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 R&D 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 percent 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.

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 fail ures, because the resulting innovations and technological changes have important economic consequences for net income and its distri bution, 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 organiza tion, economic history, welfare economics, political economy, econo metrics, 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 assump tions, particularly assumptions concerning the research lag distribu tion, the nature of the research-induced technological change, and the nature of the markets for the affected commodities.

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In this article, we examine the relationship between public investments in agricultural research and development and the productivity‐enhancing benefits they generate. Knowledge productivity functions are estimated for U.S. agriculture using data on multifactor productivity and public knowledge stocks. We examine the time‐series properties of the data and compare alternative econometric estimation procedures. The results are used to calculate economic performance measures such as internal rates of return and benefit‐cost ratios. The real rate of return to public investments in agricultural research and development in the United States is in the range of 8–10% per annum.

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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.

Measures of capital services are used in studies of production and to inform policies related to growth and development. A variety of methods have been used to measure capital stocks and service flows. We briefly review the methods commonly used to measure capital service flows, and the main assumptions. We then quantify the substantial differences between our newly constructed InSTePP series on capital use in U.S. agriculture and a comparable USDA series. We show that measures of capital services are sensitive to the treatment of interest rates, notably the use of fixed versus variable market rates, and we demonstrate the implications for measures of the quantity and productivity of agricultural capital in the United States. We conclude that when calculating capital usage in U.S. agriculture the use of a fixed rate of interest will generate more plausible estimates than the use of an annual market rate that varies from year to year. Capital Services in U.S. Agriculture: Concepts, Comparisons, and the Treatment of Interest Rates "The capital time series is one that will really drive a purist mad." Robert Solow (1957, p. 314) An accurate measure of the annual flow of capital inputs is valuable for policy makers and researchers who are interested in production and productivity. However, estimates of capital stocks and service flows are difficult to calculate and vulnerable to significant measurement errors because of data limitations and the myriad of assumptions required. Estimates of the flow of capital services are especially sensitive to underlying assumptions. Information about the implications of the alternatives provides a basis for making better-informed choices about the appropriate approaches and assumptions to apply when measuring capital stocks and flows.This article begins with a review of methods used commonly to measure capital stocks and service flows, making explicit a number of important assumptions required to construct such measures. Next, we examine and compare the measures of capital inputs in U.S. agriculture from two contemporary, state-specific panel data sets. We compare the methods used to construct the capital series, and we reveal and discuss differences in data sources, the types of data used to construct the capital measures, and the resulting estimates. We also outline some assumptions about depreciation, service lives, interest rates, aggregation methods, and the scope of goods included in each of the capital series for each data set. Finally, we examine and illustrate the extent to which certain choices

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