Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data
Abstract: Policy measures to combat low literacy are often targeted at municipalities or regions with low levels of literacy. However, current surveys on literacy do not contain enough observations at this level to allow for reliable estimates when using only direct estimation techniques. To provide more reliable results at a detailed regional level, alternative methods must be used. The aim of this article is to obtain literacy estimates at the municipality level using model-based small area estimation techniques in a hierarchical Bayesian framework. To do so, we link Dutch Labour Force Survey data to the most recent literacy survey available, that of the Programme for the International Assessment of Adult Competencies (PIAAC). We estimate the average literacy score, as well as the percentage of people with a low literacy level. Variance estimators for our small area predictions explicitly account for the imputation uncertainty in the PIAAC estimates. The proposed estimation method improves
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Veröffentlichungsversion
begutachtet (peer reviewed)
In: Journal of Official Statistics ; 36 (2020) 2 ; 251-274
- Klassifikation
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Wirtschaft
- Ereignis
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Veröffentlichung
- (wo)
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Mannheim
- (wer)
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SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- (wann)
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2020
- Urheber
- DOI
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10.2478/jos-2020-0014
- URN
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urn:nbn:de:0168-ssoar-73322-8
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:43 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Bijlsma, Ineke
- Brakel, Jan A. van den
- Velden, Rolf van der
- Allen, James Patrick
- SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
Entstanden
- 2020
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