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Estimating literacy levels at a detailed regional level: An application using Dutch data

Author

Listed:
  • Bijlsma, Ineke

    (RS: GSBE DUHR, ROA / Dynamics of the labour market)

  • van den Brakel, Jan

    (RS: GSBE EFME, QE Econometrics)

  • van der Velden, Rolf

    (Macro, International & Labour Economics, RS: GSBE DUHR)

  • Allen, James

    (RS: GSBE DUHR, ROA / Education and occupational career)

Abstract

Policy measures to combat low literacy are often targeted at the level of municipalities or regions with an above-average population with low literacy levels. However, current surveys on literacy do not contain enough respondents at this level to allow for reliable estimates, at least 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 paper 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 score, as well as the percentage of people with a low literacy level. Additional complications arise, as the PIAAC framework assumes that test scores reflect an underlying latent construct. Moreover, as an adaptive design has been used with rotating modules, not all respondents are assigned the same test items. This is why an item response model is used with multiple imputation resulting in 10 so-called plausible values for the literacy proficiency level per respondent. Variance estimators for our small area predictions explicitly account for this imputation uncertainty. The average literacy score is estimated with a unit-level model, while the percentage of low literates is estimated using an area-level model utilizing pooled variance. Optimal models are selected using a conditional Akaike information criterion score. Municipalities with less than 40,000 inhabitants were clustered with neighboring municipalities to ensure sufficiently large sample sizes. The PIAAC survey is currently carried out in 36 countries. Most of these countries also have labor force surveys that contain similar information as the one used in this analysis. This opens up the possibility of applying the same method in other countries.

Suggested Citation

  • Bijlsma, Ineke & van den Brakel, Jan & van der Velden, Rolf & Allen, James, 2017. "Estimating literacy levels at a detailed regional level: An application using Dutch data," ROA Research Memorandum 006, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    • Handle: RePEc:unm:umaror:2017006
    DOI: 10.26481/umaror.2017006
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    References listed on IDEAS

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    1. Harm Jan Boonstra & Jan A. Van Den Brakel & Bart Buelens & Sabine Krieg & Marc Smeets, 2008. "Towards small area estimation at Statistics Netherlands," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 21-49.
    2. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    3. Keith F. Rust & Eugene G. Johnson, 1992. "Chapter 2: Sampling and Weighting in the National Assessment," Journal of Educational and Behavioral Statistics, , vol. 17(2), pages 111-129, June.
    4. Moretti, Enrico, 2004. "Estimating the social return to higher education: evidence from longitudinal and repeated cross-sectional data," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 175-212.
    5. Hanushek, Eric A. & Woessmann, Ludger, 2011. "The Economics of International Differences in Educational Achievement," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 2, pages 89-200, Elsevier.
    6. Bart Buelens & Jan A. van den Brakel, 2015. "Covariate selection for small area estimation in repeated sample surveys," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 523-540, December.
    7. Eric A. Hanushek & Ludger Woessmann, 2008. "The Role of Cognitive Skills in Economic Development," Journal of Economic Literature, American Economic Association, vol. 46(3), pages 607-668, September.
    8. repec:mpr:mprres:6485 is not listed on IDEAS
    9. Serena Arima & William R. Bell & Gauri S. Datta & Carolina Franco & Brunero Liseo, 2017. "Multivariate Fay–Herriot Bayesian estimation of small area means under functional measurement error," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1191-1209, October.
    10. van der Velden, Rolf & Bijlsma, Ineke, 2017. "Skill effort: A new theoretical perspective on the relation between skills, skill use, mismatches, and wages," ROA Research Memorandum 005, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    11. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    12. Lynn M. R. Ybarra & Sharon L. Lohr, 2008. "Small area estimation when auxiliary information is measured with error," Biometrika, Biometrika Trust, vol. 95(4), pages 919-931.
    13. Serge Coulombe & Jean‐François Tremblay, 2007. "Skills, Education, And Canadian Provincial Disparity," Journal of Regional Science, Wiley Blackwell, vol. 47(5), pages 965-991, December.
    14. Florin Vaida & Suzette Blanchard, 2005. "Conditional Akaike information for mixed-effects models," Biometrika, Biometrika Trust, vol. 92(2), pages 351-370, June.
    15. Peter McHenry, 2014. "The Geographic Distribution Of Human Capital: Measurement Of Contributing Mechanisms," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 215-248, March.
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    Cited by:

    1. Caio Gonçalves & Luna Hidalgo & Denise Silva & Jan van den Brakel, 2022. "Single‐month unemployment rate estimates for the Brazilian Labour Force Survey using state‐space models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1707-1732, October.
    2. van der Velden, Rolf & Bijlsma, Ineke, 2017. "Skill effort: a new theoretical perspective on the relation between skills, skill use, mismatches, and wages," Research Memorandum 013, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. van den Brakel Jan & Michiels John, 2021. "Nowcasting Register Labour Force Participation Rates in Municipal Districts Using Survey Data," Journal of Official Statistics, Sciendo, vol. 37(4), pages 1009-1045, December.

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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