When fitting a nonlinear model such as logit (see [R] logit) or poisson (see [R] poisson), we often have two options when it comes to interpreting the regression coefficients: compute some form of marginal effect or exponentiate the coefficients, which will give us an odds ratio or incidence-rate ratio. The marginal effect is an approximation of how much the dependent variable is expected to increase or decrease for a unit change in an explanatory variable; that is, the effect is presented on an additive scale. The exponentiated coefficients give the ratio by which the dependent variable changes for a unit change in an explanatory variable; that is, the effect is presented on a multiplicative scale. An extensive overview is given by Long and Freese (2006).

This article discusses a method by Erikson et al. (2005) for decomposing a total effect in a logit model into direct and indirect effects. Moreover, this article extends this method in three ways. First, in the original method the variable through which the indirect effect occurs is assumed to be normally distributed. In this article the method is generalized by allowing this variable to have any distribution. Second, the original method did not provide standard errors for the estimates. In this article the bootstrap is proposed as a method of providing those. Third, I show how to include control variables in this decomposition, which was not allowed in the original method. The original method and these extensions are implemented in the ldecomp package.

This paper examines the factors that influence rural household decisions to clear forestland. We use a large dataset comprising 7172 households from 24 developing countries. Twenty-seven percent of sampled households had converted forest to agriculture during the previous 12 months, clearing on average 1.21 ha. Male-headed households with abundance of male labor, living in recently settled places with high forest cover, unsurprisingly tended to clear more, but regional peculiarities abounded. Households with medium to high asset holdings and higher market orientation were more likely to clear forest than the poorest and market-isolated households, questioning popular policy narratives about poverty-driven forest clearing.

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This article studies the effect of parental background on the educational attainment of the offspring. In particular, it compares the effects of parental occupation and education and investigates whether the relative importance of these resources have shifted over time. In addition, this article studies, which parent has the strongest effect on the offspring's education. Using data for the Netherlands, this article finds that occupational status has the same effect regardless of who contributed it, while for the effect of parental education, it matters whether the parent is the highest, same, or lowest educated parent. No evidence was found that the relative sizes of these effects have changed over cohorts.