resources) is not the only way of reaching the MDGs, as significant cost savings can be realized from improving efficiency in the public sector service delivery. Finally, efforts to reach the MDGs can have important distributional effects by increasing skill premia and raising inequality.

The structure of this paper is as follows. Section 2 provides a brief summary of MAMS and discusses the main policy-relevant features of the model. Section 3 gives a detailed assessment of the main lessons learned from MAMS applications in Ghana and Honduras. Section 4 offers concluding remarks.

# **2 The MAMS modeling framework**

Devarajan et al (2002) appropriately warn that: “any attempt to determine the aggregate costs of achieving the development goals is a highly speculative exercise”. Among others, two major obstacles need to be overcome: most MDGs tend to be jointly produced and future income growth rates and progress on the MDGs are both endogenous. In other words, interventions that further a given MDG are often likely to promote other MDGs and ‘double counting’ of costs can be an issue. For example, expenditures specifically aimed at improving the health of the young will help reduce child mortality but they may also improve the ability of kids to learn at school and thus promote the achievement of the education MDG.<sup>3</sup> The second obstacle consists of the simultaneous determination of economic growth and progress on social MDGs. Future growth rates are not only difficult to forecast but are also important determinants of the cost of achieving the MDGs. Future input prices, wages, exchange rates may be quite different in a fast growing economy visà-vis those in a slow growing one. On the other hand, improved health and educational outcomes can increase productivity and support higher growth rates.

No existing approach completely resolves these issues and policy makers should be aware of these limitations when using current cost estimates. MDGs costing methodologies can be classified in two main groups: bottom-up costing and economywide modeling. The stylized analytical steps of bottom-up costing consist of: a) determining needed “physical” inputs – investments, labor (at different skill levels), intermediate inputs – for each MDG; b) computing costs of providing inputs using projected or current prices, wages, and exchange rates; c) assigning costs to different agents (government, private sector, NGOs, others).<sup>4</sup> This method has some clear advantages: it is quite transparent, not very technically intensive and it is based on micro evidence. However it has some problems, too. Physical input needs by MDG are not well-defined – different combinations of the determinants can achieve the objective; MDG-specific inputs cannot be identified since some inputs contribute, directly or indirectly, to more than one MDG; and finally, marginal returns to inputs may vary depending on the value for the MDG indicator. In addition the bottom-up costing does not consider that the scaling up effort to expand social service provision may crowd out private activity and in certain cases reduce overall economic growth. This in turn can negatively influence the achievement of the goals and increase costs.

> 3 See, for example, Paxson and Schady (2005), who show that children with lower hemoglobin levels perform worse on tests with a sample of 3,000 predominantly poor pre-school age children in Ecuador. 4 See, for example, UN Millennium Project (2005), and the task force reports referenced therein.

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