employment (and value added); the treatment of households is explained further down in this section and in Appendix 1.<sup>6</sup> Given this information and related endogenous data on household incomes and spending, it is possible to compute per-capita household consumption in rural and urban areas, both of which enter some of the MDG functions (cf. Table 2.1).

The above discussion refers to how the model works in any given year. Over time, production growth is determined by growth in factor employment and TFP. Growth in capital and labor stocks is endogenous. For capital stocks, growth depends on new investment and depreciation. Growth in the aggregate labor stock depends on growth in the population in labor force age, the labor force participation rate, and school attendance. At a disaggregated level -- the labor force is disaggregated by educational attainment into segments with less than completed secondary, completed secondary but not completed tertiary, or completed tertiary – growth depends on the evolution of the educational system. For other factors (in this application agricultural land), stock growth is exogenous. TFP growth is made up of two components, one that is exogenous and one that responds to endogenous developments, in this application, growth in government infrastructure capital stocks, urbanization, and MDG 4 (as a proxy for the health status of the population). The inclusion of a link between urbanization and TFP (with a stronger link for non-agricultural sectors) is based on research indicating urban agglomeration tends to generate more rapid productivity growth.

A typical MAMS application requires an extensive data set for the application base-year – a social accounting matrix (SAM);<sup>7</sup> stocks for production factors (including different types of labor and capital), population, labor unemployment (broadly defined to include underemployment) and school enrollment; indicators for MDGs and the educational system – as well as a set of elasticities (for production, consumption, trade, and MDG, and non-MDG education relationships). The database also includes projections into the future (for growth in GDP at factor cost and the evolution of disaggregated MDG and education indicators and their determinants), to which the MAMS baseline simulation is calibrated.

The database for the current application (including its disaggregation) to a typical LIC was designed in light of data availability and the analytical objective of shedding light on the impact

> 6 Note that it is not (implausibly) assumed that the population who primarily earns its living from agriculture is rural while those depending on industry or services are urban – in LICs (and, indeed, in countries at all income levels), in terms of location and residence of workers, to varying degrees agriculture is urban and industry and services rural. For example, in Uganda, the 2005/2006 urban employment shares were 8 percent for agriculture, 44 percent for industry, 45 percent for commerce, and 51 percent for services; i.e., except for agriculture, all other sectors were quite evenly split between rural and urban employees (UBoS 2007).

7 A SAM is a square matrix with identical row and column accounts, providing a comprehensive representation of payments flows in the economy of a geographical unit (typically a country) during a period of time (typically one year). Cell entries represent payments from its column account to its row account. In a SAM without errors, row and column totals are equal. SAMs appear with widely varying degrees of disaggregation. The payments flows are expressed in current local currency or some transformation thereof – in Table A2.2, the value of each cell has been transformed into percent of GDP at market prices in the same year. For more on SAMs, see for example Reinert and Roland-Holst (1997) and Round (2003). The detailed LIC MAMS SAM for this application is available on request.

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