diff --git a/analysis/figures/figureS1-test.pdf b/analysis/figures/figureS1-test.pdf
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diff --git a/analysis/figures/figureS2-test.pdf b/analysis/figures/figureS2-test.pdf
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diff --git a/analysis/figures/figureS3-test.pdf b/analysis/figures/figureS3-test.pdf
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diff --git a/analysis/figures/figureS4-test.pdf b/analysis/figures/figureS4-test.pdf
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diff --git a/analysis/paper/.~lock.si_test.docx# b/analysis/paper/.~lock.si_test.docx#
index 8f5246a961aa71213443cdabf4c0950afa364821..c752877bd79e750f104691f591a6a239b8cd6081 100644
--- a/analysis/paper/.~lock.si_test.docx#
+++ b/analysis/paper/.~lock.si_test.docx#
@@ -1 +1 @@
-,jaccard,jaccard-Latitude-E6440,28.01.2021 18:37,file:///home/jaccard/.config/libreoffice/4;
\ No newline at end of file
+,jaccard,jaccard-Latitude-E6440,28.01.2021 18:52,file:///home/jaccard/.config/libreoffice/4;
\ No newline at end of file
diff --git a/analysis/paper/si_test.Rmd b/analysis/paper/si_test.Rmd
index 65ab63e505395860874af938f650b4bef7291ad5..3b1cb5ea2dd9cdfd4c4eebd8f75c27d329833e59 100644
--- a/analysis/paper/si_test.Rmd
+++ b/analysis/paper/si_test.Rmd
@@ -173,7 +173,8 @@ hbs = data.frame(quintile, pps_hh, pm_sector1, pm_sector2) %>%
mutate(pps_hh_sector1 = pps_hh*(pm_sector1/1000),
sector_1_shares = pps_hh_sector1/sum(pps_hh_sector1),
pps_hh_sector2 = pps_hh*(pm_sector2/1000),
- sector_2_shares = pps_hh_sector2/sum(pps_hh_sector2))
+ sector_2_shares = pps_hh_sector2/sum(pps_hh_sector2)) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(hbs, caption = "Table S1: HBS structure with calculations of quintile shares per sector.",
# escape = F,
@@ -225,7 +226,8 @@ eemrio = data.frame(q_share_of_sector, eemrio_hh_fd) %>%
q2_eemrio = q2*eemrio_hh_fd,
q3_eemrio = q3*eemrio_hh_fd,
q4_eemrio = q4*eemrio_hh_fd,
- q5_eemrio = q5*eemrio_hh_fd)
+ q5_eemrio = q5*eemrio_hh_fd) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(eemrio, caption = "Table S2: HBS income quintile shares per sector
# multiplied by EE-MRIO household final demand expenditure vector to
@@ -288,7 +290,8 @@ footprint = data.frame(eemrio,TIV) %>%
q3_footprint = q3_eemrio*TIV,
q4_footprint = q4_eemrio*TIV,
q5_footprint = q5_eemrio*TIV,
- total_footprint = eemrio_hh_fd*TIV)
+ total_footprint = eemrio_hh_fd*TIV) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(footprint, caption = "Table S3: Calculation of EE-MRIO household
# footprint decomposed by income quintile, through multiplication of
@@ -516,47 +519,11 @@ year_2005 = c("x",
"x",
"x",
"")
-
-year_1999 = c("x",
- "x",
- "",
- "",
- "",
- "x",
- "x",
- "",
- "x",
- "x",
- "x",
- "x",
- "",
- "",
- "x",
- "x",
- "",
- "x",
- "",
- "",
- "",
- "",
- "x",
- "",
- "",
- "x",
- "",
- "",
- "x",
- "",
- "",
- "",
- "x",
- "")
country_year_coverage = data.frame(geo,
year_2015,
year_2010,
- year_2005,
- year_1999)
+ year_2005)
#knitr::kable(country_year_coverage, caption = "Table S5: Country and year
# coverage between EXIOBASE and the EUROSTAT HBS. Rows with black
@@ -579,8 +546,7 @@ flextable(country_year_coverage) %>%
set_header_labels(geo = "geo",
year_2015 = "2015",
year_2010 = "2010",
- year_2005 = "2005",
- year_1999 = "1999") %>%
+ year_2005 = "2005") %>%
set_caption("Country and year
coverage between EXIOBASE and the EUROSTAT HBS. Rows with black
text show countries that are represented in EXIOBASE, and an 'x'
@@ -588,7 +554,7 @@ flextable(country_year_coverage) %>%
Rows with red text show countries where EUROSTAT HBS data exists,
but who are not represented individually in EXIOBASE (they are in
'rest-of-world' categories)") %>%
- fit_to_width(max_width = 7.5)
+ fit_to_width(max_width = 7.5)
```
@@ -639,7 +605,8 @@ hbs_bp_pps = hbs_bp %>%
pps_hh_sector1_bp,
pps_hh_sector2,
bp_share_in_pp_sector_2,
- pps_hh_sector2_bp)
+ pps_hh_sector2_bp) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(hbs_bp_pps, caption = "Table S6: Same as Table S1 but now
# with base price shares of purchaser price per sector, and
@@ -689,7 +656,8 @@ hbs_bp_shares = hbs_bp %>%
pps_hh_sector2_bp,
sector_2_shares_bp) %>%
mutate(pm_sector1_bp = (pps_hh_sector1_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000,
- pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000)
+ pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(hbs_bp_shares, caption = "Table S7: Quintile shares per sector
# in base price, with new 'pps hh' per quintile in base price and
@@ -745,7 +713,8 @@ eemrio_bp = data.frame(q_share_of_sector_bp, eemrio_hh_fd) %>%
q2_eemrio = q2*eemrio_hh_fd,
q3_eemrio = q3*eemrio_hh_fd,
q4_eemrio = q4*eemrio_hh_fd,
- q5_eemrio = q5*eemrio_hh_fd)
+ q5_eemrio = q5*eemrio_hh_fd) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(eemrio_bp, caption = "Table S8: Identical to Table S2.",
# booktabs = TRUE,
@@ -833,7 +802,8 @@ hbs_bp_pps = hbs_bp %>%
pps_hh_sector1_bp,
pps_hh_sector2,
bp_share_in_pp_sector_2,
- pps_hh_sector2_bp)
+ pps_hh_sector2_bp) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(hbs_bp_pps, caption = "Table S9: Same as Table S1 but now
# with base price shares of purchaser price per sector, and
@@ -879,7 +849,8 @@ hbs_bp_shares = hbs_bp %>%
pps_hh_sector1_bp,
pps_hh_sector2_bp) %>%
mutate(pm_sector1_bp = (pps_hh_sector1_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000,
- pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000)
+ pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(hbs_bp_shares, caption = "Table S10: Income quintile shares per sector
# in base price, with new 'pps hh' per income quintile in base price and
@@ -942,7 +913,8 @@ hbs_alt_method_fd = hbs_alt_method %>%
mean_expenditure_share,
eemrio_hh_fd,
hh_sector1,
- hh_sector2)
+ hh_sector2) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(hbs_alt_method_fd, caption = "Table S11: Same as Table S9 but
# with total EE-MRIO household final demand in purchaser price
@@ -986,7 +958,8 @@ eemrio_alt_method = hbs_alt_method %>%
spread(quintile,value) %>%
mutate(sector = dplyr::recode(sector,
"hh_sector1" = "1",
- "hh_sector2" = "2"))
+ "hh_sector2" = "2")) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(eemrio_alt_method, caption = "Table S12: EE-MRIO
# household final demand per quintile and sector.",
@@ -1027,7 +1000,8 @@ footprint_alt_method = data.frame(eemrio_alt_method, TIV) %>%
q2_footprint +
q3_footprint +
q4_footprint +
- q5_footprint)
+ q5_footprint) %>%
+ mutate_if(is.numeric, round, digits = 2)
#knitr::kable(footprint_alt_method, caption = "Table S13: EE-MRIO household
# final demand per quintile and sector multiplied by the TIV to
diff --git a/analysis/paper/si_test.docx b/analysis/paper/si_test.docx
index 89896d3ae25ff4c37dd725ce69b6c717136169f3..d52de972f2580527042f725fc1f4cd0b724e8764 100644
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