Average annual wages

Data - OECD

~/data/oecd/

Info

Data on wages

wages %>%
  arrange(-(dataset == "AV_AN_WAGE")) %>%
  mutate(Title = read_lines(paste0("~/Dropbox/website/data/", source, "/",dataset, ".Rmd"), skip = 1, n_max = 1) %>% gsub("title: ", "", .) %>% gsub("\"", "", .)) %>%
  mutate(Download = as.Date(file.info(paste0("~/Dropbox/website/data/", source, "/", dataset, ".RData"))$mtime),
         Compile = as.Date(file.info(paste0("~/Dropbox/website/data/", source, "/", dataset, ".html"))$mtime)) %>%
  mutate(Compile = paste0("[", Compile, "](https://fgeerolf.com/data/", source, "/", dataset, '.html)')) %>%
  print_table_conditional()

source	dataset	Title	Download	Compile
oecd	AV_AN_WAGE	Average annual wages	2023-09-09	2024-04-15
eurostat	earn_mw_cur	Monthly minimum wages - bi-annual data	2024-04-09	2024-04-15
eurostat	ei_lmlc_q	Labour cost index, nominal value - quarterly data	2024-04-15	2024-04-15
eurostat	lc_lci_lev	Labour cost levels by NACE Rev. 2 activity	2024-04-09	2024-04-15
eurostat	lc_lci_r2_q	Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data	2024-04-09	2024-04-15
eurostat	nama_10_lp_ulc	Labour productivity and unit labour costs	2024-04-15	2024-04-15
eurostat	namq_10_lp_ulc	Labour productivity and unit labour costs	2024-04-09	2024-04-15
eurostat	tps00155	Minimum wages	2024-04-09	2024-04-15
fred	wage	Wage	2024-04-15	2024-04-15
ilo	EAR_4MTH_SEX_ECO_CUR_NB_A	Mean nominal monthly earnings of employees by sex and economic activity – Harmonized series	2023-06-01	2023-06-01
ilo	EAR_XEES_SEX_ECO_NB_Q	Mean nominal monthly earnings of employees by sex and economic activity – Harmonized series	2023-06-01	2023-06-01
oecd	AWCOMP	Taxing Wages - Comparative tables	2023-09-09	2024-04-15
oecd	EAR_MEI	Hourly Earnings (MEI)	2024-04-08	2024-04-15
oecd	HH_DASH	Household Dashboard	2023-09-09	2024-04-15
oecd	MIN2AVE	Minimum relative to average wages of full-time workers - MIN2AVE	2023-09-09	2024-04-15
oecd	RMW	Real Minimum Wages - RMW	2024-03-12	2024-04-15
oecd	ULC_EEQ	Unit labour costs and labour productivity (employment based), Total economy	2024-04-15	2024-04-15

LAST_COMPILE

LAST_COMPILE
2024-04-16

Last

obsTime	Nobs
2022	103

Information

• OECD Website. html

This dataset contains data on average annual wages per full-time and full-year equivalent employee in the total economy. Average annual wages per full-time equivalent dependent employee are obtained by dividing the national-accounts-based total wage bill by the average number of employees in the total economy, which is then multiplied by the ratio of average usual weekly hours per full-time employee to average usually weekly hours for all employees. For more details, see: http://www.oecd.org/els/oecd-employment-outlook-19991266.htm and http://www.oecd.org/employment/emp/onlineoecdemploymentdatabase.htm.

SERIES

AV_AN_WAGE %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  group_by(SERIES, Series) %>%
  summarise(nobs = n()) %>%
  arrange(-nobs) %>%
  print_table_conditional()

SERIES	Series	nobs
USDPPP	In 2021 constant prices at 2021 USD PPPs	1173
CNPNCU	2021 constant prices and NCU	1141
CPNCU	Current prices in NCU	1141

COUNTRY

AV_AN_WAGE %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  group_by(COUNTRY, Country) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

CNPNCU - 2020 constant prices and NCU

France, Germany, Italy, Spain, U.S.

AV_AN_WAGE %>%
  filter(SERIES == "CNPNCU",
         COUNTRY %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Country) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Country" = "country")) %>%
  rename(Location = Country) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_5flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10)) +
  ylab(" 2020 constant prices and NCU (1996 = 100)") + xlab("")

CPNCU - Current prices in NCU

Table

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU") %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  group_by(COUNTRY, Country) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

France, Germany, Italy, Spain, United States

Value

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU",
         COUNTRY %in% c("FRA", "DEU", "ITA")) %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue / 1000) %>%
  left_join(colors, by = c("Country" = "country")) %>%
  rename(Location = Country) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 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("Current prices in NCU") + xlab("")

Indice 100 (1996-)

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU",
         COUNTRY %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Country) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Country" = "country")) %>%
  rename(Location = Country) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_5flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 500, 10)) +
  ylab("Current prices in NCU") + xlab("")

Switzerland, Australia, Mexico

All

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU",
         COUNTRY %in% c("AUS", "MEX", "CHE")) %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue / 1000) %>%
  left_join(colors, by = c("Country" = "country")) %>%
  rename(Location = Country) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 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("Current prices in NCU") + xlab("")

2008-

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU",
         COUNTRY %in% c("AUS", "MEX", "CHE")) %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(obsValue = obsValue / 1000) %>%
  left_join(colors, by = c("Country" = "country")) %>%
  rename(Location = Country) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 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("Current prices in NCU") + xlab("")

Implicit Deflator (2020 constant prices and NCU)

France

1990-

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("FRA"),
         SERIES != "USDPPP") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-REFERENCEPERIOD) %>%
  group_by(obsTime) %>%
  summarise(obsValue = obsValue[SERIES == "CPNCU"]/obsValue[SERIES == "CNPNCU"]) %>%
  ungroup %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Price Index") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 5))

1996-

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("FRA"),
         SERIES != "USDPPP") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-REFERENCEPERIOD) %>%
  group_by(obsTime) %>%
  summarise(obsValue = obsValue[SERIES == "CPNCU"]/obsValue[SERIES == "CNPNCU"]) %>%
  ungroup %>%
  year_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Price Index") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 5))

Germany

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("DEU"),
         SERIES != "USDPPP") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-REFERENCEPERIOD) %>%
  group_by(obsTime) %>%
  summarise(obsValue = obsValue[SERIES == "CPNCU"]/obsValue[SERIES == "CNPNCU"]) %>%
  ungroup %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Price Index") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 5))

United States

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("USA"),
         SERIES != "USDPPP") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-REFERENCEPERIOD) %>%
  group_by(obsTime) %>%
  summarise(obsValue = obsValue[SERIES == "CPNCU"]/obsValue[SERIES == "CNPNCU"]) %>%
  ungroup %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Price Index") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 5))

Individual Countries

Number of observations

AV_AN_WAGE %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  group_by(COUNTRY, Country, Series) %>%
  summarise(Nobs = n()) %>%
  spread(Series, Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

Australia

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("AUS")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Czech Republic

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("CZE"),
         SERIES != "USDPPP") %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 500, 50),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Austria

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("AUT")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Hungary

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("HUN"),
         SERIES != "USDPPP") %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 10000, 500),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Japan

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("JPN"),
         SERIES != "USDPPP") %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 10000, 50),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Switzerland

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("CHE")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Slovenia

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("SVN")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Poland

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("POL")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Israel

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("ISR"),
         SERIES != "USDPPP") %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 500, 10),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

France

Value

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("FRA")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Index

All

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("FRA")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  group_by(Series) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage (100 = 1990)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 300, 10))

1996-

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("FRA")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Series) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage (100 = 1996)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 300, 10))

Germany

Value

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("DEU")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  filter(year(date) >= 1990) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

Index

All

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("DEU")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  group_by(Series) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage (100 = 1990)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 300, 10))

1996-

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("DEU")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Series) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage (100 = 1996)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 300, 10))

United States

AV_AN_WAGE %>%
  filter(COUNTRY %in% c("USA")) %>%
  left_join(AV_AN_WAGE_var$SERIES, by = "SERIES") %>%
  year_to_date %>%
  filter(year(date) >= 1990) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Series)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + ylab("Average Wage") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, suffix = "k", prefix = ""))

First, Last

2020 constant prices and NCU (CNPNCU)

AV_AN_WAGE %>%
  filter(SERIES == "CNPNCU") %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  group_by(Country) %>%
  summarise(year1 = first(obsTime),
            year2 = last(obsTime),
            value1 = first(obsValue),
            value2 = last(obsValue),
            nobs = n()) %>%
  mutate_at(vars(value1, value2), funs(round)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

In 2018 constant prices at 2018 USD PPPs (USDPPP)

AV_AN_WAGE %>%
  filter(SERIES == "USDPPP") %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  group_by(Country) %>%
  summarise(year1 = first(obsTime),
            year2 = last(obsTime),
            value1 = first(obsValue),
            value2 = last(obsValue),
            nobs = n()) %>%
  mutate_at(vars(value1, value2), funs(round)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

Current prices in NCU (CPNCU)

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU") %>%
  left_join(AV_AN_WAGE_var$COUNTRY, by = "COUNTRY") %>%
  group_by(Country) %>%
  summarise(year1 = first(obsTime),
            year2 = last(obsTime),
            value1 = first(obsValue),
            value2 = last(obsValue),
            nobs = n()) %>%
  mutate_at(vars(value1, value2), funs(round)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

France and Germany

USDPPP - 2018 constant prices at 2018 USD PPPs

AV_AN_WAGE %>%
  filter(SERIES == "USDPPP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  left_join(AV_AN_WAGE_var[["COUNTRY"]], by = "COUNTRY") %>%
  group_by(Country) %>%
  select(Country, obsTime, obsValue) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue / 1000, color = Country)) +
  geom_image(data = . %>%
               filter(date == as.Date("2015-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(Country), ".png")),
             aes(x = date, y = obsValue / 1000, image = image), asp = 1.5) +
     scale_color_manual(values = c("#002395", "#000000")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 100, 2),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k")) +
  ylab("2018 constant prices at 2018 USD PPPs") + xlab("")

CPNCU - Current prices in NCU in Germany and France

AV_AN_WAGE %>%
  filter(SERIES == "CPNCU",
         COUNTRY %in% c("FRA", "DEU")) %>%
  left_join(AV_AN_WAGE_var[["COUNTRY"]], by = "COUNTRY") %>%
  group_by(Country) %>%
  select(Country, obsTime, obsValue) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2015-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(Country), ".png")),
             aes(x = date, y = obsValue / 1000, image = image), asp = 1.5) +
     scale_color_manual(values = c("#002395", "#000000")) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 100, 2),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = "")) +
  ylab("Current prices in NCU") + xlab("")

CNPNCU - 2018 constant prices in NCU in Germany and France

AV_AN_WAGE %>%
  filter(SERIES == "CNPNCU",
         COUNTRY %in% c("FRA", "DEU")) %>%
  left_join(AV_AN_WAGE_var[["COUNTRY"]], by = "COUNTRY") %>%
  group_by(Country) %>%
  select(Country, obsTime, obsValue) %>%
  year_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
  geom_image(data = . %>%
               filter(date == as.Date("2015-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(Country), ".png")),
             aes(x = date, y = obsValue / 1000, image = image), asp = 1.5) +
     scale_color_manual(values = c("#002395", "#000000")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 100, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = "")) +
  ylab("2018 constant prices in NCU") + xlab("")