A Modeling Approach for Uncertainty Assessment of Register-based Small Area Statistics

摘要

Statistical registers have great potentials when it comes to producing statistics at detailed spatial-demographic levels. However, population totals based on statistical registers are subjected to random variations that exist in the target populationas well as errors that are associated with the registration (or measurement) process. While the former counts for heterogeneity across the areas (or domains), i.e. genuine 'signals' of interest, the latter ones are merely 'noises' in measurement. We propose a model-based sensitivity analysis approach, which allows us to distinguish between the different sources of randomness in the data, by which means the strength of the signals can be assessed against the noises. The data from the Norwegian Employer/Employee register are used to demonstrate the existence of measurement noises in administrative data sources, and to illustrate the proposed approach. We believe that both the conceptualization of the random nature of the register data and the sensitivity analysis approach can be useful for assessing detailed statistics produced from statistical registers on various subjects.