The international transportation industry involves various sectors, shipping being one with particular characteristics which differentiates it from others especially as relevant capital risk is concerned. Within this scope, shipping banks are required to assess a number of factors in order to limit the risk from loans, considering the investment capital required. The efficiency of shipping banks is particularly important as it may affect the borrowing level and consequently the financial situation and investment activity in shipping market. This paper examines the Technical Efficiency (TE) of 71 banks operating world-wide in the maritime sector from 2005 to 2010, which is the period that the shipping industry reached its peak and one of its lowest point, making extremely difficult to secure debt finance in shipping, by using Data Envelopment Analysis (DEA) and presents the factors which may affect their technical efficiency, through the application of Regression Analysis. Based on the paper results, most banks during the study period are technically inefficient, whereas TE is proved to be higher under the assumption of variable returns to scale (VRS DEA model) when comparing to constant returns (CRS DEA model). Statistically significant variables are total deposits and total assets for both TE-CRS and TE-VRS and ROE (Return On Equity) for TE-VRS, providing significant information regarding factors on which management should further focus, in order to maintain and reinforce technical efficiency with respect to their strategy for financing shipping sector.
Shipping sector bears special characteristics that render it considerably different from all other international transport industries, forming a particularly dynamic environment with equally high risks of investment capital losses. In this context, the commercial banks, as the primary source of financing a market characterized by high capital and operating costs, play a leading role. At the same time, they are required to evaluate a broad range of different factors in order to limit the relevant risk and finally reach an efficient risk-yield balance. This becomes even more important when seen in the context of the latest international developments following the implementation of the Rules of Basel ΙΙΙ in combination with the capital lost due to one of the most prolonged downturns in the shipping market.
Considering the aforementioned, the level of ship finance available remains low while the banks seek ways to shrink their balance sheets, as a result of both regulatory and commercial restraints. Thus, the shipping banks, i.e. commercial banks that provide loans to shipping sector, have become more selective and tighter with the relevant lending volumes and terms, whereas leverage has become shorter with respect to efficiency. Efficiency of commercial banks involved in the shipping industry is crucial for their sustainability, which in turn depends on funding and effective management of operating costs. Thus, bank efficiency plays a significant role in the shipping industry, affecting financial growth or causing systematic risks.
The purpose of this paper is to assess the technical efficiency of banks involved in the shipping industry and to test independent variables that affect shipping banks’ TE for the time period from 2005 to 2010, which is the period that the shipping industry reached its peak and one of its lowest point, making extremely difficult to secure debt finance in shipping. Data Envelopment Analysis is used in order to extract efficiency scores for shipping banks worldwide. The model applied is based on the intermediate approach of banking operation with orientation in outputs (output oriented), while models are executed both with constant and variable returns to scale (CRS and VRS approaches) in order to detect any differences in banks’ TE in terms of technology. Furthermore, Regression Analysis is used, in order to test independent variables that affect shipping banks’ TE.For the purpose of this paper, technical efficiency measures the ability of a bank to produce optimal output from a given set of inputs.
This paper reveals for the first time the most important factors arising from shipping bank’s internal environment based on DEA and implicitly contributes to the development of a specific methodological tool for measuring technical efficiency with respect to bank ability to produce optimal output from a given set of inputs. Essentially, it might be considered as a decision support tool, taking into account certain bank specific factors from its internal operational environment, in order to define the level of its efficiency in the market as a whole.
The paper is structured as follows; Section 2 sets a literature review of DEA approaches for estimating bank efficiency. Section 3 presents the methodology applied, while Section 4 presents the empirical analysis and relevant results. Section 5 concludes the paper along with implications for further research.
Bank efficiency has been an important issue for analysts [
Both approaches are used in previous literature, since some researchers estimate bank efficiency by CRS approach [
DEA models for estimating bank efficiency have been widely used in previous years for several banking industries [
Either CRS or VRS DEA methods for estimating bank efficiency aim to detect the most and least efficient banks, but questions often arise about the identification of those ways that improve technical efficiency. In this frame, it is essential to identify those factors that impact overall bank efficiency.
DEA method is selected as the most suitable for the measurement of technical efficiency of a group of banks, as can process models with many inputs and outputs in different measures, enables comparisons, allows the use of input and output vectors and requires lesser degrees of freedom. Application of DEA in the banking sector refers to the estimation of the relative efficiency of each bank in a current sample in comparison with the relative efficiency of the rest of the banks comprising the total sample [
max v i , u r ( h ο ) , h ο = ∑ r = 1 s u r y r o ∑ i = 1 m v i x i o subjectedto ∑ r = 1 s u r y r j ∑ i = 1 m v i x i j ≤ 1 ∀ j = 1 , ⋯ , n u r , v i ≥ ε , i = 1 , ⋯ , m , r = 1 , ⋯ , s (1)
where h0 = the relative efficiency of bank o, o = the bank assessed by j = 1 , ⋯ , n banks of the sample, j = the number j = 1 , ⋯ , n of banks of the sample, r = the number r = 1 , ⋯ , s of outputs, i = the number i = 1 , ⋯ , m of inputs, y r j > 0 = the amount of output r of bank j ( r = 1 , ⋯ , s ), x i j > 0 = the amount of input i of bank j ( i = 1 , ⋯ , m ), and v i , u r = the coefficients of input i and output r, respectively, which maximize the objective function of the bank examined each time.
This linear fractional programming model described above is easily converted in a linear programming model as follows [
max v i , u r ( h 0 ) , h 0 = ∑ r = 1 s u r y r o subjectedto { ∑ i = 1 m v i x i o = 1 ∑ r = 1 s u r y r j − ∑ i = 1 m v i x i j ≤ 0 u r , v i ≥ 0 i = 1 , ⋯ , m j = 1 , ⋯ , n (2)
In conclusion, the model is applied once for each bank in the sample looking for the combination of inputs and outputs (ur, vi) that gives the higher degree of the bank’s efficiency (h0), without leading to a input-output ratio greater than 1 (100%) when applied to other banks in the sample. For each bank, the relative efficiency is estimated as follows:
1) h0 = 1, indicating that the bank is relatively efficient, or
2) h0 < 1, indicating that the bank is relatively inefficient.
DEA can be applied assuming either constant returns to scale (CRS) or variable returns to scale (VRS). Consequently, most researchers after having applied DEA methods to estimate technical efficiency, they estimate its determinants, assessing in the same time the degree and the nature (positive or negative) of their impacts on technical efficiency through multiple regression [
T E j = c + a 1 Z 1 + a 2 Z 2 + ⋯ + a j Z j + ε j (3)
where, TE = Technical Efficiency, Z 1 , Z 2 , ⋯ , Z j = are the independent variables affecting TE, a 1 , a 2 , a j = their coefficients and ε = error term. This model is estimated either by Time Series Ordinary Least Squares―OLS or by Panel Data Models. The regression model applied for estimating the factors affecting shipping banks’ efficiency is as follows:
t e = c + a 1 R O A + a 2 R O E + a 3 L L P _ T L + a 4 L N T T L D E P + a 5 L N S _ T A + a 6 L N _ T A + a 7 t + ε (4)
where te = the technical efficiency of bank, ROA = Return On Assets, ROE = Return On Equity, LLP_TL = Total Loan Loss Provision/Total Loans, LNTTDEP = the natural logarithm of Total Deposits, LNS_TA = Total Loans/Total Assets, and LNTA = the natural logarithm of Total Assets.
The sample of present analysis consists of seventy-one (71) banks worldwide involved in shipping finance for the time period of 2005-2010, which is the period that the shipping industry reached its peak and one of its lowest point, making extremely difficult to secure debt finance in shipping. All banks (
No | Bank | No | Bank |
---|---|---|---|
1 | Aegean Baltic Bank | 37 | Goldman, Sachs & Co., oHg |
2 | Alpha Bank AE | 38 | HSH Nordbank AG |
3 | Aozora Bank | 39 | ICICI Bank Limited |
4 | AS DnB NORD Banka | 40 | Industrial Bank of Korea |
5 | Bank Danamon Indonesia Tbk | 41 | ING Bank N.V. |
6 | Bank of China Limited | 42 | Intesa Sanpaolo |
7 | Bank of Fukuoka Ltd. | 43 | Kansai Urban Banking Corporation |
8 | Bank of Tokyo-Mitsubishi UFJ Ltd (The)-Kabushiki Kaisha Mitsubishi Tokyo UFJ Ginko | 44 | Kookmin Bank |
9 | BNP Paribas | 45 | Korea Development Bank |
10 | Bremer Landesbank Kreditanstalt Oldenburg-Girozentrale | 46 | Landesbank Hessen-Thueringen Girozentrale-HELABA |
11 | Capital One Bank (USA) National Association | 47 | Macquarie Bank Ltd |
12 | China Development Industrial Bank | 48 | Malayan Banking Berhad-Maybank |
13 | China Merchants Bank Co Ltd | 49 | Marfin Egnatia Bank SA |
14 | Citibank International Plc | 50 | National Australia Bank Limited |
15 | Commerzbank AG | 51 | National Bank of Greece SA |
16 | Corner Banca S.A. | 52 | National Federation of Fisheries Cooperatives-Suhyup Bank |
17 | Credit Agricole Corporate and Investment Bank-Credit Agricole CIB | 53 | Natixis |
18 | Crédit Industriel et Commercial―CIC | 54 | Nordea Bank AB (publ) |
19 | Credit Suisse Group AG | 55 | Piraeus Bank SA |
20 | Danske Bank A/S | 56 | Proton Bank S.A. |
21 | DBS Bank Ltd | 57 | Shinhan Bank |
22 | DekaBank Deutsche Girozentrale | 58 | Shinkin Central Bank |
23 | Deutsche Bank AG | 59 | Shinsei Bank Limited |
24 | Deutsche Schiffsbank AG | 60 | Skandinaviska Enskilda Banken AB |
25 | Dexia Bank Belgium-Dexia Bank | 61 | SpareBank 1 SR-Bank |
26 | DnB NOR Bank ASA | 62 | Sumitomo Mitsui Banking Corporation |
27 | Dresdner Bank AG | 63 | Swedbank AB |
28 | Dresdner Kleinwort Limited | 64 | T Bank S.A |
29 | DVB Bank SE | 65 | Tokyo Star Bank Ltd. |
30 | DZ Privatbank S.A. | 66 | Turkiye Garanti Bankasi A.S. |
31 | Efibanca SpA-Gruppo Bipielle | 67 | UBS AG |
32 | Emporiki Bank of Greece SA | 68 | UniCredit Bank AG |
33 | FBB First Business Bank SA | 69 | UniCredit SpA |
34 | Finansbank A.S. | 70 | WestLB AG |
35 | Fortis Bank SA/NV-BNP Paribas Fortis | 71 | Woori Bank |
located in Europe and mostly Germany, 36.61% in Asia and 2.8% in USA. All data were derived from Bloomberg professional data base and Bank scope data base provided by Bureau van Dijk.
Shipping banks’ TE is estimated by the non-parametric DEA method, both in terms of CRS and VRS, in order to test if results are verified by different production and technology circumstances, taking into account the fact that CRS models usually refer to long-term period while VRS models to short-term (Siriopoulos & Tziogkidis, 2009). Additionally, DEA method is consistent with the intermediary approach, according to Berger & Humphrey (1997) belief that this approach is best suited for the estimation of efficiency in the banking sector, since it includes interest expenses which usually are of 1 / 2 to 3 / 4 of total bank expenses. Moreover, both CRS and VRS DEA methods applied are output-oriented. Regarding input and output variables, total expenses excluding staff cost, staff cost and deposits are used as inputs, while net shipping loans are used as the only output, since it best reflects banks’ profitability. In the subsequent stage of this analysis, A regression model is used in order to test for potential variables that affect technical efficiency.
In Figures 1-6, TE of all 71 shipping banks is presented using both CRS and
VRS DEA methods, respectively. Firstly, it is proved that TE assessed under VRS hypothesis seems to be more effective in relation to CRS assumption. This is also evidenced through the box plots (
ity, especially in the case of CRS. Summarized results of TE under the CRS and VRS approaches are presented in
By presenting the descriptive statistics of the data (
The regression models applied or estimating the factors affecting shipping banks’ efficiency based on CRS and VRS approach are respectively as follows:
Year | Number of TE banks under CRS approach | Percentage (%) | Number of TE banks under VRS approach | Percentage (%) |
---|---|---|---|---|
2005 | 5 | 7.04% | 18 | 25.35% |
2006 | 5 | 7.04% | 19 | 26.76% |
2007 | 7 | 9.86% | 19 | 26.76% |
2008 | 6 | 8.45% | 15 | 21.13% |
2009 | 8 | 11.27% | 15 | 21.13% |
2010 | 7 | 9.86% | 19 | 26.76% |
Variables | Minimum | Maximum | Mean | Std. Dev. |
---|---|---|---|---|
ROA | −7.239 | 21.791 | 0.816 | 2.027 |
ROE | −130.100 | 355.700 | 8.341 | 31.286 |
LLP_TL | −1.21E−07 | 2.13E−04 | 3.18E−06 | 2.09E−05 |
LNTTLDEP | 9.590 | 20.832 | 16.895 | 2.205 |
LNS_TA | 6.810 | 94.750 | 50.518 | 20.648 |
LNTA | 11.651 | 21.863 | 17.957 | 2.007 |
CRS | b | se(b) | t | p | VRS | B | se(b) | t | p |
---|---|---|---|---|---|---|---|---|---|
(Constant) | −0.092 | 0.146 | −0.632 | 0.528 | (Constant) | −0.275 | 0.150 | −1.835 | 0.068 |
LN_TA | 0.005 | 0.001 | 6.207 | 0.000 | LNTTLDEP | 0.052 | 0.008 | 6.414 | 0.000 |
LNTTLDEP | 0.018 | 0.008 | 2.317 | 0.021 | LN_TA | 0.002 | 0.001 | 2.155 | 0.032 |
ROE | −0.001 | < 0.001 | −2.580 | 0.011 | |||||
R2 = 0.139. | R2 = 0.145. |
t e ( C R S ) = − 0.092 + 0.018 ⋅ L N T T L D E P + 0.005 ⋅ L N _ T A (5)
and
t e ( V R S ) = − 0.275 − 0.001 ⋅ R O E + 0.052 ⋅ L N T T L D E P + 0.002 ⋅ L N _ T A (6)
LNTTLDEP (Total Deposits) is positively correlated to TE under CRS and VRS approaches, meaning that more efficient banks have higher market shares. Specifically, a 1% increase in Total Deposits drives to 0.018% and 0.052% increase of technical efficiency score under CRS and VRS approach respectively. Total Assets (LN_TA) is also positively correlated with TE under both CRS and VRS approaches with the technical efficiency score to increase by 0.005% and 0.002% respectively, when Total Assets increase by 1% and vice versa, as confirmed by Hauner [
According to results, banks during the study period are technically inefficient, suggesting that market factors may influence the operation of shipping banks. Additionally, TE is proved to be higher under the assumption of variable returns to scale (VRS DEA model) when comparing to constant returns (CRS DEA model). Results obtained by the application of CRS and VRS models, respectively, seem to differ significantly, mainly due to the choices and combinations of inputs and outputs and because of the substantially high levels of TE detected in banks under review. Regarding the factors that affect TE under both CRS and VRS approach, ROA, statistically significant variables are total deposits and total assets for both te-CRS, te-VRS and ROE (Return On Equity) for te-VRS. Total Assets and Total Deposits are positively correlated with TE, denoting that pro- fitability and market power, reflected on the bank’s size, are favorable for obtaining higher levels of TE in the banking sector. In contrast and as expected, ROE is negatively correlated with TE.
Overall, the results of this research indicate banks involved in shipping fi- nance are not technical efficient over the time period examined. Additionally, regression models applied provided useful information to be considered by management regarding factors that affect TE. However, the research focused on shipping market as a whole, whereas the study period was specific. It would be of interest regarding future research to apply the proposed methodology in order to examine if the certain sub sector to be financed, i.e. dry bulk, tankers, container shipping, or the country of origin, the period to be examined, or even ownership structure of shipping banks affect their TE. It would be also interesting to define the internal factors of the operational environment of banks in combination with the external factors associated with shipping market that may affect the amount of loans for the shipping industry based on previous years’ experience. In general, the existence of non-technical efficiency in shipping banks raises questions about their decision to continue financing such a risky and heterogeneous market, despite the regulations set by the Basel Convention.
The publication of this paper has been partly supported by the University of Piraeus Research Center.
Maniati, M. and Sambracos, E. (2017) Measuring the Technical Efficiency for the Shipping Banks. Theo- retical Economics Letters, 7, 502-516. https://doi.org/10.4236/tel.2017.73038