8,318 research outputs found
Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach
Poverty maps are used to aid important political decisions such as allocation
of development funds by governments and international organizations. Those
decisions should be based on the most accurate poverty figures. However, often
reliable poverty figures are not available at fine geographical levels or for
particular risk population subgroups due to the sample size limitation of
current national surveys. These surveys cannot cover adequately all the desired
areas or population subgroups and, therefore, models relating the different
areas are needed to 'borrow strength" from area to area. In particular, the
Spanish Survey on Income and Living Conditions (SILC) produces national poverty
estimates but cannot provide poverty estimates by Spanish provinces due to the
poor precision of direct estimates, which use only the province specific data.
It also raises the ethical question of whether poverty is more severe for women
than for men in a given province. We develop a hierarchical Bayes (HB) approach
for poverty mapping in Spanish provinces by gender that overcomes the small
province sample size problem of the SILC. The proposed approach has a wide
scope of application because it can be used to estimate general nonlinear
parameters. We use a Bayesian version of the nested error regression model in
which Markov chain Monte Carlo procedures and the convergence monitoring
therein are avoided. A simulation study reveals good frequentist properties of
the HB approach. The resulting poverty maps indicate that poverty, both in
frequency and intensity, is localized mostly in the southern and western
provinces and it is more acute for women than for men in most of the provinces.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS702 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach
Small area estimation and in particular the estimation of small area income deprivation has
potential value in the development of new or alternative components of multiple deprivation
indices. These new approaches enable the development of income distribution threshold based
as opposed to benefit count based measures of income deprivation and so enable the
alignment of regional and national measures such as the Households Below Average Income
with small area measures. This paper briefly reviews a number of approaches to small area
estimation before describing in some detail an iterative proportional fitting based spatial
microsimulation approach. This approach is then applied to the estimation of small area HBAI
rates at the small area level in Wales in 2003-5. The paper discusses the results of this
approach, contrasts them with contemporary ‘official’ income deprivation measures for the
same areas and describes a range of ways to assess the robustness of the results
A map of the poor or a poor map?
This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study (LSMS) surveys. The estimation methods considered are that of Elbers, Lanjouw and Lanjouw (2003), the empirical best predictor of Molina and Rao (2010), the twofold nested error extension presented by Marhuenda et al. (2017), and finally an adaptation, presented by Nguyen (2012), that combines unit and area level information, and which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared error (MSE). Results from design-based validation show that all small area estimation methods represent an improvement, in terms of MSE, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias is unknown ex ante, methods relying only on aggregated covariates should be used with caution, but may be an alternative to traditional area level models when these are not applicable.This research was funded by Ministry of Economy, Industry and Competitiveness grant
numbers MTM2015-69638-R (MINECO/FEDER, UE) and MTM2015-72907-EXP
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