This paper has presented some evidence on the impact of managers on cost efficiency. The sample consists of 1854 observations on 309 savings and cooperative banks in 1999–2004. Each observation is assigned an efficiency estimate by running Battese and Coelli (1995) stochastic frontier translog cost function estimations. Bank output is defined according to the production approach.
There is strong statistical evidence on the impact of age and education on performance in cost minimisation, but these effects seem to be rather complicated.
In very small banks a vocational level qualification in business administration
seems to be the best education, in somewhat larger banks a university degree in business administration or economics is the best educational background. If an old manager retires, a significant cost efficiency improvement typically follows.
Other manager changes also affect efficiency, but both improvements and deteriorations are equally possible. The optimal age of a managing director depends on the educational background.
The results were not obtained in controlled experiments. Underlying manager selection and self-selection processes have probably been very complicated. Most mature managers of the sample have been recruited by the industry decades ago.
Banking groups’ recruitment policies and the attractiveness of the banking industry among young graduates may have varied, implying that if two persons are born different years but have the same education, they are not equally likely to have been employed by banks. Employees who have left cooperative and savings banks are probably no random draw either. Managers with vocational level qualifications in business administration may appear successful because the most skilled of them may have been promoted to management. Instead of being promoted, the best lawyers recruited by these banks may have been offered higher paying positions in major commercial banks. This hypothetical adverse selection would be able to create the impression that mature lawyers seldom make good managers. It is easy to present a very large number of these kinds of hypothetical selection effects. They may cause complicated statistical dependencies between age, education and various unobserved personality characteristics.
Some hypotheses of this kind can be tested, and some of the results may be worth brief reviewing. One might suggest that the sample has been partly selected by boards’ practice to lay off poor performers. If, for instance, university graduates are tolerated even if they perform poorly whereas inefficient managers without university degree are laid off, university graduates’ performance may appear poor. These kinds of hypotheses, however, are not corroborated by the data. In logit estimations with the full sample of 309 banks there seemed to be no evidence at all in favour of the hypothesis that lagged bank efficiency (in levels) would predict non-retirement manager changes. Interactions of efficiency and manager education seem to be equally irrelevant. (See Appendix 2) Managers may be laid off because of poor performance, and boards’ propensity to lay them off may correlate with observable manager characteristics, but best performers may leave equally systematically because of other reasons, making it impossible to detect the effect. As already mentioned, manager changes seemed to pass a simple endogeneity test in section 5.2. The results of a few failed experiments may be worth mentioning. In the sub-sample used in section 5.2, future manager changes did not explain efficiency changes in panel estimations, implying that the causality probably runs from manager changes to efficiency changes rather than in the opposite direction. Moreover, attempts to explain the age and education of entering new managers by past efficiency proved equally unsuccessful.
In addition to differences in skills and efforts, we may have measured differences in objectives. Some managers may not try to minimise costs because they are unwilling to lay off loyal employees in high unemployment areas. Some managers may pursue social status by hiring as many subordinates as possible. It is difficult to test whether these kinds of phenomena are present in the sample.
Because hardly any research on the impact of bank manager characteristics on cost efficiency has been published, there are many open questions for further research. The data does not contain much explicit information on experience.
Banking groups have internal training programmes, and their impact on efficiency could be estimated. Psychometric test results on cognitive skills and other personality characteristics might also be available, and they might have predictive power as efficiency determinants. Testing these kinds of effects would be relatively simple if suitable data were available.
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Appendix 1
Let us assume the price of the factor 1 in a given geographic area is g1 times the reference area price, and the price of the factor 2 is g2 times the reference area price. The relative prices in the equation to be estimated can be decomposed into reference area relative prices and a region specific effect captured by the simple dummy variable.
βnLn {g1p1/(g2p2)} = βnLn (p1/p2) + βnLn(g1/g2)
If relative input prices in the reference area differ from the national average reported in aggregate statistics, the coefficient βn captures this difference. The value of Ln(g1/g2) is unknown, but the coefficient of the geographic dummy variable adjusts accordingly.
As to the interaction between factor prices and outputs, the situation is relatively straightforward; the interactions of geographic factors and outputs must be included in the deterministic kernel.
βm Ln (g1p1/g2p2) Ln (Qi) = βmLn (p1/p2) Ln (Qi) + βmLn(g1/g2) Ln (Qi)
The logarithmic price squared can be decomposed into the squared logarithmic reference area relative price, the logarithmic reference area price and factors affecting the region specific dummy variable.
βv [Ln (g1p/1g2p2)]2 = βv [Ln(p1/p2) + Ln(g1/g2)]2 =
= βv [Ln(p1/p2)]2 + 2βv Ln(p1/p2) +2 βv Ln(g1/g2) + βv [Ln(g1/g2)]2 The explained variable can also be decomposed.
Ln[C/p2g2] = Ln [C/p2] – Ln(g2).
The term Ln(g2) can be moved to the right side of the equal sign, and it is captured by the simple geographic dummy variable.
Appendix 2
Explained variable = 1 if OTHERCH equals 1 at least once in 2000-2004.
N=309. In 69 cases explained variable = 1
1 2 3 4 5
C -0,93 -1,27 -0,78 -1,87 -1,94
(-6.0) (-1.0) (-0.6) (-0.7) (-0.7)
Ui1999 1,40 1,73 0,85 1,64 4,45
(1.3) (1.1) (0.5) (0.5) (0.3)
LnTier1 0,04 -0,02 0,01 0,03
(0.3) (-0.1) (0.0) (0.1)
Mc Fadden R squared 0,01 0,01 0,01 0,04 0,02
LR Stat 1,66 1,72 3,68 13,71 5,46
Prob of LR stat 0,20 0,42 0,45 0,47 0,60
Statistically significant variables are not denoted by stars because not a single variable is statistically significant at the 10 % level.
Gs are geographic dummy variables
UnivEduc= 1 if the manager has a university degree in any discipline, zero otherwise.
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