Agricultural Policy as a Social Engineering Tool

logistic distribution, and we can obtain the standard logistic regression model. That
model is chosen for this study because of its mathematical simplicity and because its
asymptotic characteristic constrains the predicted probabilities to a range between zero
and one (Maddala, 1983).

Using equation (5) and assuming that π_ij is a linear combination of the
explanatory variables, we can estimate the coefficient of each variable using maximum
likelihood estimation (MLE) because the data set contains individual rather than
aggregate observations (see Gujarati, 1992). The parameter estimates from the MLE are
consistent and asymptotically efficient (Pindyck and Rubinfeld, 1991).

Equation (5) can also be written as

e^βj

Pr ob (Y = j ) = —ɪ-                                (6)

∑ ex

k

where Y represents a discrete choice among j alternatives, and the set of parameters β
reflect the impact of changes in X on the probability. The marginal effects which are the
partial derivatives of probabilities with respect to the vector of characteristics and
computed at the means are given by,

∂P,       „

_x = Pj(β-∑ Pβ,).j = 1,2,....,m.

The model assumes that the probability of observing a particular outcome is
dependent on a vector of explanatory variables,X.

Variable Specification and Working Hypotheses

The dichotomous dependent variable (Restrict) in this study indicates the preference a
farm operator expresses for whether countries should be allowed to restrict trade to

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