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In this paper, we examine the determinants of bilateral export demand function of India during 1993:Q1-2015:Q1. The starting point of our study is 1993:Q1 by keeping into consideration that RBI implemented market determined managed floating flexible exchange rate system during that period. We have employed Auto Regressive Distributed Lag (ARDL) model by using the macroeconomic variables such as real exports, foreign income, nominal exchange rate (Rupee-Dollar) and relative price. We found there exist s a long run equilibrium relationship between real exports, foreign income, exchange rate and relative price. In our empirical analysis, we found that in the long run and short run, real exports are influenced more by foreign income followed by relative price. Foreign income carries a positive sign and is statistically significant, which implies that 1% increase in foreign income will increase real export by 1.63% in the long run. Likewise, relative price carries a negative sign and is statistically significant which implies 1% decrease in relative prices that will increase real exports by 0.22% in the long run. The nominal exchange rate carries a negative sign and is statistically significant (in both short run and long run) , which suggest s that depreciation of nominal exchange rate would not stimulate the volume of export during our study period. Hence, for policy point of view if any policy makers want to promote exports by depreciating , the rupee will not give fruitful results.

International trade has played an important role in the development of both developed and developing countries as countries are dependent on each other due to uneven distribution of scarce resources. The role of international trade in the development of a country is undoubtedly undeniable. Perhaps more firmly established than the relationship between exports and growth is that between fluctuations in exports and cyclical variations in economic activity. If shifts in demand were the major factor, then a study of the demand function for exports will provide a fuller understanding of the causes of economic fluctuations [

India’s exports to the whole world are showing an increasing trend during the period 1993:Q1 to 2008:Q2. India’s exports have increased from 5682.49 million USD in 1993:Q1 to 56433.8 million USD in 2008:Q2. India’s exports have increased by 10 times during this period. India’s exports to the whole world started falling since 2008:Q2 (

India’s exports to the USA fall from 6210.94 million USD in 2008:Q4 to 4199.36 million USD 2009:Q2. Though USA remained as a major export partner of India, exports had fallen during 2008:Q4 which was because of Global Financial Crisis. The volume of exports falls after some lags might be because of consumer response lag and weak consumer confidence index. Since 2009:Q2 India’s exports to the USA picked up and were showing an increasing trend (

It is believed that export growth should play an important role in economic growth in developing countries. Given the importance of export expansion of

growth and balance of payments’ concerns, the worrisome fact is that the Percentage share of the India’s exports to the USA to the whole World has declined from 22.52% in 1999 to 13.38% in 2015 (

The trade policy announced in 2014 by the government envisaged total exports of $900 billion by 2020, however, it would only be possible if exports grow by 40% per annum from now on [

Another reason for which the study of export demand function of a country becomes important is the fact that many of the developing countries are on the brinks of balance of payments problems. Keeping in view the importance of exports in the economic growth of a country, an attempt is made in this paper to examine the determinants of bilateral export demand function of India in short run and long run. As US is the major trading partner of India, we examine India-USA bilateral trade relationship in aggregate level. Rahman et al. [

Given this backdrop, the study has two fold objectives. First, is to find out the determinants of bilateral export demand function of India in short run and long run. Second, is to identify that among the macroeconomic variables which variable plays an important role in affecting exports. Finally, we hope that the conclusion of this paper will be helpful for policy makers to formulate trade policies and it will also stimulate scholars to conduct further research, which would be beneficial to policy makers in the future.

The paper is organized as follows. Following an introduction in Section I, Section II reviews a few selected literatures in this area. Section III discusses model specification. Section IV highlights the methodology used. Variables defined and data sources are presented in Section V. Section VI highlights the empirical results followed by conclusions and policy implications in Section VII.

Considering the importance of international trade to economic growth and development, especially in third world countries, a number of empirical studies onthe determinants of export demand functions have been carried out.

The contributions by Orcutt in 1950 [

The concept of elasticity of foreign demand plays an important role in international trade research. A major part of the international trade research is done in this area in the last 20 years. Exports respond significantly to changes in relative prices. This is evident from the studies by Goldstein and Khan [

Non-price factors such as patent applications and government and business characteristics of a country are also important factors for understanding of international competitiveness. Studies by Verheyen [

While most of the literature concentrates on developed economies, there is hardly few research carried out to examine the bilateral export demand function so far as emerging country like India is concerned. For instance, Raissi et al. [

We believe that these studies are not sufficient enough so far as emerging country like India is concerned. These studies are not sufficient enough to reach any definite conclusion. Hence, any new study will add to the review of literature. By keeping this in our mind, the present study is carried out to investigate the bilateral export demand function of India by applying the ARDL model and using the latest data available. We believe, our study will throw some light to the policy makers and for the scope of future research.

Export demand of a country is affected by two important factors. These are―1) foreign income, which is an indicator of the economic activity and purchasing power of trading partner, and 2) terms of trade (or competitiveness effect), which depends on the ratio of the respective price levels and the nominal exchange rate. The econometric model presented below for the empirical study shows a standard long-run relationship between real exports, nominal exchange rate, relative price and foreign income.

E x t = β 0 + β 1 F y t + β 2 E r a t e t + β 3 R p t + u t (1)

where, Export ( E x t ) = real exports of India at time t; Foreign Income ( F y t ) = foreign economic activity at time t; Exchange rate ( E r a t e t ) = nominal exchange rate at time t (Rupee per unit of USD); Relative price ( R p t ) = relative price (which is a measure of competitiveness) at time t; u t = the normally distributed error term with all classical properties.

As exports depend on the foreign country’s income, we expect a positive sign for the coefficient of foreign country’s income. However, if rise in real income is due to an increase in the production of import substitute goods, imports may decline as income increases in which case the coefficient of income in foreign country would be negative. The increase in nominal exchange rate (i.e. depreciation of the exchange rate) makes domestic goods cheaper in other countries, which increases the competitiveness of domestic goods in the international markets, raising their demand. So we expect the coefficient of the exchange rate to be positive. A fall in the relative price of a country will cause the domestic goods to be more competitive as compared to foreign goods, which would result in an increase in exports and decrease in imports and vice versa. Therefore, we expect the coefficient of relative price to be negative.

The above equation includes constant/intercept term “b_{0}” because there will be some exports even if all other variables are zero. b_{1}, b_{2}, and b_{3} are the elasticity coefficients with respect to the variables F Y t , E R A T E t , R P t respectively. U_{t} is the residual term, which shows the affects of other variables on exports not included in the model.

The exchange rate variable used here is the nominal exchange rate. We point out that the model uses nominal exchange rates, and not real exchange rate, though it is the real exchange rate that is normally understood to affect the volume of trade. We have divided real exchange rate into its two components of nominal exchange rate and relative price level. The reason for analyzing these components separately is that the volume of trade responses to real exchange rate changes may differ according to whether the real exchange rate changes are due to nominal exchange rate changes or the changes in relative price level.

Before proceeding for empirical estimation, each of the macroeconomic variables is initially tested for their stationarity properties and order of integration. The Augmented Dickey Fuller (ADF) and Phillips Perron (PP) test is used for this purpose. Subsequently, the bound testing approach to ARDL model developed by Pesaran, Shin and Smith [

The following ARDL model is estimated to check for the presence of cointegration. ARDL model is used to investigate the relationship between the real exports, foreign income, nominal exchange rate and relative price. The model specification is as follows:

Δ ln E x t = α 0 + α 1 ln E x t − 1 + α 2 ln F y t − 1 + α 3 ln E r a t e t − 1 + α 4 ln R p t − 1 + ∑ i = 1 n α 5 i Δ ln E x t − i + ∑ i = 0 n α 6 i Δ ln F y t − i + ∑ i = 0 n α 7 i Δ ln E r a t e t − i + ∑ i = 0 n α 8 i Δ ln R p t − i + π 1 E C t − 1 + e t (2)

The existence of a cointegrated relationship between the variables in the above mentioned ARDL model specifications is examined with the help of F or Wald test statistics. The Wald test examines the joint null hypothesis of zero cointegration between the variables, against the alternative hypothesis of the presence of cointegration. The calculated F-statistics are compared with two sets of critical values computed by Pesaran, Shin and Smith [

Finally, regression diagnostic tests are performed for the ARDL models estimated as per Equation (2). Lagrange Multiplier (LM) test is used to check whether the estimated ARDL model suffer from residual serial correlation. White test is used to test the null hypothesis that errors are homoscedastic and independent of the regressions, against the alternative hypothesis of the presence of heteroscedasticity of the unknown, general form. Jarque Bera (J-B) test is used to test the null hypothesis that the residuals are normally distributed. Parameter stability tests play a pivotal role to ensure reliability of policy simulations based on the model. To test for parameter stability, we have applied the CUSUM (Cumulative Sum) and Cumulative Sum of Squires (CUSUMQ) tests developed by Brown, Durbin and Evans in1975 [

We used India’s monthly exports to the USA (in USD) divided by the unit value of export price in the respective month to generate data on monthly real exports. The time series data of unit value of export price is not available on a monthly basis. Hence, we interpolated yearly unit value of export price into a monthly unit value of export price through quadratic method which is extensively used by many researchers. Though the volume of exports is expressed in USD million and rupee terms in Indian context, we prefer exports in USD million terms rather than the rupee term as USD fluctuates less compared to rupee. In addition to that, the USD is treated as one of the safe haven currencies in the international market. Unit values of exports indices are treated as price indices of exports. It is used as a deflator to compute the volume of exports from value of exports. Economic theory tells that foreign income is an important determinant of exports. We have used USAGDP to represent foreign income. The relative price (which is a measure of competitiveness) is measured by the ratio of India’s unit value of exports to USA CPI. Finally, the study uses the bilateral Rupee-Dollar nominal exchange rate as it is widely believed that exchange rate also plays an important role in affecting exports and imports of a country.

The study uses quarterly data for the period 1993:Q1 to 2015:Q1amounting to 89 observations. We have chosen this period as a starting point because the Reserve Bank of India followed market determined managed floating flexible exchange rate system during that period. In this study, we used secondary data and it has been collected from various sources. India’s exports to the USA (in Million USD) are collected from Direction of Trade and Statistics (DOTS) which is a publication of the International Monetary Fund (IMF). The Unit value of export price of India, USA GDP, CPI of USA and rupee-dollar nominal exchange rate data are collected from International Financial Statistics (IFS), which is a publication of the International Monetary Fund (IMF).

The bound testing approach to cointegration is reported in

The long run coefficient estimated from ARDL model is reported in

ADF Test | PP Test | |||
---|---|---|---|---|

Level | First differences | Level | First differences | |

Variables | ADF Tests with trend & Intercept | ADF Tests with trend & Intercept | PP Tests with trend & Intercept | PP Tests with trend & Intercept |

lnEx | −2.7627 (1) | −17.141 (1) | −4.6830 (1) | |

lnFy | −0.6551 (1) | −7.4190 (1) | −0.5100 (1) | −7.4028 (1) |

lnRp | −1.4004 (1) | −5.3629 (1) | −1.7021 (1) | −5.2177 (1) |

lnErate | −3.2716 (1) | −7.4405 (1) | −3.0211 (1) | −7.4493 (1) |

EX stands for Exports. Fy stands for foreign income (US income), RP stands for relative price. Erate stands for nominal exchange rate (Rupee/Dollar). Note: The critical values of Augmented Dickey Fuller test with trend and intercept in level and first differences are −4.0533, −3.4558 and −3.1537 at 1%, 5% and 10% level of significance and the critical values of Phillips-Perron test with trend and intercept in level and first differences are −4.0524, −3.4553 and −3.1534 at 1%, 5% and 10% level of significance. Source: Authors’ calculations.

Variable | Coefficient | Standard error | t-Statistic | Probability |
---|---|---|---|---|

lnEx (−1) | 0.336523 | 0.10547 | 3.190705 | 0.0021 |

lnEx (−2) | 0.285374 | 0.106407 | 2.68192 | 0.009 |

lnEx (−3) | −0.34252 | 0.103205 | −3.31883 | 0.0014 |

lnEx (−4) | 0.260406 | 0.099476 | 2.617784 | 0.0107 |

lnRp | −0.45415 | 0.496754 | −0.91423 | 0.3634 |

lnRp (−1) | −0.71299 | 0.836974 | −0.85187 | 0.3969 |

lnRp (−2) | 1.062461 | 0.511487 | 2.0772 | 0.0411 |

lnFy | 3.416802 | 1.398626 | 2.442971 | 0.0169 |

lnFy (−1) | 0.644429 | 2.355974 | 0.27353 | 0.7852 |

lnFy (−2) | −3.3105 | 1.441088 | −2.29723 | 0.0243 |

lnErate | −0.20884 | 0.092479 | −2.25828 | 0.0268 |

c | −4.51444 | 1.243335 | −3.63091 | 0.0005 |

Note: R Squared 0.9554, Adjusted R Squared 0.9492, Durbin Watson Test 1.82, Probability F statistic 0.0000. Source: Authors’ calculations.

Optimal Lag Lengeth | (4, 2, 2, 0) | ||
---|---|---|---|

F-Statistics (Wald Test) | 4.6528 | ||

Critical Values (T = 89) | |||

Lower bounds I (0) | Upper bounds I (1) | ||

1 Percent Level | 4.29 | 5.61 | |

5 Percent level | 3.23 | 4.35* | |

10 percent level | 2.72 | 3.77 |

Note: * denotes 5% level of significance. Source: Authors’ calculations.

Regressor | Coefficient | Standard Error | t-Statistics | Probability |
---|---|---|---|---|

lnRp | −0.22746 | 0.130538 | −1.74246 | 0.0854 |

lnFy | 1.631253 | 0.181066 | 9.009158 | 0.0000 |

lnErate | −0.45379 | 0.231529 | −1.95998 | 0.0536 |

C | −9.80941 | 1.252608 | −7.83119 | 0 |

Source: Authors’ calculations.

A fall in relative prices will cause domestic goods to become more competitive in comparison to foreign goods in the international market, therefore exports will increase and imports will fall and vice versa. So expects coefficient of relative price to be negative. Relative price carries a negative sign and statistically significant which implies 1% decrease in relative prices will increase real exports by 0.22%. Foreign income measures the economic activity and purchasing power of the trading partners. The expected signs of the coefficient of foreign income could be positive or negative. Economic theory suggests that the volume of exports to a foreign country ought to increase as the real income and purchasing power of the foreign countries rises and vice-versa. An increase in foreign income will lead to foreigners’ importing more goods from the domestic countries and hence the coefficient of foreign income is positive. However, if increase in foreign income is associated with an increase in production of import substitute goods, domestic country’s exports will fall and in that case coefficient of foreign income carries the negative sign. In our empirical analysis we found that foreign income carries a positive sign and is statistically significant, which implies that 1% increase in foreign income will increase real export by 1.63%. The nominal exchange rate carries a negative sign and is statistically significant which suggest that depreciation of nominal exchange rate would not stimulate the volume of export. On

The short run dynamics of ARDL model is shown in

Regressor | Coefficient | Standard Error | t-statistics | Probability |
---|---|---|---|---|

ΔlnEx (−1) | −0.20326 | 0.12161 | −1.67142 | 0.0987 |

ΔlnEx (−2) | 0.082113 | 0.118019 | 0.695759 | 0.4887 |

ΔlnEx (−3) | −0.26041 | 0.099476 | −2.61778 | 0.0107 |

ΔlnRp | −0.45415 | 0.496754 | −0.91423 | 0.3634 |

ΔlnRp (−1) | −1.06246 | 0.511487 | −2.0772 | 0.0411 |

ΔlnFy | 3.416802 | 1.398626 | 2.442971 | 0.0169 |

ΔlnFy (−1) | 3.310503 | 1.441088 | 2.297226 | 0.0243 |

ΔlnErate | −0.20884 | 0.092479 | −2.25828 | 0.0268 |

ECM_{t−1} | −0.46022 | 0.127534 | −3.60856 | 0.0005 |

Source: Authors’ calculations.

Model Robustness check

We want to check whether our dependent variable that is export is stable or not. For robustness checkour results, we did CUSUM and CUSUM squire test, serial correlation and Heteroscedasticity Test which is shown in

Breusch-Godfrey test is used to check whether the presence/absence of serial correlation in the data.

F-statistic | 8.021769 | Prob. F (2, 75) | 0.2137 |
---|---|---|---|

Obs*R-squared | 15.68343 | Prob. Chi-Square (2) | 0.1004 |

Source: Authors’ calculation.

F-statistic | 1.261096 | Prob. F (58, 30) | 0.2477 |
---|---|---|---|

Obs*R-squared | 63.11375 | Prob. Chi-Square (58) | 0.3005 |

Scaled explained SS | 36.17843 | Prob. Chi-Square (58) | 0.9891 |

Source: Authors’ calculation.

In this paper, we examine the determinants of bilateral export demand function of India during 1993:Q1-2015:Q1. The study uses the ARDL model by using the macroeconomic variables such as real exports, foreign income, nominal exchange rate and relative price. In our empirical estimation, we found that there exists a long run equilibrium relationship between real exports, foreign income, exchange rate and relative price. In the short run and long run, real exports are influenced more by foreign income followed by relative price. Foreign income carries positive sign and finds to be statistically significant, which implies that 1% increase in foreign income will increase real export by 1.63% in the long run. Likewise, relative price carries a negative sign and is statistically significant, which implies 1% decrease in relative prices will increase real exports by 0.22% in the long run. The nominal exchange rate carries a negative sign and is statistically significant (in both short run and long run), which suggests that depreciation of nominal exchange rate would not stimulate the volume of export during our study period. Hence, for policy point of view if any policy makers want to promote exports by depreciating, the rupee will not give desirable results. Rather, policy makers should focus more on controlling inflation for which our products will be more competitive in the international market and it will be helpful to boost our exports. Though foreign country’s income plays an important role in affecting real exports, policy makers have no control on foreign income and it is influenced by external factors.

Notwithstanding the current results provided useful information for policymakers, this should be treated with caution. The study considered only USA, as it is listed on the top of all major trading partners of India since many decades. This is one of the limitations of the study, which provides further scope for future research. Moreover, the future research has the scope to examine the bilateral analysis of export demand function with India’s top 10 major trading partners, which would further contribute to the existing literature.

The authors declare no conflicts of interest regarding the publication of this paper.

Dash, A.K., Dutta, S. and Paital, R.R. (2018) Bilateral Export Demand Function of India: An Empirical Analysis. Theoretical Economics Letters, 8, 2330-2344. https://doi.org/10.4236/tel.2018.811151