Population Size Estimation Using Multiple Incomplete Lists with Overcoverage

Di Cecco Davide (), Di Zio Marco (), Filipponi Danila () and Rocchetti Irene ()
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Di Cecco Davide: Italian National Statistical Institute, via Cesare Balbo 16, Rome00184, Italy.
Di Zio Marco: Italian National Statistical Institute, via Cesare Balbo 16, Rome00184, Italy.
Filipponi Danila: Italian National Statistical Institute, via Cesare Balbo 16, Rome00184, Italy.
Rocchetti Irene: Italian National Statistical Institute, via Cesare Balbo 16, Rome00184, Italy.

Journal of Official Statistics, 2018, vol. 34, issue 2, 557-572

Abstract: The quantity and quality of administrative information available to National Statistical Institutes have been constantly increasing over the past several years. However, different sources of administrative data are not expected to each have the same population coverage, so that estimating the true population size from the collective set of data poses several methodological challenges that set the problem apart from a classical capture-recapture setting. In this article, we consider two specific aspects of this problem: (1) misclassification of the units, leading to lists with both overcoverage and undercoverage; and (2) lists focusing on a specific subpopulation, leaving a proportion of the population with null probability of being captured. We propose an approach to this problem that employs a class of capturerecapture methods based on Latent Class models. We assess the proposed approach via a simulation study, then apply the method to five sources of empirical data to estimate the number of active local units of Italian enterprises in 2011.

Keywords: Multisource integration; capture-recapture models; latent class models; missing data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:2:p:557-572:n:14

DOI: 10.2478/jos-2018-0026

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