Tackling undercoverage in educational attainment registers using large-scale survey data: Statistics Netherlands' Educational Attainment File.

IF 2.2 Q3 HEALTH CARE SCIENCES & SERVICES
International Journal of Population Data Science Pub Date : 2026-07-06 eCollection Date: 2026-01-01 DOI:10.23889/ijpds.v11i5.3747
Christian Fang, Karen van Hedel, Bram Hogendoorn, Johan van Rooijen
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Abstract

Educational attainment is a key variable in social-scientific research. In the Netherlands, most information on educational attainment is obtained from various registers of the Ministry of Education. These cover student enrollments in nearly all Dutch education institutions from the 1980s onwards. However, some residents have attained (additional) education before these registers came into existence, abroad, or at institutions not included in these registers (e.g. private institutions). This means that for 29% of the population information on education is entirely absent, whereas for some of the observed 71%, educational attainment is underestimated. Here, we present a unique method developed to deal with these deficiencies. By combining and integrating administrative data and data from the Dutch Labour Force Survey in a unique way, Statistics Netherlands can make valid inference to the population level on the basis of incomplete coverage. We compute weights for individuals not observed in any register but who were observed in the survey, with these survey observations effectively being treated as though they are a random sample from the unobserved population. Furthermore, we impute scores for individuals observed in only registers to correct for the undercoverage of education. Finally, a bootstrap approach is applied to assess uncertainty of statistical outcomes. Taken together, this method allows valid inference to the population level, generating point estimates as well as estimates of uncertainty.

利用大规模调查数据解决教育程度登记册覆盖不足的问题:荷兰统计局的教育程度档案。
教育程度是社会科学研究的一个关键变量。在荷兰,关于教育程度的大多数资料是从教育部的各种登记册获得的。这些数据涵盖了自20世纪80年代以来几乎所有荷兰教育机构的学生入学情况。然而,一些居民在这些登记册存在之前,在国外或在这些登记册未包括的机构(例如私立机构)接受(额外)教育。这意味着29%的人口完全没有教育信息,而在观察到的71%的人口中,有些人的受教育程度被低估了。在这里,我们提出了一种独特的方法来处理这些缺陷。通过以独特的方式将行政数据与荷兰劳动力调查的数据结合和整合,荷兰统计局可以在不完全覆盖的基础上对人口水平作出有效的推断。我们计算未在任何登记中观察到但在调查中观察到的个体的权重,这些调查观察结果被有效地视为来自未观察到的人群的随机样本。此外,我们为仅在登记册中观察到的个人计算分数,以纠正教育覆盖率不足的问题。最后,采用自举法评估统计结果的不确定性。总的来说,这种方法允许对总体水平进行有效的推断,产生点估计以及对不确定性的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
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