Introduction

The current economic crisis has heightened concerns about poverty across the world. One outcome of the recent Group of Twenty (G-20) meeting in London was a call for all developed countries to fulfill their commitments on providing aid and continued development assistance to the developing world. Indeed, the Asian Development Bank (ADB) recently received a 200% General Capital Increase, while announcing a US$3 billion counter-cyclical support facility for the region.

The extent to which these measures will offset the major economic slowdown in the region remains unclear. The full extent of the slowdown's impacts on the poor is also as yet unknown, but ADB is projecting an additional 131 million poor in 2010 (ADB 2009).

While poverty reduction remains a major pillar in the development agendas of multilateral development banks, the current economic situation has placed increased stress on available resources. Not only are donor countries experiencing pressure at home to prop up their own economies, traditional aid agencies and domestic governments alike are also facing increasing, and increasingly diverse, demands for funds. Rarely has the need to maximize value for every public dollar spent been more important.

Despite the significant inroads made in the region, poverty is still a major challenge in Asia. While cases of extreme poverty (i.e. those living on US$1/day or less) have fallen, the number of those living at or near the poverty line of US$2/day remains high. Along with the rise in the number of ‘working poor' has come a rise in income inequality. Indeed, studies have shown increasing levels of income inequality over the last ten years in Asia (Ali 2008).

These trends have not been lost on policymakers. "Equitable growth" and "holistic growth" have become the new catchwords in development policy formulation. Large economies such as India and the People's Republic of China (PRC) have instituted a number of policies to address the potentially destabilizing effects of the over-concentration of benefits within particular groups in society (Ganguly 2009).

The ultimate distribution of benefits from policy or public spending is pivotal for a number of reasons. First, from a strictly economic sense, as the number of consumers grows, so do opportunities for market expansion and competition. This allows companies to reap the benefits of economies of scale and scope. Economies that support competitive markets enjoy greater innovation and increase consumer utility by widening product choice and service provision. Second, from a social policy perspective, a policy that has limited impacts on income can lead to uneven income growth. This, in turn, can lead to social unrest and the development of a permanent underclass. Aside from raising moral dilemmas, these outcomes represent real burdens on government budgets. Expanding the potential gains from any development project can ease such pressures on government budgets, while simultaneously expanding the tax base. Thus, it is imperative that policymakers understand and anticipate the potential income effects of public spending or policy initiatives, and plan accordingly.

This paper outlines one way of measuring the impact of policy initiatives on the poor. The use of the poverty headcount to measure poverty impact is well established (Bourguignon 2003; Ravallion and Dat 1999; Ravallion and Chen 1997). However, this approach usually measures the impact of policy on the poor in general, and not on specific groups. The consequences of policy reforms vary widely among various household segments, depending on their primary source of income, endowment, and consumption patterns. Therefore, it is important to stratify households according to income source and decompose their factor earnings, when attempting to determine the actual impact of policy measures. This can be done by calculating the effects of changes in the factor earnings on poverty headcount across strata (Hertel et al. 2007a).

In order to measure the impacts of various transport policy initiatives in Cambodia and Lao PDR, households were stratified according to primary income source. Five groups of households that rely almost solely (95% or more) on one source of income were identified. The households that rely on several sources of income (less than 95% on each source) were classified as diversified, and further broken down into rural and urban households. In total, there are seven strata: self employed agriculture based, self-employed non-agriculture based, rural and urban wage earners, transfer-based, and rural and urban diversified (Hertel et al. 2007a).

Income earned by households in Cambodia and Lao PDR was taken from household surveys and categorized into ten factor income sources: Land, Agriculture-Unskilled AgUnskl); Agriculture- Skilled (AgSkl); Non-agriculture-Unskilled (NagUnskl); Non-Agriculture-Skilled (NagSkl); Wage-Unskilled (WgUnskl); Wage-Skilled (WgSkl); Agriculture Capital (Agcap); Non-Agriculture-Capital (Nagcap); and Transfers. AgUnskl and AgSkl are imputed returns to self-employed agriculture labor. NagUnskl and NagSkl are imputed returns to self-employed non-agriculture labor. WgUnskl and WgSkl are labor wages that were reported directly in the survey as wage earnings. Land and Agcap are split from the residual of agriculture profits and determined by alpha (as derived from the Global Trade Analysis Project [GTAP] database). Nagcap is the residual of non-agriculture profits. Finally, transfers include both public and private transfers (Hertel et al. 2007a).

The remainder of the paper is organized as follows: Section II outlines the data cleaning procedure; Section III describes how the Cambodia and Lao PDR household data were linked to the GTAP data; and Section IV briefly discusses the methodology's strengths and weaknesses. The succeeding sections illustrate the structure of poverty around the neighborhood of the poverty line, and provide some policy implications. The last section concludes.

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