Entre conjuntos con pre ´ordenes difusos

Due to the number of countries (48) and banks (635) involved in this study, it became impossible to apply either qualitative or mixed methods in this study. As a result, quantitative method rather than qualitative or mixed methods is used in this research. Specifically, the countries and the number of banks involved in this research are too many to apply qualitative or mixed methods. Also, using quantitative methods provides greater accuracy, objectivity and generalisation of results.

Although this study treated African as a monolithic whole, the researcher recognises, that there are some differences between different countries and different parts of Africa. Such differences include, culture, bank regulations, judicial systems, population size, security systems, and the level of employment. All these

109

can affect bank risk and performance of banks in Africa. However, we feel that presenting Africa as one uniform block is justified in our case, as we did not find any differences between the banks. In addition, we use GMM to account for differences between the banks if any. As mentioned in chapter 10, future research can look at different African regions (north, south, east and west) separately to see whether any significant differences are observed.

However, the hypothesis which has been developed and will be examined in this research have been summarised below.

Hypothesis 1: There is a significant and negative association between bank risk and bank performance in Africa.

Hypothesis 2: There is a significant positive association between board size and bank risk in Africa.

Hypothesis 3: There is a significant negative association between the presence of female directors on bank board and bank risk in Africa.

Hypothesis 4: There is a significant negative association between board independence and bank risk in Africa

Hypothesis 5: There is a significant negative association between role duality and bank risk in Africa.

Hypothesis 6: There is a significant negative association between the frequency of banks board meetings and bank risk in Africa.

Hypothesis 7: There is a significant positive association between the frequency of banks board meetings and bank performance in Africa

Hypothesis 8: There is a significant negative association between role duality and bank performance in Africa.

Hypothesis 9: There is a significant positive association between the presence of female directors on bank executive board and bank performance in Africa.

Hypothesis 10: There is a significant negative association between bank board size and bank performance in Africa.

Hypothesis 11: There is a significant positive association between board independence and bank performance in Africa.

Hypothesis 12: Corporate governance moderate the relationship between bank risk and bank performance in Africa.

The study uses GMM as main estimation method, where the dependent variables were regressed on explanatory variables to examine the above hypothesis. The regression equations are specified below:

First, the study finds the relationship between bank risk and performance.

To find the relationship between bank risk and performance using LPNR as risk measure, the following econometric models were used

ROAit = β0 + β1LPNRit + β2SIZEit + β3EQTAit + β4NLTAit + β5COSTit + β6CORit + β7GDPit + δ0 + εit (1)

ROE it = β0 + β1LPNRit + β2SIZEit + β3EQTAit + β4NLTAit + β5COSTit + β6CORit + β7GDPit + δ0 + εit (2)

To find the relationship between bank risk and performance using LLGL as risk measure, the following econometric models were used

ROA it = β0 + β1LLRGLit + β2SIZEit + β3EQTAit + β4NLTAit + β5COSTit + β6CORit + β7GDPit + δ0 + εit (3)

ROE it = β0 + β1LLRGLit + β2SIZEit + β3EQTAit + β4NLTAit + β5COSTit + β6CORit + β7GDPit + δ0 + εit (4)

Second, the study finds the relationship between corporate governance and bank risk. To determine such relationship the following econometric models were used LPNRit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + δ0 + εit (5)

LLRGLit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + δ0 + εit (6)

111

Third, the study finds the relationship between corporate governance and bank performance. Below are econometric models used:

ROAit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + δ0 + εit (7)

ROEit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + δ0 + εit (8)

Fourth, the study finds the moderating effect of corporate governance on the relationship between bank risk and performance

To find the moderating effect using LPNR as risk measure, below econometric models were used

ROAit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + β12LPNRit + β13(LPNR*SIZE)it + β14(LPNR*MEETINGS)it + β15(LPNR*DUAL)it + β16 (LPNR*FEMALE)it + β17(LPNR*INDEP)it +δ0 + εit (9)

ROEit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + β12LPNRit + β13 (LPNR*SIZE)it + β14(LPNR*MEETINGS)it + β15(LPNR*DUAL)it + β16 (LPNR*FEMALE)it + β17 (LPNR*INDEP)it +δ0 + εit (10)

Finally, to find the moderating effect using LLRGL as risk measure, the following econometric models were used

ROAit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALEit + β11INDEPit + β12LLRGLit + β13 (LLRGL*SIZE)it + β14(LLRGL*MEETINGS)it + β15(LLRGL*DUAL)it + β16 (LLRGL*FEMALE)it + β17 (LLRGL*INDEP)it +δ0 + εit (11)

ROEit = β0 + β1SIZEit + β2EQTAit + β3NLTAit + β4COSTit + β5CORit + β6GDPit + β7BSIZEit + β8MEETINGSit + β9DUALit + β10FEMALE + β11INDEP + β12LLRGLit + β13(LLRGL*SIZE)it + β14(LLRGL*MEETINGS)it + β15(LLRGL*DUAL)it + β16 (LLRGL*FEMALE)it + β17 (LLRGL*INDEP)it +δ0 + εit (12)

Where,

ROAit is performance of country i at time t

ROEit is performance of country i at time t

LPNRit is loan loss provision to net interest revenue of country i at time t

LLRGLit is loan loss reserve to gross loan of country i at time t

SIZEit is bank size of country i at time t

EQTAit is equity to assets of country i at time t

NLTAit is net loans to assets of country i at time t

COSTit is cost-to-income-ratio of country i at time t

CORit is corruption of country i at time t

GDPit is gross domestic product of country i at time t

BSIZEit is board size of country i at time t

MEETINGSit is the number of board meetings of country i at time t

DUALit is role duality of country i at time t

FEMALEit is the female directors of country i at time t

INDEPit is the independent directors of country i at time t

(LPNR*BSIZE)it represents the joint effect of LPNR and BSIZE of country i at time t

(LPNR*MEETINGS)it represents the joint effect of LPNR and MEETINGS of country

i at time t

(LPNR*DUAL)it represents the joint effect of LPNR and DUAL of country i at time t

(LPNR*FEMALE)it represents the joint effect of LPNR and FEMALE of country i at

time t

(LPNR*INDEP)it represents the joint effect of LPNR and INDEP of country i at time

113

(LLRGL*BSIZE)it represents the joint effect of LLRGL and BSIZE of country i at time

t

(LLRGL*MEETINGS)it represents the joint effect of LLRGL and MEETINGS of

country i at time t

(LLRGL*DUAL)it represents the joint effect of LLRGL and DUAL of country i at time

t

(LLRGL*FEMALE)it represents the joint effect of LLRGL and FEMALE of country i

at time t

(LLRGL*INDEP)it represents the joint effect of LLRGL and INDEP of country i at time

t

β1 to β17 represent the coefficient of each variable

β0 is the intercept

δ0 is dummy for the crisis period, 1represent 2007/2008 and 0 represent other years

εit is the error term of country i at time t

5.2.5 Classification of variables: performance, risk, corporate governance,