Session Five: Productivity and R&D
Date & Time
Thursday, March 30, 2023, 9:00 AM - 10:45 AM
James MacDonald Nicholas Rada Sun Ling Wang Paul Heisey Carl Pray Julian Alston Gregory Graff

Moderated by James MacDonald, University of Maryland

Startups, private finance, and innovation in U.S. agriculture (Paper 1)

Presentation by Gregory Graff, Colorado State University

Authors: Gregory Graff, Charles DeGrazia, and Nicholas Rada

The funding and conduct of research and development (R&D) for agriculture by small and medium sized enterprises (SMEs) has historically been limited. Yet, over the last decade, private investments have poured into startups created to pursue a wide array of technologies and business models across agriculture, including development of high technologies such as software, robotics, or biotechnology as well as more traditional lines of business. We investigate the extent and the mechanisms by which private investments in startups impact innovation in the agricultural industry. How prevalent are innovative startups versus more traditional startups? How do the early-stage lifecycles of more and less innovative startups differ, in terms of growth rates, financing, rates of failure, and types of exit? Ultimately to what extent are innovative startups acquired by corporate incumbents or succeed to become competitive new entrants?

Chinese science & tech policies and their impact on agricultural productivity (Paper 2)

Presentation by Carl Pray, Rutgers, the State University of New Jersey

The Chinese government recognizes the importance of increasing agricultural total factor productivity in reaching their food security goals.  The paper lists the government investments and science and technology policies of Chinese governments aimed at increasing productivity. The paper then presents empirical evidence that some of these policy instruments have been effective in increasing research, innovation and productivity of agribusiness firms.  The next part of the paper, using a new data set of provincial policy and TFP data for the period 1980 to 2018, shows that public sector R&D and extension services of increased agricultural TFP and that private sector innovation is gradually increasing its contribution agricultural TFP growth.  The concluding section brings together evidence on technology and policy changes that are likely to contribute to Chinese productivity growth in the near future.

Patent knowledge stock and U.S. agricultural productivity growth (Paper 3)

Presentation by Sun Ling Wang, USDA Economic Research Service

Authors: Matthew Clancy, Institute for Progress and Sun Ling Wang, USDA Economic Research Service

Research findings have shown positive effects of public R&D on regional agricultural productivity growth. However, there is little study on identifying the role of private R&D in agricultural productivity growth at the local level due to data limitation. Neither do we know much about how R&D in different subsectors interact with each other and affect agricultural productivity growth. Amid declining public agricultural research (in real terms), a better understanding of what kinds of research are most related to agricultural productivity is crucial. For this study we use rich new patent data to develop knowledge stock variables for various science and technology fields. We also employ a multilateral total factor productivity (TFP) panel for 48 contiguous U.S. states so we can identify the roles of different patent knowledge stocks on regional TFP growth. The paper will first discuss trends in agricultural patenting and productivity growth at the U.S. state level. Second, we examine the impacts of private R&D on US agricultural productivity growth using a state-by-year panel spanning 1976-2015 period.  

R&D Lags in Economic Models (Paper 4)

Presentation by Julian M. Alston, University of California, Davis

Authors: Schanchao Wang, Julian M. Alston, and Philip G. Pardey

Quite different R&D lag structures predominate in studies of agricultural R&D compared with studies of R&D in other industries, and compared with studies of economic growth more broadly.  Here we compare the main models and their implications using long-run data for U.S. agriculture.  We reject the models predominantly used in studies of economic growth and industrial R&D both on prior grounds and using various statistical tests.  The preferred model is a 50-year gamma lag distribution model.  The estimated elasticity of MFP with respect to the knowledge stock is 0.28 and the implied marginal benefit-cost ratio is 23:1.

Discussant for Papers 1-2: Paul Heisey, USDA Economic Research Service, retired

Discussant for Papers 3-4: Nicholas Rada, US Patent and Trademark Office

Location Name
Ballston Room
Full Address
Virginia Tech Executive Briefing Center
900 N Glebe Rd
Arlington, VA 22203
United States
Session Type
General Session
Virtual Session Link