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The Economics Of Cattle Supply

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  • David M. Aadland

    (Utah State University)

Abstract

The primary goal of this paper is to build a more complete model of cattle supply, which could be used to both explain aggregate cattle dynamics and, ultimately, guide policy decisions. Toward that end, I build a dynamic rational expectations model describing the supply of cattle that improves on existing models by allowing cow-calf operators to make period-by-period investment decisions on both the cow and calf margins, separates the markets for fed and unfed beef, and considers a rich set of exogenous shocks. Several interesting observations have surfaced. First, it is shown that US cattle slaughter and prices do indeed exhibit cycles. The theoretical model provides mixed evidence with regard to slaughter and price cycles, with artificial slaughter data displaying more evidence of cyclical behavior than do artificial prices. To the extent that there are price cycles in the model, it is interesting to note that they are an equilibrium result from fully optimizing agents. As such, there is no opportunity to profit through countercyclical strategies (i.e., building up stocks when prices are near the trough of the cycle and selling when prices are near the peak of the cycle).Second, the model does not exhibit the short-term negative supply response noted in Jarvis (1982), even when the shock is permanent in nature. When ranchers are allowed to make decisions along both the calf and cow margins, the response to changes in relative prices will induce a positive short-run own supply response. The perverse supply response behavior noted in Jarvis instead shows up as a negative cross price response. That is, if the price of fed beef increases, ranchers optimally supply fewer cows and vice versa.And third, as shown by the impulse response functions, the dynamic response to the various cattle time series depends on the nature of the shock driving the response, whether it be a shock to retail demand, productivity, net exports, feed costs, etc. Therefore, when policymakers react to perceived changes in the cattle industry, it is critical that they understand the nature of the shock driving the dynamics.In addition to the observations above, a fully calibrated and simulated version of the model replicates several key features of US cattle time series. The model (i) produces a similar volatility ordering to that found in the US data, (ii) replicates the sign of the contemporaneous correlations between key US cattle time series, and (iii) generates cycles in cattle stocks.Although the model fits the data well in these dimensions, it fails in others. Most importantly, the model (i) understates the volatility of prices, (ii) understates the contemporaneous correlation between different stock measures, (iii) understates the length of the cycle in stocks, and (iv) only provides mixed evidence of slaughter and price cycles. In my estimation, it is these last two shortcomings that are the most pressing research items. By building in features to our existing models that "stretch" out the cattle cycle to replicate the observed cycle will be a major move forward in our understanding of cattle dynamics. The most promising extension in this regard is to formally model the age distribution of the stock of different animals, thereby allowing age effects to contribute to cyclical dynamics. Other promising extensions include credit constraints, rancher heterogeneity, variation in seasonal timing, noncompetitive behavior at the beef-packing level, and self-fulfilling prophecies.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 57.

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Date of creation: 05 Jul 2000
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Handle: RePEc:sce:scecf0:57

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Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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References

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  1. Ravn, Morten O. & Uhlig, Harald, 2001. "On Adjusting the HP-Filter for the Frequency of Observations," CEPR Discussion Papers 2858, C.E.P.R. Discussion Papers.
  2. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November.
  3. Eckstein, Zvi, 1984. "A Rational Expectations Model of Agricultural Supply," Journal of Political Economy, University of Chicago Press, vol. 92(1), pages 1-19, February.
  4. Harry J. Paarsch, 1985. "Micro-economic Models of Beef Supply," Canadian Journal of Economics, Canadian Economics Association, vol. 18(3), pages 636-51, August.
  5. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
  6. Marsh, John M., 1991. "Derived Demand Elasticities: Marketing Margin Methods Versus An Inverse Demand Model For Choice Beef," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 16(02), December.
  7. Randal R. Rucker & Oscar R. Burt & Jeffrey T. LaFrance, 1984. "An Econometric Model of Cattle Inventories," Development Research Unit Working Paper Series archive-25, Monash University, Department of Economics.
  8. Capps, Oral, Jr. & Farris, Donald E. & Byrne, Patrick J. & Namken, Jerry C. & Lambert, Charles D., 1994. "Determinants Of Wholesale Beef-Cut Prices," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 26(01), July.
  9. Edward C. Prescott, 1986. "Theory ahead of business cycle measurement," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 9-22.
  10. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-11, July.
  11. Sherwin Rosen & Kevin M. Murphy & Jose A. Scheinkman, 1993. "Cattle Cycles," NBER Working Papers 4403, National Bureau of Economic Research, Inc.
  12. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
  13. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
  14. Nerlove, Marc & Fornari, Ilaria, 1998. "Quasi-rational expectations, an alternative to fully rational expectations: An application to US beef cattle supply," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 129-161.
  15. Mathews, Kenneth H., Jr. & Hahn, William F. & Nelson, Kenneth E. & Duewer, Lawrence A. & Gustafson, Ronald A., 1999. "U.S. Beef Industry: Cattle Cycles, Price Spreads, and Packer Concentration," Technical Bulletins 33583, United States Department of Agriculture, Economic Research Service.
  16. Yair Mundlak & He Huang, 1996. "International Comparisons of Cattle Cycles," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(4), pages 855-868.
  17. Timothy Cogley & James M. Nason, 1993. "Output dynamics in real business cycle models," Working Papers in Applied Economic Theory 93-10, Federal Reserve Bank of San Francisco.
  18. John M. Marsh, 1999. "The Effects of Breeding Stock Productivity on the U.S. Beef Cattle Cycle," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 335-346.
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Cited by:
  1. Zhao, Huan & Hennessy, David A., 2009. "Rationalizing Time Series Differences Between Cow-Calf And Feeder Returns," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49486, Agricultural and Applied Economics Association.

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