parameter values selected for these links reflect our assessment of the relevant empirical literature and the situation in Liberia. With regard to (1), the internal rates of return (IRRs; the interest rates at which the net present value of the investment is zero) are as follows: 38% for energy, 34% for roads and other infrastructure, 18% for education, 15% for water and sanitation, and 9% for health.

More specifically, government spending in these areas influence growth raising TFP in selected activities. When calibrating these links, we calculate IRRs (internal rates of return) for different areas of government spending on the basis of the following variables: the initial investment cost; O&M costs linked to the resulting capital stock; gestation lags (the lag between the investment and the time when the capital stock is available for use; longer for infrastructure); productivity lags (the lag between the time when the capital stock is available and when it starts to yield positive productivity effects; longer for education); the depreciation rate (once the gestation lag is completed); the marginal productivity of the capital stock (once the productivity lag is over); and expected growth for the sectors in which productivity increases due to the investment. Among these, the marginal productivity figures are adjusted to generate targeted IRRs. For empirical data on typical IRRs (approximately matched in our parameterization, see Foster and Briceño-Garmendia (2010) for infrastructure; and Psacharopoulos and Patrinos (2004, pp. 112 and 114) for education (for the latter using social rates since they have a more complete cost coverage). With regard to the impact of health outcomes (among other things influenced by government spending on health) on growth, Jack and Lewis (2009, p. 2) conclude that they are difficult to measure and likely to be small; given this, we impose a more moderate IRR for health. Given that water and sanitation are likely to have its major effects by impacting health, we impose a similar IRR for this sector. (It should be noted that better health and better access to water and sanitation also are objectives in their own right, quite apart from the growth effects that they may have.) Our combined gestation and productivity lags follow those used for Uganda by Estache and Muñoz (2007, p. 18).

With regard to (2), a set of functions covering MDGs other than poverty, were, in a first step, calibrated to replicate projected responses given simulated growth in GNI per capita. In a second step, the sources of the gains were assigned to the following determinants: real government services in the relevant functional area (education, health, and water-sanitation). The data draw on cross-country regressions and a review of the relevant literature. However, it should be stressed that the results merely should be viewed as illustrations of plausible responses to changes under different scenarios.

For MDG 1, here covered by headcount poverty, the results are generated by a module that draws on the simulated evolution of household per-capita consumption, a Gini coefficient (which is exogenous), and an initial poverty rate, assuming that consumption is log-normally distributed. Drawing on World Bank data and the 2007 CWIQ Survey, we use a Gini coefficient real education services and, over time, in the educational attainments of the labor force, such a treatment endogenizes gains from education spending. However, due to a lack of data, this was not possible in this analysis of Liberia.

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