A structural equation model to assess the impact of agricultural research expenditure on multiple dimensions
- 72 Downloads
The economic crisis and the pressure towards efficient and effective use of public money claim for a higher accountability of research expenditure, as well as for a greater proximity of research to the needs of community. While agricultural productivity represents a worldwide goal for agricultural research as a response to growing food, feed and energy demands, other objectives besides productivity are becoming central. The challenge is to take into account broader impacts that go beyond academic and economic ones, and to improve knowledge on the causal impact-generating mechanisms. In this paper, we adopt a causal perspective and estimate the impact of agricultural research expenditure on multiple dimensions. We develop a structural equation model relating research expenditure, research activity, productivity and multiple impact indicators within a dynamic impact pathway, accounting for existing domain knowledge on causal relationships and their lag structures. The model is applied on EU 15 countries over the period 1980–2014, making use of official statistics from several European databases.
KeywordsDirected acyclic graph Dynamic causal effects Polynomial lag shape Research impact assessment Productivity Sustainable development
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant agreement No. 609448 (IMPRESA project, http://www.impresa-project.eu).
- Alston, J.M., Norton, G.W., Pardey, P.G.: Science Under Scarcity: Principles and Practice for Agricultural Evaluation and Priority Setting. Cornell University Press, Ithaca (1995)Google Scholar
- Alston, J.M., Craig, B.J., Pardey, P.G.: Dynamics in the Creation and Depreciation of Knowledge, and the Returns to Research. EPTD Discussion Paper No. 35, International Food Policy Research Institute, Washington D.C. (1998)Google Scholar
- Alston, J.M., Chan-Kang, C., Marra, M., Pardey, P.G., Wyatt, T.: Meta-Analysis of Rates of Return to Agricultural R&D: Ex Pede Herculem? Research Report No. 113, IFPRI, Washington D.C. (2000)Google Scholar
- Alston, J.M., Pardey, P.G., Ruttan, V.W.: Research Lags Revisited: Concepts and Evidence from U.S. Agriculture. Staff Paper P08-14, University of Minnesota, Department of Applied Economics and International Science and Technology Practice and Policy (InSTePP), St. Paul (2008)Google Scholar
- Bartolini, F., Coli, A., Magrini, A., Pacini, B., Porciani, L.: Assessing the impact of agricultural research: data requirements and quality of current statistics in Europe. In: Proceedings of 7th International Conference on Agricultural Statistics (ICAS 2016) (2016)Google Scholar
- Campbell, D., Caruso, J., Archambault, E.: Cross-cutting analysis of scientific publications versus other science. Technology and Innovation Indicators. Directorate General for Research and Innovation, European Commission, Brussels (2013)Google Scholar
- Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc., Ser. B 39(1), 1–38 (1977)Google Scholar
- Joly, P., Colinet, L., Gaunand, A., Lemarié, S., Matt, M.: Agricultural research impact assessment: issues, methods and challenges. OECD Food, Agriculture and Fisheries Papers, No 98, OECD Publishing, Paris (2016)Google Scholar
- Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)Google Scholar
- Renkow, M.: Assessing the environmental impacts of CGIAR research: toward an analytical framework. In: Measuring the environmental impacts of agricultural research: theory and applications to CGIAR research, Independent Science and Partnership Council Secretariat, Rome (2011)Google Scholar
- Sheng, Y., Gray, E.M., Mullen, J.D.: Public investment in R&D and extension and productivity in Australian broadacre agriculture. In: Proceedings of the 55th Conference in Australian Agricultural and Resource Economic Society, Melbourne (2011)Google Scholar