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Open access

Arnout van Delden, Boris Lorenc, Peter Struijs and Li-Chun Zhang

Open access

Joep Burger, Arnout van Delden and Sander Scholtus

Abstract

For policymakers and other users of official statistics, it is crucial to distinguish real differences underlying statistical outcomes from noise caused by various error sources in the statistical process. This has become more difficult as official statistics are increasingly based upon a mix of sources that typically do not involve probability sampling. In this article, we apply a resampling method to assess the sensitivity of mixed-source statistics to sourcespecific classification errors. Classification errors can be seen as coverage errors within a stratum. The method can be used to compare relative accuracies between strata and releases, it can assist in deciding how to optimally allocate resources in the statistical process, and it can be applied in evaluating potential estimators. A case study on short-term business statistics shows that bias occurs especially for those strata that deviate strongly from the mean value in other strata. It also suggests that shifting classification resources from small and mediumsized enterprises to large enterprises has virtually no net effect on accuracy, because the gain in precision is offset by the creation of bias. The resampling method can be extended to include other types of nonsampling error.

Open access

Arnout van Delden, Sander Scholtus and Joep Burger

Abstract

Publications in official statistics are increasingly based on a combination of sources. Although combining data sources may result in nearly complete coverage of the target population, the outcomes are not error free. Estimating the effect of nonsampling errors on the accuracy of mixed-source statistics is crucial for decision making, but it is not straightforward. Here we simulate the effect of classification errors on the accuracy of turnover-level estimates in car-trade industries. We combine an audit sample, the dynamics in the business register, and expert knowledge to estimate a transition matrix of classification-error probabilities. Bias and variance of the turnover estimates caused by classification errors are estimated by a bootstrap resampling approach. In addition, we study the extent to which manual selective editing at micro level can improve the accuracy. Our analyses reveal which industries do not meet preset quality criteria. Surprisingly, more selective editing can result in less accurate estimates for specific industries, and a fixed allocation of editing effort over industries is more effective than an allocation in proportion with the accuracy and population size of each industry. We discuss how to develop a practical method that can be implemented in production to estimate the accuracy of register-based estimates.