working in the manufacturing sector. Tables A7-A9 present regression results for farm household net revenue functions.<sup>19</sup> Net revenue for farmers increases with experience, land holdings, access to irrigation and the number of members participating in farm work.<sup>20</sup> The next step consists in using the estimated coefficients from these models to simulate counterfactual distributions. For instance, since we estimated the returns to education in two periods, we can take the estimated parameters in the first period and evaluate the earnings equations with the 2010 levels of education. This generates counterfactual earnings at the individual level, which can then be aggregated to get the corresponding household income using equation (3), which can then be used to get to a counterfactual level of consumption according to (1), and therefore a counterfactual poverty rate. In this way, changing one set of parameters at a time or one characteristic at a time, we obtain multiple counterfactual distributions and counterfactual poverty rates. The methodology for estimating each counterfactual distribution and the associated counterfactual poverty rate is detailed in Annex 2.

Finally, we compare these counterfactual poverty rates with the observed poverty rates to quantify the impact of each element being considered. Since replacing the first period parameters into last period data will yield results that are different from doing it the other way around, we calculate the counterfactual both ways and then take the average (in line with the literature). We first calculate these effects by changing one element at a time and leaving everything else constant. However, given that changes in multiple factors could have interaction effects, we also calculate the cumulative effect of these decompositions. For this purpose, we follow Bourguignon, Ferreira and Leite (2008), and begin by calculating the effects on poverty of changes in the characteristics of the population, beginning with age and gender, followed by changes in geographical, educational, occupational and sectoral structure of the population. With these results we then calculate changes in farm and non-farm earnings due to changes in the returns to these characteristics, followed by changes in non-labor incomes (first private and then public) and finally changes in the consumption-to-income ratio.

# **Results**

Focusing on the contributions to the reduction of moderate poverty based on national poverty lines, Table 6 presents the results of each counterfactual distribution when simulating only one element at a time, and keeping everything else constant. We refer to these as the marginal contributions to poverty reduction. The main result that emerges is that the largest contributions to moderate poverty reduction in all three countries came from labor market-related factors: 61 percent in Bangladesh, 75 percent in Peru and 65 percent in Thailand. Within this, it was the increase in the returns to endowments or characteristics, rather than changes in these endowments or characteristics, that explain poverty reduction, pointing to an increase in real

> 19 Net revenue was calculated using available information on total revenue stemming from agricultural production and the cost of inputs from detailed household enterprise modules included in the surveys.

> 20 The Thai household survey does not contain information on land holding and access to irrigation.

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