The aggregate approach to the evaluation of production oriented expenditure in the United States has consistently shown high rates of return to agricultural research investments. These efforts, while showing the value of agricultural research, are limited in the information they can provide policy and budget decisionmakers. More recently, efforts such as the analysis of four major commodity groups by Bredahl and Peterson and cross sectional studies by Evenson, White, and Havlicek have begun refining the level of analysis to individual regions and states and for specific commodities (Evenson, Bredahl and Peterson, White and Havlicek). The objective of this study is to further disaggregate these commodity groupings of Bredahl and Peterson into individual commodities and to begin investigating the impact of interregional research "spillovers." Case studies of corn, wheat, and sorghum are made, using individual states as observation units over the time period for which research data on individual commodities are available. The empirical results presented in this paper are the results of some first attempts at estimation. Further work is being done to improve specification of variables measuring weather, cash inputs, risk, and other factors influencing yield response of grain commodities. A special cross sectional-time series algorithm is used in parameter estimation. Theoretical Framework, Model, and Data The conceptual framework of this study is particularly constrained by the availability of only 11 years of research expenditure data on individual commodities (1967-1977). The short number of years prohibits use of a 12-, to 13 year polynominal Almon distributed lag which has been used in previous studies to investigate the research lag structure (White, et al., Quance and Lu). The nonavailability of production input data for specific commodities for other than farm census years also limits utilizing the traditional aggregate production function approach to the analysis of agriculture research expenditure. Faced with these data limitation problems, the framework of a supply response analysis is developed as an alternative to investigating the impact on research expenditures on productivity of individual grain commodities. The supply response model which is derivable from the production function, expresses the quantity of a commodity offered for sale as a function of input and output prices, technical parameters, and a variety of shifters, such as weather and technological change. Supply analysis studies have typically represented the effects of technological change as a linear trend variable. While the research evaluation studies have not focused on the processes of technological adoption and diffusion, their analyses have shown that public investments in agricultural research activities have contributed to increases in productivity. On this basis, lagged research expenditures on individual commodities will be used as the technological change shifter in the commodity response functions.


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