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U.S. farm productivity growth has direct consequences for sustainably feeding the world’s 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.

We use newly constructed state-specific data to explore the implications of common modeling choices for measures of research returns. Our results indicate that state-to-state spillover effects are important, that the research and development lag is longer than many studies have allowed, and that misspecification can give rise to significant biases. Across states, the average of the own-state benefit-cost ratios is 21:1, or 32:1 when the spillover benefits to other states are included. These ratios correspond to real internal rates of return of 9% or 10% per annum, much smaller than those typically reported in the literature, partly because we have corrected for a methodological flaw in computing rates of return.

Measures of productivity growth are often procyclical. This paper focuses on measurement errors in capital inputs, associated with unobserved variations in capital utilization rates, as an explanation for the existence of pro-cyclical patterns in measures of productivity. Recently constructed national and state-specific indexes of inputs, outputs, and productivity in U.S. agriculture for 1949-2002 are used to estimate production functions that include proxy variables for changes in the utilization of durable inputs. The proxy variables include an index of farmers' terms of trade and an index of local seasonal growing conditions. We find that utilization responses by farmers are significant and bias measures of productivity growth in a pro-cyclical pattern. We quantify the bias, adjust the measures of productivity for the estimated utilization responses, and compare the adjusted and conventional measures.

Agricultural research has transformed agriculture and in doing so contributed to the transformation of economies. Economic issues arise because agricultural research is subject to various market failures, because the resulting innovations and technological changes have important economic consequences for net income and its distribution, and because the consequences are difficult to discern and attribute. Economists have developed models and measures of the economic consequences of agricultural R&D and related policies in contributions that relate to a very broad literature ranging across production economics, development economics, industrial organization, economic history, welfare economics, political economy, econometrics, and so on. A key general finding is that the social rate of return to investments in agricultural R&D has been generally high. Specific findings differ depending on methods and modeling assumptions, particularly assumptions concerning the research lag distribution, the nature of the research-induced technological change, and the nature of the markets for the affected commodities.

Some studies have reported a slowdown in U.S. farm productivity growth, but the prevalent view among economists is to reject or downplay the slowdown hypothesis, implying that the rates of productivity growth experienced over the past half century can be projected forward. We set out to resolve this issue, which matters both for understanding the past and anticipating the future. Using newly compiled multifactor and partial-factor productivity estimates, developed for the purpose, we examine changes in the pattern of U.S. agricultural productivity growth over the past century. We detect sizable and significant slowdowns in the rate of productivity growth. Across the 48 contiguous states for which we have very detailed data for 1949-2007, U.S. multifactor productivity (MFP) growth averaged just 1.18 percent per year during 1990-2007 compared with 2.02 percent per year for the period 1949-1990. MFP in 44 of the 48 states has been growing at a statistically slower rate since 1990. Using a longer-run national series, since 1990 productivity growth has slowed compared with its longer-run growth rate, which averaged 1.52 percent per year for the entire period, 1910-2007. More subtly, the historically rapid rates of MFP growth during the 1960s, 1970s and 1980s can be seen as an aberration relative to the long-run trend. A cubic time-trend model fits the data very well, with an inflection around 1962. We speculate that a wave of technological progress through the middle of the twentieth century--reflecting the progressive adoption of various mechanical innovations, improved crop varieties, synthetic fertilizers and other chemicals, each in a decades long process--contributed to a sustained surge of faster-than-normal productivity growth throughout the third quarter of the century. A particular feature of this process was to move people off farms, a one-time transformation of agriculture that was largely completed by 1980.

This study examines the role of public agricultural research and development (R&D) in the process of knowledge production and productivity growth in U.S. agriculture from a new perspective. The seminal work of Griliches (1967) established the relationship between investments in R&D, the process of knowledge production, and the productivity enhancing benefits they create. In the literature on estimating knowledge production functions measures of multi-factor productivity (MFP) are regressed against measures of knowledge stocks, thereby enabling the researcher to quantify the relationship between the stream of investment expenditures and the productivity enhancing benefits they produce. A critical aspect of this research involves how to handle inter-temporal research spillovers, or in other words how to sum current and previous R&D expenditures into a measure that best represents the current stock of knowledge. This research expands on recently published work by Alston et al (2010) related to estimating the ideal lag structure for summing R&D investments, using data from the International Science and Technology Practice and Policy (InSTePP) Center at the University of Minnesota. We reproduce some of the analysis in the Alston et al (2010) research related to estimating knowledge production functions, but substitute an alternative measure of MFP in the analysis. Specifically, we re-estimate the knowledge production functions using a dual as opposed to a primal measure of productivity. The authors of this study have not seen this approach utilized in the literature, and we believe this novel approach will provide additional valuable insight on the process of knowledge production and productivity growth. Griliches and Jorgenson (1967) formalized the relationship between a dual and a primal measure of MFP. Commonly, a primal measure of MFP is defined as a measure of aggregate output divided by aggregate input. A dual measure can be defined as the ratio aggregate input to output prices. We outline the theoretical reasons why there may differences between the primal and dual measures of MFP. Furthermore, we compare the empirical measures of primal and dual MFP from the InSTePP database and identify differences in these measures over time. Many different lag structures for estimating knowledge stocks have been considered in the literature, including geometric, gamma, and trapezoidal distributions to name a few, and both the shape as well as the length of the distribution are important. The gamma distribution embodies several favorable characteristics: 1) all lag weights determined by the function are non-negative; 2) the shape implied is relatively smooth; 3) the gamma distribution is unimodal; 4) the distribution can be skewed to give more weight to more recent or more distant lags; and 5) the distribution can be characterized by only two parameters. We construct two grids of 64 gamma distributions based on a research lags of 35 and 50 years. The distributions can be represented by altering two parameters. The goal is to examine the best lag structure to represent the relationship between R&D expenditures, knowledge production, and the resulting productivity enhancing benefits. We do this by estimating knowledge production functions under the different lag specifications, and choose the specification that produces the lowest Sum-of-Squared Errors (SSE) in the regressions. The primary objective is to compare and contrast the results of the regression analysis with regards to the preferred lag structure using the dual as opposed to primal measure of MFP. Do the results from a primal and dual approach support a similar lag structure for summing R&D expenditures or do they contradict one another? The methodology of this study is well established, the results have direct significance to an important field of agricultural economics, and the potential to generate discussion and debate is high.