A MULTINOMIAL LOGISTIC MODEL OF GLOBAL TROPICAL DEFORESTATION by

Researchers have failed to reach on a consensus about the impact of different factors on tropical deforestation. Such failure has its origins in the problems with: deforestation data, hypothesizing the one-way effect of explanatory variables on deforestation, and estimation techniques. In this paper, these problems are dealt with. The data problem is taken care of by classifying the data on rate of deforestation into three classes. Our hypothesis is that any explanatory variable may have both positive and negative effect on deforestation, and the net effect will vary in different circumstances. Multinomial logistic regression is used to address some of the estimation issues. Only underlying causes of deforestation from different sectors of an economy are used as the independent variables. Data set for 64 tropical countries spread over Africa, Asia, and Latin America for two periods, 1980-90 and 1990-95 is used to estimate the deforestation model. Results indicate that same explanatory variable has different impacts on deforestation across the countries of three classes – high, medium, and low deforestation