Small area estimation of poverty indicators

By Isabel Molina, J. Rao. 2010

325 citations recorded by the source corpus.

The authors propose to estimate nonlinear small area population parameters by using the empirical Bayes (best) method, based on a nested error model. They focus on poverty indicators as particular nonlinear parameters of interest, but the proposed methodology is applicable to general nonlinear parameters. They use a parametric bootstrap method to estimate the mean squared error of the empirical best estimators. They also study small sample properties of these estimators by model‐based and design‐based simulation studies. Results show large reductions in mean squared error relative to direct area‐specific estimators and other estimators obtained by “simulated” censuses. The authors also apply the proposed method to estimate poverty incidences and poverty gaps in Spanish provinces by gender with mean squared errors estimated by the mentioned parametric bootstrap method. For the Spanish data, results show a significant reduction in coefficient of variation of the proposed empirical best estimators over direct estimators for practically all domains. The Canadian Journal of Statistics 38: 369–385; 2010 © 2010 Statistical Society of Canada

Subfields: General Agricultural and Biological Sciences, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f27deb82624d7967bee8ebacc24d0eb4a44addbd

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In General Agricultural and Biological Sciences

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Small area estimation of complex parameters under unit‐level models with skew‐normal errors (2018, methodology): “Empirical studies of the traditional ELL method by Molina and Rao (2010) have revealed very high MSEs compared with the best EB estimator and even higher than the MSE of the direct estimator under the Normal model. In the simulation study of Section 6, similar results as in Molina and Rao (2010) are”
New important developments in small area estimation (2013, methodology): “” For α = 1,2 it is practically impossible to assign a distribution for the measures Fαij , and in order to estimate the means Fαi in sampled and nonsampled areas, Molina and Rao (2010) assume the existence of a one-to-one transformation yij = T (Eij) such that the transformed outcomes yij satisfy t”
Poverty mapping in small areas under a twofold nested error regression model (2017, methodology): “According to Molina and Rao (2010), for a sampled subdomain t, the EB estimator of δdt is obtained by preserving the h.Ydtj/ values corresponding to the sample units and predicting those corresponding to the non-sampled units, i.e. δ̂ EB dt = 1 Ndt { ∑ j∈sdt h.Ydtj/+ ∑ j∈Udt−sdt E[h.Ydtj/|ys; θ̂] }…”
Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach (2014, methodology): “Hierarchical Bayesian models have been extensively used in small area estimation; see Rao (2003), Chapter 10 and Datta (2009).”