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(searched for: doi:10.2307/1237038)
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, Philip G. Pardey, Xudong Rao
Published: 22 August 2021
American Journal of Agricultural Economics, Volume 104, pp 502-529;

The publisher has not yet granted permission to display this abstract.
Matthew A. Andersen
Australian Journal of Agricultural and Resource Economics, Volume 63, pp 205-220;

The publisher has not yet granted permission to display this abstract.
Kobayashi Hajime
Geographical review of Japan series A, Volume 91, pp 376-394;

, Frikkie Liebenberg,
African Journal of Science, Technology, Innovation and Development, Volume 10, pp 463-472;

Agricultural research programmes in Africa have experienced waning state financial allocations. Efforts to change these funding trends have been fettered by the limited evidence of research investment benefits and the long lags associated with these returns. In a bid to provide such information, this article seeks to calculate the benefits of investments in the Agricultural Research Council’s peach and nectarine research programme – one of Africa’s successful and oldest research programmes. It uses the supply response function to model South Africa’s peach and nectarine industry and estimates the effect of deciduous fruit prices, production costs, research investment and weather on production. A lag distribution of research and development (R&D) investment is estimated using the polynomial distribution function and the derived elasticities are used to calculate the marginal internal rate of return. The study’s results reveal that investment in the peach and nectarine programme is associated with a marginal internal rate of return of 55.9%. This means that every R100 invested yields a R55.9 increase in value in the peach and nectarine industry. In light of these findings, it is concluded that R&D investment is worthwhile and recommends that the funding allocated to this programme be increased.
C. Ford Runge
Published: 15 February 2018
The publisher has not yet granted permission to display this abstract.
José E. Bervejillo, , Kabir P. Tumber
Australian Journal of Agricultural and Resource Economics, Volume 56, pp 475-497;

The publisher has not yet granted permission to display this abstract.
Julian M. Alston, Matthew A. Andersen, Jennifer S. James, Philip G. Pardey
Published: 23 August 2011
American Journal of Agricultural Economics, Volume 93, pp 1257-1277;

, George Rapsomanikis
American Journal of Agricultural Economics, Volume 92, pp 985-998;

A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of (R&D) on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverses the model space. In addition to producing estimates and standard deviations for the coefficients, the probability that a given lag (or break) enters the model is estimated. The approach is extended to select models populated with gamma distributed lags of different frequencies. Results are consistent with the hypothesis that R&D positively drives productivity. Gamma lags are found to retain their usefulness in imposing a plausible structure on lag coefficients, and their role is enhanced through the use of model averaging.
Daniel A. Sumner, Julian M. Alston, Joseph W. Glauber
American Journal of Agricultural Economics, Volume 92, pp 403-423;

Jean-Paul Chavas, Robert G. Chambers, Rulon D. Pope
American Journal of Agricultural Economics, Volume 92, pp 356-375;

This article is a reflection on the path taken by production economics and farm management over the last century, and the progress made in understanding the economics of the farm. The accumulated knowledge has helped refine our assessment of the efficiency of farm management decisions and the evolving role of agriculture in modern society.
Julian M. Alston, , Jennifer S. James, Matthew A. Andersen
Published: 10 October 2009
Annual Review of Resource Economics, Volume 1, pp 537-566;

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.
D. G. Russell
Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Volume 23, pp 29-52;

The publisher has not yet granted permission to display this abstract.
Giannis Karagiannis
Published: 19 March 2008
Journal of Productivity Analysis, Volume 30, pp 67-68;

The publisher has not yet granted permission to display this abstract.
Gary W. Williams, C. Richard Shumway, H. Alan Love
Agricultural and Resource Economics Review, Volume 31, pp 97-111;

U.S. soybean producers have been cooperatively investing in both production research and demand promotion for nearly four decades to enhance the profitability and international competitiveness of their industry. Have producers benefitted from their contributions to soybean checkoff program activities over the years? How has the return to investments in soybean production research compared to that of soybean demand promotion investments? The overall positive returns to producers over the study period resulted primarily from promotion activities. Production research contributed negatively to overall producer returns from soybean checkoff investments.
Siddhartha Dasgupta, Carole R. Engle
Published: 1 December 2000
Aquaculture Economics & Management, Volume 4, pp 141-155;

Economic returns to the investment in shrimp research in Honduras by Auburn University researchers, as a part of the Pond Dynamics/Aquaculture Collaborative Research Support Program (1993 to 1998), were estimated using a nonparametric approach. A survey of shrimp growers in Honduras provided data on yield, input application, and prices for their first year of production and for the year 1997. Research investment data included funding from both public and private sectors. Results showed that total factor productivity indices increased from 1995 to 1997 indicating technical progress due to research. When both private and public investment were considered, the internal rate of return to the investment in research was 46%. However, the internal rate of return to public‐sector investment alone was above 6,681%. This indicated that the public funds invested in shrimp research in Honduras have been leveraged effectively with private‐sector capital to generate technological progress.
R F Townsend
Published: 30 November 1999
Studies in Economics and Econometrics, Volume 23, pp 49-61;

Livestock supply in South Africa is examined using an error correction model with a polynomial lag formulation applied to determine price and technology dynamics. Data from 1947 to 1995 were used for the analysis. Time series properties of the data were tested and short and long run elasticities were derived for variables influencing livestock supply. An average total lag effect on livestock output of 7 years was derived with respect to real livestock producer prices and 15 years with respect to livestock research and development expenditures. These results suggest that reductions in research expenditures will have a significant negative long run effect on output. Correcting for the decline in output, with a subsequent increase in research expenditures, will take several years to achieve due to the lagged effects, with a potential of 15 years of lost growth. An inelastic response to livestock prices was derived for both the short and the long run.
Rob Townsend, Johan Van Zyl
Published: 1 June 1998
Journal: Agrekon
Agrekon, Volume 37, pp 189-210;

This article evaluates the impact of research and technology development in the wine grape industry in order to determine the rate of return (ROR) to these investments, and to make specific recommendations on funding. The analysis illustrates the applied and adaptive nature of the research conducted in the industry, with RORs of roughly 40 percent for R&D and extension. This is high, providing excellent motivation for increased investment in R&D.
R. F. Townsend, J. Van Zyl, C. Thirtle
Published: 1 December 1997
Journal: Agrekon
Agrekon, Volume 36, pp 585-597;

This paper focuses on assessing the benefits of research expenditures on maize production in South Africa. Both the production and supply function approaches are used to calculate elasticities of research expenditure on output and yield. Cointegration is used to establish long-run relationships between variables in these models. The lag structure of R&D expenditures on output is examined making use of the unrestricted, polynomial, beta and gamma distributions. The coefficients of these lag distributions were then used to calculate a rate of return to maize research expenditure, which was estimated as being between 28% and 39% per annum. These rates of return are high, mitigating in favour of more research expenditure rather than less.
John Farrington, Colin Thirtle, Simon Henderson
Published: 31 October 1997
Agricultural Systems, Volume 55, pp 273-300;

The publisher has not yet granted permission to display this abstract.
Jean-Paul Chavas, , Thomas L. Cox
Published: 1 August 1997
The review of economics and statistics, Volume 79, pp 482-492;

This paper proposes a methodology to investigate the process of technical change with a focus on the dynamic effects of R&D investments on productivity, and on the induced innovation hypothesis for both inputs and outputs. The approach builds on a nonparametric representation of the underlying technology. An application to U.S. agriculture is presented. By distinguishing between private and public R&D investments, the analysis provides useful insights into the source and the dynamic nature of technical progress. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
A. David McDonald, Anthony D.M. Smith
Published: 23 June 1997
Natural Resource Modeling, Volume 10, pp 185-216;

The publisher has not yet granted permission to display this abstract.
Yougesh Khatri, Colin Thirtle
Published: 1 January 1996
Journal of Agricultural Economics, Volume 47, pp 338-354;

The publisher has not yet granted permission to display this abstract.
Kenneth J. Meier, Robert D. Wrinkle, J.L. Polinard
Published: 1 October 1995
American Politics Quarterly, Volume 23, pp 427-460;

This study is a quantitative time-series analysis of politics and agricultural policy in the United States from 1950 to 1990. Agricultural policy is an area that generally does not fit the assumptions of the principal-agent model but rather relies on cooperative relationships between politicians and bureaucrats. Congress is the most active political institution, and bureaucracy has substantial expertise. The result is a mix of political controls and bureaucratic discretion that shapes major agricultural programs. The impact of these agricultural programs, especially those relevant to agricultural research, is assessed on several indicators of the farm sector's economic health.
X. M. Gao, Anderson Reynolds
Published: 1 June 1994
Journal of Productivity Analysis, Volume 5, pp 123-139;

The publisher has not yet granted permission to display this abstract.
Robert G. Chambers, Ramón López
Published: 1 August 1993
Journal of Public Economics, Volume 52, pp 73-82;

James D. Leiby, Gregory D. Adams
Northeastern Journal of Agricultural and Resource Economics, Volume 20, pp 1-14;

Estimates of the marginal internal rate of return to expenditures for research by the Maine Agricultural Experiment Station are presented. Estimates are performed using ridge regression under an array of specifications, including alternative functional forms, lag structures, costs of public funds, and variable specifications. The results are consistent with many previous results that imply an underinvestment in agricultural research.
Philip G. Pardey, Barbara Craig, Michelle L. Hallaway
Published: 31 October 1989
Journal: Research Policy
Research Policy, Volume 18, pp 289-296;

W. S. Wise
Published: 1 May 1986
Journal of Agricultural Economics, Volume 37, pp 151-161;

The publisher has not yet granted permission to display this abstract.
George W. Norton, Joseph D. Coffey, E. Berrier Frye
Journal of Agricultural and Applied Economics, Volume 16, pp 121-128;

The majority of decisions concerning investment and allocation of public funds for agricultural research, extension, and teaching (RET) are made at the state-level, while most of the quantitative RET evaluations are made on a national basis. This paper illustrates an approach for conducting a disaggregated state-level evaluation of agricultural research, extension, and teaching. Ridge regression is employed to handle multicollinearity problems.
Blair L. Smith, George W. Norton, Joseph Havlicek Jr.
Journal of the Northeastern Agricultural Economics Council, Volume 12, pp 109-115;

This paper illustrates differences in estimated returns to public agricultural research investments for the U.S. and the Northeast when value-added (VA) as opposed to gross production (GP) functions are estimated. Commodity groups considered are dairy, poultry, other livestock, and cash grains. Sizable differences are evident in returns estimated with VA as opposed to GP functions, with the VA estimates generally being larger. Cash grains research yields the largest returns at the margin. Dairy research is more productive in the Northeast than the rest of the country.
Joseph Havlicek Jr., Fred C. White
Journal of the Northeastern Agricultural Economics Council, Volume 12, pp 19-30;

The contribution of research to agricultural production is measured by estimating a production function which includes variables to reflect conventional inputs as well as agricultural research. Conventional inputs considered are hired labor, feed and livestock, seed and fertilizer, and capital and depreciation. Investment in agricultural research and extension within the region and investment in agricultural research in other production regions of the U.S. are included in the production function. Marginal products and internal rates of return axe derived for the own region and outside-the-region investments in agricultural research. The empirical results indicate that sane agricultural production regions have a greater capacity for exporting agricultural research results while some have a greater capacity for importing agricultural research results from other production regions. Of the ten agricultural production regions of the U.S., the Northeast had the lowest marginal product per dollar invested in agricultural research during the 1977–81 period and the lowest internal rate of return to investment in agricultural research. For the same time period the average annual spillovers from the Northeast were approximately 3.3 times as large as the average annual regional benefit and the spillovers from the Northeast were about 2.3 times as large as the spill-ins into the Northeast region. The ratio of federal to state expenditures on agricultural research in the Northeast was 1.03 and compared to a ratio of spillover's to regional benefits of 3.3 suggests that the Northeast does not fare well in terms of federal support of agricultural research benefiting other regions of the U.S.
E. C. Pasour, Marc A. Johnson, E. C. Pasour Jr.
Published: 1 January 1982
Journal: Public Choice
Public Choice, Volume 39, pp 301-317;

The publisher has not yet granted permission to display this abstract.
N.M. Garren, F.C. White
Published: 31 July 1981
Agricultural Administration, Volume 8, pp 279-287;

The publisher has not yet granted permission to display this abstract.
Fred C. White, Joseph Havlicek Jr.
Journal of Agricultural and Applied Economics, Volume 11, pp 107-111;

The interregional transfer of agricultural research results has long been recognized by sociologists and economists [10, pp. 524–526]. The first major economic study in this area was reported in 1957 by Griliches [7]. However, many economists have failed to account for this type of transfer in estimating rates of return for agricultural research investment at the state level. A possible explanation for the failure to account for this transfer is that many analyses at the state level are modeled after national studies. Though researchers estimating a national rate of return may not feel a need to account for interregional transfers, these transfers clearly cannot be ignored at the state or regional levels. Latimer and Paarlberg [9] and Bauer and Hancock [2] estimated aggregate production functions for states and had difficulty finding a statistically significant relationship between research expenditures within the state and agricultural output. Bauer and Hancock finally estimated a lagged relationship that is in conflict with other conceptual and empirical models. Latimer and Paarlberg concluded that research is so pervasive that there are no measurable differences in levels of farm income attributable to differences in research inputs by states [9, p. 239]. More recently, Bredahl and Peterson [3] examined the differences in rates of return to cash crops, dairy, poultry, and livestock research among states. These estimates are appropriate if agricultural research results are limited by state boundaries. The interregional transfer of agricultural research results needs to be taken into account in estimating the returns to agricultural research at a regional level.
C.Richard Shumway
Published: 31 July 1977
Agricultural Administration, Volume 4, pp 191-201;

The publisher has not yet granted permission to display this abstract.
Larry L. Bauer, Curtis R. Hancock
Journal of Agricultural and Applied Economics, Volume 7, pp 117-122;

With current high food prices and increasing talk about a world food crisis, there is renewed interest in production agriculture and in the allocation of resources to agriculture. It would seem, therefore, that estimates of agricultural production functions and their associated marginal products would be useful to those responsible for resource allocation to the agricultural sector. This paper intended to give policymakers information on which to base decisions relative to the impact of investments in agricultural research and extension activities. The level of appropriations to such activities can be considered a proxy measure of technology. Most researchers familiar with this area feel that the total effect of new technology on production does not occur at one momemt in time, but may be spread over a number or years. Considering this, a distributed lag on research and extension expenditures was incorporated into the production function estimated in this paper.
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