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Agricultural R&D Expenditure in Africa: An Analysis of Growth and Volatility

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Agricultural research and development (R&D) investment is positively associated with high returns, but these returns take time – often decades – to develop. Consequently, the inherent lag from the inception of research to the adoption of new technologies calls for sustained and stable R&D funding. This article introduces a quantitative measure to assess volatility in agricultural R&D spending. It reveals that agricultural R&D spending in Sub-Saharan Africa (SSA) has been substantially more volatile than in other developing regions, which is the consequence of low levels of government funding, coupled with a high dependence on short-term and ad hoc donor and development bank funding. Rather than relying too much on external funding, SSA governments need to clearly identify long-term priorities, design focused and coherent agricultural R&D programmes accordingly, and commit sufficient funding for their implementation, while donor funding needs to be better aligned with national priorities. Moreover, diversification of funding sources is needed to better absorb funding shocks.


L’investissement en R&D agricole est positivement associé à des rendements élevés, mais ces rendements prennent du temps, souvent plusieurs décennies, à se développer. Par conséquent, le décalage inhérent entre le début de la recherche et l’adoption de nouvelles technologies rappelle le besoin de financements soutenus et stables pour la R& D. Cet article introduit une mesure quantitative pour évaluer la volatilité des dépenses en R&D. Il révèle que les dépenses en R&D agricole en Afrique sub-saharienne (ASS) sont beaucoup plus volatiles que dans d’autres régions en développement, ce qui est la conséquence du faible niveau de financement du gouvernement, allié à une forte dépendance sur les financements à court terme et ad hoc des bailleurs de fonds et des banques de développement. Plutôt que de trop compter sur un financement externe, les gouvernements d’Afrique subsaharienne doivent définir clairement leurs priorités à long terme, concevoir en conséquence des programmes de R&D agricole ciblés et cohérents, et engager des fonds suffisants pour leur mise en œuvre, tandis que le financement des bailleurs de fonds doit être mieux aligné sur les priorités nationales. En outre, la diversification des sources de financement est nécessaire pour mieux absorber les chocs de financement.

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  1. For more information on ASTI methodology, visit

  2. Cariolle (2012) identifies three main measures to quantify volatility: volatility as the standard deviation of the growth rate of a variable, volatility as the standard deviation of the residual of an econometric regression and volatility as the standard deviation of the cycle isolated by a statistical filter. These techniques vary in terms of the choice of reference value and the way in which deviations from the reference value are calculated. We recognize some of the limitations of calculating volatility based on the standard deviation of the growth rate, but given the relatively small sample of just 31 SSA countries, we believe the method is sufficient to highlight cross-country differences in volatility in agricultural R&D spending.

  3. This is an unweighted average. Country-level agricultural R&D expenditure data for 2009–2011 were unavailable for Asia-Pacific and Latin America.

  4. Agricultural GDP data were taken from World Bank (2013).

  5. The sample includes 82 large agricultural research agencies in Benin, Botswana, Burkina Faso, Burundi, Republic of Congo, Côte d’Ivoire, Eritrea, Ethiopia, Gabon, The Gambia, Ghana, Guinea, Kenya, Madagascar, Mali, Mauritania, Mauritius, Namibia, Nigeria, Senegal, Sudan, Tanzania, Togo, Uganda and Zambia. Combined, these agencies accounted for 31 per cent of total agricultural R&D spending in SSA in 2011. Given that log transform can only be applied to non-zero values, a value of 0.001 was added to each agency’s salary, operating and capital investments. This had a negligible impact on the individual institutes’ and overall volatility coefficients, and allowed for the calculation of volatility coefficients of institutes without capital spending during a certain year.

  6. Although the data allowed for the calculation of a volatility coefficient for funding derived from commodity levies and producer organizations, this coefficient was irrelevant at the SSA level as only a handful of countries generate funding for agricultural R&D this way and therefore the mean would be skewed.


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Stads, GJ., Beintema, N. Agricultural R&D Expenditure in Africa: An Analysis of Growth and Volatility. Eur J Dev Res 27, 391–406 (2015).

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