The Empirical Model and Estimation Method

To test the null hypotheses posited above, the following dynamic model is constructed:

FPIi,t = α0 + β1FPIi,t-1 + δRGIi,t + γXi,t + ζBKi,t+ θZi,t + β2Islamic_Dummy + β3Crisis_Dummy +

εi,t (3.1) Where:

FPIi,t stands for the Financial Performance Indicator (measured by ROAA, ROAE and Cost-to- Income) of bank i at time t, FPIi,t-1 is the lagged value of the financial performance indicator,

RGIi,t is the Risk Governance Index for bank i at time t, Xi,t is a matrix of bank accounting explanatory variables, BKi,t is a matrix of bank specific control variables, Zi,t is a matrix of macroeconomic variables. Islamic_Dummy is the dummy variable that permits to account for the distinctiveness of the Islamic banks’ business model compared to their conventional counterparts in the four equations as will follow19_{. Crisis_Dummy is the dummy variable that}

permits to distinguish between the years before the inception of the crisis (that is 2006 and 2007) and the period following the inception of the crisis (that is from 2008 to 2012). α0 is the constant, β1, δ, γ, ζ, θ, β2 and β3 are the vectors of parameter estimates for their respective matrices and ε is the error term.

19_{Please note that in Models 2 and 4, where the focus is on the case of RGI in Islamic banks, an interaction term} is incorporated in the models whereby the RGI scores in Islamic banks only acts as an additional variable from which an inference of the effect on every financial performance indicator is drawn.

92 Table 3.1: Description of Variables Used in the Study

Variables Definition, Coding and Data Source

Panel A: Dependent Variables

ROAA Return on Average Assets as equal to the ratio of Net Income on Average Assets (Source: BankScope)

ROAE Return on Average Equity as equal to the ratio of Net Income on Average Equity (Source: BankScope)

C2I Cost to Income Ratio as equal to Operating Expenses divided by Operating Income (Source: BankScope)

Panel B: Explanatory Variables

RGI Risk Governance Index developed by author (Source: Annual Reports and corporate governance reports)

Islamic Dummy Dummy variable that takes the value of 1 when the bank is Islamic and 0 otherwise

Crisis Dummy Dummy variable that takes the value of 1 when the year of the observation is during and post crisis (that is 2008 to 2012) and 0 otherwise

TEA Total Earning Assets concern assets that generate interest or dividends. It includes stocks, bonds, income from rental property, certificates of deposit and other interest or dividend earning accounts or instruments. (Source: BankScope)

NL Net Loans defined as interest-earning balances with central banks and loans and advances to banks net of impairment value including loans pledged to banks as collateral, incl. reverse repos with banks (Source: BankScope)

Op_Inc Operating Income that is income gained from the operating activities (Source: BankScope)

Ovh_C Overhead Costs which refer to all the costs in the income statement including accounting fees, insurance, rent, repairs among others (Source: BankScope)

Bank Level Control Variables

LnTA Natural Logarithm of total assets (Source: BankScope). Total assets include: cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax, discontinued operations, other assets

DSTF Total deposits and short-term funding (Source: BankScope) Eq_TA Leverage ratio as equal to Equity divided by Total Assets (Source:

BankScope)

NL_TA Ratio of Net Loans to Total Assets (Source: BankScope)

LLR_GL Ratio of Loan Loss Reserves to Gross Loans (Source: BankScope) IRS Interest Rate Spread as equal to lending rate minus deposit rate in %

(Source: World Bank Database)

Macroeconomic Control Variables

lnGDP_Grw Natural Logarithm of Gross Domestic Product Growth Rate (Source: World Bank Database)

Infl Inflation rate (Source: World Bank Database)

Pol_Stab Political stability is defined by the World Bank as a measure of the “perceptions of the likelihood of political instability and/or

politically-motivated violence, including terrorism”. Estimates ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance. (Source: World Bank Database)

Gov_eff Government efficiency is defined by the World Bank as reflecting the “perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political

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pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies”. Estimates ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance. (Source: World Bank Database) Reg_Qual Regulatory quality is defined by the World Bank as reflecting the

“perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development”. Estimates ranges from approximately - 2.5 (weak) to 2.5 (strong) governance performance. (Source: World Bank Database)

The econometric model is estimated using twostep generalized methods of moments (GMM) with instrumental variables. Therefore, the first-differences of the dependent variables are used to remove the unobserved time-invariant country-specific effects and the explanatory variables are instrumented in these first differences equations by using levels of their series lagged two periods (Blundell and Bond, 1998). Theoretically, GMM suits the study for various reasons. First, the sample size entails more cross-sectional variables than time periods (Blundell and Bond 1998, Roodman 2008). As the number of time periods is relatively small, a straightforward fixed effects model is unlikely to handle the potential dynamic panel bias20

where the lagged value of the dependent variable is correlated with the fixed effects in the error term (Nickell, 1981). Second, the financial performance is very likely to be dynamic as the realizations of the ROAA, ROAE and Cost-to-income at time t are influenced by their past realizations, i.e. at t-1. Also, as demonstrated by Caselli et al (1996) and Bond et al (2001), GMM dynamic panel estimation enables correction for frequent severe econometric issues such as: unobserved heterogeneity, omitted variable bias and measurement error that loom when modelling panel data. Also, when heteroscedasticity is present, GMM is more efficient in dealing with endogeneity as compared to the use of simple instrumental variables estimator. Indeed, the suspected endogenous variables, i.e. ones that might be correlated with the error term, are specified in the dynamic model and their lags are used as instruments.

Therefore, heteroscedasticity is controlled for in the three econometric models and the second lag of the following variables is defined (defined in Table 3.1) as endogenous regressors: RGI IS_DV lnTA TEA NL Op_Inc Ovh_Cost EQ_TA and NL_TA for models where ROAA and ROAE are the dependent variables and the following RGI IS_DV Op_Inc Ovh_Cost NL IRS LLR_GL lnTA EQ_TA NL_TA for the model with Cost-to-Income (C2I) as the dependent variable. With regards to the exogenous variables, the following are used as instruments in the

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STATA command in the three models: Crisis_DV DSTF Pol_Stab Gov_eff Reg_Qual lnGDP_Grw and Infl.