In pttry/ficomp: Short Run Price Competitiveness of Finland
knitr::opts_chunk$set(
collapse = TRUE,
echo = FALSE,
message = FALSE,
warning = FALSE,
comment = "#>"
)
library(tidyverse)
library(ggptt)
# library(ficomp)
library(pttdatahaku)
devtools::load_all()
start_year <- 2000
set_board_theme(base_size = 10)
fig_translation <- pRoductivity::fig_translation
geo_wage <- c("BE", "DK", "DE", "ES", "FR", "IT", "NL", "AT", "FI", "SE", "UK", "NO", "US")
ameco7_0 <- ficomp::read_ameco(table_num = 7, ameco_file = here::here("data-raw/AMECO7_2022spring.TXT"))
ameco7_I <-
ameco7_0 |>
separate(code, into = c("geo", NA, "desc", "unit_code", "rel", "vars"), sep = "\\.", remove = FALSE) |>
mutate(geo = as_factor(countrycode::countrycode(geo, "iso3c", "eurostat",
nomatch = NULL,
custom_match = c(ROM = "RO")))) |>
mutate_if(is.character, as_factor) |>
filter(desc %in% c(0,1)) # Only current values weights, 2 is PPS weights (it seems)
# ameco7_I |>
# filter(geo == "US",
# rel == "0") |>
# unite("title_unit", title, unit) |>
# filter(time >= 2021) |>
# droplevels() |> #str()
# select(title_unit, unit_code ,time, values) |>
# spread(time, values) |>
# knitr::kable()
fig_height <- 10
Komission ennusteet
Ameco tietokanta sisältää komission ennusteet.
EU ja EA aggregaatit eivät vastaa ennustejulkaisun Statistics annexin lukuja, vaikka maiden luvut vastaavat.
Sain selitykseksi tämän:
Please note that table 26 in the Statistical Annex is based on full-time equivalents when available. See note 6 in the Annex:
“Employment data used in tables 24 and 26-30 are based on full-time equivalents (FTEs), where available. Currently, Spain, France, Italy, and the Netherlands report FTE data. In the absence of FTE data, employment is based on numbers of persons. In the calculation of EU and euro-area aggregates, priority is given to FTE data, as this is regarded as more representative of diverse patterns of working time”
By contrast the AMECO variable is based on numbers of persons.
Nominal compensation per employee: total economy
ameco7_I |>
filter(
rel == "0",
# Standard aggregation
desc == "0",
title == "Nominal compensation per employee: total economy ",
# national currency
unit_code == "0"
) |>
filter(time >= max(time)-4) |>
select(country, time, values) |>
complete(country, time) |>
group_by(country) |>
mutate(values = pc(values, 1, order_by = time)) |>
ungroup() |>
filter(time >= max(time)-3) |>
spread(time, values) |>
drop_na() |>
arrange(across(last_col())) |>
knitr::kable(digits = 1)
ameco7_I |>
filter(
geo %in% geo_wage,
rel == "0",
# Standard aggregation
desc == "0",
title == "Nominal compensation per employee: total economy ",
# national currency
unit_code == "0"
) |>
filter(time >= start_year) |>
# select(country, time, values) |>
group_by(country) |>
mutate(values = pc(values, 1, order_by = time)) |>
ungroup() |>
ggplot(aes(time, values, colour = country, size = geo == "FI")) +
geom_line()+
scale_size_manual(values = c(1,3)) +
labs(
title = "Nominal compensation per employee"
)
Suhteelliset
Ameco sisältää myös suhteellisia
ameco7_I |>
filter_recode(
geo = "FI",
# rel == "0",
# Standard aggregation
desc = "0",
title = c(
"EU-15" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to the rest of the former EU-15: double export weights ",
"Teollisuusmaat_24" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to the rest of 24 industrial countries: double export weights : EU-15, TR CH NR US CA JP AU MX and NZ",
"Teollisuusmaat_12" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to 12 industrial countries (DK SE UK TR CH NO US CA MX JP AU NZ)",
"Teollisuusmaat_9" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to 9 industrial countries (TR CH NO US CA MX JP AU NZ)",
"Teollisuusmaat_37" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to the rest of 37 industrial countries: double export weights "),
# national currency
unit_code = "0"
) |>
filter(time >= start_year) |>
# select(country, time, values) |>
# group_by(title) |>
# mutate(values = pc(values, 1, order_by = time)) |>
# ungroup() |>
ggplot(aes(time, values, colour = title, size = geo == "FI")) +
geom_line()+
scale_size_manual(values = c(1,3)) +
labs(
title = "Nominal compensation per employee"
)
Suhteelliset eivät kuitenkaan sisällä ennusteita, joten oma suhteellinen ennusteiden kanssa:
ameco7_rel <-
ameco7_I |>
filter_recode(
geo = c(geo17),
rel = "0",
# Standard aggregation
desc = "0",
title = "Nominal compensation per employee: total economy ",
# national currency
unit_code = "0"
) |>
filter(time >= start_year) |>
group_by(geo) |>
mutate(values = rebase(values, time, 2015)) |>
group_by(time) |>
mutate(rel = weight_index2(values, geo, time, geos = geo17, weight_df = weights_ecfin37)) |>
ungroup()
ameco7_rel |>
filter(geo == "FI") |>
ggplot(aes(time, rel)) +
geom_line()+
# scale_size_manual(values = c(1,3)) +
labs(
title = "Nimelliset palkansaajakorvaukset suhteessa kauppakumppaneihin\nEU komission ennuste"
)
eo_rel <-
eo_a_dat |>
select(geo, time, values = D1_aper__CP_MNAC) |>
filter(time >= start_year) |>
group_by(geo) |>
mutate(values = rebase(values, time, 2015)) |>
group_by(time) |>
mutate(rel = weight_index2(values, geo, time, geos = setdiff(geo17, "CH"),
weight_df = weights_ecfin37)) |>
ungroup()
p <- bind_rows(
EU = select(ameco7_rel, geo, time, values, rel),
OECD = eo_rel,
.id = "source"
) |>
filter(geo == "FI") |>
mutate(other_w = 100 * values / rel) |>
select(time, Suomi = values, Verrokkimaat = other_w, source) |>
gather(vars, value, Suomi, Verrokkimaat) |>
group_by(vars, source) |>
mutate(value = 100 * (value / lag(value, 1) -1)) |>
ungroup() |>
unite(vars2, vars, source, sep = ", ", remove = FALSE) |>
ggplot(aes(time, value, colour = vars2, size = vars2)) +
# facet_wrap(~source) +
geom_rect(aes(xmin=2021.5, xmax=2023.5, ymin=-Inf, ymax=Inf),
fill="grey", colour = "grey", size = 1, alpha=0.9) +
geom_line() +
scale_x_continuous(limits = c(2000, 2023.5), expand = c(0,0)) +
scale_size_manual(values = c(2, 1,2,1)) +
scale_colour_manual(values = c("#0072B2", "#fc7d0b")[c(1,1,2,2)]) +
geom_h0() +
labs(
title = "Palkansaajakorvaukset työllistä kohden",
y = "%, vuosimuutos",
caption = "Lähde: OECD, AMECO, Tuottavuuslautakunta"
) +
the_title_blank("xl") +
the_legend_bot() +
guides(colour = guide_legend(nrow = 2), size = guide_legend(nrow = 2))
p
save_board_figs("Kuvio_2_6", plot = p, height = fig_height, save_data = TRUE)
save_board_figs("Kuvio_2_6_en", plot = ggptt::translate_plot(p + labs(caption = NULL), fig_translation), height = fig_height)
OECD Economic outlook
Compensation rate, total economy
per palkansaajat (EE)
eo_a_dat |>
filter(geo %in% geo_wage) |>
select(geo, time, values = D1_aper__CP_MNAC) |>
filter(time >= start_year) |>
# select(country, time, values) |>
group_by(geo) |>
mutate(values = pc(values, 1, order_by = time)) |>
ungroup() |>
ggplot(aes(time, values, colour = geo, size = geo == "FI")) +
geom_line() +
scale_size_manual(values = c(1,3)) +
labs(
title = "Compensation rate, total economy"
)
Yksikkötyökustannukset
ameco7_rel_ulc <-
ameco7_I |>
filter_recode(
geo = c(geo17),
rel = "0",
# Standard aggregation
desc = "1",
vars = "PLCD",
# title = "Nominal unit labour costs (ratio of compensation per employee to real GDP per employee) (Ratio of compensation per employee to real GDP per person employed.) ",
# national currency
unit_code = "0"
) |>
filter(time >= start_year) |>
group_by(geo) |>
mutate(values = rebase(values, time, 2015)) |>
group_by(time) |>
mutate(rel = weight_index2(values, geo, time, geos = geo17, weight_df = weights_ecfin37)) |>
ungroup()
ameco7_rel_ulc |>
filter(geo == "FI") |>
ggplot(aes(time, rel)) +
geom_line()+
# scale_size_manual(values = c(1,3)) +
labs(
title = "Nimelliset yksikkötyökustannukset suhteessa kauppakumppaneihin\nEU komission ennuste"
)
eo_rel_ulc <-
eo_a_dat |>
select(geo, time, values = nulc) |>
filter(time >= start_year) |>
group_by(geo) |>
mutate(values = rebase(values, time, 2015)) |>
group_by(time) |>
mutate(rel = weight_index2(values, geo, time, geos = setdiff(geo17, "CH"),
weight_df = weights_ecfin37)) |>
ungroup()
p <- bind_rows(
EU = select(ameco7_rel_ulc, geo, time, values, rel),
OECD = eo_rel_ulc,
.id = "source"
) |>
filter(geo == "FI") |>
mutate(other_w = 100 * values / rel) |>
select(time, Suomi = values, Verrokkimaat = other_w, source) |>
gather(vars, value, Suomi, Verrokkimaat) |>
group_by(vars, source) |>
mutate(value = 100 * (value / lag(value, 1) -1)) |>
ungroup() |>
unite(vars2, vars, source, sep = ", ", remove = FALSE) |>
ggplot(aes(time, value, colour = vars2, size = vars2)) +
# facet_wrap(~source) +
geom_rect(aes(xmin=2021.5, xmax=2023.5, ymin=-Inf, ymax=Inf),
fill="grey", colour = "grey", size = 1, alpha=0.9) +
geom_line() +
scale_x_continuous(limits = c(2000, 2023.5), expand = c(0,0)) +
scale_size_manual(values = c(2, 1,2,1)) +
scale_colour_manual(values = c("#0072B2", "#fc7d0b")[c(1,1,2,2)]) +
geom_h0() +
labs(
title = "Nimelliset yksikkötyökustannukset",
y = "%, vuosimuutos",
caption = "Lähde: OECD, AMECO, Tuottavuuslautakunta") +
the_title_blank("xl") +
the_legend_bot() +
guides(colour = guide_legend(nrow = 2), size = guide_legend(nrow = 2))
# +
# guides(size = guide_legend(override.aes = list(colour = "grey50"), title = ""),
# colour = guide_legend(title = "Lähde:"))
p
save_board_figs("Kuvio_2_7", plot = p, height = fig_height, save_data = TRUE)
save_board_figs("Kuvio_2_7_en", plot = ggptt::translate_plot(p + labs(caption = NULL), fig_translation), height = fig_height)
pttry/ficomp documentation built on June 11, 2024, 9:53 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, echo = FALSE, message = FALSE, warning = FALSE, comment = "#>" )
library(tidyverse) library(ggptt) # library(ficomp) library(pttdatahaku) devtools::load_all() start_year <- 2000 set_board_theme(base_size = 10) fig_translation <- pRoductivity::fig_translation geo_wage <- c("BE", "DK", "DE", "ES", "FR", "IT", "NL", "AT", "FI", "SE", "UK", "NO", "US") ameco7_0 <- ficomp::read_ameco(table_num = 7, ameco_file = here::here("data-raw/AMECO7_2022spring.TXT")) ameco7_I <- ameco7_0 |> separate(code, into = c("geo", NA, "desc", "unit_code", "rel", "vars"), sep = "\\.", remove = FALSE) |> mutate(geo = as_factor(countrycode::countrycode(geo, "iso3c", "eurostat", nomatch = NULL, custom_match = c(ROM = "RO")))) |> mutate_if(is.character, as_factor) |> filter(desc %in% c(0,1)) # Only current values weights, 2 is PPS weights (it seems) # ameco7_I |> # filter(geo == "US", # rel == "0") |> # unite("title_unit", title, unit) |> # filter(time >= 2021) |> # droplevels() |> #str() # select(title_unit, unit_code ,time, values) |> # spread(time, values) |> # knitr::kable() fig_height <- 10
Komission ennusteet
Ameco tietokanta sisältää komission ennusteet.
EU ja EA aggregaatit eivät vastaa ennustejulkaisun Statistics annexin lukuja, vaikka maiden luvut vastaavat.
Sain selitykseksi tämän: Please note that table 26 in the Statistical Annex is based on full-time equivalents when available. See note 6 in the Annex: “Employment data used in tables 24 and 26-30 are based on full-time equivalents (FTEs), where available. Currently, Spain, France, Italy, and the Netherlands report FTE data. In the absence of FTE data, employment is based on numbers of persons. In the calculation of EU and euro-area aggregates, priority is given to FTE data, as this is regarded as more representative of diverse patterns of working time”
By contrast the AMECO variable is based on numbers of persons.
Nominal compensation per employee: total economy
ameco7_I |> filter( rel == "0", # Standard aggregation desc == "0", title == "Nominal compensation per employee: total economy ", # national currency unit_code == "0" ) |> filter(time >= max(time)-4) |> select(country, time, values) |> complete(country, time) |> group_by(country) |> mutate(values = pc(values, 1, order_by = time)) |> ungroup() |> filter(time >= max(time)-3) |> spread(time, values) |> drop_na() |> arrange(across(last_col())) |> knitr::kable(digits = 1)
ameco7_I |> filter( geo %in% geo_wage, rel == "0", # Standard aggregation desc == "0", title == "Nominal compensation per employee: total economy ", # national currency unit_code == "0" ) |> filter(time >= start_year) |> # select(country, time, values) |> group_by(country) |> mutate(values = pc(values, 1, order_by = time)) |> ungroup() |> ggplot(aes(time, values, colour = country, size = geo == "FI")) + geom_line()+ scale_size_manual(values = c(1,3)) + labs( title = "Nominal compensation per employee" )
Suhteelliset
Ameco sisältää myös suhteellisia
ameco7_I |> filter_recode( geo = "FI", # rel == "0", # Standard aggregation desc = "0", title = c( "EU-15" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to the rest of the former EU-15: double export weights ", "Teollisuusmaat_24" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to the rest of 24 industrial countries: double export weights : EU-15, TR CH NR US CA JP AU MX and NZ", "Teollisuusmaat_12" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to 12 industrial countries (DK SE UK TR CH NO US CA MX JP AU NZ)", "Teollisuusmaat_9" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to 9 industrial countries (TR CH NO US CA MX JP AU NZ)", "Teollisuusmaat_37" = "Nominal compensation per employee: total economy: Relative performance: double export weights :- Performance relative to the rest of 37 industrial countries: double export weights "), # national currency unit_code = "0" ) |> filter(time >= start_year) |> # select(country, time, values) |> # group_by(title) |> # mutate(values = pc(values, 1, order_by = time)) |> # ungroup() |> ggplot(aes(time, values, colour = title, size = geo == "FI")) + geom_line()+ scale_size_manual(values = c(1,3)) + labs( title = "Nominal compensation per employee" )
Suhteelliset eivät kuitenkaan sisällä ennusteita, joten oma suhteellinen ennusteiden kanssa:
ameco7_rel <- ameco7_I |> filter_recode( geo = c(geo17), rel = "0", # Standard aggregation desc = "0", title = "Nominal compensation per employee: total economy ", # national currency unit_code = "0" ) |> filter(time >= start_year) |> group_by(geo) |> mutate(values = rebase(values, time, 2015)) |> group_by(time) |> mutate(rel = weight_index2(values, geo, time, geos = geo17, weight_df = weights_ecfin37)) |> ungroup() ameco7_rel |> filter(geo == "FI") |> ggplot(aes(time, rel)) + geom_line()+ # scale_size_manual(values = c(1,3)) + labs( title = "Nimelliset palkansaajakorvaukset suhteessa kauppakumppaneihin\nEU komission ennuste" )
eo_rel <- eo_a_dat |> select(geo, time, values = D1_aper__CP_MNAC) |> filter(time >= start_year) |> group_by(geo) |> mutate(values = rebase(values, time, 2015)) |> group_by(time) |> mutate(rel = weight_index2(values, geo, time, geos = setdiff(geo17, "CH"), weight_df = weights_ecfin37)) |> ungroup() p <- bind_rows( EU = select(ameco7_rel, geo, time, values, rel), OECD = eo_rel, .id = "source" ) |> filter(geo == "FI") |> mutate(other_w = 100 * values / rel) |> select(time, Suomi = values, Verrokkimaat = other_w, source) |> gather(vars, value, Suomi, Verrokkimaat) |> group_by(vars, source) |> mutate(value = 100 * (value / lag(value, 1) -1)) |> ungroup() |> unite(vars2, vars, source, sep = ", ", remove = FALSE) |> ggplot(aes(time, value, colour = vars2, size = vars2)) + # facet_wrap(~source) + geom_rect(aes(xmin=2021.5, xmax=2023.5, ymin=-Inf, ymax=Inf), fill="grey", colour = "grey", size = 1, alpha=0.9) + geom_line() + scale_x_continuous(limits = c(2000, 2023.5), expand = c(0,0)) + scale_size_manual(values = c(2, 1,2,1)) + scale_colour_manual(values = c("#0072B2", "#fc7d0b")[c(1,1,2,2)]) + geom_h0() + labs( title = "Palkansaajakorvaukset työllistä kohden", y = "%, vuosimuutos", caption = "Lähde: OECD, AMECO, Tuottavuuslautakunta" ) + the_title_blank("xl") + the_legend_bot() + guides(colour = guide_legend(nrow = 2), size = guide_legend(nrow = 2)) p save_board_figs("Kuvio_2_6", plot = p, height = fig_height, save_data = TRUE) save_board_figs("Kuvio_2_6_en", plot = ggptt::translate_plot(p + labs(caption = NULL), fig_translation), height = fig_height)
OECD Economic outlook
Compensation rate, total economy per palkansaajat (EE)
eo_a_dat |> filter(geo %in% geo_wage) |> select(geo, time, values = D1_aper__CP_MNAC) |> filter(time >= start_year) |> # select(country, time, values) |> group_by(geo) |> mutate(values = pc(values, 1, order_by = time)) |> ungroup() |> ggplot(aes(time, values, colour = geo, size = geo == "FI")) + geom_line() + scale_size_manual(values = c(1,3)) + labs( title = "Compensation rate, total economy" )
Yksikkötyökustannukset
ameco7_rel_ulc <- ameco7_I |> filter_recode( geo = c(geo17), rel = "0", # Standard aggregation desc = "1", vars = "PLCD", # title = "Nominal unit labour costs (ratio of compensation per employee to real GDP per employee) (Ratio of compensation per employee to real GDP per person employed.) ", # national currency unit_code = "0" ) |> filter(time >= start_year) |> group_by(geo) |> mutate(values = rebase(values, time, 2015)) |> group_by(time) |> mutate(rel = weight_index2(values, geo, time, geos = geo17, weight_df = weights_ecfin37)) |> ungroup() ameco7_rel_ulc |> filter(geo == "FI") |> ggplot(aes(time, rel)) + geom_line()+ # scale_size_manual(values = c(1,3)) + labs( title = "Nimelliset yksikkötyökustannukset suhteessa kauppakumppaneihin\nEU komission ennuste" ) eo_rel_ulc <- eo_a_dat |> select(geo, time, values = nulc) |> filter(time >= start_year) |> group_by(geo) |> mutate(values = rebase(values, time, 2015)) |> group_by(time) |> mutate(rel = weight_index2(values, geo, time, geos = setdiff(geo17, "CH"), weight_df = weights_ecfin37)) |> ungroup() p <- bind_rows( EU = select(ameco7_rel_ulc, geo, time, values, rel), OECD = eo_rel_ulc, .id = "source" ) |> filter(geo == "FI") |> mutate(other_w = 100 * values / rel) |> select(time, Suomi = values, Verrokkimaat = other_w, source) |> gather(vars, value, Suomi, Verrokkimaat) |> group_by(vars, source) |> mutate(value = 100 * (value / lag(value, 1) -1)) |> ungroup() |> unite(vars2, vars, source, sep = ", ", remove = FALSE) |> ggplot(aes(time, value, colour = vars2, size = vars2)) + # facet_wrap(~source) + geom_rect(aes(xmin=2021.5, xmax=2023.5, ymin=-Inf, ymax=Inf), fill="grey", colour = "grey", size = 1, alpha=0.9) + geom_line() + scale_x_continuous(limits = c(2000, 2023.5), expand = c(0,0)) + scale_size_manual(values = c(2, 1,2,1)) + scale_colour_manual(values = c("#0072B2", "#fc7d0b")[c(1,1,2,2)]) + geom_h0() + labs( title = "Nimelliset yksikkötyökustannukset", y = "%, vuosimuutos", caption = "Lähde: OECD, AMECO, Tuottavuuslautakunta") + the_title_blank("xl") + the_legend_bot() + guides(colour = guide_legend(nrow = 2), size = guide_legend(nrow = 2)) # + # guides(size = guide_legend(override.aes = list(colour = "grey50"), title = ""), # colour = guide_legend(title = "Lähde:")) p save_board_figs("Kuvio_2_7", plot = p, height = fig_height, save_data = TRUE) save_board_figs("Kuvio_2_7_en", plot = ggptt::translate_plot(p + labs(caption = NULL), fig_translation), height = fig_height)
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