Abstract
The future path and pace of agricultural productivity growth areinextricably intertwined with investments in food and agricultural research and development (R&D). Looking back over half a century of evidence, we find that the lay of the global food and agricultural R&D land is changing, with indications that we are in the midst of an historic transition. The more notable trends are as follows: (1) for the first time in modern history (in purchasing power parity, PPP, terms), the middle-income countries now outspend the rich countries in terms of public-sector investments in food and agricultural R&D; (2) the shifting public shares reflect a continuing decline in the rate of growth of food and agricultural R&D spending by the rich countries, along with a generally sustained and substantial growth in spending by the middle-income countries (especially China, India, and Brazil); (3) in PPP terms, China now spends more than the United States on both public- and private-sector food and agricultural R&D; (4) the global share of food and agricultural R&D being conducted by the private sector has increased, especially in the high- and rapidly growing middle-income countries; and (5) the low-income countries are losing ground and account for an exceptionally small share of global spending. The mean and median values of the reported rates of return to food and agricultural R&D based on the IRR are high and remain so, with no signs of a diminution in the payoffs to more recent (compared with earlier) investments in R&D. But the available evidence on the returns to food and agricultural R&D is not fully representative of the institutional (i.e., public versus private), locational, or commodity orientation of the research and the agricultural sector itself.
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Notes
- 1.
- 2.
Of course a host of other measurement matters remain to be resolved, such as accounting for inflation (or, more specifically, the changing price of R&D inputs), standardizing currency units of measurement among countries (and over time), and so on.
- 3.
See Pardey et al. (2016) for complete details on data construction and data sources.
- 4.
Here, “rich countries” are synonymous with high-income countries. Countries are classified into income classes according to the World Bank (2015) data and income classifications. High-income countries are those with 2013 GNI (gross national income) per capita of $12,746 or more; upper middle-income countries had 2013 GNI per capita between $4126 and $12,745; lower middle-income countries had 2013 GNI per capita between $1046 and $4125; and low-income countries had 2013 GNI per capita less than or equal to $1045.
- 5.
The IRR is the interest rate that equates the present value of benefits from an investment to the present value of its costs. The BCR is the ratio of the present value of benefits from an investment to the present value of its costs.
- 6.
These concerns were noted earlier by Alston et al. (2000).
- 7.
In more recent work, Rao et al. (2017b) were able to recalibrate 2165 of the published IRR estimates, resulting in a median MIRR of 17.0% per year (conditional on a research lag length of 30 years and a discount rate of 5% per year), well less than the corresponding 39.0% per year median IRR.
- 8.
Dehmer et al. (2017) report total global gross domestic expenditures on R&D (GERD) of $1492 billion (2009 PPP$) for 2011 GERD (inclusive of countries from the former Soviet Union and Eastern Europe, FSU&EE). The $1397 billion in total science spending quoted and the time series of agGERD estimates reported in this paper exclude estimates of R&D spending by countries of the former Soviet Union and Eastern Europe. In a set of “satellite accounts,” InSTePP estimates (with less confidence compared with the rest-of-world estimates) that in 2011 the 28 countries that make up this group spent $1.9 billion on agGERD. Unless otherwise stated, all dollar-denominated R&D estimates in this chapter are expressed in base year 2009 purchasing power parity (PPP) dollars.
- 9.
Spending by the international agricultural research centers that constitute the CGIAR totaled $668.4 million (2009 prices) in 2011, such that public (including national or “domestic” and international) plus private spending totaled $69.9 billion in 2011 (Fig. 2).
- 10.
The current status and future prospects for agricultural R&D funding in Brazil are conditioned by (1) the 40% reduction in overall R&D funding during 2013–2016 as a consequence of reduced public spending and (2) the prospects of a freeze on overall federal government spending for the next two decades (Angelo 2016).
- 11.
Cross-country relativities reflect a currency conversion issue. Using purchasing power parities to adjust for differences in prices across countries is common, but has well-known issues. Namely, while the goal is to account for differences in the unit costs of scientific staff and other R&D inputs across countries, the PPP conversion factors reflect price differences for a standard basket of goods and services related to GDP rather than a targeted basket of goods and services related to R&D costs. For example, using market exchange rates for the currency conversions has the United States outspending China in 2011 with US$4.40 billion of agPERD in the United States versus US$2.18 billion for China (Table 1).
- 12.
We use the term “evaluation” to refer to a particular model parameterization or design giving rise to a stream of benefits and a stream of costs that could give rise to multiple alternative “estimates” of summary statistics, depending on other assumptions. A single evaluation (of a particular technology) within a given study may report either an estimate of an IRR, of a BCR, or both. Version 3.0 of the InSTePP returns-to-research database includes 2827 evaluations, and it is shares of evaluations (not estimates) that are mostly reported throughout this chapter.
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Pardey, P.G. et al. (2018). The Shifting Structure of Agricultural R&D: Worldwide Investment Patterns and Payoffs. In: Kalaitzandonakes, N., Carayannis, E., Grigoroudis, E., Rozakis, S. (eds) From Agriscience to Agribusiness. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-67958-7_2
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