Adjustment of unemployment estimates based on small area estimation in Korea

Abstract

The Korean Economically Active Population Survey (EAPS) has been conducted in order to produce unemployment statistics for large areas such as metropolitan cities and provincial levels. Large areas have been designated as planned domains in the EAPS and local self-government areas (LSGAs) as unplanned domains. In this study, we suggest small area estimation methods to adjust for the unemployment statistics of LSGAs within large areas estimated directly from current EAPS data. We suggest synthetic and composite estimators under the Korean EAPS system, and for the model-based estimator we put forward the hierarchical Bayes (HB) estimator from the general multi-level model. The HB estimator we use here was introduced by You and Rao (2000). The mean square errors of the synthetic and composite estimates are derived from the EAPS data by the Jackknife method, and are used as a measure of accuracy for the small area estimates. Gibbs sampling is used to obtain the HB estimates and their posterior variances, which we use to measure precision for small area estimates. The total unemployment figures of the 10 LSGAs within the ChoongBuk Province produced by the December 2000 EAPS data have been estimated using the small area estimation methods suggested in this study. The reliability of small area estimates is evaluated by the relative standard errors or the relative root mean square errors of these estimates. Here, under the current Korean EAPS system, we suggest that the composite estimates are more reliable than other small area estimates.