The Adoption of Agricultural Technology - Food Security Group

opposite strategy of throwing as many independent variables as possibleinto the equation is not advisable either. The use of such techniques will beefficient only if we have a clear understanding of the data with which weare working.CoeffIcIentProIMIbIIftyConstantTENMONAGE-1.3670.8931.868-0.0120.4810.3530.2660.009-2.8422.5317.016-1.4450.0050.0110.0000.148Sample sizeLog of likelihood function318-192.7Chi-square statistic for significance of equation 71.4Degrees of freedom for chi-square statistic 3Significance level for chi-square statistic 0.000Cases correctly predicted 72.4%One way of presenting information from probit or logit regressions is toshow how changing one independent variable alters the probability that agiven individual is an adopter. In ordinary least squares regression, acoefficient can be interpreted directly as the change in the vi.llue of thedependent variable associated with a change in one unit of the independentvariable associated with that coefficient. This is not true in probit or logitregressions; the change in probability of adoption given a change in one ofthe independent variables depends not only on the coefficient of thatvariable but also on the levels of all the other independent variables.As an example, the estimated probability of adoption in a logit model isgiven by:probability of adoption = F(b'x),where:F(b'x)1 + e-b'xis the cumulative logistic probability distribution. The expression b'x isdefined as:78