2026-09-23T17:31:23Zhttps://mro.massey.ac.nz/server/oai/requestoai:mro.massey.ac.nz:10179/20532025-11-12T14:03:51Zcom_10179_294col_10179_296
DSpace at Massey University
author
Isidro, Marissa Cinco
2011-01-12T19:29:44Z
NO_RESTRICTION2011-01-12T19:29:44Z
2010
http://hdl.handle.net/10179/2053
Q112883561
https://www.wikidata.org/wiki/Q112883561
Small area estimation (SAE) involves tting statistical models to generate statistics
for areas where the sample size of the survey data is insu cient for generating precise
estimates. A recent application of SAE techniques is in estimating local level poverty
measures in Third World countries necessary for aid allocation and monitoring of
the Millennium Development Goals (MDGs). The SAE technique commonly known
as ELL method (Elbers et al., 2003) is extensively implemented by the World Bank
in collaboration with national statistical agencies in most Third World countries.
This technique generates estimates by tting a linear mixed model to household level
income or consumption using the survey and census data. The ELL method di ers in
various ways from the mainstream SAE techniques, two of which are emphasized in
this thesis: (1) the ELL model does not include area level e ects and (2) the model
tting technique follows a non-standard weighted generalized least squares (GLS).
Under the ELL method the survey and the census data are assumed to have been
conducted at the same time period, hence generating updated estimates of poverty
measures during non-census years is a problem. The method for SAE updating developed
in this thesis is called the Extended Structure Preserving Estimation (ESPREE)
method, an extension of the classical SAE technique called the structure preserving
estimation (SPREE) method - an approach to SAE based on a categorical data analysis
framework. The ESPREE method is structured within a generalized linear model
(GLM) framework and uses information from the most recent survey and pseudocensus
(census replicates) data to generate updated small area estimates under a
superpopulation.
The World Bank in collaboration with the National Statistical Coordination Board
in the Philippines has conducted an intercensal updating project using an ELL-based
method requiring time invariant variables. Comparison of the estimates generated
from the ELL-based and ESPREE updating method revealed substantial di erences.
The ESPREE method but not the ELL updating method generated unbiased estimates.
An in-country validation exercise conducted in the Philippines supported
the view that ESPREE based estimates, besides having theoretical advantages, also
conformed better to local experts' opinion on current poverty levels.
en
The Author
Small area estimation (SAE)
Statistical models
ELL method
Extended Structure Preserving Estimation (ESPREE)
Categorial data analysis framework
Local level poverty measures
Intercensal updating of small area estimates : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
Thesis
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