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This paper presents a Bayesian analysis of poverty rates in urban Ho Chi Minh City and rural Nghe An province in Vietnam. Using mixtures of beta distributions as priors for the poverty rates, we find that, when the prior is reasonably informative, our approach yields more accurate estimated poverty rates than a frequentist approach. On the other hand, we find that, in the presence of poor/non-poor misclassification, average probabilities of posterior credible intervals for poverty rates can fall well short of .95 even with sample sizes such as 2000 or 3000 when the width of the interval is for example four percentage points. In general, we suggest reporting prior and posterior means and standard deviations along with traditional frequentist measures. Our results rely on techniques due to Nandram and Sedransk (1993) and Rahme, Joseph and Gyorkos (2000), and make use of the software WINBUGS.