  Demand for reliable statistics at a local area (small area) level has greatly
increased in recent years. Traditional area-specific estimators based on
probability samples are not adequate because of small sample size or even zero
sample size in a local area. As a result, methods based on models linking the
areas are widely used. World Bank focused on estimating poverty measures, in
particular poverty incidence and poverty gap called FGT measures, using a
simulated census method, called ELL, based on a one-fold nested error model for
a suitable transformation of the welfare variable. Modified ELL methods leading
to significant gain in efficiency over ELL also have been proposed under the
one-fold model. An advantage of ELL and modified ELL methods is that
distributional assumptions on the random effects in the model are not needed.
In this paper, we extend ELL and modified ELL to two-fold nested error models
to estimate poverty indicators for areas (say a state) and subareas (say
counties within a state). Our simulation results indicate that the modified ELL
estimators lead to large efficiency gains over ELL at the area level and
subarea level. Further, modified ELL method retaining both area and subarea
estimated effects in the model (called MELL2) performs significantly better in
terms of mean squared error (MSE) for sampled subareas than the modified ELL
retaining only estimated area effect in the model (called MELL1).
