chapter -vi determinants of financial performance of ethiopian commercial banks

225

CHAPTER - VI

DETERMINANTS OF FINANCIAL PERFORMANCE OF

ETHIOPIAN COMMERCIAL BANKS

The financial performance of a bank may be influenced by factors such as bank

size, capital adequacy, liquidity, market structure, and economic growth. Some of

these factors may have a positive impact on the bank’s profitability while the others

could have a negative impact. Further, some of the factors that affect the profitability

of a bank could be under the control of the bank’s management (internal factors) and

the others could be beyond its control (external factors). Knowledge of the underlying

internal and external factors that affect the financial performance of a bank is vital for

policy makers and bank supervisors and regulators in framing future policies aimed at

improving the financial performance of the banking sector (Kosmidou1, 2008; Sufian

and Chong2, 2008). It is necessary, therefore, to identify the determinants of financial

performance of the Ethiopian commercial banks.

This, the chapter presents the key factors that influence the financial performance

of Ethiopian commercial banks over the study period. The chapter also presents the

trends in the market structure of the Ethiopian banking sector over the study period.

Accordingly, the trend in the market structure of the sector is presented in subsection

6.1; factors influencing bank profitability are presented in subsection 6.2; factors that

affect bank efficiency are presented in subsection 6.3, and; the conclusion of the

chapter is given in subsection 6.4.

226

6.1. Trend of market structure of the banking sector

An assessment of the market structure of the banking sector provides insights

as to the level of concentration and hence the degree of competition among the banks

in the sector. Particularly, the Ethiopian banking sector which is characterized as

highly dominated by the state owned banks is of interest to see as to whether its

market structure has shown any change over the study period following the gradual

financial reforms the government has taken on the banking sector. Using both the K-

bank concentration ratios and HHI, the trend of the market structure of the sector is

examined on the basis of two variables: Deposits and Total assets. The K-bank

concentration ratios are presented in section 6.1.1, while the HHI of the sector is

given in section 6.1.2.

6.1.1. K-bank concentration ratios of the banking sector

The K-bank concentration ratios measure the degree of bank concentration

taking into account the K-banks in the banking sector. The K-bank concentration

ratios used in the study are 1BCR and 3BCR. In the 1BCR, the largest bank in terms

of total deposits and total assets in the banking sector is considered. In the 3BCR, the

largest three banks in the sector on total deposits and total assets are taken into

account. The trends in K-bank concentration ratios during 1999-00 to 2010-11 are

indicated in Table 6.1.

On the deposit market, the share of the largest public commercial bank

(1BCR-Deposits), CBE, steadily declined over the period 1999-00 to 2010-11. The

share of the bank in the deposit market declined from 85.3 percent in 1999-00 to

60.88 percent in 2010-11. Yet, CBE continued to dominate the deposit market and

takes the lion’s share of the total deposits in the banking sector. The market structure

using the 3BCR-Deposits also indicates the same trend. The share of the three largest

227

banks in the deposit market declined from 91.79 percent in 1999-00 to 75.09 percent

in 2010-11. The study suggests that, though the banking sector gradually gets less and

less concentrated over the study period, the deposit market is still highly concentrated.

The bank concentration ratios in terms of total assets show the same trend as

in the deposit market. Though the largest bank, CBE, continued to dominate the

banking sector, its share in the total assets of the banking sector (1BCR-Assets)

decreased over the study period. The share of the bank in terms of total assets of the

banking sector declined from 82.76 percent in 1999-00 to 62.57 percent in 2010-11.

The same trend has been observed in the 3BCR-Assets. The share of the largest three

banks in the total assets of the banking sector decreased from 90.43 percent in 1999-

00 to 76.44 percent in 2010-11.

Table 6. 1 K-bank concentration ratios of Ethiopian banking sector

Year No. of

banks 1 BCR-Deposits 3 BCR-Deposits 1 BCR -Assets 3 BCR-Assets

1999-00 8 0.8530 0.9179 0.8276 0.9043

2000-01 8 0.8300 0.9030 0.8111 0.8892

2001-02 8 0.7983 0.8896 0.7815 0.8742

2002-03 8 0.7655 0.8733 0.7537 0.8593

2003-04 8 0.7411 0.8619 0.7338 0.8504

2004-05 9 0.7005 0.8323 0.7019 0.8214

2005-06 9 0.6696 0.8178 0.6628 0.8014

2006-07 10 0.6415 0.7971 0.6431 0.7892

2007-08 10 0.6133 0.7766 0.6148 0.7690

2008-09 12 0.5775 0.7487 0.5884 0.7484

2009-10 14 0.5721 0.7421 0.5811 0.7400

2010-11 14 0.6088 0.7509 0.6257 0.7644

Source: Own computation

228

Figure 6. 1 K-Bank concentration ratio of the banking sector


6.1.2. Herfindahl-Hirschman Index of the banking sector

Though the 1BCR and 3BCR provide useful information about the market

structure, these measures do not take into account the number of banks operating in

the banking sector. However, the number of market participants in the industry has a

direct impact on issues of concentration and competition, and thus should be taken

into account. To take account of this, Herfindahl- Hirschman Index (HHI) is used in

the study since the relative size and number of banks in the market is considered in

measuring HHI. Table 6.2 summarizes HHI on total deposit and total assets of the

Ethiopian baking sector over the period 1999-00 to 2010-11.

The values of HHI for all the major indicators of the market structure of the

banking sector decrease over the period of the study. The HHI-Deposits of the

Ethiopian banking sector decreased from 73.16 percent in 1999-00 to 39.02 percent in

55

60

65

70

75

80

85

90

95

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Year-end of June

1 BCR-Deposits 3 BCR-Deposits

1 BCR-Assets 3 BCR-Assets

229

2010-11. The HHI-Assets of the Ethiopian baking sector also showed the same trend.

It declined from 69.03 percent in 1999-00 to 40.80 percent in 2010-11. According to

the US merger guideline3, HHI values below 0.1000 (or 1000) indicate non-

concentrated, between 0.1000 (or 1000) and 0.1800 (or 18000) moderately

concentrated and indices above 0.1800 (or 18000) imply highly concentrated. Hence,

all the HHI measures (HHI-Assets and HHI-deposits) suggest that Ethiopia’s banking

sector is characterized as highly concentrated. It is important to note that the changes

in the concentration measures could reflect changes in coverage since the number of

banks covered in the study increased over the study period from 8 banks in 1999-00 to

14 banks in 2010-11.

Table 6. 2 Trends in HHI of the banking sector

Year No. of banks HHI-Deposits HHI-Assets

1999-00 8 0.7316 0.6903

2000-01 8 0.6940 0.6640

2001-02 8 0.6445 0.6188

2002-03 8 0.5956 0.5781

2003-04 8 0.5610 0.5503

2004-05 9 0.5055 0.5066

2005-06 9 0.4667 0.4572

2006-07 10 0.4326 0.4333

2007-08 10 0.3996 0.4003

2008-09 12 0.3600 0.3703

2009-10 14 0.3526 0.3607

2010-11 14 0.3902 0.4080


230

6.2. Determinants of bank profitability

This section presents the factors that influence the profitability of banks in

Ethiopia. The profitability of the banks is measured using the ROAA against which a

number internal and external factors including market structure is regressed using the

fixed effects model. The descriptive statistics of the variables used in the fixed effects

model is presented in subsection 6.2.1. The pair wise correlation analysis of the

explanatory variables is given in subsection 6.2.2. The result of the fixed effects

model is presented and discussed in subsection 6.2.3.

6.2.1. Descriptive statistics of the variables used in the fixed effects model

Table 6.3 shows the summary data for the variables used in the analysis. The

data are averaged across years and reported showing the trend of the key variables

over the period 1999-00 to 2010-11. It shows that during 1999-00 to 2010-11 the

average profit level, ROAA, of Ethiopian banks is 2.14 percent. As it can be seen

from Table 6.3, the profit condition of the banks has improved over the study period.

The ROAA, which is a measure of bank profitability, increases from 1.54 percent in

1999-00 to 3.4 percent in 2010-11. However, there is much difference in profitability

among the banks as witnessed by the values of the standard deviations.

Capital adequacy30

(Capadq) is one of the determinant variables. Capadq is

proxied by the ratio of equity to total assets. The Capadq of the banks ranges between

14.38 percent in 1999-00 and 13.97 in 2010-11. On average the Capadq of the

commercial banks is 14.49 over the study period. The study indicates that the banks

remain strong in terms of capital over the period 1999-00 to 2010-11.

30

The Ethiopian banking sector overall is well capitalized and the capital adequacy ratio

(measured by Regulatory Tier 1 capital to risk-weighted assets) for the sector has been around 18

percent in 2011, well above the regulatory 8 percent minimum (IMF, 2011).

231

The size of the banks in terms of total assets is provided in Table 6.3 (in

millions of Ethiopian currency). The total assets of the commercial banks have

significantly increased. The total asset of the commercial banking sector has increased

from Birr23, 959 million in 1999-00 to Birr 183, 483 million in 20010-11. The

expansion of branch net work and the entry of new banks into the banking sector may

attribute to the increase in the total assets of the Ethiopian commercial banking sector.

The loan intensity measured by the ratio of net loans and advances to total assets

(Nlta) of the banks averages to 49.72 percent over the study period. The ratio steadily

declined from 57.31 percent in 1999-00 to 38.15 percent in 2010-11. Liquidity of the

banks is proxied by the ratio of loans to deposits (Ldr). The liquidity ratio of the

banks decreases over the study period from 94.85 percent in 1999-00 to 53.07 percent

in 2010-11. The study indicates that the Ldr of the banks averages to 73.44 percent

over the study period.

Diversification (Dfn) is measured by taking the ratio of non-interest income to

total income. The share of non-interest income to total income of the banks ranges

between 31.04 percent in 1999-00 and 50.96 percent in 2010-11. The non-interest

income of the banks has particularly shown a steady increment over the period 2007-

08 to 2010-11, indicating that the banks have continued to involve in providing non-

traditional banking services. The study also indicates that the banks have earned

almost half of their income from non-traditional banking services such as commission

and advisory fees in 2010-11.On average non-interest income of the banks constitutes

about 40 percent of the total income earned by the banks over the study period.

Another important factor that is considered by the study is the loan loss

reserve to total loans (Llrs). The Llrs of the banks which measures the asset quality of

the banks ranges between 2.56 percent in 1999-00 and 3.66 percent in 2010-11. As it

232

can be seen from the table, the ratio declined particularly over the period 2004-05 to

2009-10, though it increased in 2010-11. On average the Llrs of the banks is found to

be 3.02 over the study period. The operating costs of the banks averages to about 48

percent of the total income of the banks over the study period. This ratio (Operat Eff.)

indicates how efficient bank managers are in controlling the operating costs of banks.

As it is shown in Table 6.3, the ratio ranges between 42.23 percent in 1999-00 and

33.14 percent in 2010-11. For the HHI index, the value is 50.32 percent for the

commercial banks over the period 1999-00 to 2010-11. This figure demonstrates that

although the Ethiopian banking system has undergone financial reforms, it still

features that the market is highly concentrated. However, the trend in HHI index

shows that the banking sector is getting gradually less concentrated. The HHI-assets

decreases from 60.03 percent in 1999-00 to 40.92 percent in 2010-11.

As far as market share is concerned, Table 6.3 shows that the variation in

market share among the banks has declined as witnessed by the steady decline in the

values of the Std.Dev. On average the market share of a bank is about 10 percent over

the study period. On the macro economic variables, on average the country’s real

GDP grows at the rate of 9.05 over the study period, while inflation grows at an

average rate of 11.58 percent (NBE4, 2011).

233

Table 6. 3 Descriptive statistics of variables used in the fixed effects model

1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 Ave.

ROAA

Mean 1.54 1.86 0.82 1.40 2.00 2.12 2.64 2.26 2.66 1.57 2.69 3.40 2.18

Std.Dev. 0.96 1.56 1.53 0.55 1.02 1.66 1.93 2.35 1.45 2.44 2.22 1.04 1.78

Min 0.12 0.09 -2.16 0.48 0.40 -1.71 -2.37 -3.76 -0.20 -3.95 -3.19 2.2 -3.95

Max 2.74 4.86 2.99 2.35 3.28 3.55 3.92 4.22 4.02 3.91 6.72 6.35 6.72

Capadq

Mean 14.38 13.39 12.57 10.63 10.00 18.04 15.3 17.89 14.85 15.07 15.61 13.97 14.49

Std.Dev. 8.31 7.82 7.56 4.49 3.38 25.97 15.00 13.15 6.55 7.06 7.92 5.90 10.57

Min 6.50 6.05 3.74 5.28 5.35 4.31 4.20 9.01 9.04 8.48 7.49 5.44 3.74

Max 27.97 29.44 28.02 19.04 14.24 86.82 54.46 50.75 29.79 33.64 35.22 29.75 86.82

Bnksz 23, 959 26, 493 28, 338 32, 110 38, 125 47, 254 54, 089 67, 572 82, 004 100, 970 127, 745 183, 483

Nlta

Mean 57.31 62.05 54.91 55.48 53.16 46.11 58.77 54.42 49.49 43.85 40.02 38.15 49.72

Std.Dev. 13.34 11.36 10.03 12.83 13.04 20.79 14.83 16.71 9.92 6.99 4.71 5.20 13.69

Min 37.34 40.48 33.22 25.10 22.51 2.35 21.35 19.26 31.41 34.09 31.77 28.87 2.35

Max 77.62 77.58 67.85 65.56 60.96 63.16 69.96 67.32 60.10 57.47 49.27 46.34 77.62

Dfn

Mean 31.01 29.47 29.16 37.37 39.94 38.93 35.77 35.74 36.63 42.95 50.31 50.96 39.53

Std.Dev. 10.56 10.40 8.19 8.64 8.19 15.44 14.41 9.23 7.03 14.07 11.65 9.92 12.83

Min 14.08 12.5 18.18 23.46 21.14 6.45 4.03 24.34 26.29 20.22 29.25 32.01 4.03

Max 42.86 40.90 41.63 48.38 46.67 57.27 53.23 54.02 48.15 74.51 76.69 72.14 76.69

Ldr

Mean 94.85 95.16 80.53 81.63 76.07 66.27 89.29 80.72 71.58 63.24 58.24 53.07 73.44

Std.Dev. 32.60 30.99 20.00 21.88 20.98 22.93 28.12 22.88 15.06 10.24 10.35 9.55 23.83

Min 65.84 61.00 52.62 43.28 36.95 20.00 32.86 29.67 46.07 48.07 43.95 40.06 20.00

Max 162.22 160.59 119.57 114.95 107.16 92.64 129.59 116.98 93.13 85.77 80.35 74.55 162.22

234

Llrs

Mean 2.56 3.07 3.75 4.53 4.48 3.20 3.06 2.89 2.76 2.04 1.54 3.66 3.02

Std.Dev. 2.65 3.06 3.56 3.15 2.49 1.91 2.00 2.65 2.32 2.02 1.58 3.23 2.60

Min 0 0 0.64 1.49 2.22 0 0.45 0.38 0.37 0.31 0.38 1.03 0

Max 7.25 9.12 10.81 10.24 9.08 6.09 7.40 9.90 7.60 7.02 6.04 10.48 10.81

Operat. Eff.

Mean 42.23 39.17 48.83 49.24 43.85 81.34 46.17 56.06 39.80 60.34 45.83 33.14 48.49

Std.Dev. 11.77 8.63 26.18 11.13 11.12 135.60 47.81 56.23 20.23 57.64 35.00 11.60 48.67

Min 24.09 28.13 28.89 25.50 31.18 22.08 20.50 22.45 19.18 13.46 20.97 16.67 13.46

Max 60 52.20 111.15 58.06 64.00 441.96 172.74 209.52 80.73 211.54 150.92 62.98 441.94

MS-assets*

Mean 12.5 12.5 12.5 12.5 12.5 11.11 11.11 10 10 8.33 7.14 7.14 10.17

Std.Dev. 28.40 27.75 26.56 25.44 24.65 22.23 20.80 19.25 18.27 16.15 14.92 16.09 20.29

Min 0.60 0.81 1.11 1.46 1.77 0.27 0.41 0.39 0.70 0.32 0.30 0.43 0.27

Max 82.8 81.11 78.15 75.37 73.38 70.19 66.28 64.31 61.48 58.84 58.11 62.47 82.76

HHI-assets 69.03 66.40 61.88 57.81 55.03 50.66 45.72 43.33 40.03 37.03 36.07 40.92 50.32

GDPr 5.9 7.4 1.6 -2.1 11.7 12.6 11.5 11.8 11.2 9.9 10.4 11.5 9.05

Infr 4.2 -1.4 -5.5 4.6 5.6 7.2 8.4 19.2 20.8 29.4 10.1 19.4 11.58


235

6.2.2. Pair wise correlation analysis between the independent variables

Table 6.4 shows the correlation between the explanatory variables used in the

analysis. The matrix shows that in general the correlation between the explanatory

variables is not strong suggesting that multicollinearity problems are either not severe

or non-existent. According to Kennedy5 (2008), multicollinearity will be a severe

problem when the correlation is above 0.80.

236

Table 6. 4 Pair wise correlation between the independent variables

Capadq Bnksz Nlta Dfn Llrs Lqdty GDPr Infr Operat. Eff. MS-assets HHI-

assets

Capadq 1.000

Bnksz -0.5610***

1.000

Nlta -0.2531***

-0.3836***

1.000

Dfn -0.1941**

0.2557***

-0.4465***

1.000

Llrs -0.4452***

.3567***

0.0303 -0.0419 1.000

Ldr -0.0152 -0.49150***

0.4848***

-0.4468***

0.1518***

1.000

GDPr 0.1215 0.2354**

-0.2316**

0.2461***

-0.1230 -0.2368***

1.000

Infr 0.0813 0.2978***

-0.3209***

0.2998***

-0.1290 -0.3285***

0.5262***

1.000

Operat. Eff. 0.4850***

-0.4461***

-0.3301***

-0.2151**

-0.1953**

-0.1128 0.0280 0.0166 1.000

MS-assets -0.3154***

0.7213***

-0.4556***

0.0510 0.3718***

-0.3949***

-0.0533 -0.0730 -0.0533 1.000

HHI-assets -0.0943 -0.3741***

0.4207***

-0.4334***

0.1664*

0.4449***

-0.5979***

-0.7929***

-0.0162 0.0915 1.000

Source: Author’s computation

*** Significant at 1%,

** Significant at 5%, Capadq is the ratio of equity to total assets, Bnksz is the size of a bank measured as log of total asset of a bank, Nlta is the

ratio of net loans to total assets, Operat. Eff. is the ratio of operating costs to income, Ldr is loans to deposit ratio, Dfn is diversification measured by the ratio of non-interest

income to total income, MS-assets is the market share of a bank measured by the ratio of the assets of a bank to the total assets of the commercial banking sector, HHI-assets

is the market concentration measured using Herfindahl- Hirschman Index, Llrs is the ratio of loan loss provision to total loans, GDPr is the annual real GDP growth rate, and

Infr annual inflation rate.

237

6.2.3. Empirical result

The fixed effects model has been used to identify the factors that influence the

profitability of the banks. The preference of the fixed effects model to the random effects

model is made following the result of the Hausman test which is indicated in Table 6.5.

Table 6. 5 Hausman test

The result of the fixed effects model taking ROAA as a dependent variable is reported

in Table 6.6. The R-squared-within, R-squared-between, and R-squared-overall are

reported to be 74.74 percent, 36.45 percent and 53 percent respectively. This implies that

the variables considered in the study explain about 74.74 percent of the variation within a

given bank’s profitability, 36.45 percent of the variation between the banks’ profitability

and 53 percent of the variation among all the banks’ profitability. The key findings of the

study on determinants of bank profitability are presented below.

Capital adequacy is found to be one of the key determinants of bank profitability in

Ethiopia. The equity to total asset ratio, which is used as a proxy for capital strength, is

significantly and positively associated with bank profitability (ROAA). A positive and

Prob>chi2 = 0.0046 = 26.98 chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg operationa~y -.0399119 -.0339282 -.0059837 .0009583 hhiassets .0597335 .0335293 .0262043 .0146694 msassets -.0072983 -.0313195 .0240212 .0382096 infr -.0085261 -.0092971 .000771 . gdp .0444192 .061747 -.0173278 . llrs -.0112804 .096419 -.1076994 .0520664 ldr -.065676 -.0260921 -.0395839 .008396 dfn .050616 .0450594 .0055566 .0106642 nlta .1210663 .0585005 .0625658 .0149874 bnksz 2.511902 1.792093 .7198093 .481818 capadq .2110678 .1407747 .0702931 .0120511 fixed random Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients

. hausman fixed random

238

significant correlation between equity to total assets and profitability indicates that

commercial banks in Ethiopia over the period 1999-00 to 2010-11 were focusing on

making sound lending decisions which reaffirms that banks with more capital tended to

engage in higher loan risk lending for higher profits. On the other hand, the study implies

that banks that are relatively poorly capitalized were so conservative in extending loans

and thus their profitability inauspiciously affected. The empirical finding of this study is

consistent with those found by Berger6 (1995), Alexiou and Sofoklis

7 (2009), Demirguc-

Kunt and Huizinga8 (1999), Staikouras and Wood

9 (2003), Haron

10 (2004), Goddard

11 et

al. (2004), Pasiouras and Kosmidou12

(2007), Sufian and Chong13

(2008), and

Kosmidou14

(2008), which indicate that well-capitalized banks face lower risks of going

bankrupt, building their credit worthiness, thus reducing their cost of funding which will

ultimately enhance their profit margin. From this perspective, strengthening the capital

structure of Ethiopian commercial banks is crucial since it provides the banks an

additional buffer to endure financial crises and offers better safety for depositors during

unstable macroeconomic conditions.

The ratio of non-interest income to total income, which measures the level of

diversification of a bank’s activities, is found to have statistically significant and positive

impact on bank profitability. A positive and significant association between bank

profitability and activity diversification has been reported in the literature (Flamini15

et

al., 2009; Sufian and Chong16

, 2008; Alper and Anbar17

, 2011). A positive and significant

association between this variable and profitability reveals that commercial banks in

Ethiopia that earned a higher proportion of their income from sources other than interest

tend to report higher level of profits over the study period.

239

The other key determinant of bank profitability in Ethiopia, as evidenced by its

highest coefficient in Table 6.4, is Bank size (Bnksz). Bnksz is proxied by log of total

assets of a bank. The variable is found to have statistically significant and positive impact

on the profitability of the banks. The positive coefficient indicates that larger commercial

banks tend to earn higher profits than smaller commercial banks. This is consistent with

the relative market power hypothesis. The relative market power hypothesis states that

only larger banks are able to exercise market power in pricing their products to earn

above normal profits. The finding of the study provides support to the earlier studies

finding economies of scale for larger banks (Flamini18

et al., 2009; Sufian and

Habibullah19

, 2009, and; Kosmidou20

et al., 2006; Alexiou and Sofoklis21

, 2009; Alper

and Anbar22

, 2011). As cited by Sufian and Habibullah23

(2009), Hauner (2005) gives

two potential explanations vis-à-vis the positive association between bank size and bank

performance. First, if this link relates to market power, large banks should pay less for

their inputs. Second, there may be increasing returns to scale through the prioritization of

fixed costs over a higher volume of services or through efficiency gains from a

specialized workforce. Thus, the positive association between bank size and profitability

of commercial banks in Ethiopia confers two important implications. One, larger

commercial banks in Ethiopia may be benefited from scale economies. Two, larger

commercial banks may exert market power through strong brand image which ultimately

enable them to reap more profits. With regards to operational efficiency, as measured by

the ratio of operating costs to total income, the ratio is statistically significant and is

negatively correlated with profitability. The result of the study implies that more

operationally efficient commercial banks reported higher profits than those commercial

240

banks that have poor expense management over the study period. Thus, the empirical

result of the study reveals that a reduction in costs increases the profits of the commercial

banks. This denotes that commercial banks in Ethiopia have much to profit if they are

able to exercise efficient cost management practices. The study suggests that the

commercial banks have to redesign their operations to reduce costs and ensure effective

cost management to enhance their profit margin. To this effect, the bank managers have

to identify the areas for cost reduction through conducting an in-depth examination of all

the cost drivers of the bank they are in charge to manage. The result of the study is in line

with other banking studies (Pasiouras and Kosmidou, 2007; Alexiou and Sofoklis, 2009,

and; Sufian and Chong, 2008). These studies reinforce the maxim that poor operational

efficiency is one of the key factors that contributes to weak profitability, and thus

efficient cost management is a precondition for bank profitability.

The other calculated parameter is bank liquidity (Ldr), measured by the ratio of loans

to deposits. The variable is found to have a negative and significant relationship with

profitability. The estimated coefficient corresponding to this particular proxy suggests

that an increase in liquidity will have a negative impact in profitability. One possible

reason for this could be the cost of funding these loans through higher rates of non-

performing loans (Guru24

et al., 2002). As opposed to the findings of Haron (2004),

Kosmidou et al. (2006), and Pasiouras and Kosmidou (2007) who found a positive

association between bank profitability and liquidity, the study is consistent with the

findings of Dietrich and Wanzenried25

(2011), Funacova and Poghosyam26

(2011), Guru

et al. (2002), Faisal27

(2010), and Alexiou and Sofoklis (2009). The finding brings to light

the trade-off between liquidity and profitability; the more resources that are tied up to

241

meet future liquidity demands, the lower the bank’s profitability. Thus, bank managers

need to possess the required skills to strike the balance between liquidity and

profitability.

As far as the ratio of net loans to total assets (Nlta) is concerned, the study finds the

variable to have a positive and statistically significant impact on bank profitability. This

ratio is a very important financial ratio as it shows how well a bank is financially

operating to enhance its interest income. The positive coefficient of this variable shows

that as banks make use of their assets productively, profitability increases. However, care

should be taken by the management of the banks as extremely higher ratio of this variable

may also impair the liquidity of the banks. The result of the study is consistent with the

finding of Sufian and Habibullah (2009). With regards to the loan loss reserves to total

loans (Llrs), which measures a bank’s credit risk, the study finds a negative but

statistically insignificant association with profitability. Previous studies in banking show

that the effect of Llrs on profitability is negative (Alexiou and Sofoklis, 2009; Sufian and

Chong, 2008 ;). This negative association denotes that the higher the credit risk assumed

by the banks, the higher the loan defaults. Though the variable is found to be statistically

insignificant, its negative coefficient requires the attention of bank managers. This

implies that bank managers should exert efforts on implementing credit risk management

to evaluate credit risk more effectively to avoid problems associated with loan defaults.

Market concentration (HHI-assets) is found to have positive and significant impact on

bank profitability. Since market share is found to have negative and insignificant impact

on bank profitability, the study provides evidence on the structure-conduct performance

(SCP) hypothesis. The Structure –Conduct- Performance hypothesis posits that the

242

degree of market concentration is inversely related to the degree of competition since

market concentration encourages firms to collude. According to the Structure-Conduct

Performance hypothesis, collusion may result in higher rates being charged on loans and

less interest rates being paid on deposits. The result of the study characterizes the nature

of Ethiopian banking sector as an oligopoly type, a market dominated by few banks, each

of which has control over the market where there is a high level of market concentration.

This highly concentrated bank market can reduce firms’ demand for credit and ultimately

affect the level of intermediation and impede the growth of the economy by increasing

the cost of credit. The study indicates the need for modifying the environment in which

the banks operate to enhance more competition and more entry into the banking market.

Similar to the finding of the present study, few studies provide evidence for the PCP

hypothesis in the banking literature (Kosmidou et al., 2006)

As far as the macroeconomic variables are concerned, the study indicates that

inflation (Infr) negatively impacts bank profitability though its impact is low. The other

macroeconomic variable is the real GDP growth rate (GDPr). GDPr is found to be

statistically significant and positively associated with bank profitability. Similar to the

finding of this study, a positive and significant association between bank profitability and

real GDP growth rate has been found in previous studies (Heffernan and Fu28

, 2010;

Pasiouras and Kosmidou, 2007; Kosmidou et al., 2006). The positive sign of this variable

supports the notion that economic growth positively affects bank profitability. Economic

growth can boost the profitability of banks by increasing the demand for financial

services, thereby increasing the banks’ cash flows, non-interest earnings and profits. As

the economy grows, the demand for loans increases allowing banks to earn more interest

243

income and enhance their profit margins. When the economy performs better, borrowers

of banks are less likely to default. This allows banks to have lower non-performing loans

and thus higher margin of profit.

Table 6. 6 Result of the fixed effects model (ROAA as dependent variable)


6.3. Robustness test

Thus far, this chapter discusses the results from the regression analysis regarding

determinants of banks’ profitability in the Ethiopian banking sector. Two further tests are

performed to examine whether the findings are robust to changes in variable or changes

in the methodology. Whenever the statistical procedure is insensitive to the initial

assumptions and choices of the model, the results are thought to be robust. Firstly, as

there are different choices of measuring market concentration, the 3BCR-assets than

HHI-assets is included as a measure of market structure in the fixed effects model to see

rho .61312189 (fraction of variance due to u_i) sigma_e .90168917 sigma_u 1.1351237 _cons -28.73466 9.253513 -3.11 0.008 -48.72566 -8.74366 hhiassets .0597335 .0305178 1.96 0.072 -.0061961 .1256632 msassets -.0072983 .0162452 -0.45 0.661 -.0423939 .0277973operationa~y -.0399119 .0091227 -4.38 0.001 -.0596202 -.0202036 infr -.0085261 .0189384 -0.45 0.660 -.0494401 .0323878 gdp .0444192 .0179657 2.47 0.028 .0056068 .0832317 llrs -.0112804 .0743825 -0.15 0.882 -.171974 .1494133 ldr -.065676 .0275532 -2.38 0.033 -.1252012 -.0061508 dfn .050616 .0144464 3.50 0.004 .0194064 .0818256 nlta .1210663 .0377263 3.21 0.007 .0395636 .2025689 bnksz 2.511902 .8330598 3.02 0.010 .7121858 4.311618 capadq .2110678 .0378156 5.58 0.000 .1293721 .2927636 roaa Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust (Std. Err. adjusted for 14 clusters in typbank)

corr(u_i, Xb) = -0.5271 Prob > F = 0.0000 F(11,13) = 20047.72

overall = 0.5567 max = 12 between = 0.3647 avg = 8.4R-sq: within = 0.7442 Obs per group: min = 2

Group variable: typbank Number of groups = 14Fixed-effects (within) regression Number of obs = 118

> s hhiassets, fe robust cluster(). xtreg roaa capadq bnksz nlta dfn ldr llrs gdp infr operationalefficiency msasset

244

whether the use of the 3BCR-assets in the model will bring changes in the outcome. The

hausman test still provides preference to the fixed effects model to the random effects

model when concentration is measured using 3BCR-assets. The hausman test is indicated

in Table 6.7. Table 6.8 shows the result of the fixed effects regression model. The result

is similar to the one that is indicated in Table 6.6 wherein market concentration is

measured using HHI-assets. The only difference that is observed is that GDPr was found

to be statistically insignificant when market concentration is measured using 3BCR-

assets.

Secondly, the panel data methodology is adapted from the fixed effects model to

more simple regression technique (OLS). The pooled OLS estimators are not adjusted for

non-normality, hetroscedasticiy, endogeneity or autocorrelation in the disturbance term.

Therefore, the p-values are calculated using robust standard errors to make the hypothesis

rejection area more conservative in the presence of non-independent error items and

hetroscedasticiy. Results obtained from the pooled ordinary least squares regression is

very similar to those obtained from the fixed effects model; most variables retain their

sign and significance. Although, there are some interesting differences to mention, (i) the

variable Llrs is found to be significant and positive determinant of banks’ profitability

and (ii) the variable market share (MS-assets) is, found to be significant and negative, and

(iii) the variable HHI-assets is relatively less significant and positive in the OLS

regression. The significant results for Llrs (positive) and MS-assets should be carefully

treated since the violations of the assumptions under pooled OLS increase the likeliness

of Type 1 and 2 errors. Turning to the results of the random effects model significant

results under pooled OLS remain the same for the variables: Llrs and MS-assets.

245

Moreover, the variable HHI-assets has relatively less significant and positive impact

under both the pooled OLS as well as the random effects regression.

Table 6. 7 Hausman test (3 BCR-assets)


Prob>chi2 = 0.0020 = 29.29 chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg msassets -.0065751 -.0327901 .026215 .0377417 cr3assets .1416377 .0720286 .0696091 .03429operationa~y -.0403381 -.0341266 -.0062115 .0007701 infr -.0073328 -.0088292 .0014964 . gdp .0415906 .0608805 -.0192899 . llrs -.0309219 .091313 -.1222349 .0531409 ldr -.0654563 -.0251689 -.0402874 .0081324 dfn .0507679 .0449451 .0058228 .0104054 nlta .1179076 .0550315 .0628761 .0134602 bnksz 2.761777 1.827454 .9343225 .5256072 capadq .2134423 .1403695 .0730728 .0120084 fixed random Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients

. hausman fixed random

246

Table 6. 8 Robustness test 1 (3BCR-Assets)


Table 6. 9 Robustness test 2 (OLS)


rho .64526534 (fraction of variance due to u_i) sigma_e .89607162 sigma_u 1.2085374 _cons -39.43555 12.22795 -3.23 0.007 -65.85244 -13.01866 cr3assets .1416377 .0596628 2.37 0.034 .0127441 .2705312 msassets -.0065751 .0170778 -0.39 0.706 -.0434694 .0303193operationa~y -.0403381 .008846 -4.56 0.001 -.0594487 -.0212275 infr -.0073328 .01856 -0.40 0.699 -.0474293 .0327636 gdp .0415906 .0187463 2.22 0.045 .0010918 .0820894 llrs -.0309219 .077853 -0.40 0.698 -.199113 .1372692 ldr -.0654563 .026088 -2.51 0.026 -.121816 -.0090967 dfn .0507679 .0136439 3.72 0.003 .021292 .0802438 nlta .1179076 .0341656 3.45 0.004 .0440972 .191718 bnksz 2.761777 .8406309 3.29 0.006 .9457044 4.57785 capadq .2134423 .0368256 5.80 0.000 .1338854 .2929991 roaa Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust (Std. Err. adjusted for 14 clusters in typbank)

corr(u_i, Xb) = -0.5735 Prob > F = 0.0000 F(11,13) = 4458.98



> s cr3assets, fe robust cluster(). xtreg roaa capadq bnksz nlta dfn ldr llrs gdp infr operationalefficiency msasset

_cons -19.80833 7.617936 -2.60 0.011 -34.91163 -4.705034 hhiassets .0335293 .02538 1.32 0.189 -.0167891 .0838476 msassets -.0313195 .0144357 -2.17 0.032 -.0599396 -.0026993operationa~y -.0339282 .0075193 -4.51 0.000 -.048836 -.0190204 infr -.0092971 .0184382 -0.50 0.615 -.0458527 .0272584 gdp .061747 .0196642 3.14 0.002 .0227608 .1007332 llrs .096419 .0481168 2.00 0.048 .0010227 .1918154 ldr -.0260921 .0097234 -2.68 0.008 -.0453697 -.0068145 dfn .0450594 .0162458 2.77 0.007 .0128505 .0772683 nlta .0585005 .0148363 3.94 0.000 .0290861 .0879148 bnksz 1.792093 .6974872 2.57 0.012 .4092566 3.174929 capadq .1407747 .0313761 4.49 0.000 .0785686 .2029809 roaa Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = 1.0207 R-squared = 0.7022 Prob > F = 0.0000 F( 11, 106) = 21.56Linear regression Number of obs = 118

> iassets, robust. reg roaa capadq bnksz nlta dfn ldr llrs gdp infr operationalefficiency msassets hh

247

Table 6. 10 Robustness test 3 (Rand effects model)


6.4. Testing for time fixed effects

It is quite important to capture the time difference impact on bank performance

since the dataset used in the study differ across different years. Thus, time variables are

created and included in the fixed effects model. Table 6.10 shows the result of the test for

time-fixed effects. This test is required to see if the dummies for all years are equal to

zero. The test fails to reject the null hypothesis that all years coefficients are jointly equal

to zero (p = 0.2764), implying time has no discernible impact on the performance of the

banks.

rho 0 (fraction of variance due to u_i) sigma_e .90168917 sigma_u 0 _cons -19.80833 7.617936 -2.60 0.009 -34.73921 -4.877453 hhiassets .0335293 .02538 1.32 0.186 -.0162147 .0832732 msassets -.0313195 .0144357 -2.17 0.030 -.0596129 -.003026operationa~y -.0339282 .0075193 -4.51 0.000 -.0486658 -.0191906 infr -.0092971 .0184382 -0.50 0.614 -.0454354 .0268411 gdp .061747 .0196642 3.14 0.002 .0232059 .1002881 llrs .096419 .0481168 2.00 0.045 .0021118 .1907263 ldr -.0260921 .0097234 -2.68 0.007 -.0451497 -.0070345 dfn .0450594 .0162458 2.77 0.006 .0132182 .0769006 nlta .0585005 .0148363 3.94 0.000 .0294219 .087579 bnksz 1.792093 .6974872 2.57 0.010 .425043 3.159143 capadq .1407747 .0313761 4.49 0.000 .0792787 .2022707 roaa Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust (Std. Err. adjusted for clustering on typbank)

corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(11) = 237.11


Group variable: typbank Number of groups = 14Random-effects GLS regression Number of obs = 118

> s hhiassets, re robust cluster(). xtreg roaa capadq bnksz nlta dfn ldr llrs gdp infr operationalefficiency msasset

248

Table 6. 11 Testing for time fixed effects


Prob > F = 0.2764 F( 8, 85) = 1.26

Constraint 10 dropped Constraint 9 dropped Constraint 3 dropped (11) _Iyear_2011 = 0 (10) _Iyear_2010 = 0 ( 9) _Iyear_2009 = 0 ( 8) _Iyear_2008 = 0 ( 7) _Iyear_2007 = 0 ( 6) _Iyear_2006 = 0 ( 5) _Iyear_2005 = 0 ( 4) _Iyear_2004 = 0 ( 3) _Iyear_2003 = 0 ( 2) _Iyear_2002 = 0 ( 1) _Iyear_2001 = 0

. testparm _Iyear*

F test that all u_i=0: F(13, 85) = 3.62 Prob > F = 0.0002 rho .67410455 (fraction of variance due to u_i) sigma_e .89184771 sigma_u 1.2826707 _cons -46.51054 12.97444 -3.58 0.001 -72.3072 -20.71389 _Iyear_2011 -.9612928 .6097916 -1.58 0.119 -2.173722 .2511364 _Iyear_2010 (dropped) _Iyear_2009 (dropped) _Iyear_2008 .2777939 .3671868 0.76 0.451 -.4522717 1.00786 _Iyear_2007 .1664787 .4169629 0.40 0.691 -.6625551 .9955126 _Iyear_2006 .6523978 .4951164 1.32 0.191 -.3320263 1.636822 _Iyear_2005 .5645579 .5057215 1.12 0.267 -.440952 1.570068 _Iyear_2004 .1577643 .5362491 0.29 0.769 -.9084426 1.223971 _Iyear_2003 (dropped) _Iyear_2002 -.6769168 .4612688 -1.47 0.146 -1.594043 .2402092 _Iyear_2001 -.1960529 .490832 -0.40 0.691 -1.171958 .7798526 hhiassets .1237881 .0443216 2.79 0.006 .0356649 .2119114 msassets -.0666609 .0518947 -1.28 0.202 -.1698414 .0365195operationa~y -.0435701 .0045152 -9.65 0.000 -.0525475 -.0345927 infr .00029 .0205053 0.01 0.989 -.0404799 .04106 gdp .0010837 .0463512 0.02 0.981 -.091075 .0932424 llrs .058558 .0914601 0.64 0.524 -.1232891 .2404051 lqdty -.0712649 .0137427 -5.19 0.000 -.0985891 -.0439408 dfn .0483647 .0164061 2.95 0.004 .015745 .0809843 nlta .1165133 .027558 4.23 0.000 .0617207 .171306 bnksz 4.212401 1.132321 3.72 0.000 1.961043 6.463758 capadq .2373323 .0313985 7.56 0.000 .1749037 .2997609 roaa Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.6294 Prob > F = 0.0000 F(19,85) = 15.08



i.year _Iyear_2000-2011 (naturally coded; _Iyear_2000 omitted)> assets hhiassets i.year, fe. xi:xtreg roaa capadq bnksz nlta dfn lqdty llrs gdp infr operationalefficiency ms

249

6.5. Conclusion

This chapter has presented the trends in the market structure of Ethiopian banking

sector as well as the empirical results on determinants of financial performance of banks

in Ethiopia. All the measures of concentration used in the study indicate that the

Ethiopian banking sector is highly concentrated though it gets less and less concentrated

over the study period. The findings of the study showed that the performance of the

Banks has been highly capricious with the banks recoding negative profits in the early

periods of the study. The study also revealed that diversification, operational efficiency,

capital adequacy, bank size, loan intensity, market concentration and real GDP growth

rate are significant key factors that influence banks’ profitability in Ethiopia.

Nevertheless, market share, credit risk and annual inflation rate did not have any

significant effect on the banks’ profitability.

Furthermore, the robustness checks indicate that the results presented earlier in this

chapter are to some extent sensitive to relaxations of the assumptions or changes in

regression models. The significance of the variables is very sensitive to pooled OLS.

Overall, credit risk and market share gain a positive significant coefficient under OLS

and the random effects model. With the exception of these two variables, the remaining

variables retain their sign and significance under the fixed effects model, the random

effects model and the OLS regression, reflecting the validity of the findings of the study.

250

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