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The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

Correction to: Relaxing the import proportionality assumption in multi-regional input–output modelling

The Original Article was published on 09 October 2021

Correction to: Econ Struct (2021) 10:20 https://doi.org/10.1186/s40008-021-00250-8

The original version of this article unfortunately contained a mistake. The resolution of Figs. 3–7 was low (Schulte et al. 2021).

The correction contains the high-resolution figures (Figs. 3, 4, 5, 6 and 7).

Fig. 3
figure 3

A The relative variability (CV) of the national footprints compared to their absolute size assuming proportional import shares. Boxplots show the distributions of the CVs across the 49 regions to facilitate comparison between the different types of footprints. Country codes according to ISO 3166-1 alpha-3 except RoW regions (see Additional file 5). B The sample distributions of the national footprints exemplary for some selected interesting regions. The footprints where normalised by dividing each sample by the mean of all 4897 samples. The violin plots show the probability density and the range of all samples. The boxplots show the inter-quartile range (IQR) where 50% of all samples are situated (boxes), the sample’s median (horizontal line) and the range from the 2.5th to the 97.5th percentile (whiskers). The red points show the footprints size assuming proportional import shares, also normalised by the mean of all samples

Fig. 4
figure 4

The absolute variability of the national footprints expressed as variability in the contribution to the global total impacts. The bars cover the range between the 2.5th and 97.5th percentiles. The grey whiskers extend to the min/max of the sample. The regions are ordered by their SD from top (high) to bottom (low). All regions with a share of less than 1% of the global impact are not included in the figure

Fig. 5
figure 5

The relative variability (CV) of the industry footprints compared to their absolute size assuming proportional import shares. The 10 industries with the smallest non-zero footprint and all industries with a zero footprint are not shown. Boxplots show the distributions of the CVs across all industries with a non-zero footprint to facilitate comparison between the different types of footprints

Fig. 6
figure 6

The sample distributions of the industry footprints exemplary for selected industries. The footprints where normalised by dividing each sample by the mean of all 4897 samples. The violin plots show the probability density and the range of all samples. The boxplots show the IQR (boxes), the sample’s median (vertical line) and the range from the 2.5th to the 97.5th percentile (whiskers). The red points show the footprints size assuming proportional import shares, also normalised by the mean of all samples

Fig. 7
figure 7

The sample distributions of the top five industries with the highest SD for each footprint category. The industries are ordered by their SD from left (high) to right (low). The red points show the footprints size assuming proportional import share

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Schulte, S., Jakobs, A. & Pauliuk, S. Correction to: Relaxing the import proportionality assumption in multi-regional input–output modelling. Economic Structures 10, 26 (2021). https://doi.org/10.1186/s40008-021-00258-0

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