This repository contains all code to reproduce the results from our paper on ‘Relaxing the import proportionality assumption in multi-regional input-output anlysis’ submitted to the Journal of Economic Structures.
In the absence of data on the destination industry of international trade flowsmost multi-regional input-output (MRIO) tables are based on the importproportionality assumption. Under this assumption imported commodities areproportionally distributed over the target sectors (individual industries and finaldemand categories) of an importing region.Here, we quantify the uncertainty arising from the import proportionalityassumption on the four major environmental footprints of the different regionsand industries represented in the MRIO database EXIOBASE. We randomise theglobal import flows by applying an algorithm that randomly assigns importedcommodities block-wise to the target sectors of an importing region, whilemaintaining the trade balance.We find the variability of the national footprints in general below a coefficientof variation (CV) of 4%, except for the material, water and land footprints ofhighly trade-dependent and small economies. At the industry level the variabilityis higher with 25% of the footprints having a CV above 10% (carbon footprint),and above 30% (land, material and water footprint), respectively, with maximumCVs up to 394%.We provide a list of the variability of the national and industry environmentalfootprints in the online SI so that MRIO scholars can check if a industry/regionthat is important in their study ranks high, so that either the database can beimproved through adding more details on bilateral trade, or the uncertainty canbe calculated and reported.
The individual scripts should be run according to their numbering. Note, that for running the script you need the 2011 table from EXIOBASE Version 3.4 in the industry-by-industry version. You can find it on https://exiobase.eu/.
For questions you can contact me via e-mail or raise an issue on github.
All code was tested with the following setup:
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
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## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] RColorBrewer_1.1-2 tikzDevice_0.12.3.1 ape_5.4-1
## [4] cowplot_1.1.0 ggalluvial_0.9.1 easyalluvial_0.2.3
## [7] ggpubr_0.4.0 scales_1.1.1 ggthemes_4.1.0
## [10] ggrepel_0.8.0 gridExtra_2.3 plotly_4.8.0
## [13] viridis_0.5.1 viridisLite_0.3.0 pbmcapply_1.5.0
## [16] my.utils_0.0.0.9000 Rfast_2.0.1 RcppZiggurat_0.1.6
## [19] Rcpp_1.0.6 tictoc_1.0 forcats_0.5.0
## [22] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
## [25] readr_1.3.1 tidyr_1.1.2 tibble_3.0.5
## [28] ggplot2_3.3.2 tidyverse_1.3.0 data.table_1.13.6
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## loaded via a namespace (and not attached):
## [1] colorspace_2.0-0 ggsignif_0.6.0 ggridges_0.5.1
## [4] ellipsis_0.3.1 class_7.3-18 rio_0.5.16
## [7] fs_1.5.0 rstudioapi_0.13 prodlim_2019.11.13
## [10] fansi_0.4.2 lubridate_1.7.9.2 xml2_1.3.2
## [13] codetools_0.2-18 splines_3.6.3 knitr_1.31
## [16] jsonlite_1.7.2 caret_6.0-84 broom_0.7.0
## [19] dbplyr_1.4.4 compiler_3.6.3 httr_1.4.2
## [22] backports_1.2.1 assertthat_0.2.1 Matrix_1.3-2
## [25] lazyeval_0.2.2 cli_2.2.0 prettyunits_1.1.1
## [28] htmltools_0.5.1.1 tools_3.6.3 gtable_0.3.0
## [31] glue_1.4.2 reshape2_1.4.4 carData_3.0-4
## [34] cellranger_1.1.0 vctrs_0.3.6 filehash_2.4-2
## [37] nlme_3.1-151 iterators_1.0.13 timeDate_3043.102
## [40] gower_0.2.2 xfun_0.20 openxlsx_4.2.3
## [43] rvest_0.3.6 lifecycle_0.2.0 rstatix_0.6.0
## [46] MASS_7.3-53 ipred_0.9-9 hms_1.0.0
## [49] yaml_2.2.1 curl_4.3 rpart_4.1-15
## [52] stringi_1.5.3 foreach_1.5.1 e1071_1.7-4
## [55] zip_2.1.1 lava_1.6.8.1 rlang_0.4.10
## [58] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41
## [61] recipes_0.1.6 htmlwidgets_1.5.3 tidyselect_1.1.0
## [64] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [67] generics_0.1.0 DBI_1.1.1 pillar_1.4.7
## [70] haven_2.3.1 foreign_0.8-71 withr_2.4.1
## [73] survival_3.2-7 abind_1.4-5 nnet_7.3-15
## [76] modelr_0.1.8 crayon_1.4.0 car_3.0-2
## [79] rmarkdown_2.6 progress_1.2.2 readxl_1.3.1
## [82] blob_1.2.1 ModelMetrics_1.2.2.2 reprex_1.0.0
## [85] digest_0.6.27 stats4_3.6.3 munsell_0.5.0