production activities to changes in factor wages. On the supply side of the labor market, unemployment is endogenous – the model includes a wage curve (a supply curve) that is upward-sloping until full employment is reached, at which point it becomes vertical (see Figure 2.2; its wage curve assumes a minimum unemployment rate of 5%). Unemployment is defined more broadly than in official statistics to include un- and under-employment. In the simulations, a broad definition of unemployment increases the scope for the existing labor force to generate a larger (smaller) amount of effective labor if the incentives to work were to improve (deteriorate) without any change in the labor-force participation rate; in most developing country contexts, this seems realistic. For non-labor factors, the supply curves are typically vertical.

The above discussion did not refer to the evolution of the economy over time. In MAMS, the economy grows over time due to accumulation of capital (determined by investment and depreciation), labor (determined by demography and the educational system), and other factors (following exogenous growth trends), as well as because of improvements in total factor productivity (TFP). Apart from an exogenous component, TFP depends on the levels of government capital stocks.<sup>8</sup> In the context of factor markets, these developments lead to rightward shifts in both demand and supply curves.

# _MDG and education module_

MAMS includes a set of HD (MDG and education) indicators. A built-in poverty module computes the three standard Foster-Green-Thorbecke poverty indicators (headcount poverty, poverty gap, and severity of poverty) on the basis of the assumption that, in both rural and urban areas, per-capita (goods and service) consumption follows a lognormal distribution parameterized on the basis of a Gini coefficient and an initial headcount poverty rate.<sup>9</sup> The prevalence of undernourishment (the share of the population that does not satisfy its dietary energy requirements, i.e. suffer from calorie poverty) is computed on the basis of similar assumptions and data (per-capita calorie consumption, a Gini coefficient for calorie consumption, and an initial rate of undernourishment). However, in the absence of more

> 8 In Appendix 1 (on the MAMS database), we discuss our treatment of the links between productivity and government capital stocks. 9 It is widely accepted that the lognormal provides a good approximation for within-country income and consumption distributions even though it may fail to account for phenomena such as consumption smoothing (Easterly 2007, pp. 5-6; Lopez and Servén 2006, p. 2). However, in this application, some of the simulations (especially the targeted transfer schemes) invalidate the assumption of an unchanged distribution. To account for this, the following procedure was applied in the computation of poverty and inequality results: (1) a synthetic distribution was generated on the basis of initial rural and urban Gini coefficients; and (b) for each simulation, household consumption observations were scaled on the basis of the change in household consumption per capita for the model household to which the observation was mapped. Poverty and inequality indicators were computed from the resulting distribution. The mapping between model households and observations was possible given that the model households initially represent the top and bottom halves of the income distribution in rural and urban areas. Except for the targeted transfer simulations, this procedure had little impact on the results; however, for the targeted transfer simulations, it made a significant difference.

5