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Challenges in Deriving Educational Attainment from Administrative Data and Surveys

Presentation on methodological issues when deriving educational attainment from various data sources. Discusses the use of administrative data for official statistics, quality concerns, and micro-integration processes for harmonization, corrections, and deriving data. Concludes with insights on the complexities of combining data sources.

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Challenges in Deriving Educational Attainment from Administrative Data and Surveys

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  1. Could that be true?Methodological issues when deriving educational attainment from different administrative datasources and surveys Presentation for the IAOS Conference on Reshaping Official Statistics Shanghai, October 14-16, 2008 Bart F.M. Bakker Manager Section Socio-Economic State Statistics Netherlands

  2. The problem • Increasing use of administrative data for official statistics, because: • lower costs • smaller response burden • covering all elements of the population for small domain statististics • Surveys only additional • The problem: • unknown or poor quality of part of the administrative data • unknown or poor quality of statistical outcomes if administrative sources are combined

  3. General idea • Administrative data are collected with one or more traditional survey techniques, so: • they have the same errors • as traditional surveys • The size of the errors depends on the audits the register keeper execute • Variables that are important to the register keeper are assumed to be of better quality

  4. An example: educational attainment • The goal of the project • Determining the educational attainment of as many persons as possible • that can be used to derive a background variable for all kinds of research • and, if the validity is reasonable, can be used for the estimation of the educational attainment in small areas and small subgroups • not one register available

  5. Sources • CRIHO: students in higher education from 1986 • ERR: students who did an exam in general secondary education from 1999 • Education Number Registers: students in secondary general education from 2004 • CWI: job-seekers who are registered as such in the employment exchange from 1990 • WSF: students with student grants from 1999 • LFS: 1% samples from the population aged >15 from 1996

  6. Micro-integration: harmonisation • Determine the classification of educational attainment • Harmonise the copied information on the training programmes • Derive the classification • Derive information whether certificates are attained • The date that the certificates are attained

  7. Micro-integration: correction for measurement errors • Is the educational attainment valid at the reference date? • Border that the probability is <5% that someone will attain a higher level • 2. Probability <5% that someone has attained a higher level since the latest certificate is attained • Both empirically determined with the use of life tables

  8. Micro-integration: correction for measurement errors • For one person on one reference date more than one valid score on educational attainment is available • Choose the source with the best quality: • CRIHO, Education Number Register, ERR • LFS • WSF CWI only for weighting

  9. Derive educational attainment • Derive the highest educational level attained from: • all followed training programmes before reference date • the certificates that are attained before reference date • validity on reference date • choose source with best quality • downgrade the followed training programmes not ended with a certificate • impute with the use of age <15 years

  10. 100% 90% 80% register 15+ 70% 60% 50% LFS 15+ 40% coverage 30% PR 0-14 + register 15+ 20% + LFS 15+ 10% 0% 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 Results: coverage age

  11. Weighting the data • Coverage shows selectivity • underrepresentation of vocational education on secondary level • overrepresentation of youngsters • Weight to the population, result in two vectors • the valid scores on educational attainment on reference date and • a weight

  12. Conclusions • Administrative data have the same errors as traditional surveys • And some more… • Combining data from registers and surveys is promising • But complicated • Always do research on the quality of the administrative data

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