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Loredana Di Consiglio and Tiziana Tuoto

Abstract

Data integration is now common practice in official statistics and involves an increasing number of sources. When using multiple sources, an objective is to assess the unknown size of the population. To this aim, capture-recapture methods are applied. Standard capture-recapture methods are based on a number of strong assumptions, including the absence of errors in the integration procedures. However, in particular when the integrated sources were not originally collected for statistical purposes, this assumption is unlikely and linkage errors (false links and missing links) may occur. In this article, the problem of adjusting population estimates in the presence of linkage errors in multiple lists is tackled; under homogeneous linkage error probabilities assumption, a solution is proposed in a realistic and practical scenario of multiple lists linkage procedure.

Open access

Loredana Di Consiglio and Tiziana Tuoto

Abstract

The Capture-recapture method is a well-known solution for evaluating the unknown size of a population. Administrative data represent sources of independent counts of a population and can be jointly exploited for applying the capture-recapture method. Of course, administrative sources are affected by over- or undercoverage when considered separately. The standard Petersen approach is based on strong assumptions, including perfect record linkage between lists. In reality, record linkage results can be affected by errors. A simple method for achieving linkage error-unbiased population total estimates is proposed in Ding and Fienberg (1994). In this article, an extension of the Ding and Fienberg model by relaxing their conditions is proposed. The procedures are illustrated for estimating the total number of road casualties, on the basis of a probabilistic record linkage between two administrative data sources. Moreover, a simulation study is developed, providing evidence that the adjusted estimator always performs better than the Petersen estimator.