Post-stratification sampling in small area estimation (SAE) model for unemployment rate estimation by Bayes approach

Abstract

This research implemented unemployment rate in Indonesia that based on Poisson distribution. It would be estimated by modified the post-stratification and Small Area Estimation (SAE) model. Post-stratification was one of technique sampling that stratified after collected survey data. It’s used when the survey data didn’t serve for estimating the interest area. Interest area here was the education of unemployment which separated in seven category. The data was obtained by Labour Employment National survey (Sakernas) that’s collected by company survey in Indonesia, BPS, Statistic Indonesia. This company served the national survey that gave too small sample for level district. Model of SAE was one of alternative to solved it. According the problem above, we combined this post-stratification sampling and SAE model. This research gave two main model of post-stratification sampling. Model I defined the category of education was the dummy variable and model II defined the category of education was the area random effect. Two model has problem wasn’t complied by Poisson assumption. Using Poisson-Gamma model, model I has over dispersion problem was 1.23 solved to 0.91 chi square/df and model II has under dispersion problem was 0.35 solved to 0.94 chi square/df. Empirical Bayes was applied to estimate the proportion of every category education of unemployment. Using Bayesian Information Criteria (BIC), Model I has smaller mean square error (MSE) than model II.