Introduction

The overall economic performance of economies in South Asia in recent years has been impressive. India has led the way with an 8–9% growth in gross domestic product (GDP) since 2003, followed by Bhutan with 7–8%, and Pakistan, Bangladesh, and Sri Lanka each with 6–7%. Only Nepal has experienced relatively slow growth. However, there is concern that an aging and increasingly inadequate infrastructure may limit the potential for further growth and economic development.

A critical infrastructure component is the transportation network. Although South Asia inherited an integrated transport infrastructure from the British, this infrastructure was fractured by the partition of India and its political aftermath, and now needs to be rebuilt within the context of greater political harmony in South Asia (Asian Development Bank [ADB] 2007: 1). Transport infrastructure in many areas has fallen into disuse, raising the cost of travel and trade. The ADB is financing several transportation infrastructure projects (in addition to projects on energy, tourism, and the environment) in the South Asia Subregional Economic Cooperation (SASEC) region, connecting Nepal, eastern India, Bangladesh, and Bhutan.

An important question is how these infrastructure developments might affect the broader economy in SASEC, and in particular impact on income distribution and poverty. South Asia is one of the poorest regions in the world. Table 1 reviews the poverty/income distribution statistics in the region. These have been drawn from the World Bank (2007), and we have extracted the latest available year for each economy in South Asia for which data are available.

The most basic measure of poverty is the headcount ratio, the proportion of the population that falls below a defined poverty line. Commonly used criteria are the international US$1/day and the US$2/day standards, with the higher standard more widely applied to countries with higher average incomes.¹ The overall percentage of the population under the poverty line in India has been falling since 1996. The depth and severity of poverty has also fallen over that period. Nonetheless, the proportion of the population living in poverty in India remains high, and there is also considerable variation in poverty levels between urban and rural populations. In Bangladesh, the poverty headcount is even higher, at 35%, while it is 25% in Nepal. In Pakistan and Sri Lanka, the rates are much lower, at 9% and 6%, respectively. Nonetheless, poverty remains an issue; at the US$2/day level the corresponding rates are 60% and 41%. Income distribution is also uneven throughout South Asia, especially in Sri Lanka and Nepal.

Two other measures are provided in Table 1 [ PDF 19.6KB | 1 page ], both of which attempt to address the issue of poverty depth. The poverty gap measure is the mean distance below the poverty line as a proportion of the poverty line. The squared poverty gap weights individual poverty gaps by the gaps themselves, and provides a measure of inequality among the poor. The areas with the greatest depth of poverty are again Bangladesh, rural India, and Nepal. Finally, the Gini coefficient is a commonly used measure of overall income inequality, with the greatest levels of inequality in Nepal and Sri Lanka.

The purpose of this study, as defined by the ADBI, was to try to quantify the impact of transportation infrastructure developments in the SASEC region by applying appropriate empirical methods to ascertain the economic outcomes from identified cross-border transportation projects in the region, and, as far as possible, by analyzing the socioeconomic implications. The impacts on households in the region, poverty, and income distribution are analyzed, as well as general economic conditions and impacts on sectors.

Given the desire to derive economy-wide and sector level impacts, computable general equilibrium (CGE) is the most appropriate methodological approach. CGE models are numerical simulation tools based on general equilibrium theory. Their objective is to turn the abstract models of theory into a practical tool for policy analysis. The typical applied model adds complexity to, but retains the basic structure of, textbook general equilibrium models. Since policy and other changes in an economic system often have repercussions beyond the sector in which they occur, by linking markets, CGE techniques are effective at capturing the relevant feedback and flow-through effects. CGE techniques have been widely used for ex ante trade policy analysis, and more recently for poverty analysis (for surveys see Scollay and Gilbert 2000; Gilbert and Wahl 2002; Robinson and Thierfelder 2002; and Lloyd and MacLaren 2004).

The primary objective of this draft paper is to describe the building of a new CGE model for South Asia constructed with the support of the study, and its applications for understanding the socioeconomic aspects of developments in cross-border transport infrastructure. The model covers India, Sri Lanka and Bangladesh, Nepal, and Pakistan. The model incorporates modifications to the household structure to capture implications of reform for intra-household income changes. It is general in purpose, with the possibility of future applications to related problems. It is also extensible, so that alternate datasets for other countries inside the region may be added in the future.

The paper is organized as follows. In Section 2, we outline the features of the proposed SASEC project, which involves a series of improvements in transportation network infrastructure between India, Bangladesh, Nepal, and Bhutan. In Section 3, we review the existing CGE studies of socioeconomic impacts of policy reforms in the SASEC region and South Asia more generally. The majority of studies featuring household impacts have focused on trade liberalization. In Section 4, we then describe the regional model that we have built for this study. In Section 5, we consider the results of our simulations and the policy implications. Finally, in Section 6, we offer our concluding comments.

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