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Africa’s Agricultural R&D Funding Rollercoaster: An Analysis of the Elements of Funding Volatility
This document analyzes the volatility of agricultural R&D funding in sub-Saharan Africa, highlighting a significant increase in funding from 2000 to 2008, driven mainly by a few countries, while many others saw declines. Despite some overall increases, funding remains below the NEPAD target and is characterized by severe fluctuation, particularly in low-income countries, which hinders long-term planning and stability in research programs. To mitigate this volatility, the document calls for long-term commitments from national governments, donors, and development banks, emphasizing the need for funding diversification.
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Africa’s Agricultural R&D Funding Rollercoaster: An Analysis of the Elements of Funding Volatility
- 1. Africa’s Agricultural R&D Funding Rollercoaster An Analysis of the Elements of Funding Volatility Gert-Jan Stads 5–7 December 2011, Accra, Ghana
- 2. Background: Trends inAgricultural R&D Investment in Sub-Saharan Africa Source: Beintema and Stads 2011 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.
- 3. Investment challenge: Underinvestment Source: Beintema and Stads 2011 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.
- 4. Trends in AgriculturalR&D spending in the “Big Eight” since 2008 30 Change 2008-2010 (%) 20 10 0 -10 -20 -30
- 5. Investment challenge: Volatility FASTEN YOUR SEAT BELT Keep arms and legs inside vehicle at all times
- 7. Severe fluctuations inannual agricultural R&D investment levels, 1981–2008 Burkina Faso Niger 8 40 8 35 Billion 2005 CFA francs Billion 2005 CFA francs Million 2005 PPP dollars Million 2005 PPP dollars 6 30 6 26 4 20 4 18 2 10 2 9 0 0 0 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 1.5 South Africa 415 1.0 Gabon 3.9 Billion 2005 CFA francs Million 2005 PPP dollars Million 2005 PPP dollars Billion 2005 rand 1.2 332 0.8 3.1 0.9 249 0.6 2.3 0.6 166 0.4 1.5 0.3 83 0.2 0.8 0.0 0 0.0 0.0 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 1991 1993 1995 1997 1999 2001 2003 2005 2007
- 8. Economic Theory onVolatility 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.
- 9. Why is StableAgricultural 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.
- 10. Volatility coefficient of agricultural R&D spending Growth in agricultural R&D spending (gs) was expressed as follows: = ln −1 s=1,…, N, 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: 1 2, 1 V= =1 − where = =1 .
- 11. Volatility in African agricultural R&D spending 0.12 (Asia–Pacific 1992–2002) 0.21 > (SSA 2001–2008) 0.14 (Latin America 2004–2006) 0.09 (SSA agricultural output, 2001–2008)
- 12. Volatility coefficient 0.0 0.1 0.2 0.3 0.4 0.5 Mauritania Gabon Tanzania Burkina Faso very high Ethiopia Namibia Gambia, The Mali Côte d'Ivoire high Calculated from Beintema and Stads (2011) Sierra Leone Eritrea Guinea Sudan Togo Nigeria Burundi Botswana Benin Senegal Zambia Uganda moderate Kenya Cross-Country Variation Ghana Niger Volatility Coefficients 2001–08 Mauritius Madagascar South Africa Malawi low Congo, Rep.
- 13. Volatility and CountryGroupings 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)
- 14. Volatility of agriculturalR&D spending across cost categories Salaries Operating costs Capital investments 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Volatility coefficient
- 15. Funding sources foragricultural 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
- 16. Benin (INRAB) Botswana (DAR) Burkina Faso (INERA, IRSAT, CNSF) Government Burundi (ISABU) Donors Côte d'Ivoire (CNRA) Producer organizations Eritrea (NARI) Own income Gambia, The (NARI) Guinea (IRAG) Other Kenya (see footnote) Madagascar (FOFIFA) Source: Beintema and Stads (2011) Mali (IER) Mauritania (CNERV, CNRADA) Mauritius (FARC, MSIRI) Mozambique (IIAM, IIP) Namibia (DRT) Niger (INRAN) Rwanda (ISAR) Senegal (ISRA, ITA) Sierra Leone (SLARI) South Africa (ARC) Sudan (ARC) Tanzania (DRD) Togo (ITRA) Uganda (NARO) Zambia (ZARI) 0 20 40 60 80 100 Share of total funding (%)
- 17. Drivers of FundingVolatility in African Agricultural R&D Government Sale of goods and services Donors and development banks Total Indicates that in many cases shocks in one 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 funding source are to Volatility coefficient some extent absorbed by reverse shocks in other funding sources
- 18. Donor dependency andfunding volatility Average and spreadShare of funding as a % of of donor Volatility 100 total agriculturaldonorfunding, 2001–08 R&D funding coefficient <10% 0.19 80 >10% 0.28 >40% 0.31 Share of donor funding in total annual funding (%) 60 40 20 0
- 19. Funding sources andcost categories for DRD (Tanzania) and INERA (Burkina Faso), 2001–08 40 40 DRD – cost categories DRD – funding sources Million 2005 PPP dollars Million 2005 PPP dollars 30 30 20 20 10 10 0 0 2001 2002 2003 2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 2006 2007 2008 Salaries Operational Capital Government Donors, development banks, SROs Producer organizations Sales of goods and services 30 30 INERA – cost categories INERA – funding sources Million 2005 PPP dollars Million 2005 PPP dollars 20 20 10 10 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Salaries Operational Capital Government Donors, development banks, SROs Sales of goods and services
- 20. 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).
- 21. Thank you Will Africa’s bumpy rollercoaster ride end here? 2013 2012 2011
Editor's Notes
- #6 Besides severe underinvestment, African AgR&D is also characterized by severe fluctuations in annual AgR&D investments. Before we start analyzing the elements that cause volatility in year-to-year AgR&D spending in Africa, I wanted to show you the following short clip first. This clip is representative of what many African agricultural R&D have gone through over the past 20 years. So, are you ready? Here we go….
- #8 Although this clip may look like an exaggeration, it is actually not so far off the truth when it comes to long-term AgR&D trends in Africa. Many African countries have had extremely volatile agricultural R&D funding levels over the past decades as these figures show. If Africa were a theme park full of country rollercoasters, true thrill seekers would ride the Burkina Faso or Gabon rollercoasters; the South African roller coaster would be for small children or less adventurous people, and the Niger rollercoaster would really be for the die-hards. All jokes aside, what these figures reveal is a very worrisome trend. Many African countries are characterized by extreme fluctuations in their agricultural R&D spending levels from one year to the next.
- #9 A wide body of literature exists on the impact of macroeconomic volatility on economic growth and performance in developing countries. This literature has focused primarily on volatility across countries, thereby setting the issue within an international context. (bullet point 1)This is unsurprising given the broad consensus that high macroeconomic volatility likely slows down investment (because investment flows depend on expected rewards and risks), as well as biasing investments toward short-term returns. High macroeconomic volatility has also been associated with lower investment in human capital, for similar reasons.In addition, a vast amount of literature has focused on the volatility of aid flows to developing countries. (bullet point 2)The findings on macroeconomic volatility and aid volatility suggest that extreme volatility in agricultural R&D funding is similarly harmful to the institutional stability and long-term outputs of agricultural R&D. This is supported by substantial anecdotal evidence. Numerous examples across Africa indicate that, upon the completion of multimillion dollar projects, agricultural R&D agencies have been plunged into financial hardship and an uncertain future, forcing them to cut research programs and lay off staff. Large fluctuations in yearly investment levels are therefore thought to have a detrimental impact on the release of new varieties and technologies in the long run, which in turn can have a negative impact on agricultural productivity growth and poverty reduction.
- #11 In order to measure the degree of volatility in yearly agricultural R&D spending levels across SSA countries, a commonly used method of calculating price volatility in finance and output volatility in macroeconomics was applied to ASTI’s agricultural R&D spending data. The so-called volatility coefficient quantifies volatility in agricultural R&D spending by applying the standard deviation formula to average one-year logarithmic growth of agricultural R&D spending over a certain period
- #16 In order to analyze the main causes of volatility in yearly agricultural R&D investment levels, it is important to gain insight into how agricultural R&D is funded across SSA
- #18 In order to reduce future volatility, it is important to identify the main drivers of funding volatility in agricultural R&D across countries over the past decade. The volatility coefficient, introduced earlier, is a useful tool for comparing the relative stability of different funding sources over time and across countries. It is important to note, however, that not all volatility is bad per se. A sudden injection of government or donor funding to rehabilitate R&D infrastructure after a civil war, for example, is of course a positive thing. Based on sample of 49 large government agencies from 22 countriesThe fact that donor and development bank funding for agricultural R&D shows a much higher degree of volatility than other funding sources is worrying, given that many national agricultural R&D institutes in SSA, particularly those in low-income countries, derive a significant share of their total funding from donors, development banks, and SROs. In many countries, the bulk of government appropriations is spent on salaries, which leaves the costs of operating research programs and investing in necessary infrastructure largely dependent on volatile funding from donors, competitive grants, or the private sector. Although competitive salaries are crucial to maintaining a critical mass of qualified researchers, it is equally important to provide these scientists with well-funded research programs and well-equipped research laboratories, which requires long-term, sustainable investment in nonsalary expenditures.
- #19 The dots in this figure indicate the average share of donor funding in total agricultural R&D funding for the main agencies in each country during 2001–08. The lines intersecting the dots range from the highest share of donor funding in total agricultural R&D funding during 2001–08 to the lowest share. The shorter the line, the lower the spread in the share of donor funding over time.AgR&D in middle-income countries is much less dependent on donor funding and has shown a considerably lower degree of volatility