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Commit 2e8f6fce authored by Ingram Jaccard's avatar Ingram Jaccard
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edit si-test

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......@@ -164,8 +164,15 @@ hbs = data.frame(quintile, pps_hh, pm_sector1, pm_sector2) %>%
flextable(hbs) %>%
autofit() %>%
set_header_labels(quintile = "quintile ya", pps_hh = "pps hh") %>%
set_caption("Table S1: HBS structure with calculations of quintile shares per sector.", autonum = NULL) %>%
set_header_labels(quintile = "quintile",
pps_hh = "pps hh",
pm_sector1 = "s1 (pm)",
pm_sector2 = "s2 (pm)",
pps_hh_sector1 = "s1 (pps hh)",
sector_1_shares = "s1 (q share)",
pps_hh_sector2 = "s2 (pps hh)",
sector_2_shares = "s2 (q share)") %>%
set_caption("HBS structure with calculations of quintile shares per sector.") %>%
fit_to_width(7.5)
```
......@@ -213,11 +220,25 @@ eemrio = data.frame(q_share_of_sector, eemrio_hh_fd) %>%
# "q5 fd")) %>%
# kable_styling(latex_options = "HOLD_position")
round(2)
regulartable(eemrio) %>%
flextable(eemrio) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(sector = "sector",
q1 = "q1 share",
q2 = "q2 share",
q3 = "q3 share",
q4 = "q4 share",
q5 = "q5 share",
eemrio_hh_fd = "eemrio hh fd",
q1_eemrio = "q1 fd",
q2_eemrio = "q2 fd",
q3_eemrio = "q3 fd",
q4_eemrio = "q4 fd",
q5_eemrio = "q5 fd") %>%
set_caption("HBS income quintile shares per sector
multiplied by EE-MRIO household final demand expenditure vector to
calculate EE-MRIO household final demand expenditure per
income quintile and sector.") #%>%
#fit_to_width(7.5)
```
......@@ -265,9 +286,28 @@ footprint = data.frame(eemrio,TIV) %>%
# "total fp")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(footprint) %>%
flextable(footprint) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(sector = "sector",
eemrio_hh_fd = "eemrio hh fd",
q1_eemrio = "q1 fd",
q2_eemrio = "q2 fd",
q3_eemrio = "q3 fd",
q4_eemrio = "q4 fd",
q5_eemrio = "q5 fd",
TIV = "TIV",
q1_footprint = "q1 fp",
q2_footprint = "q2 fp",
q3_footprint = "q3 fp",
q4_footprint = "q4 fp",
q5_footprint = "q5 fp",
total_footprint = "total fp") %>%
set_caption("Calculation of EE-MRIO household
footprint decomposed by income quintile, through multiplication of
EE-MRIO household final demand expenditure per income quintile and
sector by the total intensity vector (TIV) calculated in the EE-MRIO
(with only one total intensity per sector).") #%>%
#fit_to_width(7.5)
```
......@@ -293,9 +333,15 @@ labels = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Exi
# column_spec(1, width = "30em") %>%
# scroll_box(width = "100%", height = "200px")
regulartable(labels) %>%
flextable(labels) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(V2 = "exiobase industry production sector",
coicop = "coicop consumption category",
five_sectors = "aggregate consumption category") %>%
set_caption("Mapping EXIOBASE industry
production sectors to COICOP consumption categories and
five aggregate consumption categories") #%>%
#fit_to_width(7.5)
```
......@@ -502,9 +548,21 @@ country_year_coverage = data.frame(geo,
# row_spec(c(20,21,28,34), color = "red") %>%
# scroll_box(width = "100%", height = "200px")
regulartable(country_year_coverage) %>%
flextable(country_year_coverage) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(geo = "geo",
year_2015 = "2015",
year_2010 = "2010",
year_2005 = "2005",
year_1999 = "1999") %>%
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'
for those years where EUROSTAT HBS data also exists for that country.
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(7.5)
```
......@@ -574,9 +632,22 @@ hbs_bp_pps = hbs_bp %>%
# "s2 (pps hh bp)")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(hbs_bp_pps) %>%
flextable(hbs_bp_pps) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(quintile = "quintile",
pps_hh = "pps hh",
pm_sector1 = "s1 (pm)",
pm_sector2 = "s2 (pm)",
pps_hh_sector1 = "s1 (pps hh)",
bp_share_in_pp_sector_1 = "s1 bp",
pps_hh_sector1_bp = "s1 (pps hh bp)",
pps_hh_sector2 = "s2 (pps hh)",
bp_share_in_pp_sector_2 = "s2 bp",
pps_hh_sector2_bp = "s2 (pps hh bp)") %>%
set_caption("Same as Table S1 but now
with base price shares of purchaser price per sector, and
sectoral expenditure per quintile in pps hh base price.") #%>%
#fit_to_width(7.5)
```
......@@ -609,9 +680,20 @@ hbs_bp_shares = hbs_bp %>%
# "s2 (pm bp)")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(hbs_bp_shares) %>%
flextable(hbs_bp_shares) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(quintile = "quintile",
pps_hh_bp = "pps hh bp",
pps_hh_sector1_bp = "s1 (pps hh bp)",
sector_1_shares_bp = "s1 (q share bp)",
pps_hh_sector2_bp = "s2 (pps hh bp)",
sector_2_shares_bp = "s2 (q share bp)",
pm_sector1_bp = "s1 (pm bp)",
pm_sector2_bp = "s2 (pm bp)") %>%
set_caption("Quintile shares per sector
in base price, with new 'pps hh' per quintile in base price and
new sectoral pm values in base price.") #%>%
#fit_to_width(7.5)
```
......@@ -655,9 +737,22 @@ eemrio_bp = data.frame(q_share_of_sector_bp, eemrio_hh_fd) %>%
# "q5 fd")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(eemrio_bp) %>%
flextable(eemrio_bp) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(sector = "sector",
q1 = "q1 share bp",
q2 = "q2 share bp",
q3 = "q3 share bp",
q4 = "q4 share bp",
q5 = "q5 share bp",
eemrio_hh_fd = "eemrio hh fd",
q1_eemrio = "q1 fd",
q2_eemrio = "q2 fd",
q3_eemrio = "q3 fd",
q4_eemrio = "q4 fd",
q5_eemrio = "q5 fd") %>%
set_caption("Identical to Table 2.") #%>%
#fit_to_width(7.5)
```
......@@ -731,9 +826,22 @@ hbs_bp_pps = hbs_bp %>%
# "s2 (pps hh bp)")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(hbs_bp_pps) %>%
flextable(hbs_bp_pps) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(quintile = "quintile",
pps_hh = "pps hh",
pm_sector1 = "s1 (pm)",
pm_sector2 = "s2 (pm)",
pps_hh_sector1 = "s1 (pps hh)",
bp_share_in_pp_sector1 = "s1 bp",
pps_hh_sector1_bp = "s1 (pps hh bp)",
pps_hh_sector2 = "s2 (pps hh)",
bp_share_in_pp_sector2 = "s2 bp",
pps_hh_sector2_bp = "s2 (pps hh bp)") %>%
set_caption("Same as Table S1 but now
with base price shares of purchaser price per sector, and
sectoral expenditure per income quintile in pps hh base price.") #%>%
#fit_to_width(7.5)
```
......@@ -761,9 +869,19 @@ hbs_bp_shares = hbs_bp %>%
# "s2 (pm bp)")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(hbs_bp_shares) %>%
flextable(hbs_bp_shares) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(quintile = "quintile",
pps_hh_bp = "pps hh bp",
pps_hh_sector1_bp = "s1 (pps hh bp)",
pps_hh_sector2_bp = "s2 (pps hh bp)",
pm_sector1_bp = "s1 (pm bp)",
pm_sector2_bp = "s2 (pm bp)") %>%
set_caption("Income quintile shares per sector
in base price, with new 'pps hh' per income quintile in base price and
new sectoral pm values in base price ('pps hh bp' is the sum of
's1 (pps hh bp)' and 's2 (pps hh bp)').") #%>%
#fit_to_width(7.5)
```
......@@ -815,9 +933,20 @@ hbs_alt_method_fd = hbs_alt_method %>%
# "hh fd s2")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(hbs_alt_method_fd) %>%
flextable(hbs_alt_method_fd) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(quintile = "quintile",
pps_hh = "pps hh",
pm_sector1 = "s1 (pm)",
pm_sector2 = "s2 (pm)",
mean_expenditure_share = "mean exp",
eemrio_hh_fd = "eemrio hh fd",
hh_sector1 = "hh fd s1",
hh_sector2 = "hh fd s2") %>%
set_caption("Same as Table S9 but
with total EE-MRIO household final demand in purchaser price
instead of base price.") #%>%
#fit_to_width(7.5)
```
......@@ -844,9 +973,17 @@ eemrio_alt_method = hbs_alt_method %>%
# "q5 fd")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(eemrio_alt_method) %>%
flextable(eemrio_alt_method) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(sector = "sector",
q1 = "q1 fd",
q2 = "q2 fd",
q3 = "q3 fd",
q4 = "q4 fd",
q5 = "q5 fd") %>%
set_caption("EE-MRIO
household final demand per quintile and sector.") #%>%
#fit_to_width(7.5)
```
......@@ -885,9 +1022,24 @@ footprint_alt_method = data.frame(eemrio_alt_method, TIV) %>%
# "total fp")) %>%
# kable_styling(latex_options = "HOLD_position")
regulartable(footprint_alt_method) %>%
flextable(footprint_alt_method) %>%
autofit() %>%
fit_to_width(7.5)
set_header_labels(sector = "sector",
q1 = "q1 fd",
q2 = "q2 fd",
q3 = "q3 fd",
q4 = "q4 fd",
q5 = "q5 fd",
TIV = "TIV",
q1_footprint = "q1 fp",
q2_footprint = "q2 fp",
q3_footprint = "q3 fp",
q4_footprint = "q4 fp",
q5_footprint = "q5 fp") %>%
set_caption("EE-MRIO household
final demand per quintile and sector multiplied by the TIV to
calculate footprint per quintile and sector.") #%>%
#fit_to_width(7.5)
```
......
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