Principales características y condiciones del Acuerdo de Alcance Parcial de

In addition to our previous pooled-model analysis, we conducted five regression analyses for four different regions (MENA, East Asia and Pacific, South Asia, and EU and Central Asia) and one pooled region (MENA, East Asia and Pacific, South Asia). Appendices 11, 12, 13 and 14 present four RE models that include, individually, the former four-region dummy variables in order to determine the drivers of PTE. Moreover, Appendix 10 shows the four region dummy variables pooled into a single RE in order to estimate the determinants of PTE.

6.2.1.1. MENA Model

Similar to our previous findings, three bank- and one country-level variables exhibit positive relationships to the PTE. The coefficient on the capitalization variable (EQ/TA) is positive and statistically significant at the 1% level. This indicates that well capitalized banks are more efficient. This result is in line with previous studies (Berger and Mester, 1997; Isik and Hassan, 2003; Berger and Bonaccorsi di Patti, 2006; Reda and Isik, 2006; Naceur, 2011). Moreover, the coefficient on ratio of Net Loans to Total Assets (NT/TA) is both positive and significant at the 1% level of

significance; this is consistent with the findings of Fuentes and Vergara, 2003, and Carvallo and Kasman, 2005.

Table 6.13: Determinants of PTE of MENA

VRS500 Coefficient Standard _Error z _|z|>Z*Prob 95% Confidence_Interval

ISMDUM -0.04256 0.04921 -0.86 0.3871 -0.139 0.05388 ISFCRIS 0.0358 0.03426 1.04 0.296 -0.03134 0.10294 FCRISIS -0.01494 0.02361 -0.63 0.5268 -0.06121 0.03133 NL_TA .00408*** 0.00099 4.12 0 0.00214 0.00602 LLP_GL .27696D-04 0.4156D-04 0.67 -0.5051 0.53754D-04 0.10914D-03 EQ_TA .00633*** 0.00135 4.7 0 0.00369 0.00897 SIZERT .08499*** 0.01652 5.14 0 0.05261 0.11737 RES_YPC -0.0041 0.01804 -0.23 0.8204 -0.03946 0.03126 YGR -0.00317 0.00349 -0.91 0.3635 -0.01002 0.00367 HHI 0.51486 0.41434 1.24 0.214 -0.29722 1.32694 INFL 0.00094 0.00121 0.77 0.4386 -0.00143 0.0033 MKTCY 0.00121*** 0.00033 3.67 0.0002 0.00056 0.00185 VACC 0.00018 0.00197 0.09 0.9267 -0.00368 0.00404 REGQ -0.00131 0.00147 -0.89 0.3713 -0.00419 0.00156 Constant -0.62221*** 0.16238 -3.83 0.0001 -0.94046 -0.30396 ***, **, *==> Significance at 1%, 5%, 10% level 777 observations

Random Effects Model: v(i,t) = e(i,t) + u(i) Estimates: Var[e] = 0.021056 SD.[e] = 0.145107 Var[u] = 0.030502 SD.[u] = 0 .174648 Corr[v(i,t),v(i,s)] = 0.591605 Sum of Squares 17.0256 R-squared 0.332510 [1 degrees of freedom, prob. Value = 0.000000]

Note: We estimated the model by using bank random effects; standard errors are heteroscedasticity adjusted.

Since the higher figures of the ratio denote lower liquidity (level of liquid assets held by the bank), the results imply that the relatively less (more) liquid banks tend to exhibit higher (lower) efficiency levels. The results show, also, that the relationship between PTE and bank size (SIZERT) is positive (and statistically significant at the 1% level). The result is consistent with the findings of Akhavein et al. (1997), Cornett et al. (2006), Al-Sharkas et al. (2008), Olson and Zoubi (2011), Sufian et al. (2012) which concluded that the larger the total assets, the greater the efficiency. The large banks can take advantage of economies of scale by sharing costs in the production process. At the country-level, the market capitalization variable (MKTCY) relates both positively and significantly related to pure technical efficiency at the 1% level of significance level. This is in line with the findings of Demirguc-Kunt and Levine (1996), Beck et al. (2000), and Dietsch and Lozano-Vivas (2000) which suggest that the stock market acts as a complement to, rather than substitute to potential borrowers (banking sector).

6.2.1.2 East Asia and Pacific Model

Similar to previous findings, the PTE of East Asia and Pacific region show positive relationships with three bank-level variables (i.e. Net loans-to-Total Assets ratio (NL/TA), Equity-to-Total Assets ratio (EQ/TA), and Bank Size (SIZERT)). However, in addition to these former variables, a particular variable, namely the Islamic Banks- During Crisis dummy (ISFCRIS), shows both a positive and significant influence on the pure technical efficiency of East Asia and Pacific banks. This suggests that, in the countries of East Asia and the Pacific region, the Islamic banks showed resilience towards the crisis (2007-2009). Moreover, (by 0.07) the IBs are more efficient than conventional banks. This outperformance is explained by the fact that Islamic banks transactions and instruments may comply with Shari’ah law, and are well regulated and supervised by responsible financial authorities.

Table 6.14: Determinants of PTE of East Asia and Pacific countries

VRS500 Coefficient Standard _Error z _|z|>Z*Prob 95% Confidence_Interval

ISMDUM -0.11625 0.07575 -1.53 0.1249 -0.26472 0.03221 ISFCRIS .07371** 0.0329 2.24 0.0251 0.00922 0.13819 FCRISIS -0.04292 0.03067 -1.4 0.1616 -0.10303 0.01718 NL_TA .00223** 0.00098 2.27 0.0231 0.0003 0.00415 LLP_GL 0.00096 0.00303 0.32 0.7511 -0.00497 0.00689 EQ_TA .00758*** 0.00167 4.53 0 0.0043 0.01087 SIZERT .07149*** 0.0249 2.87 0.0041 0.02269 0.12028 RES_YPC 0.03861 0.02749 1.4 0.1601 -0.01526 0.09249 YGR 0.00051 0.0062 0.08 0.934 -0.01163 0.01266 HHI -0.50074 1.36598 -0.37 0.7139 -3.17802 2.17653 INFL -0.0004 0.00352 -0.11 0.9102 -0.00729 0.0065 MKTCY -0.00014 0.00037 -0.38 0.7076 -0.00087 0.00059 VACC -0.00275 0.00432 -0.64 0.5245 -0.01122 0.00572 REGQ 0.00244 0.00252 0.97 0.3339 -0.00251 0.00738 Constant -0.16227 0.35813 -0.45 0.6505 -0.86421 0.53966 ***, **, *==> Significance at 1%, 5%, 10% level 315 observations

Random Effects Model: v(i,t) = e(i,t) + u(i) Estimates: Var[e] = 0.015392 SD.[e] = 0.124065 Var[u] = 0.054454 SD.[u] = 0.233354 Corr[v(i,t),v(i,s)] = 0.779628 Sum of Squares 0.395607E+08 R-squared 0.098889 [ 1 degrees of freedom, prob. value = 0.000000]

6.2.1.3 South Asia Model

In the South Asia region, Bank Size (SIZERT) was the only explanatory variables that had a significant effect on the banks’ PTE. This outcome may suggest that the countries of South Asia region are considered to be poor and underdeveloped and that they encounter adverse socioeconomic conditions. Unlike previous findings, SIZERT exhibits a negative relationship with PTE. This is in line with the findings of Berger and Hannan (1994), Beck et al. (2013) and Johnes et al. (2014). This result might be explained by the fact that larger banks may tend to be less efficient due to their managements seeking quiet lives by pursuing other objectives or by maintaining the advantages which their market power produces (Berger and Hannan, 1994).

Table 6.15: Determinants of PTE of South Asia countries

VRS500 Coefficient Standard _Error z _|z|>Z*Prob 95% Confidence_Interval

ISMDUM 0.06204 0.06531 0.95 0.3422 -0.06596 0.19004 ISFCRIS -0.02679 0.02443 -1.1 0.2727 -0.07467 0.02108 FCRISIS -0.00704 0.02118 -0.33 0.7398 -0.04855 0.03448 NL_TA 0.00129 0.00105 1.23 0.2185 -0.00076 0.00334 LLP_GL 0.0016 0.00125 1.29 0.198 -0.00084 0.00405 EQ_TA 0.00119 0.00103 1.15 0.2497 -0.00084 0.00321 SIZERT -.08538*** 0.0163 -5.24 0 -0.11733 -0.05343 RES_YPC -0.0312 0.08996 -0.35 0.7287 -0.20753 0.14512 YGR 0.00768 0.00992 0.77 0.4389 -0.01177 0.02713 HHI -0.124 0.48215 -0.26 0.797 -1.06899 0.82099 INFL -0.00048 0.00304 -0.16 0.8735 -0.00645 0.00548 MKTCY 0.00021 0.00185 0.11 0.9111 -0.00343 0.00384 VACC 0.00035 0.00356 0.1 0.9223 -0.00664 0.00733 REGQ -0.00436 0.00972 -0.45 0.6534 -0.02341 0.01468 Constant .78845*** 0.24144 3.27 0.0011 0.31524 1.26165 ***, **, *==> Significance at 1%, 5%, 10% level 182 observations

Random Effects Model: v(i,t) = e(i,t) + u(i) Estimates: Var[e] = 0.004937 SD.[e] = 0.070266 Var[u] = 0.023552 SD.[u] = 0.153465 Corr[v(i,t),v(i,s)] = 0.826694 Sum of Squares 4.97527 R-squared 0.415404 [ 1 degrees of freedom, prob. value = 0.000000]

Note: We estimated the model by using bank random effects; standard errors are heteroscedasticity adjusted.

6.2.1.4 EU and Central Asia Model

Similar to previous findings, Bank Size (SIZERT), Equity-to-Total Assets ratio (EQ/TA), and the Herfindahl–Hirschman Index (HHI) are the three explanatory variables which have a significant effect on the PTE, the former at the 1% level of

significance and the latter two latter at the 5% level of significance. These three variables exhibit positive relationship with the PTE. This was in line with the findings of Olson and Zoubi (2011) and Figueira et al. (2009) who reported both a positive and statistically significant relationship between efficiency and concentration (HHI); Berger and Mester (1997), Isik and Hassan (2003), Berger and Bonaccorsi di Patti (2006), Reda and Isik (2006) and Naceur (2011) who presented both a positive significant relationship between efficiency and Capitalization (EQ/TA); and Akhavein et al. (1997), Cornett et al. (2006), Al-Sharkas et al. (2008), Olson and Zoubi (2011), Sufian et al. (2012)- who concluded that the larger the total assets (SIZERT) the greater the efficiency.

Table 6.16: Determinants of PTE of EU and Central Asia countries

VRS500 Coefficient Standard _Error z _|z|>Z*Prob 95% Confidence_Interval

ISMDUM 0.0006 0.0552 0.01 0.9913 -0.10759 0.1088 ISFCRIS -0.00216 0.03671 -0.06 0.9531 -0.0741 0.06979 FCRISIS -0.03646 0.03432 -1.06 0.2882 -0.10373 0.03082 NL_TA 0.00202 0.00124 1.63 0.1025 -0.00041 0.00445 LLP_GL .00511* 0.00305 1.68 0.0935 -0.00086 0.01109 EQ_TA .00335** 0.00138 2.42 0.0156 0.00063 0.00606 SIZERT .11626*** 0.03125 3.72 0.0002 0.05502 0.1775 RES_YPC -0.0471 0.0393 -1.2 0.2307 -0.12412 0.02993 YGR 0.00371 0.00343 1.08 0.2801 -0.00302 0.01043 HHI .98305** 0.47871 2.05 0.04 0.04481 1.9213 INFL -0.00576 0.0053 -1.09 0.2769 -0.01615 0.00462 MKTCY -.00131** 0.00057 -2.28 0.0227 -0.00243 -0.00018 VACC .01805*** 0.00352 5.12 0 0.01115 0.02496 REGQ -.01141*** 0.00374 -3.05 0.0023 -0.01875 -0.00408 Constant -.96813*** 0.36658 -2.64 0.0083 -1.68661 -0.24966 ***, **, *==> Significance at 1%, 5%, 10% level 119 observations

Random Effects Model: v(i,t) = e(i,t) + u(i) Estimates: Var[e] = 0.004868 SD.[e] = 0.069772 Var[u] = 0.007440 SD.[u] = 0.086257 Corr[v(i,t),v(i,s)] = 0.604489 Sum of Squares 0.802277 R-squared 0.504480 [ 1 degrees of freedom, prob. value = 0.000000]

Note: We estimated the model by using bank random effects; standard errors are heteroscedasticity adjusted.

On the other hand, three macroeconomic (country-level) variables (voice and accountability, regulation quality, and market capitalization) show different significant relationships with PTE. Relative to our previous findings- at the 1% level of significance level- the former (VACC) exhibit both a negative and statistically significant influence on the pure technical efficiency of EU and Central Asia banks whereas the latter two variables (REGQ) and (MKTCY) show a positive relationship with the PTE at the 1% and 5% levels of significance respectively. Unlike our

previous findings, VACC suggests that more developed and democratic systems are conducive to the more efficient operations of financial institutions. It implies, also, that the capacity of government to formulate effectively, implement sound policies, and promote socially desirable investments can enhance efficiency in the industry and the welfare of the economy. Despite our previous outcomes, the negative relationship between REGQ and PTE may be explained by the fact that banks’ greater independence from government control allows the bank boards to be accountable to their shareholders while limited financial freedom can distort the incentives of bankers’ boards accountable to government bodies and which strive to meet particular government imposed regulations. Moreover, limited financial freedom may encourage financial institutions to create opaque new instruments and miscalculate risk. In addition, the banking sector and the capital market are complementary and government imposed regulations may have a negative impact in the case of competition between them. As for Market capitalization variable, the result shows that higher levels of market capitalization lead to lower PTE of EU and Central Asia banks; this suggests that that there is competition between the banking sector and the capital market (stock market) . This is in line with the findings of Johnes et al. (2014). In addition to the previous findings’ variables, the ratio of LLP/Total Loans shows significant positive influence on the PTE at 10% significance level. In this case, the higher the level of reserves (and, hence, the greater degree of protection for the bank from bad loans) provides more PTE. This suggests that banks, which behave prudently in terms of insuring against bad loans, reap rewards in terms of PTE.

6.2.1.5 MENA-East Asia and Pacific-South Asia Region Model

Similar to our previous findings, three bank-level (i.e. Net loans-to-Total Assets ratio (NL/TA), Equity-to-Total Assets ratio (EQ/TA), and Bank Size (SIZERT)) and three country-level variables (i.e. the Herfindahl–Hirschman Index (HHI), Market Capitalization (MKT) and Voice and Accountability (VACC)) exhibit significant relationships to pure technical efficiency. The coefficient on ratio of Net Loans to Total Assets (NT/TA) is both positive and significant at the 1% level; this is consistent with the findings of Fuentes and Vergara, 2003, and Carvallo and Kasman, 2005. Since the higher figures denote lower liquidity (level of liquid assets held by the bank), the results imply that the relatively less (more) liquid banks tend to exhibit

higher (lower) efficiency levels. Moreover, the coefficient on the capitalization variable (EQ/TA) is both positive and statistically significant at the 1% level; this indicates that well capitalized banks are more efficient. This result is in line with previous studies (Berger and Mester, 1997; Isik and Hassan, 2003; Berger and Bonaccorsi di Patti, 2006; Reda and Isik, 2006; Naceur, 2011).

Table 6.17: Determinants of PTE of MENA-East Asia and Pacific-South Asia region VRS500 Coefficient Standard _Error z _|z|>Z*Prob 95% Confidence_Interval

ISMDUM -0.04958 0.04246 -1.17 0.2429 -0.13279 0.03364 ISFCRIS 0.03194 0.01999 1.6 0.1101 -0.00725 0.07112 FCRISIS -0.01678 0.01347 -1.25 0.2129 -0.04318 0.00962 NL_TA .00310*** 0.00064 4.83 0 0.00184 0.00436 LLP_GL _{-.21397D-04 .4689D-04 -0.46 -0.6482 .11330D-03 .70508D-04} EQ_TA .00571*** 0.00086 6.62 0 0.00402 0.0074 SIZERT .04020*** 0.01282 3.14 0.0017 0.01508 0.06533 RES_YPC 0.00223 0.01058 0.21 0.8331 -0.0185 0.02296 YGR -.00551** 0.00228 -2.42 0.0156 -0.00997 -0.00104 HHI .58028*** 0.19376 2.99 0.0027 0.20051 0.96005 INFL .00160* 0.0009 1.78 0.0755 -0.00016 0.00337 MKTCY .00043** 0.00017 2.54 0.0112 0.0001 0.00077 VACC -.00318** 0.00139 -2.28 0.0227 -0.00044 -0.00591 REGQ 0.0013 0.00101 1.29 0.1983 -0.00068 0.00329 Constant -.32212*** 0.11827 -2.72 0.0065 -0.55393 -0.09031 ***, **, *==> Significance at 1%, 5%, 10% level 1274 observations

Random Effects Model: v(i,t) = e(i,t) + u(i) Estimates: Var[e] = 0.016560 SD.[e] = 0.128685 Var[u] = 0.055247 SD.[u] = 0.235047 Corr[v(i,t),v(i,s)] = 0.769384 Sum of Squares 0.242987E+09 R-squared -0.226190

[1 degrees of freedom, prob. value = 0.000000]

Note: We estimated the model by using bank random effects; standard errors are heteroscedasticity adjusted.

Also, the results show that the relationship between PTE and bank size (SIZERT) is positive (statistically significant at the 1% level). The result is consistent with the findings of Akhavein et al. (1997), Cornett et al. (2006), Al-Sharkas et al. (2008), Olson and Zoubi (2011), Sufian et al. (2012); they concluded that the larger the total assets, the higher the efficiency. The large banks can take advantage of economies of scale by sharing costs in the production process. At the country-level, the market capitalization variable (MKTCY) is related both positively and significantly to PTE at the 5% level of significance. This is in line with the findings of Demirguc-Kunt and Levine (1996), Beck et al. (2000), and Dietsch and Lozano-Vivas (2000); they suggest that the stock market acts as a complement to rather than substitute to potential borrowers (banking sector). The HHI relates positively and significantly to

pure technical efficiency at the 10% level of significance. This supports the efficient structure theory, which states that the most efficient firms will be able to increase their market share, resulting in higher concentration. This is in line with the findings of Figueira et al. (2009) and Olson and Zoubi (2011). Regarding the relevance of Voice and Accountability (VACC), the findings show that, at the 5% level of significance, a higher level of media independence has a negative influence on pure technical efficiency. This outcome may be justified by the fact that effective supervision on media may prevent negative rumors, regardless of their validity, which can damage a bank’s reputation and can have a negative effect on the investors’ (lenders or depositors) sentiments and result in a run on the bank and deterioration in its performance. Thus, a higher level of effective supervision on media independence promotes a bank’s PTE. This is consistent with the findings of Asongu and Nwachukwu (2015).

In addition to the previous findings’ variables, three macroeconomic (country-level) variables show significance in the PTE equations. Firstly, the coefficient of the Growth in real GDP (YGR) variable shows a negative sign (statistically significant at the 5% level). This suggests that, under expansive demand conditions, banks may feel less pressure to control their inputs and, thus, become less efficient. This is in line with Pasiouras (2008) who found a negative relationship between the growth of GDP and efficiency. Secondly, at the 10% level of significance, inflation shows both a significant and positive influence on PTE. This suggests that a full anticipation of the rate of inflation may raise profits since banks can appropriately adjust interest rates to increase revenues. This is consistent with the findings of Bourke (1989), Molyneux and Thornton (1992), Demirguc-Kunt and Huizinga (1999).