Concluding Comments

The main contribution of this study is to bring the multiple-representative household CGE approach to a model of the entire South Asian region, as opposed to the single-country models examined earlier in the paper, and to apply this model to changes in transportation infrastructure. The model that we have developed is a multi-country CGE with 16 production sectors and 47 regional households. In principle, the model should have applications for numerous policy questions concerning South Asia, especially in relation to international trade and economic integration.

The scenarios that we considered in this paper reflect the potential implications of proposed investments in land transport infrastructure in the SASEC region. These investments should result in reductions in the land transport component of international transport margins, which vary bilaterally by commodity. We found that all SASEC economies benefited from reductions in aggregate welfare, with the largest gains to India in absolute terms but the largest relative gains to Nepal, followed by Bangladesh, and Sri Lanka, when the margin reduction is prorated to intra-South Asian trade rather than just SASEC. Hence, in terms of overall impact, the largest gains are to the smallest economies in the region.

We used the discounted present value of the EV stream to provide a (lower bound) estimate of the total “one-off” benefit of the margin reductions, which is suitable for comparison with project cost estimates should these become available. In terms of household level distribution, the picture is mixed. While the outcomes are clearly pro-poor in some countries such as Nepal, the impacts are more ambiguous in other countries. Examination of the extent of predicted structural changes suggests that there would be only minor potential adjustment costs, although these would be somewhat more significant for the smaller economies in the region.

Ideally, we would like to allow for endogenous switching of mode of transportation and to incorporate internal transportation margin information within the model. The lack of these variables means that we are probably understating the potential gains from land transport infrastructure investment. Similarly, there is a question of what other pathways for impacts of the infrastructure developments can/should be implemented in the model and experimental design. As it stands, the model traces the impact of policies only through the price mechanism. Changes in transportation costs alter the costs of final goods. These affect households directly through their consumption and indirectly through their ownership of factors, the prices of which shift in response to output price changes. Changes in transfers such as tax revenues are also altered.

These are important forces, but there may be others. Improved transportation networks may lead to better access to education, for example, over time increasing the skilled/unskilled labor ratio. There are also potential costs that are difficult to quantify within a formal modeling framework. For example, the model only indirectly accounts for adjustment costs in production, and cannot account for dislocation impacts on household welfare. Transportation networks that connect the hinterland to more developed regions may result in substantial migration flows that strain urban resources. Exploring whether it is possible to bring this type of variable into the modeling process is an area for future research.

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