National poverty rates are traditionallymeasured using survey data. To allow for frequent monitoringand 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 orexpenditure survey cannot produce statistically reliablepoverty estimates for small geographic units. This reportdiscusses two means of addressing the issue. The first iscommonly referred to as poverty mapping, and derivesestimates of monetary poverty as it was officially measuredin Tajikistan at the time of the surveys used in theanalysis. The second is a multi-dimensional poverty index(MPI) that combines information about individualdeprivations to summarize a complimentary, but unofficial,measure of poverty incidence. Poverty mapping is a powerfulapproach to measuring welfare for highly disaggregatedgeographic units. A variety of poverty mapping methods havebeen devised to overcome the increasing imprecision ofpoverty estimates as they are disaggregated. The standardstrategy for estimating a poverty map involves three mainstages: (a) identify a comparable set of variables thatappear in both the census and the household survey; (b)estimate consumption as a function of the comparable set ofvariables; and (c) compute welfare indicators on censusrecords based on the parameters derived from the estimationscarried out on data from the household survey.