National poverty rates are traditionally measured using survey data. To allow for frequent monitoring and to contain the costs of gathering detailed information, such surveys sample only a small subset of the population. This approach necessarily leads to sampling errors however, and as a consequence, a typical household income or expenditure survey cannot produce statistically reliable poverty estimates for small geographic units. This report discusses two means of addressing the issue. The first is commonly referred to as poverty mapping, and derives estimates of monetary poverty as it was officially measured in Tajikistan at the time of the surveys used in the analysis. The second is a multi-dimensional poverty index (MPI) that combines information about individual deprivations to summarize a complimentary, but unofficial, measure of poverty incidence. Poverty mapping is a powerful approach to measuring welfare for highly disaggregated geographic units. A variety of poverty mapping methods have been devised to overcome the increasing imprecision of poverty estimates as they are disaggregated. The standard strategy for estimating a poverty map involves three main stages: (a) identify a comparable set of variables that appear in both the census and the household survey
(b) estimate consumption as a function of the comparable set of variables
and (c) compute welfare indicators on census records based on the parameters derived from the estimations carried out on data from the household survey.