Predictive Inference on Finite Populations Segmented in Planned and Unplanned Domains

In this paper, we develop a new model-based method to inference on totals and averages of nite populations segmented in planned domains or strata. Within each stratum, we decompose the total as the sum of its sampled and unsampled parts, making inference on the unsampled part using Bayesian nonparametric methods. Additionally, we extend this method to make inference on totals of unplanned domains simultaneously modelling, within each stratum, the underlying uncertainty about the composition of the population and the totals across unplanned domains. Making inference on population averages is straightforward in both frameworks. To illustrate these methods, we develop a simulation exercise and evaluate the uncertainty surrounding the gender wage gap in Mexico.