Determinants Intl Banking

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Determinants of International Banking Activity: Models and their Estimation 1

Sukumar Nandi

Indian Institute of Management Lucknow

Money is an important economic institution invented and developed in its presentform to facilitate the exchange of commodities in a multidimensional form and to derive the efficiencygain out of it so that society as a whole gets additional welfare. Banking system came along with thedevelopment of money as an institution. As civilisation narroweddown the social distances and mankind learned about the benefits of exchanging commodities across

political boundaries, the present-day international trade developed. The transaction of commoditiesacross countries, required financial intermediation in the international level and thus international

banking business was born. What started with movement of gold and silver across country-borders became ultimately an efficient institution of international transfer of not only yellow metal butcurrencies of sovereign countries. Thus the emergence and growth of international banking is closelyinterwoven with the development of international trade and international capital movement.

The above gives the general perspective of the growth of international banking. Butthere are many aspects of this development. From a historical standpoint, the recent growth of international banking can be regarded as a reversion to the situation before World War I whenEuropean banks dominated the world capital market (Darby, 1986; Lees, 1982). During the period1940-1960 regulatory control on capital flow and convertibility of the currencies reduced theimportance of international banking. But from 1960 onwards globalisation of capital market started

and the emergence of surplus in petro-dollars in the seventies gave the much needed liquidity to theinternational banking business. The latter has been characterised by an increasing turn over ininternational trade, a phenomenal increase in the international flow of capital and also an increasingflow of funds from the banks to non-bank sectors. To understand the causative factors properly theliterature has attempted to identify the factors supporting the internationalisation of banking

business. Thus factors like non-financial multinational corporations (Grubel, 1977; Gray and Gray,1981), the proximity to customers abroad (Brimmer and Dahl, 1975) the competitive advantage with

better information technology (Nigh et al., 1986) and the benefits due to international diversification(Rugman, 1979; Tschoegl, 1983) have been mentioned in the literature in the contexts when these

become relevant.

The literature abounds in the exploration of the causative link in the development of international banking, but not many studies are found testing the theory empirically. There have beenseveral studies which attempt to measure empirically the role of the different factors behind thegrowth of the US banks in the international fields (Murphy, 1981; Fieleke, 1977; Goldberg and1 Chapter of the book___ Sukumar Nandi, Essays in International Finance : AnIndian Perspective, NIBM, 1997, Pune.


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Saunders, 1981; Nigh et al., 1986; Sagari,1986).Goldberg and Johnson (1990) has tested a model of the growth of US bank branches abroad. In fact,two models are examined. The first model explains the assets of the foreign branches of US banksand the second model treats number of branches abroad of US banks.

Determinants of Foreign Bank Growth : A ModelIn today's world no country can afford to be auturkic either in the field of

international trade or in international banking. But the latter is subject to much more restrictions inalmost all the countries compared to the former. What determines the growth of international banksin the domestic banking sector of a particular country? Analytically we can proceed asfollows:

Since international trade is closely related to international banking, volume of international trade (imports and exports together) is a determinant of the growth of international

banking and the relationship is direct. Assuming that no specific restrictions are imposed on theoperation of foreign banks so far as their operations are concerned in international banking vis-a-visthe practice of international banking done by home country's banks, it can be said that an increase inthe turnover of international trade should have positive impact on the growth of international

banking. Alternatively, the ratio of export to gross domestic product can be taken as the explanatoryvariable. This alternative formulation can be tested.

Foreign direct investment has been cited as an important determinant for the

expansion of international banking (Grubel, 1977). In fact, the presence of international banksfacilitates the inflow of foreign capital and so it is expected that the increase in foreign directinvestment should have a positive impact on the growth of international banking.

Banking service as a commodity is supposed to have positive income elasticity. As

national income is growing, demand for banking service should increase. To what extent the increasein income will help the growth of foreign banking activity in domestic soil depends on the preferenceof the consumers and also the participation of the foreign banks in the trade, both domestic andinternational, of the host country. If we take per capita income as the explanatory variable for thegrowth of international banking activity, then the growth of per capita income may facilitate thegrowth of international banking in the host country on the assumption that foreign banks havecomplementary role in the domestic banking structure.

The growth of domestic deposit should have influence on the activity of foreign banks. An increase in domestic deposit is supposed to have positive influence on the depositmobilisation of all banks including the foreign banks. That helps the building up of the asset

portfolio. To what extent deposit mobilisation will affect the activities of foreign banks depends onthe competition between domestic banks and the foreign banks in the host country. But a higher deposit mobilisation will have a negative influence on the asset structure of foreign banks.

Again, the exchange rate changes (EX) affects the activities of the foreign banks. Anincreased volatility of the exchange rate increases the risk factor in international banking, and unless


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this aspect is properly taken care of, this acts negatively so far as the growth of international bankingis concerned. But when exchange rate is defined as the value of domestic currency in terms of foreign currency, an increased value of Ex means appreciation of domestic currency. Thus the valueof assets of foreign banks in terms of host country's currency will fall. A negative impact is expected.

An index of activity of foreign banks may be their aggregate asset structure, thoughthe number of branches may be another indicator. Some studies take both (Goldberg and Johnson,1990). In this case we take the aggregate asset structure as the index of the activities of foreign

banks. This is the dependent variable the behaviour of which is supposed to be explained by theindependent variables explained above.

Based on the discussion above we can write the formalmodel as

A = F(TR, FDI, PCI, DEP, EX) ...... (1)when

A = total assets of foreign banksTR = total trade volume i.e., total of export and importPCI = per capita incomeDEP = aggregate domestic depositsEx = exchange rate of domestic currencyFDI = total foreign direct investment

For empirical estimation we write the model in linear formA = b o + b 1 T R + b 2 FDI + b 3 PCI + b 4 DEP + b 5 E x + u

----- (2)

and the presumptive sign of the parameters are b1 > 0, b 2 > 0, b 3 > 0, b 5 < 0 and b 4 < 0The stochastic term u has the usual distributional properties.The logarithmic transformation of the model (1) will beLn A = a 0 + a1 ln TR + a 2 ln FDI + a 3 ln PCI + a 4 ln DEP + a 5 ln Ex + u 1 ...... (3)In this case the parameters will be the elasticities .Here natural logarithm is used.An alternative formulation of the model (1) can be by replacing the total trade volume

(TR) in the set of explanatory variable by a suitable index of the international exposure of thecountry. The latter can be captured by the ratio of exports in national income as in Goldberg andJohnson (1990) or ratio of exports and imports taken together as a proportion of national income.Suppose we define T1 in the following way :

T1 = ratio of exports to national income Now the model (1) changes toA = f (T1, FD1, PC1, DEP, Ex)


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The idea of working with both TR and T1 in two separate models is to see which formulations work better. Thus the estimable equation (2) will be accordingly,

A = b 0 + b1 T1 + b 2 FDI + b 3 PCI + b 4 DEP + b 5 Ex + u ....... (2)'

The sign of the parameters will have the same explanations. Further, the log-linear form of equation(3) will change as

ln A = a 0 + a 1 ln T1 + a 2 ln FDI + a 3 ln PCI + a 4 ln DEP +a 5 ln EX + U 2 ....... (3)'

Another problem associated with data of our reference countries (the USA and UK)is that the figures of foreign direct investments are mostly in the negative. The logarithmictransformation of these negative values are not possible. So we have dropped the variable foreigndirect investment (FDI) in the logarithmic transformation. Thus the estimation of both equations (3)and (3)' have been made without the FDI term as an explanatory variable. We are aware of the factthat the dropping of one variable (FDI) may lead to mis-specification of the model, and in that casethe linear model stands as the model of reference. Of course, a suitable proxy can be used in place of FDI and in case of India this has been done.

Indian Case

In the literature both assets and number of branches of foreign banks are used asdependent variable to ascertain the quantitative influences of the determinants on both. In case of thecountries like the USA and UK we have used only the asset as the dependent variable. Here also thenumber of branches of foreign banks are not available over the years. Of course, any one of the two,i.e., either aggregate assets of foreign banks or the number of branches can serve the main purpose,which is to quantify the nature of influences on the growth of foreign banks in the host country.

In case of India time series data of the assets of foreign banks are difficult to collect.Because of this, we are using the number of branches of foreign banks as the dependent variable.Thus the model becomes

N = F 1 (T1, NB, PC1, DEP, Ex) ....... (4)

Here the variable FD1 is replaced by net borrowing from abroad (NB) as the data on FD1 are notavailable. Thus in equation (4)

N = number of branches of foreign banksThe model in estimable form becomes

N = K 0 + K 1 TI + U 2 NB + K 3 PCI + K 4 DEP + K 5 Ex + u ...... (5)Again the logarithmic transformation of the model (4)isln N = d 0 + d 1 ln TI + d 2 ln NB + d 3 ln PCI + d 4 ln DEP + d 5 ln Ex + u 1 ...... (6)


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We need not write the presumptive sign of the parameters again, which are stated with equation (2).But the formulations of the models and the theoretical explanations can help to put the testablehypothesis briefly.

From the above analysis we can briefly state thehypothesis we are going to test in this framework.

H1 : The greater the volume of international trade (exportsand imports taken together) in the host country, themore becomes the activity of the foreign banks.

H2 : The greater the size of foreign direct investment intothe host country, the more becomes the activity of theforeign banks.

H3 : The higher the per capita income, the higher the sizeof activities of foreign banks H4 : The higher the domestic deposit

mobilisation, the more negative will be the effect on the assets of foreign banks.

H5 : The higher the volatility of the exchange rate of thehost country, the lower will be the activity of theforeign banks.

Data and Methodology

The data are collected from various issues of Federal Reserve Bulletin, Bank for International Settlement and International Financial Statistics. The time-period covered for the UK is1975-1990, while for the US it is 1980-1990. The consumer price indices are used to deflate all thenominal figures to get the real values. The estimation is made on the annual time-series data. For

Indian case the period of study is 1974-1989.

The logarithmic transformation of the model has one problem regarding estimationand that is the negative values of most of the figures of foreign direct investment (FD1) in case of

both the USA and UK. Thus this variable is dropped in the logarithmic form.

The t-values are reported as significant when they are significant at 5 per cent or lesslevel of significance. Otherwise, the level of significance at two-tail tests are mentioned.

Table 6.1 Regression Results of the USA and UK Equations 2

_________________________________________________________________ Independent Variable Dependent Variable : Asset

_________________________________


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U.S.A.U.K._________________________________________________________________

Constant 158.62 -278901.94

(0.526) (-1.205)TR 0.479 -4.752

(2.055) (-3.336)FDI -0.755 1.857

(-1.487) * (0.678)PCI -0.013 -293.82

(-0.38) (5.28)DEP -0.006 -0.132

(0.042) (-0.595)Ex -143.14 -235739.29

(-1.373) ** (-7.124)

Other StatisticsAdjusted R 2 0.82 0.96D W Statistics 2.31 2.41SEE 1640.85 857 X 10 7

n 11 16 _________________________________________________________________

Figures in the parenthesis are t-statistics * Significant at 20 per cent level ** Significant at 23 per cent level

Table 6.2Regression Results of USA and UK Equation 2 TR Replaced by T1

_________________________________________________________________ Independent Variables Dependent Variable : Asset

_______________________________ U.S.A. U.K.


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_________________________________________________________________ Constant -432.95 3829.76

(-1.268) (0.118)T1 2059.27 2255967.3

(2.123) (-2.02)FDI -0.90 1.779

(-1.741) * (0.50)PCI 0.035 199.0

(1.49) ** (3.899)DEP -0.005 -0.348

(-0.04) (-1.058)Ex -96.28 -209221.15

(-0.836) (-5.50)Other StatisticsAdjusted R 2 0.82 0.94D W Statistic 2.63 1.70SEE 1591.32 1.29 x 10 9

n 1116_________________________________________________________________

Figures in the parenthesis are t-statistics ** Significant at 20 per cent level * Significant at 14 per cent level Table 6.3 Regression Result : Log-Linear Form : USA and UK Model * : ln A = a 0 + a 1 ln TR + a 3 ln DEP + a 4 ln Ex


______________________________ U.S.A. U.K.

_________________________________________________________________ Constant 5.90 -1.81(0.223) (-0.473)

ln TR 1.549 -2.35(1.777) ** (-5.212)

ln PCI -1.237 4.97(-0.277) (6.62)

ln DEP 0.084 -0.0035(0.061) (-0.033)

ln Ex -1.506 -1.315(-2.30) (-10.097)

Other StatisticsAdjusted R 2 0.80 0.97D W Statistics 1.59 2.35SEE 0.1283 0.0522n 11 16

_________________________________________________________________


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Note : * The variable FDI dropped as negative values create problem in logarithmictransformation.

** Significant at 28 per cent level

Table 6.4Regression Result : Log-Linear Form : USA and UK Model * :ln A = a 0 + a 1 ln T1 + a 2 ln PCI + a 3 ln DEP + a 4 ln Ex **


_____________________________ U.S.A. U.K.

_________________________________________________________________ Constant -17.435 -12.155

(-0.985) (-1.817)ln T1 0.372 -1.138

(0.708) (-1.58) b

ln PCI 2.843 2.837(1.122) a (2.89)

ln DEP -0.607 -0.084(-0.495) (-0.388)

ln Ex -1.645 -1.157(-2.343) (-5.36)

Other StatisticsAdjusted R 2 0.77 0.92D W Statistic 1.69 0.917SEE 0.1458 0.1476

n 1116_________________________________________________________________

Note : a : Significant at 30 per cent level b : Significant at 14 per cent level * : ThevariableFDI is droppedfromthemodel as logarithmic transformation of negativevalues are problematic.

Figures in parenthesis are t-statistics** : Here T1 is inserted instead of TR and T1 is defined as export divided by national

income.Table 6.5

Regression Results : India Equations 5 and6 _________________________________________________________________ Independent Variables Dependent Variable: N : Number of Branches

__________________________________________ Linear Form Independent Log-Linear Equation 5 Variable

form Equation6_________________________________________________________________

Constant 113.571 Constant 3.436


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(21.125) (9.797)T1 -140.552 ln T1 -0.05

(-5.486) (-4.976)NB -0.0285 ln NB -0.0038

(-0.815) (-0.922)PCI 0.0077 ln PCI 0.177

(2.956) (3.195)DEP -0.011 ln DEP -0.041

(-2.51) (-2.71)Ex 1.226 ln Ex 0.077

(5.907) (6.32)Other StatisticsAdjusted R 2 0.95 Adjusted R 2 0.96DW Statistic 2.84 DW 2.71SEE 3.603 SEE 0.0002n 16 n 16

_________________________________________________________________ Empirical Test of the Model

Four variants of the model (equation (1)) have been used for estimation. First, thelinear form (equation 2) has been estimated for the two countries, the USA and UK and this includestotal trade volume (TR) as one explanatory variable. From the result (Table 6.1) we see that thecoefficient of TR has expected positive sign in case of the USA. While it has negative sign in case of UK. In both cases the estimators are significant. The coefficient of FDI is positive but not significantin case of UK and negative and significant at 20 per cent level in case of the USA. In the latter casethe hypothesis is rejected. The coefficients of exchange rate have the expected negative sign in bothcases, though it is significant only at 23 per cent level in case of the USA. All other coefficients arenot statistically significant. The coefficient of PCI is positive and significant in case of UK andnegative but not significant in case of the USA.

The coefficients of determination i.e., the values of adjusted R 2 are good in case of both countries and the DW statistics do not show any serious auto-correlation.

Second, the variable TR is replaced by T1 in the model, where T is the ratio of exportto national income of the country. The results are shown in Table 6.2. We find that the coefficient of T1 is positive and significant in case of the USA, andnegative and significant in case of UK. The latter result does not conform to the hypothesis. Thecoefficient of FDI is negative and significant at 14 per cent level in case of the USA, but positive andnot significant in case of UK. The coefficient of PCI is positive and significant for UK, and positiveand significant at 20 per cent level for the USA. The coefficients of DEP are negative for both thecountries, but not significant. The signs of the coefficient of exchange rate (Ex) are on expected line,i.e., these are negative, but it is not significant, in case of the USA. The values of adjusted R 2 aregood for both the countries, it is 0.82 for the USA and 0.94 for UK. The values of Durbin WatsonStatistics in both the cases do not show any serious autocorrelation.

Third, the log-linear version of the model (equation 1) does not contain FDI (because


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of negative value) but it contains TR. The results are reported in table 6.3. The coefficient are allelasticities and their numerical values show the percentage change in the dependent variable due toone per cent change in the independent variable. The sign before the coefficient shows the dimensionof the change.

We find that the sign of the coefficient of ln TR is positive and significant at only 28 per cent level in case of the USA, but it is negative and significant in case of UK. The elasticity of PCI is negative and not significant in case of the

USA but it is positive and significant in case of UK. The coefficients of ln DEP are not significant for both the countries, though it is positive for the USA and negative for UK. The elasticities of exchange rates are negative and significant for both the countries. The values of adjusted R 2 aregood for both the countries and the value of DW Statistics show some autocorrelation problem for the USA, but for UK no significant autocorrelation is there.

Fourth, in the log-linear version T1 replaces TR. We find from table 6.4 that theelasticity of T1 is positive but not significant for the USA, but it is negative and significant at 14 per cent level for UK. The elasticities of PCI are positive for both the countries, and it is significant for UK and significant at 30 per cent level only for the USA. The sign of the elasticity of domesticdeposit (DEP) is negative for both the countries. This is on expected line, but both the figures arenot significant. The elasticities of exchange rate are negative and significant for both the countries.

The values of adjusted R 2 are good for both the countries. The value of DWStatistics for the USA is more or less good without any serious autocorrelation problem, but for UK there is autocorrelation problem as the DW Statistics shows.

Regression Result : India

The model used for estimation in case of India is given in equation (4). Both thelinear and log-linear forms are estimated and the results are in table 6.5. We find that in the linear form the coefficient of T1 is negative and significant. This is contrary to the hypothesis. Similar isthe situation with the coefficient of NB, though it is not significant. Both the coefficients of PCI andDEP are of expected sign and these are significant. In case of India, the exchange rate is defined asthe value of US dollar in terms of Indian rupees. This is just the reverse of the definition of exchangerate used in the case of the USA and UK. So the expected sign of the coefficient of exchange rateshould be positive. We find that the coefficient of the exchange rate is positive and significant.Again, the value of adjusted R 2 is 0.95, which is good, though the value of D W statistic shows somedegree of autocorrelation.

The results of the log-linear form give the values of the coefficient which areelasticities. The coefficient of ln T1 is negative but significant. This does not confirm thehypothesis. The coefficient of NB is negative but not significant. Again, the coefficient of PCI is


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positive and significant, which supports the hypothesis. Thus for 1 percent increase in PCI thenumber of branches should increase by 0.177 per cent. The elasticity of domestic deposit is negativeand significant, while the elasticity of exchange rate is positive and significant.

The value of adjusted R 2 is 0.96 and the value of D W statistic shows some problemof autocorrelation.

Looking at the overall results of all the cases of three countries we find that the fit of the multiple regression is remarkably good which are reflected in the values of the coefficient of determination i.e., adjusted R 2. The problem of autocorrelation, though not completely absent, is notserious except in one case (log linear form for the country, UK in table 6.4). So far as the hypothesisare concerned we find mixed result. Again, studying the correlation matrices in the linear modelestimation we find some problem of multicollinearity, which is natural given the nature of data of themacro variables.

There is one limitation to the study of this type. The setting up of branches of international bank is mostly determined in recent time on reciprocity basis and thus market forcesdoes not play in the strict sense of the term. This is more true when the number of branches of foreign banks is taken as the dependent variable. Here, we are to assume that randomness still

persists in the demand for opening up of new branches of foreign banks. In this perspective this typeof quantitative study gives some idea about the potential demand of international banking in anycountry. When the relevant macro variables are changing with overall economic development.

Another important observation is that data base of international banking is not good particularly in some areas like the asset position and deposit mobilisation of foreign banks.Continuous time series data of these variables are difficult to get for many countries. An improveddata base in this regard can help more meaningful study of the problems of international banking.

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Determinants Intl Banking