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COMMERCIAL BANK INTEREST MARGINS AND
PROFITABILITY: EVIDENCE FOR SOME EU COUNTRIES
Margarida Abreu
Assistant Professor
Instituto Superior de Economia e Gestão CISEP
R. do Quelhas 6
Lisboa PORTUGAL
Ph: 351-21-3925968
email: mabreu@iseg.utl.pt
Victor Mendes
Associate Professor
Faculdade de Economia do Porto CEMPRE
R. Dr. Roberto Frias
4200 Porto PORTUGAL
Ph: 351-21-3177163
Fax: 351-21-3537077
email: victormendes@cmvm.pt
Preliminary draft
2
COMMERCIAL BANK INTEREST MARGINS AND
PROFITABILITY: EVIDENCE FOR SOME EU COUNTRIES
Objectives: The main goal of this paper is to study the determinants of bank interest
margins and profitability for some European countries in the last decade. We use a set
of bank characteristics, macroeconomic and regulatory indicators as well as financial
structure variables in order to explain interest margins and profitability. We intend to
evaluate whether European countries, sharing a common bond (EU membership) also
share the same interest margin and profitability determinants. In particular, we want to
check whether inflation, exchange rates, economic growth, bank size and
capitalisation, bank product mix, among others, could be accepted as explanatory
variables for interest margins and profitability. At the same time, we evaluate the
impact of the EMS crisis of 1992/3/4 on the net interest margin and bank profitability,
as well as the impact of the liberalisation of capital movements (occurred in Portugal
in 1992 and in Spain in 1993) on Portuguese and Spanish banks.
Background: This paper follows in the footsteps of Demirguç-Kunt and Huizinga
(1999), Bartholdy, Boyle and Stover (1997) and Barth, Nolle and Rice (1997), and
several specifications of the equation
(1) ijtjjjtjijti0ijt uCXB+β+β+β+β=Π
will be estimated (with Πijt the net interest margin or ROA/ROE for bank i in country j
at time t, B
ijt represents a vector of characteristics of bank i in country j at time t, X
jt is
a vector of control variables for country j at time t, and C
j is a vector of country
dummy variables).
The focus of the paper will be the investigation of possible influences of a standard set
of bank-specific explanatory variables along with other variables taking account of
cross-country differences in the regulatory environment in which banks do operate on
bank profitability and interest margins. Although in many studies empirical results are
essentially unchanged with respect to the used measure of bank performance, we will
3
use three different indicators of ex-post bank performance: the robustness of our
results is at stake. The bank specific variables we use are commonly used variables
such as market share, operating costs, capital to asset ratio and loan to asset ratio (to
account for bank-specific risk insofar as the dependent variable is not risk-adjusted).
Among the macroeconomic variables we use the inflation rate, the unemployment
rate, and the nominal effective exchange rate. We will also use dummy variables to
account for the range of permissible activities as well as the existence of crises of the
European Monetary System.
Some authors have claimed that the relationship between the explanatory and
explained variables is not linear and is not stable (v.g. Swamy et all 1996). On the
other hand, it is not easy to design a single model that completely describes bank
performance. Therefore we will test different specifications of the general model (1)
in order to avoid the risk of misspecifying the functional form of the relationship.
Data and Methods: In this paper we will use balance sheet and income statement
data from Datastream for the period 1986-99, as well as from other sources
(Économie Européenne).
Data set:
Banks from four different EU countries (Portugal, Spain, France and Germany).
Number of banks from each country in the data set.
Years Portugal Spain France Germany TOTAL
1986 8 5 0 8 21
1987 8 6 0 8 22
1988 8 7 2 8 25
1989 8 7 2 8 25
1990 8 7 3 8 26
1991 8 7 3 9 27
1992 8 7 14 9 38
1993 8 6 15 9 38
1994 8 7 15 9 39
1995 8 7 17 9 41
1996 8 13 18 10 49
4
1997 8 14 19 10 51
1998 0 14 19 10 43
1999 0 8 15 9 32
Variable definition:
The dependent variable is a measure of ex-post bank performance. In order to test the
robustness of our results we use four different variables: Interest Margin (IM =
Interest received Interest paid), Return on Assets (ROA) and Return on Equity
(ROE). NIM is alternatively defined as IM/Total Assets or IM/Equity. ROA is Pre-tax
Profit/Total Assets and ROE=Pre-tax Profits/Equity.
The explanatory variables are the following:
1) Labor/Assets = Total Employment Costs/Total Assets. It is a proxy for
operating costs; it is expected that banks with higher operating costs will have
higher net interest margins (in order to survive) and lower ROA and ROE
(everything else constant, banks will have lower pre-tax profits). Differences
in operating costs may also capture differences in business and product mix or
even differences in the range and quality of services offered.
2) Equity/Total Assets. We expect that the higher equity-to-asset ratio, the lower
ne ed to external funding and therefore higher NIM and profits. It is also a sign
that well-capitalized banks face lower costs of going bankrupt and thus their
cost of funding is reduced.
3) Loans/Assets = Total debtors and equivalent/Total Assets. Traditionally, banks
are intermediaries between lenders and borrowers. Other things constant, the
more deposits are transformed into loans, the higher the interest margin and
profits. However, if a bank needs to incur higher risk in order to have a higher
loan-to-asset ratio, then profits may decrease.
4) Bank market share (MS), defined as bank’s Loans/Country’s Domestic Credit.
We were unable to get information on total bank loans at the country level.
Therefore, the denominator is Domestic Credit of the country.
5) Unemployment rate (UR). The Eurostat definition (% of civilian active
population).
5
6) Inflation rate (INF). The annual % change of the GDP deflator at market
prices.
7) Exchange rate (EXR). The nominal effective exchange rate (base 100=1991;
performance vis-à-vis the rest of the 22 industrialized countries).
8) CRIS: Dummy variable, equal to 1 if the year is 1992 or 1993 (all countries),
or 1994 and the country is Portugal.
9) DCFPS: Dummy variable, equal to 1 if the country is Portugal and the year is
1992 and beyond, or the country is Spain and the year is 1993 and beyond.
10) D1: Dummy variable, equal to 1 if the country is Portugal.
11) D2: Dummy variable, equal to 1 if the country is Spain.
12) D3: Dummy variable, equal to 1 if the country is France.
13) YEAR: Time trend.
Sources:
Accounting data from DATASTREAM is used for banks from Spain, France and
Germany. Accounting data from banks’ annual balance sheet and income statement
for Portuguese banks. As for UR, INF, EXR and Domestic Credit, we use “Économie
Européenne”, nº 70, 2000 (Commission Européenne, Direction Générale ‘Affaires
Économiques et Financières’).
Descriptive statistics of some variables (%)
IM
----------
ASSETS
PTPROFIT
--------------
ASSETS
EQUITY
-----------
ASSETS
LABOR
----------
ASSETS
LOAN
--------
DEP
MS
Mean 2.54 1.06 6.25 1.43 109.45 4.18
Max 8.01 6.29 26.02 3.14 989.36 33.24
Min -1.67 -2.62 0.98 0.11 27.48 0.02
Std. Dev. 1.57 0.95 3.52 0.60 70.60 5.98
6
Results:
LS // Dependent Variable is IM/ASSETS
Sample (adjusted): IF EQUITY>0
Included observations: 477 after adjusting endpoints
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient
Std. Error
t-Statistic
Prob.
C -0.799791
0.366014
-2.185135
0.0294
LABOR/ASSETS 1.397429
0.136901
10.20757
0.0000
EQUITY/ASSETS 0.110296
0.023534
4.686731
0.0000
LOAN/ASSETS 0.023527
0.004637
5.073225
0.0000
MS 0.008137
0.007843
1.037518
0.3000
UR -0.030525
0.034397
-0.887425
0.3753
INF -0.102139
0.036798
-2.775660
0.0057
EXR -1.77E-05
0.000147
-0.120431
0.9042
CRIS 0.002852
0.001449
1.968705
0.0496
DCFPS -0.013428
0.003592
-3.738193
0.0002
D1 0.023207
0.004198
5.527786
0.0000
D2 0.019599
0.006007
3.262728
0.0012
D3 -0.002845
0.001698
-1.675304
0.0946
YEAR 0.017382
0.007952
2.185830
0.0293
YEAR*YEAR -9.60E-05
4.24E-05
-2.263364
0.0241
LS // Dependent Variable is ROA
Sample (adjusted): IF EQUITY>0
Included observations: 477 after adjusting endpoints
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient
Std. Error
t-Statistic
C 0.197540
0.224592
0.879554
0.3796
LABOR/ASSETS 0.094415
0.087026
1.084907
0.2785
EQUITY/ASSETS 0.200242
0.014238
14.06423
0.0000
LOAN/ASSETS 0.007695
0.002590
2.971416
0.0031
MS 0.015280
0.003841
3.978588
0.0001
UR -0.037868
0.019437
-1.948246
0.0520
INF -0.043461
0.020308
-2.140148
0.0329
EXR 2.00E-06
8.09E-05
0.024734
0.9803
CRIS 0.000682
0.000890
0.765738
0.4442
DCFPS -0.004374
0.001891
-2.313033
0.0212
D1 0.001854
0.002211
0.838656
0.4021
D2 0.012096
0.003240
3.733250
0.0002
D3 -0.003270
0.001042
-3.138169
0.0018
YEAR -0.004278
0.004881
-0.876439
0.3812
YEAR*YEAR 2.25E-05
2.61E-05
0.862377
0.3889
7
LS // Dependent Variable is IM/EQUITY
Sample (adjusted): IF EQUITY>0
Included observations: 477 after adjusting endpoints
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient
Std. Error
t-Statistic
Prob.
C -3.366639
6.259955
-0.537806
0.5910
LABOR/ASSETS 15.57204
2.036915
7.644910
0.0000
EQUITY/ASSETS -3.260323
0.346372
-9.412780
0.0000
LOAN/ASSETS 0.404142
0.076693
5.269616
0.0000
MS 0.154858
0.142458
1.087040
0.2776
UR -0.592039
0.568037
-1.042253
0.2978
INF -1.754957
0.711952
-2.464992
0.0141
EXR -0.002360
0.002590
-0.911101
0.3627
CRIS 0.054085
0.024212
2.233803
0.0260
DCFPS -0.200159
0.067908
-2.947486
0.0034
D1 0.419235
0.071076
5.898438
0.0000
D2 0.281686
0.093371
3.016853
0.0027
D3 0.024557
0.035286
0.695953
0.4868
YEAR 0.095506
0.136731
0.698497
0.4852
YEAR*YEAR -0.000591
0.000729
-0.810675
0.4180
LS // Dependent Variable is ROE
Sample (adjusted): IF EQUITY>0
Included observations: 477 after adjusting endpoints
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient
Std. Error
t-Statistic
Prob.
C 5.077019
3.607479
1.407359
0.1600
LABOR/ASSETS 1.268978
1.358656
0.933995
0.3508
EQUITY/ASSETS 0.775668
0.193757
4.003293
0.0001
LOAN/ASSETS 0.044995
0.039711
1.133073
0.2578
MS 0.340045
0.073922
4.600064
0.0000
UR -0.862172
0.284189
-3.033792
0.0026
INF -1.152596
0.442833
-2.602776
0.0095
EXR 0.000456
0.001291
0.353369
0.7240
CRIS 0.001792
0.017467
0.102610
0.9183
DCFPS -0.061173
0.034003
-1.799072
0.0727
D1 0.026513
0.042200
0.628263
0.5301
D2 0.210882
0.048157
4.379001
0.0000
D3 -0.042555
0.021934
-1.940138
0.0530
YEAR -0.105481
0.079320
-1.329819
0.1842
YEAR*YEAR 0.000560
0.000426
1.312674
0.1899
8
Summary of results
IM/ASSETS IM/EQUITY ROA ROE
C - ** - + +
LABOR/ASSETS + * + * + +
EQUITY/ASSETS + * - * + * + *
LOAN/ASSETS + * + * + * +
MS + + + * + *
UR - - - *** - *
INF - * - ** - ** - *
EXR - - + +
CRIS + ** + ** + +
DCFPS - * - * - ** - ***
D1 + * + * + +
D2 + * + * + * + *
D3 - *** + - ** - ***
YEAR + ** + - -
YEAR*YEAR - ** - + +
* significant at the 1% level (two-tailed).
** significant at the 5% level (two-tailed).
*** significant at the 10% level (two-tailed).
Some comments on the results:
1. The determinants of NIM and Pre-tax Profits are not the same and this holds true
when we use either total assets or equity on the denominator of the ratios. In
particular, we have found that CRIS and Labor/Assets impact on NIM only, whilst
MS and Ur are relevant for explaining ROA(E).
2. Results do not significantly change when we use Equity (instead of total assets) in
the denominator of the dependent variable, meaning that results are robust.
3. Regarding bank-specific variables, the net interest margin reacts positively to
operating costs, but pre-tax profits do not. This means that less efficient banks
(that is, banks with higher operating costs) charge higher interest rates on loans (or
pay lower rates on deposits), therefore passing those costs onto customers.
However, competition does not allow them to ‘overcharge’ and thus all banks
achieve similar profitability ratios.
4. Well-capitalised banks (ie, banks with higher equity/assets) face lower expected
bankruptcy costs and thus lower funding costs and higher interest margins on
assets. In general, this advantage ‘translates’ into better profitability ratios.
9
5. The loan-to-asset ratio has a positive impact on interest margins and profitability.
This could mean that in our sample period banks did watch carefully the lending
process. That is, they did not grant credit at all costs (relaxing credit selection and
monitoring), just for the sake of organic growth. Thus, they seem to have been
able to maintain low levels of non-performing loans, thereby increasing profits
and margins.
6. The market share variable is not significant when we explain the Net Interest
Margin. If we consider that MS captures product differentiation as well as market
power, then it appears that banks do not differentiate traditional loan and deposit
products (and do not exert market power in these markets) but rather less
‘conventional’ bank products and services. It also means that market structure is
not relevant in those traditional activities; however, they do exert market power in
some other bank products and services such as off-balance activity.
7. Although with a negative sign in all regressions, the unemployment rate (as a
proxy for the cyclical behavior of the economy) is relevant in the two last
equations only. Results are not better if we use the GDP growth rate instead.
8. The inflation rate is relevant in all models. Inflation brings along higher costs but
also higher income. It seems that bank costs increase more than do bank revenues.
This contradicts findings from other studies (Barth et al 1997, Claessens,
Demirguç-Kunt and Huizinga 1998, Hanson and Rocha 1986, Demirguç-Kunt and
Huizinga 1999, Demirguç-Kunt and Huizinga 2000, Denizer 2000), but goes
along the lines of earlier research (Wallich 1977, Petersen 1986).
9. The nominal effective exchange rate does not have any impact on net interest
margins and profitability.
10. The EMS crisis of 1992/3/4 seems to have had a positive impact on the net interest
margin on assets but not on bank profitability. Under pressure, European
authorities reacted by increasing short-term interest rates and that has had some
impact on median and long-term rates. However, credit rates react generally faster
than do deposit rates and thus the positive impact on the interest margin. At the
same time, exchange rate instability increases risk in cross-border bank activity
and losses could have occurred in foreign exchange transactions. Other bank costs
may have also increased, thus offsetting increased bank revenues.
11. Portuguese and Spanish banks suffered from the liberalisation of capital
movements (occurred in Portugal in 1992 and in Spain in 1993), both in terms of
10
interest margin and profitability. Given the increased competition brought about
by liberalisation, fund holders did look for more efficient banking systems and
more profitable applications, thus flowing out of these two countries.
12. Banks in Portugal and Spain perform generally better than banks in Germany.
However, French banks at the lower end of the spectrum. We can consider that we
have in these four countries bank-based financial systems. And “after controlling
for the level of financial development, there is some evidence that a more market-
based financial structure would lead to lower levels of bank profits” (Demirguç-
Kunt and Huizinga 2000, p.12). That seems to be the case of Portugal and Spain
vis-à-vis France and Germany. The bank sector in Iberian countries thus
represents for firms a larger source of funds than does the capital market, leading
to superior performances for Portuguese and Spanish banks. As for France and
Germany, 1995 data shows that total bank assets represent 119% of GDP in
Germany (against the 99% in France), whilst stock market capitalization
represents 34% of GDP in France and 24% in Germany (Demirguç-Kunt and
Huizinga 1999, table3). Using the same reasoning, banks in France face more
intense competition from the stock market and therefore show lower interest
margins and profitability.
13. No clear time trend, except for IM/Assets.
REFERENCES
Barth, J. R., D. E. Nolle, and T. N. Rice (1997). “Commercial Banking Structure,
Regulation, and Performance: An International Comparison”, Comptroller of the
Currency Economics WP 97-6.
Claessens, S., A. Demirguç-Kunt, and H. Huizinga (1998). “How Does Foreign Entry
Affect the Domestic Banking Market?”,
Demirguç-Kunt, A. and H. Huizinga (1999). “Determinants of Commercial Bank
Interest Margins and Profitability: Some International Evidence”, The World Bank
Economic Review, vol. 13, nº 2, 379-408.
Demirguç-Kunt, A. and H. Huizinga (2000). “Financial Structure and Bank
Profitability”, World Bank Policy Research WP 2430.
Denizer, C. (2000). “Foreign Entry in Turkey’s Banking Sector, 1980-97”, World
Bank Policy Research WP 2462.
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Hanson, J. A., and R. R. Rocha (1986). “High Interest Rates, Spreads, and the Cost of
Intermediation: Two Studies”, World Bank Industry and Finance Series 18.
Petersen, W. M. (1986). “The Effects of Inflation on Bank Profitability”, in Recent
Trends in Commercial Bank Profitability A Staff Study, Federal Reserve Bank of
New York, 89-114.
Swamy, P.A.V.B., J. R. Barth, R. Y. Chou and J. S. Jahera Jr. (1996). “Determinants
of US Commercial Bank Performance: Regulatory and Econometric Issues”, Research
in Finance, vol 14, 117-156.
Wallich, H. C. (1977). “Inflation is Destroying Bank Earnings and Capital
Adequacy”, Bankers Magazine, Autumn 1977, 12-16.
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Inflation is Destroying Bank Earnings and Capital Adequacy
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