Very simple marginal effects in some discrete choice models

I show a simple back-of-the-envelope method for calculating marginal effects in binary
choice and count data models. The approach suggested here focuses attention on
marginal effects at different points in the distribution of the dependent variable rather
than representative points in the joint distribution of the explanatory variables. For binary
models, if the mean of the dependent variable is between 0.4 and 0.6 then dividing the
logit coefficient by 4 or multiplying the probit coefficient by 0.4 should be moderately
accurate.