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IFPRI
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Estimating the cost and financing gap for
meeting CAADP growth and MDG1
targetsSam 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, 2010
IFPRI
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 MDGs
IFPRI
Approaches and limits to MDGs costing
Source: Nallari and Heuty, 2004
IFPRI
Estimated required resources to meet
MDG1
Methodology Studies Estimates Remarks
Intervention-based Anti-poverty
program
$24 billion
Aggregate unit costs Rosegrant et
al. (2005)
$238 billion from
1997-2025
UN Reports
(2005)
$ per capita in
2006: Ghana=80;
Tanzania=96;
Uganda=92
ICOR Devarajan et
al. (2002)
$54-62 billion
per year
Input-outcome
elasticity
Zedillo
report
$20 billion per
year
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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 outcome
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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 growth
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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.
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Review of the evidence
Elasticities and growth rates
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Elasticity of poverty with respect to agricultural
and non-agricultural growth
Import table from Fan et al
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Elasticity of agricultural productivity with respect to
public agricultural spendingIndicator 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
from Africa)Research Ag Output 0.038
Non-research Ag Output –0.070
Research (R&D) Ag GDP per hectare
All countries
SSA
Asia
Latin America
0.442
0.363
0.344
0.197
Thirtle et al. 2003 (48 developing countries, including 22
from Africa)
Research (R&D) Ag GDP per capita
All countries
SSA
Asia
Latin America
0.304
0.264
0.231
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
farms)
TFP 0.041–0.063 Rosegrant and Evenson, 1995 (India)
Domestic research (scientists per ha of
arable land)
TFP 2.69 Johnson and Evenson, 2000 (90 Least developed
countries)
Foreign research (spending per ha of
arable land)
TFP 10.27
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Elasticity of agricultural productivity with respect to
public non-agricultural spendingIndicator 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
education
Ag output per capita –0.11 Benin et al., 2008b (Ghana)
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
a health center
Ag output per capita –0.81 Benin et al., 2008b (Ghana)
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)
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Effect of public spending on factors of
agricultural production and input useDependent variable Value of
coefficient
Source/Country
Indicator of public agricultural
investment
Investment on irrigation Agricultural labor –0.233 Teurel and Kuroda, 2005 (Philippines)
Investment on irrigation Intermediate inputs –0.501
Investment on irrigation Agricultural capital 0.650
Government expenditures on
agriculture
Household total agricultural
expenditures per capita
0.148 Benin et al., 2008b (Ghana)
Indicator of public non-agricultural
investment
Share of people completed at
least primary education
Household total agricultural
expenditures per capita
0.459 Benin et al., 2008b (Ghana)
Share of people living more than
15 minutes of a health center
Household total agricultural
expenditures per capita
–0.359
Feeder road density Household total agricultural
expenditures per capita
–0.045n
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
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Crowding-in and crowding-out effects of
public on private investmentsIndicator of public investment Dependent variable (Indicator of
private investment or market)
Value of coefficient Source/Country
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
research
Expenditures on private applied
research
0.25–0.28 Malla and Gray, 2005 (USA)
Expenditures on public basic
research
Expenditures on private applied
research
0.20–0.22
Subsidy 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 –0.001n
Expenditures 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)
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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
per acre
–0.804; –0.076n Pender and Gebremedhin, 2006
(Ethiopia). Estimates are for two
different methods.Fertilizer and soil bund Household agricultural output
per acre
0.369n; –0.455
Fertilizer and irrigation Household agricultural output
per acre
0.663n; 0.131n
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
countries)Decentralization Public education spending 0.134–0.271
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
services to spending on
economic services
–0.82 to –1.51 Ramajo et al., 2007 (Spain)
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Public agricultural spending growth rates
Country
Total
Expenditure
Agriculture
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 d'Ivoire 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
Togo 5.48 14.48
Tunisia 5.30 3.85
Uganda 0.79 -4.95
Source: Nin Pratt and Yu, 2009
IFPRI
Growth rates of factors and productivity, and
information on other parameters
Fertilizer
per hectare
Tractors
per
hectare
Animal stock
per hectare
Worker
per
hectare
Output
per
hectare
Output
per
worker
TFP
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 0.03
Source: 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 range
IFPRI
Africa-wide estimates
Michael
IFPRI
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.
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Annual average growth (%) in aggregate public agricultural expenditures
required to achieve CAADP growth and MDG1 (2005-15)
CAADP MDG1
baseline low high low high
Malawi 13.8 34.8 24.1 37.2 24.1
Rwanda -6.5 30.3 15.2 45.6 22.6
Uganda 14.8 35.1 23.1 35.1 23.1
Zambia 8.4 31.9 20.1 44.6 26.4