1. bookVolume 37 (2021): Issue 3 (September 2021)
    Special Issue on Population Statistics for the 21st Century
Journal Details
License
Format
Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
access type Open Access

Optimal Sampling for the Population Coverage Survey of the New Italian Register Based Census

Published Online: 13 Sep 2021
Page range: 655 - 671
Received: 01 Sep 2019
Accepted: 01 Nov 2020
Journal Details
License
Format
Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
Abstract

For the first time in 2018 the Italian Institute of Statistics (Istat) implemented the annual Permanent Population Census which relies on the Population Base Register (PBR) and the Population Coverage Survey (PCS). This article provides a general overview of the PCS sampling design, which makes use of the PBR to correct population counts with the extended dual system estimator (Nirel and Glickman 2009). The sample allocation, proven optimal under a set of precision constraints, is based on preliminary estimates of individual probabilities of over-coverage and under-coverage. It defines the expected sample size in terms of individuals, and it oversamples the sub-populations subject to the risk of under/over coverage. Finally, the article introduces a sample selection method, which to the greatest extent possible satisfies the planned allocation of persons in terms of socio-demographic characteristics. Under acceptable assumptions, the article also shows that the sampling strategy enhances the precision of the estimates.

Keywords

Alleva, G. 2017. “The new role of sample surveys in official statistics.” Paper presented at 5th Italian Conference on Survey Methodology – Itacosm 2017, June 14–17, 2017, Bologna. Available at: https://www.istat.it/it/files//2015/10/Alleva_ITACOSM_14062017.pdf (accessed September 2019). Search in Google Scholar

Buglielli, T., De Vitiis C., Barcaroli G. 2013. MAUSS-R. R package version 1.1. Available at: https://www.istat.it/en/methods-and-tools/methods-and-it-tools/design/design-tools/mauss-r (accessed June 2021). Search in Google Scholar

Cochran, W.G. 1977. Sampling Techniques. Wiley. New York. Search in Google Scholar

Falorsi, P.D., and P. Righi. 2015. “Generalized framework for defining the optimal inclusion probabilities of one-stage sampling designs for multivariate and multi-domain surveys.” Survey Methodology 41: 215–236. Available at: https://www150.statcan.gc.-ca/n1/pub/12-001-x/2015001/article/14149-eng.pdf (accessed June 2021). Search in Google Scholar

Istat. 2016. Istat’s Modernisation Programme. Available at: https://www.istat.it/en/files/2011/04/IstatsModernistionProgramme_EN.pdf (accessed June 2020). Search in Google Scholar

Kish, L. 1965. Survey Sampling. New York: Wiley. Search in Google Scholar

Mancini, L., and A. Ronconi. 2017. “La rilevazione sperimentale 2015 per l’indagine C-sample del censimento permanente della popolazione: copertura delle liste anagrafiche comunali e confronto con i risultati del 2011 (with abstract in English).” Istat Working Paper 2017. DOI: https://doi.org./10.13140/RG.2.2.28529.79202. Search in Google Scholar

Nirel, R., and H. Glickman. 2009. “Sample Surveys and Censuses.” In Handbook of Statistics ed. D. Pfeffermann and C.R. Rao. 539–565. Elsevier. Search in Google Scholar

Pfeffermann, D. 2015. “Methodological Issues and Challenges in the Production of Official Statistics.” Journal of Survey Statistics and Methodology, 3: 425–483. DOI: https://doi.org/10.1093/jssam/smv035. Search in Google Scholar

Singh, A.C., and C.A. Mohl. 1996. “Understanding Calibration Estimators in Survey Sampling.” Survey Methodology 22: 107–115. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/1996002/article/2973-eng.pdf?st=8C3F8n32 (accessed June 2021). Search in Google Scholar

Tillé, Y. 2006. Sampling Algorithm. New York: Springer. Search in Google Scholar

Wolter, K.M. 1986. “Some Coverage Error Models for Census Data.” Journal of the American Statistical Association, 81: 338–346. DOI: https://doi.org/10.1080/01621459.1986.10478277. Search in Google Scholar

Zardetto, D. 2020. Package ReGenesees R Evolved Generalized Software for Sampling Estimates and Errors in Surveys. R package version 2.0. Available at: https://www.istat.it/en/methods-and-tools/methods-and-it-tools/process/processing-tools/regenesees (accessed June 2021). Search in Google Scholar

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