Empirical Model

8.3. Empirical Model

Trade flows between countries are determined by a host of domestic as well as external factors. The bilateral exchange rate between the trading nation and its partners often serves as a proxy for the relative price of domestic goods in foreign markets. Barring the individual effect of changes in home currency, movements in the competitors’ currencies do exert significant impact on the exports of a trading nation. In many important markets, India and China are significant suppliers and movements in their real exchange rates have an important bearing on their trade flows to third countries. After the recent global economic crisis, the undervaluation of Chinese renminbi, has emerged as the single most contentious issue in the global trading arena over the past few years as discussed in the earlier section. For India, China is not only a major competitor in the Asian region but also in many of its major trade destinations. China has a strong export presence in many sectors where India is an important supplier. Chinese exports, supported by its aggressive export financing approach along with scores of other incentives to importer, have been detrimental to India’s presence in these markets. Both countries are suppliers to these destinations in many sectors. Often the size of Chinese market share in specific product segments in these destinations matters for India in its active engagement in export. In this context, the expected gains for India in exports resulting from a Trade flows between countries are determined by a host of domestic as well as external factors. The bilateral exchange rate between the trading nation and its partners often serves as a proxy for the relative price of domestic goods in foreign markets. Barring the individual effect of changes in home currency, movements in the competitors’ currencies do exert significant impact on the exports of a trading nation. In many important markets, India and China are significant suppliers and movements in their real exchange rates have an important bearing on their trade flows to third countries. After the recent global economic crisis, the undervaluation of Chinese renminbi, has emerged as the single most contentious issue in the global trading arena over the past few years as discussed in the earlier section. For India, China is not only a major competitor in the Asian region but also in many of its major trade destinations. China has a strong export presence in many sectors where India is an important supplier. Chinese exports, supported by its aggressive export financing approach along with scores of other incentives to importer, have been detrimental to India’s presence in these markets. Both countries are suppliers to these destinations in many sectors. Often the size of Chinese market share in specific product segments in these destinations matters for India in its active engagement in export. In this context, the expected gains for India in exports resulting from a

Exports from India = ƒ (demand of importers, movement in renminbi, Chinese exports, structure of Indian exports) ………...………… (3)

The underlying argument for export gains to India from yuan appreciation lies in the fact that currency appreciation deteriorates China’s export competitiveness and thereby creates opportunities for its competitors like India to export more. Whether this applies to all commodities or not depends on the commodity structure of India’s exports. In order to account for the asymmetric effect of trade sectors to renminbi revaluation the model considers an interaction term involving both bilateral sectoral exports and structure of India’s exports. We consider the revaluation effects of the yuan on India’s exports may be different in various sectors. Factoring in these dynamisms in the model, we have the following export function:

ln Indijt = ƒ (ln GDP jt , ln Chn ijt , ln REER chn,t , ln POP jt , ln Imp jt , Zind ijt ) ……………. (4) Where Indx ijt denotes India’s exports from i-th trade sector to j-th trade partner,

GDP jt denotes GDP of the partner country, POP jt denotes population of partner countries, Imp jt denotes imports of partner countries, REER chnt denotes real exchange rate of China, Chnx ijt denotes China’s exports from i-th trade sector to j-th trade partner, Zind it denotes interactive variable of India’s sectoral structure of exports with India’s bilateral sectoral exports to j-th trade partner, and ‘t’ stands for time (year).

As discussed earlier, equation (4) represents India’s sectoral export function with its major exports destinations. India’s substantial exports are segregated into five broad sectors. For each sector, we will be having a separate Equation (4). Therefore, in this analysis, variants of five different export sectors are considered in the framework of a panel cointegration model.

For empirical estimation of equation (4), several econometric techniques could

be considered. These include single-equation standard pooled OLS, LSDV, panel fixed and random effects, dynamic models like Arellano and Bond (1991), generalised method of moments (GMM), and system GMM estimators. Most of these are stationary models with variables in first differences accounting for endogeneity and correction for serial correlation in residuals. With increasing application of time series techniques like VAR and cointegration in panel data, traditional panel data models are not used extensively in the current literature.

Panel cointegration-based estimation procedures are used in contemporary empirical research. In this context, other sets of models including Pedroni (1999), Pedroni (2004), Kao (1999), Maddala and Wu (1999), etc among others, are used to Panel cointegration-based estimation procedures are used in contemporary empirical research. In this context, other sets of models including Pedroni (1999), Pedroni (2004), Kao (1999), Maddala and Wu (1999), etc among others, are used to

regression estimator (Park, 1992), fully-modified OLS (FMOLS) (Phillips and Hansen, 1990; Pedroni, 2000), and dynamic OLS estimators (Stock and Watson, 1993; Kao and Chiang, 2000). Based on the practice and utility of these models in the field of international trade, we consider the panel cointegration approach to estimate equation (4).

The exogenous variables used in this study are four, and three control variables (GDP, population and imports of partner countries) are tried alternatively in the models for different sectors. Improvement in income in foreign markets raise their purchasing power, and therefore the sign of the foreign demand coefficient in the export equation (4) is expected to be positive. Besides GDP, we are trying with the alternative control variable like population and import of partner countries in the equation and the signs are expected to be similar to GDP. Similarly, a positive relationship is assumed to hold for yuan in the export model. Given the possibility of the contemporaneous relationship between Indian exports and Chinese exports, the inclusion of Chinese exports in the export equation seem theoretically plausible. Since its empirical behaviour is unknown the sign of Chinese exports is ambiguous in the equation. India’s bilateral exports to third countries is expected to rise in certain sectors with an appreciation of Chinese renminbi because Chinese exports would be more expensive to India’s competing product. If India’s bilateral export rises in its export destinations, the sign of the interactive variable will be positive.

8.3.1. Data and Variable Definitions The panel cointegration model has been estimated to test the possible impact of

renminbi revaluation on India’s exports in third markets. The bilateral export equation aims to capture the effects on India’s exports across five sectors. Each Sector is represented by a separate panel. The joint effect of renminbi revaluation and the associated shift in global demand are captured in a multi-country export

equation. Each panel data 58 for the empirical exercise covers 25 countries and 7 years from 2003 to 2010.

We have identified the most important trade destinations for both India and China separately employing bilateral trade flows using the Direction of Trade Statistics online database, IMF. Taking top ranking countries from both lists, 25 most important countries are for the panel (see Appendix IV). Among the top 25 countries, 7 countries are from the European Union, 5 from other developed countries, 8 from developing Asia, 2 from the Middle-East, and 3 are from the Emerging economies. These 25 countries are represented in different panels used for the cointegration analysis.

Disaggregated time series trade data is taken from the UN-Comtrade at 6-digit HS with the nomenclature of HS 2002. The UN Statistical Division developed the Broad

58 Trade data used for the model is in HS 2002 nomenclature at 6-digit level.

Economic Categories (BEC) 59 which is consistent with the NAS and presented a correspondence table between BEC and Harmonised System. Under the broad

framework of the BEC, trade data is grouped into three categories namely primary, intermediate and final goods, and latter two categories are again subdivided into two sub-groups each using concordance table. Therefore, substantial trade data is segregated into five groups namely primary, semi-finished, parts and components, consumption and capital goods. As UN-Comtrade provides time series bilateral trade flows (exports and imports separately) data, new bilateral trade series are constructed for each destination and for all the five categories. Such trade series are formed for India and China separately to prepare sector-specific panels.

The variables considered in the model are widely used in the empirical literature on exchange rate effects on exports. By using quarterly data for China for the period 1996-2009, Ahmad (2009) finds that real exchange rate appreciations have contemporaneous and lagged effects on real export growth. Foreign consumption representing economic activity in top ten trade destinations has a positive effect on Chinese exports. Estimating a dynamic OLS model, Thorbecke (2011) establishes a strong causality among exchange rate, foreign income and exports. The study observes that a generalised appreciation in both China and East Asia produces a large drop in processed exports. Foreign income is positively related to export of processed goods from these economies. Likewise, Yu (2009) observes a dampening effect of renminbi revaluation on Chinese exports to the United States. Garcia- Herrero and Koivu (2009) find that real appreciation of the renminbi not only affects processed exports but also ordinary exports of China in the long-run. A similar effect holds for imports to China also. Unlike other studies, this study observes a positive but weak effect of foreign demand on Chinese exports. These papers provide the rationale for including Chinese REER and foreign demand as explanatory variables for the export equation.

The variable definitions are given below. All variables except ‘z’ and ‘zind_sector’ are expressed in natural logarithms.

logind_sector:- Sectoral exports from India to country ‘j’ in US billion dollars. ‘Sector’ includes ‘cap’ (capital goods), ‘cons’ (consumer goods), ‘pc’ (parts and components), ‘pri’ (primary goods) and ‘semi’ (semi-finished goods). Bilateral trade data is taken from UN-Comtrade.

logreer: Real effective exchange rate of China (2005 =100). REER of China is obtained from International Financial Statistics, IMF.

logmpart: Imports of partner country ‘j’ from the world in US billion dollars. Global imports for India’s major trade destinations are drawn from Direction of Trade Statistics, IMF.

59 For details, see http://unstats.un.org/unsd/class/intercop/expertgroup/2007/AC124-8.PDF 59 For details, see http://unstats.un.org/unsd/class/intercop/expertgroup/2007/AC124-8.PDF

April 2011. logpop: Population of partner country ‘j’ in millions. Population of partner

countries are drawn from World Economic Outlook, April 2011. logchn_sector: Sectoral exports of China to country ‘j’ in US billion dollars. ‘Sector’

includes ‘cap’ (capital goods), ‘cons’ (consumer goods), ‘pc’ (parts and components), ‘pri’ (primary goods) and ‘semi’ (semi-finished goods).

z: Commodity structure of India’s exports measured as the ratio of sectoral exports to country ‘j’ to its total exports to country ‘j’.

zind_sector: Interaction term between ‘z’ and ‘ind_sector’, and ‘ind_sector’

represents India’s sectoral exports to country ‘j’ where j =1,2,3,………,25 The estimation procedure in the study is carried out in three steps. We apply the

panel unit root tests in order to find the stationary of the variables in the panel. The second step in our analysis is to test whether the variables are cointegrated, as presented in equation (4). In the last step, the panel cointegration test is applied to show the existence of a long-run relationship between India’s bilateral exports to third countries in the event of an appreciation of the Chinese renminbi.