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

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Approaches and limits to MDGs costing

Source: Nallari and Heuty, 2004

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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

…

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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

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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

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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

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Africa-wide estimates

Michael

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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