Africa’s Agricultural R&D Funding Rollercoaster

An Analysis of the Elements of Funding Volatility

5–7 December 2011, Accra, Ghana

Gert-Jan Stads

Background: Trends in Agricultural R&D Investment in Sub-Saharan Africa

Investments (and human capacity) in agricultural R&D increased by more than 20% during 2000–08.

Most of this growth was driven by just a handful of countries (mainly following boosts in salaries and rehabilitation of infrastructure).

In many other countries (particularly in francophone West Africa), investments have declined since 2000.

Source: Beintema and Stads 2011

Investment challenge: Underinvestment

NEPAD target: Allocation of at least 1 % of GDP to R&D In 2008, Africa spent $0.61 for every $100 of AgGDP on agricultural R&D. Despite an overall increase in recent years, Africa is widely underinvesting

in agricultural R&D.

Source: Beintema and Stads 2011

Trends in Agricultural R&D spending in the “Big Eight” since 2008

-30

-20

-10

0

10

20

30

Chan

ge 20

08-2

010

(%)

Investment challenge: Volatility

FASTEN YOUR SEAT BELT

Keep arms and legs inside vehicle at all times

Severe fluctuations in annual agricultural R&D investment levels, 1981–2008

0

10

20

30

40

0

2

4

6

8

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

Billi

on 2

005

CFA

fran

cs

Burkina Faso

Million 2005 PPP dollars

0

9

18

26

35

0

2

4

6

8

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

Billi

on 2

005

CFA

fran

cs

Niger

Million 2005 PPP dollars

0.0

0.8

1.5

2.3

3.1

3.9

0.0

0.2

0.4

0.6

0.8

1.0

1991 1993 1995 1997 1999 2001 2003 2005 2007

Billi

on 2

005

CFA

fran

cs

Gabon

Million 2005 PPP dollars

0

83

166

249

332

415

0.0

0.3

0.6

0.9

1.2

1.5

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

Billi

on 2

005

rand

South AfricaM

illion 2005 PPP dollars

Economic Theory on Volatility

Increased macroeconomic volatility has a negative impact on economic growth, or is at least closely associated with slower growth (Aghion et al. 2005; Fatás and Mihov 2006; Hnatkovska and Loayza 2004; Perry 2009).

Aid flows in developing countries are more volatile than government revenues, household consumption, or gross domestic product (GDP), and aid volatility tends to reinforce macroeconomic instability and slow down economic growth (Bulíř and Hamann 2003; Desai and Kharas 2010; Fielding and Mavrotas 2008).

No literature was found on R&D funding volatility in developing countries.

Why is Stable Agricultural R&D Funding Important?

Agricultural R&D investment is positively associated with high returns, but these returns take time—commonly decades—to develop.

Consequently, the inherent lag from the inception of research to the adoption of a new technology or the introduction of a new variety calls for sustained and stable R&D funding.

Severe fluctuations in annual agricultural R&D funding exacerbate uncertainty at the institute level and renders long-term R&D budget, staffing, and planning decisions more difficult.

Therefore, the continuity of research programs is imperiled in the short run, as is the release of new varieties and technologies in the long run.

Volatility coefficient of agricultural R&D spending

Growth in agricultural R&D spending (gs) was expressed as follows:

where s is agricultural R&D spending (in constant prices), and t represents the year.

A country’s volatility coefficient (V) of agricultural R&D expenditures was calculated by taking the standard deviation of growth in annual agricultural R&D spending:

𝑔𝑠 = lnቀ 𝑠𝑡𝑠𝑡−1ቁ s=1,…, N,

V = ට1𝑁σ ሺ𝑔𝑠 −𝜇ሻ2𝑁𝑠=1 , where 𝜇= 1𝑁σ 𝑔𝑠𝑁𝑠=1 .

Volatility in African agricultural R&D spending

0.21 (SSA 2001–2008)

0.12 (Asia–Pacific 1992–2002)

0.09(SSA agricultural output, 2001–2008)

0.14(Latin America 2004–2006)

>

Volatility Coefficients 2001–08Cross-Country Variation

Calculated from Beintema and Stads (2011)

Mau

ritan

ia

Gab

on

Tanz

ania

Burk

ina

Faso

Ethi

opia

Nam

ibia

Gam

bia,

The

Mal

i

Côte

d'Iv

oire

Sier

ra L

eone

Eritr

ea

Gui

nea

Suda

n

Togo

Nig

eria

Buru

ndi

Bots

wan

a

Beni

n

Sene

gal

Zam

bia

Uga

nda

Keny

a

Gha

na

Nig

er

Mau

ritius

Mad

agas

car

Sout

h Af

rica

Mal

awi

Cong

o, R

ep.0.0

0.1

0.2

0.3

0.4

0.5

Vol

atilit

y co

effici

ent

very high high moderate low

Volatility and Country Groupings

Agricultural R&D spending in low-income countries (0.23) is on average more volatile than spending in middle-income countries (0.16)

Average volatility was higher in West (0.23) and East (0.22) Africa than in Southern Africa (0.14)

Spending at NARS with less than 100 FTEs (0.24) is on average more volatile than spending at NARS with more than 100 FTEs (0.19)

AgR&D expenditures in countries spending less than 0.5% of AgGDP on AgR&D (0.23) are on average more volatile than those in countries spending more than 1.0% of AgGDP on AgR&D (0.16)

Volatility of agricultural R&D spending across cost categories

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Capital investments

Operating costs

Salaries

Volatility coefficient

Funding sources for agricultural R&D

National government funding: either through direct allocations or competitive funding schemes

Donors and development banks: high donor dependency in low-income countries worldwide

Production or export levies (mostly on export crops): e.g. cocoa in Ghana; tea in Tanzania and Kenya; sugarcane in Mauritius, etc.

Sale of goods and services: e.g. on-demand research for private companies

Zambia (ZARI)Uganda (NARO)

Togo (ITRA)Tanzania (DRD)

Sudan (ARC)South Africa (ARC)

Sierra Leone (SLARI)Senegal (ISRA, ITA)

Rwanda (ISAR)Niger (INRAN)Namibia (DRT)

Mozambique (IIAM, IIP)Mauritius (FARC, MSIRI)

Mauritania (CNERV, CNRADA)Mali (IER)

Madagascar (FOFIFA)Kenya (see footnote)

Guinea (IRAG)Gambia, The (NARI)

Eritrea (NARI)Côte d'Ivoire (CNRA)

Burundi (ISABU)Burkina Faso (INERA, IRSAT, CNSF)

Botswana (DAR)Benin (INRAB)

0 20 40 60 80 100

Government DonorsProducer organizationsOwn incomeOther

Share of total funding (%)

Source: Beintema and Stads (2011)

Drivers of Funding Volatility in African Agricultural R&D

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Total

Donors and development banks

Sale of goods and services

Government

Volatility coefficient

Indicates that in many cases shocks in one funding source are to some extent absorbed by reverse shocks in other funding sources

Donor dependency and funding volatility

Average and spread of donor funding as a % of total agricultural R&D funding, 2001–08

0

20

40

60

80

100

Shar

e of

don

or fu

ndin

gin

tota

l ann

ual f

undi

ng (

%)

Share of Volatilitydonor funding coefficient

<10% 0.19 >10% 0.28 >40% 0.31

Funding sources and cost categories for DRD (Tanzania) and INERA (Burkina Faso), 2001–08

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007 2008

Salaries Operational Capital

Milli

on 20

05 P

PP do

llars

DRD – cost categories

0

10

20

30

40

2001 2002 2003 2004 2005 2006 2007 2008

Government Donors, development banks, SROs

Producer organizations Sales of goods and services

Milli

on 20

05 P

PP do

llars

DRD – funding sources

0

10

20

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Salaries Operational Capital

Milli

on 20

05 P

PP do

llars

INERA – cost categories

0

10

20

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Government Donors, development banks, SROs Sales of goods and services

Milli

on 20

05 P

PP do

llars

INERA – funding sources

Concluding Remarks:Putting a Halt to Volatility

Agricultural R&D spending in SSA has been far from stable in recent years. Problem is more pronounced in donor-dependent low-income countries. Halting excessive volatility in yearly agricultural R&D investment levels

requires a long-term commitment from national governments, donors and development banks, as well as the private sector.

Stable and sustainable levels of government funding are key, not just to secure salaries (which are fundamentally important), but also to enable necessary nonsalary expenditures.

Donor and development bank funding needs to be better aligned with national priorities, and consistency and complementarities among donor programs need to be assured.

Mitigating the effects of any single donor’s abrupt change in aid disbursement is crucial. Need for greater funding diversification (e.g. through the sale of goods and services or private sector funding).

Will Africa’s bumpy rollercoaster ride end here?

Thank you

2011

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2013