Robust Estimation of Monthly Employment Growth Rates for Small Areas in the Current Employment Statistics Survey

Julie B. Gershunskaya and Partha Lahiri


Each month, the Bureau of Labor Statistics publishes estimates of employment for industrial supersectors at the metropolitan statistical area (MSA) level. The survey-weighted ratio estimator that is used to produce estimates for large domains is generally less reliable for MSA level estimation due to the unavailability of adequate sample from a given MSA. We also note that the effect of a few establishments, which are influential in terms of unusual employment numbers or sampling weights or both, could be prominent for the small area estimation. In this paper, we develop an empirical hierarchical Bayes method based on a unit level model. Empirical evaluation using the population data from administrative file shows our proposed method to be less sensitive to influential establishments when compared to the direct survey-weighted ratio estimator or estimators based on an area level model.