Two micro-decomposition approaches are explored. The first is a simple accounting approach, which adapts the Barros et al. (2006) method to quantify the contributions to distributional changes on account of changes in demographics, changes in employment and labor income and changes in non-labor income, including remittances, public transfers and other private transfers. The second approach adapts the Bourguignon, Ferreira and Lustig (2005) methodology to further distinguish between distributional changes on account of changes in endowments or/and returns to those endowments, changes in occupational choice and changes in geographical, age, and gender structure of the population, along with the non-labor dimensions mentioned above. These methods have been mostly used on income-based measures of poverty and inequality. Since most countries measure welfare through household expenditures or consumption, this paper modifies the existing methodologies to decompose consumption-based measures of poverty and inequality. These decompositions do not allow for the identification of causal effects, but they are useful to focus attention on the elements that are quantitatively more important in describing changes in poverty. In particular, we would like to capture the heterogeneity of impacts throughout the distribution and account for the contributions that demographics, sectoral, occupational and other labor and non-labor dimensions had in reducing poverty.<sup>3</sup> As recognized in the literature (Bourguignon, Ferreira and Lustig, 2005), these decompositions are path dependent, and as such, sensitive to the order in which the variables are simulated to account for the overall changes in distribution. The best-known way to remedy path dependence is to calculate the decomposition across all possible paths and then take the average. These are also known as the Shapley-Shorrocks estimates of each component.<sup>4</sup> Although we do not propose to do this for all of the components, in this paper we group the variables into smaller sets, calculate the Shapley-Shorrocks estimates of these, and assess whether path dependence is indeed empirically important.

The paper focuses on Peru, Bangladesh and Thailand. These three economies have experienced fast poverty reduction during the last decade, with falls in the moderate national poverty headcount rate in each country of over 12 percentage points. Growth was very high during the decade in all three countries, well above 4 percent per annum during the period 2002-2008. In the case of Peru and Thailand, there was a sharp deceleration due to the financial crisis in 2009, only to rebound very fast the following year. Bangladesh came through the crisis unscathed.<sup>5</sup> In all countries employment and public social transfers increased, as did remittances. However,

> 3 Panel data that can track the life and labor histories of households over time can be used to answer questions about economic mobility and poverty dynamics. However, panels are often not available with the frequency required. Moreover, panel data are often not representative of the population as a whole; and if they initially are, it is unlikely that over the course of a decade the panel would remain representative of the population. Alternative methods using repeated cross sections have been used. One approach is to construct pseudo panels, which can delve into some issues of economic mobility (Lanjouw et al., 2011). However, these models are often troubled by their lack of precision and the fact that they often do not measure the contributions of different factors to poverty reduction.

> 4 See Shapley (1953) and Shorrocks (1999).

> 5 See Bangladesh Poverty Assessment, World Bank (2012a).

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