COMBINE produces error-corrected figures on the number of individuals receiving a social assistance benefit in the Netherlands and the transition rate out of social assistance. Social assistance benefits are an important pillar of the welfare state. Policy making on social assistance benefits is based on official statistics that are produced by National Statistical Institutes with the use of register data. COMBINE is carried out in joint efforts of the Vrije Universiteit Amsterdam and Statistics Netherlands.
COMBINE produces error-corrected figures on the number of individuals receiving a social assistance benefit in the Netherlands and the transition rate out of social assistance. Social assistance benefits are an important pillar of the welfare state. Policy making on social assistance benefits is based on official statistics that are produced by National Statistical Institutes with the use of register data. COMBINE is carried out in joint efforts of the Vrije Universiteit Amsterdam and Statistics Netherlands.
COMBINE is a collaborative project between the Vrije Universiteit Amsterdam and Statistics Netherlands that enhances the accuracy of official statistics on social assistance benefits. It aims to produce error-corrected figures on the number of individuals receiving social assistance and their transition rate out of assistance.
Social assistance benefits are a fundamental pillar of the welfare state. However, traditional register data used to compile official statistics often contain measurement errors , which can distort policy-making decisions. COMBINE seeks to correct these inaccuracies to provide more reliable insights .
The project applies a model-based error-detection and correction method using a hidden Markov model. This approach triangulates data from the municipal register and the employment register to minimize uncertainty and improve the precision of official statistics.
Unlike conventional data-cleaning methods, COMBINE’s statistical model does not require prior knowledge of the causes of measurement errors. This makes it a more efficient and scalable solution for improving data quality across various official statistics.
By implementing model-based error correction, COMBINE will boost confidence in official statistics and encourage evidence-based policy-making. The methodology can be applied beyond social assistance benefits, improving a wide range of statistics managed by National Statistical Institutes.
COMBINE is working on a proof of concept, an implementation plan for extending the method to other official statistics, and a dissemination plan to engage policymakers and key stakeholders.
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