First stage analysis: Estimation of DEA-Efficiencies

In this section, through using the methodology explained in chapter four, we analyze empirically the efficiency scores of Islamic and conventional banking sectors during the period of study from 2006 to 2012. This section discusses and analyses the results of the efficiency scores (PTE, OTE and SE) which we obtained by applying the DEA approach and the bootstrapping technique. Moreover, it reports the most efficient banking sector operating in the 21 countries.

Section 6.2 investigates the impact of country-specific and bank-specific variables on Islamic and conventional bank performance (i.e. pure technical efficiency). In order to calculate each bank’s efficiency in a given year, we built a “common frontier” by pooling the observations of the 7 years (from 2006 to 2012) rather than building a “year specific” best-practice frontier. By pooling the data across these years, we assumed that, during the period of study, all banks operated in the same environment. However, one may argue that, since the banks operated in different years, the macroeconomic indicators, which existed in those years, could have had an effect on their respective performance. Consequently, in the second stage of this research, we analyze the impact of these environmental variables on efficiency. By creating a pooled frontier, it is possible to measure and compare for each of the years between 2006 and 2012 each banking sector relative to the same frontier by treating each sector as a different entity in each period. Furthermore, a “common frontier” approach can indicate a trend in the efficiency of a banking sector (as single entity); this would be unavailable if we had applied a “year specific frontier” approach was applied. Therefore, over time, the “common frontier” approach provides variations in the banks’ efficiency. We applied this comparison across time by using the same principle as that related to the global frontier in Portela and Thanassoulis (2010). We measured the correlations between the inputs and outputs of the DEA model in order to certify that the inputs and outputs were isotonic. According to Avkiran (2006), a

high correlation is preferable. Table 6.2 shows correlation coefficients between an input and an output pair.

Table 6.1a: Descriptive statistics for the first stage variables' original values (three inputs and two outputs)

Note: All variables are reported in US $ millions. The number of observations in each year are 104 Islamic banks and 95 conventional banks. The number of observations in each year varies because of data availability. DEPSTF: Deposits and Short-term funding, FXASSET: Fixed Assets, PEREXP: Personnel Expenses, NLOANS: Net Loans, NINCOME: Net Income

Table 6.1b: Descriptive statistics for the first stage variables' adjusted positive values (three inputs and two outputs)

Note: A value of USD 757.3 million is added to the net income observations in order to offset negative values and consequently obtain values ≥ 0.

Table 6.2: Correlation coefficient between inputs and outputs

Table 6.2 shows that, for all pairs, the correlation coefficients between an input and an output pair are more than 0.49. The outcomes cannot be considered to be a low correlation and, therefore, it can be claimed that the variables pass the isotonicity test. Tables 6.1, 6.2 and 6.3 respectively report estimated overall and technical efficiency and scale efficiency. These show that, on average, the overall technical efficiency of Islamic Banks (referred hereinafter as IBs) is 0.4016 and ranges from 0.3540 to 0.4460, while the pure technical efficiency averages 0.4330 and ranges from 0.3915 to

Variable Mean Standard Deviation Minimum Maximum Cases Missing values DEPSTF 3655.404 7321.008 0 79761.15 1256 137 FXASSET 62.75741 193.3967 0 2753.051 1250 143 PEREXP 44.64251 80.10163 0.1 691.49 1223 170 NLOANS 2732.374 5148.302 0 45928.96 1227 166 NINCOME 72.39466 214.5395 -757.3 2102.59 1284 109

Variable Mean Standard Deviation Minimum Maximum Cases Missing values DEPSTF 3655.404 7321.008 0 79761.15 1256 137 FXASSET 62.75741 193.3967 0 2753.051 1250 143 PEREXP 44.64251 80.10163 0.1 691.49 1223 170 NLOANS 2732.374 5148.302 0 45928.96 1227 166 NINCOME 829.6947 214.5395 0.0 2859.89 1284 109

Cor.Mat. DEPSTF FXASSET PEREXP NLOANS NINCOME DEPSTF 1.00000 0.63515 0.91315 0.97332 0.48325

FXASSET 0.63515 1.00000 0.68412 0.65825 0.48938

PEREXP 0.91315 0.68412 1.00000 0.91796 0.96843

NLOANS 0.97332 0.65825 0.91796 1.00000 0.54528

0.4778. These estimates suggest that, if the IBs were operating on the most efficient frontier under the CRS and VRS models respectively, the same levels of output can be produced with 40.16% and 43.30% of their current inputs. On the other hand, the overall technical efficiency of conventional banks is, on average, 0.3073 and ranges from 0.2614 to 0.3549, whereas the pure technical efficiency is, on average, 0.4188 and ranges between 0.3889 and 0.4681. These outcomes suggest that, if operating on the efficient frontier, the conventional banks could produce the same levels of output by using 30.73% and 41.88% of their current inputs under CRS and VRS respectively. Based on these results, the IBs show higher average overall technical efficiency (CRS) and pure technical efficiency (VRS) than their conventional counterparts. As mentioned previously, the CRS assumption is justifiable only when all the decision- making units operate at an optimal scale. However, in the real world, often this optimal behaviour is unachievable due to a variety of circumstances such as different types of market power, constraints on finances, externalities, imperfect competition, etc.

The CRS specification, as produced by Charnes, Cooper and Rhodes (1978), yields misleading measures of technical efficiency in the sense that technical efficiency scores, reported under that set of constraints, are biased by scale efficiencies. Therefore, under the VRS assumption, we used pure technical efficiency as the major measure to assess the technical efficiency. However, we conducted the CCR model, run under the CRS assumption, to extract the scale efficiency. The difference between the calculated overall technical efficiency (CRS) and pure technical efficiency (VRS) indicates that the firm has scale inefficiency. Therefore, the calculation of scale efficiency is done by deconstructing overall technical efficiency into pure technical efficiency and scale efficiency; namely, CRS efficiency= VRS efficiency x Scale efficiency. Accordingly, the IBs’ estimated average efficiency scale would be 0.9274 and 0.7335 for their conventional counterparts. The deconstruction of overall efficiency into its pure technical and scale efficiency components seem to suggest that, in the entire Islamic banking sector, pure technical inefficiency outweighs scale inefficiency in determining the overall technical inefficiency. The findings imply that, although overall IBs were operating at a relatively optimal scale of operations, they were managerially inefficient in controlling their operating costs and utilizing to the

fullest their resources in terms of deposits, capital (as Fixed asset) and labour (as personnel expense). Therefore, annually for both Islamic and conventional banks, we attributed bank inefficiency to Pure Technical Efficiency (PTE) rather than to scale inefficiency.

Table 6.2’s results show that, during the entire period of study, the PTE values of IBs and conventional banks were very close. IBs demonstrated slightly higher mean in pure technical efficiency for each year 2006 (0.4778), 2007 (0.4540), 2008 (0.4431), 2009 (0.4221), 2011 (0.3983) and 2012 (0.4442) when compared to conventional banks 2006 (0.4681), 2007 (0.4293), 2008 (0.4219), 2009 (0.3917), 2011 (0.3889) and 2012 (0.4389). However, in 2010, the conventional banks showed a slightly higher PTE of 0.3926 as compared to the IBs’ 0.3915. As noted, the IBs’ PTE scores followed a declining trend from 2006 to 2010. The IBs’ worst year was 2010 when the PTE was at lowest level scoring 39.15 % and was slightly below the conventional banks' average PTE of 39.26 %. During the 2008 financial crisis, the gap between the Islamic and conventional banks' PTE widened since the impact of the first wave of the world financial crisis (2007–2008) hit the conventional banks severely because they were exposed directly to subprime mortgage portfolios (Toxic assets). For instance, in 2007−08, the Gulf International Bank (GIB, a Bahraini wholesale CB) incurred losses of about US$1.3 billion in securities investments in debt-based toxic assets (mortgage backed collateralized debt obligations) and in American banks such as Lehman Brothers. The GIB’s shareholders injected US$1 billion of new capital and bought toxic asset-backed securities worth $4.8 billion.

The Arab Banking Corporation (a Bahraini wholesale CB) incurred losses of $1.2 billion due to similar investments and its shareholders injected $1 billion of new capital. In addition, the Gulf Bank (a Kuwaiti CB) incurred losses of $1.4 billion due mainly to derivatives activities, with the bank‘s shareholders and the Kuwait Investment Authority injecting an equivalent amount of capital. In 2008, the National Commercial Bank (NCB), the largest Saudi conventional bank, lost more than one billion riyal on changes in fair value for financial instruments (Hassan and Dridi, 2010). In contrast, when compared to their conventional counterparts, IBs wereprohibited from dealing or trading in derivatives. Therefore, this made them

relatively resilient to the impact of the first wave of the global crisis (2007-2008). Consequently, the conventional banks' PTE declined to attain a lowest level of 39.17%. However, these banks were able to restrain the second round effects of the global crisis (economic wave), and showed a stable PTE trend during 2010 (39.26%) and 2011 (38.89%) followed by a significant recovery with, on average, PTE increasing to 43.89%. However, many IBs, which are financed by way of Murabahah, determine their profit or mark-up on the basis of the current interest rate; they use mostly LIBOR (London Inter-bank offered rate) as the criterion (Usmani, 2007). No doubt, it cannot be considered to be desirable to use the rate of interest to determine a halal profit. Certainly, at least in appearance, it makes the transaction resemble an interest-based financial item and, keeping in view the severity of prohibition of interest, even this apparent resemblance should be avoided as far as possible. However, one should not ignore the fact that the most important requirement for validity of Murabahah is that it is a genuine sale with all its ingredients and necessary consequences. If a Murabahah transaction fulfils all the conditions of

Sharia'compliance, merely using the interest rate as a benchmark for determining the profit of Murabahah does not render the transaction to be either invalid, haram or prohibited. This is because the deal itself does not contain interest. During the global crisis, the cost of funds (interest rate) increased due to liquidity's scarcity and default risk (vulnerability). For these reasons, Islamic banks were entitled to a higher payment obligation towards investors; in other words, they needed to pay a higher profit share ratio to fund suppliers. Consequently, the IBs’ PTE declined slightly from 44.31% (2008) to 42.21% (2009).

IBs had shown more resilience to the direct subprime exposure (Financial wave). However, starting from 2009, they were subject to the second round effect of the global crisis, the so-called economic wave, (The Economist, 2009; El-Said and Ziemba, 2009). IBs were unaffected since the global financial crisis originated from sub-prime mortgage portfolios that were spun off into securitized instruments and these were offered subsequently as investments. This was because Islamic finance was based on a close link between financial and productive flows. However, the protacted duration of the financial crisis effected IBs because, rather than these

institutions being exposed directly to derivative instruments, Islamic banking contracts were based on asset-backed transactions. With the downturn in the global economy, there was a decline in the property markets of a number of countries where IBs had a significant presence. This carries negative implications for these banks since a large number of contracts are backed by real estate and property as collateral. In such a situation, there was increased credit risk due to the erosion in the value of the collateral and especially in highly leveraged countries, like the UAE (Dubai) and Qatar, where a large share of financing was channeled to the once-booming real estate market (Hasan and Dridi, 2010).

The findings imply that, when compared to conventional banks, IBs showed significant higher mean Overall Technical Efficiency (OTE) and Scale Efficiency (SE) previous to, during and post-crisis. On the other hand, the Islamic and conventional banks’ PTE scores demonstrated insignificant difference since their values were very close and showed similar trends over the years. This is in line with conclusions from previous studies derived using SFA and DEA (Bader, 2008; Hassan et al., 2009; Grigorian and Manole, 2005; Belanes and Hassiki, 2012).

Table 6.3 shows descriptive statistics for different regions and countries based on total assets (in millions of USD) for 7 years from 2006 to 2012. Please see Appendix 15 for the number of year observations for each country and each year.

Figure 6.1

:

Table 6.3: Average Overall Technical Efficiency (OTE) for Islamic and conventional banks Average Overall Technical Efficiency

Year Islamic Banks Conventional Banks

2006 0.4460 0.3549 2007 0.4157 0.3430 2008 0.4338 0.3202 2009 0.4007 0.3048 2010 0.3726 0.2822 2011 0.3540 0.2614 2012 0.3882 0.2848 Mean 0.4016 0.3073 0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 2006 2007 2008 2009 2010 2011 2012

Overall Technical Efficiency

146

Figure 6.2

:

Table 6.4:Average Pure Technical Efficiency (PTE) for Islamic and conventional banks

Average Pure Technical Efficiency

Year Islamic Banks Conventional Banks

2006 0.4778 0.4681 2007 0.4540 0.4293 2008 0.4431 0.4219 2009 0.4221 0.3917 2010 0.3915 0.3926 2011 0.3983 0.3889 2012 0.4442 0.4389 Mean 0.4330 0.4188 0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000 2006 2007 2008 2009 2010 2011 2012

Pure Technical Efficiency

147

Figure 6.3

:

Table 6.5:Average Scale Efficiency (SE) for Islamic and conventional banks

Average Scale Efficiency

Year Islamic Banks Conventional Banks

2006 0.9334 0.7582 2007 0.9156 0.7988 2008 0.9791 0.7590 2009 0.9492 0.7784 2010 0.9518 0.7190 2011 0.8888 0.6723 2012 0.8738 0.6488 Mean 0.9274 0.7335 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 2006 2007 2008 2009 2010 2011 2012

Scale Efficiency

148