Matthew A. Andersen

The Federal Role

The history of agricultural R&D and related government policy in the United States is one of jointly evolving state and federal, public- and private-sector roles. The private role has always emphasized more-patentable inventions, or at least innovations from which the returns seem more appropriable by a variety of intellectual property rights or other means. In agriculture, in particular, however, it is difficult for individuals to fully appropriate the returns from their research investments, leading to a general consensus that some government action is warranted to ensure an adequate investment in R&D.

A Brief History of U.S. Agriculture

Almost 150 years have passed since U.S. public-sector agricultural research and development (R&D) began in earnest with the establishment of the U.S. Department of Agriculture and the passage of the Morrill Land Grant College Act in 1862, to be followed 25 years later by the passage of the Hatch Experiment Station Act in 1887. During that time, and especially in the more recent decades, U.S. agriculture changed dramatically. Public and private agricultural R&D played a major role in bringing about those changes, and the R&D systems and institutions evolved alongside and as part of agriculture.

Research Lags and Spillovers

Modeling and measuring the productivity consequences of R&D is a tricky business. The challenge in attributing productivity to R&D is to establish which research, conducted by whom, and when, was responsible for a particular productivity increase. In other words, in modeling the effects of research on agricultural productivity the two principal areas of difficulty are in the treatment of knowledge spillovers (i.e., the “by whom” part of the attribution problem) and in identifying the lag structure linking research spending, knowledge stocks, and productivity (i.e., the “when” part).

A Century of U.S. Farm Productivity Growth: A Surge Then a Slowdown

&NA; U.S. farm productivity growth has direct consequences for sustainably feeding the worlds still rapidly growing population, as well as U.S. competitiveness in international markets. Using a newly expanded compilation of multifactor productivity (MFP) estimates and associated partial‐factor productivity (PFP) measures, we examine changes in the pattern of U.S. agricultural productivity growth over the past century and more. Considering the evidence as a whole, we detect sizable and significant slowdowns in the rate of productivity growth in recent decades. U.S. multifactor productivity grew at an annual average rate of just 1.16% per year during 1990‐2007 compared with 1.42% per year for the period 1910‐2007. U.S. yields of major crops grew at an annual average rate of 1.17% per year for 1990‐2009 compared with 1.81% per year for 1936‐1990. More subtly, but with potentially profound implications, the relatively high rates of MFP growth during the third quarter of the century are an historical aberration relative to the long‐run trend.

Econometric Estimation and Results

The implementation of the general model developed in Chapter 9 requires some specific choices about the detail of the model, beginning with the functional form. In the present chapter we discuss those choices, and present and interpret the resulting econometric estimates, along with the results of some analysis of the sensitivity of the estimates to model specification choices. In Chapter 11 we report the results from using the econometric estimates to compute a range of benefit-cost ratios for alternative types of research spending, and in Chapter 12 we interpret and assess the results from both the econometric estimation and the benefit-cost analysis.

Research Funding and Performance

How has public and private sector participation in agricultural R&D in the United States changed over the decades, and how has research spending on agriculture fared relative to research spending in all areas of science? Given the international interdependencies in agricultural R&D, how has research spending in the United States evolved relative to spending elsewhere in the world? This chapter describes public and private investments in R&D directed to agriculture in the United States, placing them in the context of both the overall spending on all sciences, and global spending on agricultural R&D. Then, we explore patterns of public spending on agricultural R&D within the United States, both in aggregate and among the states, with attention to the separate and joint roles of the federal government through its USDA intramural labs and the State Agricultural Experiment Stations. We consider the evolving sources of funding as well as the evolving patterns of spending. In Chapter 7, these trends in the funding and performance of agricultural R&D are linked to legislative and other policy changes.

Models of Research and Productivity

In this chapter we develop the structure of our models for estimating the effects of U.S. public agricultural research on U.S. agricultural productivity. We begin by laying out a general model relating research spending to agricultural productivity. Because this general specification includes too many parameters to be estimated individually with any degree of precision, some restrictions must be imposed. A conventional approach is to model productivity as a function of knowledge stocks that are created as a result of past research and can be represented using a distributed lag model defined by a small number of parameters. In this chapter we describe our approach for creating research knowledge stocks, including the specification of the research lag structure and state-to-state (and federal-to-state) spillovers used to construct the knowledge stocks. In later chapters we evaluate the effects of specification choices on our estimates and the implied benefit-cost ratios.

Productivity Patterns and Research Benefits

In Chapter 10 we reported the results from estimating models of productivity as a function of variables representing agricultural research and extension knowledge stocks. Various transformations of these models can be used to derive implications that are of interest to economists and policymakers. For instance, we can use the estimated model to evaluate the roles of policies in influencing the pas time path and spatial pattern of agricultural productivity. Alternatively, we can use the model to evaluate the future time path and spatial pattern of agricultural productivity given actual past and likely future research spending patterns or alternative counterfactual spending patterns. Or, we can compare productivity patterns under alternative scenarios of research spending patterns and infer measures such as benefit-cost ratios or internal rates of return.

Agricultural Productivity Patterns

At the center of our empirical work is a model of state-specific productivity growth as a function of investments in agricultural research. While the notions of productivity and changes in productivity are intuitive, it is not easy to develop meaningful measures of productivity or to identify the productivity consequences of investments in agricultural R&D. Schultz (1953) argued that the root reason for an interest in the sources of productivity growth is an interest in the sources of output growth. And, expressing an idea he attributed to Zvi Griliches, Schultz (1956, p. 758) wrote.

Interpretation and Assessment of Benefit-Cost Findings

Agricultural production, input use, and productivity have been evolving over time, with substantially different patterns among U.S. states; so, too, has the pattern of spending on agricultural research and extension by the federal and state governments. In our econometric models linking these patterns of R&D spending and agricultural productivity, we have imposed a great deal of structure on the research lag and state-to-state spillover relationships. So long as they are appropriate and do not lead to estimation bias, these restrictions are helpful in reducing the number of free parameters to be estimated and improving the precision with which they are estimated. The resulting estimates indicate strong linkages between research and extension spending and productivity, and high payoffs to past investments. They also signal slower future productivity growth, especially if the past slowdown in the rate of growth in spending on farm-productivity-oriented research and extension spending will be sustained into the future.