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cross-country data, whether the HIV/AIDS incidence<sup>and prevalenceratesimpact economicgrowth.</sup> Given the difficulties in isolating the effects of HIV/AIDS<sup>from otherfactors, theevidenceis mixed.</sup> Using an econometric model based on EPIMODEL<sup>(Chin,1990),BloomandMahal (1997)find no</sup> significant correlation between the HIV/AIDS incidence<sup>rateandeconomicgrowth. Ontheother</sup> hand, using panel data on prevalence rates and<sup>GDPper capita, Bonnel(2000)findsthat growth rates</sup> in Africa's most affected countries could have<sup>beenhigher by1 percentagepointinthe absenceof</sup> HTV/AIDS. A second tradition uses household surveys<sup>tostudytheimpactsof theepidemicon</sup> household income. Bollinger et al. (1999), for example,<sup>find that rural householdin Kenya couldsee</sup> their income fall by over _50%_ as a result of AIDS. In<sup>Kagar-Tanzania,Overetal.(1999)infer that</sup> household food expenditure per capita after the death of<sup>a householdmember is reducedby32%and</sup> food consumption by 15%. A third tradition, to<sup>whichourpapersubscribes, usessimulationmodels</sup> to explore the impacts of H1V/AIDS on the economy<sup>throughthreemain channels:prematuredeaths</sup> that affect the size and composition of the labor<sup>force,increasesin morbidityand healthexpenditures</sup> that affect total factor productivity, and potentially<sup>lowersavingratesthataffect investmentsand</sup> growth. Micro-simulations by Soucat (2001) in the<sup>caseof Chad and Elliset al.(1997)inthe caseof</sup> India, for example, predict that health expenditures could<sup>increasebyafactorof2to3.Macro-</sup> simulation models, on the other hand, predict that<sup>inaffectedcountries(countrieswhere prevalence</sup> rates have surpassed 5% and continue on an upward<sup>trend) reductionsin per capita GDP growthrates</sup> could be in the order of 0.5% to 1.5% per year<sup>(see,forinstance,Over,1992foranapplicationto</sup> Sub-Saharan Africa; Cuddington, 1993 for an application<sup>toTanzania; Leighton,1993foran</sup> application to Thailand; Nalo and Aoko,<sup>1994for an applicationto Kenya;Lewis,2000and Quattek,</sup> 2000 for an application to South Africa; and MacFarlan<sup>and Sgherri,2001for a recentapplicationto</sup> Botswana). The structure of these models has evolved<sup>over timefrom theonesectorSolow-type</sup> model (Cuddington 1993), to models that allow to<sup>differentiatethe effectsthatthe epidemichas on</sup> skilled, unskilled, and unemployed workers (see MacFarlan<sup>andSgherri,2001),andwherethe</sup> savings rate of the economy is endogenous (see, Robalino<sup>etal.,2002for anapplicationto estimate</sup> optimal reduction targets for the HIV/AIDS incidence<sup>ratein Kenya).GeneralEquilibriumModels</sup> have also been developed to evaluate the impact of the<sup>epidemicacrosssectorsandrelative prices</sup> (see, Kambou et al., 1992 for an application to Cameroon).<sup>Whilethese models necessarily leave out</sup> important aspects of the interplay between the<sup>economy and the epidemic(e.g.,the role of migration,</sup> multi-sectorial heterogeneity, social costs related to<sup>the increasein singleparentshouseholds and the</sup> number of orphans, distortionary effects introduced<sup>whenmobilizing resourcestocureAIDS</sup> patients), they are useful to put boundaries on the magnitude<sup>of the potential economicimpacts of the</sup> epidemic. The main limitation, however, is that model<sup>parameters rarely result fromrobuststatistical</sup> inference and standard sensitivity analysis leave wide<sup>regions of the parameterspace unexplored.</sup> The model used in this paper has many of the<sup>featuresofothermacro-models inthe</sup> economics literature. The main addition is that it<sup>incorporatesanH[V/AIDSdiffusioncomponent</sup> where the virus transmission occurs through sexual intercourse<sup>andtheexchangeof infectneedles</sup> among IDUs. This allows us to simulate the impact of<sup>interventions suchasimproving accessto</sup> condoms and clean needles for IDUs. Nonetheless,<sup>ourefforts havenotconcentrated onmodel</sup> design, but rather on simulation design. Indeed, we<sup>recognizethatthe economyandtheepidemic</sup> create a complex system that is structurally unpredictable.<sup>This unpredictabilitydeepenswhendata to</sup> estimate model parameters is scant. The approach<sup>taken herehas been to construct wideconfidence</sup> intervals for model parameters, based on estimates<sup>fromtheliterature andknowneconomic</sup> constraints, and then conduct an extensive exploration<sup>ofthe parameterspacetocharacterizethe</sup> ensemble of plausible futures that the model can generate<sup>givencountries'initialconditions(see</sup> Bankes, 1993; Lempert et al., 1996; and Robalino<sup>andLempert,2000,for otherapplicationsof</sup> exploratory modeling methods). Finally, we analyze<sup>therobustnessof policyinterventionsagainst</sup> the wide range of futures.