The production functions for the MDGs have been calibrated to assure that, under base-year conditions, base-year performance is replicated and that, under a set of other conditions identified by HD sector studies, the relevant targets are fully achieved. This approach also implies that the modeling of MDG production functions does _not_ mechanically link more inputs, i.e. increased production of social services, to higher attainments, but reproduces, in reduced form, the functioning of the social sectors and the interactions of demand and supply within them. Thus, the model consolidates knowledge from sector studies and incorporates various bottlenecks that are likely to hinder MDG achievement. The following studies were consulted for estimates of costs required to reach the MDGs. For education, the Ghana MAMS model relies on the Education Strategic Plan published by the Ministry of Education in the Government of Ghana (Government of Ghana, 2003). For health, the main source of information is the Marginal Budgeting for Bottlenecks (MBB) model developed by the human development experts at the World Bank. For water and sanitation, the main input is “Getting Africa on Track to Meet the MDGs on Water and Sanitation: A Status Review of Sixteen African Countries” (Africa Ministers’ Council on Water, 2006).

The cross-MDG synergies in the current application have been specified in accordance with the available sector evidence. Thus, the positive impact of improvements in access to water and sanitation on the health outcomes is consistent with the Ghana MBB model, while the importance of infant health as well as water and sanitation targets for achieving universal primary completion is clearly delineated in the Education Strategic Plan. The model considers only the _public_ provision of MDG services, given the lack of detailed data on private service delivery and the fact that the incidence of private service falls mainly on the wealthy parts of the population.

MAMS is compatible with several alternative methods of tracking the progress on poverty reduction. A simple option is to use an estimated elasticity of poverty reduction with respect to growth in households’ per capita consumption. But of course, this simple approach fails to take into account the underlying characteristics of the distribution of income and consumption. This paper takes a more sophisticated approach by utilizing 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. The data for our microsimulations come from the most recent household survey for Ghana (GLSS5), where employment and labor earnings by skill and sector are easily identified. The simulations then apply changes in employment, skill levels, relative wages, and consumption per capita from MAMS to each household in the survey, producing a new distribution of income and translating the evolution of macro variables into poverty outcomes.<sup>7</sup> Unlike the simpler poverty elasticity-based methods, the micro-simulation approach allows for four main avenues of escaping poverty: (i) moving from agricultural employment to nonfarm activities where the wages tend to be higher, (ii) upgrading individual skills (through schooling), (iii) changes in relative wages across sectors, and (iv) an economywide growth component that equally benefits all households (Table 1).

> 7 The micro-simulations are carried out at the household level due to the difficulties of accounting for within-household distribution of income. Therefore, we introduce a certain imprecision in our results as the entire household shares in the labor market outcomes of the household head (e.g., if the head of the household is an unskilled worker in agriculture, then the per capita welfare metric for the entire household will change following the changes in unskilled agricultural wages in MAMS).

7