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Poverty maps are used to aid important political decisions such as allocationof development funds by governments and international organizations. Thosedecisions should be based on the most accurate poverty figures. However, oftenreliable poverty figures are not available at fine geographical levels or forparticular risk population subgroups due to the sample size limitation ofcurrent national surveys. These surveys cannot cover adequately all the desiredareas or population subgroups and, therefore, models relating the differentareas are needed to 'borrow strength" from area to area. In particular, theSpanish Survey on Income and Living Conditions (SILC) produces national povertyestimates but cannot provide poverty estimates by Spanish provinces due to thepoor precision of direct estimates, which use only the province specific data.It also raises the ethical question of whether poverty is more severe for womenthan for men in a given province. We develop a hierarchical Bayes (HB) approachfor poverty mapping in Spanish provinces by gender that overcomes the smallprovince sample size problem of the SILC. The proposed approach has a widescope of application because it can be used to estimate general nonlinearparameters. We use a Bayesian version of the nested error regression model inwhich Markov chain Monte Carlo procedures and the convergence monitoringtherein are avoided. A simulation study reveals good frequentist properties ofthe HB approach. The resulting poverty maps indicate that poverty, both infrequency and intensity, is localized mostly in the southern and westernprovinces and it is more acute for women than for men in most of the provinces.

Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach