Small Area Estimation for ILO-Unemployment

Stephen Haslett
Massey University

Alasdair Noble
Massey University

Felibel Zabala
Statistics New Zealand

This research fits hierarchical Bayes models under a superpopulation structure to provide sound Territorial Local Authority level estimates of International Labour Organisation (ILO) unemployment. The models are fitted via R and WinBUGS using Markov Chain Monte Carlo techniques and are based on strong priors developed from extensive historical information. Unemployed count models combine survey information on ILO unemployment from the quarterly Household Labour Force Survey (HLFS) with monthly Ministry of Social Development (MSD) information on registered unemployed for the period first quarter 2001 to first quarter 2006. The accuracy of estimates is good for levels at which sample sizes in HLFS are otherwise too small, and the method also allows monitoring of changes of model parameters over time. Relative risk models, which incorporate census population projections, are also fitted. The outcome is improved and potentially publishable ILO based estimates of unemployment at a finer geographic level than is currently possible from the HLFS alone. The research was funded under the Statistics New Zealand OSRDAC Official Statistics Research programme.

Session 2d, Social Data: 16:20 — 16:40, Room 031

Presentation Program