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Knowledge accumulation in US agriculture: research and learning by doing
Journal of Productivity Analysis ( IF 2.3 ) Pub Date : 2020-09-04 , DOI: 10.1007/s11123-020-00586-6
Sansi Yang , C. Richard Shumway

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.

中文翻译:

美国农业中的知识积累:边做边学

我们通过对美国农业生产部门的实证研究,研究了公共研究投资(R&D)和边做边学(LBD)在提高生产力中的作用。我们构建了一个双重模型,并利用一个多世纪的数据来跟踪研发和LBD对规模回报,生产成本和投入需求的影响。贝叶斯方法用于维持经济理论所隐含的规律性条件。我们发现,将R&D和LBD都包括在生产过程中,美国农业显示出规模收益增加的重要证据。研发和LBD在降低成本方面是互补的,因为一种库存的增加会显着增强另一种库存的降低成本的效果。研发和LBD对规模经济,成本和投入需求的直接影响对研发的选择敏感 滞后结构,LBD代理,LBD知识折旧率和数据周期。但是输入需求价格弹性在整个模型规范中都非常强大。
更新日期:2020-09-04
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