Files
Abstract
In this paper, a continuous version of the
Markov Chain Model (MCM) is proposed to project the
number and the population structure of farms. It is then
applied to the population of professional French farms.
Rather than working directly with transition
probabilities as in the traditional, discontinuous, MCM,
this approach relies on the close but not identical
concept of growth rate probabilities and exploits the
Gibrat’s law of proportionate effects which appears to
be supported by the French data. It is shown that the
proposed continuous MCM is a more general approach,
since it enables to derive more in-depth detail on the
distribution of the projected population and the
traditional MCM transition probability matrix can be
easily reconstructed from the estimated growth rate
probabilities. Though the continuous MCM is presented
in this paper in a stationary framework, it should be
possible to develop a non-stationary version in a similar
way traditional MCMs are now made non-stationary.