight asset are obtained from Bankscope database.

5.3.2.6. Bank Size (SIZE)

Prior studies commonly control for bank size to take into account bank loan loss provisioning that depend on the size of the bank (e.g. Anandarajan et al., 2003, 2007; Kilic et al., 2012). Anandarajan et al. (2003) suggest that large banks may keep higher loan loss provisions when they have higher levels of business activities and would ensure that the level of loan loss provision is commensurate with their level of activities. The natural logarithm of bank total asset is commonly used to control for provisioning that depends on bank size. Consistently, I take the natural logarithm of total assets. Data for total asset is obtained from Bankscope database.

5.3.2.7. Loan to asset ratio (LOTA)

The literature demonstrate that bank loan to asset ratio reflects the default risk of bank loan portfolio (e.g. Bouvatier and Lepetit, 2008; El Sood, 2012; Bouvatier et al., 2014). For instance, Bouvatier and Lepetit (2008) suggest that banks with high loan to asset ratio would have high default risk and will keep higher loan loss provisions to compensate for the increase in default risk on the loan portfolio, implying a positive relationship between LLP and LOTA. For instance, Bouvatier and Lepetit (2008) report a

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positive relation between loan loss provisions and loan to asset ratio while Bikker and Metzemakers (2005) report a positive relationship for banks in OECD countries but the relation is not significant for European banks. Consistently, I expect a positive relation between loan loss provisions and bank loan to asset ratio for African banks.

5.3.2.8. Growth in real gross domestic product (ΔGDP)

Growth in real gross domestic product captures macroeconomic fluctuation. The literature demonstrate that banks keep higher loan loss provisions during economic downturns or recession and keep fewer loan loss provisions during periods of economic prosperity (e.g. Cavallo and Majnoni, 2002; Laeaven and Majnoni, 2003; Bikker and Metzemakers, 2005). Consistently, I control for bank provisioning that depend on fluctuation in the economic cycle.

I perform additional sensitivity test to detect whether African banks use loan loss provisions to smooth earnings when they are going through a recession or when they are going through periods of economic boom or prosperity. Beatty and Liao (2009) and El Sood (2012) observe that US banks delay provisions during recessionary periods in order to smooth earnings upward during recessionary periods while Liu and Ryan (2006) find that US banks smooth earnings to lower too high earnings during economic boom periods. I extend these studies by incorporating two dummy variables: REC and BOOM. REC dummy variable take the value of one if ΔGDP is negative and zero otherwise, reflecting economic downturns or recessionary periods; and BOOM dummy variable take the value of one if ΔGDP is above-the-median ΔGDP for the full sample and zero otherwise, reflecting periods of economic prosperity. The interaction

of REC with EBTP detect whether African banks use loan loss provisions to smooth reported earnings when they are in recessionary periods while the interaction of BOOM with EBTP detect whether African banks use loan loss provisions to smooth reported earnings during economic boom periods. Finally, data on real gross domestic product growth rate is obtained from World Economic Forum archived in World Bank database.

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5.3.2.9. Lagged Loan Loss Provisions (LLPt-1)

Lagged loan loss provision is beginning loan loss provisions (or loan loss provisions in the previous period). The lagged provisions variable captures the dynamic behaviour of bank provisioning. Laeven and Majnoni (2003) argue that banks adjust loan loss provisions to account for non-performing loans that take more than one year to be fully realised. Several studies including Laeven and Majnoni (2003), Fonseca and Gonzalez (2008), Bikker and Metzemakers (2005), Bonin and Kosak (2013) and Bouvatier et al. (2014) also use this adjustment to control for dynamic bank provisioning. Laeven and Majnoni (2003), Bikker and Metzemakers (2005), Fonseca and Gonzalez (2008) and Bonin and Kosak (2013) use one-year and two-year lag of the dependent variable (LLP) and find that the dynamic adjustment of loan loss provisions is concentrated only in the one-year lag (i.e. the first year), therefore, I use the one-year lag of the dependent variable in the analysis for the thesis. A positive sign on the coefficient of the lagged loan loss provisions variable would indicate that higher loan loss provisions in the previous period is

accompanied by higher loan loss provisions in the subsequent period while a negative sign on the coefficient of the lagged loan loss provisions variable would indicate that higher loan loss provisions in the previous period is accompanied by lower loan loss provisions in the subsequent period. I do not have a definite prediction for the coefficient sign of the lagged LLP variable for African banks.