africa’s agricultural r&d funding rollercoaster: an analysis of the elements of funding...
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By Gert-Jan Stads. Presented at the ASTI-FARA conference Agricultural R&D: Investing in Africa's Future: Analyzing Trends, Challenges, and Opportunities - Accra, Ghana on December 5-7, 2011. http://www.asti.cgiar.org/2011confTRANSCRIPT
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
2012
2013