A data‐driven supply‐side approach for estimating cross‐border Internet purchases within the European Union

Q. A. Meertens, C. G. H. Diks, H. J. van den Herik and F. W. Takes

Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 1, 61-90

Abstract: The digital economy is a highly relevant item on the European Union's policy agenda. We focus on cross‐border Internet purchases, as part of the digital economy, the total value of which cannot be accurately estimated by using existing consumer survey approaches. In fact, they lead to a serious underestimation. To obtain an accurate estimate, we propose a three‐step data‐driven approach based on supply‐side data. For the first step, we develop a data‐driven generic method for firm level probabilistic record linkage of tax data and business registers. In the second step, we use machine learning to identify webshops based on website data. Then, in the third step, we implement recently developed bias correction techniques that have hitherto been overlooked by the machine learning community. Subsequently, we claim that our three‐step approach can be applied to any European Union member state, leading to more accurate estimates of cross‐border Internet purchases than those obtained by currently existing approaches. To justify the claim, we apply our approach to the Netherlands for the year 2016 and find an estimate that is six times as high as current estimates, having a standard deviation of 8%. Hence, we may conclude that our new approach deserves more investigation and applications.

Date: 2020
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