preceding years (the details depend on the gestation lags), by investments that sufficiently raise the level of the capital stock. This ensures that O&M costs are scaled up after the completion of roads and that additional teachers are not employed without already having a new school in place. Each year, the government budget clears via adjustments in real government consumption and capital stock growth rates in a manner that is unbiased across the different government functions.

In addition to payments involving the government (as payer or payee), the model covers payments involving from the rest of the world to domestic non-government actors. FDI (split between mining and other private capital), and transfers from abroad to UNMIL and households – all of these payments are exogenous in US dollars. Mining and non-mining FDI follow time profiles that are extracted from IMF (2010) and consistent with more recent data (IMF 2012). Transfers to households from the rest of the world (remittances and other) grow gradually over time (at a rate similar to GDP). Transfers to UNMIL decline rapidly during the period up to 2017, after which they continue at a very low level. Other non-government payments include investment and private savings. The rest of the world finances its investment (FDI) via the balance of payments. The household (marginal and average) savings rate is gradually increased (from 7.7 percent of household disposable income in 2012 to 13.0 percent in 2030) on the assumption that the investment climate will improve, encouraging domestic private savings and investment. As a result, by 2030, the GDP share for private investment is around 16 percent with domestic and FDI GDP shares of 12 and 4 percent, respectively; these figures are similar to recent averages for low- and middle-income countries.

# <u>Results for the Base Scenario</u>

The results for the base scenario are summarized in Figures 2.1-2.8. Tables A3.1-A3.7 in Appendix 3 provide additional simulation results focused on the base scenario.

The evolution of Liberia’s economy during the AfT period (2013-2017) is dominated by two events: (a) very high levels of mining FDI; these permit strong expansion in mining production and in government receipts from mining; and (b) the near-elimination of UNMIL transfers; this dampens import growth and domestic consumption (as UNMIL today represents large shares of both). By contrast, the post-AfT period (2018-2030) is less eventful with more uniform growth across sectors and macro aggregates. With respect to mining, the base scenario is based on IMF (2010 and 2012); earlier data were adjusted in light of more recent projections.

Figures 2.1-2.4 summarize the evolution of key macro aggregates under the base. Figures 2.12.2 show absorption, GDP, exports and imports in constant 2009 US$ and indexed to 100 in 2012. From both Figures it can be gleaned that export grow is strongly relative to imports, thus narrowing the export-import and absorption-GDP gaps.<sup>7</sup> In the background, the narrowing of

> 7 Note that, in the different Tables and Figures of this paper, (exponential) growth rates are computed on the basis of two data points, those of an initial and a final year. This is a good summary of developments for indicators that grow at a regular pace. However, for variables that vary significantly from year to year (in this case investment and

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