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Table 2 Estimated regression results of the learning model (Eq. 12)

From: Impact of learning through credit and value creation on the efficiency of Japanese commercial banks

  (1)
EVA
(2)
FISIM (GO)
(3)
Credit (tloans)
(4)
Secinv
(5)
TI
Panel A: Full Sample      
\(ln{w}_{p}\) 1.1432** 0.1157** 0.3080*** 0.3886*** 0.2398***
  (0.5014) (0.0485) (0.0365) (0.1071) (0.0585)
\(ln{w}_{d}\) 0.5630*** 0.0795 0.1145*** 0.1173* 0.1258***
  (0.1988) (0.0598) (0.0389) (0.0684) (0.0442)
\(ln{w}_{k}\) − 0.2209 0.0815 0.0730 0.0597 0.0705
  (0.3734) (0.0946) (0.0929) (0.1110) (0.0976)
\(lnNPA\) − 0.1623 − 0.0095 0.0254 − 0.0164 0.0338
  (0.1817) (0.0124) (0.0333) (0.0817) (0.0456)
\({\varnothing }_{it-1}\) 0.1033** − 0.4050*** − 0.3910*** − 0.6314*** − 0.3841***
  (0.0491) (0.0609) (0.0357) (0.0357) (0.0349)
\(\_Con\) 2.2048 6.5630*** 1.8128* 5.8399*** 1.6899
  (3.5557) (0.8573) (0.9951) (1.6061) (1.1574)
\(Obs\) 1687 1687 1687 1687 1687
\(R\_square\) 0.1841 0.5453 0.6336 0.6822 0.6911
\(F\) 16.1579 70.1076 58.9292 121.3465 79.2705
\(p\) 0.0000 0.0000 0.0000 0.0000 0.0000
\(\boldsymbol{r}\boldsymbol{m}\boldsymbol{s}\boldsymbol{e}\) 1.0850 0.1147 0.0954 0.1562 0.0957
Panel B: City Banks      
\(ln{w}_{p}\) 1.1972* − 0.1342 0.5235** 0.6591*** 0.5378***
  (0.4494) (0.1899) (0.1323) (0.0975) (0.0943)
\(ln{w}_{d}\) 0.1521 0.4139** 0.3010*** 0.4229*** 0.3643***
  (0.1146) (0.1094) (0.0167) (0.0486) (0.0186)
\(ln{w}_{k}\) 0.1167 − 0.6238** − 0.4587** − 0.5941** − 0.5551***
  (1.1646) (0.1661) (0.1541) (0.1434) (0.1205)
\(lnNPA\) 0.3705 0.1319* 0.0487 − 0.0631 0.0217
  (0.4150) (0.0595) (0.0299) (0.1094) (0.0616)
\({\varnothing }_{it-1}\) 0.3070** − 0.4488* − 0.0098 − 0.3748*** − 0.0975**
  (0.0674) (0.1648) (0.0328) (0.0528) (0.0253)
\(\_Con\) − 11.0532 5.7353 − 5.5345** 2.7306 − 3.9304*
  (10.3760) (3.2280) (1.7063) (1.8650) (1.6947)
\(Obs\) 74 74 74 74 74
\(R\_square\) 0.6145 0.4830 0.8563 0.8272 0.8548
\(F\) 14.9797 23.5488 21.9727 14.9797 127.8709
\(p\) 0.0000 0.0000 0.0000 0.0000 0.0000
\(\boldsymbol{r}\boldsymbol{m}\boldsymbol{s}\boldsymbol{e}\) 0.4101 0.2494 0.0723 0.1536 0.0870
Panel A: Regional Bank I      
\(ln{w}_{p}\) 0.9437 0.2676*** 0.3611*** 0.4180*** 0.2892***
  (0.6065) (0.0400) (0.0432) (0.1259) (0.0616)
\(ln{w}_{d}\) 0.5708*** 0.0466 0.1091** 0.1154 0.1243**
  (0.2008) (0.0531) (0.0426) (0.0710) (0.0472)
\(ln{w}_{k}\) − 0.0950 0.0895 0.0804 0.0681 0.0782
  (0.3064) (0.0886) (0.0893) (0.1049) (0.0938)
\(lnNPA\) − 0.0878 − 0.0095 0.0147 − 0.0053 0.0300
  (0.2304) (0.0129) (0.0366) (0.0911) (0.0511)
\({\varnothing }_{it-1}\) 0.1171** − 0.4278*** − 0.4435*** − 0.5837*** − 0.3957***
  (0.0505) (0.1825) (0.0408) (0.0473) (0.0400)
\(\_Con\) 2.2965 6.8054*** 2.5769** 5.0590*** 1.8071
  (4.2180) (2.3168) (1.0362) (1.8960) (1.2762)
\(Obs\) 999 999 999 999 999
\(R\_square\) 0.1925 0.5964 0.6557 0.6945 0.7059
\(F\) 11.0512 61.5595 48.3803 109.2990 63.5233
\(p\) 0.0000 0.0000 0.0000 0.0000 0.0000
\(\boldsymbol{r}\boldsymbol{m}\boldsymbol{s}\boldsymbol{e}\) 1.1277 0.1027 0.0981 0.1481 0.0971
Panel A: Regional Bank II      
\(ln{w}_{p}\) 1.3381** 0.2253*** 0.1478*** 0.2893* 0.0644
  (0.6588) (0.0395) (0.0344) (0.1664) (0.0627)
\(ln{w}_{d}\) 0.7009** 0.0388 0.0998** 0.0993 0.1053**
  (0.2908) (0.0441) (0.0431) (0.0706) (0.0485)
\(ln{w}_{k}\) − 0.7036 0.0858 0.0636 0.0550 0.0632
  (0.7483) (0.0958) (0.0992) (0.1188) (0.1026)
\(lnNPA\) − 0.3441** − 0.0174 0.0325 − 0.0223 0.0392
  (0.1411) (0.0153) (0.0274) (0.0749) (0.0386)
\({\varnothing }_{it-1}\) 0.0798 − 0.2411*** − 0.4115*** − 0.7257*** − 0.4315***
  (0.0717) (0.0426) (0.0506) (0.0279) (0.0511)
\(\_Con\) − 0.0217 3.6700*** − 0.0308 4.7133*** − 1.9340**
  (2.6294) (0.6625) (0.9250) (1.3106) (0.9227)
\(Obs\) 614 614 614 614 614
\(R\_square\) 0.2163 0.6437 0.6663 0.7215 0.7241
\(F\) 16.4636 49.2820 57.9316 57.8995 63.2943
\(p\) 0.0000 0.0000 0.0000 0.0000 0.0000
\(\boldsymbol{r}\boldsymbol{m}\boldsymbol{s}\boldsymbol{e}\) 1.0539 0.0989 0.0827 0.1538 0.0843
  1. Driscoll–Kraay standard errors in parenthesis.
  2. *** p < 0.01, ** p < 0.05, * p < 0.1
  3. \(\text{EVA} \,\sim \,\text{economic value added,}\)
  4. FISIM(GO)\(\sim \,\text{Financia Intermediation Services Indirectly Measured (Gross Output)}\)
  5. \(\text{secinv}\,\sim\, \text{tota security investment},\)
  6. \(\text{tloans}\,\sim\, \text{total loans},\)
  7. \(\text{TI} \,\sim \,\text{Total Investment}\)
  8. \({\varnothing }_{it-1}\sim \,\text{learning elasticity}\)