[
Arima, S., W.R. Bell, G.S. Datta, C. Franco, and B. Liseo. 2017. “Multivariate Fay-Herriot Bayesian estimation of small area means under functional measurement error.” Journal of the Royal Statistical Society, Series A 180: 1191–1209 DOI: https://doi.org/10.1111/rssa.12321.10.1111/rssa.12321
]Search in Google Scholar
[
Battese, G.E., R.M. Harter, and W.A. Fuller. 1988. “An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data.” Journal of the American Statistical Association 401: 28 – 36. DOI: https://doi.org/10.1080/01621459.1988.10478561.10.1080/01621459.1988.10478561
]Search in Google Scholar
[
Boonstra, H.J. 2015. Package ‘hbsae’ (version 1.0). Available at: https://cran.r-project.org/web/packages/hbsae/hbsae.pdf (accessed December 2015).
]Search in Google Scholar
[
Boonstra, H.J., J.A. van den Brakel, B. Buelens, S. Krieg, and M. Smeets. 2008. “Towards small area estimation at Statistics Netherlands.” METRON International Journal of Statistics LXVI: 21–49. Available at: https://EconPapers.repec.org/RePEc:mtn:ancoec:080102 (accessed April 2020).
]Search in Google Scholar
[
Buisman, M., J. Allen, D. Fouarge, W. Houtkoop, and R. van der Velden. 2013. PIAAC: Kernvaardigheden voor werk en leven. Resultaten van de Nederlandse survey 2012, Den Bosch/Maastricht: ECBO/ROA.
]Search in Google Scholar
[
Casas-Cordero, C., J. Encina, and P. Lahiri. 2016. “Poverty mapping for the Chilean Comunas.” In Analysis of Poverty Data by Small Area Estimation, edited by M. Pratesi, 379–403. Hoboken: Wiley. DOI: https://doi.org/10.1111/j.1467-9787.2007.00538.x10.1111/j.1467-9787.2007.00538.x
]Search in Google Scholar
[
Coulombe, S. and J.F. Tremblay. 2007. “Skills, Education, and Canadian Provincial Disparity.” Journal of Regional Science 47: 965–991. DOI: https://doi.org/10.2307/2669921.10.1080/01621459.1999.10473860
]Search in Google Scholar
[
Datta, G., P. Lahiri, T. Maiti, and K. Lu. 1999. “Hierarchical Bayes Estimation of Unemployment Rates for the States of the U.S.” Journal of the American Statistical Association 448: 1074–1082.10.1080/01621459.1999.10473860
]Search in Google Scholar
[
Elbers, C., J.O. Lanjouw, and P. Lanjouw. 2003. “Micro estimation of poverty and inequality.” Econometrica 71: 355 – 364. DOI: https://doi.org/10.1111/1468-0262.00399.10.1111/1468-0262.00399
]Search in Google Scholar
[
Fay, R.E. and R.A. Herriot. 1979. “Estimates of income for small places: An application of James-Stein procedures to census data.” Journal of the American Statistical Association 366: 269–277. DOI: https://doi.org/10.2307/2286322.10.2307/2286322
]Search in Google Scholar
[
Ganzeboom, H.B.G., P.M. de Graaf, and D.J. Treiman. 1992. “A Standard International Socio-Economic Index of Occupational Status.” Social Science Research 21: 1–56. DOI: https://doi.org/10.1016/0049-089X(92)90017-B.10.1016/0049-089X(92)90017-B
]Search in Google Scholar
[
Gibson, A. and P. Hewson. 2012. “2011 Skills for Life Survey: Small Area Estimation Technical Report.” BIS Research Report 81C. Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/36077/12-1318-2011-skills-for-life-small-area-estimation-technical.pdf (accessed November 2018).
]Search in Google Scholar
[
Hanushek, E.A. and L. Woessmann. 2008. “The Role of Cognitive Skills in Economic Development.” Journal of Economic Literature 46: 607–668. DOI: https://doi.org/10.3386/w15949.10.3386/w15949
]Search in Google Scholar
[
Hanushek, E.A. and L. Woessmann. 2011. The Economics of International Differences in Educational Achievement. In Handbook of the Economics of Education, Vol. 3: 89–200. Amsterdam: North Holland.10.1016/B978-0-444-53429-3.00002-8
]Search in Google Scholar
[
Hastie, T., R. Tibshirani, and J. Friedman. 2001. The elements of statistical learning. Springer: New York.10.1007/978-0-387-21606-5
]Search in Google Scholar
[
Hodges, J.S. and D.J. Sargent. 2001. “Counting degrees of freedom in hierarchical and other richly parameterized models.” Biometrika 88: 367–379. DOI: https://doi.org/10.1093/biomet/88.2.367.10.1093/biomet/88.2.367
]Search in Google Scholar
[
Johnson, E.G. and K.F. Rust. 1992. “Sampling and Weighting in the National Assessment.” Journal of Educational and Behavioral Statistics 17: 111–129. DOI: https://doi.org/10.2307/1165165.10.2307/1165165
]Search in Google Scholar
[
Lohr, S. and N. Prasad. 2003. “Small Area Estimation with Auxiliary Survey Data.” The Canadian Journal of Statistics 31: 383–396. DOI: https://doi.org/10.2307/3315852.10.2307/3315852
]Search in Google Scholar
[
McHenry, P. 2014. “The Geographic Distribution of Human Capital: Measurement of Contributing Mechanisms.” Journal of Regional Science 54: 215–248. DOI: https://doi.org/10.1111/jors.12067.10.1111/jors.12067
]Search in Google Scholar
[
National Research Council. 2000. “Small Area Estimates of School-Age Children in Poverty: Evaluation of current methodology.” Committee on National Statisitcs, edited by C.F. Citro and G. Kalton. Washington, DC: National Academy Press.
]Search in Google Scholar
[
OECD. 2013a. OECD skills outlook 2013: first results from the survey of adult skills. Paris: OECD Publishing. DOI: https://doi.org/10.1787/9789264204256-en.10.1787/9789264204256-en
]Search in Google Scholar
[
OECD. 2013b. The Survey of Adult Skills – Reader’s Companion. Paris: OECD Publishing. DOI: https://doi.org/10.1787/9789264204027-en.10.1787/9789264204027-en
]Search in Google Scholar
[
OECD. 2013c. Technical Report of the Survey of Adult Skills (PIAAC). Available at: http://www.oecd.org/site/piaac/publications.htm (accessed December 2015).
]Search in Google Scholar
[
Pokropek, A. and M. Jakubowski. 2013. Package ‘PIAAC tools’ (version 4.3). Available at: https://ideas.repec.org/c/boc/bocode/s457728.html (accessed September 2016).
]Search in Google Scholar
[
Pfeffermann, D. 2013. “New Important Developments in Small Area Estimation.” Statistical Science 28: 40–68. DOI: https://doi.org/10.1214/12-STS395.10.1214/12-STS395
]Search in Google Scholar
[
PricewaterhouseCoopers. 2013. Laaggeletterdheid in Nederland kent aanzienlijke maatschappelijke kosten. Internal Rapport, PWC, Amsterdam.
]Search in Google Scholar
[
Rao, J.N.K. and I. Molina. 2015. Small Area Estimation, Second Edition. New York: John Wiley and Sons.10.1002/9781118735855
]Search in Google Scholar
[
Rubin, D.B. 1996. “Multiple Imputation After 18 þ Years.” Journal of the American Statistical Association 434: 473–489. DOI: https://doi.org/10.2307/2291635.10.2307/2291635
]Search in Google Scholar
[
Särndal, C.E., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer Verlag.10.1007/978-1-4612-4378-6
]Search in Google Scholar
[
Schmid, T., F. Bruckschen, N. Salvati, and T. Zbiranski. 2017. “Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal.” Journal of the Royal Statistical Society Series A (Statistics in Society) 180: 1163–1190. DOI: https://doi.org/10.1111/rssa.12305Y.
]Search in Google Scholar
[
Statistics Netherlands. 2010. “Methoden en definities Enquête Beroepsbevolking 2010.” Available at: https://www.cbs.nl/nl-nl/onze-diensten/methoden/onderzoeksomschrijvingen/aanvullende%20onderzoeksbeschrijvingen/enquete-beroepsbevolking-uitgebreideonderzoeksbeschrijving-2010 (accessed March 2018).
]Search in Google Scholar
[
Statistics Netherlands. 2011. “Methoden en definities Enquête Beroepsbevolking 2011.” Available at: https://www.cbs.nl/nl-nl/onze-diensten/methoden/onderzoeksomschrijvingen/aanvullende%20onderzoeksbeschrijvingen/enquete-beroepsbevolking-uitgebreideonderzoeksbeschrijving-2011 (accessed March 2018).
]Search in Google Scholar
[
Statistics Netherlands. 2012. “Methoden en definities Enquête Beroepsbevolking 2012.” Available at: https://www.cbs.nl/nl-nl/onze-diensten/methoden/onderzoeksomschrijvingen/aanvullende%20onderzoeksbeschrijvingen/enquete-beroepsbevolking-uitgebreideonderzoeksbeschrijving-2012 (accessed March 2018).
]Search in Google Scholar
[
Taylor, J., G. Moon, and L. Twigg. 2016. “Using geocoded survey data to improve the accuracy of multilevel small area synthetic.” Social Science Research 56: 108–116. DOI: https://doi.org/10.1016/j.ssresearch.2015.12.006.10.1016/j.ssresearch.2015.12.00626857175
]Search in Google Scholar
[
Thao, L.T.P. and R. Geskus. 2019. “A comparison of model selection methods for prediction in the presence of multiply imputed data.” Biometrical Journal 61: 343–356. DOI: https://doi.org/10.1002/bimj.201700232.10.1002/bimj.201700232649221130353591
]Search in Google Scholar
[
Tighe, E., D. Livert, M. Barnett, and L. Saxe. 2010. “Cross-Survey Analysis to estimate low-incidence religious groups.” Sociological Methods & Research 39: 56–82. DOI: https://doi.org/10.1177/0049124110366237.10.1177/0049124110366237
]Search in Google Scholar
[
Vaida, F. and S. Blanchard. 2005. “Conditional Akaike information for mixed effect models.” Biometrika 92: 351–370. DOI: https://doi.org/10.1093/biomet/92.2.351.10.1093/biomet/92.2.351
]Search in Google Scholar
[
Van den Brakel, J.A. and B. Buelens. 2015. “Covariate selection for small area estimation in repeated sample surveys.” Survey Methodology and Statistics in Transition, Special issue on Small Area Estimation, Vol.16: 523–540. DOI: https://doi.org/10.21307/stattrans-2015-031.10.21307/stattrans-2015-031
]Search in Google Scholar
[
Van den Brakel, J.A. and S. Krieg. 2015. “Dealing with small sample sizes, rotation group bias and discontinuities in a rotating panel design.” Survey Methodology 41: 267 – 296. Available at: https://www150.statcan.gc.ca/n1/pub/12-001-x/2015002/article/14231-eng.pdf (accessed April 2020).
]Search in Google Scholar
[
Van der Velden, R. and I. Bijlsma. 2018. “Effective skill: a new theoretical perspective on the relation between skills, skill use, mismatches and wages.” Oxford Economic Papers, Advance articles. DOI: https://doi.org/10.1093/oep/gpy028.10.1093/oep/gpy028
]Search in Google Scholar
[
World Bank. 2002. “How Low Can You Go? Combining Census and Survey Data for Mapping Poverty in South Africa.” Journal of African Economies 11: 169–200. DOI: https://doi.org/10.1093/jae/11.2.169.10.1093/jae/11.2.169
]Search in Google Scholar
[
Yamamoto, K. 2014. Using PIAAC Data for Producing Regional Estimates. Working Paper, Educational Testing Service, Princeton.
]Search in Google Scholar
[
Ybarra, L.M.R. and S.L. Lohr. 2008. “Small area estimation when auxiliary information is measured with error.” Biometrika 95: 919–931. DOI: https:///doi.org/10.1093/biomet/asn048.10.1093/biomet/asn048
]Search in Google Scholar
[
You, Y., J.N.K. Rao, and P. Dick. 2004. “Benchmarking Hierarchical Bayes Small Area Estimators in the Canadian Census Undercoverage Estimation.” Statistics in Transition 6: 631–640. Available at: https://www.semanticscholar.org/paper/BENCHMARKING-HIERARCHICAL-BAYES-SMALL-AREA-IN-THE-You-Rao/efaafa565aa134-fe0943f03bbad15278eb228e3a (accessed April 2020).
]Search in Google Scholar
[
You, Y., J. Rao, and J. Gambino. 2003. “Model-based unemployment rate estimation for the Canadian Labour Force Survey: A Hierarchical Bayes approach.” Survey Methodology 29: 25–32. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20030016602 (accessed April 2020).
]Search in Google Scholar
