disaggregated data, this calorie computation is done at the national level.<sup>10</sup> Per-capita calorie consumption is a constant-elasticity (CE) function of per-capita real food consumption. A CE function is also used to model the link between per-capita calorie consumption and labor productivity; it is assumed that, across all production activities, an increase in average percapita consumption raises the productivity of the segment of the labor force with less than completed secondary education (the least educated segment) across all production activities. This group of workers was singled out on the assumption that they are more likely to do physical work that is relatively energy-demanding at the same time as they also are more likely to belong to households that are calorie deprived.<sup>11</sup> A different treatment is used for other MDG and education indicators: MDGs 4 (under-five mortality), 5 (maternal mortality), 7w (improved water access), 7s (improved sanitation access); net intake to first grade of primary, promotion to next grade in each of three education cycles, and continuation into next cycle (for primary and secondary). For these indicators, a two-level formulation is used. At the bottom level, an intermediate variable specific to each indicator is expressed as a CE function of a set of determinants, summarized in Table 2.1. At the top level, each MDG and education indicator is a logistic function of its intermediate variable. The parameters of these two-level functions are calibrated to replicate data on projected progress for the top-level indicators under the _base_ scenario, with upper or lower limits imposed on the basis of logic or cross-country experience (e.g. the net intake rate has an upper limit of 100 percent; mortality rates cannot fall below some low positive value). As shown in Table 2.1, the determinants include the supply of government services (measured per student for each cycle of education and per-capita for health), the stock of government infrastructure, real household consumption per-capita (an indicator of the ability of households to make purchases in support of stronger MDG and education outcomes), and other MDG indicators (to reflect the fact that progress for one MDG may have a positive impact on other MDGs).<sup>12</sup> In the absence of crosscountry data for the provision of water and sanitation services, an alternative variable, absorption per capita (an indicator of overall capacity to support the living standards of the population), is the single determinant for MDGs 7w and 7s.<sup>13</sup> A wage incentive indicator is a determinant specific to education; it is expressed as the ratio between the wages for labor at the next higher and current levels of education for the student in question (an indicator of payoff from continued education). From a broader perspective, the education module tracks base-year stocks of students and new entrants through the different grades of the three cycles (primary, secondary, and tertiary). In each year, students will successfully complete their grade,

> 10 Given our focus on food prices, our nutritional analysis is focused on calorie undernourishment – the link between food prices, food consumption and calorie consumption is relatively direct and clear-cut. For other nutrition indicators, like wasting (weight for age) or stunting (height for age), the relationships are more long run while data on the impact of food prices and food consumption on these indicators are lacking.

> 11 Empirically, the impact of changes in per-capita calorie consumption on labor productivity turned out to be negligible in the context of moderate changes in calorie consumption and plausible elasticities of labor productivity with respect to calorie consumption.

12 Sadoulet and de Janvry (1995, p. 16) observe that absorption is a better measure of the welfare of the population than GDP, which is an indicator of the aggregate level of production.

13 In cross-country regressions (in log form) of MDGs 7w and 7s on absorption per capita, the coefficients for the absorption variable are highly significant (at the 1 percent level), with an R2 of 0.41 and 0.39, respectively.

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