Knowledge accumulation in US agriculture: research and learning by doing

Sansi Yang () and C. Shumway

Journal of Productivity Analysis, 2020, vol. 54, issue 2, No 1, 87-105

Abstract: Abstract We investigate the role of public research investment (R&D) and learning by doing (LBD) in improving productivity through an empirical examination of the US agricultural production sector. We construct a dual model and track R&D and LBD impacts on returns to scale, production cost, and input demand utilizing data for more than a century. A Bayesian approach is used to maintain regularity conditions implied by economic theory. We find that US agriculture shows significant evidence of increasing returns to scale when both R&D and LBD are included in the production process. R&D and LBD are complementary in reducing cost as an increase in one stock significantly strengthens the cost-reducing effect of the other. The direct impacts of R&D and LBD on scale economies, cost, and input demands are sensitive to choices of R&D lag structure, LBD proxy, LBD knowledge depreciation rate, and data period. But input demand price elasticities are highly robust across model specification.

Keywords: Cost function; Input demand; Knowledge accumulation; Learning by doing; Research investment; R&D (search for similar items in EconPapers)
JEL-codes: D24 Q16 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11123-020-00586-6

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