An emerging body of knowledge has established that poorer households in forest adjacent
communities in developing countries are generally more forest reliant (higher forest income share)
while richer households tend to extract more and generate higher absolute forest income. These
studies commonly categorize households based on observed income in cross-section data,
presenting a snap-shot reflecting both inter-household and inter-annual income variation. In this
paper we introduce a new approach to categorize households based on a combination of the
observed one-year income and predicted income by an augmented asset approach. Applying this
approach on household data from Tanzania, we find forest reliance to be high among structurally
poor households (low observed income and assets). The highest forest reliance is, however, found
among the stochastically non-poor households (high observed income and low assets), and this
group also has the highest absolute forest income.