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Assessing Economic Impacts of Connectivity Corridors in North East India

An Empirical Investigation

Prabir De (prabirde@hotmail.com) teaches at, Sunetra Ghatak (sunetra.ghatak@ris.org.in) is a research associate, and Durairaj Kumarasamy (durairaj@ris.org.in) is a consultant at Research and Information System for Developing Countries, New Delhi.

One of the main constraints to development in North East India is the lack of connectivity. How the existing East–West Corridor and the proposed transboundary corridors such as the Trilateral Highway, the Kaladan Multimodal Transit Transport Project, and the Bangladesh–China–India–Myanmar Economic Corridor connecting India with neighbouring countries in the eastern neighbourhood would stimulate economic activities in the North East is examined. It is found that the corridor-based development projects may generate economic activities and regional development, which, in turn, would influence economic growth through higher production and consumption.

(Maps 2 to 5 accompanying this paper are available on the EPW website.)

The authors are grateful to Ajitava Raychaudhuri for his guidance on this paper. They are thankful to Sanjib Pohit, Masudur Rahman, C Veeramani and Dibyendu Maiti for their detailed comments on an earlier draft, which have helped improve the quality of the paper. The authors gratefully acknowledge the comments and suggestions received from the anonymous referee of the journal, which have helped in improving the paper substantially.

Empirical studies reveal that infrastructure, such as corridor-based development, promotes economic growth and regional development through easing the demand for infrastructure, reducing time and cost of the transaction due to increase of transportation activities, encourages fragmentation of production in a region, creates employment opportunities, and also contributes to poverty reduction (ADB 2008; Kumagai et al 2009; Brooks and Hummels 2009; Ghosh and De 2005; De et al 2013). Therefore, infrastructure endowment along with geographical location and agglomerative sectoral structure is the potential determinant for a regional development (Capello 2007).

In the case of India’s infrastructure development, activities on upgrading and creating new roads and highways, railways, airports, inland waterways, and ports, etc, are expected to provide cost-effective and efficient logistic services to promote trade and development. In addition, India’s focus on the North East Region (NER) by developing deeper connectivity would enhance the scope of intra- and cross-border trade and socio-economic benefits in that region. Especially, the existing East–West Corridor (EWC)1 connecting North East India with Gujarat improves the connectivity of the NER with the rest of the country. There are also other ongoing and proposed corridors connecting the NER with the neighbouring countries of India, namely the Trilateral Highway (TH), the Kaladan Multimodal Transit Transport Project (KMTTP), and the Bangladesh–China–India–Myanmar Economic Corridor (BCIM-EC), which are expected to bring potential benefits to the region. These corridors are significant for India and the NER in particular in view of the ambitious Act East Policy (AEP) that aims to strengthen connectivity between India and South East Asia.

The NER has the potential to grow faster than its current pace, by improving connectivity, logistics and trade facilitation, more particularly with Bangladesh, Myanmar and other South East and East Asian countries. Development of (transport) corridors, which connect the NER with the other states of India and the neighbouring countries, can enhance both trade and connectivity. The NER has a number of challenges in terms of lack of connectivity, barriers in trade across the border, and lag in economic activities in comparison with the rest of India. The slow progress of the the NER’s economy is reflected in the low growth in income. For instance, NER contributes only 3% to India’s gross domestic product (GDP),2 despite having huge unexplored natural resources, including several critical minerals, petroleum, natural gas, hydroelectricity, etc. Therefore, the NER needs drastic improvement in terms of connectivity to the rest of India and facilitate border infrastructure to trade with the neighbouring countries. In this context, the ongoing and proposed economic corridors through the NER can encourage economic activities. Besides, it would enhance the agriculture and manufacturing activities, along with the existing limited industries mostly on some mineral-based industries and some micro-household unorganised sectors such as handloom and handicrafts, small food processing units, etc.

In view of the above, this paper attempts to assess the economic impact of the ongoing and proposed transport corridors such as the TH, the KMTTP, and the BCIM-EC along with the EWC. This paper makes an important contribution to the existing literature on the economic corridor and economic development-related issues. First, there is no single study on measuring the impact of the economic corridor with reference to the NER till date. Second, we have developed a theoretical model based on the constant elasticity of substitution (CES) production and consumption function to illustrate how the existing and the proposed corridors would enhance the economic activities in the NER. Third, we have empirically estimated the theoretical model using the panel data approach and projected the impact of the economic corridor on economic activity until 2040. Our study finds that corridor-based development may lead to generate further economic activities and regional development. The existing and the proposed corridors with the given level of economic growth can influence the freight movement across the NER, which would also help to facilitate the economic agglomeration. In addition to this, connecting the geographical space will encourage production and consumption of that region to promote further economic activities.

Literature Review

Corridors are often characterised by public good features—non-rivalry and non-excludability—though their extent could vary across services.3 For instance, a corridor can be national (for example, Leipzig–Frankfurt corridor, Tokyo–Osaka corridor), regional (for example, the Greater Mekong Subregion (GMS) or the Central Asia Regional Economic Cooperation corridors), or even international (for example, submarine telecommunication cables or energy pipelines) thereby connecting country or countries across the region. Corridor-based infrastructure development has received worldwide attention when the GMS countries jointly decided to promote economic corridors for improvement and expansion of economic opportunities by linking cities and towns with urban centres (ADB 2004).4 A corridor helps strengthen industrial (or services) agglomeration over time through the establishment of industrial zones (or special economic zones [SEZs]) and facilitates the cluster-type development of enterprises.

Several studies showed that corridors which cut across a geographical space generate economic agglomeration, subject to location, where transportation costs and time are critical to such agglomeration.5 The low costs related to transportation activities create an effect of increasing productivity. For example, when the freight rate of an imported goods falls, the profit rate gained from trade increases, and more goods and services are produced (Brooks and Hummels 2009). Besides, infrastructure development linking with industrial location significantly reduces the border trade (Kumagi et al 2009). Better infrastructure (for example, services links, logistics services) encourages fragmentation of production in a region, and enhances regional and global trade, expediting regional integration (Ghosh and De 2005; ADB 2006; ADB 2008). Few recent studies have pointed out that improvement in trade would also lead to job creation and more income opportunities, which will further add on to the local production. Therefore, regional and national economic corridors are complementary to each other, by facilitating a conducive environment for trade and investment activities (Sen 2014), besides contributing to poverty reduction (De et al 2013). A set of literature indicates that, ceteris paribus, corridors would generate further economic activities (Brooks and Hummels 2009; Ghosh and De 2005; De et al 2013; Sen 2014). States that have better connectivity and access to the neighbouring markets through improved corridors may gain from intra- and inter-state economic activities as well as with neighbouring countries (De 2013). On the other hand, the states that have less proximity to the transport corridors might face more connectivity challenges and relatively little access to the international market (Pal 2016).

India has taken several initiatives to upgrade and develop infrastructure facilities by launching several flagship projects such as Sagarmala, Bharatmala, the Golden Quadrilateral, and North–East–South–West (NESW) corridor and so on, that would ensure the time-bound creation of world-class infrastructure in the country. Within India, keeping the NER in focus, there is much to be desired in terms of infrastructure development and growth of trade. However, given its natural resources and strategic location, the region has the potential to be an important player in India’s trade and investment. Not only natural resources, the NER also enjoys greater geoeconomic space over other Indian regions. Moreover, the NER imports almost every consumer goods from outside the region. The absence of adequate institutional and physical infrastructure, both national and international, coupled with political disturbances and insurgency in part, have slowed down the NER’s growth process. For instance, physical infrastructure such as electricity, communication, transportation, and banking and finance are very sporadic and unevenly distributed among urban and rural areas. Amenities are limited in nature, and the lack of economic opportunities encourages migration, particularly that of skilled people to work and live in better-developing parts of India. The main constraint to development is high transportation cost, which has been negating the NER’s
advantage of having a vast international border. Hence, it has been argued that if cross-border corridors are developed, the NER may show better performance in medium to long run.

The NER has the potential to grow faster than its current pace, by improving its connectivity, logistics and trade facilitation, more particularly with Bangladesh, Myanmar and other South East and East Asian countries such as Thailand, Malaysia, and China (De and Majumdar 2014). Therefore, unlike other states, the development of the NER is reliant on the connectivity to South East Asia, including Bangladesh and Myanmar. Most importantly, the border trade potential of the NER states with Bangladesh and Myanmar gives a new potential to grow in terms of trade. Therefore, the corridors that are either passing through or proposed to pass through the NER would have potential benefits to the region (Map 1).

The EWC, a national project, which is under the Golden Quadrilateral (GQ) projects, starts at Silchar (Assam) and ends at Porbandar (Gujarat), and aims to improve the connectivity of the NER with the rest of India through a 3,300 kilometre (km)-long four-lane divided highway between Silchar and Porbandar. This corridor forms a key part of the Indian highway network, connecting many important manufacturing, commerce and cultural centres of the northern part of India. While the larger portion of this corridor has been completed, small phases are still under construction in the NER. According to the National Highway Authority of India (NHAI), reasons for the delay include, inter alia, problems in land acquisition, forest clearance for cutting trees, transfer of electric poles, etc.6

The TH is an international project that is designed to connect Moreh in Manipur to Mae Sot in the Tak province of Thailand via Myanmar. Importantly, along with this corridor, there are two border crossings (India–Myanmar, and Myanmar–Thailand), four customs checkpoints, three international time zones, three customs Electronic Data Interchange (EDI) systems, two different vehicle-driving standards, and three different motor vehicle laws. The challenge is to reach convergence in standards and procedures along the corridor. This project would help in establishing trilateral connectivity between India, Myanmar, and Thailand.

Another international project, the KMTTP has been jointly identified by India and Myanmar for creating a multimodal transportation project that will carry cargo from the eastern ports of India to Myanmar as well as to the NER of India through Myanmar. The KMTTP is aimed to provide an alternate route for transportation of goods to north-eastern India through Myanmar. The components of this project include
(i) construction of an integrated port and Inland Water Transport (IWT) terminal at Sittwe, including dredging; (ii) development of navigational channel along river KMTTC from Sittwe to Paletwa (158 km); (iii) construction of an IWT—Highway transhipment terminal at Paletwa; (iv) construction of six IWT barges (each with a capacity of 300 tonnes) for transportation of cargo between Sittwe and Paletwa; and (v) building a highway (109 km) from Paletwa to the India–Myanmar border (Zorinpui) in Mizoram. The framework agreement and two protocols (Protocol on Transit Transport and Protocol on maintenance) were signed by India and Myanmar on 2 April 2008.

Lastly, the BCIM-EC is aimed at connecting China, Bangladesh, and Myanmar with India. The BCIM-EC encompasses Kolkata in India to Kunming in China’s Yunnan province, passing through Bangladesh and Myanmar. There are four border crossings between China–Myanmar; Myanmar–India; and two in India–Bangladesh, eight customs checkpoints, four international time zones, two different working weeks, four customs EDI systems, two different vehicle-driving standards, and four different motor vehicle laws.

Methodology and Data

The model: To assess the economic impact of transport corridors on economic activities in Indian states with special reference to the NER states, we have followed the classical CES function, for both production (CES production function) and consumption (CES utility function).7

In a closed economy framework, each state i potentially produces varieties of goods and engages in interstate production and consumption, thereby generating trade across states. The preferences of consumer in state j given the supplier of varieties of goods from state i would be as follows:

... (1)

The utility function is the CES functional form, which is the sum of the consumer preference of varieties of goods (v) from state i (each of which is weighted equally), also called as
Armington aggregator.8xij is the quantity of goods traded from state i that is consumed in state j, given the unit price of xij can be pij. λ is a preference parameter related to the share of
expenditure by state j spent on the goods from state i, assuming θ = σ / (1- σ) and σ is the CES.

Given the utility function of the varieties of goods v from state i, the demand function derived from the CES utility function9 can be written as:

... (2)

 

where Xijis state i demand for the product from state j (that is, xijpij). is the real consumption from state j, given γij=xijpij and τij is the trade cost. We derived the trade cost by relaxing the assumption of closed economy framework by considering India’s inter- and intra-trade relations with neighbouring countries and assessing the impact of economic corridors that are proposed to pass through some of the Indian states, especially the NER states, to the neighbouring countries such as Bangladesh, Myanmar, Thailand and China. In this case, we consider that the trade relation exists among within the state and across the borders with the neighbouring countries. Thus, states that are closer to or passing through the transport corridors incur less trade costs compared to the states that are relatively far away from the corridors. Therefore, the price of the goods consumed in state i would vary depending on how close or far away from the production and transportation of the goods is from state/country j.

Further, we extended the equation (2) by including the factors determining the demand for product between the states, such as road density and speed of vehicle, as a proxy for infrastructure development, and dummy variables indicating the existing and proposed transport corridors, respectively. The augmented model with other external factors considered in the model is given as:

Impact in Connectivity Corridors

Xit =α+β1 Yit–β2τij3 Iit4 Cki5 Zii ... (3)

The empirical model in equation (3) attempts to examine the impact of differences in the connectivity corridors given that the level of economic growth at the state level in India would influence the freight movement across the states and neighbouring countries. For instance, transport corridors help strengthen industrial (or services) agglomeration over time through the establishment of industrial zones (or SEZs) and facilitates the cluster-type development of enterprises. In addition, an improvement in transport corridors influences production and household consumption through a reduction in transportation costs. This may generate redistribution effects among economic groups and also among regions. Therefore, transport corridors emphasise the integration of infrastructure improvement in a region and promote economic activity through the movement of goods and services, which in turn would provide employment opportunities and other social outcomes of increased connectivity (ADB 2008). In this context, equation (3) explores how the NER economy having a proposed economic corridor would benefit from the economic activity by gaining inter- and intra-regional trade movements within and between the states and neighbouring countries. Given the geographical location of the NER and its long isolation from the rest of India in terms of economy and connectivity, the infrastructure development in terms of the proposed corridors would facilitate to promote trade and investment within and across the region.

Here, we proxied Xij, the state i demand for the product from state j as total freight movement between states i and j. Therefore, the total consumption of a good in state i is equal to its endowment.

... (4)

where Xi is the total freight by aggregating the freight movement via land, air and sea routes at states.10 In case of China, we consider freight at the province level, and in case of
Bangladesh, Myanmar, and Thailand, we consider total freight at the country level.11

Yi is the real expenditure on a good consumed from state j, which is proxied by state-wise GDP for India and China; and country-level GDP for Bangladesh, Myanmar, and Thailand. in equation (2) is not really observable, besides, both state-level and country-level GDP include the consumption component. Hence, to avoid the multicolinearity problem, we omit the consumption variable.

Trade cost, τij is proxied by the remoteness measure to capture the distance between state/country capitals. The remoteness measure (D1i) is a relative measure to capture the location-specific advantages and disadvantages through a ratio of the aerial distances between India’s capital (C) Delhi to respective states capital (ci) by the aggregate distance from Delhi to all the capitals.

... (5)

Numerator is the distance between India’s capital Delhi to the i-th state’s capital and the denominator represents the aggregate distance from Delhi to rest of the (i-1)-th states’ capitals. D1i accesses the accessibility and availability of services of the particular state or country that is in closer the proximity to Delhi. Suppose two particular places (that is, the capitals of two states) are the same in the aerial distance from Delhi. But as per their location (D1i), we can capture the barriers to service access in relative terms. We assume that the closer the distance the better the accessibility and economic activity.

Ii presents the infrastructure development in the state/country, particularly accessibility and quality of physical connectivity. To measure better access to land route connectivity, we have used road density, the ratio of total road in km to the total area in square km of that state/country. We expect to have a positive relationship with the freight movement, as higher the road density the better would be the freight flow between and within the states. Similarly, to measure the quality of physical connectivity, we proxy it by the average speed of the vehicle.12 Both durability and stretch of the road connectivity tend to improve timely delivery and increase the freight movement in a country.

Cki is the dummy variable to capture the effect of proposed and ongoing transport corridors on state-level freight movement, where, corridor (k) is the EWC, the KMTTP, the TH and the BCIM-EC, respectively. We keep this corridor in our model as categorical variable, where it takes ‘1’ when the corridor (k) crosses the respective states/country or ‘0’ otherwise.

Zi is the series of interaction variables to capture the effect of economic corridors on freight movement. We have interacted the corridor (k) dummy variable with Yi, to capture the state-/province-level GDP effect of corridor versus non-corridor state on freight movement.

Forecasting the impact of economic corridors: We have further forecasted the impact of corridors till 2040. To carry out the future projections over a long time horizon, the growth rate of freight movement i has been obtained using equation (3). Here, we assume static relation in economic indicators such as road density, speed of vehicle except state GDP growth.13 Therefore, the growth rate of freight movement i can be derived using equation (12), ceteris paribus:

lnXit=α+βit1 lnYi ... (6)

Differentiation of the demand equation (12) for freight movement i with respect to time yields the relation (where a hat (^) on the top of a variable denotes its rate of growth):

... (7)

This simpler equation can be used to project future freight demand by using the income elasticity αit of the freight demand and the expected future growth rate g of the GDP. Our projections till 2040 are based on the average growth rate of state-level real GDP for the past 10 years (2004 to 2014). Correspondingly, we have forecasted the growth rate of state-level GDP for the years 2020, 2030 and 2040. We consider that the forecasted growth rate of state-level GDP is the baseline scenario, and they are appropriate for long-term growth of demand for freight movement and are not meant as accurate short-term forecasts.

Empirical Analysis and Results

We have used Prais–Winsten regressions with panel-corrected standard errors (PCSE) model and the Driscoll–Kraay standard error of the estimates model (spatial correlation consistent [SCC] estimator)14 for our analysis based on the country- and state-level data of India, China, Thailand, Myanmar, and Bangladesh for the period 2010 to 2014. The list of 29 Indian states and other selected countries included in our analysis are given in Appendix 1 (p 60). The list of Indian states and countries passing through the economic corridors of the EWC, the KMTTP, the TH, the BCIM-EC are given in Appendix 2 (p 60). The definitions of variables and their corresponding data source are given in the Appendix 3 (p 60).

The Prais–Winsten PCSE estimates cross-sectional time series models are estimated in our analysis based on fixed effect regression model. The PCSE assumes that residuals can be either heteroscedastic across panels or heteroscedastic and contemporaneously correlated across panels. Similarly, Driscoll and Kraay’s (1998) standard error estimates of commonly applied covariance matrix estimation propose a non-parametric covariance matrix estimator which produces heteroscedasticity consistent standard errors that are robust to general forms of spatial and temporal dependence. Therefore, assuming fixed effect models15 suffer from cross-section dependence and heteroscedasticity, whereas, both the PCSE and SCC models are supposed to give robust estimates.

Table 1 investigates the impact of different economic corridors on freight movement to capture the individual corridor effect in the regression models. Model 1 and Model 2 include dummy for the existing EWC, Model 3 and Model 4 include dummy for the TH, Model 5 and Model 6 include the KMTTP, and Model 7 and Model 8 include, the BCIM-EC, respectively. It is clearly evident from the Table 1 that the coefficients of the core variables are robust and consistent between the PCSE and the SCC models.

Table 1 shows that the state GDP and road density (proxy for infrastructure development) are the important determinants of freight flow. For every 1% increase in GDP, total freight in the region is expected to increase by 0.5% over time. Road density and speed variables have a positive effect on freight. The positive and significant relationship between road density and the freight implies that the higher the road density, the higher would be the flow of freight between and within the states. The relative remoteness measure with the negative sign implies that the states closer to the capital region get better political attention, in terms of sanctioning infrastructure projects that leads to more economic activities compared to the states that are away from the capital region.

The existing EWC in all the models shows positive and significant coefficient, thereby suggesting the states which connect the EWC perform better than other (non-EWC) states in India. In terms of dummies for the proposed corridor, it shows negative and mostly insignificant results. Dummy for the TH corridor shows negative and insignificant estimates in Model 3 and Model 4, whereas the dummy for the KMTTP shows negative and mixed results in Models 5 and 6, whereas the dummy for BCIM-EC shows negative and significant results. This may be due to the fact that the growth of freight is considerably higher in non-corridor states, compared to the corridor states like the north-eastern states of India and the other neighbouring countries such as Myanmar and Bangladesh. In the case of the dummy for BCIM-EC, both corridor and non-corridor states in China’s provinces are expected to benefit substantially, whereas, the corridor and non-corridor states in India are likely to witness mixed results due to the structural differences in economic size. The results also reflect the relatively poor performance of proposed corridor states in terms of freight. Overall, Table 1 shows that the growth of state GDP and better infrastructure would positively influence the growth of freight.

It is clearly evident from Table 1 that state GDP has positive influence on freight. Therefore, we would like to investigate the contribution of state GDP growth in corridor and non-corridor states on freight. Table 2 shows the interaction effect of GDP with dummy for the EWC, the TH, the KMTTPr and the BCIM-EC along with other core variables. The estimated coefficient of GDP shows the expected results, which have come out positive and significant in all the models. Remoteness measure shows negative and significant in most of the models. Coefficient of road density has come out positive and significant in every model, thereby implying that infrastructure is crucial to increase freight movement. The coefficient of speed shows mixed results.

The coefficient of the existing EWC has a positive and significant impact on freight movement in all the models. In terms of GDP interaction, Models 1 to 8 in Table 2 clearly show that the coefficients of corridor states and GDP interaction variable are highly positive and statistically significant. In fact, the size of coefficient of interaction variable with GDP has come out greater than that of coefficient of GDP in non-corridor states, implying that the NER states having corridors would gain relatively higher freight, compared to the non-corridor states in both existing and proposed corridors. For instance, the coefficient of interaction effect of GDP with dummy for the TH shows that 1% rise in GDP growth in corridor states would lead to 0.6% rise in freight, compared to the non-corridor states. Similarly, the interaction effect of the GDP with the KMTTP and the BCIM-EC shows significant higher impact on freight movement in the NER states. We then use the coefficient of GDP and the interaction variable with GDP to forecast the freight for different corridors and the likely growth of freight flow till 2040.

We forecast the freight movement for the Indian states based on the estimated parameters of Table 2. In order to do it, we have used the annualised growth rate of state-level real GDP for the past 10 years (2004 to 2014); and correspondingly, we have projected the growth rate of state-level GDP for the years 2020, 2030 and 2040. The projections for corridors linking the NER within India with the existing EWC and with the neighbouring countries such as Bangladesh, Myanmar, Thailand, and China through the TH, the KMTTP and BCIM-EC are given in Table 3 (p 59).16 It briefly summarises the effect of corridor and non-corridor states on the freight flow till 2040. Table 3 clearly shows that the existing EWC would contribute about 90% growth in freight movement in 2040. It is also evident that the transport corridor has immense potential to boost economic activity across the states connected to the corridor and also has spillover effect to the other neighbouring states with “no corridor.” In the case of the KMTTP, the Indian states, mostly NER states, may witness an increase in freight movement by 74.37% in 2040, compared to the states with “no corridor.” Similarly, in the case of the TH, the states with corridor would witness an increase of the freight by 34.75% in 2040, compared to the states with “no corridor.”

Maps 2 to 5 present the graphical illustration to understand the state-level impact of GDP on freight movement due to corridors for both existing and proposed corridors till 2040. The existing EWC given in Maps 2(a), 2(b) and 2(c) show the annualised growth rate of projected freight for the periods 2014–20, 2020–30 and 2030–40, respectively. From Map 2, it is clearly evident that the dark (blue) colour states in India may witness higher growth of freight till 2040. The EWC with the distance of 3,300 km, under the Golden Quadrilateral project connecting Gujarat (Porbandar), Madhya Pradesh, Rajasthan, Uttar Pradesh, Bihar, West Bengal and Assam (Silchar) would benefit the NER states.

In the case of the KMTTP, Maps 3(a), 3(b) and 3(c) show the annualised growth rate of projected freight for the periods 2014–2020, 2020–2030 and 2030–2040, respectively. From Map 3, it is clearly evident that the dark (blue) colour states in India may witness higher growth of freight till 2040. The KMTTP is planned to connect the NER states and West Bengal with Myanmar. We find that the growth rates of freight have come out high in the NER states, particularly, Manipur, Meghalaya, Nagaland, Assam, Sikkim, Tripura, and West Bengal through which the KMTTP is designed to pass.

Similarly, Map 4 presents the projected growth of freight for the TH. This project connects the NER states with the neighbouring South-east Asian countries such as Thailand and
Myanmar. Maps 4(a), 4(b) and 4(c) demonstrate that darker states are likely to witness higher growth in freight till 2040, compared to non-corridor states. The pattern has remained the same and is also visible in all the three maps; the growth of freight is likely to be higher in states like Arunachal Pradesh, Assam, Bihar, Jharkhand, Manipur, Meghalaya, Mizoram, Nagaland, Odisha, Sikkim, Tripura, and West Bengal as compared to the states not falling in the direct catchment of the TH. Similarly, Map 5(a), 5(b) and 5(c) for the BCIM-EC shows a positive impact on freight in the NER states, particularly Assam, Bihar, Jharkhand, Manipur, Tripura, and West Bengal.

Therefore, in view of the analysis carried out in this paper, it may be concluded that the NER states are forecasted to gain more in terms of freight from the KMTTP, the TH and the BCIM-EC. The caveat is that this is a static analysis and may miss many dynamic relations between the known variables. The analysis does not talk about either causal direction or relations between freight and development. It simply illustrates a likely future scenario based on simulations of the economic geography model. Therefore, we need to interpret the results with caution.

Conclusions

The NER is crucial to India’s growing economic and strategic partnership with South-east and East Asia. The NER is also central to India’s Look East–Act East Policy, and acts as a land bridge between India and South East Asia. Owing to its strategic location, several national and international corridors may pass through the NER either as a place of origin or a place of destination. In this study, we have examined how the existing EWC and the proposed corridors like the TH, the KMTTC, and the BCIM-EC connecting India with countries in its eastern neighbourhood would stimulate economic activities in the NER. We find that the freight movement will pick up pace in the NER only when these international corridors become functional. The corridor-based development project may generate further economic activities and regional development, which in turn will influence economic growth through higher production and consumption.

This study indicates that the NER states are likely to gain more in terms of growth in freight from the existing EWC and, the proposed KMTTC, TH and BCIM-EC, respectively. Gains are robust and highly significant in case of states like Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura, and eastern Indian states like West Bengal, Bihar, Jharkhand, and Odisha. However, we need to interpret the results with caution. The operational models, which we have developed to trace the effects of changes in corridors on regional development, provide strong policy implications.

Notes

1 East–West Corridor (EWC) is a part of Golden Quadrilateral project, initiated by the National Highways Authority of India (NHAI) under the National Highways Development Project (NHDP) in 1998.

2 For details, see Annual Reports of the Ministry of Development of North Eastern Region for various years.

3 Refer Rimmer (2014) for a detailed discussion on corridors in Asia–Pacific region.

4 A detailed discussion is available at several seminal publications of Asian Development Bank (2004).

5 Refer Weber (1929), Isard (1956), Krugman (1991) to mention a few.

6 Based on the conversation the authors had with the NHAI.

7 Several studies have used CES function (Villar 2001; De and Rout 2008 and Kumagai and Ison 2011) to estimate the economic impact of connectivity.

8 Refer, Armington (1969).

9 For brevity, we have explained the model briefly. The detailed derivation of the model is available upon request.

10 Due to difficulties in collecting inter-state freight movement data, inflows and outflows of freight movement data are not available.

11 State-level freight data are not readily available in these countries.

12 Based on authors’ own observations.

13 Therefore, our results should be described with caution.

14 Several studies have used Input–Output Model (Shen 1960; Schaffer 1972; Stokes et al 1991), Economic Simulation Models (Weisbrod and Beckwith 1992), Spatial Computable General Equilibrium Model (Ivanova 2004; Martino et al 2005), and Geographical Simulation Model (Kumagai et al 2009; 2011). Due to a lack of data availability at the state level in India, and for other countries considered in the model, we are constrained to use panel fixed effect related model for our estimation.

15 In the initial stage of our analysis, we ran both the fixed effect and random effect models. The diagnostic test of these models suggests that it suffers from contemporaneous correlation within the panel. For instance, if the unobserved components are correlated across the cross-section with the explanatory variables, then the fixed effect would be biased as well as inconsistent. Therefore, the panel suffers from cross-section dependence due to spatial dependence affecting reliability of the estimates. We do not report the analysis in this paper, due to word constraint. But the results of fixed and random effect model would be available upon request.

16 The detailed state-level projected results are available upon request.

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Updated On : 20th Mar, 2019

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