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India’s trade balance: the role of income and exchange rates
Journal of Policy Modeling 24 (2002) 437–452
India’s trade balance: the role of income and exchange rates Tarlok Singh∗ School of Economics, The University of New South Wales, Sydney 2052, Australia Received 30 May 2000; received in revised form 9 January 2002; accepted 1 March 2002
Abstract This study estimates the balance of trade model on Indian data from 1960 to 1995 using a reduced-form specification similar to Rose [J. Int. Economics, 30 (1991) 301]. The results show that the real exchange rate (trade weighted) and domestic income play a significant, while the world income plays an insignificant or a less significant role in affecting the balance of trade in India. The trade effects of real exchange rate are different from those of nominal exchange rate. The study suggests the need to monitor the real rather than nominal exchange rate, and in this major focus is required on weighted and more specifically the trade weighted real effective exchange rate. Besides, the devaluation-based adjustment policies need to be supplemented by stabilisation policies to ensure domestic price stability and achieve the desired effects of nominal exchange rate changes (devaluation) on the balance of trade. © 2002 Society for Policy Modeling. Published by Elsevier Science Inc. All rights reserved. Keywords: Trade balance; Income; Exchange rate
1. Introduction The role of income and exchange rate in affecting the trade flows is well recognised in the literature and a large number of studies have been conducted analysing the effects of these factors on exports, imports and the balance of trade (payments). The studies conducted until the late 1980s have mostly used a single ∗ Present address: Department of Economic Analysis and Policy, Reserve Bank of India, 9th Floor, New Central Office Building, Shahid Bhagat Singh Road, Fort, Post Box No. 1036, Mumbai 400 001, India. Tel.: +91-22-2675937; fax: +91-22-264-1154. E-mail address: [email protected] (T. Singh).
0161-8938/02/$ – see front matter © 2002 Society for Policy Modeling. PII: S 0 1 6 1 - 8 9 3 8 ( 0 2 ) 0 0 1 2 4 - 2
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equation framework (Goldstein & Khan, 1985) with little cognisance of the unit root problems associated with level variables, while those conducted subsequently have mainly relied on the use of a maximum likelihood (ML) systems estimator of Johansen (1991) and have taken into consideration the time series properties of model variables. In addition to the development of a systems based Johansen (1991) estimator, the early 1990s has also witnessed significant improvements in single equation methodology. Saikkonen (1991) and Phillips and Loretan (1991) have developed optimal single equation estimators for cointegrated systems which generally have better asymptotic properties as compared to conventional single equation estimators. But to date no systematic attempt has been made to use both ML systems estimator and the optimal single equation estimators and analyse the sensitivity of results to the use of different methodologies. This study uses both these estimators and estimates the balance of trade model for India. Besides, most studies have drawn conclusions from the model estimated using mainly a single exchange rate series along with other variables like domestic and foreign incomes. Such exchange rate was measured in terms of either unweighted or less comprehensively weighted (imports or exports) exchange rate. This study uses a comprehensive set of both weighted (trade and export) and unweighted (official and black market) exchange rates and shows the sensitivity of results to the measure of exchange rate used. The scheme of the study is as follows. Section 2 presents a discussion on the specification and estimation of the balance of trade model. Section 3 presents the empirical results. Section 4 contains the conclusions emerging from the study.
2. Model specification and estimation The study uses a reduced-form balance of trade model similar to Rose (1991) to analyse the effects of the exchange rate and domestic and foreign incomes on the balance of trade in India. Rose uses a “two-country” model of trade which assumes that the imports and domestically produced goods are imperfect substitutes. The import (export) demand and supply functions are modelled as the functions of relative prices of imports (exports) and expenditure or output. Specifically, the demand for imports depends on real income and the relative price of imported goods, while the demand for a country’s exports (foreign imports) depends on foreign income and the foreign relative price of imports. The supply of exportables in each country is assumed to depend only on the relative prices of exportables. Rose uses market clearing equilibrium conditions to solve these demand and supply functions and obtain a reduced-form specification. The trade balance is expressed as a function of real exchange rate and the domestic and foreign real incomes. A log-linear approximation to Rose specification can be written as ln TB(t) = 0 + 1 ln Y (t) + 2 ln Y ∗ (t) + 3 ln R(t) + ε(t)
(1)
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where TB(t) represents the trade balance (expressed as the difference between real exports and real imports), Y(t) is domestic real income, Y∗ (t) is foreign real income, R(t) is real exchange rate and ε(t) is the error term. The real exchange rate is defined as the number of units of foreign currency per unit of domestic currency and accordingly an increase in R(t) indicates an appreciation and a decrease denotes the depreciation. Conventional economic theory predicts that 1 , 3 < 0 and 2 > 0 in model (1). Krugman and Baldwin (1987) use a similar model to explain trade flows in the USA, France, Germany, Japan and Korea. Model (1) is estimated on annual data from 1960 to 1995 using both ML systems estimator of Johansen (1991) and the optimal single equation estimators of Saikkonen (1991) and Phillips and Loretan (1991). Since the trade balance TB(t) as specified in model (1) generally takes negative values, it is not possible to take its logarithmic transformation. Therefore, the trade balance is measured in terms of logarithms of the ratio of real exports to real imports of goods and services (ln RXM), obtained by deflating nominal exports and imports of goods and services (Rs. crores) by the unit value indices of exports and imports respectively (both at base: 1980–1981 = 100). The domestic real income Y(t) is measured as real gross domestic product at market prices (GDPMP) at 1980–1981 prices (Rs. crores), while world real income Y∗ (t) is taken in terms of the index of world gross domestic product (GDP∗ ; base: 1980 = 100). The real exchange rate R(t) is measured alternatively in terms of the trade (REERTW) and export (REERXW) weighted real effective exchange rate (both based on 36-country bilateral weights; base: 1980 = 100) and the official (RER) and black market (BLKRER) unweighted real exchange rate. The nominal series of the trade (NEERTW) and export (NEERXW) weighted and the official (NER) and black market (BLKNER) unweighted exchange rates are also used to estimate the model.1 A rise in exchange rate indicates an appreciation and a decline denotes the depreciation of the Rupee.
3. Empirical results The trade weighted real effective exchange rate REERTW theoretically provides the best measure of real exchange rate for the reasons of its comprehensive coverage of both exports and imports, as compared to the export weighted real effective exchange rate REERXW with coverage of only exports and the unweighted official 1 The indices both of nominal and real effective exchange rate are based on 36-country bilateral weights (base: 1980 = 100). In the construction of these indices, the exchange rate of a currency is expressed as the number of units of numeraire (SDRs) which equal one unit of the currency (SDRs per currency). The unweighted nominal series of official exchange rate (NER) is transformed into real (RER) series by using world consumer price index relative to domestic wholesale price index. Since a substantial amount of black market transactions are effected in US dollars, the US consumer price index relative to domestic wholesale price index is used to transform nominal (BLKNER) into real (BLKRER) series of black market exchange rate. The unweighted official and black market exchange rates represent the number of units of foreign currency (which is US$) per unit of Indian Rupee.
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RER and black market BLKRER real exchange rates with no regard to the pattern of trade. To examine the sensitivity of results to the measure of exchange rate used, four variants of model (1) are estimated using, along with other variables, the real exchange rate series of REERTW (Model I), REERXW (Model II), RER (Model III) and BLKRER (Model IV). These real exchange rate series are then replaced by the corresponding nominal series (with other variables retained in real terms) and the model is re-estimated to analyse whether the coefficient of real exchange rate can be interpreted as the measure of approximate response of trade balance to nominal exchange rate. The Dickey–Fuller tests for the null of a unit root (Dickey & Fuller, 1981) suggest that all the series are I(1) in log levels and I(0) in first log differences. The model selection criteria AIC and SC and the Sims (1980) LR test consistently suggest the lag length choice of one for all the alternative VAR models, and therefore the lag length one is used in the Johansen estimator.2 The residuals of these VAR models estimated with lag one were found to have desired normality and white-noise properties as indicated by various univariate and multivariate residual misspecification tests.3 In the optimal single equation estimators of Saikkonen (1991) and Phillips and Loretan (1991), the model structure with 1 year lead–lag and contemporaneous regressors is used.4 3.1. Cointegrating relationship and long-run elasticities The results obtained from the Johansen estimator for the cointegrating relationship among variables are presented in Table 1. In the model estimated using trade weighted real effective exchange rate, both trace and -max statistics consistently reject the null of r ≤ 0 against the alternative r ≥ 1 at 5% level and provide a strong evidence for the presence of one cointegrating vector in the model (Model I). The results obtained from the model estimated using export weighted real effective exchange rate show that the model variables continue to be characterised by the presence of long-run equilibrium relationship, but at a relatively reduced level of significance. Both trace and -max statistics reject the null of r ≤ 0 against the alternative r ≥ 1 at 10% level (Model II). The use of unweighted official and black 2 The results for the (i) Dickey–Fuller unit root tests, (ii) model selection criteria including Akaike’s Information Criterion (AIC) and Schwarz Criterion (SC) and the (iii) Sims (1980) Likelihood Ratio (LR) test, are not reported to conserve space. 3 The univariate test statistics are based on estimated residuals of each of the VAR equation and include three central moments of estimated residuals (standard deviation, skewness and kurtosis), LM test (Engle, 1982) for autoregressive conditional heteroskedasticity (ARCH) and modified version of Shenton–Bowman test for normality of individual residual series (Doornik & Hansen, 1994; Shenton & Bowman, 1977). The multivariate test statistics are based on the estimated auto- and cross-correlations of the residuals of overall VAR system and include the LM test for the first and fourth order autocorrelation (Godfrey, 1988) and 2 test for normality. The 2 test for normality is based on the multivariate version of the univariate Shenton–Bowman test. The results of these tests are not reported to conserve space. 4 The estimation of the model with two year lead–lag and contemporaneous regressors did not make any qualitative difference to results and therefore these results are not reported to conserve space.
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Table 1 Johansen’s cointegration test for India’s balance of trade modela Null hypothesis
Eigenvalues
Trace test
Maximal Eigenvalue test -max
95% critical values
Model I: Y = [ln RXM ln GDPMP ln REERTW ln GDP∗ ] r≤0 0.5511 48.38 47.210 r≤1 0.2770 21.14 29.680 r≤2 0.2189 10.12 15.410 r≤3 0.0492 1.72 3.762
27.23 11.03 8.40 1.72
27.067 20.967 14.069 3.762
Model II: Y = [ln RXM ln GDPMP ln REERXW ln GDP∗ ] r≤0 0.5369 47.05b 47.210 r≤1 0.2701 20.87 29.680 r≤2 0.2162 10.17 15.410 r≤3 0.0540 1.89 3.762
26.18b 10.70 8.28 1.89
27.067 20.967 14.069 3.762
Model III: Y = [ln RXM ln GDPMP ln RER ln GDP∗ ] r≤0 0.4268 37.68 47.210 r≤1 0.2344 18.76 29.680 r≤2 0.2148 9.68 15.410 r≤3 0.0419 1.46 3.762
18.92 9.08 8.22 1.46
27.067 20.967 14.069 3.762
Model IV: Y = [ln RXM ln GDPMP ln BLKRER ln GDP∗ ] r≤0 0.3171 27.87 47.210 r≤1 0.2663 15.67 29.680 r≤2 0.1639 5.76 15.410 r≤3 0.0010 0.03 3.762
12.20 9.91 5.73 0.03
27.067 20.967 14.069 3.762
Trace
95% critical values
The sample period for VAR model with log of black market real exchange rate (ln BLKRER) is 1961–1993. The r denotes number of cointegrating vectors and ln stands for logarithms of the series. The 95% critical values are from Osterwald-Lenum (1990) (Table D.1). a Based on real exchange rates; VAR lag = 1. b Statistical significance at 10% level and hence the rejection of the null hypothesis at this level. The 90% critical values are not reported to conserve space.
market real exchange rates further eliminate the long-run relationship and show the complete absence of cointegration among model variables. Both trace and -max statistics consistently do not reject the null of r ≤ 0 against the alternative r ≥ 1 in both the models estimated using these unweighted official and black market real exchange rates (Models III and IV).5 The long-run elasticity coefficients of trade balance obtained from the Johansen estimator provide an interesting comparison with those obtained from the optimal single equation estimators of Saikkonen and Phillips and Loretan. In both the models estimated using REERTW and REERXW, all the coefficients carry theoretically predicted signs (Table 2). In the model estimated using REERTW (Model I), 5 The Engle and Granger (1987) estimator also provides the similar evidence and show that the null of no cointegration among variables is rejected in Models I and II and accepted in Models III and IV.
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the long-run elasticity of real trade balance with regard to domestic real income GDPMP is: −1.87 as per Johansen estimator, −1.98 as per Saikkonen estimator (henceforth SKN) and −2.18 as per Phillips and Loretan estimator (hereafter PL), and all these coefficients are statistically significant at 1% level. Similarly, with the estimating procedure shown in parenthesis, the long-run elasticity of trade balance with regard to REERTW is: −2.33 (Johansen), −2.06 (SKN) and −2.18 (PL), and all these coefficients are significant at 1% level. The elasticity coefficient of world real income GDP∗ is found to be both dimensionally weak at 0.35 (Johansen), 0.64 (SKN) and 0.74 (PL) and statistically insignificant. These results consistently provide a strong evidence for the significant role of domestic income and relative prices in explaining the trade balance in India. The world real income does not play any significant role in affecting the trade balance. In the model estimated using REERXW (Model II), the coefficients of GDPMP and GDP∗ are somewhat higher than those obtained from the model estimated with REERTW. The long-run elasticity of trade balance with regard to GDPMP is: −2.50 (Johansen), −2.54 (SKN) and −2.78 (PL) and all these coefficients are significant at 1% level (Table 2). The elasticity of trade balance with respect to GDP∗ is: 1.02 (Johansen), 1.60 (SKN) and 1.47 (PL); the first coefficient being statistically insignificant and the latter two being significant at 10% level. Similarly, the elasticity of trade balance with regard to REERXW is found to be: −2.30 (Johansen), −1.66 (SKN) and −2.03 (PL); the first coefficient is significant at 1% level and the latter two coefficients are significant at 5% level. These results show some dimensional variations in terms of the magnitude and statistical significance of the elasticity coefficients as compared to the results obtained from the model estimated using REERTW. But broadly these results reinforce the significant effect of similar variables including domestic income and relative prices in explaining the behaviour of trade balance in India. The world income plays either less significant or an insignificant role in affecting the balance of trade. This evidence suggests that a small open economy like India has not been very susceptible to world economic aberrations and hence it could largely pursue its independent economic policies. In both the models estimated using REERTW and REERXW, the results are consistent across alternative estimators. The results obtained from the trade balance model estimated using unweighted official (Model III) and black market (Model IV) real exchange rates are presented in Table 3. These results do not make any meaningful comparison with those obtained from the model estimated using weighted exchange rates. The long-run elasticity coefficients of various variables are characterised by unduly large (small) magnitudes and perverse signs in some cases. The elasticity coefficients are statistically insignificant in most cases. Besides, these coefficients are not consistent and show wide variations across alternative estimators. All these results provide strong empirical support to the theoretically predicted usefulness of trade weighted followed by export weighted real exchange rate, and show that the unweighted real exchange rates are not useful in modelling the balance of trade.
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3.1.1. Effect of nominal exchange rate The results presented so far show the responsiveness of trade balance to the real exchange rate (relative prices), while the devaluation-based adjustment policies are effected through the nominal exchange rate. For policy purposes, it is important to analyse whether the coefficient of real exchange rate can be interpreted as the measure of approximate response of trade balance to nominal exchange rate. If prices remain stable and the proposition of relative price stickiness holds, then the changes in nominal exchange rate would reflect in the corresponding changes in real exchange rate. In such case, the trade effects of both nominal and real exchange rates would be similar and the adjustment policies can be successful in effecting the desired changes in the balance of trade. On the contrary, in the absence of price stability, trade effects of real exchange rate can be different from those of nominal exchange rate and accordingly adjustment policies would need to be supplemented by stabilisation policies. To empirically test the proposition of relative price stickiness with its implications for trade balance, the real exchange rate series are replaced with nominal exchange rate series (with all other variables retained in real terms) and the model is re-estimated using the Johansen (1991) and Phillips and Loretan (1991) estimators. Four variants of model (1) are estimated using, along with other variables, the nominal exchange rate series of NEERTW (Model Ia), NEERXW (Model IIa), NER (Model IIIa) and BLKNER (Model IVa). In the model estimated using trade weighted nominal effective exchange rate NEERTW, both trace and -max statistics consistently do not reject the null of r ≤ 0 against the alternative r ≥ 1, showing the absence of long-run cointegrating relationship among the model variables (Table 4). These results are in sharp contrast to the significant evidence for the presence of cointegrating relationship obtained in the model estimated using real exchange rate. In the model estimated using export weighted nominal effective exchange rate NEERXW, both trace and -max statistics consistently do not reject the null of r ≤ 0 against the alternative r ≥ 1. The results obtained from the model estimated using unweighted series of official NER and black market BLKNER nominal exchange rates continue to be characterised by the absence of long-run cointegrating relationship among variables and the lack of any meaningful economic interpretation for the elasticity coefficients. These results are further supported by the long-run elasticity coefficients obtained from both the Johansen as well as the Phillips and Loretan estimators (results not reported to conserve space). The long-run elasticity coefficients of explanatory variables obtained from the model estimated with nominal exchange rates show wide variations as compared to the corresponding elasticity coefficients obtained from the model estimated using real exchange rates. All these results suggest that the trade effects of nominal exchange rate are different from those of real exchange rate. This evidence is further reinforced by the absence of long-run cointegrating relationship between various nominal and real exchange rates. Both trace and -max statistics do not reject the null of r ≤ 0 against the alternative r ≥ 1 in all the bivariate models for nominal and real exchange rates (Table 5). These results provide evidence against the proposition of relative price stickiness. The
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Table 4 Johansen’s cointegration test for India’s balance of trade modela Null hypothesis
Eigenvalues
Trace test
Maximal Eigenvalue test -max
95% critical values
Model Ia: Y = [ln RXM ln GDPMP ln NEERTW ln GDP∗ ] r≤0 0.4044 39.63 47.210 r≤1 0.2676 22.01 29.680 r≤2 0.2494 11.43 15.410 r≤3 0.0479 1.67 3.762
17.62 10.59 9.76 1.67
27.067 20.967 14.069 3.762
Model IIa: Y = [ln RXM ln GDPMP ln NEERXW ln GDP∗ ] r≤0 0.4176 40.61 47.210 r≤1 0.2659 22.23 29.680 r≤2 0.2535 11.72 15.410 r≤3 0.0511 1.78 3.762
18.38 10.51 9.94 1.78
27.067 20.967 14.069 3.762
Model IIIa: Y = [ln RXM ln GDPMP ln NER ln GDP∗ ] r≤0 0.4027 42.96 47.210 r≤1 0.3308 25.44 29.680 r≤2 0.2554 11.78 15.410 r≤3 0.0503 1.75 3.762
17.52 13.66 10.03 1.75
27.067 20.967 14.069 3.762
Model IVa: Y = [ln RXM ln GDPMP ln BLKNER ln GDP∗ ] r≤0 0.3827 35.13 47.210 r≤1 0.3158 19.69 29.680 r≤2 0.2057 7.55 15.410 r≤3 0.0055 0.18 3.762
15.44 12.15 7.37 0.18
27.067 20.967 14.069 3.762
Trace
95% critical values
The sample period for VAR model with log of black market real exchange rate (ln BLKRER) is 1961–1993. The r denotes number of cointegrating vectors and ln stands for logarithms of the series. The 95% critical values are from Osterwald-Lenum (1990) (Table D.1). a Based on nominal exchange rates; VAR lag = 1.
changes in domestic prices relative to foreign prices distort the long-run relationship between nominal and real exchange rates and, as a result, the policy changes (devaluation) effected through nominal exchange rate may not have desired effects on the balance of trade. Two major policy implications emerge from the above analysis. First, it is important to monitor the real rather than nominal exchange rate, and in this major focus is required on weighted and more specifically the trade weighted real effective exchange rate. The immediate policy changes are effected in unweighted nominal exchange rate and the magnitude of their pass-through into the weighted (trade) real effective exchange rate depends on the adoption of supporting policy measures particularly concerning exports, imports and the rate of inflation. The unweighted official and black market real exchange rates are not useful in modelling the balance of trade. Secondly, the devaluation-based adjustment policies need to be supplemented by stabilisation policies to ensure domestic price stability and avoid countervailing effects of price variations on real exchange rate. In India, the
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Table 5 Johansen’s cointegration test for nominal and real exchange ratesa Null hypothesis
Eigenvalues
Trace test Trace
Maximal Eigenvalue test 95% critical values
-max
95% critical values
Y = [ln NEERTW ln REERTW] r≤0 0.062 r≤1 0.0032
2.29 0.11
15.410 3.762
2.18 0.11
14.069 3.762
Y = [ln NEERXW ln REERXW] r≤0 0.065 r≤1 0.0018
2.35 0.06
15.410 3.762
2.29 0.06
14.069 3.762
17.70 4.33
15.410 3.762
13.37 4.33
14.069 3.762
8.11 1.00
15.410 3.762
7.11 1.00
14.069 3.762
Y = [ln NER ln RER] r≤0 0.3252 r≤1 0.1195 Y = [ln BLKNER ln BLKRER] r≤0 0.1992 r≤1 0.0308
The sample period for VAR model with log of black market real exchange rate (ln BLKRER) is 1961–1993. The r denotes number of cointegrating vectors and ln stands for logarithms of the series. The 95% critical values are from Osterwald-Lenum (1990) (Table D.1). a [VAR lag = 1].
failure of first (June 1966) in contrast to the success and sustainability of second (July 1991) liberalisation reforms further corroborates the need for such policies so as to ensure the effectiveness of exchange rate adjustments. In the first liberalisation reforms, the nominal exchange rate was discernibly devalued, on June 6, 1966, by 57.6% from Rupees 4.76 to 7.50 per US dollar. But the effects of this devaluation were partially offset by the imposition of countervailing duties on traditional exports, removal of import entitlements and other subsidies on various non-traditional exports, and the rise in domestic inflation.6 In the second liberalisation reforms, two-step devaluation of exchange rate in July 1991 (18–19% against major international currencies) was followed by several policy initiatives to ensure the effectiveness of devaluation and enable the sustainability of reforms.7 The major policy initiatives in the external sector included the 6 Bhagwati and Srinivasan (1975) show that total net devaluation on visible trade account approximates to 21.6 for exports and 42.3 for imports; the estimates for entire current account (including invisibles) are 22.3% for receipts and 44.8% for payments. Nayyar (1976) finds that the imposition of export duties reduced the de facto devaluation for most traditional exports to a range of 15–25%, as against the de jure devaluation of 57.6%. 7 The severity of crises was quite unanticipated and the current account deficit, as a proportion to GDP at current market prices, soared to 3.2% in 1990–1991 and this was quite unsustainable. The system of managed float with exchange rate linked to a basket of currencies came under severe pressure. Despite the withdrawals of US$2.5 billion from the IMF under the Reserve Tranche, the foreign currency assets dipped from US$3.4 billion at the end of March 1990 to a low of US$975 million on July 12, 1991, equivalent to barely a week’s imports.
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adoption of dual (official and market determined) exchange rate system (March 1992), unification of exchange rate (March 1993), current account convertibility initiated in March 1994 and completed in August 1994, reduction in quantitative and tariffs restrictions on imports, move towards capital account convertibility (1997) and encouragement to the entry of certain multinational companies and foreign investment; both direct and portfolio. These measures in the external sector were supplemented with policy measures in the domestic sector to contain inflation and develop a sound and efficient banking and financial system so as to facilitate the efficient conduct of monetary policy and enable the sustainability of market determined exchange rate. This policy environment has not only allayed the apprehensions regarding the continuation of crises, but also provided a safe cushion for attracting foreign investment. Despite the adoption of market determined exchange rate, the Reserve Bank did not relinquish its right to intervene in market to enable orderly conduct, and all this boosts the confidence of foreign investors who are interested in risk-adjusted returns. The model suggests that the devaluation of real effective exchange rate leads to an improvement in trade balance. The policy reforms of the 1990s substantially reversed the earlier policy framework and led to an improvement in trade balance, rise in foreign exchange reserves and increase in foreign investment. The large market potential is a major attraction for foreign investment in India, but the lack of adequate infrastructure and transparency impinge upon the inflow of such investment. The strengthening of infrastructure and reforms in legal framework are further required to encourage the inflow of foreign investment in India. 3.2. Error correction model (ECM) This section estimates the ECM to capture the short-run dynamics of trade balance. The Granger Representation Theorem developed in Engle and Granger (1987) implies that if a set of variables are cointegrated, then there exists a valid error correction representation of the time series. The ECM which combines features of both differenced and long-run equilibrium models is specified as ln RXM(t) = ␣0 +
k
(i) ln RXM(t − i)
i=1
+
k
X(t − i)γ (i) + ␣z(t − 1) + (t)
(2)
i=1
In ECM (2), ln RXM is the ratio of real exports to real imports and X is a column vector containing the real exchange rate, domestic real income and the world real income (these variables are as defined earlier). The lagged error correction term z(t −1) is obtained from the cointegrating model estimated using the trade weighted real effective exchange rate REERTW. Two sets of ECM are estimated; one based on z(t − 1) computed from the long-run model estimated using Johansen estimator
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Fig. 1. Actual and predicted balance of trade in India.
and the other based on z(t − 1) computed from the long-run model estimated using Phillips and Loretan estimator (Model I, Table 2). The results obtained from both the sets of ECMs show that the coefficients of the lagged explanatory variables do not carry the signs consistent with those obtained in the long-run model. All these coefficients are statistically insignificant, with the exception of the coefficient of domestic income which is significant at 1% level in most specifications (Table 6). The ECM basically shows the significant effects of lagged equilibria and reinforces the results obtained from the long-run cointegrating model. In all the specifications, the coefficients of z(t − 1) are significant and show a quite rapid speed of adjustment with more than 40% of the disequilibrium eliminated in 1 year (Table 6). The root mean square error is low and the model can be used to forecast the balance of trade. Based on the ECM estimated with z(t − 1) obtained from the long-run estimates of Johansen estimator, the forecast values of ln RXM are computed. The Fig. 1 shows that the forecast values closely track the actual values of trade balance during the sample period. The out-of-sample simulation is made using the available data from 1996 to 1999 and this also shows that the simulated values reasonably track the actual values of trade balance.
4. Conclusions This study has estimated the balance of trade model on Indian data from 1960 to 1995 using a reduced-form specification similar to Rose (1991). The results are broadly consistent across alternative estimators, but are sensitive to the measure of exchange rate used. In the model estimated using trade weighted real effective
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exchange rate, the Johansen test shows the presence of a long-run cointegrating relationship among the variables. The parameter estimates obtained from the Johansen, Saikkonen and the Phillips and Loretan estimators consistently provide a strong evidence for the significant role of both domestic income and real effective exchange rate (trade weighted) in explaining the behaviour of balance of trade in India. World income is not found to play any significant role in affecting the trade balance. Broadly similar evidence is obtained from the model estimated using export weighted real effective exchange rate. The unweighted official and black market real exchange rates are not very useful in modelling the balance of trade. The trade effects of nominal exchange rate are quite different from those of real exchange rate. The study suggests the need to monitor the real rather than nominal exchange rate, and in this major focus is required on weighted and more specifically the trade weighted real effective exchange rate. The immediate policy changes are effected in unweighted nominal exchange rate and the magnitude of their pass-through into weighted (trade) real effective exchange rate depends on the adoption of supporting policy measures particularly concerning exports, imports and the rate of inflation. The devaluation-based adjustment policies need to be supplemented by stabilisation policies to ensure domestic price stability and achieve the desired effects of nominal exchange rate changes (devaluation) on the balance of trade. The policy reforms need to be continued to strengthen infrastructure and simplify legal framework so as to encourage the inflow of foreign investment in India.
Acknowledgments I am extremely grateful to Dr. Glenn Otto of the University of New South Wales, Sydney, Australia for his valuable comments and incisive suggestions which helped to improve the paper substantially. I am also grateful to the editors of the journal for giving very useful comments and suggestions. However, I am solely responsible for any error and omission that may remain in the paper. I am grateful to Professor Ross Milbourne and the Faculty of Commerce and Economics of the University of New South Wales for providing me the Dean’s research grant. The views expressed in the article are the personal views of the author and not of the institution he is associated with.
References Bhagwati, J. N., & Srinivasan, T. N. (1975). Foreign trade regimes and economic development: India. New York: National Bureau of Economic Research. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072. Doornik, J. A., & Hansen, H. (1994). An omnibus test for univariate and multivariate normality (Working Paper). Oxford: Nuffield College.
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Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007. Engle, R. F., Granger, C. W. J. (1987). Cointegration and error correction: Representation, Estimation and Testing. Econometrica, 55, 251–276. Godfrey, L. G. (1988). Misspecification tests in econometrics: The Lagrange Multiplier Principle and other approaches. Cambridge: Cambridge University Press. Goldstein, M., & Khan, M. S. (1985). Income and price effects in foreign trade. In R. W. Jones, & P. B. Kenen (Eds.), Handbook of International Economics (Vol. II, pp. 1041–1105). Amsterdam: North-Holland. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, 1551–1580. Krugman, P. R., & Baldwin, R. E. (1987). The persistence of the US trade deficit. Brookings Papers on Economic Activity 1 (pp. 1–43). Washington, D.C.: The Brookings Institution. Nayyar, D. (1976). India’s exports and export policies in the 1960s. Cambridge: Cambridge University Press. Osterwald-Lenum, M. (1990, January). Recalculated and extended tables of the asymptotic distribution of some important maximum likelihood cointegration test statistics. Unpublished manuscript, Institute of Economics, University of Copenhagen. Phillips, P. C. B., & Loretan, M. (1991). Estimating long-run economic equilibria. The Review of Economic Studies, 58, 407–436. Rose, A. K. (1991). The role of exchange rate in a popular model of international trade: Does the Marshall–Lerner condition hold? Journal of International Economics, 30, 301–316. Saikkonen, P. (1991). Asymptotically efficient estimation of cointegration regressions. Econometric Theory, 7, 1–21. √ Shenton, L. R., & Bowman, K. O. (1977). A bivariate model for the distribution of b1 and b2 . Journal of the American Statistical Association, 72, 206–211. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48, 1–48.
India’s trade balance: the role of income and exchange rates Tarlok Singh∗ School of Economics, The University of New South Wales, Sydney 2052, Australia Received 30 May 2000; received in revised form 9 January 2002; accepted 1 March 2002
Abstract This study estimates the balance of trade model on Indian data from 1960 to 1995 using a reduced-form specification similar to Rose [J. Int. Economics, 30 (1991) 301]. The results show that the real exchange rate (trade weighted) and domestic income play a significant, while the world income plays an insignificant or a less significant role in affecting the balance of trade in India. The trade effects of real exchange rate are different from those of nominal exchange rate. The study suggests the need to monitor the real rather than nominal exchange rate, and in this major focus is required on weighted and more specifically the trade weighted real effective exchange rate. Besides, the devaluation-based adjustment policies need to be supplemented by stabilisation policies to ensure domestic price stability and achieve the desired effects of nominal exchange rate changes (devaluation) on the balance of trade. © 2002 Society for Policy Modeling. Published by Elsevier Science Inc. All rights reserved. Keywords: Trade balance; Income; Exchange rate
1. Introduction The role of income and exchange rate in affecting the trade flows is well recognised in the literature and a large number of studies have been conducted analysing the effects of these factors on exports, imports and the balance of trade (payments). The studies conducted until the late 1980s have mostly used a single ∗ Present address: Department of Economic Analysis and Policy, Reserve Bank of India, 9th Floor, New Central Office Building, Shahid Bhagat Singh Road, Fort, Post Box No. 1036, Mumbai 400 001, India. Tel.: +91-22-2675937; fax: +91-22-264-1154. E-mail address: [email protected] (T. Singh).
0161-8938/02/$ – see front matter © 2002 Society for Policy Modeling. PII: S 0 1 6 1 - 8 9 3 8 ( 0 2 ) 0 0 1 2 4 - 2
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equation framework (Goldstein & Khan, 1985) with little cognisance of the unit root problems associated with level variables, while those conducted subsequently have mainly relied on the use of a maximum likelihood (ML) systems estimator of Johansen (1991) and have taken into consideration the time series properties of model variables. In addition to the development of a systems based Johansen (1991) estimator, the early 1990s has also witnessed significant improvements in single equation methodology. Saikkonen (1991) and Phillips and Loretan (1991) have developed optimal single equation estimators for cointegrated systems which generally have better asymptotic properties as compared to conventional single equation estimators. But to date no systematic attempt has been made to use both ML systems estimator and the optimal single equation estimators and analyse the sensitivity of results to the use of different methodologies. This study uses both these estimators and estimates the balance of trade model for India. Besides, most studies have drawn conclusions from the model estimated using mainly a single exchange rate series along with other variables like domestic and foreign incomes. Such exchange rate was measured in terms of either unweighted or less comprehensively weighted (imports or exports) exchange rate. This study uses a comprehensive set of both weighted (trade and export) and unweighted (official and black market) exchange rates and shows the sensitivity of results to the measure of exchange rate used. The scheme of the study is as follows. Section 2 presents a discussion on the specification and estimation of the balance of trade model. Section 3 presents the empirical results. Section 4 contains the conclusions emerging from the study.
2. Model specification and estimation The study uses a reduced-form balance of trade model similar to Rose (1991) to analyse the effects of the exchange rate and domestic and foreign incomes on the balance of trade in India. Rose uses a “two-country” model of trade which assumes that the imports and domestically produced goods are imperfect substitutes. The import (export) demand and supply functions are modelled as the functions of relative prices of imports (exports) and expenditure or output. Specifically, the demand for imports depends on real income and the relative price of imported goods, while the demand for a country’s exports (foreign imports) depends on foreign income and the foreign relative price of imports. The supply of exportables in each country is assumed to depend only on the relative prices of exportables. Rose uses market clearing equilibrium conditions to solve these demand and supply functions and obtain a reduced-form specification. The trade balance is expressed as a function of real exchange rate and the domestic and foreign real incomes. A log-linear approximation to Rose specification can be written as ln TB(t) = 0 + 1 ln Y (t) + 2 ln Y ∗ (t) + 3 ln R(t) + ε(t)
(1)
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where TB(t) represents the trade balance (expressed as the difference between real exports and real imports), Y(t) is domestic real income, Y∗ (t) is foreign real income, R(t) is real exchange rate and ε(t) is the error term. The real exchange rate is defined as the number of units of foreign currency per unit of domestic currency and accordingly an increase in R(t) indicates an appreciation and a decrease denotes the depreciation. Conventional economic theory predicts that 1 , 3 < 0 and 2 > 0 in model (1). Krugman and Baldwin (1987) use a similar model to explain trade flows in the USA, France, Germany, Japan and Korea. Model (1) is estimated on annual data from 1960 to 1995 using both ML systems estimator of Johansen (1991) and the optimal single equation estimators of Saikkonen (1991) and Phillips and Loretan (1991). Since the trade balance TB(t) as specified in model (1) generally takes negative values, it is not possible to take its logarithmic transformation. Therefore, the trade balance is measured in terms of logarithms of the ratio of real exports to real imports of goods and services (ln RXM), obtained by deflating nominal exports and imports of goods and services (Rs. crores) by the unit value indices of exports and imports respectively (both at base: 1980–1981 = 100). The domestic real income Y(t) is measured as real gross domestic product at market prices (GDPMP) at 1980–1981 prices (Rs. crores), while world real income Y∗ (t) is taken in terms of the index of world gross domestic product (GDP∗ ; base: 1980 = 100). The real exchange rate R(t) is measured alternatively in terms of the trade (REERTW) and export (REERXW) weighted real effective exchange rate (both based on 36-country bilateral weights; base: 1980 = 100) and the official (RER) and black market (BLKRER) unweighted real exchange rate. The nominal series of the trade (NEERTW) and export (NEERXW) weighted and the official (NER) and black market (BLKNER) unweighted exchange rates are also used to estimate the model.1 A rise in exchange rate indicates an appreciation and a decline denotes the depreciation of the Rupee.
3. Empirical results The trade weighted real effective exchange rate REERTW theoretically provides the best measure of real exchange rate for the reasons of its comprehensive coverage of both exports and imports, as compared to the export weighted real effective exchange rate REERXW with coverage of only exports and the unweighted official 1 The indices both of nominal and real effective exchange rate are based on 36-country bilateral weights (base: 1980 = 100). In the construction of these indices, the exchange rate of a currency is expressed as the number of units of numeraire (SDRs) which equal one unit of the currency (SDRs per currency). The unweighted nominal series of official exchange rate (NER) is transformed into real (RER) series by using world consumer price index relative to domestic wholesale price index. Since a substantial amount of black market transactions are effected in US dollars, the US consumer price index relative to domestic wholesale price index is used to transform nominal (BLKNER) into real (BLKRER) series of black market exchange rate. The unweighted official and black market exchange rates represent the number of units of foreign currency (which is US$) per unit of Indian Rupee.
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RER and black market BLKRER real exchange rates with no regard to the pattern of trade. To examine the sensitivity of results to the measure of exchange rate used, four variants of model (1) are estimated using, along with other variables, the real exchange rate series of REERTW (Model I), REERXW (Model II), RER (Model III) and BLKRER (Model IV). These real exchange rate series are then replaced by the corresponding nominal series (with other variables retained in real terms) and the model is re-estimated to analyse whether the coefficient of real exchange rate can be interpreted as the measure of approximate response of trade balance to nominal exchange rate. The Dickey–Fuller tests for the null of a unit root (Dickey & Fuller, 1981) suggest that all the series are I(1) in log levels and I(0) in first log differences. The model selection criteria AIC and SC and the Sims (1980) LR test consistently suggest the lag length choice of one for all the alternative VAR models, and therefore the lag length one is used in the Johansen estimator.2 The residuals of these VAR models estimated with lag one were found to have desired normality and white-noise properties as indicated by various univariate and multivariate residual misspecification tests.3 In the optimal single equation estimators of Saikkonen (1991) and Phillips and Loretan (1991), the model structure with 1 year lead–lag and contemporaneous regressors is used.4 3.1. Cointegrating relationship and long-run elasticities The results obtained from the Johansen estimator for the cointegrating relationship among variables are presented in Table 1. In the model estimated using trade weighted real effective exchange rate, both trace and -max statistics consistently reject the null of r ≤ 0 against the alternative r ≥ 1 at 5% level and provide a strong evidence for the presence of one cointegrating vector in the model (Model I). The results obtained from the model estimated using export weighted real effective exchange rate show that the model variables continue to be characterised by the presence of long-run equilibrium relationship, but at a relatively reduced level of significance. Both trace and -max statistics reject the null of r ≤ 0 against the alternative r ≥ 1 at 10% level (Model II). The use of unweighted official and black 2 The results for the (i) Dickey–Fuller unit root tests, (ii) model selection criteria including Akaike’s Information Criterion (AIC) and Schwarz Criterion (SC) and the (iii) Sims (1980) Likelihood Ratio (LR) test, are not reported to conserve space. 3 The univariate test statistics are based on estimated residuals of each of the VAR equation and include three central moments of estimated residuals (standard deviation, skewness and kurtosis), LM test (Engle, 1982) for autoregressive conditional heteroskedasticity (ARCH) and modified version of Shenton–Bowman test for normality of individual residual series (Doornik & Hansen, 1994; Shenton & Bowman, 1977). The multivariate test statistics are based on the estimated auto- and cross-correlations of the residuals of overall VAR system and include the LM test for the first and fourth order autocorrelation (Godfrey, 1988) and 2 test for normality. The 2 test for normality is based on the multivariate version of the univariate Shenton–Bowman test. The results of these tests are not reported to conserve space. 4 The estimation of the model with two year lead–lag and contemporaneous regressors did not make any qualitative difference to results and therefore these results are not reported to conserve space.
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Table 1 Johansen’s cointegration test for India’s balance of trade modela Null hypothesis
Eigenvalues
Trace test
Maximal Eigenvalue test -max
95% critical values
Model I: Y = [ln RXM ln GDPMP ln REERTW ln GDP∗ ] r≤0 0.5511 48.38 47.210 r≤1 0.2770 21.14 29.680 r≤2 0.2189 10.12 15.410 r≤3 0.0492 1.72 3.762
27.23 11.03 8.40 1.72
27.067 20.967 14.069 3.762
Model II: Y = [ln RXM ln GDPMP ln REERXW ln GDP∗ ] r≤0 0.5369 47.05b 47.210 r≤1 0.2701 20.87 29.680 r≤2 0.2162 10.17 15.410 r≤3 0.0540 1.89 3.762
26.18b 10.70 8.28 1.89
27.067 20.967 14.069 3.762
Model III: Y = [ln RXM ln GDPMP ln RER ln GDP∗ ] r≤0 0.4268 37.68 47.210 r≤1 0.2344 18.76 29.680 r≤2 0.2148 9.68 15.410 r≤3 0.0419 1.46 3.762
18.92 9.08 8.22 1.46
27.067 20.967 14.069 3.762
Model IV: Y = [ln RXM ln GDPMP ln BLKRER ln GDP∗ ] r≤0 0.3171 27.87 47.210 r≤1 0.2663 15.67 29.680 r≤2 0.1639 5.76 15.410 r≤3 0.0010 0.03 3.762
12.20 9.91 5.73 0.03
27.067 20.967 14.069 3.762
Trace
95% critical values
The sample period for VAR model with log of black market real exchange rate (ln BLKRER) is 1961–1993. The r denotes number of cointegrating vectors and ln stands for logarithms of the series. The 95% critical values are from Osterwald-Lenum (1990) (Table D.1). a Based on real exchange rates; VAR lag = 1. b Statistical significance at 10% level and hence the rejection of the null hypothesis at this level. The 90% critical values are not reported to conserve space.
market real exchange rates further eliminate the long-run relationship and show the complete absence of cointegration among model variables. Both trace and -max statistics consistently do not reject the null of r ≤ 0 against the alternative r ≥ 1 in both the models estimated using these unweighted official and black market real exchange rates (Models III and IV).5 The long-run elasticity coefficients of trade balance obtained from the Johansen estimator provide an interesting comparison with those obtained from the optimal single equation estimators of Saikkonen and Phillips and Loretan. In both the models estimated using REERTW and REERXW, all the coefficients carry theoretically predicted signs (Table 2). In the model estimated using REERTW (Model I), 5 The Engle and Granger (1987) estimator also provides the similar evidence and show that the null of no cointegration among variables is rejected in Models I and II and accepted in Models III and IV.
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the long-run elasticity of real trade balance with regard to domestic real income GDPMP is: −1.87 as per Johansen estimator, −1.98 as per Saikkonen estimator (henceforth SKN) and −2.18 as per Phillips and Loretan estimator (hereafter PL), and all these coefficients are statistically significant at 1% level. Similarly, with the estimating procedure shown in parenthesis, the long-run elasticity of trade balance with regard to REERTW is: −2.33 (Johansen), −2.06 (SKN) and −2.18 (PL), and all these coefficients are significant at 1% level. The elasticity coefficient of world real income GDP∗ is found to be both dimensionally weak at 0.35 (Johansen), 0.64 (SKN) and 0.74 (PL) and statistically insignificant. These results consistently provide a strong evidence for the significant role of domestic income and relative prices in explaining the trade balance in India. The world real income does not play any significant role in affecting the trade balance. In the model estimated using REERXW (Model II), the coefficients of GDPMP and GDP∗ are somewhat higher than those obtained from the model estimated with REERTW. The long-run elasticity of trade balance with regard to GDPMP is: −2.50 (Johansen), −2.54 (SKN) and −2.78 (PL) and all these coefficients are significant at 1% level (Table 2). The elasticity of trade balance with respect to GDP∗ is: 1.02 (Johansen), 1.60 (SKN) and 1.47 (PL); the first coefficient being statistically insignificant and the latter two being significant at 10% level. Similarly, the elasticity of trade balance with regard to REERXW is found to be: −2.30 (Johansen), −1.66 (SKN) and −2.03 (PL); the first coefficient is significant at 1% level and the latter two coefficients are significant at 5% level. These results show some dimensional variations in terms of the magnitude and statistical significance of the elasticity coefficients as compared to the results obtained from the model estimated using REERTW. But broadly these results reinforce the significant effect of similar variables including domestic income and relative prices in explaining the behaviour of trade balance in India. The world income plays either less significant or an insignificant role in affecting the balance of trade. This evidence suggests that a small open economy like India has not been very susceptible to world economic aberrations and hence it could largely pursue its independent economic policies. In both the models estimated using REERTW and REERXW, the results are consistent across alternative estimators. The results obtained from the trade balance model estimated using unweighted official (Model III) and black market (Model IV) real exchange rates are presented in Table 3. These results do not make any meaningful comparison with those obtained from the model estimated using weighted exchange rates. The long-run elasticity coefficients of various variables are characterised by unduly large (small) magnitudes and perverse signs in some cases. The elasticity coefficients are statistically insignificant in most cases. Besides, these coefficients are not consistent and show wide variations across alternative estimators. All these results provide strong empirical support to the theoretically predicted usefulness of trade weighted followed by export weighted real exchange rate, and show that the unweighted real exchange rates are not useful in modelling the balance of trade.
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3.1.1. Effect of nominal exchange rate The results presented so far show the responsiveness of trade balance to the real exchange rate (relative prices), while the devaluation-based adjustment policies are effected through the nominal exchange rate. For policy purposes, it is important to analyse whether the coefficient of real exchange rate can be interpreted as the measure of approximate response of trade balance to nominal exchange rate. If prices remain stable and the proposition of relative price stickiness holds, then the changes in nominal exchange rate would reflect in the corresponding changes in real exchange rate. In such case, the trade effects of both nominal and real exchange rates would be similar and the adjustment policies can be successful in effecting the desired changes in the balance of trade. On the contrary, in the absence of price stability, trade effects of real exchange rate can be different from those of nominal exchange rate and accordingly adjustment policies would need to be supplemented by stabilisation policies. To empirically test the proposition of relative price stickiness with its implications for trade balance, the real exchange rate series are replaced with nominal exchange rate series (with all other variables retained in real terms) and the model is re-estimated using the Johansen (1991) and Phillips and Loretan (1991) estimators. Four variants of model (1) are estimated using, along with other variables, the nominal exchange rate series of NEERTW (Model Ia), NEERXW (Model IIa), NER (Model IIIa) and BLKNER (Model IVa). In the model estimated using trade weighted nominal effective exchange rate NEERTW, both trace and -max statistics consistently do not reject the null of r ≤ 0 against the alternative r ≥ 1, showing the absence of long-run cointegrating relationship among the model variables (Table 4). These results are in sharp contrast to the significant evidence for the presence of cointegrating relationship obtained in the model estimated using real exchange rate. In the model estimated using export weighted nominal effective exchange rate NEERXW, both trace and -max statistics consistently do not reject the null of r ≤ 0 against the alternative r ≥ 1. The results obtained from the model estimated using unweighted series of official NER and black market BLKNER nominal exchange rates continue to be characterised by the absence of long-run cointegrating relationship among variables and the lack of any meaningful economic interpretation for the elasticity coefficients. These results are further supported by the long-run elasticity coefficients obtained from both the Johansen as well as the Phillips and Loretan estimators (results not reported to conserve space). The long-run elasticity coefficients of explanatory variables obtained from the model estimated with nominal exchange rates show wide variations as compared to the corresponding elasticity coefficients obtained from the model estimated using real exchange rates. All these results suggest that the trade effects of nominal exchange rate are different from those of real exchange rate. This evidence is further reinforced by the absence of long-run cointegrating relationship between various nominal and real exchange rates. Both trace and -max statistics do not reject the null of r ≤ 0 against the alternative r ≥ 1 in all the bivariate models for nominal and real exchange rates (Table 5). These results provide evidence against the proposition of relative price stickiness. The
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Table 4 Johansen’s cointegration test for India’s balance of trade modela Null hypothesis
Eigenvalues
Trace test
Maximal Eigenvalue test -max
95% critical values
Model Ia: Y = [ln RXM ln GDPMP ln NEERTW ln GDP∗ ] r≤0 0.4044 39.63 47.210 r≤1 0.2676 22.01 29.680 r≤2 0.2494 11.43 15.410 r≤3 0.0479 1.67 3.762
17.62 10.59 9.76 1.67
27.067 20.967 14.069 3.762
Model IIa: Y = [ln RXM ln GDPMP ln NEERXW ln GDP∗ ] r≤0 0.4176 40.61 47.210 r≤1 0.2659 22.23 29.680 r≤2 0.2535 11.72 15.410 r≤3 0.0511 1.78 3.762
18.38 10.51 9.94 1.78
27.067 20.967 14.069 3.762
Model IIIa: Y = [ln RXM ln GDPMP ln NER ln GDP∗ ] r≤0 0.4027 42.96 47.210 r≤1 0.3308 25.44 29.680 r≤2 0.2554 11.78 15.410 r≤3 0.0503 1.75 3.762
17.52 13.66 10.03 1.75
27.067 20.967 14.069 3.762
Model IVa: Y = [ln RXM ln GDPMP ln BLKNER ln GDP∗ ] r≤0 0.3827 35.13 47.210 r≤1 0.3158 19.69 29.680 r≤2 0.2057 7.55 15.410 r≤3 0.0055 0.18 3.762
15.44 12.15 7.37 0.18
27.067 20.967 14.069 3.762
Trace
95% critical values
The sample period for VAR model with log of black market real exchange rate (ln BLKRER) is 1961–1993. The r denotes number of cointegrating vectors and ln stands for logarithms of the series. The 95% critical values are from Osterwald-Lenum (1990) (Table D.1). a Based on nominal exchange rates; VAR lag = 1.
changes in domestic prices relative to foreign prices distort the long-run relationship between nominal and real exchange rates and, as a result, the policy changes (devaluation) effected through nominal exchange rate may not have desired effects on the balance of trade. Two major policy implications emerge from the above analysis. First, it is important to monitor the real rather than nominal exchange rate, and in this major focus is required on weighted and more specifically the trade weighted real effective exchange rate. The immediate policy changes are effected in unweighted nominal exchange rate and the magnitude of their pass-through into the weighted (trade) real effective exchange rate depends on the adoption of supporting policy measures particularly concerning exports, imports and the rate of inflation. The unweighted official and black market real exchange rates are not useful in modelling the balance of trade. Secondly, the devaluation-based adjustment policies need to be supplemented by stabilisation policies to ensure domestic price stability and avoid countervailing effects of price variations on real exchange rate. In India, the
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Table 5 Johansen’s cointegration test for nominal and real exchange ratesa Null hypothesis
Eigenvalues
Trace test Trace
Maximal Eigenvalue test 95% critical values
-max
95% critical values
Y = [ln NEERTW ln REERTW] r≤0 0.062 r≤1 0.0032
2.29 0.11
15.410 3.762
2.18 0.11
14.069 3.762
Y = [ln NEERXW ln REERXW] r≤0 0.065 r≤1 0.0018
2.35 0.06
15.410 3.762
2.29 0.06
14.069 3.762
17.70 4.33
15.410 3.762
13.37 4.33
14.069 3.762
8.11 1.00
15.410 3.762
7.11 1.00
14.069 3.762
Y = [ln NER ln RER] r≤0 0.3252 r≤1 0.1195 Y = [ln BLKNER ln BLKRER] r≤0 0.1992 r≤1 0.0308
The sample period for VAR model with log of black market real exchange rate (ln BLKRER) is 1961–1993. The r denotes number of cointegrating vectors and ln stands for logarithms of the series. The 95% critical values are from Osterwald-Lenum (1990) (Table D.1). a [VAR lag = 1].
failure of first (June 1966) in contrast to the success and sustainability of second (July 1991) liberalisation reforms further corroborates the need for such policies so as to ensure the effectiveness of exchange rate adjustments. In the first liberalisation reforms, the nominal exchange rate was discernibly devalued, on June 6, 1966, by 57.6% from Rupees 4.76 to 7.50 per US dollar. But the effects of this devaluation were partially offset by the imposition of countervailing duties on traditional exports, removal of import entitlements and other subsidies on various non-traditional exports, and the rise in domestic inflation.6 In the second liberalisation reforms, two-step devaluation of exchange rate in July 1991 (18–19% against major international currencies) was followed by several policy initiatives to ensure the effectiveness of devaluation and enable the sustainability of reforms.7 The major policy initiatives in the external sector included the 6 Bhagwati and Srinivasan (1975) show that total net devaluation on visible trade account approximates to 21.6 for exports and 42.3 for imports; the estimates for entire current account (including invisibles) are 22.3% for receipts and 44.8% for payments. Nayyar (1976) finds that the imposition of export duties reduced the de facto devaluation for most traditional exports to a range of 15–25%, as against the de jure devaluation of 57.6%. 7 The severity of crises was quite unanticipated and the current account deficit, as a proportion to GDP at current market prices, soared to 3.2% in 1990–1991 and this was quite unsustainable. The system of managed float with exchange rate linked to a basket of currencies came under severe pressure. Despite the withdrawals of US$2.5 billion from the IMF under the Reserve Tranche, the foreign currency assets dipped from US$3.4 billion at the end of March 1990 to a low of US$975 million on July 12, 1991, equivalent to barely a week’s imports.
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adoption of dual (official and market determined) exchange rate system (March 1992), unification of exchange rate (March 1993), current account convertibility initiated in March 1994 and completed in August 1994, reduction in quantitative and tariffs restrictions on imports, move towards capital account convertibility (1997) and encouragement to the entry of certain multinational companies and foreign investment; both direct and portfolio. These measures in the external sector were supplemented with policy measures in the domestic sector to contain inflation and develop a sound and efficient banking and financial system so as to facilitate the efficient conduct of monetary policy and enable the sustainability of market determined exchange rate. This policy environment has not only allayed the apprehensions regarding the continuation of crises, but also provided a safe cushion for attracting foreign investment. Despite the adoption of market determined exchange rate, the Reserve Bank did not relinquish its right to intervene in market to enable orderly conduct, and all this boosts the confidence of foreign investors who are interested in risk-adjusted returns. The model suggests that the devaluation of real effective exchange rate leads to an improvement in trade balance. The policy reforms of the 1990s substantially reversed the earlier policy framework and led to an improvement in trade balance, rise in foreign exchange reserves and increase in foreign investment. The large market potential is a major attraction for foreign investment in India, but the lack of adequate infrastructure and transparency impinge upon the inflow of such investment. The strengthening of infrastructure and reforms in legal framework are further required to encourage the inflow of foreign investment in India. 3.2. Error correction model (ECM) This section estimates the ECM to capture the short-run dynamics of trade balance. The Granger Representation Theorem developed in Engle and Granger (1987) implies that if a set of variables are cointegrated, then there exists a valid error correction representation of the time series. The ECM which combines features of both differenced and long-run equilibrium models is specified as ln RXM(t) = ␣0 +
k
(i) ln RXM(t − i)
i=1
+
k
X(t − i)γ (i) + ␣z(t − 1) + (t)
(2)
i=1
In ECM (2), ln RXM is the ratio of real exports to real imports and X is a column vector containing the real exchange rate, domestic real income and the world real income (these variables are as defined earlier). The lagged error correction term z(t −1) is obtained from the cointegrating model estimated using the trade weighted real effective exchange rate REERTW. Two sets of ECM are estimated; one based on z(t − 1) computed from the long-run model estimated using Johansen estimator
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Fig. 1. Actual and predicted balance of trade in India.
and the other based on z(t − 1) computed from the long-run model estimated using Phillips and Loretan estimator (Model I, Table 2). The results obtained from both the sets of ECMs show that the coefficients of the lagged explanatory variables do not carry the signs consistent with those obtained in the long-run model. All these coefficients are statistically insignificant, with the exception of the coefficient of domestic income which is significant at 1% level in most specifications (Table 6). The ECM basically shows the significant effects of lagged equilibria and reinforces the results obtained from the long-run cointegrating model. In all the specifications, the coefficients of z(t − 1) are significant and show a quite rapid speed of adjustment with more than 40% of the disequilibrium eliminated in 1 year (Table 6). The root mean square error is low and the model can be used to forecast the balance of trade. Based on the ECM estimated with z(t − 1) obtained from the long-run estimates of Johansen estimator, the forecast values of ln RXM are computed. The Fig. 1 shows that the forecast values closely track the actual values of trade balance during the sample period. The out-of-sample simulation is made using the available data from 1996 to 1999 and this also shows that the simulated values reasonably track the actual values of trade balance.
4. Conclusions This study has estimated the balance of trade model on Indian data from 1960 to 1995 using a reduced-form specification similar to Rose (1991). The results are broadly consistent across alternative estimators, but are sensitive to the measure of exchange rate used. In the model estimated using trade weighted real effective
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exchange rate, the Johansen test shows the presence of a long-run cointegrating relationship among the variables. The parameter estimates obtained from the Johansen, Saikkonen and the Phillips and Loretan estimators consistently provide a strong evidence for the significant role of both domestic income and real effective exchange rate (trade weighted) in explaining the behaviour of balance of trade in India. World income is not found to play any significant role in affecting the trade balance. Broadly similar evidence is obtained from the model estimated using export weighted real effective exchange rate. The unweighted official and black market real exchange rates are not very useful in modelling the balance of trade. The trade effects of nominal exchange rate are quite different from those of real exchange rate. The study suggests the need to monitor the real rather than nominal exchange rate, and in this major focus is required on weighted and more specifically the trade weighted real effective exchange rate. The immediate policy changes are effected in unweighted nominal exchange rate and the magnitude of their pass-through into weighted (trade) real effective exchange rate depends on the adoption of supporting policy measures particularly concerning exports, imports and the rate of inflation. The devaluation-based adjustment policies need to be supplemented by stabilisation policies to ensure domestic price stability and achieve the desired effects of nominal exchange rate changes (devaluation) on the balance of trade. The policy reforms need to be continued to strengthen infrastructure and simplify legal framework so as to encourage the inflow of foreign investment in India.
Acknowledgments I am extremely grateful to Dr. Glenn Otto of the University of New South Wales, Sydney, Australia for his valuable comments and incisive suggestions which helped to improve the paper substantially. I am also grateful to the editors of the journal for giving very useful comments and suggestions. However, I am solely responsible for any error and omission that may remain in the paper. I am grateful to Professor Ross Milbourne and the Faculty of Commerce and Economics of the University of New South Wales for providing me the Dean’s research grant. The views expressed in the article are the personal views of the author and not of the institution he is associated with.
References Bhagwati, J. N., & Srinivasan, T. N. (1975). Foreign trade regimes and economic development: India. New York: National Bureau of Economic Research. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072. Doornik, J. A., & Hansen, H. (1994). An omnibus test for univariate and multivariate normality (Working Paper). Oxford: Nuffield College.
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Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007. Engle, R. F., Granger, C. W. J. (1987). Cointegration and error correction: Representation, Estimation and Testing. Econometrica, 55, 251–276. Godfrey, L. G. (1988). Misspecification tests in econometrics: The Lagrange Multiplier Principle and other approaches. Cambridge: Cambridge University Press. Goldstein, M., & Khan, M. S. (1985). Income and price effects in foreign trade. In R. W. Jones, & P. B. Kenen (Eds.), Handbook of International Economics (Vol. II, pp. 1041–1105). Amsterdam: North-Holland. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, 1551–1580. Krugman, P. R., & Baldwin, R. E. (1987). The persistence of the US trade deficit. Brookings Papers on Economic Activity 1 (pp. 1–43). Washington, D.C.: The Brookings Institution. Nayyar, D. (1976). India’s exports and export policies in the 1960s. Cambridge: Cambridge University Press. Osterwald-Lenum, M. (1990, January). Recalculated and extended tables of the asymptotic distribution of some important maximum likelihood cointegration test statistics. Unpublished manuscript, Institute of Economics, University of Copenhagen. Phillips, P. C. B., & Loretan, M. (1991). Estimating long-run economic equilibria. The Review of Economic Studies, 58, 407–436. Rose, A. K. (1991). The role of exchange rate in a popular model of international trade: Does the Marshall–Lerner condition hold? Journal of International Economics, 30, 301–316. Saikkonen, P. (1991). Asymptotically efficient estimation of cointegration regressions. Econometric Theory, 7, 1–21. √ Shenton, L. R., & Bowman, K. O. (1977). A bivariate model for the distribution of b1 and b2 . Journal of the American Statistical Association, 72, 206–211. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48, 1–48.