We develop a poverty decomposition method that is based on a consumption regression
model. Because this method uses an integral of the partial derivatives of a poverty
measure with respect to time, the resulting poverty decomposition satisfies
time-reversion consistency and sub-period additivity. Unlike the existing poverty
decomposition methods, it allows us to ascribe the observed change in poverty to
various covariates of interest collected at a disaggregate level. This method is applied to
two datasets from Tanzania to assess, among others, the short- and long-term impacts of
infrastructure and market access on poverty