External Commercial Borrowings in India and Its Sensitivity to Macroeconomic Factors: An Empirical Analysis

7/26/2019 External Commercial Borrowings in India and Its Sensitivity to Macroeconomic Factors: An Empirical Analysis

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*Correspondence concerning this article should be addressed to Surya Dev, Finance Area, Institute ofManagement and Information Science, Swagat Vihar, Bankuala, Bhubaneswar-751002. Email [email protected]/[email protected]

External Commercial Borrowings in India and its Sensitivity

to Macroeconomic Factors: An Empirical Analysis

By

Dr Surya Dev*, IMIS Bhubaneswar

Abstract

The share of external commercial borrowing (ECB) in the total external borrowing is rising in

India. The government is also progressively relaxing the rules to raise ECB. The present study

empirically examines ECB in India and its relationship with the exports, imports, index of

industrial production (IIP), foreign investment (FI), exchange rate (ER) and interest rate

differential (IRD) for the period September 1999 to September 2012 on a quarterly basis.It also

tries to ascertain the cost of ECB, which normally is believed to be cheaper, against the three

currencies like US Dollar (USD), Japanese Yen (JPY) and Great Britain Pound (GBP) for the

period 1978-2011.The methodology adopted for this study is based on the application of time

series econometrics.It is observed, on application of Augmented Dicky Fuller Tests and Philips

Pheron tests, that the time series of each variable is non-stationary at level and stationary at

first difference and, therefore, is subjected to the analysis as a Vector Error Correction Model(VECM). From the cointegrating vector it is found that there is a significant long term positive

relationship with IIP, IRD and ER and a negative relationship with imports and FI. In the short

run, imports, IRD, ER and FI have positive relationship with ECB, while exports and IIP show a

negative relationship. The Granger causality tests show that there is a unidirectional causality.

The variance decomposition analysis shows that most of the movements in ECB are explained by the

interest rate differential followed by the index of industrial production. The ECB in JPY has been

found to be cheaper than in the GBP or in USD in most of the years.

JEL Classification Code: F21, F31, F34, F37

Keywords: External Commercial borrowings, Interest Rate Differential, Automatic Route,

Approval route, Unit root tests, Granger Causality, Johansen Cointegration and VECM


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Section1: Introduction

In terms of total external debt, India is ranked the 30 thin the world (The World Factbook, 2011).

Since independence, India has been one of the largest borrowers from the external sources.

During 1948-49, following the partition, the country faced a severe balance of payment crisis

and stood at a massive deficit of US$1881 million. This deficit was entirely financed by running

down the sterling balances. The strain on the sterling reserves was eased as the external

assistance picked up from 1958-59 (Kapur, 1997). The imports, which increased at a faster

pace, in comparison to exports raised the demand of external finance. When the flow of external

assistance failed to meet the need for external finance, the recourse was taken to external

commercial borrowings (ECB) (Kapur, 1997; Singh M. ; 1993; Khanna, 1992; Patnaik, 1987).

Since 1980 there has been significant dependence on private capital flows in the form of ECB

and deposits from NRIs (Reddy, 2001). Presently, ECB is considered as an effective source of

external finance and preferred to domestic debt due to the interest rate advantage. But that the

external borrowing in South Asian countries, in late nineties of the last century, had led to

unprecedented financial crisis (Jalan, 2003), makes it imperative to look critically at the growing

dependence on ECB in India which is around 35 percent of total debt and the government

raised the ceiling on the borrowing to $ 30 billion, and even allowed the borrowers to raise in

Chinese Yuan (Kisan, 2011).

The present study attempts to establish, in the Indian context, the long run and short run

relationship between ECB, and different macroeconomic variables like imports, exports, index of

industrial production, exchange rate, interest rate differential and foreign investment to the

country. The study also examines if the ECB was really cheaper as stated. The cost of ECB is

calculate for three currencies namely, Japanese Yen, Great Britain Pound and the US Dollar.

The study is divided into eight sections. Section 1 gives the introduction to the paper. Section 2

deals with the review of the literature and section 3 with the objective of study. Sections 4, 5 and

6, respectively, discuss ECB and their trends, methodology undertaken and data analysis.

Section 7 elaborates on the cost of ECB, and section 8 states the conclusion of the study.

Section 2: Review of the Literature

In literature, there is no dearth of studies on gross external debt, which, however, largely focus

on its relationship with economic growth. While in some studies it is shown that external debt


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and economic growth are inversely related (Geiger, 1990; Savvides, 1992; Afxentious, 1993;

Swada, 1994; Deshpande, 1997; Fosu, 1999; and Karagol, 2002), in some other studies the

relationship is shown to be positive(Amoateng and Amoako-Adu, 1996; Choudhry, 1994; Smyth

and Hsing, 1995; Butts, Ivor, & Albert, 2012). Such conflicting conclusions arise as the nature

of external debt is unique to a specific country or a group of countries. But not much study is

found to have been carried out on the components of external debt.

Commercial Borrowing, which is essentially a component of external debt, only finds mention in

the studies on gross capital flows and their impact on the economy. Guillermo, Leiderman, &

Reinhart, (1993) argue that external conditions like recession and lower interest rates in the US

have increased the capital inflows into Latin America, whereas the domestic conditions like

terms of trade, monetary and fiscal policy, and exchange rate policies cause the fluctuations in

capital inflows. Nwala (2008) observes that countries that are unable to save enough to invest in

capital stock rely on savings of other countries for their economic development. Private sources

of foreign savings take the form of direct investment, portfolio investment, commercial bank

loans and trade credits. Banks loans have been preferred to FDI in 70s and 80s due to greater

flexibility in their usuage.

Similar studies have also been carried out on Indian economy. Khanna (1992) observes that

total foreign liabilities since independence till 1983 were within the manageable limits of $ 28.7

billion. Post 1983, the countrys debt increased with structural changes being observed with

short term and commercial borrowings constituting a greater portion of the liabilities as most of

the companies relied more on import than on exports (Singh ,1993 ). The large trade deficits

were mostly due to reliance on petroleum and bulk imports. The country faced crisis in 79-80

and 1990 due to oil shock. These rising deficits were financed by costly commercial borrowings

as the climate for cheaper and concessional debt had turned adverse. Kohli (2001) finds that

the capital inflows were mostly in the form of Foreign Direct Investment (FDI), Foreign

Institutional Investment (FII) and External Commercial Borrowing (ECB). FDI and FII arevoluntary in nature while ECB is discretionary and monitored very closely. Commercial

Borrowings have been higher in normal times than in the years of recession. The declining

interest rate in credit market, relaxation of rules and higher interest rate differential have

attracted ECB to India (Saxena ,2009; Ghosh & Chandrasekhar,2009); a component of which is

being raised by domestic firms through syndicated loans. Singh (2007) examines the

macroeconomic factors during the period 1993-2007 that had driven Indian corporate

preference towards overseas borrowings. The author has found out that long run demand for


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the overseas commercial loans is influenced by the pace of domestic activity which in turn is

measured by the index of industrial production (IIP), interest rate differential between domestic

and international markets, liquidity conditions measured by money supply. Similarly, Verma

Prakash (2011) provides empirical evidence of sensitivity of capital flows to interest rate

differential. They find equity flows, i.e. FDI and FII for the period 2000-01 to 2009-10 on a

quarterly basis are not sensitive to interest rate differential but the debt flows, i.e. ECB,

NR(E)RA and FCNR(B) exhibit statistically significant sensitivity. The authors also observe that

the interest rate differential, IIP and Current Account Deficit (CAD) induce ECB while exchange

rate moves opposite to it.

The emphasis on capital inflows is to accelerate the industrial and capital growth. But,

Mazumdar (2005), in his work, observes that capital flows have not contributed to IIP or GDP

growth for the period 1971-72 to 1999-00.

Section 3: Objectives of the present study

It is quite evident from the review of the literature that not much study has been carried out

exclusively on the ECB. The present study, therefore, proposes to focus on ECB and their

relationship with macroeconomic variables like imports, exports, index of industrial production,

exchange rate, foreign investment, and interest rate differential for the period Sept 1999 to Sept

2012 on a quarterly basis. The study tries to find out whether ECB had been costlier or cheaper

in the period of study spread across three currencies i.e. US Dollar (USD), Japanese Yen (JPY)

and Great Britain Pound (GBP).

Section 4: ECB in India and their trends

External Commercial Borrowings (ECB) refer to commercial loans in the form of bank loans,

buyers credit, suppliers credit, securitized instruments (e.g. floating rate notes and fixed rate

bonds, non-convertible, optionally convertible or partially convertible preference shares) availed

from non-resident lenders with a minimum average maturity of 3 years (master circular of

Reserve Bank of India updated till Jan 20, 2012). The ECB can be availed either through

automatic route or approval route, and the RBI guidelines lay down the purposes for which it

can or cant be availed.

During the period of study, ECB, as a percentage of total external debt, has risen from 28.13

percent to 34.9388 percent (fig 1). The corporations preferred the automatic route to approval

route for raising ECB. It is significant to find that between March 2004 and Sept 2012, there are


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5,871 cases, where ECB is raised through the automatic route as against only 585 cases

through approval route. (Refer Fig 2).

Fig 1: Ratio of Total ECB to total External Debt, imports and exports

Fig 2: Number of Proposals of ECB through the automatic route and approval route

Source: Data collected from RBIs website


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Of many purposes for which ECB can be raised conforming to the RBI guidelines, the

corporates have mostly raised it for import of capital goods, modernization, new projects and

rupee expenditure for loc CG. These purposes account for 84 percent of the total number of

proposals in automatic route, and 70 percent of the proposals in approval route. Between March

2004 and Sept 2012, import of capital goods constituted 23.57 percent and 27.18 percent,

respectively, of the total proposals in the automatic route and approval route. ECBs were also

raised to repay the previous loans like ECB, FCCB buyback and refinancing of the old loans. In

spite of the guidelines not permitting ECB for working capital and general corporate use, it is

observed that ECB had also been raised for such purposes.

ECB has been mostly a medium term borrowing. The tenure of these loans, in majority cases,

range from 5 to 10 years followed by 3 to 5 years. There had been a few proposals which were

beyond 20 years, and also less than three years. (table 1)

Table 1: Maturity period of ECB under Automatic Route and Approval Route for the

period March 2004-Sept 2012

Duration Automatic route Approval route

Less than 3 years 88 13

3-5 years 1532 87

5-10 years 3660 377

10-15 years 453 90

15-20 years 130 14

20 years and above 12 3

The compounded annual growth rates (CAGR) mentioned in table 2 are calculated using semi-

log equation. During the period from Sept 1999 to Sept 2012, ECB in India grew with a

compounded annual growth rate (CAGR) of 14.52 percent while total external debt grew at a

rate of 11.952 percent. This indicates declining external assistance. Since the imports were

growing at a faster rate than the exports the current account deficit led to a faster growth in

ECB. It can also be observed from figure 1, that ECB to imports ratio was always more than 1

except for a few years, which shows that imports are not the only purpose for raising ECB.

While ECB to exports ratio was always greater than 1.It means that exports revenues are not

enough to repay the commercial borrowings. The most disturbing trend is the rate at which the

short term external debt is growing. It is growing at a rate of 31.61 percent and if it persists then


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the short term debt will become equal to long term debt in 6.13 years. It will certainly be a matter

of concern for the Indian economy as the majority of the South East Asian economies faced

crisis because of such borrowings (Singh, 1998).

Among the different components of ECB, Commercial Bank Loans grew at a rate of 19.04

percent and appeared to be the most popular channel for accessing external commercial

borrowings. (Refer to table 2)

Table 2- Growth Rates of different components of ECB

Items CAGR

Buyers credit 16.305%

Suppliers credit -5.923%

Export credit component of bilateral credit 3.3961%

Commercial bank loans 19.45%

Securitised borrowings 9.71%loans/ securitised borrowing with multilateral and bilateralguarantee 2.127%

total ECB 14.52%

total external debt 11.952%

Long term External Debt 9.73%

Short term External Debt 31.61%

Exports 18.63%

Imports 21.58%

IIP 7.19%Note: Calculated from the data collected from the Ministry of Finance Website

Section 5: Data and Methodology

5.1: Data and Variables

The data taken, on a quarterly basis, for the present study belong to the period spanning from

Sept 1999 to Sept 2012, and the sources are Handbook of Indian Statistics published by RBI,

Ministry of Finance data on debt and Federal Reserve website for the dollar interest rates. The

variables IMP, EXPORTS, INTDIFF100, IIP, ER, FI and TECB, used in the present study,

denote respectively imports, exports, interest rate difference, index industrial production,

exchange rate, foreign investment and total ECB. The interest rate differential is reckoned

taking the difference between yields of 10 year maturity bonds of government of India and US

treasury. Foreign investment is taken as the sum of FDI (Foreign Direct Investment) and FII

(Foreign Institutional Investment).


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In this paper, Johansen Cointegration and vector error correction tests are used to determine

the long term and short term causal relationship among TECB and other macroeconomic

variables mentioned above. All the variables, except INTDIFF100 as it is expressed in

percentages, are converted into natural log. The process is divided into three steps, namely,

unit root tests to test the stationarity of the data series followed by Johansen Jusellius

cointegration test to study the long term relationship, and finally Error Correction Model to

estimate the long run and short run relationships.

5.2: Econometric Methods

5.2.1: Stationarity Tests

The tests relied upon are the augmented Dicky-Fuller (ADF) test and Philips Pheron (PP) test.

Three possible forms of the ADF test are given by the following first difference equations.

(Gujurati & Sangeetha, 2010)

-------------------------------------- (i)

----------------------------------(ii)

-------------------------(iii),

where denotes the variable at time t. These three equations are, however, based on threedifferent assumptions regarding the presence of the deterministic elements and . TheADF test procedure has been undertaken by examining the optimal lag length using Scwartz

Information Criterion before proceeding to identify the probable order of stationarity.

The Philips Pheron is a non parametric test. Implicit in it an automatic correction to the DF

procedure that allows for auto correlated residuals. The test provides similar conclusions. The

test regression for PP test is AR (1) process:

------------------------------------------- (iv)

The null hypothesis states that the series has unit root in other words it is non-stationary,

whereas according to the alternative hypothesis the series is assumed to be stationary. Thus

whether a series is stationary or non-stationary depends on the comparison between the test

alternative value and the critical value; if the test alternative value is more negative than the


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critical value then the series under test is stationary and the null hypothesis of a unit root stands

rejected (Brooks, 2008).

5.2.2: Testing for Cointegration and VECM (Johansen approach)

A vector time series Yt [y1t,y2t,y3t,ynt] is said to be cointegrated if each yit (i=1,2,3n) is

I(1), that is nonstationary with a unit root, but some linear combination of the series aYt is

stationary or I(0) for some non zero (nx1) vector a. Cointegration shows that although many

developments can cause permanent changes in the individual yt s of vector time series Yt, there

exists some long run equilibrium relation represented by a linear combination ay t of the

individual yts. In this study, cointegrating test, using the Johansen technique based on Vector

Auto Regression (VAR), has been undertaken. The general co-integration equation is given as

--------------------------------------------- (v)Where, k shows the number of lags. If optimal lag is 1, the equation above reduces to

----------------------------------------------------- (vi)

This equation can be reformulated in a Vector Error Correction Model (VECM) as

where

are constants in the long run model i.e. the cointegrating equation and short run model(VAR).

=, includes the speed of adjustment to equilibrium coefficients, while is the long run

matrix of coefficients.

Yt-1is equivalent to the error correction term.

There are two test statistics for cointegration under this approach.


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Where, r is the no.of cointegrating vectors under the null hypothesis and i is the estimated

value for the ith ordered Eigen value from matrix.

The optimum lag has been determined using Schwartz Information criterion and also other

residual tests have been undertaken to check for the serial correlation, heteroschedasticity and

VAR Granger causality. A variance decomposition analysis is also undertaken.

In this study seven endogenous variables have been taken i.e. Yt = [LTECBt, LIMPt,

LEXPORTSt, LIIPt, LERt, LFIt, INTIDIFF100], where L stands for natural log. Using Schwartz

Information Criterion in VAR Lag order selection criteria, we find in our study that the optimal lag

is 1.

Section6: Data Analysis and Findings

6.1: Results from the Stationarity tests

The results of both the ADF and PP tests of all the variables for the three alternative models at

levels and at first differences are mentioned, below, in tables 3 and 4. These tables show the

presence of unit roots at levels, and rejection of null hypothesis at 5% level of significance in

their first difference. The stationarity of the variables at first difference suggests that they are

integrated to order one i.e. I(1), except for exports, which, under the assumption of constant

and trend, is I(0). However, the robustness of the other two models helps in accepting it to be

I(1) and not I(0) for the test of cointegration. The results of the PP test are reported in table 4

and these confirm to those of the ADF tests.

Table 3 :ADF Test Results

Unit Root Tests of Logarithm Levels

variables Constant

(Lag)

Constant and Trend

(Lag)

None (Lag)

TECB 1.502046 (0) -1.35937 (0) 3.873763(0)

IMP 0.145632 (0) -3.36148 (0) 3.524506 (0)

EXPORTS -0.31375 (0) -3.88427* (0) 3.566031 (0)

IIP -0.61531 (5) -3.08887 (4) 2.255318 (5)

ER -2.68128 (2) -2.67588 (2) 0.657391 (4)

INTDIFF100 -1.3594 (0) -1.98208 (0) -0.54087 (0)


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FI -1.61746 (0) -1.17569 (0) 0.73357 (0)

Unit Root Tests at First Differences

variables Constant (lag) Constant and Trend None

TECB -5.91123* (0) -6.2768* (0) -4.86345* (0)

IMP -6.25986* (2) -6.28317* (2) -5.58282* (0)

EXPORTS -6.46736* (2) -6.40842* (2) -6.44011* (0)

IIP -2.97804* (4) -2.93982 (4) -1.773 (4)

ER -4.68026* (3) -4.7326* (3) -4.66143* (3)

INTDIFF100 -6.67077* (0) -7.06749* (0) -6.7356* (0)

FI -7.48043* (0) -7.69829* (0) -7.3809* (0)

* it denotes significance at 5% level and rejection of null hypothesis of non-stationary.

Table 4: PP Test Results at Levels

Unit Root Tests of Logarithm Levels

variables Constant (BW) Constant and Trend

(BW)

None (BW)

TECB 1.308073 (3) -1.40504 (3) 3.183795 (4)

IMP 1.38041 (51) -3.1528 (13) 9.515803 (47)

EXPORTS 0.196952 (40) -3.5729* (8) 11.30987(33)

IIP -1.0212 (15) -3.48285 (7) 4.109007 (14)

ER -1.63775 (3) -1.71451 (3) 0.774354 (3)

INTDIFF100 -1.50384 (3) -1.98208 (0) -0.56388 (2)

FI -1.60239 (1) -0.97201(1) 0.868987 (2)

Unit Root Tests at First Differences

variables Constant (BW) Constant and Trend None

TECB -5.94195* (3) -6.25596* (2) -5.07422* (4)

IMP -9.37817* (35) -9.40733* (31) -5.63088* (5)

EXPORTS -20.2024* (50) -19.7024* (50) -6.4365* (1)

IIP -9.96976* (16) -9.95629* (16) -7.60085* (12)

ER -5.43146* (3) -5.45651* (3) -5.39336* (3)

INTDIFF100 -6.66218 (2) -7.06948 (2) -6.72895 (2)

FI -7.47288 (2) -7.71213 (2) -7.3809 (0)

* it denotes significance at 5% level and rejection of null hypothesis of non-stationary.


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6.2: Results of Johansen Cointegration Test

From table 5, it is evident that there is one cointegrating relationship among the variables. The

null hypothesis of no cointegrating relationship can be rejected in favour of the alternative

hypothesis i.e. there is one cointegrating relationship as the trace statistic is greater than the

critical value. The lag order has been fixed using SBC criterion in unrestricted VAR model.

Table 5: Johansen Cointegration test

Unrestricted cointegration rank test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.778144 163.5456 125.6154 0

At most 1 0.443228 86.7535 95.75366 0.1771

At most 2 0.366125 56.88788 69.81889 0.3431

At most 3 0.225588 33.63683 47.85613 0.5218

At most 4 0.176915 20.59859 29.79707 0.3831

At most 5 0.128055 10.6691 15.49471 0.2327

At most 6 0.069627 3.680639 3.841466 0.055

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.778144 76.79207 46.23142 0

At most 1 0.443228 29.86562 40.07757 0.4327

At most 2 0.366125 23.25106 33.87687 0.5114

At most 3 0.225588 13.03824 27.58434 0.8828

At most 4 0.176915 9.929488 21.13162 0.7513

At most 5 0.128055 6.988461 14.2646 0.4905

At most 6 0.069627 3.680639 3.841466 0.055


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Table 6: Long Run Normalized Co integrating Equation (taking TECB to be the dependent

variable)

Variables Coefficients T statistics

LTECB(-1) 1 -

LIIP(-1) 5.394022 15.3667

LIMP(-1) -1.178441 -6.33924

LEXPORTS(-1) 0.116038 0.50185

LER(-1) 1.699964 5.89309

LFI(-1) -0.089875 -1.72127

INTDIFF100(-1) 9.806614 11.5155C -6.154277

From table 6, it is observed that there is positive relationship of index of industrial production

(IIP), Interest Rate Differential (INTDIFF100), exports (EXPORTS), exchange rate (ER) with

ECB in the long run and negative relationship with imports (IMP) and foreign investment (FI). All

the coefficients, except for that of exports and foreign investment, are significant. Thus, the long

run cointegrating equation becomes

LTECB = -6.15427 + 5.394022 LIIP -1.178441 LIMP + 0.116038 LEXPORTS + 1.699964 LER -

0.089875 LFI +9.806614 INTDIFF100

6.3: Results from Vector Error Correction Model

By specifying the long run relationships in the error correction model, the short run and the long

run relationships among the variables on the right hand side were estimated. The coefficient of

the error correction term contains information about the effect of the past values on the

variables. A significant coefficient implies that the past equilibrium errors play a role in thecurrent outcomes. The short run dynamics are captured through the individual coefficients while

the information from ECM shows the speed of adjustment for long run equilibrium. The

adjustment coefficient is negative and also statistically significant, which shows that the

deviation from the long run equilibrium gets corrected supporting a long run relationship among

the variables. The deviation gets corrected by 0.1606 per period moving towards long term

equilibrium.


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Table 7: Short term Causality for the time series data (DLTECB)

Variables Coefficient T-statistics

ECM -0.1606 -2.45337

D(LTECB(-1)) 0.06618 0.46819

D(LIMP(-1)) 0.260756 2.33345

D(LEXPORTS(-1)) -0.187219 -1.33032

D(LIIP(-1)) -0.122796 -0.43605

D(LER(-1)) 0.336783 1.06439

D(LFI(-1)) 0.028253 0.85393

D(INTDIFF100(-1)) 2.986775 1.87094C 0.026517 2.17890

2SC (1) 49.10168 (0.469)*

2Norm (1) 16.7581 (0.2693)

2Het (1) 451.95 (0.4389)

*The figures in the brackets show the probability value.

In the short run, imports and interest rate differential have a positive impact on external

commercial borrowings and the coefficients are statistically significant. Exports and IIP have,

however, negative impact on external commercial borrowing, while exchange rate and foreign

investment are positively related to external commercial borrowing, though the coefficients of

the same are not statistically significant. From the diagnostic 2 applied to the data implies that

there is no serial correlation, the residuals follow the normal distribution and there is no

heteroscedasticity.

6.4: Results from Granger Causality Tests

The results of the Granger Causality tests are reported in table 8. It can be seen that the

variables combined reject the null hypothesis in favour of alternative, and that there is a

unidirectional causality. Further among the individual factors imports and interest rate

differential cause movements in ECB.


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Table 8: Results from VEC Granger Causality

Sample: 1999Q3 2012Q3 (Included observations: 51)

Dependent variable: D(LTECB)

Excluded Chi-sq df Prob.

D(LIMP) 5.445009 1 0.0196

D(LEXPORTS) 1.769757 1 0.1834

D(LIIP) 0.190135 1 0.6628

D(LER) 1.132934 1 0.2872

D(LFI) 0.729198 1 0.3931

D(INTDIFF100) 3.500426 1 0.0614

All 12.77821 6 0.0467

6.5: Results from Variance Decomposition Analysis

The variance decomposition analysis arising from the error correction model provides an important

insight into the relative contribution of the various factors to the behavior of ECB. It can be

observed that most of the movements in ECB, for the 10 quarters horizon, are explained by the

INTDIFF100 followed by the IIP. Both the factors combined explain 28 percent of the movements. The

contribution of imports to ECB fluctuation, though small, has increased over the periods while the

contributions from the other factors are insignificant. (Refer table 9)

Table 9: Variance Decomposition of LTECB

Period S.E. LTECB LIIP LIMP LEXPORTS LER LFI INTDIFF100

1 0.058025 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.091959 89.45440 2.584993 0.690099 0.041626 0.641717 0.095183 6.491981

3 0.121628 79.93032 7.223286 1.021793 0.582864 0.617519 0.084259 10.539964 0.145132 75.84768 8.893759 1.166916 0.565288 0.512339 0.081774 12.93224

5 0.165018 73.41078 9.800073 1.297046 0.606690 0.441706 0.067364 14.37634

6 0.182492 72.10354 10.26797 1.359758 0.653866 0.412635 0.055807 15.14643

7 0.198614 71.08444 10.75369 1.376158 0.675267 0.394066 0.047121 15.66925

8 0.213518 70.36160 11.06632 1.395797 0.676543 0.373977 0.040834 16.08493

9 0.227365 69.83981 11.25595 1.421921 0.684449 0.358762 0.036013 16.40309

10 0.240416 69.42985 11.41798 1.438233 0.696458 0.349781 0.032230 16.63547


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Section 7: Cost of ECB

From the variance decomposition analysis, it can be clearly seen that ECB fluctuations are

mostly due to the change in the interest rates between the two countries followed by the

movements in the real activities within the economy. In this context, it becomes more important

to find if ECB were really cheaper than domestic borrowings taking into account the exchange

rate fluctuations.

7.1: Calculation of Cost of ECB

With regard to the calculation of the cost of ECB, the methodology adopted is as follows:

Let X be the amount in dollars raised at a given time t. If the E(t) is the USD-INR exchange rate

prevailing at time t, then rupee equivalent of $X in INR is X E0(t). If T (in years) is the time period

of repayment of this borrowed amount, r1, r2, r3, .. rTare the annual rates of interest in US and

R1,R2,R3, . RTare domestic annual rates of interest, and E(t+T )is the exchange rate in

the Tth year, then the borrowed amount of X dollars in US demands payment of ,which in rupee term becomes

Whereas the rupee equivalent of X dollar that is X E 0(t) as domestic borrowing would demand

repayment

It is assumed that interest rates are constant for the year and change in each year and both the

loans are taken for the same period. It is clear that the ECB is either costlier or cheaper

depends on whether

> , or < which implies either or ,

where


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Thus if calculated forward exchange rate is less than actual future spot rate, then the ECB is necessarily costlier. In this study, the value of T is fixed as 10 yearsbecause the maturity period of ECB is found to be ranging from 5 to 10 years.

7.2. Results of Calculation

In the present study, the cost of external commercial borrowing has been calculated applying

the above methodology considering the exchange rates of three currencies namely Japanese

yen, US Dollar and UK pound. The exchange rates and the lending rates, across different

countries for the period from 1978 to 2012, are, respectively, taken from the websites of RBI

and the World Bank. It is observed that (table 10) US $ is the most preferred currency for

external borrowing. In this currency the borrowing has increased from 41.4 percent in 1994 to

55 percent in 2012 while borrowings in Japanese yen and in Pound Sterling have declined.

Table 10: Currency Composition of External Debt (in percent)

Currency

End-

March1994

End-

March2000 2001 2005 2006 2007 2008 2009 2010 2011 2012

US Dollar $ 41.4 51.4 55 48 48.8 51.1 55.3 54.1 53.2 53.6 55

SDR 14.9 13.2 12.8 14.2 14.3 12.4 10.6 9.8 10.7 9.7 8.7

Indian

Rupee 14.8 11.6 12.4 19.6 18.8 18.5 16.2 15.4 18.7 19.5 21.4

Japanese

Yen 13.7 12.7 10.1 10.5 10.9 11.4 12 14.3 11.5 11.3 9.1

Euro* 6.9 5.8 4.5 4.4 3.9 3.5 4.1 3.6 3.7 3.7

Pound

Sterling 3.3 2.9 2.8 2.6 2.6 2.4 2.2 1.9 1.8 1.7 0.9

Others 2 0.8 1 0.5 0.2 0.3 0.2 0.4 0.5 0.5 1.2

Source: Report on external debt MOF various issues.


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Table 11: Result on Cost of ECB

No.of times it is cheaper no.of times it is costlier

time ofMaturity Currency

1978-2011

1978-1990

1991-2011

1978-2011

1978-1990

1991-2011

1

JPY 20 7 13 13 5 7

GBP 21 5 16 12 7 4

US$ 17 5 12 16 7 8

2

JPY 20 6 13 12 5 6

GBP 20 5 15 12 6 4

US$ 16 6 8 16 5 11

3

JPY 19 5 12 12 5 6

GBP 19 5 14 12 5 4

US$ 16 5 8 15 5 10

4

JPY 19 5 12 11 4 5

GBP 18 5 13 12 4 4

US$ 15 5 7 15 4 10

5

JPY 19 5 12 10 3 4

GBP 17 5 12 12 3 4

US$ 14 5 6 15 3 10

6

JPY 18 5 11 10 2 4GBP 16 5 11 12 2 4

US$ 14 5 6 14 2 9

7

JPY 17 4 10 10 2 4

GBP 15 4 10 12 2 4

US$ 14 4 6 13 2 8

8

JPY 16 4 9 10 1 4

GBP 14 3 9 12 2 4

US$ 14 3 6 12 2 7

9

JPY 15 4 8 10 0 4

GBP 14 2 9 11 2 3US$ 14 2 6 11 2 6

10

JPY 14 3 7 10 0 4

GBP 13 1 8 11 2 3

US$ 14 1 6 10 2 5

Further ECB was not always cheaper to finance in comparison to domestic loans, as there were

many years when ECB was costlier(Refer table 11). Moreover, it can also be seen that with the


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variation in tenure of loan number of times ECB has been cheaper has also varied. ECB in JPY

and GBP would have been cheaper than US $ as we can find that difference between number

of times cheaper is always more than number of times costlier. But US $ ECB is mostly opted

due its liquidity. Thus, interest rate differential may motivate the corporate to borrow huge

amounts of loans in the form of ECB, but it is certainly not necessary that they will pay less in

comparison to the domestic loan as exchange rate keeps fluctuating.

Section 8: Conclusion

ECB has been mostly a medium term borrowing, ranging from 5 to 10 years. Mostly it has been

raised for import of capital goods, modernization, new projects and rupee expenditure for loc

CG. It has been for these four purposes that 84% of the ECB is raised through automatic route

and 70% of the ECB is raised through approval route. During this period, ECB as a percentage

of total external debt has risen from 28.13 % to 34.9388 %. Of different components of ECB,

Commercial Bank Loans have grown at a rate of 19.04 percent and have been the most popular

channel for accessing ECB. It is, however, disturbing to find that the risky short term external

debt is growing at a rate of 31.61 percent and if it persists then the short term debt will become

equal to long term debt in 6.13 years.

On subjecting the data to appropriate econometric analysis, the study further reveals that the

long run ECB is influenced by the import, index of industrial production, interest differential,

exchange rate, and foreign investment. While ECB exhibits a positive relationship with IIP,

INTDIFF100, andER, it exhibits negative relationship with IMP and FI. In the short run, imports

and interest rate differential have positive relationship with ECB, while, export, though its

coefficient is not significant, exhibits a negative relationship. From Granger causality tests,

unidirectional causality is observed with all the factors as a whole. Among the individual factors

imports and interest rate differential mainly cause movements in ECB.

From the calculation of cost of ECB it is observed that ECB route has always not been cheaper

and mostly depends on the exchange rate movements, tenure, interest rate and type of

currency.

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External Commercial Borrowings in India and Its Sensitivity to Macroeconomic Factors: An Empirical Analysis