metadata: authors: 0: author: Robalino, David A.
1: author: Jenkins, Carol
2: author: El Maroufi, Ka
count: Middle East and North Africa,Morocco,Jordan,Lebanon,Tunisia,Iran, Islamic Republic of,Egypt, Arab Republic of,Djibouti,Algeria,Yemen, Republic of
docna: 0: docna: Risks and macroeconomic impacts of HIV/AIDS
            in the Middle East and North Africa : why waiting to
            intervene can be costly
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Disease Control & Prevention,Public Health Promotion,HIV AIDS,Cholera,Communicable Diseases,Leprosy
teratopic: Health, Nutrition and Population
abstracts: cdata!: The authors develop a model of optimal
            growth to assess the risks of an HIV/AIDS epidemic and the
            expected economic impact in nine countries in the Middle
            East and North Africa region-Algeria, Djibouti, Egypt, Iran,
            Jordan, Lebanon, Morocco, Tunisia, and Yemen. The model
            incorporates an HIV/AIDS diffusion component based on two
            transmission factors-sexual intercourse and exchange of
            infected needles among intravenous drug users. Given high
            levels of uncertainty on the model parameters that determine
            the dynamics of the epidemic and its economic impact, the
            authors explore large regions of the parameter space. The
            prevalence rates in year 2015 would be below 1 percent in 16
            percent of the cases, while they would be above 3 percent in
            50 percent of the cases. On average, GDP losses across
            countries for 2000-2025 could approximate 35 percent of
            today's GDP. In all countries it is possible to observe
            scenarios where losses surpass today's GDP. The authors
            quantify the impact of expanding condom use and access to
            clean needles for intravenous drug users. They show that
            these interventions act as an insurance policy that
            increases social welfare. They also show that delaying
            action for five years can cost, on average, the equivalent
            of six percentage points of today's GDP.
geo_reg_key: 517202,119229,119229,517191
repnme: repnme: Risks and macroeconomic impacts of HIV/AIDS
            in the Middle East and North Africa : why waiting to
            intervene can be costly
display_title: Risks and macroeconomic impacts of
            HIV/AIDS in the Middle East and North Africa : why waiting
            to intervene can be costly
listing_relative_url: /research/2002/08/1976188/risks-macroeconomic-impacts-hivaids-middle-east-north-africa-waiting-intervene-can-costly
topicv3: Pandemics m1327520 2167,HIV/AIDS m1327522 1416,Health m1504661 3480,Condoms m1504697 3617,Condom Use m1504745 3536
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population
origu: Human Development Group (MNSHD)
no_of_pages: 40
model_extractions: I POLICY RESEARCH WORKING PAPER 2874

# Abstract

Robalino, Jenkins, and El Maroufi develop a model of optimal growth to assess the risks of an HIV/AIDS epidemic and the expected economic impact in nine countries in the Middle East and North Africa regionAlgeria, Djibouti, Egypt, Iran, Jordan, Lebanon, Morocco, Tunisia, and Yemen. The model incorporates an HIV/AIDS diffusion component based on two transmission factors-sexual intercourse and exchange of infected needles among intravenous drug users. Given high levels of uncertainty on the model parameters that determine the dynamics of the epidemic and its economic impact, the authors explore large regions of the parameter space. The prevalence rates in year 2015 would be below 1 percent in 16 percent of the cases, while they would be above 3 percent in 50 percent of the cases. On average, GDP losses across countries for 20002025 could approximate 35 percent of today's GDP. In all countries it is possible to observe scenarios where losses surpass today's GDP. The authors quantify the impact of expanding condom use and access to clean needles for intravenous drug users. They show that these interventions act as an insurance policy that increases social welfare. They also show that delaying action for five years can cost, on average, the equivalent of six percentage points of today's GDP.

This paper-a product of the Human Development Group, Middle East and North Africa Region-is part of a larger effort in the region to raise awareness about the social and economic cost of HIV/AIDS. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cheikh Fall, room H8-118, telephone<sup>202-</sup> 473-0632, fax 202-477-0036, email address cfalll@worldbank.org. Policy Research Working Papers are also posted<sup>on</sup> the Web at http://econ.worldbank.org. David Robalino may be contacted at drobalino@worldbank.org. August 2002. (34 pages)

> _The Policy Research Working Paper Series disseminates the findiRzgs eork of in progress to encoafrage the excbange of ideas about development issues. An objective of the series is to get the finidings ouit quickly, even if the presentations are less thanl fuilly polished._<sup>_The_</sup> _papers carry the names of the autbors and should be cited accordingly. The findings, interpretations, and concluisions expressed in this paper are entirely those of the authors. They do not necessarily represenit the view of the World Bank, its Executive Directors, or the countries they represent._

Produced by the Research Advisory Staff
# Risks and Macro-Economic Impacts of HIV/AIDS in the Middle East and North Africa:

Why waiting to intervene can be costly by David A. Robalino drobalino@woldbank.org Carol Jenkins Karim El Maroufl
# **Introduction**

In 1991, with the evidence available at the time, experts estimated that by the year 2000, 1520 million adults and 5-10 million children cumulatively would become infected with HIV. By the end of 2001, The World Health Organization (WHO) reported 40 million people were living with HIV/AIDS and 5 million new infections had occurred during the year. The great difference between earlier projections and current estimates reflects both the unexpected spread of the virus and the inadequacy of statistics used to track the epidemic. HIV does not always spread rapidly, though it certainly can if conditions permit. Rapid social and economic changes underlie the virus's spread in most of Africa, Russia, Central Asia, Eastern Europe, China, and elsewhere. HIV epidemics have been particularly sensitive to large migrations of people, wars, economic downturns, and other drastic alterations in social stability. WHO estimates that 1.2% of all adults on earth are presently infected with HIV and all experts agree that the worst effects of the pandemic are yet to come.

This paper is a first attempt to evaluate the risks of an epidemic in 9 Middle East and North Africa (MENA) countries' and its potential economic costs, while at the same time assessing the welfare implications of two preventive interventions: expanding condom use and access to clean needles for Injecting Drug Users (IDUs). WHO estimated that approximately 80,000 persons were newly infected with HIV in the MENA region in the year 2001, and that about 0.2% of adults in the region are currently infected. These figures are relatively low, compared to Africa, South and SE Asia and the Caribbean, but do not assure safety for the region. Inadequate methods of surveillance, universal in the region, can easily miss outbreaks in hidden populations. Further, even in low prevalence nations, the situation can change rapidly, as has occurred in Indonesia and Nepal.

Unfortunately, in MENA countries, continued low levels of case detection through mostly mandatory screening, the lack of appropriate behavioral data, and over-confidence in the protective effects of social and cultural conservatism have dictated low priority for HIV/AIDS Yet, there is evidence that the necessary risk factors to the spread of the epidemic are present. The first group is comprised of those with known risky behaviors, such as prostitutes, their clients, IDUs, Men who have Sex with Men (MSM), and those who acquire Sexually Transmitted Diseases (STDs). These persons are immediately at-risk. While it is likely that they represent a minority in any country, our analysis shows that any such group can.become the core of spread into the rest of the population, depending upon the extent and nature of social linkages and networks. The next groups are those who may be considered vulnerable (i.e., could be at-risk if and when their life situation changes). For example, migrants going to work abroad, refugees, tourists traveling for fun and recreation, noninjecting drug users who may switch to injecting when the availability or price of the drug changes, and young people in general, in that some proportion will engage in non-marital sex under certain conditions. Further, structural factors, such as poor and dysfunctional health care systems, raising unemployment and poverty rates, and income and gender inequality create an environment that is suitable for the diffusion of the epidemic (see Over, 2000; and Jenkins and Robalino, 2002, Ch. 4, for econometric analysis of the socioeconomic determinants of IRV prevalence rates using cross-country data).

Clearly, there are methodological difficulties to predict the dynamics of the epidemic in different countries and to evaluate economic impacts. These difficulties are amplified by the lack of substantial IRV-related social or behavioral research in the region. Our analysis builds upon the large body of applied research on the economics of HIV/AIDS that has been conducted over the past 10 years. One tradition within this literature has focused on estimating econometrically, on the basis of The countries included in the analysis are: Algeria, Djibouti, Egypt, Iran, Jordan, Lebanon, Morocco, Tunisia, and Yemen.
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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.
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The paper is organized in 4 sections. Section 1 develops the model used in the analysis. Section 2 applies the model to explore alternative diffusion scenarios and potential macroeconomic impacts in MENA countries. Section 3 evaluates the welfare gains that could be induced by two classic interventions: distributing condoms and improving access to clean needles for IDUs. Finally, Section 4 summarizes the major conclusions from the analysis and discusses some of its limitations.

# 1. The Model

Our model focuses on 4 channels through which the epidemic affects the economy: the size and composition of the labor force; productivity growth; health expenditures; and the savings rate of the economy. Other channels, such as reductions in human capital resulting from an increase in the number of orphans, who are less likely to fully develop their physical and intellectual capacities<sup>2</sup> , are ignored for methodological reasons. Thus, it can be said that our model underestimates the economic cost of the HIV/AIDS epidemic.

The model has been modified from its original version (see Robalino et al., 2002) in two important ways. First, we introduce three types of labor than can be affected differently by the epidemic: skilled labor, unskilled labor, and unemployed labor. Second, we couple the macroeconomic model to an HIV/AIDS diffusion model that simultaneously takes into account sexual transmission and transmission through the sharing of infected needles among IDUs (the two major transmission mechanisms in the case of MENA countries). This is a convenient feature of the model as it allows us to simulate the welfare implications of specific interventions such as increasing condom use and access to clean needles for IDUs.

Another important feature of the model is that the savings rate of the economy is computed endogenously to maximize inter-temporal social welfare. This enables us to simulate the economic impacts of the epidemics under "ideal conditions" and to take a normative stance in terms of when and how governments should intervene3. In fact, the assumption introduced in most models that savings are reduced as a result of the epidemic is not supported by empirical data (see Robalino, 2002). In the absence of well functioning insurance markets, rational agents may actually increase savings to protect themselves against potentially high future health expenditures (see Appendix A for a technical discussion). The ultimate effect that HIV/AIDS has on the savings rate of the economy will depend on how it affects agents' preferences (risk aversion, discount rates) and the govermnent fiscal balance. For simplicity, our simulations will keep preferences constant and assume that societies respond optimally to the shock resulting from the epidemic. This, again, would underestimate the welfare impacts of the epidemic.

> 2 Research has shown that beyond the psychological impacts, among low-income populations, the death of one parent is associated with a deterioration of nutritional status and lower school enrollment rates (see Ainsworth M. and Koda G., 1993).

3 The implicit assumption is that the consumption of health services to treat AIDS patients is included in the consumption bundle.
_-4 -_

# _Modeling the economy_

The model is based on the assumption that the output of a given economy can be represented as a simple function of human and produced capital.

> where Y is GDP, K is the stock of produced capital, L is quality adjusted labor, our proxy for human capital, and A is a scale factor. The growth rate of A captures changes in total factor productivity and its dynamic is determined exogenously, we have:

where ya is the growth rate of aggregate labor productivity and **8a** is the yearly change in this growth rate (see Pfizer, 1998 for a similar formulation).

> Human capital is defined by:

where N, is the number of workers of type i, a, is the productivity of labor type i, and I is the set of labor types. It is convenient to rewrite equation (3) as: where q, is the average quality of labor at time t, given by: For simplicity in this application we use three types of labor: skilled (a= l), unskilled (a=0.5), and unemployed (a=O). Given little knowledge about the workings of the labor markets in the MENA region, we have avoided modeling explicitly its dynamics (i.e., by formalizing labor supply and demand decisions). Instead, we make assumptions about transition probabilities between different types of labor and treat the growth rate of the aggregate labor force exogenously. This treatment is sufficient to achieve our objective of been able to simulate the impact of alternative distributions of the burden of FIV/AIDS across the labor force. Thus, we postulate:

> where N is a (3 _,1)_ vector giving the number of workers in each labor class, **Tlj** is a (3 _,3)_ matrix of

> transition probabilities, D is a (3 _,1)_ vector giving the number of deaths due AIDS in each labor class,

> Th is a (1 _,3)_ vector giving the share of new workers going to each labor class, and AN is the number of new workers. The dynamics of the total number of workers is characterized as follows:
_5-_ (7) where H is the total number of HIV/AIDS infected individuals and fl is the aggregate HIV/AIDS prevalence rate. The prevalence rate is determined by the HIV/AIDS diffusion model described in the next section.

To derive the optimal savings rate, and therefore the dynamics of the stock of produced capital, we assume that the economy operates along a 'path that maximizes social utility, assumed to be a function of aggregate consumption. More specifically, we solve the following optimization problem: where S is the depreciation rate for produced capital, p is a discount factor, and X is the coefficient of risk aversion. We use a standard constant elasticity of substitution utility function that is population weighted. By aggregating across the population we are able to capture part of the welfare losses due to the reduction of the labor force resulting from AIDS related deaths<sup>4</sup> .

It can be shown that under optimality conditions the dynamics of the stock of produced capital can be approximated by: where T, _= q,A'('-°),_ and al and a2 are functions of the sequence {qt}, the growth rate of the labor force, and of the parameters _ya,_ k _H, X,_ and p (see Appendix B).

Up to here, the HV/AIDS epidemic affects the size of the labor force and its composition, thus perturbing the quality of the labor force (through equation 5) and the savings rate of the economy (through equation 9). In addition, we consider the possibility that the HIV/AIDS prevalence rate affects the aggregate level of productivity (see for instance Haccker, 2001). This may occur as labor turnover and absenteeism increase and as firms divert resources to preventive activities. This can also occur if economic efficiency decreases as resources are allocated to treat HIV/AIDS patients, or as social capital is eroded. To formalize this idea we simply postulate that the realized level of productivity is given by:

> 4 Notice that as long as c< _IUlN = (I+ )'C+ N' iC -' fCiaN >o,_ so that a reduction in N, as a result of HIV/AIDS, results in a welfare loss.
_- 6-_ where d1 and d2 are the parameters determining productivity losses,<sup>and</sup><sup>**cb**is theshare of HlV/AIDS</sup> health related expenditures in GDP. In the presence of HIV/AIDS,<sup>A* replacesAinequations(1)</sup> and (3) to (9). We notice that the implicit assumption behind this formulation<sup>isthatHIV/A1DSdoes</sup> not have _permanent_ effects on total factor productivity. In other words, if<sup>the prevalence rate drops to</sup> zero at time t, total factor productivity will return to its steady-state<sup>level(A*=A).Thisagainis a</sup> conservative assumption. (for a case where the impacts are permanent<sup>seeMacFarlan andSgherri,</sup> 2001).

Health expenditures are simply modeled by keeping track of an<sup>averageexpenditureper</sup> patient (which depends on the level of GDP per capita) and a given<sup>level of access to healthservices.</sup> We have: where X0 and XI are the parameters determining the average cost of treating<sup>apatientas a functionof</sup> GDP per capita (y), and X2 gives the share of the stock H of<sup>infected individualswho haveaccessto</sup> curative services.

# **_Modeling the diffiusion of_** _HIV/AIDS_

As with any other infectious disease, the diffusion of HIV/AIDS depends on its _reproduction rate._ This is the average number of people who are infected by a person<sup>carryingthevirus during</sup> his/her life span. The higher the reproduction rate, the faster the<sup>diffusionof thedisease.Diseases</sup> that have a reproduction rate lower than 1 gradually disappear. In the case of HIV/AIDS, the reproduction rate is determined by the types of contagion mechanisms<sup>(e.g.,sexualintercourse,</sup> sharing of infected needles among drug users, mother to child, blood<sup>transfusion,orrepeateduseof</sup> needles in hospitals and health centers), and three elements associated<sup>with each of them: the</sup><sup>_duration_</sup> _of the period during which an individual can transmit the disease_ through the<sup>given mechanism;the</sup> _risk of transmission_ per contact; and _the frequency and heterogeneity of contacts._ In this study we simulate the diffusion of the HIV epidemic trough two channels: sharing of infected needles among IDUs and sexual intercourse. The former is based<sup>on Law(2001), while the</sup> latter is based on the model AVERT developed in Rehle et al. (1998).<sup>AVERTwasmodifiedto</sup> consider additional population groups (the original model has only two<sup>populationgroups)andto</sup> introduce time (the original model is static)<sup>5</sup> .

Our model divides a country's population aged 15-49 into<sup>5</sup> groups:<sup>sexworkers,IDUs</sup> female, IDUs male, low-risk female, and low-risk male. In<sup>termsof infectionsrelatedtoneedle</sup> sharing, the model assumes that needles are shared at random (this<sup>assumptionisintroducedfor</sup> tractability). Hence, the probability that an uninfected IDU would become<sup>infectedbetween time t-l</sup> and t, is given by: 5 An alternative to the current model would have been a more complex agent-based model.<sup>Nonetheless,the</sup> time needed for calibration, simulation, and analysis of agent-based models tends<sup>to increase considerably. The</sup> current model is considered an adequate compromise. Moreover, AVERT, despite<sup>its simple formulation,has</sup> shown to produce reasonable forecasts of infections averted as a result of<sup>interventionssuchasincreasing</sup> condom use or reducing the prevalence of STDs. For other non-agent<sup>models of theepidemicseeStover and</sup> Way, (1995) and Stover (1997).
_-7_ where D is the vector of IDUs population groups (in<sup>this case D has twoelements:maleand female),</sup> *d iS the share of the population of group d in the total<sup>populationof IDUs,P& istheH[V/A]DS</sup> prevalence in the population group d, ridu is the<sup>probabilityof infection afteraninjectionwithan</sup> infected needle, n is the total number of injections<sup>betweentime t- 1 and time t, u is the shareof these</sup> injections which use safe needles, and dU is a<sup>policyvariableintroducedtosimulate reductionsin</sup> sharing practices. In equation (10) the expression between<sup>{}istheprobabilityofremaining</sup> uninfected after an injection with a shared needle.<sup>Theprobabilitythattheneedlewillcomefrom</sup> group d is given by d Within this group, the probability that the needle<sup>is infected is given by0,L the</sup> HIV/AIDS prevalence rate in group d. Finally, if the needle is infected, the probability<sup>thatthe</sup> individual will not acquire HIV is given by<sup>l-ridu.Toobtainthejointprobabilityofremaining</sup> uninfected after n injections, taking into account that a<sup>shareI-u-dUof these injectionstakesplace</sup> through shared needles, we simply raise the expression<sup>in brackets to the n(I -u-dU)power.</sup> In the simulations, before considering infections due to sexual<sup>intercourse,the prevalencerate</sup> for the IDUs population groups is updated on the basis of<sup>equation(10).We get:</sup> where Hd, is the stock of HIV/A1DS infected individuals<sup>at timet in groupd.At this stage deathsare</sup> not yet taken into account.

In terms of infections sexually transmitted we follow<sup>thesamemechanics thanthemodel</sup> AVERT. The probability that an HIV negative individual<sup>would becomeinfectedat timet is given</sup> by: where J is the vector of population groups (5 in this case),<sup>j,is the HI V/AIDS prevalenceobservedin</sup> group j at time t, S is a vector containing different<sup>STDsstatus(four casesareconsidered:non-STDs,</sup> ulcerative STDs, non-ulcerative STDs, and both ulcerative<sup>andnon-ulcerative),w, istheprobability</sup> of observing state s, r, is the probability of HD/AIDS<sup>transmissionafter intercourseinstatuss, fis</sup> the probability of using condoms, e is the effectiveness<sup>of thecondoms,mijis theaveragenumberof</sup> sexual partners that individuals of group i have in<sup>groupj,andn</sup> 1<sup>jisthe averagenumberof</sup> intercourses with each partner between time t and t+l.<sup>Similartothecaseof equation(12), the</sup> expression between {} gives the probability that an uninfected<sup>individual in groupiwouldremain</sup> uninfected after sexual intercourse with mij partners in<sup>group j.The productover the J groupsgives</sup> the joint probability of remaining uninfected after sexual<sup>intercourseswiththe partnersinall groups.</sup> Inside the brackets, the probability that the partner will<sup>beinfectedwith HIVis approximatedby the</sup> prevalence rate ij. If the partner is infected,<sup>theprobabilityofremaininguninfected aftern,j</sup> intercourses is given by the expression in parenthesis.<sup>Theprobabilityof infectionisgivenbyrs,</sup> which depends on whether the partner suffers from<sup>an STD.As previously mentioned, the probability</sup> of having different types of STDs is given by w,. Therefore,<sup>the sum gives the expected probability</sup>
-8 -

of remaining uninfected after one intercourse with a partner, taking into account the prevalence of STDs.

Under these assumptions, the dynamics of the HIV/AIDS prevalence rate is given by:

# _Calibrating models parameters_

The model parameters can be grouped into three categories: economic parameters (those influencing growth and the dynamics of labor markets); parameters determining the economic impact of the epidemic for a given prevalence rate; and parameters affecting the diffusion of the epidemic (see Appendix C for a summary of parameter values and a description of calibration methods). Key economic parameters were estimated in order to replicate targets in terms of economic growth and savings rates, on the basis of the World Bank Country Assistance Strategies for each of the countries included in the analysis (World Bank 1997, 1999b, 1999c, 2000, 2001a, 2001b, 2001c, 2001d, 2002a). For the parameters determining the diffusion of the epidemic and potential economic impacts (for instance the distribution of deaths among skilled and unskilled workers), given the lack of data, we defined uniform probability distributions. The supports of these distributions were calibrated on the basis of results from the literature, in particular the works of Haacker (2001), MacFarlan and Sgherri (2001), Bonnel (2000), and Ainsworth and Over (1994), for economic impacts; Floyd and Gilks (2001), United Nations (2001), and Cavallini et al. (2000), for estimates about health expenditures; Jenkins et al. (2002) and Rehle et al. (1998), for sexual and drug use behavior; and UNAIDS and Jenkins and Robalino (2002), for initial prevalence rates.

## 2. Simulating the costs of inaction

Given high levels of uncertainty in terms of model parameters traditional simulation methods, where outcome variables are analyzed for a handful of scenarios, are not appropriate. Here we adopt an exploratory modeling approach (see Bankes, 1995). Hence, for each country, we sample a large number of points in the parameters space from the uniform probability distributions described in the previous section (our priors). At each of these points we compute the value of five variables of interest: the present value of total GDP and its average growth rate during the period 2000-2025; the size of the population in year 2025; and the HIV/AIDS prevalence rate and HIV/AlDS related health expenditures in year 2015.

As an illustration, Figure 1 graphs losses in the present value of total GDP and the HIV/AIDS prevalence rate for 100 sampled points in the case of Tunisia. We observe that there is a wide range of plausible futures (see Appendix D for a sample of diffusion profiles). The HIV/AIDS in year 2015 could fluctuate anywhere between 0.18% and 15%, while losses in the present value of GDP for the period 2000-2025 could be equivalent to 2% to 300% of today's GDP. Clearly, not all the futures have the same likelihood. For instance, in 35% of the cases explored the HIV/AIDS prevalence rate in year 2015 would be below 2%. That being said, only in 16% of the cases the HIV/AIDS prevalence would be below 1%. In fifty percent of the cases, the prevalence rate would be above 3%.
-9-

**Figure 1: Tunisia: plausible futures for the**<sup>**H1IV/AIDSprevalencerate and lossesIn GDP**</sup> **(2000-2015)**

<!-- Start of picture text --> 2%IX0%<br>1J150%<br>MO1~% - 11<br>8 50.0%<br>0.0% ~ 00% I 2L9<br>0.0% 50# 10.0% 150% 20.0%<br>HIV/AIDS prevalence rate In year 2015<br><!-- End of picture text --> The same type of analysis was conducted for<sup>the 9 countries included in the study.For each</sup> country we summarize the distribution of the<sup>outputvariablesofinterestby reportingthemean,</sup> standard deviation, and the minimrum and<sup>maximurnvalues(seeTable1).</sup> Graphics of the full distributions are provided in Appendix E. All<sup>distributionsdeviatefrom the normaldistribution,and</sup> therefore one needs to be careful when interpreting<sup>the means.</sup>
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Table 1: Descriptive statistics for Selected Output **Varables** (Status-quo)

|Country|Statistic|pvGDPI2000-20251 loss(%<br>today'sGDP)|AverageGDP<br> growthrate(2000-<br>2025)|Population<br>changeIn2025<br>(%/)|HIVprevalence<br>2015(*/*)|Health<br> expenditures2015<br>(%GDP)|
|---|---|---|---|---|---|---|
|Algeria|Mean|36.2%|-0.32%|-3.5%|3.9<sup>0</sup>%|1.3%|
||std|36.7/o|0.37/o|2.8%|3.5%|1.2%|
||min-|4.5%|-2.39%|-13.7P/|0.4%|0.1%|
||max|222.2%|-0.03%|-0.6%|17.1%|_5.8%_|
|**Djibouti**|mean|227.1%|-1.64%|-22.2%|18.3%|6.4%|
||std|121.5%|1.24%|10.6%|12.4%|4.4%|
||min|85.2%|-7.19%|-53.6%|2.5%|0.9%/0|
||max|698.2%|-0.36%|-8.2%|55.3%|19.9%|
|**Egypt**|mean|44.3%|-0.33%|-3.2%|3.8%|1.3%|
||std|47.6%|0.40%|2.6%|3.3%|1.1%|
||min|4.4%|-2.61%|-12.7/o|0.4%|0.1%|
||max|285.4%|-0.03%|-0.5%|16.1%|5.5%|
|**Iran**|mean|33.6%|-0.33%|-3.3%|3.7%|1.3%|
||std|36.0%|0.41%|2.6%|3.3%|1.1%|
||min|3.3%|-2.67/o|-12.8%|0.4%|0.1%|
||imax|215.8%<br>|-0.03%<br>|-0.5%|15.9%|5.4%|
|Jordan|mean|27.9<sup>0</sup>%|-0.27<sup>0</sup>/o|-2.6%|3.2%|1.1%<br>|
||std|31.0%|0.34%0|_<br>2.1%|2.8%|1.0<sup>0</sup>h/|
||nmin|23%|-2.18<sup>0</sup>%|-10.5%|0.3%|0.1%|
||max|187.8%|-0.02%|-0.3%|14.00/o|4.8%|
|**Lebaon**|mean|26.3%.<br>|-0.36%|-3.8%|4.1%|1.3%|
||std|26.9<sup>0</sup>/o|0.43%|3.0%|3.6%|1.2%|
||min|3.1%|-2.77/o|-14.8%|0.4%|0.1%|
||max|161.8%|-0.03%|-0.6%0|.<br>17.5%|5.7%|
|Morocco|**mean**|33.2%|-0.33%|-3.3%|3.7/o|1.3%|
||std|35.3%|0.400/o|2.6%|3.3%|1.1%|
||miin|3.5%|-2.55%|-13.1%|0.4%|0.1%|
||-<br>max|211.5%|-0.03%|-0.5%|16.0%|5.4%|
|Tunisia|mean|45.5%|-0.34%|-3.5%|3.8%|1.2%|
||std|47.70/|0.41%|2.8%|3.3%|1.1%|
||min|5.2%|-2.62%|-13.P/o|0.4%|0.1%|
||max|286.1%|-0.03%|-0.5%|16.4%|5.3%|
|Yemen|**mean**|31.4%|-0.27/o|-2.5%|3.3%|1.2%|
||std|34.8%|0.33%|2.0%|2.9%|1.1%|
||min|2.6%|-2.170/o|-9.8%|0.3%|0.1%|
||nmax|211.7%|-0.02%|-0.3%|14.1%|5.2%|
The level of uncertainty surrounding the dynamics<sup>of HIV/AlDSis considerable.Given that</sup> all countries start from similarly low baselines,<sup>the rangeof variation of the prevalencein year 2015</sup> is similar (between 0.3% and 17% excluding<sup>Djibouti).Acrosscountries, losses in the presentvalue</sup> of GDP per capita could range between close<sup>to2.3%of today'sGDPtoover1000/0(inDjibouti</sup> maximum impacts could be equivalent to<sup>7 timescurrentGDP).Average real GDPgrowthratesfor</sup> the period could be reduced by 0.02% to 2.7%<sup>peryear.</sup> The size of the population in year 2025 could be reduced by 0.3% to 15%, and HIV/AIDS<sup>relatedexpenditurescouldincrease by 0.1% to6%</sup> of GDP in year 2015.

On average, impacts are likely to be considerable. Excluding Djibouti, the average HIV/AIDS prevalence rate in year 2015 across<sup>scenariosapproximates4%.The GDP growthrate for</sup> the period 2000-2025 could be reduced, on<sup>average,by0.27%peryearinYemen andJordan,to</sup> 0.36% in Lebanon, and over 1.6% in Djibouti.<sup>Lower economic growth would result in output losses</sup> for the period 2002-2025 equivalent to 31.4%<sup>of today's GDPin Yemen,36%inAlgeria,44.3%in</sup> Egypt, and 227% in Djibouti. In year 2015,<sup>HIV/AIDSrelatedhealthexpenditurescouldaverage</sup> 1.2% of GDP. By year 2025, the labor force<sup>couldhave beenreduced by 2.5%in Yemento 4% in</sup> Algeria and 22% in Djibouti. Thus, regardless of the distribution of factors<sup>that affectthe</sup> vulnerability of different countries (differences<sup>inunemploymentrates,theshareof laborintotal</sup> inputs, the growth rate of labor productivity),<sup>HIV/AIDSposes a considerabletreat.</sup> The HIV/AIDS epidemic presents a typical<sup>problem of decisionmakingunder conditionsof</sup> deep uncertainty. Prevalence rates could remain<sup>atlow levels,butthereis alsoariskthatthe</sup> epidemic reaches alarming levels and causes<sup>notonly economicdamage,butalsogrieveas</sup> individuals in their most productive ages<sup>die.</sup> The next section discusses the type of<sup>policy</sup> interventions that could be implemented to<sup>insure societies againstthese risks.</sup>

# 3. Social gains **from preventive** interventions

It has been extensively discussed in the policy<sup>arenathatgovernmentshave a keyrolein</sup> developing and financing the implementation of policies<sup>to confront HIV/AIDS. Indeed,individuals</sup> alone could not devise appropriate mechanisms<sup>to containthe epidemic.First,individualsdo not take</sup> into account the social costs of the risks they take,<sup>or the socialbenefitsof the preventive measure</sup> they adopt. In an unregulated market we would<sup>observe anexcessof risky behavior and toolittle</sup> prevention from a social point of view. A second reason is given by information<sup>problems.</sup> Individuals may not have enough information<sup>about therisks of HWVand may lack knowledgeabout</sup> preventive behaviors. Finally, cultural/religious<sup>values mayconstraintindividuals'actionsinways</sup> that render them, and society, more vulnerable<sup>to HIV/AIDS.The role of Governmentsin providing</sup> information and subsidizing interventions to reduce<sup>risky behaviors is therefore critical.</sup> Governments can only intervene, however, if<sup>therearecost-effectiveinterventionsat their</sup> disposable. Fortunately, international experience has demonstrated<sup>thatthisisthecasewith</sup> HIV/AIDS. Recent studies show that interventions which<sup>focusonreducingrisks(through</sup> information and preventive behaviors and services)<sup>in thosepopulation groups most likely to contract</sup> and spread HIV can be highly cost-effective<sup>(seeKahn,1996).Interventionssuchasreproductive</sup> health and HIV/AIDS education in schools, targeted<sup>STD treatmentfor highly vulnerable groups, and</sup> harm reduction for IDUs have also proved to be<sup>cost-effective(seeJenkins,2002).Ingeneralearly</sup> interventions bring higher benefits and lower<sup>costs.</sup> The question of interest for policymakers in MENA<sup>countries, where prevalencerates remain</sup> low and future dynamics are highly uncertain,<sup>is whether thereare stillinterventionsthat governnent</sup>
-12-

could/should implement. We focus on two classical interventions: condoms distribution and expanding access to safe needles for IDUs. As previously discussed, heterosexual transmission and transmission through the sharing of infected needles among IDUs are the two major mechanisms that could sustain the development of the epidemic.

To implement these policies we affect the parameters dU (equation 12) and dF (equation 14), which are respectively the reduction in the probability of sharing a needle and the increase in the probability of using a condom. The total costs of these interventions is given by: where s is the time when the policy is in effect, sex(.) is a function giving the total number of sexual contacts, drug(.) is a function given the total number of needles consumed (both defined on the basis of equations 12 and 14), and Pc and pn are respectively the average costs of distributing a condom and a needle.

We simulate two policies that are summarized in Table 2. In both cases, dU is set equal to 0.20 (that is the probability of sharing a needle is reduced by 20 percentage points) and dF is set equal to 0.30 (the probability of using a cotndom increases by 30 percentage points). The first case sets s=1 (i.e., the policy -is implemented immediately), while the second case sets s=5 (the policy is implemented after 5 years). Since we prefer to underestimate benefits and overestimate costs, we work with high-end estimates from the literature. Thus, we assume that the average cost of distributing a condom is equal to USD 0.5 while the average cost of distributing a needle is equal to USD 1.5 (see Table 3).

Table 2: Two interventions: expanding condom **use and access to safe needles for IDUs**

||PolicyA|PolicyB|
|---|---|---|
|Increaseincondomuse(dF)|30%|30%|
|Reduction inneedlesharing(dU)|200/o|20%|
|Yearwhenthepolicyisimplemented(s)|1|5|
|[Average**cost**condominUSD(Pc)|0.5|0.5|
|[AveragecostneedleinUSD(Pn)|1.5|1.5|

**Table 3: Unit costs for a needle distribution intervention**

|**Input**|**Cost**|**Unit**|
|---|---|---|
|Costneedleandsyringe|0.1|perunit|
|Rentdrop-in center|165|month|
|lectricitydrop-in center|35|month|
|Supervisordrop-incenter|3,000|year|
|Costdistributor|2|day|

We assume that all costs are directly subtracted from GDP<sup>6</sup> . For each sampled point in the parameter space we then re-compute the same five output variables than in the non-intervention case.

6 At the macro level this is not necessarily the case, since the production and distribution of condoms and needles can also contribute to GDP. A more realistic approach, but less conservative, would have been to look at the distortionary effects of reallocating resources to the production and/or importation and distribution of needles and condoms.
- 13 -

In Table 4 we summarize the new set of descriptive statistics when the policy is implemented today, as well as changes in the means with respect to the non-intervention<sup>casesand thestandard</sup> error of the estimate of this change (in italics). We measure the efficiency of the policy<sup>intervention</sup> by its impact on total GDP. We observe that, averaging across scenarios, GDP losses during the period 2000-2025 are significantly reduced, by an equivalent of 15.5% of today's<sup>GDPin the caseof</sup> Jordan, to 27% in the case of Egypt, and 71% in the case of Djibouti. This is after taking<sup>into account</sup> the costs of distributing condoms and needles. Lower losses result from a significant<sup>reduction in the</sup> average H1V/AIDS prevalence rate in year 2015 (minus 2.8 percentage points). We observe<sup>that the</sup> minimum and maximum loss (that is the extremes of the support of the distribution)<sup>arereducedas</sup> well. The latter in particular is reduced by an equivalent of 100% of today's<sup>GDPinthecaseof</sup> Yemen, to 200% in the case of Egypt. This is a clear illustration of how to mitigate<sup>the risks</sup> associated with HIV/AIDS. In other words, even if the likelihood of<sup>eachscenarioremainsdesame,</sup> the loss in each case would be considerably reduced. Implementing the policy<sup>can then beconsidered</sup> as a form of insurance against the risks of the epidemic.

The policy intervention also produces statistically significant reductions in population<sup>losses</sup> and health expenditures. In most countries the total population in year 2025<sup>couldbe2.4%higher</sup> than in the case of the status-quo. In Djibouti, the total population would be, on average,<sup>9% higher.</sup> Health expenditures related to the treatment of HIV/AIDS, on the other hand,<sup>would bereducedby</sup> close to one percentage point of GDP.

A clear message from these results is that, in the face of an uncertain future<sup>regarding the</sup> dynamics of the HIV/AIDS epidemic and its economic impacts, countries in the<sup>MIENAregion will</sup> be better-off if interventions such as the ones simulated here are adopted.
- 14-

**Table 4: Descriptive statistics for Selected Output Variables (Policy A)**

|ountry|Statistic|pvGDPI2000-20251<br>loss(%today's<br>GDP)|AverageGDP<br>growthrate<br>(2000-2025)|Population<br>change**In**<br>2025(%)|HIV<br>prevalence <br>year2015<br>(°/O)|Health<br>expenditures<br>2015(%<br>GDP)|<br>Loss<br>g<br>pvGDP|Loss<br>GDP<br>rowth<br>rate|**Pop**|HIV<br>|Health|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Ageria|mean|15.8%|-0.1%|-1.2%|1.2%|0.4%|-|0.20!|2.3/|-2.8!|-0.99/|
||std.dev.|12.70%|0.1%|1.0%|1.2%|0.4%|**_0.104_**|**_0.00_**|**_0.0"A_**|**_0.0"A_**|**_0.0_**|
||min|2.9%|-0.6%|-5.9%|0.0%|0.0°/°|-1.6°/|1.8"!|7.80/|-0.44!|-0.1"!|
||max|70.5%|0.0%|-0.1%|6.9%|2.3%|-151.6°/|_0.0"!_|0.5"!|-10.1"!|-3.4/|
|Djibouti|mean|155.8%|-0.8%|-13.5%|8.2%|2.90%o||0.8"!|8.7"!|-10.1"!|-3.5"!|
||std.dev.|69.5%|0.6%|5.8%|6.6%|2.3%|_1.60_|**_0.0S_**|**_0.0S_**|**_0.0S_**|**_0.O"_**|
||min|76.7/o|4.0%|-33.7%|0.8%|0.3%|-8.5"!|3.2"!|19.9"!|-1.7"!|-0.6"!|
||max|485.1%|-0.3%|-6.3%|31.9%|11.3%|-213.0!|0.1"|1.9"!|-23.4"!|-8.6!|
|**Egypt**|mean|16.8%|-0.1%|-0.9%|1.0%|0.3%|_ANN_|0.2"!|2.4"!|-2.8"!|-0.9"!|
||std.dev.|14.9%|0.1%|0.9%|1.1%|0.4%|0.2S|**_0.0"S_**|**_0.0"_**|**_0.0"_**|**_0.0"_**|
||min|2.4%|-0.6%|-5.5%|0.0%|0.0%|-1.9!|2.0"!|7.3"_|-0.4"|-0.1"|
||max|82.00o|0.0%|0.0%!0|6.6%|2.2%|-203.4"°|OO."|0.5Y|-9.5"!|-3.2"|
|Iran|mean|13.0%|-0.1%|-0.9%h|0.9%|0.3%|_|0.2"!|2.4"|-2.8"|0.9"|
||**std.**dev.|11.3%|0.1%|0.9%|1.1%|0.4%|**O.I"**|**_0.0"_**|**_0.0"_**|**_0.0"_**|**_0._**|
||min|2.0%|-0.6%|-5.5%|0.0%|0.0%|-1.30Y|2.1°!|7.3!|-.4"!|-0.1"|
||max|62.1%|0.0%|0.0%|6.5%|2.2%|-153.7"!|0.0"O|_0.5"_|-9.4Y|-3.2|
|Jordan|mean|12.4%|-0.1%|-0.7%|0.9%|0.3%||0.0.<br>2"!|1.9"!|-2.4!<br>|-0.8"!<br>|
||std. dev.|10.1%|0.1%|0.7%|0.9%/0|0.3%|**_0.1"_**|**_0.0"_**|_0.09_|**_0.0a_**|**_0.0_**|
||min|2.3%|-0.5%|-4.5%|0.0%|0.0%|_0.0"!_|1.7"!|6.00!|-0.3"!|4-0.!4|
||max|55.4%|0.0%|0.0%|5.8%|2.0%|-132.3"!|0.0"|0.3"!|-8.2"!|-2.8"!|
|Lebanon|mean|9.5%|-0.1%|-1.1%|1.1%|0.4%|**.;**|0.3"!|2.70!|-3.0"!|-1.0"|
||std.dev.|8.4%|0.1%|1.0%|1.2%|0.4%|**0.1"**|**_0.0s_**|**_0.0"_**|**_o0.0_**|**_0.0a_**|
||min|1.5%|-0.6%|-6.3%|0.0%|0.0%|-1.6"!|2.1"!|8.4"!|-0.4"!|-0.I|
||max|45.6%|0.0%|-0.1%|7.1%|2.3%|-116.3"!|0.0"!|0.5"|-10.4"!|-3.4"!|
|Morocco|mean|13.1%|-0.1%|-0.9%/|0.9%|0.3%||0.2"|2.4"!|-2.8Y|-0.9"!|
||std.dev.|11.2%|0.1%|0.9%|1.1%|0.4%|**_0.1"_**|**_0.0"_**|_0.0"_|**_0.0o_**|<br>**_.0o,_**|
||min|2.2%|-0.6%|-5.6%|0.0%|0.0%|-1.40"|2.0"!|7.59|-0.44!|-0.1"|
||max|62.6%|0.0%/0|0.0%|6.6%|2.2%|-148.9"!|0.0"!|0.4"!|-9.4"!|-3.2"!|
|Tunisia|mean|15.1%|-0.1%|-1.0%/|1.0%!o|0.3%|W|0.3%!|2.5"!|-2.8"!|-0.9"!|
||std. dev.|14.4%|0.1%|1.0%|1.1%|0.4%|**_0.2"_**|**_0.a0_**|**_"0.s_**|**_.o_**|**_a0_**|
||nmin|2.0%|-0.6%|-5.9%|0.0%|0.0%|-3.2"!|2.0"!|7.89!|-0.4"!|-0.1"!|
||max|80.8%<br>|.<br>0.0%!o|-0.1%|6.7%|2.2%|-205.3Y|0.0"!|_0.5"_|-9.6"!|-3.1"!|
|Yemen|mean|29.2%|-0.1%|-0.8%|1.0%|0.4%0/||0.1"!|1.7"!|-2.3"|-0.8"|
||std.dev.|16.7%|0.1%|0.7%|1.0%|0.4%|**_0.1"_**|**_0."_**|**_0._**|**_0.0"_**|**_o0.0_**|
|**_**|min|8.7%/|-0.6%/|-4.2%|0.0%/|0.0%/|6.0"!/|ISV|5.6Y~|-0.3"!/|<br>-0.1"!X|
||max|94.8%|0.0%|0.<br>**Om"**|5.7%|2.1%|-116.9"!j|0.|3°<br>**0.3**-|8.4"!|-3.1"!|

What are the social costs of delaying action? This a second question that policymakers would like to have answered. Here- we have simulated the impact of **a** relatively small delay in the policy intervention: 5 years. The new descriptive statistics are summarized in Table 5. The results suggest that delaying the intervention for 5 years could cost an equivalent of 4% of today's GDP in the case of Yemen and Jordan, to 8% in the case of Egypt and Tunisia, and 20% in the case of Djibouti. Five years of inaction would increase the average HP//AIDS prevalence rate in year 2015 by 0.4 to 0.6 percentage points. This represents more than 60,000 additional infections in the case of Algeria,
- 15 _-_

more than 100,000 in Egypt, and over 400,000 adding<sup>across the9 countries included in thisanalysis.</sup> Thus, the costs of delaying action are considerable.<sup>Governmentsoughttointervenetodaywhen</sup> **HIV/AIDS** prevalence rates are still low.

Table 5: Descriptive statistics for Selected Output Variables<sup>(Policy B)</sup>

|Countr|Statistic|**pvGDP[2000-**<br>**20251loss(% **<br>todan'sGDP)|**Average**<br>**GDP**<br>**growthrate**<br>(2000-2025)|**opulationp **<br> **changein** <br><sup>2025(%)</sup>|**HIV**<br>**revalencee **<br><sup>**year2015**</sup><br>(%)|**Health**<br>**xpenditures**<br>**2015**(%<br>GDP)|**L**<br>**Loss**<br>vGDP|**ossGDP**<br>**growth**<br>rate|Pop|HIV|Health|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Algeria|mean|21.5%|-0.1%|-1.8%|1.7%|0.6%|.6__<br>t|-0.0350!|0.5960/|0.5010/|0.1690!|
||I____std.<br>dev.|_17.5%_|0.1%|1.4%|1.6%|0.5%|_0.03590_|_0.000"4_|_0.000"_|_0.0000_|_0.000"_|
||min|4.0%|-1.0%|-7.2%|0.1%|0.0%|-2.401°/!<br>|-0.3320!<br>|1.2840!<br>|0.074°!<br>|0.0250/<br>|
||imax|115.3%|0.0%|-0.2%|8.1%|2.7%|-0.013°/c|-0.0050/|0.123Y/|1.2000/|0.405°/|
|Djibouti|mean|176.0%|-1.0%|-15.5%|9.4%|3.3%|C9m|-0.1150|"!2.053"!|1. 1**730/**|0.412°/|
||std.dev.|81.3%|0.7%|6.8%|7.4%|2.6%|_0.827A_|_0.000R_|_0.006O_|_0.007"_|_0.001A_|
||rnmi|81.3%|-4.4%|-38.5%|0.9%|0.3%|-7.907°/c|-0.4220/|4.722"!|0.1090/|0.038"/|
||max|544.4%|-0.3%|-6.9%|35.4%|12.5%|-0.3420/|-0.0230!-|0.5620/|3.4430/|1.247°!|
|Eyt|mean|25.1%|-0.1%,|-1.6%/|1.6%|0.5%|1 -1<br>N<br>_I_|-0.045"!4|0.698Y/|0.6410"!|0.217"|
||std.dev.|21.8%|0.1%|1.3%|1.5%|0.5%|_0.053"_<br>|_0.000"_<br>|_0.000"_<br>|_0.000"_<br>|_0.000O_<br>|
||nmin|4.1%|-1.0%!o|-6.7%|0.1%|0.0%|-2.594"/c|-0.416"!|1-.264!|0.0970!|0.0330/c|
||max|143.7%|0.0%!o|-0.2%|7.8%|2.6%|-0.011°/|-0.0070!<br>|0.1490!<br>|1.2020!<br>|0.4070/<br>|
|Iran|mean|19.2%|-0.1%|-1.6%|1.6%|0.5%|MVA|-0.0440!|0.692°|0.6310!|0.2130/|
||std.dev.|16.6%|0.1%|1.3%|1.5%|_0.5%_|_0.031%_|_0.00OA_|_0.000"_|_0.000"_|_0.000"_|
||mnin|3.3%|-1.0%|-6.8%|0.1%|0.0%|-2.657"!|-0.427"!|1.245!|0.095"!|0.032"!|
||max|109.5%|0.0%|-0.2%|7.7%|2.6%|-0.011!/|-0.0060!|0.146°|1.171"!|0.397!/|
|Jordan|mean|16.7%|-0.1%|-1.2%|1.3%|0.4%|_fi5c|-0.029°!|0.454!|0.457"!|0.157"!|
||std.dev.|14.3%|0.1%|1.0%|1.3%|0.4%|_0.023"_|_0.000"_|_0.000"_|_O.0000_|_0.R000_|
||m_____<br>win|_<br>2.5%|-0.8%|-5.5%|0.0%|0.0%|-2.182%!<br>|-0.328"!<br> <br>|1.005"!<br>|0.021"!<br>|0.007"!<br>|
||max|93.9%|0.0%|0.0%|6.8%|2.3%|-0.007"/!|-0.001<br>|4.024"|1.003"!|0.344"!|
|Lebanon|mean|14.5%|-0.1%|-1.9%|1.8%|0.6%|i<br>c|-0.048"!|0.792"!|0.684"!|0.223"!|
||_std.dev.|12.5%|0.2%|1.5%|1.6%|0.5%|_0.0017°A_|_0.000A_<br>|_0.000_<br>|_0.000_<br>|_0.000"_<br>|
||min|3.0%|-1.1%|-7.7%|0.1%|0.0%|-2.7660/c|-0.4560!<br>|1.400°<br>|0.122"!<br>|0.0400!<br>|
||max|82.90%|0.0%|-0.3%|8.4%|2.7%|-0.015/!|-0.010"! <br>|-0.226!<br>|12.2700!<br>|0.415"!<br>|
|Morocco|mean|19.2%|-0.1%|-1.6%|1.5%|0.5%||-0.044°!|0.666"|0.609"!|0.206"!|
||std.dev.|16.40%|0.1%|1.3%|_1.5%_|_0.5%_|_0a030_<br>|_0.000_<br>|_0.000"_<br>|_0.00-_<br>|_0.000"_<br>|
||min|2.90/o|-1.0%W|-6.90/o|0.1%|0.0%|-2.554°/c|-0.397°<br>|1.300"!<br>|0.057°<br>|0.019"!<br>|
||max|107.3%|0.0%|-0.1%|7.8%|2.6%|-0.010/c|-0.004"!|0.086|1.225"!|0.416"!|
|Tunisia|mean|23.2%|-0.1%|-**1.7/o**|1.6%|0.5%|**';.**|-0.046"!|0.690"!|0.618"!|0.201"!|
||std.dev.|21.3%|0.1%|1.3%|1.5%|0.5%|_0.050"4_|<br>_0.000"_|_0.00_|_0.000"_|_4_0.0005"|
||nmin|3.0%|-1.0%|-7.3%|0.1%|0.0%|-2.617/!|<br>-0.408Y|1.357"!|0.059"!|0.019"!|
||max|139.8%|0.0%|-0.2%|8.0%|2.6%|-0.010!|-0.004"!|0.090"|1.255"!|0.409"!|
|Yemen|mean|33.2%|-0.1%|-1.2%|1.4%|0.5%||-0.022"|.389"!|0.373!|0.O137"!|
|**____s**|**td.**<br>dev.<br>|20.5%-|0.1%|1.0%/|1.3%|_0.5%_|_0.051_<br>|_0.0_<br>|_0.00_<br>|_o_**_0_**_"_<br>_0 0"_<br>|_.000_<br>|
||min|9.2%|-0.90/|-5.1%|0.1%|0.0%|-2.249"!|.0241°|0.893"!|0.048"|!0.017"!|
||**_______**<br>max|132.1%|0.0%|-0.1%|6.6%|2.4%|-0.020!|-0.003"|0.066!|0.919"|!0.338°!|
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# 4. **Conclusions and discussion**

This paper develops a model of optimal growth coupled to a diffusion model of the HIV/AIDS epidemic based on two transmission factors: sexual intercourse and exchange of infected needles among IDUs. The model is used to assess the risks of a major HIVtAIDS epidemic and expected economic impacts. The paper argues that the necessary conditions to support the diffusion of HIV/AIDS are present in MENA countries. Clearly, high levels of uncertainty pervade any projection of prevalence rates. Thus, in the simulations we explore large regions of the parameter space and derive distributions for the dynamics of 5 variables of interest: the HIV/AIDS prevalence rate, total GDP, the growth rate of GDP, total labor force, and HIV/AIDS related health expenditures. The results show that in only 16% of the cases analyzed would prevalence rates in year 2015 be below 1%. On average, GDP losses across countries for the period 2000-2025 could approximate 35% of today's GDP. However, in all countries it is possible to observe scenarios where losses surpass today's GDP. So while HIV/AIDS prevalence rates could remain low, there are also risks that they continue to increase and in this case economic costs can be considerable.

The analysis shows, however, that there are policies that governments could put in place to insure against these risks. The two policies considered in this study (expanding condom use by 30% and expanding access to clean needles for IDUs by 20%), could reduce GDP losses across the 9 countries by an average of 19% of today's GDP. The analysis also shows that delaying action can be considerably costly. For instance, waiting for 5 years before intervening could cost an equivalent of 6% of today's GDP.

It is important to note that in this paper we have only looked at two types of interventions that are not tailored to specific countries. Clearly, the total amount of resources that societies ought to invest to fight HIV/AIDS and the allocation across alternative interventions depend on countries' characteristics. Indeed, the costs and effectiveness of the different interventions are given by factors such as the level of development of the epidemic, social and economic constraints on safe behavior, underlying pattems of sexual and drug-injecting behavior, local costs, and implementation capacity. Several methodologies are available to guide these allocations (see Kaplan and Pollack, 2000). These methodologies, however, require baseline data. Therefore, one of the priorities among MENA countries is to strengthen surveillance and information systems. Strategies would then need to be designed with strong involvement from civil society. Programs that are implemented should be carefully monitored to evaluate costs and impacts, thus allowing adjustments/corrections when necessary.

Another limitation of our analysis is that it doesn't shade light on the question of how the epidemic affects different population groups, in particular the poor. This is important when designing policy interventions. There is some evidence that the economic impact of the epidemic is likely to be higher among the poor, because their main or only source of revenue is their labor force (the non-poor can hedge with other assets losses in wage-income related to AIDS). Furthermore, coping mechanisms for the poor are more limited and usually involve changes in consumption patterns (e.g., reducing education, food, and health expenditures) or sending children to work. These mechanisms produce human capital losses as a result, among others, of high child malnutrition or lower school enrollment rates. While in MENA countries informal coping mechanisms to manage risks are diverse - ranging from family support and kinship ties to religious charitable organizations - research has shown that they are usually insufficient to hedge against adverse shocks (see World Bank, 2002b). Studies show that reductions in consumption in low-income households following the death of an adult household member would reduce food expenditures by 32% and food consumption by 15% (see Over et al., 2001). This occurs not only as household income is lost and funeral expenditures need to
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be financed (on average households<sup>spend50%/omore,$800-$900,onfunerals thanthey dofor</sup> medical care), but also because households<sup>thatexperienceadeathcutback onthenumber of hours</sup> they work for wages (Beegle, 1996).<sup>InmostMENA countries,thepooralreadyfaceproblemsof</sup> access to health services As health systems<sup>become financially constrained,these problemscan be</sup> exacerbated. At the same time, the<sup>poorare more exposedtoinfectiousdiseasesandcomplicated</sup> with malnutrition, and thus are more<sup>vulnerableto the deterioration of their immunesystem.</sup> With all its limitations, four important<sup>conclusionscanbederivedfromtheanalysis</sup> developed in this paper: i) the risk<sup>of an increaseintheHIV/AIDSprevalencerate inMENA</sup> countries is real; ii) expected costs<sup>over the next 25 yearscould beconsiderable;iii) there are actions</sup> that can be implemented to prevent the<sup>spread of the epidemicand the costsof theseactions would be</sup> more than compensated by the savings<sup>they generate;and iv)the time to actis today when prevalence</sup> rates are still low.