1. Macroeconomic effects of the response to HIV: Defining the path to long run impact analysis XVII International AIDS Conference Mexico, 5 August 2008 E. Lamontagne 1, B. Ventelou 2, B. Walters 3, R. Greener 1, Y. Videau 2, E. Orgiazzi 2 With additional contributions from J.P. Moatti 2 and S. Luchini 2 1 UNAIDS, Economics and Development Analysis Unit, Geneva, Switzerland, 2 Health and Medical Research National Institute, Marseille, France, 3 University of Manchester, School of Social Sciences, Manchester, UK

2. Aim of the study To provide an economic model to: 1.Analyse the long term economic impact of the scaling up of HIV programmes 2.Identify economic factors that could enhance or limit its implementation

3. Two phases: 1.Preparation Phase (Oct 07 – June 08) –Model, variables, data 2.Development phase (Sept 08 March 09) –Definition and calibration of a long run endogenous growth model

4. Background HIV crisis: long wave event that superimposes on other long wave trends: –Endemic poverty –Poor governance –Health sector and other social sectors in crisis –Increasing social cost of economic transition programmes

5. Macroeconomic Models Previous models = the impact of the epidemic Two conclusions from previous modelling: 1.More empirical evidence is needed Behavioural effects of individuals and HH affected by HIV 2.Must be dynamic AIDS not a short-term shock but a cumulative shock

6. New concerns The importance of human capital in determining HIV economic impact Households and individuals are at the centre of the HIV epidemic –The source of human capital –Savings and consumption (ordinary goods or health goods) –Health status affects productivity and education attainment –Poverty and vulnerability are at individual and HH level

7. An augmented Solow-type model “Real production model”, oriented to long-run issues and endogenous mechanisms 1.Will not include exchange rate and monetary mechanisms Because Dutch disease effect unlikely to be generated by extra aid for HIV –Most high infection countries = limited aid dependence –Where aid for HIV is high = low aid dependence countries

8. Augmented Solow model to include (cont.) 2. Dealing not only with health but with healthcare access and cons => a multiple factor production function for health H = H ( G i, G H ) Where: G i : healthcare infrastructures and medical staff (health sector support) G H : HIV-related medical goods (treatment and care) Cross elasticity : can reflect health system absorption capacity 3. A human capital function determined by education and health status To emphasize the positive relation : health and economic growth Translate the OVC component

9. Augmented Solow model to include (cont.) 4. The economic outcome of prevention activities Challenges: How to model the infections averted by prevention activities? How to estimate the economic returns of prevention activities ? Outcomes interact at different time periods Possibilities under discussion: Might try to integrate an epidemiological model to estimate the impact of prevention on prevalence Coefficient could be built on cost effectiveness values from existing literature

10. The response to HIV and economic growth short term horizon 2/ ARV treatment Better health status Better mental and physical capacities Drop in sickness leave Decrease of MTCT Increase of life expectancy Better annual productivity per worker Better hourly productivity per worker Increase of the labour market participation rate Better productivity per capita 3/ Prevention Lower transmission Better career productivity 1/ Support to health sctr (facilities, human resources) Absorptive capacity constraint

11. The response to HIV and economic growth medium term horizon 2/ ARV treatment Better health status Better mental and physical capacities Drop in sickness leave Decrease of MTCT Drop in infant mortality Increase of life expectancy Better annual productivity per worker Better hourly productivity per worker Increase of the labour market participation rate Better productivity per capita Economic growth 3/ Prevention Lower HIV incidence Lower transmission Saving and education investment Better career productivity 1/ support to health sctr (facilities, human resources) Absorptive capacity constraint Non HIV+ people

12. The response to HIV and economic growth long term horizon 2/ ARV treatment Better health status Better mental and physical capacities Drop in sickness leave Decrease of MTCT Drop in infant mortality Increase of life expectancy Better annual productivity per worker Better hourly productivity per worker Increase of the labour market participation rate Better productivity per capita Economic growth 3/ Prevention & VAW Lower HIV incidence Lower transmission Saving and education investment Better career productivity 1/ support to health sctr (facilities, human resources) Absorptive capacity constraint Non HIV+ people 4/ OVC

13. Conclusion The new proposed model will: Take into consideration the endogenous impact of the epidemic on the behaviour of individuals and HH Enable to translate the long term impact of different HIV programmes on these behaviours: –elasticity of GDP to alternative combinations of HIV activities Enable selection and analysis of economic added value of National HIV Programmes

14. Thank you

15. Macroeconomic effects of the response to HIV: Defining the path to long run impact analysis Contact person: Erik Lamontagne lamontagnee@unaids.org E. Lamontagne 1, B. Ventelou 2, B. Walters 3, R. Greener 1, Y. Videau 2, E. Orgiazzi 2 With additional contributions from J.P. Moatti 2 and S. Luchini 2 1 UNAIDS, Economics and Development Analysis Unit, Geneva, Switzerland, 2 Health and Medical Research National Institute, Marseille, France, 3 University of Manchester, School of Social Sciences, Manchester, UK

16. Annexes Dutch Disease Effect and aid dependence

17. Annex 1 : Aid Dependence (net aid flows to GDP) in % Year19951996199719981999200020012002200320042005 Country/Group Botswana1.91.62.42.01.10.5 0.70.40.50.7 Central African rep.15.216.19.211.711.48.06.95.84.38.47.0 Cote d'Ivoire12.18.64.18.03.83.61.79.92.01.10.8 Gabon3.42.60.81.11.20.30.21.7-0.20.60.7 Haiti24.712.010.010.76.25.54.84.67.26.812.1 Kenya8.45.03.53.02.44.13.63.13.64.1 Lesotho8.58.16.75.42.73.46.08.96.06.33.9 Malawi32.221.913.125.425.826.124.019.930.027.028.4 Mozambique51.433.129.528.421.324.727.456.222.622.420.7 Namibia5.2 4.55.25.34.43.44.33.13.02.0 South Africa0.3 0.4 0.50.40.3 Swaziland4.02.31.82.52.00.92.11.91.80.91.7 Tanzania16.913.612.512.111.611.413.612.716.615.712.5 Uganda14.611.213.09.89.914.114.312.416.018.014.0 Zambia62.819.816.411.520.925.810.118.014.122.513.9 Zimbabwe7.24.54.24.64.32.51.60.92.44.111.4 World Development Indicators (April 2007)

18. Annex 2: commitments for HIV/AIDS as a proportion of total commitments from all sources (in %) Year200020012002200320042005 Botswana12.454.815.156.240.134.2 Central African Rep.0.326.61.318.17.00.7 Cote d'Ivoire1.58.90.75.93.511.8 Gabon 0.10.40.22.60.6 Haiti1.03.942.32.77.62.8 Kenya13.86.017.814.47.512.0 Lesotho6.81.74.111.97.920.9 Malawi3.26.1 21.94.26.1 Mozambique1.34.71.38.47.76.9 Namibia3.06.112.813.727.739.3 South Africa5.55.710.423.921.814.6 Swaziland0.14.89.246.27.037.4 Tanzania1.31.93.46.54.619.0 Uganda2.38.05.78.112.25.1 Zambia2.36.714.814.95.95.3 Zimbabwe21.714.016.614.333.213.4 Source: OECD DAC database (2007)

19. Data The model will be calibrated for 5 -7 countries Using country specific data Parameter from the literature