"Estimating the cost and financing gap for meeting CAADP growth and MDG1 targets_2010
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"Estimating the cost and financing gap for meeting CAADP growth and MDG1 targets", presentation by Sam Benin at the USAID, IFPRI Financial Gap Analysis Workshop held at the World Bank, January 7, ...

"Estimating the cost and financing gap for meeting CAADP growth and MDG1 targets", presentation by Sam Benin at the USAID, IFPRI Financial Gap Analysis Workshop held at the World Bank, January 7, 2010.

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    "Estimating the cost and financing gap for meeting CAADP growth and MDG1 targets_2010 "Estimating the cost and financing gap for meeting CAADP growth and MDG1 targets_2010 Presentation Transcript

    • IFPRI Estimating the cost and financing gap for meeting CAADP growth and MDG1 targets Sam Benin International Food Policy Research Institute USAID/World Bank Workshop on “Agricultural investment priorities and financing gaps for achieving growth and poverty reduction targets: Review of evidence and methodology” January 7, 2010INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
    • Introduction  Existence of several cost estimates for attaining the MDGs has raised the need the most appropriate methodology to obtain consistent and reliable projections  However, the issue is not merely technical. There is need to also consider the political motivations  As relatively “large” or “small” estimates will generate different reactions in donor and developing countries, developing accurate methodologies appears critical for both parties  Four main approaches have been used to cost MDGsIFPRI
    • Approaches and limits to MDGs costingSource: NallariIFPRI Heuty, 2004 and
    • Estimated required resources to meet MDG1 Methodology Studies Estimates Remarks Intervention-based Anti-poverty $24 billion program Aggregate unit costs Rosegrant et $238 billion from al. (2005) 1997-2025 UN Reports $ per capita in (2005) 2006: Ghana=80; Tanzania=96; Uganda=92 ICOR Devarajan et $54-62 billion al. (2002) per year Input-outcome Zedillo $20 billion per elasticity report yearIFPRI
    • Sources of discrepancies in MDGs costs estimates  Interpretation of targets and baselines  Countries covered  Underlying assumptions (economic growth, population growth, resource mobilization and allocation, institutional reform, etc.)  Data sources  Unit costs and elasticity parameters  Alternative scenarios …IFPRI
    • Estimating agricultural spending required to achieve CAADP growth and the MDG1  Rationale » Previous studies focused on costing the MDGs (whether at the global, regional, or country level) have ignored agricultural financial resources  Elasticity approach » From the policy perspective of using public spending for stimulating growth and reducing poverty, methods based on expenditure-growth, expenditure- poverty, and growth-poverty elasticities are conceptually sound » Elasticity measures of the relative change in the outcome with respect to change in expenditures (or inputs), taking into account any conditioning and confounding factors, including lag between expenditures and realization of the outcomeIFPRI
    • Issues to consider  Relative effect of public investment in the agricultural and non-agricultural sectors  Public investment is not be growth-neutral: different types of public investment (across and within sectors) affect growth and poverty differently via different pathways and at different levels  Relative productivity or efficiency of public versus private investment in overall economic growth  Plausible crowding-out effect of public investment on private investment  Interaction effects among the different types of investment  Initial conditions of development and pattern of growthIFPRI
    • Estimation of required growth and spending  Poverty-growth elasticity » decompose “elasticity of poverty with respect to growth” into effects of agricultural and non-agricultural growth and an interaction term that captures a linkage or multiplier effect  Growth-spending elasticity » decompose “elasticity of agricultural (and non- agricultural) growth with respect to public spending” into the effects of growth in different types agricultural and non-agricultural spending and interaction terms that captures complementarity (substitution) effects among different types of spending  Initial conditions of development and pattern of growth » Resource endowments, climate, institutions, etc.IFPRI
    • Review of the evidence Elasticities and growth ratesIFPRI
    • Elasticity of poverty with respect to agricultural and non-agricultural growth  Import table from Fan et alIFPRI
    • Elasticity of agricultural productivity with respect to public agricultural spending Indicator of public agricultural investment Dependent variable Elasticity Source/Country Government investment: Agriculture Ag Output 0.085 Fan et al., 2008a (44 Developing countries, including 17 Research Ag Output 0.038 from Africa) Non-research Ag Output –0.070 Research (R&D) Ag GDP per hectare Thirtle et al. 2003 (48 developing countries, including 22 All countries 0.442 from Africa) SSA 0.363 Asia 0.344 Latin America 0.197 Research (R&D) Ag GDP per capita All countries 0.304 SSA 0.264 Asia 0.231 Latin America 0.093 Research and extension Ag output per capita 0.189 Fan et al., 2004 (Uganda) Agriculture Ag output per capita 0.153 Benin et al., 2008b (Ghana) Research Ag GDP per capita 0.085 Fan et al., 2002 (China) Irrigation Ag GDP per capita 0.101 Research Ag output per worker 0.464 Fan et al., 2008c (Thailand) Research TFP 0.049–0.066 Rosegrant and Evenson, 1995 (India) Research TFP 0.255 Fan, Hazell and Thorat, 2000 (India) Irrigation TFP 0.036 Soil and water conservation TFP 0.002n Irrigation TFP 0.003 Teurel and Kuroda, 2005 (Philippines) Non-government investment: Official development assistance (ODA) Ag GDP 0.03 Schuh and Norton, 1991 (98 developing countries) Other indicators: Agricultural extension (staff per 1000 TFP 0.041–0.063 Rosegrant and Evenson, 1995 (India) farms) Domestic research (scientists per ha of TFP 2.69 Johnson and Evenson, 2000 (90 Least developed arable land) countries) Foreign research (spending per ha of TFP 10.27 arable land) IFPRI
    • Elasticity of agricultural productivity with respect to public non-agricultural spending Indicator of public non-agricultural investment Dependent variable Value of coefficient Source/Country Education Literacy rate Ag Output 0.362n Fan et al., 2008a (44 Developing countries, including 17 from Africa) Rural literacy rate Ag output per capita 0.332 Fan et al., 2004 (Uganda) Share of people completed at least primary Ag output per capita –0.11 Benin et al., 2008b (Ghana) education Spending on education Ag GDP per capita 0.197 Fan et al., 2002 (China) Expenditure on rural education TFP 0.047 Fan, Hazell and Thorat, 2000 (India) Spending on education Ag output per worker 0.578 Fan et al., 2008c (Thailand) Health Share of people sick last month Ag output per capita –0.465 Fan et al., 2004 (Uganda) Share of people living more than 15 minutes of Ag output per capita –0.81 Benin et al., 2008b (Ghana) a health center Spending on public health and welfare TFP 0.012n Fan, Hazell and Thorat, 2000 (India) Roads Density (km/1000km2) Ag Output –0.092n Fan et al., 2008a (44 Developing countries, including 17 from Africa) Distance to feeder road Ag output per capita –0.139 Fan et al., 2004 (Uganda) Feeder road density Ag output per capita 0.13 Benin et al., 2008b (Ghana) Spending on rural roads Ag GDP per capita 0.037 Fan et al., 2002 (China) Road density TFP 0.042 Zhang and Fan, 2004 (India) Investment on rural roads TFP 0.057 Fan, Hazell and Thorat, 2000 (India) Spending on rural roads Ag output per worker 0.119 Fan et al., 2008c (Thailand) Investment on roads TFP 0.015 Teurel and Kuroda, 2005 (Philippines) Other public investments Spending on rural power TFP 0.004n Fan, Hazell and Thorat, 2000 (India) Spending on rural power Ag GDP per capita 0.009n Fan et al., 2002 (China) Spending on rural power Ag output per worker 0.198 Fan et al., 2008c (Thailand) Investment on electrification TFP 0.002 Teurel and Kuroda, 2005 (Philippines) Spending on rural development TFP 0.022n Fan, Hazell and Thorat, 2000 (India) Crop area under public irrigation TFP 0.036 Fan, Hazell and Thorat, 2000 (India) Spending on rural telecommunications Ag GDP per capita 0.021 Fan et al., 2002 (China) IFPRI
    • Effect of public spending on factors of agricultural production and input use Dependent variable Value of Source/Country coefficientIndicator of public agriculturalinvestment Investment on irrigation Agricultural labor –0.233 Teurel and Kuroda, 2005 (Philippines) Investment on irrigation Intermediate inputs –0.501 Investment on irrigation Agricultural capital 0.650Government expenditures on Household total agricultural 0.148 Benin et al., 2008b (Ghana)agriculture expenditures per capitaIndicator of public non-agriculturalinvestment Share of people completed at Household total agricultural 0.459 Benin et al., 2008b (Ghana) least primary education expenditures per capita Share of people living more than Household total agricultural –0.359 15 minutes of a health center expenditures per capita Feeder road density Household total agricultural –0.045n expenditures per capita Investment on roads Agricultural labor –1.189 Teurel and Kuroda, 2005 (Philippines) Investment on roads Intermediate inputs –1.052n Investment on roads Agricultural capital 1.806 Investment on electrification Agricultural labor –0.099 Teurel and Kuroda, 2005 (Philippines) Investment on electrification Intermediate inputs –0.216 Investment on electrification Agricultural capital 0.499 IFPRI
    • Crowding-in and crowding-out effects of public on private investmentsIndicator of public investment Dependent variable (Indicator of Value of coefficient Source/Country private investment or market)Public investment Private investment 0.027–0.067n Ashipala and Haimbodi, 2003 (South Africa)Public investment Private investment 0.312–1.108n Ashipala and Haimbodi, 2003 (Namibia)Public investment Private investment –0.021 to 0.022n Ashipala and Haimbodi, 2003 (Botswana)Expenditures on public applied Expenditures on private applied 0.25–0.28 Malla and Gray, 2005 (USA)research researchExpenditures on public basic Expenditures on private applied 0.20–0.22research researchSubsidy on research Expenditures on private research 0.10 Görg and Strobl, 2006 (Ireland)Stocks of public R&D Stocks of private R&D 0.035–1.918 Sadraoui and Ben Zina, 2006 (23 countries including 3 from Africa)Share of public investment in GDP Share of private investment in GDP –0.082 Ramirez and Nazmi, 2003 (9 Latin American countries)Ratio of public to private investment Overall TFP –0.23 del Mar Salinas-Jimemez, 2004 (Spain)Ratio of public to private investment Ag TFP –0.001nExpenditures on public irrigation Crop area under private irrigation 0.08 Fan, Hazell and Thorat, 2000 (India) (%)Crop area under public irrigation (%) Crop area under private irrigation 0.92 (%)Spending on research Rural wages 0.033 Fan, Hazell and Thorat, 2000 (India)Public wages Private wages 0.212–0.357 Afonso and Gomes, 2008 (16 OECD countries) IFPRI
    • Interaction effects among different types of public spending Explanatory variable Dependent variable Value of coefficient Source/Country Interactions Fertilizer and stone terrace Household agricultural output –0.804; –0.076n Pender and Gebremedhin, 2006 per acre (Ethiopia). Estimates are for two different methods. Fertilizer and soil bund Household agricultural output 0.369n; –0.455 per acre Fertilizer and irrigation Household agricultural output 0.663n; 0.131n per acre Neighborhood effects Tax rate of neighbors Tax rate 0.158–0.314 Hauptmeier et al., 2009 (Germany) Public spending of neighbors Public spending 0.178–0.507 Public social spending Public education spending 0.265–0.410 Busemeyer, 2007 (21 OECD Decentralization Public education spending 0.134–0.271 countries) Decentralization Public health spending 0.015n Decentralization Public social spending –0.042 to –0.099 Decentralization Public total spending 0.046 Public total spending Ratio of spending on other –0.82 to –1.51 Ramajo et al., 2007 (Spain) services to spending on economic servicesIFPRI
    • Public agricultural spending growth rates Total Agriculture Country Expenditure Expenditure Benin 7.66 12.98 Botswana 2.41 -2.48 Burkina Faso 21.42 11.05 Burundi 16.84 19.80 Cameroon 3.83 8.21 Central African Republic 15.69 -4.46 Chad -0.18 3.70 Congo, Dem. Rep. 26.95 30.21 Congo, Rep. -21.78 -1.09 Cote dIvoire 3.09 4.26 Djibouti 7.17 51.90 Egypt, Arab Rep. -0.19 3.84 Ethiopia 10.97 38.62 Ghana 21.47 35.32 Guinea-Bissau 18.03 5.57 Kenya 16.60 13.91 Lesotho 10.16 -2.37 Madagascar 19.10 21.86 Malawi 12.13 36.44 Mali 11.09 6.76 Mauritania 0.20 -4.42 Morocco 8.52 -7.66 Mozambique 9.26 -20.12 Namibia 8.94 -1.64 Niger -1.36 -13.96 Nigeria -0.10 13.55 Sao Tome and Principe 28.09 56.47 Senegal 11.07 23.33 Seychelles -2.36 5.80 Sierra Leone 0.52 -1.41 Swaziland 12.25 20.99 Tanzania 15.20 17.72 Source: Nin Pratt Togo Yu, 2009 and 5.48 14.48 Tunisia 5.30 3.85IFPRI
    • Growth rates of factors and productivity, and information on other parameters Fertilizer Tractors Animal stock Worker Output Output TFP per hectare per per hectare per per per hectare hectare hectare worker Benin 6.37 -3.58 -2.73 -3.60 0.07 3.81 1.67 Burkina Faso -1.26 -1.07 0.52 -1.46 1.25 2.76 1.32 Cameroon 1.29 0.00 2.19 0.69 2.62 1.91 1.84 Chad 10.71 -0.31 2.52 0.89 2.71 1.80 2.48 Congo 4.14 -0.54 1.59 0.23 1.68 1.45 1.39 Cote d’Ivoire 4.75 -0.25 1.01 -0.40 2.09 2.50 1.60 Ethiopia 1.70 -1.79 1.63 0.68 2.49 1.79 2.55 Gabon -2.76 -0.73 0.00 -1.53 0.75 2.31 2.31 Ghana 5.27 -3.96 -1.48 -1.31 1.57 2.92 1.79 Guinea -3.05 -0.27 2.41 -0.72 0.87 1.60 0.42 Guinea-Bissau 10.36 -2.73 -1.17 -0.80 -0.13 0.67 0.45 Kenya 0.29 0.77 -0.82 1.18 1.30 0.11 1.05 Malawi 7.34 -2.63 -1.20 -1.48 3.23 4.78 3.35 Mali 3.97 -0.44 2.04 0.52 2.25 1.72 2.85 Mauritania -2.55 1.01 3.87 1.60 2.02 0.41 1.44 Mauritius -2.12 0.00 2.75 -1.75 0.97 2.76 0.93 Mozambique 4.63 -1.85 -1.01 0.04 2.79 2.75 3.32 Nigeria -5.45 0.85 0.82 -0.06 2.02 2.08 2.12 Sudan 0.19 -0.29 1.39 -0.14 1.64 1.78 3.19 Tanzania -14.94 0.38 1.69 0.66 0.74 0.09 2.79 Togo 2.97 -2.27 1.33 0.55 0.96 0.41 0.59 Zambia 1.25 -0.03 0.46 0.77 1.23 0.46 0.03Source: Nin Pratt and Yu, 2009 IFPRI
    • Application  Successful application depends on the extent to which information on the different parameters is available  It is unlikely, actually unrealistic, to obtain information on all the parameters for every country in Africa  Parameter estimates from similar countries or the regional level would have to be used in the cost calculations for countries where such information is lacking  How the value of the parameters change over time (or do not change) would have to be decided upon  Obtaining a range of estimates would be more prudent than point estimates » the lower end of the range would correspond to an optimistic spending scenario characterized by (e.g. high spending efficiency, greater crowding-in effect on private investments, and positive interaction effect with other types of spending) » vice versa for the upper end of the rangeIFPRI
    • Africa-wide estimates  MichaelIFPRI
    • Country-level estimates Use evidence from different countries to assess the aggregate public agricultural expenditures (PAE) required to reach the CAADP and MDG1 growth targets in the next 10 years (2005-15) for selected countries Elasticity of agricultural productivity with respect to public agricultural spending: 0.15 and as low- and high-end values or a less and more optimistic public spending efficiency scenario, respectively. Scenarios: » Baseline: public agricultural and non-agricultural spending in 2004 constant prices continue to grow according to their respective recent (1999-2005) trends. Other factors (e.g. interactions between different types of spending, crowding effects of public spending on private investments, non-spending factors affecting agricultural growth) remain unchanged. » Accelerated public agricultural and non-agricultural expenditure growth speeds up too to match with the higher growth rate required in the agricultural and non-agricultural GDP. For the latter, we use low-end and high-end elasticity values of 0.15 and 0.25, respectively. Other assumptions » Interaction effects remain unchanged as in the baseline scenario and are already reflected in the estimated elasticities with respect agricultural and non-agricultural spending » Non-spending factors that affect agricultural growth (e.g. weather, policies, prices) are difficult to model and so are assumed to remain unchanged as in the baseline scenario. IFPRI
    • Annual average growth (%) in aggregate public agricultural expenditures required to achieve CAADP growth and MDG1 (2005-15) CAADP MDG1 baseline low high low highMalawi 13.8 34.8 24.1 37.2 24.1Rwanda -6.5 30.3 15.2 45.6 22.6Uganda 14.8 35.1 23.1 35.1 23.1Zambia 8.4 31.9 20.1 44.6 26.4 IFPRI