Impact of Container Ports on Economic Development:- A Comparative Study Among Selected Top Seven Container Ports in India

Transcription

1 Impact of Container Ports on Economic Development:- A Comparative Study Among Selected Top Seven Container Ports in India Dr. Jonardan Koner Professor of Economics and Dean (ARNP), National Institute of Construction Management and Research, Pune Prof. Avinash Purandare Associate Professor, National Institute of Construction Management and Research, Pune 1. Introduction Sea Ports are planned to serve the country s strategic needs on one hand and international trade on the other. They are a critical and inextricable part of the country s economic and social growth process. The importance of efficient ports for the growth of the foreign trade stems from the chain linkages between production, performance of individual ports and overseas transportation leading to exports. Ports alone handle over 80% of the country s merchandise trade. Recent trends in international trade have led to the increasing importance of ports as prime nodes in transportation (Haralambides et al, 2001). Container ports and terminals form an essential component of the modern economy. Containerisation since the middle of the 20th century has dramatically reduced the transport cost of international trade. Since the introduction of the first internationally-standardised container in the 1960s, container trade has grown rapidly to reach an estimated 143 million in TEU and 1.24 billion in tonnage (UNTCAD, 2008), comprising over 70% of the value of world international seaborne trade (Drewry Shipping Consultants, 2006). The sea ports of India have played a historical role in the development of maritime trade and economy in India. Indeed maritime trade in India is synonymous with India s overseas trade, accounting for over 90% of India s overseas trade. India has a natural peninsular coastline of around 6000 km strategically located on the crucial east-west trade route which links Europe and the Far-East. The coast line has 12 major ports and about 180 other minor and intermediate ports. Ports have assumed enormous importance in the era of globalization with a phenomenal expansion in world trade. The volume of cargo traffic in India has also expanded significantly. Total tonnage handled by the major ports taken together was million tons in an increase of almost 27 times since the beginning of the first five year plan when India embarked on the path of economic development. The development of port infrastructure is vital to India s economic growth as exports and imports are important components of the Indian economy. Exports contribute 25% of the country s GDP (gross domestic products) and are expected the reach a level of 500 billion USD by The important container ports in India as per the percentage of containerised cargo handled by them are JNPT (Jawaharlal Nehru Port Trust) 60%, Chennai 17.7%, Tuticorin 5.59%, Kolkata 5.51%, Cochin 4.22%, Kandla 2.14% and Haldia 1.8%. 2. Objectives of the Study The study considers the following objectives. 1) To find out the impact of container traffic flow on gross domestic product on in India. 2) To measure the discriminations of impact if any on gross domestic product from the selected container ports in India. 268

2 3. Review of Literature A number of theoretical developments and empirical studies have taken place on various aspects of the functioning of container ports and the impact of exports, imports and gross domestic product on container traffic flow in India and similarly on rest of the world. Here an attempt is made to give a broad outline of the existing studies. Li and Li (2008) 1 argue that infrastructure investment is very important to boost national economic growth and prove this with the results of infrastructure investment and the GDP in China from 1997 to De Langen (2004) 2 stated that ports can be considered as important clusters of economic activities which include value added logistics activities. Clark et al. (2004) 3 stated that port efficiency has been found to be of key importance in determining transport costs and, hence, international trade among countries. Goss (1990) 4 stated that the transport economics literature, however, has stressed that ports drive economic development because they increase competition through enlargement of the market areas of firms, thereby reducing prices for consumers. Love and Chandra (2004) 5 suggested that trade and economic growth exhibits a feedback relationship. Chen (2008) 6 concluded in their study that Guangzhau Port and other small and medium ports in China should develop policies to adapt the ever growing container transport demand and economic growth of the surrounding areas. Zou, Zhang, Zhuang and Song (2008) 7 analyzed data from China and found that higher economic growth level comes to a greater extent from better transport infrastructure and that public investment on road construction in poor areas is crucial to growth and poverty alleviation. Feder (1983) 8 and Ram (1985) 9 have analysed the export led economic growth hypothesis where they argued that exports are likely to alleviate foreign exchange constraints and thereby facilitate importation of better technologies and production methods. Balassa (1978) 10 applied simple regression analysis to a sample of 10 countries and found trade export volume were positively related to a country s rate of economic growth. Michaely (1977) 11 found a strong positive correlation between economic growth and international trade. Ahmad and Harnhirun (1996) 12 examined causality between exports and economic growth for five countries 1 2 Li, Y., & Li, Z. (2008), Grey Relational Analysis between Infrastructure Investment and Economical Growth in China from 1997 to 2006, Proceedings of 2008 International Conference on Construction & Real Estate Management (1) and (2), pp De Langen, P.W. (2004), The Performance of Seaport Clusters, ERIM PhD Series, Rotterdam. 3 Clark, X., Dollar, D., and Micco, A. (2004), Port efficiency, maritime transport costs, and bilateral trade, Journal of Development Economics, 75 :pp Goss, R. O. (1990), Economic policies and seaports, Maritime Policy and Management, 17(3), pp Love, J. and Chandra, R. (2004), Testing Export-Led Growth in India, Pakistan, and Sri Lanka Using a Multivariate Framework, The Manchester School, 72 (4): Chen, L. (2008), The Market Driven Trade Liberalization and East Asian Regional Integration, HEID Working Paper No. 12/2008. Geneva: The Graduate Institute. 7 W. Zou, F. Zhang, Z. Zhuang, and H. Song (2008), Transport Infrastructure, Growth and Poverty Alleviation: Empirical Analysis of China. Annals of Economics and Finance 9(2), Feder G. (1983), On Exports and Economic Growth, Journal of Development Economics, 12(1-2): Ram, R. (1985), Exports and Economic Growth: Some Additional Evidence, Economic Development and Cultural Change, 33 (2), Balassa, B. (1978), Exports and Economic Growth: Further evidence, Journal of Development Economics, 5(2): Michaely, M. (1977), Exports and Growth: An Empirical Investigation, Journal of Development Economics, 4 (1): Ahmad, J. and Harnhirun, S. (1996), Cointegration and Causality between Exports and Economic growth: Evidence from the ASEAN countries, Canadian Journal of Economics, 29(2):

3 of the Association of Southeast Asian Nations (ASEAN). Dutt and Ghosh (1996) 13 studied causality between exports and economic growth for a relatively large sample of countries using the Error Correction Model (ECM) for the countries in which they found Co-integration. After that Vector Error Correction (VEC) model was estimated, and tests for Granger Causality were performed. Fujita and Mori (1996) 14 proposed a model based on new economic geography assumptions and argue that the construction of a port in a relatively backward region may deteriorate local economic conditions. 4. Research Methodology The study incorporates multiple regression analysis to measure the impact of containerization on economic growth in India. In this model, we estimate the impact of container traffic flow (throughput) of the selected major container ports on gross domestic product in India during the selected time period from to The study incorporates the multiple regression equation as follows. GPD INDIA = α + β 1 CTF JNPT + β 2 CTF CHENNAI + β 3 CTF TUTICORIN + β 4 CTF KOLKATA + β 5 CTF COCHIN + β 6 CTF KANDLA + β 7 CTF HALDIA....(1) Where, GPD INDIA represents the gross domestic product in India in crore rupees. CTF JNPT represents the container traffic flow of JNPT port in thousand tonnes. CTF CHENNAI represents the container traffic flow of Chennai port in thousand tonnes. CTF TUTICORIN represents the container traffic flow of Tuticorin port in thousand tonnes. CTF KOLKATA represents the container traffic flow of Kolkata port in thousand tonnes. CTF COCHIN represents the container traffic flow of Cochin port in thousand tonnes. CTF KANDLA represents the container traffic flow of Kandla port in thousand tonnes. α is the intercept and βs are the slope coefficients for the multiple regression equations. 5. Multiple Regression Analysis In the regression equation no. 2, the dependent variable is gross domestic product in India in crore rupees (GPD INDIA ) and explanatory variables are CTF JNPT (container traffic flow of JNPT port in thousand tonnes), CTF CHENNAI (container traffic flow of Chennai port in thousand tonnes), CTF TUTICORIN (container traffic flow of Tuticorin port in thousand tonnes), CTF KOLKATA (container traffic flow of Kolkata port in thousand tonnes), CTF COCHIN (container traffic flow of Cochin port in thousand tonnes) and CTF KANDLA (container traffic flow of Kandla port in thousand tonnes). The estimated multiple regression equation is given below. GPD INDIA = CTF JNPT CTF CHENNAI CTF TUTICORIN CTF KOLKATA CTF COCHIN CTF KANDLA CTF HALDIA..(2) The estimated coefficients of equation no. 34 are individually significant, as their p values of the estimated t coefficients are very small. The estimated coefficients of the explanatory variable CTF JNPT is positive (0.97), which indicates that if the container traffic flow of JNPT port increases then it will be positively impacted to the GDP of India and also significant at 1 % level (two tail test). The estimated coefficients of the explanatory variable CTF CHENNAI is positive and the magnitude of coefficient is 0.83, which indicates the positive impact of container traffic flow by Chennai port on gross domestic product in India and the result is significant at 1 % level (two tail test). 13 Dutt, S.D and Ghosh, D. (1996), The Export Growth Economic Growth Nexus: A Causality Analysis, The Journal of Developing Areas, 30(2): Fujita, A. and Mori, T. (1996), The role of ports in the making of major cities: self-agglomeration and hub-effect, Journal of Development Economics, 49_

4 Dependent Variable: GPD INDIA Method: Least Squares Table 1: Estimated Results of Multiple Regressions Analysis Variable Coefficient Std. Error t-statistic Prob. α CTF JNPT CTF CHENNAI CTF TUTICORIN CTF KOLKATA CTF COCHIN CTF KANDLA CTF HALDIA R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.17E+05 Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Similarly, the coefficients of the explanatory variables CTF TUTICORIN, CTF COCHIN, CTF KANDLA and CTF HALDIA are positive and their magnitudes are 0.57, 0.67, 0.65 and 0.72 respectively, which indicate the positive impact of container traffic flow by these port on gross domestic product in India and the result is significant at 1 % level (two tail test). For the explanatory variable CTF KOLKATA, the estimated coefficient is positive (0.59), but significant at 5% level (two tail test), which also indicates positive impact of container traffic flow by Kolkata port on gross domestic product in India. The value of R-Square is 0.56, which indicates that the selected explanatory variables are able to explain 56 % of variations of the dependent variable. The D-W statistics is 2.00, which means that the data set is free from autocorrelations. 6. Conclusion In the multiple regression analysis, the estimated coefficients of the explanatory variable CTF JNPT is positive (0.97), which indicates that if the container traffic flow of JNPT port increases then it will be positively impacted to the GDP of India and also is significant at 1 % level (two tail test). The estimated coefficients of the explanatory variable CTF CHENNAI is positive and the magnitude of coefficient is 0.83, which indicates the positive impact of container traffic flow by Chennai port on gross domestic product in India and the result is significant at 1 % level (two tail test). Similarly, the coefficients of the explanatory variables CTF TUTICORIN, CTF COCHIN, CTF KANDLA and CTF HALDIA are positive and their magnitudes are 0.59, 0.67, 0.65 and 0.72 respectively, which indicate the positive impact of container traffic flow by these port on gross domestic product in India and the result is significant at 1 % level (two tail test). For the explanatory variable CTF KOLKATA, the estimated coefficient is positive (0.59), but significant at 5% level (two tail test), which also indicates positive impact of container traffic flow by Kolkata port on gross domestic product in India. The value of R-Square is 0.56, which indicates that the selected explanatory variables are able to explain 56 % of variations of the dependent variable. The major limitations of the study are i) the study is restricted on seven important container ports, whereas there are total twelve container ports in India and ii) the study is limited to the period of to

5 Every research study ends with its own outcome and further scope of studies. Similar way this study also opening up few new angle of research. They are i) the present work will encourage further research in the field of container ports and its impact on socio economic development in India and ii) another study can be done by considering more number of container ports; even one can do the same by including similar type of container ports from this geographical region (South East Asia). References 1. Li, Y., & Li, Z. (2008), Grey Relational Analysis between Infrastructure Investment and Economical Growth in China from 1997 to 2006, Proceedings of 2008 International Conference on Construction & Real Estate Management (1) and (2), pp De Langen, P.W. (2004), The Performance of Seaport Clusters, ERIM PhD Series, Rotterdam. 3. Clark, X., Dollar, D., and Micco, A. (2004), Port efficiency, maritime transport costs, and bilateral trade, Journal of Development Economics, 75 :pp Goss, R. O. (1990), Economic policies and seaports, Maritime Policy and Management, 17(3), pp Love, J. and Chandra, R. (2004), Testing Export-Led Growth in India, Pakistan, and Sri Lanka Using a Multivariate Framework, The Manchester School, 72 (4): Chen, L. (2008), The Market Driven Trade Liberalization and East Asian Regional Integration, HEID Working Paper No. 12/2008. Geneva: The Graduate Institute. 7. W. Zou, F. Zhang, Z. Zhuang, and H. Song (2008), Transport Infrastructure, Growth and Poverty Alleviation: Empirical Analysis of China. Annals of Economics and Finance 9(2), Feder G. (1983), On Exports and Economic Growth, Journal of Development Economics, 12(1-2): Ram, R. (1985), Exports and Economic Growth: Some Additional Evidence, Economic Development and Cultural Change, 33 (2), Balassa, B. (1978), Exports and Economic Growth: Further evidence, Journal of Development Economics, 5(2): Michaely, M. (1977), Exports and Growth: An Empirical Investigation, Journal of Development Economics, 4 (1): Ahmad, J. and Harnhirun, S. (1996), Cointegration and Causality between Exports and Economic growth: Evidence from the ASEAN countries, Canadian Journal of Economics, 29(2): Dutt, S.D and Ghosh, D. (1996), The Export Growth Economic Growth Nexus: A Causality Analysis, The Journal of Developing Areas, 30(2): Fujita, A. and Mori, T. (1996), The role of ports in the making of major cities: self-agglomeration and hub-effect, Journal of Development Economics, 49_