Hourly Earnings (MEI) - EAR_MEI

Data - OECD

Last observation: Q3 2023 (N = 71) · nov. 2023 (N = 5) · 2022 (N = 93)

First observation: 1955 (N = 15) · janv. 1955 (N = 10) · Q1 1955 (N = 15)

Last data update: 02 août 2026, 08:57

Last compile: 20 sept. 2026, 23:56

Structure

SUBJECT

LOCATION

FREQUENCY

TIME_FORMAT

UNIT

POWERCODE

REFERENCEPERIOD

Detail

The Hourly Earnings (MEI) dataset contains predominantly monthly statistics, and associated statistical methodological information, for the 38 OECD member countries and for selected non-member economies.

The MEI Earnings dataset provides monthly and quarterly data on employees’ earnings series. It includes earnings series in manufacturing and for the private economic sector. Mostly the sources of the data are business surveys covering different economic sectors, but in some cases administrative data are also used. The target series for hourly earnings correspond to seasonally adjusted average total earnings paid per employed person per hour, including overtime pay and regularly recurring cash supplements. Where hourly earnings series are not available, a series could refer to weekly or monthly earnings. In this case, a series for full-time or full-time equivalent employees is preferred to an all employees series.

SUBJECT

EAR_MEI |>
  left_join(EAR_MEI_var$SUBJECT, by = "SUBJECT") |>
  group_by(SUBJECT, Subject) |>
  summarise(nobs = n()) |>
  arrange(-nobs) |>
  print_table_conditional()

SUBJECT	Subject	nobs
LCEAMN01_IXOBSA	Manufacturing, Index, SA	19040
LCEAMN01_IXOB	Manufacturing, Index	15978
LCEAPR_IXOBSA	Private Sector, Index, SA	6568

FREQUENCY

EAR_MEI |>
  left_join(EAR_MEI_var$FREQUENCY, by = "FREQUENCY") |>
  group_by(FREQUENCY, Frequency) |>
  summarise(nobs = n()) |>
  arrange(-nobs) |>
  print_table_conditional()

FREQUENCY	Frequency	nobs
M	Monthly	22249
Q	Quarterly	15555
A	Annual	3782

LOCATION

All

EAR_MEI |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Location x Frequency

EAR_MEI |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location, FREQUENCY) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Annual

EAR_MEI |>
  filter(FREQUENCY == "A") |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Quarterly

EAR_MEI |>
  filter(FREQUENCY == "Q") |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Monthly

EAR_MEI |>
  filter(FREQUENCY == "M") |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

obsTime

EAR_MEI |>
  group_by(obsTime) |>
  summarise(Nobs = n()) |>
  arrange(desc(obsTime)) |>
  print_table_conditional()

Europe VS U.S.

LCEAMN01_IXOBSA - Manufacturing, Index, SA

EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  arrange(date) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

LCEAPR_IXOBSA

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  arrange(date) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Eurozone, Japan, United States, United Kingdom

All

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "JPN", "EA19", "GBR", "HUN", "POL")) |>
  quarter_to_date() |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

2015-

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "JPN", "EA19", "GBR", "HUN", "POL")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Eurozone, United States, United Kingdom

All

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

1996-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("1996-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("1996-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

All

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2019-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  arrange(date) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

All

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, France, Germany, Italy, Belgium

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "DEU", "BEL")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "DEU", "BEL")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Poland, Hungary

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("POL", "HUN")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_2flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("POL", "HUN")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Sweden, Netherlands, Luxembourg, Hungary, Poland, Finland

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("SWE", "NLD", "LUX", "FIN", "DNK", "IRL")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_4flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("SWE", "NLD", "LUX", "FIN", "DNK", "IRL")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, France, Germany, Italy, Belgium

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2020-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = seq(as.Date("2015-01-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2022-

Real

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("FRA", "DEU", "ITA", "BEL", "ESP", "PRT", "AUT")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2021-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, France

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Eurozone, United States, United Kingdom, Germany

2015-

Nominal

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA") + xlab("")

Real

EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  arrange(date) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2021-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  arrange(date) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2021-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

US, Eurozone, Germany, France

2019Q4-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  arrange(date) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2017-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  arrange(date) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2017-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2019-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  arrange(date) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

2021-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAPR_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "DEU", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  arrange(date) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2021-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Private Sector, Index, SA, Real") + xlab("")

Manufacturing, Index, SA

Table

EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA") |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

U.S., Europe, U.K., Germany

EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

U.S., Europe, U.K., Germany, France

2015-

EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_5flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) + add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

2019-Q4-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

  ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_5flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Plus Italy, Spain, etc.

2019-Q4-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR", "DEU", "FRA", "ITA", "ESP")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_7flags + add_6flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Germany, Italy, Netherlands, Spain, France

2019-Q4-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("DEU", "ITA", "NLD", "ESP", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_5flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Germany, Italy, Netherlands, Spain, France

2019-Q4-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("DEU", "ITA", "NLD", "ESP", "FRA")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2019-10-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))

ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + 
  add_5flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

United States, Euro, UK

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19", "GBR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))


ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_3flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

United States, Euro, UK

2015-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2015-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))


ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

2017-

df <- EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "Q",
         LOCATION %in% c("USA", "EA19")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  left_join(PRICES_CPI_CPALTT01_IXOB, by = c("date", "Location", "LOCATION")) |>
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
  mutate(obsValue = obsValue/CPALTT01_IXOB) |>
  add_flag_color("Location") |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue / obsValue[date == as.Date("2017-01-01")]) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))


ggplot(data = df) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() + add_2flags +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_yearqtr(labels = date_format("%Y Q%q"),
                  breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
  add_6flags +
  scale_y_log10(breaks = seq(10, 500, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = "", prefix = "")) +
  ylab("Manufacturing Sector, Index, SA, Real") + xlab("")

Switzerland, Australia, Mexico

EAR_MEI |>
  filter(SUBJECT == "LCEAMN01_IXOBSA",
         FREQUENCY == "A",
         LOCATION %in% c("AUS", "MEX", "CHE")) |>
  left_join(EAR_MEI_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  add_flag_color("Location") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k", prefix = "")) +
  ylab("Manufacturing, Index, SA") + xlab("")

Related Data

Wage Data

source	dataset	Title	Updated
ecb	EWT	ECB wage tracker - EWT	2026-09-17
ecb	INW	Indicator of negotiated wage rates - INW	2026-09-17
ecb	STS	Short-Term Statistics - STS	2026-09-18
eurostat	lc_lci_r2_q	Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data - lc_lci_r2_q	2026-09-17
eurostat	nama_10_lp_ulc	Labour productivity and unit labour costs - nama_10_lp_ulc	2026-09-11
eurostat	namq_10_lp_ulc	Labour productivity and unit labour costs - namq_10_lp_ulc	2026-09-17
fred	wage	Wage - wage	2026-09-19
oecd	EAR_MEI	Hourly Earnings (MEI) - EAR_MEI	2026-08-02
oecd	RMW	Real Minimum Wages - RMW	2026-08-02