Benefits of Public R & D in US Agriculture: Spill-Ins, Extension, and Roads

Abstract

This paper uses panel data for the 1980-2004 period to estimate the contributions of public research to US agricultural productivity growth. Local and social internal rates of return are estimated accounting for the effects of R & D spill-in, extension activities and road density. R & D spill-in proxies were constructed based on both geographic proximity and production profile to examine the sensitivity of the rates of return to these alternatives. We find that extension activities, road density, and R & D spill-ins, play an important role in enhancing the benefit of public R & D investments. We also find that the local internal rates of return, although high, have declined through time along with investments in extension, while the social rates have not. Yet, the social rates of return are not robust to the choice of spill-in proxy.

Slow Magic: Agricultural Versus Industrial R&D Lag Models

“…More recently, in their application using long-run, state-specific data on US agriculture, Alston et al (2010Alston et al ( , 2011 tested for longer lags and found in favor of a gamma lag distribution model with a much longer research lag than most previous studies used-a research lag of at least 35 years and up to 50 years for US agricultural research, with a peak lag around year 24. A similar gamma lag distribution model has been adopted by several studies since then (examples include Andersen & Song 2013, Andersen 2015, Fuglie et al 2017, Khan et al 2017, Fuglie 2018, Baldos et al 2019, Yang & Shumway 2020, Wang et al 2023, though some others continue to use the trapezoidal lag model (Plastina & Fulginiti 2011;Wang et al 2012Wang et al , 2017Jin & Huffman 2016;Yang & Shumway 2016Huffman 2018). All of these models imply longer overall lags, and considerably longer mean lags, compared with both the agricultural R&D models used prior to the 1990s and the industrial R&D models up to the present.…”

Theoretical Production Restrictions and Agricultural Technology in the United States

“…The panel was specifically developed to measure agricultural productivity; therefore, it seems natural to use it in the estimation of the U.S. agricultural production function. Earlier versions of the data were used to evaluate agricultural productivity by means of a dual (cost function) representation of the production technology (Morrison Paul et al 2001;Huffman et al 2002;Wang et al 2012;Wang et al 2017), as well as primal (output and input distance functions) representations of the production technology (O'Donnell 2014; Plastina and Lence 2018). The present study is the first one to use the USDA panel dataset to calibrate a stochastic production function representation of U.S. agricultural technology.…”

Impacts of US Public R&D Investments on Agricultural Productivity and GHG Emissions

“…To ensure long-term farm productivity growth, continued investments in public agricultural R&D are needed. The economic gains from R&D-driven productivity growth are well researched (Alston et al, 2011;Andersen and Song, 2013;Fuglie, 2017;Jin and Huffman, 2016;Plastina and Fulginiti, 2011;Wang et al, 2017). In a recent meta-analysis of 492 studies, Rao et al (2019) estimated that the median internal rate of return from agricultural R&D is around 34.0% per year for developed countries.…”