<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
<p>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 the precision of the area estimates, making it possible to break down the national figures by municipality.</p>
</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 the precision of the area estimates, making it possible to break down the national figures by municipality.

Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data

Online veröffentlicht: 15. Juni 2020

Seitenbereich: 251 - 274

Eingereicht: 01. Nov. 2018

Akzeptiert: 01. Jan. 2020

DOI: https://doi.org/10.2478/jos-2020-0014

Schlüsselwörter
Literacy, basic skills, municipality, region, small area estimation

© 2020 Ineke Bijlsma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Details zum Zusatzmaterial

Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data

Online veröffentlicht: 15. Juni 2020

Seitenbereich: 251 - 274

Eingereicht: 01. Nov. 2018

Akzeptiert: 01. Jan. 2020

DOI: https://doi.org/10.2478/jos-2020-0014

SchlüsselwörterLiteracy, basic skills, municipality, region, small area estimation

© 2020 Ineke Bijlsma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Details zum Zusatzmaterial

Schlüsselwörter
Literacy, basic skills, municipality, region, small area estimation