MDGs, and demand-side effects (see Table 1). Expansion of per capita service delivery requires increased commitments of three broad categories of inputs: labor (which is disaggregated according to skill/education levels), capital and intermediate goods. In addition to these inputs, which account for spending on specific MDG interventions, the aggregate measure of MDG service delivery is also determined by complementary policies. For example, reaching the education MDG requires additional schooling services, but is also facilitated by improvements in health conditions (proxied by MDGs 4 and 5), by better infrastructure (e.g., better roads to schools), by higher income levels (better-off parents may not need their children to work), and by good returns to education (proxied by the wage premium paid to skilled workers). The aggregate measure is strictly increasing in all of its components, and does not capture potential bottlenecks and/or the decreasing returns to scale as the target approaches (due to the difficulty of reaching the most remote parts of the population or, for example, necessity of high-level medical care to reduce maternal mortality beyond a certain threshold). In order to account for these effects, the top level of the MDG production nest links this aggregate measure of MDG service delivery to actual MDG outcomes by requiring greater and greater improvements in the former for the same rate of improvement in the latter.<sup>9</sup>

**<u>Table 1: Determinants of MDG Achievement in MAMS</u>**

||||**Other Det**<br>|**erminants**<br>||
|---|---|---|---|---|---|
|**MDG**|**Per-capita**<br>**Service Delivery**|**Per-capita**<br>**consumption**|**Wage**<br>**incentives**|**Public**<br>**infrastructure**|**Other**<br>**MDGs**|
|1 Poverty||X||||
|2 Education|X|X|X|X|4|
|4 Infant Mortality|X|X||X|7a,7b|
|5 Maternal Mortality|X|X||X|7a,7b|
|7a Access to Water|X|X||X||
|7bAccess to Sanitation|X|X||X||

In addition to being a key input in the production of MDGs, improvements in public infrastructure also contribute to overall growth by adding to the productivity of private activities. This means that real GDP growth—and, indirectly, poverty reduction—is partly influenced by government policies and in particular by its investment in infrastructure services (roads, ports, energy, etc). However, given the lack of consensus on the strength of this relationship and the non-linear relationship between growth and poverty reduction, the MAMS model does not include explicit policy instruments for targeting the achievement of MDG 1. Instead, the model is capable of tracking the progress on poverty reduction by means of several alternative methods. A simple option is to use an estimated elasticity of poverty reduction with respect to growth in households’ per capita consumption. A more sophisticated approach (and one that is utilized in the case studies considered in this paper) is to rely on a macro-micro framework where a set of aggregate results from MAMS are passed on to household survey data by means of a micro-simulation module.<sup>10</sup> The simulations involve applying changes in employment, skill levels, relative wages, and consumption per capita from

> 9 This is accomplished by using a logistic function with the MDG outcome as a dependent variable and the aggregate measure of MDG service delivery as an independent variable.

> 10 This approach follows the methodologies developed in Bourguignon and Pereira da Silva (2003), Chen and Ravallion (2003), and Bussolo et al (2005).

5