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Assessing structural change in agriculture with a parametric Markov chain model. Illustrative applications to EU-15 and the USA

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  • Piet, Laurent

Abstract

The Markov chain model (MCM) has become a popular tool in the agricultural economics literature to explain the past evolution of and simulate the future developments in the number and size distribution of farms. In this paper, I show that the way MCMs have been implemented by agricultural economists so far suffers from the fact that transition probabilities are estimated as almost independent variables (up to adding-up to unity constraints). The alternative parametric MCM I propose addresses the deriving issues since (i) it is parsimonious in terms of parameters; (ii) it can be estimated with simple econometric techniques; (iii) it reveals detailed information on the structural change processes at hand. Applying it to experimentally controlled data with noise shows that the proposed model behaves well and competes with the traditional approach without any significant shortcoming. Two illustrative empirical applications, one using data from the EU-15 Farm Accounting Data Network (FADN) and the other using data from the USA Agricultural Resource Management Survey (ARMS), reveal the rich information that can be derived regarding the economic size changes experienced annually by farms in both regions.

Suggested Citation

  • Piet, Laurent, 2011. "Assessing structural change in agriculture with a parametric Markov chain model. Illustrative applications to EU-15 and the USA," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114668, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaae11:114668
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    References listed on IDEAS

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    1. Jeffrey M. Gillespie & Joan R. Fulton, 2001. "A Markov chain analysis of the size of hog production firms in the United States," Agribusiness, John Wiley & Sons, Ltd., vol. 17(4), pages 557-570.
    2. Stokes, Jeffrey R., 2006. "Entry, Exit, and Structural Change in Pennsylvania's Dairy Sector," Agricultural and Resource Economics Review, Cambridge University Press, vol. 35(02), pages 357-373, October.
    3. Karantininis, Kostas, 2002. "Information-based estimators for the non-stationary transition probability matrix: an application to the Danish pork industry," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 275-290, March.
    4. Krenz, Ronald D., 1964. "Projection of Farm Numbers for North Dakota With Markov Chains," Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, issue 3.
    5. T. C. Lee & G. G. Judge & T. Takayama, 1965. "On Estimating the Transition Probabilities of a Markov Process," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 47(3), pages 742-762.
    6. Axel Tonini & Roel Jongeneel, 2009. "The distribution of dairy farm size in Poland: a markov approach based on information theory," Applied Economics, Taylor & Francis Journals, vol. 41(1), pages 55-69.
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    Cited by:

    1. Saint-Cyr, Legrand D. F., 2016. "Farm segmentation and agricultural policy impacts on structural change: evidence from France," 149th Seminar, October 27-28, 2016, Rennes, France 244789, European Association of Agricultural Economists.
    2. repec:bla:jorssc:v:66:y:2017:i:4:p:777-795 is not listed on IDEAS
    3. Saint-Cyr, Legrand D. F. & Piet, Laurent, 2014. "Movers and Stayers in the Farming Sector: Another Look at Heterogeneity in Structural Change," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183068, European Association of Agricultural Economists.
    4. Saint-Cyr, Legrand D. F., 2016. "Accounting for farm heterogeneity in the assessment of agricultural policy impacts on structural change," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235778, Agricultural and Applied Economics Association.
    5. Legrand D. F. Saint-Cyr & Laurent Piet, 2017. "Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 777-795, August.

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    Keywords

    Agribusiness; Farm Management;

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