The Econometrics of Risk

  • Matthew T. Holt
  • Jean-Paul Chavas
Part of the Natural Resource Management and Policy book series (NRMP, volume 23)


The purpose of this chapter is to review some of the recent relevant literature on the empirical implementation and testing of risk in agricultural production decisions. As well, it suggests several potentially fruitful areas for future investigation. In many respects agriculture and risk are synonymous. Agricultural economists have long recognized risk as a primary distinguishing feature of the agriculture sector relative to other sectors of the economy. Two overriding factors have contributed significantly to the observed variability in price, income, and production realizations for agricultural producers over time: the fact that production — and especially field crop production — is stochastic, and the received empirical truth that the demands, both domestic and foreign, for many agricultural products are highly price inelastic. The inherent biological lags that describe most physical agricultural production processes and, as well, the reliance of the agricultural sector on highly volatile international markets have also added to the instability in agriculture markets. Given these fundamental characterizing features of agricultural production and marketing environments, a primary question is: Do agricultural decision makers account for these inherent risks in any meaningful and systematic way when making production and investment decisions? As well, to what extent does the intrinsic underlying stochastic nature of agricultural production make it necessary to modify extant economic models, both theoretical and empirical, of production and producer decision making? Attempts to address these two fundamental questions have been the underlying motivating force for the research programs of many professional agricultural economists during the past twenty-five years (Pope 1982).


Risk Aversion Risk Preference Initial Wealth Conditional Moment Constant Relative Risk Aversion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Matthew T. Holt
    • 1
    • 2
  • Jean-Paul Chavas
    • 1
    • 2
  1. 1.North Carolina State UniversityMadisonUSA
  2. 2.University of WisconsinMadisonUSA

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