or more,<sup>5</sup> using comparable consumption or income data in the decade 2000 to 2010. For this set of countries, growth explains most of the observed reduction in poverty for 17 of the 21 countries. Redistribution was found to be more important only in the cases of Argentina, Mongolia, Paraguay and the Philippines.

While these estimates of the reduced-form relationship between economic growth, inequality and poverty have been useful to identify empirical regularities, they are unable to make explicit the links between growth and poverty reduction (Ferreira, 2012). In particular, we would like to capture the heterogeneity of impacts throughout the distribution, and be able to account for the contributions that demographics, sectorial, occupational and other labor and non-labor dimensions had in reducing poverty.<sup>6</sup> For this, we propose a micro-decomposition method, which generate entire counterfactual distributions, allowing us to decompose the contributions of changes in different sources of income and in individual and household characteristics to the observed distributional changes.

Several potential factors may have contributed to poverty reduction experienced in Sri Lanka over the first decade of the XXI century. We look at each in turn. First, Sri Lanka is in the final stages of its demographic transition, as lower fertility rates over the last two decades have meant that there is a relatively large share of the population who is at working age. Although population growth has recently increased following the end of hostilities, the overall trend has been an increase in the share of adults per household and therefore lower dependency rates (Figure 4 and Table 1), which should lead to an increase in household incomes, and therefore a decline in poverty.

However, despite the increase in the working-age population, there has also been a decline in the share of employed adults per household for most of the decade. What was the net effect on employment? Between 2002 and 2009/10 the net effect of these forces was a decline in the share of employed adults (Table 1), mostly on account of fewer working women (partly due to lower labor force participation), and an increase in the share of elderly per household (Table 1). However, between 2009/10 and 2012/13 both male and female employment started growing again, leading to an increase in the share of employed adults per household, thus returning to about the same levels observed in 2002, at about 41 percent.

In addition to demographic characteristics of the household and changes in employment patterns, growth in earnings can be an important source of poverty reduction. Figure 5 shows growth in total incomes by percentiles of per capita income. The curve is mainly flat around the mean growth rate, with the income of those at the bottom of the distribution growing slightly slower than the incomes of the non-poor.

Since growth in labor incomes was more or less flat across the distribution, with the income of those at the very bottom of the distribution (the bottom 5 percent) growing faster than average, one can conclude that the slower growth of those at the bottom of the total income distribution (Figure 5) was mostly on account of a slower growth in non-labor incomes for the very poor.

> 5 Note that Latin American countries typically measure poverty using household income, while most other countries around the world use a consumption aggregate.

> 6 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.

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