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The Structure of U.S. Agricultural Technology, 1910–78

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  • John M. Antle

Abstract

This paper utilizes 1910–78 time-series data and a single product aggregate translog profit function to measure the structure of U.S. agricultural technology. Duality relations are used to devise a multifactor measure of biased technical change. A measure of nonhomotheticity is introduced which indicates the effects scale change has had on aggregate cost shares. The empirical analysis finds that different, nonhomothetic technologies characterized the prewar and postwar periods. Differing technical change biases are consistent with relative price trends during the two periods, showing that the long-run structure of U.S. agricultural technology has been consistent with the Hayami-Ruttan induced innovation theory.

Suggested Citation

  • John M. Antle, 1984. "The Structure of U.S. Agricultural Technology, 1910–78," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(4), pages 414-421.
    • Handle: RePEc:oup:ajagec:v:66:y:1984:i:4:p:414-421.
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    File URL: http://hdl.handle.net/10.2307/1240919
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    Citations

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    Cited by:

    1. Nganje, William E. & Mazzocco, Michael A. & McKeith, Floyd K., 1999. "Food Safety Regulation, Product Pricing, And Profitability: The Case Of Haccp," AE Series 23077, North Dakota State University, Department of Agribusiness and Applied Economics.
    2. Tsigas, Marinos E. & Hertel, Thomas W. & Preckel, Paul V., 1990. "Modeling The U.S. Grains Programs: A Microeconomic Approach," 1990 Annual meeting, August 5-8, Vancouver, Canada 270990, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Jean-Paul Chavas & Thomas L. Cox, 1994. "A Primal-Dual Approach to Nonparametric Productivity Analysis: the Case of U.S. Agriculture," Wisconsin-Madison Agricultural and Applied Economics Staff Papers 372, Wisconsin-Madison Agricultural and Applied Economics Department.
    4. Thirtle, Colin, 1988. "Induced Innovation Theory and Agricultural Development in LDCs: An Appraisal," Manchester Working Papers in Agricultural Economics 232807, University of Manchester, School of Economics, Agricultural Economics Department.
    5. Ka Kei Gary Wong & Min Qiang Zhao, 2023. "Induced innovation and its impact on productivity growth in China: a latent variable approach," Empirical Economics, Springer, vol. 65(1), pages 371-399, July.
    6. Silva, F.D.F. & Fulginiti, L. & Perrin, R., 2018. "Agricultural productivity and forest preservation in the Brazilian Amazon," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277167, International Association of Agricultural Economists.
    7. Sandhu, Gurmit S. & Phillips, W.E. & Percy, Mike, 1992. "Economic Structural Analysis of the Canadian Agricultural Production Sector," Staff Paper Series 232540, University of Alberta, Department of Resource Economics and Environmental Sociology.
    8. Asci, Serhat & Yamazaki, Fumiko & Paggi, Mechel, 2016. "Forecasting California Pesticide Demand using PUR Dataset," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230126, Southern Agricultural Economics Association.
    9. Bullock, David S. & Dadakas, Dimitrios & Katranidis, Stelios D., 2009. "Measuring the Effects of Technology Change in Multiple Markets : Application to the Greek Cotton Yarn Industry," MPRA Paper 67204, University Library of Munich, Germany, revised 2012.

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