Wfp_Project

SCHOOL FEEDING World Food Programme School Feeding: A Sound Investment Results from investment case study February 2009 Work in Progress Acknowledgements The investment case study has been developed jointly developed by the World Food Programme (WFP) and The Boston Consulting Group (BCG) BCG has been engaged on a pro bono basis under the corporate partnership with WFP Guidance for has been provided by members of staff of the World Food Program (WFP), in particular Nancy Walters, Marc Regnault de la Mothe, Carmen Burbano, Luca Molinas and Federica Carfagna. Many others have contributed in many ways. Aulo Gelli, from Imperial College, has served as academic expert to discuss, review and further refine the methodology. The country offices of Lao PDR, in particular Maiko Tajima, and of Kenya, in particular Rene McGuffin, Alex Muindi, Dennis Tiren and Danston Ondachi have supported the analysis by providing in country data and estimates for these. Rome, June 2009. 1 Work in Progress Disclaimer The financial analyses presented herein are based on data been obtained from internal WFP and public sources believed to be reliable, but BCG makes no representation as to the accuracy or completeness of such information. Changes in the underlying data or assumptions that are explicitly or implicitly part of the investment model will clearly impact the analyses and conclusions. A variety of factors could cause actual results to be materially different from any results that may be expressed or implied by the investment model. BCG does not intend or assume any obligation to update or revise this model in light of results which differ from those anticipated. All results are provided for informational purposes only and are not intended to be and should not be construed as a recommendation to invest in the interventions mentioned in this file. 2 Work in Progress Agenda Executive Summary Objectives & Scope Methodology & framework Results Conclusions Appendix 3 Work in Progress Agenda Executive Summary Objectives & Scope Methodology & framework Results Conclusions Appendix 4 Work in Progress School Feeding provides a value transfer Executive summary (1/4) School Feeding provides additional resources to households for consumption The value constituted by the food transferred to the household also frees up income, which the household invests in productive assets Evidence from developing countries shows that the poorest households on average consume 85.5% of added income and spend the remainder 14.5% in productive assets (McKinzie and Woodruff 2003) • Hence, for every US$100 of value transferred by means of School Feeding, US$14.5 is invested in productive assets. • Of these US$14.5, the median return on investment in developing countries is estimated 54% per year (Banerjee, Duflo 2004). 5 Work in Progress School Feeding improves education and nutrition Executive summary (2/4) School feeding leads to an increase in time spent in school through increased enrolment, attendance and decreased drop out rates (Ahmed 2004) • In Laos, evidence shows that attendance increases by 5.5% per year, enrolment by 16% and dropout reduces by 9% School feeding leads to an increase in cognition: learning is improved. • One additional year of school feeding leads to an increase in cognition of 0.09 SD test scores. • Additionally 1 SD increase leads to 11% increase in wages over productive life (Kristjansson 2007) School feeding leads to improved micronutrient status and decreased prevalence of intestinal parasites • This leads to decreased morbidity - children are less sick in school and attend more. less school days are lost The lifetime increase to 1% increase in attendance rate is 0.055%. (Grigorienko et. al 2007; Brooker et al 2007) 6 Work in Progress School feeding leads to higher lifetime earning Executive summary (3/4) Evidence from the World Bank shows that every additional year of primary schooling leads to a 5 % increase in future wages • When children are well nourished during primary school age, they will be healthier and more productive during their future working years. Higher income leads to 1% increase in life expectancy (Wigley, 2004) • 1 year of additional schooling raises disease awareness, in particular related to HIV and decreases HIV prevalence by 6.7% (De Walde, 2004), leading to longer life expectancy and higher productivity. Higher productivity and longer productive lifetime, taken together, result in higher lifetime earnings - over a longer lifetime • Increased productivity (thanks to improved education and cognition) • a prolonged productive life ..and added household income due to higher savings invested in productive assets all together lead to higher returns on investment for school feeding 7 Work in Progress Main conclusions from the investment case Executive summary (4/4) School Feeding is a unique intervention thanks to the reinforcing and multiplication effects between the various outcomes Investing in school feeding creates significant economic value School feeding is a unique safety net driven by the interdependency between various outcomes, and combines short-, mid- and long-term benefits from nutrition, education and value transfer. 8 Work in Progress Agenda Executive Summary Objectives & Scope Methodology & framework Results Conclusions Appendix 9 Work in Progress School feeding investment case objectives Assess monetary cost and economic benefits of providing school feeding • All main recognized outcomes: nutrition/health, educational benefits and value transfer Calculate School Feeding Benefit/Cost (B/C) ratio and Net Present Value (NPV) • B/C ratio: how many $ are generated from investing 1$ in school feeding ' • NPV: how much value is generated from a school feeding program (overall and per capita)' • Perform sensitivity analysis to prove investment robustness under different scenarios Identify value created in terms of increased education, improved health & nutrition and value transfer to the beneficiaries Point out synergies when using school feeding as delivery platform for other interventions • Does investment case improve by adding further interventions' 10 Work in Progress Laos and Kenya chosen as sample countries to build the case on actual WFP projects Laos Development 1 Project1 Kenya 10078.1 1 Country Programme1 10264.0 Beneficiaries ~90,000 primary school children • remote, food-insecure areas of Northern Laos • 1,084 schools covered ~M1.2 children in primary school2 • rural, food-insecure areas as well as urban slums • 3,847 schools covered: Modalities In-school snacks to ~90,000 children: • Annual costs / beneficiary3 : $18 • Food basket: CSB (50g), Sugar (10g), Oil (10g) • Delivery frequency: 166 days (morning) Take-home rations ~90,000 children: • Annual costs / beneficiary3 : $67 • Food basket for Girls: Rice (30kg), Fish (5 cans), Salt (1 kg); Boys: Rice (15kg), Fish (5 cans), Salt (1kg) • Delivery frequency: Twice per year (salt only once per year) In-school snacks to ~70,000 children: • Annual costs / beneficiary3 : $6 • Daily food basket: CSB (40g) • Delivery frequency: 142 out of 195 planned days (morning) In-school meals to ~M1.2 children: • Annual costs / beneficiary3 : $22 • Daily food basket: Cereals (150g), Pulses (40g), Oil (5g) • Delivery frequency (time): 142 out of 195 planned days (lunch-time) 1. Data for 2008 operations; both, Laos and Kenya provide de-worming through other institutions – benefits may be captured through indicators while they are not costed. 2. Programme serves also pre-primary school children which have been taken out of the school feeding analysis 3. Costs include estimates for government and community contributes that are directly associated with offering school feeding to the children Source: WFP Standardized Project Reports, WFP country office Laos, WFP country office Kenya 11 Work in Progress Agenda Executive Summary Objectives & Scope Methodology & framework Results Conclusions Appendix 12 Work in Progress Calculation of investment case via 4-steps approach BCG methodology for social impact applied to investment case Input Output Impact Value Creation Resources provided to the programme1 Interventions received by beneficiaries Measurable benefits on beneficiaries Monetization of benefits Framework already used for REACH 1. Financial and non-financial Source: BCG experience 13 Work in Progress School feeding investment case framework Seven immediate outcome parameters considered for impact Input Output 1 2 3 Impact G Household income G Enrolment G Attendance G Dropout G Cognition G Intestinal parasites3 G Micronutrient deficiency3 Value Creation G HH income + Returns from resulting higher investments G Productivity x G Productive life years $ invested1 in school feeding # of children receiving school feeding 4 5 6 7 = G Lifetime earnings Wider Socio Economic Benefits2 1. Includes WFP expenditures (Commodity, Transport, LTSH, DOC, DSC & ISC), government expenditures and community contributions; 2. Not quantified in the model; 3. Captured through other immediate outcome parameters (attendance and cognition) Source: BCG analysis and experience 14 Work in Progress All main recognized benefits of school feeding considered Impact Intervention type Immediate outcome Quantifiable impact G HH income Value Transfer G Household income Returns from investments G Enrolment Behaviour change via incentive for education G Attendance G Dropout Attendance G Cognition G Productivity Nutrition / Health G Intestinal parasites G Micronutrient deficiency G Productive life years (G DALYs) 1. Captured through other immediate outcome parameters (attendance and cognition) Source: BCG analysis and experience 15 Work in Progress Value transfer offers households the opportunity to generate additional value through investments Value creation Government & Community costs Return on Investment1 Investment WFP other costs WFP Commodity costs Local market value of food Additional consumption Additional consumption School feeding costs Additional Income G Household income available to HH Household employment of additional income Total Value transfer to Household 1. Returns on investment in productive assets, such as livestock, farming technology, fertilizers Source: BCG analysis and experience 16 Work in Progress Nutrition/health and educational benefits mutually reinforcing effects make school feeding a unique and robust intervention .. generates higher productivity .. Time spent in school Time spent in school G Enrolment G Attendance I Increased time spent in school and better school time quality .. Productivity .. resulting in higher earnings over longer and healthier life Productivity I G Dropout Better Education School time quality Time spent in school I X Lifetime Earnings Lifetime years Productivity x School time quality Time spent in school G Cognition G Intestinal parasites G Micronutrient deficiency .. enhances disease awareness and productive life years.. I I X x I Productive life School time quality Productivity I I Lifetime years I School time quality Lifetime years 17 Work in Progress Investment case core assumptions Full upward social mobility • beneficiaries to reach avg. GDP per capita in the baseline scenario Gap between WFP SF schools and Control Group1 100% driven by SF intervention only and constant over time • e.g. +15% in enrolment relative to control group (70% vs 85%) due to SF Controlling for potential decrease in quality of education by keeping student / teacher ratio constant • associated costs considered No significant long-term benefits from micronutrient intake • Weak evidence on avoidance of chronic diseases or on long-term cognitive brain development 1. Control group = the country average 2. xxxx 3. xxxx Source: BCG 18 Work in Progress Agenda Executive Summary Objectives & Scope Methodology & framework Results Conclusions Appendix 19 Work in Progress Investing in school feeding creates significant economic value Laos Development Project 10078.1 Kenya Country Programme 10264.0 An investment of $375 per child generates a total value of ~$2 500 over the lifetime • Benefit / cost ratio ~7 At country level an investment program in school feeding generates a discounted NPV of ~$354M • 10 years investment serving 90.000 children annually • Time to positive returns 3 years Benefit / cost ratio of 1.5 in the most conservative scenario and B/C of ~40 in most optimistic case • Most sensitive to discount rate and growth rate An investment of $146 per child generates a total value of ~$2400 • Benefit / cost ratio ~16.5 At country level an investment program of $200M generates a discounted NPV of ~$3B • 10 years investment serving 1.2M children annually • Time to positive returns 9 years Benefit / cost ratio of ~3 in the most conservative scenario and B/C of ~90 in most optimistic case • Most sensitive to discount rate, growth rates and returns to education 20 Work in Progress An investment of $375 per child over the primary school in Laos creates a value of ~$2500 with a benefit/cost ratio of ~7 $ 150 Transfer value increases household 100 income... $90 Transfer Value 50 Better education and health increase productivity and wages during working life... Better education, health and higher income lead to increased lifetime... $1852 Productivity increase $147 ROI $206 Increase in disability adjusted life years 0 Additional income partially invested yielding returns... -50 $375 Costs -100 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Age 21 Source: BCG Analysis and experience Work in Progress At country level an investment program of 10 years serving 90.000 children generates NPV of ~$354M % 4% 3% Investment returns (6%) Productivity increase (74%) Value transfer 2% (12%) Prolonged productive life years (8%) 1% 0% 0 10 20 30 40 50 60 70 Years 1. xxxx 2. xxxx 3. xxxx Note: Graph base on a 10 years investment scenario Source: BCG analysis 22 Work in Progress Sensitivity analysis shows robustness of investment case Laos Parameter Worst Base Best 1 Benefit/Cost ratio of 1 Change in Enrolment Rate Change in Dropout Rate Country and project parameters investment, lifetime and education Change in Attendance Rate Change in Cognition in SD2 (per school yr.) Wage increase from 1SD higher test score2 Wage increase due to add. yr. of schooling Lifetime incr. due to 1% more schooling Lifetime incr. (red. HIV inf.) due to schooling Lifetime incr. from 1% increased income Investment rate of household Average return on investment (p.a.) Method. ass. Discount Rate Projected Growth Rate1 GDP p.c. (average vs bottom quintile) Benefit-cost- ratio NPV 11% -6 % 3.9 % 0 SD 5% 4% 0.04 % 0% 0.05 % 0% 10 % 8.5 % 2.5 % 270$ 1.5 $23M 16 % -9 % 5.5 % 0.09 SD 11 % 5% 0.055 % 6.7 % 0.07 % 14.5 % 54 % 6.5 % 3.5 % 711 $ 7.1 $354M 21 % -12 % 7.2 % 0.11 SD 20 % 7% 0.07 % 9% 0.1 % 19 % 140 % 4.5 % 4.5 % n/a 41.3 -5.5 $2412M +34 -331 2058 -4 -0 -1 -1 -0 1 0 Benefit/Cost ratio 0 7.1 1 NPV 0 Break-even point 0 $354M -33 -45 -10 -62 -34 -72 33 48 10 19 51 72 0 0 -0 0 0 0 -25 -22 8 43 335 135 -1 0 -1 1 -1 1 0 0 0 0 0 0 0 Individual parameter analysis -0 1 -2 -2 2 5 -152 -91 -209 Worst / Best case3 scenario Note: Scenarios calculated using Laos data – where available; effects calculated using controls and conditionality Base case Worst case Best case 1. Displayed sensitivities assume all growth rates to be at worst (best) value; depicted for values is growth rate yrs. 1-10 2. Cognition captures attentiveness in class as measured by better test score performance. Better test scores have been shown to increase wages; 3. Excludes sensitivity on income (GDP p.c.) Source: Data sources as in documentation; BCG analysis and experience 23 Work in Progress An investment of $146 per child over 8 yrs of primary school in Kenya creates ~$2400 with a benefit/cost ratio of ~16.5 $ 250 200 Better education, health and higher income lead to increased lifetime... 150 100 Better education and health increase productivity and wages during working life... $511 Increase in disability adjusted life years Transfer value increases household income... 50 $1782 Productivity increase $90 Transfer Value $41 ROI 0 $146 Costs -50 Additional income partially invested yielding returns... 15 20 25 30 35 40 45 50 55 60 65 70 5 10 Age 24 Source: BCG Analysis and experience Work in Progress At country level an investment program of 10 years serving 1.2M children generates NPV of almost $3B % 5% 4% Productivity increase (73%) 3% Prolonged productive life years (21%) 2% Investment returns (2%) Value transfer (4%) 1% 0% 0 10 20 30 40 50 60 Years 25 Work in Progress Sensitivity analysis shows robustness of investment case Based on Kenya 10-yr investment case key drivers are discount rate and projected growth rate Parameter Worst Base Best 1 Benefit/Cost ratio of 1 Change in Enrolment Rate Change in Dropout Rate Country and project parameters investment, lifetime and education Change in Attendance Rate Change in Cognition in SD2 (per school yr.) Wage increase from 1SD higher test score2 Wage increase due to add. yr. of schooling Lifetime incr. due to 1% more schooling Lifetime incr. (red. HIV inf.) due to schooling Lifetime incr. from 1% increased income Investment rate of household Average return on investment (p.a.) Method. ass. Discount Rate Projected Growth Rate1 GDP p.c. (average vs bottom quintile) Benefit-cost- ratio NPV 1.4% -4 % 2.1 % 0 SD 5% 4% 0.04 % 0% 0.05 % 0% 10 % 8.5 % 2.5 % 220 $ 2.9 $279M 2.0 % -5 % 3.0 % 0.06 SD 11 % 5% 0.055 % 6.7 % 0.07 % 14.5 % 54 % 6.5 % 3.5 % 786 $ 15.7 $2942M 2.6 % -7 % 3.9 % 0.08 SD 20 % 7% 0.07 % 9% 0.1 % 19 % 140 % 4.5 % 4.5 % n/a 90.3 -12.8 $18291M +74.5 -2663 15,349 -11 Benefit/Cost ratio 0 15.7 -0 0 NPV 26 0 Break-even point 0 $2942M -66 67 691 91 211 577 520 94 90 28 18 125 -3 3 -0 -3 0 1 -569 -89 -698 -382 -516 -41 Individual parameter analysis -2 3 -3 3 -0 -2 0 0 -301 -21 -59 -64 -0 0 -0 -0 -6 -4 5 0 1 11 -1,265 -762 -2,131 2,550 1,073 Worst / Best case3 scenario Note: Scenarios calculated using Kenya data – where available; effects calculated using controls and conditionality Base case Worst case Best case 1. Displayed sensitivities assume all growth rates to be at worst (best) value; depicted for values is growth rate yrs. 1-10 2. Cognition captures attentiveness in class as measured by better test score performance. Better test scores have been shown to increase wages; 3. Excludes sensitivity on income (GDP p.c.) Source: Data sources as in documentation; BCG analysis and experience 26 Work in Progress Investment case in Ghana based on the 607,000 kindergarten & primary school children that benefited from school feeding in 2008 ~607,000 children in kindergarten and primary school ~596,000 children benefiting from in school meals • Menu set by caterer for Ghana School Feeding Programme • Standard ration for WFP & GES ~11,000 children receiving take home rations from WFP & GES • Another 24,000 girls in secondary school receive take home rations • Standard ration of maize, oil, and salt 27 Work in Progress An investment of $288 per child over 6 yrs of primary school in Ghana creates ~$1,986 with a benefit/cost ratio of ~8 $ 150 100 Transfer value increases household income... Better education and health increase productivity and wages during working life... Better education, health and higher income lead to increased lifetime... 50 $210 Transfer value 0 $96 ROI $1,407 Productivity increase $560 Increase in disability adjusted life years $288 Costs -50 5 10 Additional income partially invested yielding returns... 15 20 25 30 35 40 45 50 55 60 65 Results show how investing in school meals creates significant economic value Age 28 Work in Progress At country level an investment program of 10 years serving 607k children generates NPV of almost $1.8B % 5% 4% Productivity increase (63%) Prolonged productive life years (24%) 3% Investment returns (4%) 2% 1% 0% 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Years Value transfer (9%) 29 Work in Progress Sensitivity analysis shows robustness of investment case Parameter Worst Base Best Benefit/Cost ratio 0 7.9 Benefit/Cost ratio of 1 Change in Enrolment Rate Change in Dropout Rate Country and project parameters investment, lifetime and education Change in Attendance Rate Change in Cognition in SD2 (per school yr.) Wage increase from 1SD higher test score2 Wage increase due to add. yr. of schooling Lifetime incr. due to 1% more schooling Decrease in HIV infections per school yr Lifetime incr. from 1% increased income Investment rate of household Average return on investment (p.a.) Method. ass. Discount Rate Projected Growth Rate1 5% -4% 12% 0.00 5% 4% 0.04% 0% 0.05% 0% 10% 9% 3% 7% -6% 18% 0.06 11% 5% 0.06% 7% 0.07% 15% 54% 7% 4% 9% -8% 23% 0.08 20% 7% 0.07% 9% 0.10% 19% 140% 5% 5% -0.3 -0.9 -1.0 -0.9 -0.5 -1.3 -0.3 -0.8 0.3 1.0 0.9 0.2 0.7 1.3 0.2 0.2 NPV 26 -4,000 0 Break-even point 0 $1,986 -85 85 282 273 73 200 365 61 65 4,000 -265 -273 -245 -134 -365 -91 Individual parameter analysis -217 -0.1 0.0 -0.3 -0.4 -2.8 -1.8 0.1 0.7 5.7 2.6 -16 16 -96 29 201 -103 -860 -519 1,808 747 Worst / Best case3 scenario Benefit-cost- ratio NPV 1.1 $35 7.9 $1,986 59.7 $12,379 -7 52 -1,951 10,393 Note: Scenarios calculated using Ghana data – where available; effects calculated using controls and conditionality Base case Worst case Best case 1. Displayed sensitivities assume all growth rates to be at worst (best) value; depicted for values is growth rate yrs. 1-10 2. Cognition captures attentiveness in class as measured by better test score performance. Better test scores have been shown to increase wages; 3. Excludes sensitivity on income (GDP p.c.) Source: Data sources as in documentation; BCG analysis and experience 30 Work in Progress Investment case in Zambia based on the 250,000 primary school children that benefited from school feeding in 2008 ~250,000 children benefiting from in school meals • Standard ration of maize, oil, and salt 31 Work in Progress An investment of $165 per child over 9 yrs of primary school in Zambia creates ~$1,640 with a benefit/cost ratio of ~11 $ 150 100 $1,649 Productivity increase 50 $64 Transfer Value 0 $29 ROI $62 Increase in disability adjusted life years $165 Costs -50 Child age 5 10 15 20 25 30 35 40 45 32 Investment case based on Bangladesh Country Programme ~545,000 children in primary school supported by WFP Country Programme • ~2,850 schools in Gaibandha, Kishoreganj and Kurigram Receive a 75g pack of fortified biscuits for each school day, for 240 feeding days in a year 33 An investment of $124 per child over 5 yrs of primary school in Bangladesh creates ~$630 with a benefit/cost ratio of ~5 $ 40 Transfer value increases household income... 30 20 Better education and health increase productivity and wages during working life... 10 $46Transfer value $21 ROI $563 Productivity increase 0 -10 $124 Costs -20 Additional income partially invested yielding returns... -30 -40 5 10 15 20 25 30 35 40 45 50 55 60 65 Results show how investing in school meals creates significant economic value Age 34 Work in Progress Findings School feeding generates educational, value transfer, nutritional & health benefits School feeding generates both short-term and long-term benefits • Immediate relief through increased household income that can be invested in productive assets and reduce negative coping strategies • Long-term returns through increased lifetime earnings from increased productivity, reduced morbidity, and prolonged lifetime School feeding benefits not only complementary but reinforce each other through multiplication effect • Benefit from increased time spent in school multiplies with better cognition while in school • Increased productivity multiplies with additional disability adjusted lifetime Investing in school feeding creates significant economic value at a Benefit / Cost ratio of ~8 Understanding synergies and potential benefits of add-on interventions key to reap additional benefits • School feeding logistics ensure highest coverage and lowest cost of health interventions • Local procurement helps make school feeding sustainable and ensures community ownership 35 Work in Progress School feeding unleashes multiple other benefit categories that are worthwhile pursuing Add-on interventions Complementary activities that use school feeding logistics or school infrastructure to promote additional benefits 1 1. Local Procurement • Use the stable demand from school feeding activities as a foundation for growing local agricultural economy • Build long-run perspective for self-sustainable school feeding programme Wider socio-economic benefits School feeding brings about additional benefits that have not been quantified in core investment case 3 Gender Equality 3. • School feeding particularly attracts women to school • Better educated women use reliable family planning and are better informed about their childrens' nutritional requirements 2. 2 Health Interventions • Target beneficiaries in remote areas by using school network as platform to reach out to additional community members • Raise synergies by optimizing the logistics chain over different development interventions 4. 4 Intergenerational and spillover effects • Parental education will strongly influence next generation's status (intergenerational benefits) • Younger siblings also benefit from school feeding programs indirectly (spillover effects) 36 Work in Progress 1 Local procurement contributes to short- and long-term benefits Providing food produced and purchased from country's local farmers Annual amount and value of locally procured food • • Laos: 750mt CSB, 105mt Salt, 621mt Rice, 180mt Sugar worth $735'000; equivalent to annual income of ~1020 farmers1 Kenya: 635mt CSB, 5792mt Maize worth ~$1'460'000; equivalent to annual income of ~2000 farmers1 Short-term benefits • Financial relief of the poor – increased farmer's income – increased investment in production technologies • Platform to overcome market imperfections – increasing credit access and market transparency – removing vulnerability to post-harvest losses Long-term benefits • Agricultural development – increase of productivity – increase quality of agricultural production – better management of natural resources • Creating an enabling environment – fosters dev. of institutions, increases employment – guarantees increased, stable market demand Kenya outputs3: • Productivity increase (better technology) ensures increased supply • Demand from school feeding can be met even without price increases • Benefits of supplying 50M children in Sub-Saharan Africa worth ~$1.6B3 Malawi outputs2: • Agro-dealers moved goods worth $ 900k in 2 years • Fertilizers sales from $ 125k to $676k in 1 year (+441%) • Default rate on credit guarantees less than 1% 1. Assumes annual farmer income to be 2$ per day; commodity rates as reported by country offices – using cereals rate for maize in Kenya; 2. Based on Case study: "Supporting supply of agricultural inputs in Kenya, Malawi and Uganda" from the Rockefeller Foundation; 3. Based on modelling exercise by Ahmed and Sharma (2004): "Food for Education Programs with locally-procured Food" Source: WFP Standardized Project Reports and academic studies (cp. footnotes) 37 Work in Progress 2 Providing health interventions via school channel allows increased coverage with additional synergies and benefits A Breastfeeding educ. (BF) Educate pregnant and lactating mothers about benefits of breastfeeding • Suboptimal BF leads to significant child mortality (12% of 2030 Life expectancy Avg. start working Life Lao Avg. End working Life Lao Life expectancy at birth Lao Laos secondary schooling until 16 years of age (source: UNESCO) World Bank; Country Profile Lao World Bank; Country Profile Lao # # # 17 66 64 UN Country Data 2007 Economist Intelligence Unit Economist Intelligence Unit OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 $ % % % % % $711 3,00% 5,00% 3,50% 3,00% 2,00% 50 Work in Progress Assumptions and sources for model parameters - Laos Costing data Laos Laos Source Unit Coefficient Input Parameters Parameter General Duration of intervention per child enrolled Years 5 to 10 (Primary School Laos) Years 5 Costs Total WFP School Feeding Project 10078.1 Duration Total WFP Project Costs Thereof Once-off costs Additional Once-off costs not included in total WFP Project Costs 2008 Project Expenditures Once-off costs 2008 as % of total annual costs Markup for Gov't costs (annual costs that accrue but are not part of WFP project costs mentioned above) Markup for Community costs (annual costs that accrue but are not part of WFP project costs mentioned above) Markup for Other costs (annual costs that accrue but are not part of WFP project costs mentioned above) Additional annual costs due to School Feeding Enrolment impact (New Schools and Teachers) SPR 2008 Laos, Project ID 10078.1 Project documents (SPR and project document), 10078.1 Top-down estimate Laos country office Estimation with Laos CO: $100 per school * 1300 schools; Working time cost equivalent to set up: 360 days * 1300 school * $1 a day as salary SPR 2008 Laos, Project ID 10078.1 Top-down estimate Laos CO Top-down estimate Laos CO Estimate: (1 day per week for wood collection etc. * 1,300 schools * 52 weeks * $1 per day) Top-down estimate Laos CO See cost detail sheet of the model Years $ % $ $ % % $ % $ 6 $28.000.000 15% $598.000 $7.306.833 10% 1% $67.600 0% $110.410 51 Work in Progress Assumptions and sources for model parameters - Laos Costing and beneficiary data Laos Laos Parameter Modality Split Mid-morning Snack as % of SF Costs Take-home ration costs as % of SF costs Source Unit Coefficient See cost detail sheet of model; calculated using commodity baskets See cost detail sheet of model; calculated using commodity baskets % % 21% 79% Mid-morning Snack - cost per beneficiary See input&output sheet of model; calculated Take-home ration - cost per beneficiary See input&output sheet of model; calculated Mid-morning Snack – cost for avg child Calculation using cost p. beneficiary Take-home ration – cost for avg child Calculation using cost p. beneficiary $ $ $18 $67 $ $ $18 $67 Beneficiaries Overall Project Beneficiaries Total WFP school children Thereof receiving mid-morning snack Thereof receiving take-home rations SPR 2008 Laos, Project ID 10078.1 SPR 2008 Laos, Project ID 10078.1 SPR 2008 Laos, Project ID 10078.1 SPR 2008 Laos, Project ID 10078.1 # # # # 291.854 89.859 89.859 89.859 52 Work in Progress Assumptions and sources for model parameters - Laos Impact and immediate outcome parameters Impact & Value Creation Parameters Parameter General Impact on HIV / AIDS Severity factor WHO Disability Weights HIV/AIDS (a) HIV: 0.135 (range: 0.123 - 0.136) (b) AIDS (no treatment): 0.505 (c) AIDS with treatment (ART): 0.167 (range: 0.145 0.469) Unesco Global Monitoring Report 2006 WHO Source Unit Laos Laos Coefficient # 0,135 HIV prevalence rate HIV mortality rate % % 0,10% 0,02% Immediate Outcome Parameters Value Transfer Full value transfer for beneficiary from modality Transfer Value for beneficiary receiving MMS Calculation using food baskets and local market prices as stated by Kenya CO (compare cost detail sheet of model) Calculation using food baskets and local market prices as stated by Kenya CO (compare cost detail sheet of model) $ $12 Transfer Value for beneficiary receiving THR $ $61 Value transfer for average child from modality Transfer Value for average SF child receiving MMS Transfer Value for average SF child receiving THR Calculated using beneficiary numbers and transfer value per beneficiary Calculated using beneficiary numbers and transfer value per beneficiary $ $ $12 $61 53 Work in Progress Assumptions and sources for model parameters - Laos Impact parameters – education1 and value transfer Impact Parameters Parameter Impact of Education % increase in wages due to one additional year of primary school Median of various: Psacharopoulos and Patrinos "Returns to Investment in Education"; Onphanhdala and Suruga "Education and Earnings in Lao PDR: Further Results; Leveling the Playing Field; Miguel and Kremer "Worms: Identifying impact on education" - Kenya (Econometrica study) Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Damien de Walque, The World Bank, Development Research Group, "How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment' Evidence from Rural Uganda" Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Growth Theory through the lens of development economics, Massachusetts Institute of Technology Department of Economics Working Paper Series, December 2004 Various (see detail sheet) Top-down assumption Source: Leveling the Playing Field, Chapter 6 Source Unit Coefficient Laos Laos % 5,00% 1% increase in average years of schooling directly increases life expectancy by 0.0553% Decrease of HIV infections per additional school year % 5,53% % 6,70% Impact of Income 100% increase in per capita income increases life expectancy by 7.3954% Impact of Value Transfer Investment rate % 7,40% % % # % 14,50% 54,00% 10 11% 54 Avg. ROI p.a. Lifetime of investment Impact of Cognition Increase in wage per SD increase in test scores 1. Enrolment data, attendance data and dropout data used can be seen directly from the sensitivity analysis sheet; they have been derived using standardized project reports, estimations from the country offices and ministries of education data; for more detailed information see model, assumptions sheet Work in Progress Agenda Kenya 55 Work in Progress Assumptions and sources for model parameters - Kenya Modeling and macroeconomic parameters Kenya Kenya Source Unit Coefficient Assumption Parameters Parameter Modeling Parameters Discount Rate Approx. World Bank Social Discount Rate; see paper The Social Discount Rate: Estimates for Nine Latin American Countries by Humberto Lopez % 6,5% Macroeconomic Parameters GDP 2007 GDP per Capita Kenya Kenya GDP growth rate 2008 2009 GDP growth rate Projected growth rate 2010-2020 Projected growth rate 2020-2030 Projected growth rate >2030 Life expectancy Avg. start working Life Kenya Avg. End working Life Kenya Kenya secondary schooling until 17 years of age (source: UNESCO, Global Monitoring Report) Ministry of State for Public Service; Statutory retirement age; retirement age of civil servants adjusted from 55 to 60 years at beginning of 2009 World Bank; Country Profile Kenya # # # 18 60 54 UN Country Data 2007 Economist Intelligence Unit, Alacra, downloaded 13.06.2009 Economist Intelligence Unit, Alacra downloaded 13.06.2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 $ % % % % % $786 1,80% 3,50% 3,50% 3,00% 2,00% Life expectancy at birth Kenya 56 Work in Progress Assumptions and sources for model parameters - Kenya Costing data Kenya Kenya Source Years 5 to 13 (8 years of primary school in Kenya) SPR 2008 Kenya, Project ID 10264.0 running from March 2004 until December 2008 SPR 2008 Kenya, Project ID 10264.0 Top-down estimate Kenya country office Estimate based on experience values Kenya CO Estimate country office: How much of total project costs are due to SF; based on data from SPR 2008 Kenya, Project ID 10264.0 Top-down estimate Kenya country office (assumption that because project already in maturity phase once-off cost for conservative estimate no higher than 10% - still require replacement of items) Calculation by Kenya Country Office (Govt covers significant fraction of LTSH costs - total of M$2.6 per annum=~13%) Calculation by Kenya Country Office based on study by Aulo Gelli et al. (including cash contributions of communities) Kenya Country Office estimation: $220,000p.a. from including Feed The Children supporting infrastructure in slums, etc; does not include costs for deworming provided by Feed the Children; FTC children counted into beneficiaries; Additionally costs from transportation by other NGOs (each 110,000 p.a.) See cost detail sheet of the model Unit Years Years $ % $ $ Coefficient 8 4,75 $95.110.945 10% $592.000 $19.355.526 Input Parameters Parameter General Duration of intervention per child enrolled Costs Total WFP School Feeding Project 10264.0 Duration Total WFP Project Costs Thereof Once-off costs Additional Once-off costs not included in total WFP Project Costs 2008 Project Expenditures Once-off costs 2008 as % of total annual costs % 10% Markup for Gov't costs (annual costs that accrue but are not part of WFP project costs mentioned above) Markup for Community costs (annual costs that accrue but are not part of WFP project costs mentioned above) Markup for Other costs (annual costs that accrue but are not part of WFP project costs mentioned above) % 13% $ $1.513.000 % 1% Additional annual costs due to School Feeding Enrolment impact (New Schools and Teachers) $ $2.476.789 57 Work in Progress Assumptions and sources for model parameters - Kenya Costing and beneficiary data Kenya Kenya Parameter Modality Split Mid-morning Snack as % of SF Costs In-school meal costs as % of SF costs Source Unit Coefficient See cost detail sheet of model; calculated using commodity baskets See cost detail sheet of model; calculated using commodity baskets See input&output sheet of model; calculated See input&output sheet of model; calculated % % 2% 98% Mid-morning Snack - cost per beneficiary In-school meal - cost per beneficiary $ $ $6 $22 Mid-morning Snack - cost for avg child In-school meal - cost for avg child Calculation using cost p. beneficiary Calculation using cost p. beneficiary $ $ $0 $22 Beneficiaries Overall Project Beneficiaries Total WFP school children SPR 2008 Kenya, Project ID 10264.0 Kenya Country Office information: total school feeding beneficiaries (1211824) deducted by 52500 as these are pre-primary school Kenya Country Office information: 70000 children in urban slums receive mid-morning snack in addition to meal Kenya Country Office information: total school feeding beneficiaries (1211824) deducted by 52500 as these are pre-primary school # # 1.314.159 1.158.924 Thereof receiving mid-morning snack # 70.000 Thereof receiving in-school meals # 1.158.924 58 Work in Progress Assumptions and sources for model parameters - Kenya Impact and immediate outcome parameters Kenya Kenya Source Unit Coefficient Impact & Value Creation Parameters Parameter General Impact on HIV / AIDS Severity factor WHO Disability Weights HIV/AIDS (a) HIV: 0.135 (range: 0.123 - 0.136) (b) AIDS (no treatment): 0.505 (c) AIDS with treatment (ART): 0.167 (range: 0.145 0.469) Unesco Global Monitoring Report 2006 WHO # 0,135 HIV prevalence rate HIV mortality rate % % 6,70% 13,00% Immediate Outcome Parameters Value Transfer Full value transfer from modality Transfer Value for child beneficiary receiving MMS Calculation using food baskets and local market prices as stated by Kenya CO (compare cost detail sheet of model) Calculation using food baskets and local market prices as stated by Kenya CO (compare cost detail sheet of model) Calculated using beneficiary numbers and transfer value per beneficiary Calculated using beneficiary numbers and transfer value per beneficiary $ $5 Transfer Value for child beneficiary receiving ISM $ $14 Value transfer for average child from modality Transfer Value for average child receiving MMS Transfer Value for average child receiving ISM $ $ $0 $14 59 Work in Progress Assumptions and sources for model parameters - Kenya Impact parameters – education1 and value transfer Impact Parameters Parameter Impact of Education % increase in wages due to one additional year of primary school Median of various: Psacharopoulos and Patrinos "Returns to Investment in Education"; Onphanhdala and Suruga "Education and Earnings in Lao PDR: Further Results; Leveling the Playing Field; Miguel and Kremer "Worms: Identifying impact on education" - Kenya (Econometrica study) Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Damien de Walque, The World Bank, Development Research Group, "How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment' Evidence from Rural Uganda" Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Growth Theory through the lens of development economics, Massachusetts Institute of Technology Department of Economics Working Paper Series, December 2004 Various (see detail sheet) Top-down assumption Source: Leveling the Playing Field, Chapter 6 Source Unit Coefficient Kenya Kenya % 5,00% 1% increase in average years of schooling directly increases life expectancy by 0.0553% Decrease of HIV infections per additional school year % 5,53% % 6,70% Impact of Income 100% increase in per capita income increases life expectancy by 7.3954% Impact of Value Transfer Investment rate % 7,40% % % # % 14,50% 54,00% 10 11% 60 Avg. ROI p.a. Lifetime of investment Impact of Cognition Increase in wage per SD increase in test scores 1. Enrolment data, attendance data and dropout data used can be seen directly from the sensitivity analysis sheet; they have been derived using standardized project reports, estimations from the country offices and ministries of education data; for more detailed information see model, assumptions sheet Work in Progress Assumptions and sources for model parameters - Kenya Impact parameters – education1 and value transfer Impact Parameters Parameter Impact of Education % increase in wages due to one additional year of primary school Median of various: Psacharopoulos and Patrinos "Returns to Investment in Education"; Onphanhdala and Suruga "Education and Earnings in Lao PDR: Further Results; Leveling the Playing Field; Miguel and Kremer "Worms: Identifying impact on education" - Kenya (Econometrica study) Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Damien de Walque, The World Bank, Development Research Group, "How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment' Evidence from Rural Uganda" Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Growth Theory through the lens of development economics, Massachusetts Institute of Technology Department of Economics Working Paper Series, December 2004 Various (see detail sheet) Top-down assumption Source: Leveling the Playing Field, Chapter 6 Source Unit Coefficient Kenya Kenya % 5,00% 1% increase in average years of schooling directly increases life expectancy by 0.0553% Decrease of HIV infections per additional school year % 5,53% % 6,70% Impact of Income 100% increase in per capita income increases life expectancy by 7.3954% Impact of Value Transfer Investment rate % 7,40% % % # % 14,50% 54,00% 10 11% 61 Avg. ROI p.a. Lifetime of investment Impact of Cognition Increase in wage per SD increase in test scores 1. Enrolment data, attendance data and dropout data used can be seen directly from the sensitivity analysis sheet; they have been derived using standardized project reports, estimations from the country offices and ministries of education data; for more detailed information see model, assumptions sheet Work in Progress Assumptions and sources for model parameters - Ghana Modeling and macroeconomic parameters Assumption Parameters Parameter Modeling Parameters Discount Rate Source Approx. World Bank Social Discount Rate; see paper The Social Discount Rate: Estimates for Nine Latin American Countries by Humberto Lopez Unit Coefficient % 6,5% Macroeconomic Parameters GDP 2007 GDP per Capita Ghana Ghana GDP growth rate 2008 2009 GDP growth rate Projected growth rate 2010-2020 Projected growth rate 2020-2030 Projected growth rate >2030 Life expectancy Avg. start working Life Ghana Avg. End working Life Ghana Life expectancy at birth Ghana Ghana secondary schooling until 17 years of age (EFA 2009) AARP international World Bank; Country Profile Ghana # # # 18 60 57 UN Country Data 2007 IMF world economic outlook database IMF world economic outlook database OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 $ % % % % % $739 8% -14% 3,50% 3,00% 2,00% 62 Work in Progress Assumptions and sources for model parameters – Ghana Costing data Input Parameters Parameter General Duration of intervention per child enrolled Years 5 to 11 (6 years of primary school in Ghana) Years 8 Source Unit Coefficient Costs Total School Feeding Project Duration Total Project Costs Thereof Once-off costs Additional Once-off costs not included in total WFP Project Costs 2008 Project Expenditures Once-off costs 2008 as % of total annual costs Markup for Gov't costs Markup for Community costs (annual costs that accrue but are not part of WFP project costs mentioned above) Markup for Other costs (annual costs that accrue but are not part of WFP project costs mentioned above) Additional annual costs due to School Feeding Enrolment impact (New Schools and Teachers) GSFP started in late 2005 (WFP started in 2006) GSFP/ WFP Ghana Country Office GSFP/ WFP Ghana Country Office GSFP/ WFP Ghana Country Office $ GSFP/ WFP Ghana Country Office GSFP/ WFP Ghana Country Office GES costs included in programme costs Community costs survey carried out at GSFP/GES/WFP schools 2009 GSFP/ WFP Ghana Country Office % See cost detail sheet of the model $ $5,137,982 0% $ % % $ 0 $25,702,677 0.5% 0% $2,893,030 Years $ % 3 $44,793,357 2% 63 Work in Progress Assumptions and sources for model parameters - Ghana Costing and beneficiary data Parameter Modality Split Take home rations as % of SF Costs In-school meal costs as % of SF costs Source Unit Coefficient WFP Ghana country office GSFP/ WFP Ghana country office % % 7% 93% Take home rations - cost per recipient In-school meal - cost per beneficiary See input&output sheet of model; calculated See input&output sheet of model; calculated $ $ $73 $56 Take home rations - cost for avg child In-school meal - cost for avg child Calculation using cost p. beneficiary Calculation using cost p. beneficiary $ $ $1 $40 Beneficiaries Overall Project Beneficiaries GSFP/ WFP Ghana country office – Includes preprimary school meals recipients and secondary school take home rations recipients Reduction in beneficiaries to school children due to the fact the take home rations aims to feed a family of 5 WFP Ghana country office – includes 23,968 in secondary school GSFP/ WFP Ghana country office – includes preprimary recipients # 685,185 Total school children # # # 606,898 10,786 596,501 Thereof receiving take home rations Thereof receiving in-school meals 64 Work in Progress Assumptions and sources for model parameters – Ghana Impact and immediate outcome parameters Impact & Value Creation Parameters Parameter General Impact on HIV / AIDS Severity factor WHO Disability Weights HIV/AIDS (a) HIV: 0.135 (range: 0.123 - 0.136) (b) AIDS (no treatment): 0.505 (c) AIDS with treatment (ART): 0.167 (range: 0.145 0.469) Ghana National Aids Control Porgramme WHO (2002) Source Unit Coefficient # 0.135 HIV prevalence rate HIV mortality rate % % 1.7% 15% Immediate Outcome Parameters Value Transfer Full value transfer from modality Transfer Value for child beneficiary receiving THR Transfer Value for child beneficiary receiving ISM Calculation using food baskets and local market prices as stated by Ghana CO Calculation using 0.40GHC provided to caterers per child per day, assuming 80% if cash transfer is used to procure commodities (GSFP AOP 2009) Calculated using beneficiary numbers and transfer value per beneficiary Calculated using beneficiary numbers and transfer value per beneficiary $ $ $30 $41 Value transfer for average child from modality Transfer Value for average child receiving THR Transfer Value for average child receiving ISM $ $ $1 $40 65 Work in Progress Assumptions and sources for model parameters - Ghana Impact parameters – education1 and value transfer Impact Parameters Parameter Impact of Education % increase in wages due to one additional year of primary school Median of various: Psacharopoulos and Patrinos "Returns to Investment in Education"; Onphanhdala and Suruga "Education and Earnings in Lao PDR: Further Results; Leveling the Playing Field; Miguel and Kremer "Worms: Identifying impact on education" - Kenya (Econometrica study) Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Damien de Walque, The World Bank, Development Research Group, "How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment' Evidence from Rural Uganda" Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Growth Theory through the lens of development economics, Massachusetts Institute of Technology Department of Economics Working Paper Series, December 2004 Various (see detail sheet) Top-down assumption Source: Leveling the Playing Field, Chapter 6 Source Unit Coefficient % 5% 1% increase in average years of schooling directly increases life expectancy by 0.0553% Decrease of HIV infections per additional school year % 5.5% % 6.7% Impact of Income 100% increase in per capita income increases life expectancy by 7.3954% Impact of Value Transfer Investment rate % 7.4% % % # % 14.5% 54% 10 11% 66 Avg. ROI p.a. Lifetime of investment Impact of Cognition Increase in wage per SD increase in test scores 1. Enrolment data, attendance data and dropout data used can be seen directly from the sensitivity analysis sheet; they have been derived using standardized project reports, estimations from the country offices and ministries of education data; for more detailed information see model, assumptions sheet Assumptions and sources for model parameters - Bangladesh Modelling and macroeconomic parameters Assumption Parameters Parameter Modeling Parameters Discount Rate Source Approx. World Bank Social Discount Rate; see paper The Social Discount Rate: Estimates for Nine Latin American Countries by Humberto Lopez Unit Coefficient % 6,5% Macroeconomic Parameters GDP 2007 GDP per Capita Bangladesh Bangladesh GDP growth rate 2008 2009 GDP growth rate Projected growth rate 2010-2020 Projected growth rate 2020-2030 Projected growth rate >2030 Life expectancy Avg. start working Life Bangladesh Avg. End working Life Bangladesh Life expectancy at birth Bangladesh Bangladesh Bureau of Statistics (BBS) Bangladesh Bureau of Statistics (BBS) Human Development Report 2009 # # # 15 63 65.7 UN Country Data 2007 IMF world economic outlook database IMF world economic outlook database OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 OECD Long-run GDP growth framework and scenarios for the world economy 2009 $ % % % % % $428 6.19% 5.90% 3,50% 3,00% 2,00% 67 Assumptions and sources for model parameters - Bangladesh Costing data Input Parameters Parameter General Duration of intervention per child enrolled Years 6 to 10 (5 years of primary school in Bangladesh) WFP Bangladesh Country Office WFP Bangladesh Country Office WFP Bangladesh Country Office WFP Bangladesh Country Office $ WFP Bangladesh Country Office WFP Bangladesh Country Office WFP Bangladesh Country Office WFP Bangladesh Country Office $ WFP Bangladesh Country Office % See cost detail sheet of the model $ $3,710,019 0% 0 $ % % 0 $10,930,000 0% 1% Years 5 Source Unit Coefficient Costs Total School Feeding Project Duration Total Project Costs Thereof Once-off costs Additional Once-off costs not included in total WFP Project Costs 2008 Project Expenditures Once-off costs 2008 as % of total annual costs Markup for Gov't costs Markup for Community costs (annual costs that accrue but are not part of WFP project costs mentioned above) Markup for Other costs (annual costs that accrue but are not part of WFP project costs mentioned above) Additional annual costs due to School Feeding Enrolment impact (New Schools and Teachers) Years $ % 4 $53,209,147 0% 68 Assumptions and sources for model parameters - Bangladesh Costing and beneficiary data Parameter Modality Split In-school snack costs as % of SF costs In-school snack - cost per beneficiary Source Unit Coefficient WFP Bangladesh country office See input&output sheet of model; calculated % $ 100% $31 Beneficiaries Overall Project Beneficiaries Total school children Thereof receiving in-school snack WFP Bangladesh country office WFP Bangladesh country office # WFP Bangladesh country office – includes 23,968 in secondary school # 525,000 525,000 # 5,298,129 69 Assumptions and sources for model parameters - Bangladesh Impact and immediate outcome parameters Impact & Value Creation Parameters Parameter General Impact on HIV / AIDS Severity factor WHO Disability Weights HIV/AIDS (a) HIV: 0.135 (range: 0.123 - 0.136) (b) AIDS (no treatment): 0.505 (c) AIDS with treatment (ART): 0.167 (range: 0.145 0.469) WHO (2002) WHO (2002) Source Unit Coefficient # 0.135 HIV prevalence rate HIV mortality rate % % 10% 1% Immediate Outcome Parameters Value Transfer Full value transfer from modality Transfer Value for child beneficiary receiving in-school snack Estimated cash saving for the family based on information collected by WFP country office (3 takas per child per day) $ $10 70 Assumptions and sources for model parameters - Bangladesh Impact parameters – education1 and value transfer Impact Parameters Parameter Impact of Education % increase in wages due to one additional year of primary school Median of various: Psacharopoulos and Patrinos "Returns to Investment in Education"; Onphanhdala and Suruga "Education and Earnings in Lao PDR: Further Results; Leveling the Playing Field; Miguel and Kremer "Worms: Identifying impact on education" - Kenya (Econometrica study) Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Damien de Walque, The World Bank, Development Research Group, "How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment' Evidence from Rural Uganda" Simon Wigley and Arzu Akkoyunlu-Wigley (2006), Human Capabilities versus human capital: Guaging the value of education in developing countries Growth Theory through the lens of development economics, Massachusetts Institute of Technology Department of Economics Working Paper Series, December 2004 Various (see detail sheet) Top-down assumption Source: Leveling the Playing Field, Chapter 6 Source Unit Coefficient % 5% 1% increase in average years of schooling directly increases life expectancy by 0.0553% Decrease of HIV infections per additional school year % 5.5% % 6.7% Impact of Income 100% increase in per capita income increases life expectancy by 7.3954% Impact of Value Transfer Investment rate % 7.4% % % # % 14.5% 54% 10 11% Avg. ROI p.a. Lifetime of investment Impact of Cognition Increase in wage per SD increase in test scores 1. Enrolment data, attendance data and dropout data used derived from the study performed by Friedman School of Nutrition Science and Policy, Tufts University (2004) ; for more detailed information see model, assumptions sheet 71 Work in Progress REFERENCES Adelman, S., H. Alderman, D. O. Gilligan and J. Konde-Lule (2008a) The Impact of Alternative Food for Education Programs on Child Nutrition in Northern Uganda. Washington DC: International Food Policy Research Institute. Adelman, S., D. O. Gilligan and K. Lehrer (2008b) How Effective Are Food For Education Programs': A Critical Assessment of the Evidence From Developing Countries. Washington DC: International Food Policy Research Institute, 9. Ahmed, A. U. 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A Study of the Outcomes of Take-home Ration Food Rations for Orphans and Vulnerable Children in Malawi. Research Report. Nairobi: UNICEF ESARO. Gelli, A. (2006). Food For Education Works: A Review of WFP programme monitoring and evaluation 2002-2006. Rome, World Food Programme. Gelli, A. and C. Burbano. (2009). School Feeding, Seasonality and Schooling Outcomes: A case study from Malawi, forthcoming. Gelli, A., N. Al Shaiba and F. Espejo (forthcoming). The Costs and Cost-efficientcy of Providing Food Through Schools in Areas of High School Insecurity. Food and Nutrition Bulletin. Gelli, A., K. Izushi, Z. Islam, M. Mattthew and F. Espejo (2006). The Costs and Outcomes of Fortified Biscuit Interventions on Prmary School-Age Children. Rome, World FoodProgramme. Gelli, A., U. Meir and F. Espejo. (2006). Supporting Girls’ Education. Rome, World Food Programme. Gelli, A., U. Meir and F. Espejo (2007). 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