Policies and Productivity Growth in African Agriculture

Keith Fuglie and Nicholas Rada*Economic Research Service

U.S. Department of AgricultureWashington, DC

*The views expressed in this presentation are the authors’ own and not necessarily those of the Economic Research Service.

Is agriculture in SSA taking off?

• Higher rates of agricultural GDP growth following structural adjustment– From 1.4% per year (1970-1984) to 2.9% per year (1985-2009)

• Possible reasons for higher agricultural growth– Macroeconomic & political stability (Binswanger-Mkhize & McCalla, 2009)– Improved agricultural terms of trade (Anderson and Masters, 2008)– Technology diffusion and greater productivity (Block, 1995; Nin-Pratt & Yu,

2008; Alene & Coulaby, 2009)• Aims of study:

– Has growth been primarily resource-led or productivity-led?– What are the policy drivers for agricultural growth? Especially, what is the

role of national and international agricultural research?

Framework for Analysis: Decomposing and Explaining Growth

Yield growth

Area growth

Input intensification

TotalFactor

Productivity(TFP) growth

Out

put

grow

th

Area growth

Productivity-led growth

Research & extensionHuman capitalInstitutions & incentivesInfrastructure

Resource-led growth

Prices & costsInput policiesExchange ratesInfrastructure

Analytical strategy

X1 X2

Y1

Y2

Output Y

Input X

Productivity-led growthTotal factor productivity = f(policy)

Resource-led growthY = f(X)

Explaining Total Factor Productivity (TFP) Growth

CGIAR technology dissemination

CGIAR research

National research

Enabling factors

CGIAR technology dissemination

National research

Enabling factors

Agricultural TFP growth

Agricultural Production Function Estimates

Coefficient(elasticity)

(all significant at 1% level)

Production inputs

Labor 0.248Assume constant returns to scale (elasticities sum to 1.00)

Production elasticity = input cost share under competitive market equilibrium

Land 0.315

Livestock Capital 0.357

Machinery Capital 0.024

Fertilizers 0.055

Resource quality variables

Irrigation (%) 68% Coefficient indicate % increase in yield over unfavorable rainfed cropland areaFavorable area (%) 125%

R 2 overall 0.700

R 2 between countries 0.706

R 2 within countries 0.700

Increase in output growth has been primarily resource-led with some rise in TFP growth

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

1961-1984 1985-2009

Ave

rage

Ann

ual G

row

th

TFP

Input/Cropland

Cropland

Agricultural TFP index for sub-Saharan Africa (1961=100)

Agricultural TFP index for sub-Saharan Africa (1961=100)

Agricultural TFP index for sub-Saharan Africa (1961=100)

Can R&D investments explain TFP growth?

• International (CGIAR) agricultural research– Invests about $200 million in SSA (25% for crops)– 1200 international scientists (40% for SSA)– Improved crop technology adopted on about 20% of

cropland

• National agricultural research in SSA– $US 350 million ($PPP 960 million) – 9000 scientists (15% with PhD) in 2000– Low and declining level of research intensity

National & international agricultural research in sub-Saharan Africa

New technologies have impacted at least 25% of SSA cropland by 2001-05

Source: Compiled by author from case studies of technology adoption and impact. These technologies Originated primarily from CGIAR centers except NRM technologies, which are primarily farmer innovations.

Macro and price policies have become less discriminating against agriculture since 1985

Source: Anderson and Masters (2008).

Data coverage for policy variables

R&D, School, NRA, Roads (9+, Obs=273)

R&D, School (27, Obs=783)

R&D (31, Obs=899)

R&D, Roads (17+, Obs=611) R&D, NRA (17, Obs=467)

Some Findings from Regression Models

• Lack of data coverage constrains analysis– Road data too limiting to draw inference

• Technology policy– CGIAR technology adoption strongly correlated with TFP growth– NARS – absolute size seems to matter (small country problem)– CGIAR raises returns to NARS

• Other factors– Economic policy and incentives matter – Schooling leads to higher adoption but not higher yield given

adoption– War and HIV/AIDS (strongly) depress growth

Returns to Agricultural Research in SSA

Median for countries grouped by size IRR(%)

IRR without CGIAR

B/C ratio

(10% discount rate)

Large countries Output > $3 bil. 25.0 19.5 4.0

Medium-size countries Output, $1-$3 bil. 17.4 12.8 2.2

Small countries Output < $1 bil. 7.4 3.9 0.7

Returns to CGIAR in SSA 44.5  — 8.6

Conclusions

• Agricultural growth acceleration has been primarily resource-led

• Some evidence of productivity improvement, especially in West Africa

• Robust drivers of productivity and growth:– CGIAR research & technology dissemination– NARS R&D (except for small countries)– Improved economic policy

• Implications for national R&D policy– Underinvestment by medium and large countries– Evidence for economies of size in NARS (small country problem)– Important to be open to international sources of technology